Adaptive OFDM Modulation for Underwater Acoustic Communications_ Design Considerations
Adaptive OFDM Modulation for Underwater Acoustic Communications:Design Considerations and
Experimental Results
Andreja Radosevic,Student Member,IEEE,Rameez Ahmed,Tolga M.Duman,Fellow,IEEE,
John G.Proakis,Life Fellow,IEEE,and Milica Stojanovic,Fellow,IEEE
Abstract—In this paper,we explore design aspects of adaptive modulation based on orthogonal frequency-division multiplexing (OFDM)for underwater acoustic(UWA)communications,and study its performance using real-time at-sea experiments.Our design criterion is to maximize the system throughput under a target average bit error rate(BER).We consider two different schemes based on the level of adaptivity:in the?rst scheme,only the modulation levels are adjusted while the power is allocated uniformly across the subcarriers,whereas in the second scheme, both the modulation levels and the power are adjusted adaptively. For both schemes we linearly predict the channel one travel time ahead so as to improve the performance in the presence of a long propagation delay.The system design assumes a feedback link from the receiver that is exploited in two forms:one that conveys the modulation alphabet and quantized power levels to be used for each subcarrier,and the other that conveys a quantized estimate of the sparse channel impulse response.The second approach is shown to be advantageous,as it requires signi?cantly fewer feedback bits for the same system throughput.The effectiveness of the proposed adaptive schemes is demonstrated using computer simulations,real channel measurements recorded in shallow water off the western coast of Kauai,HI,USA,in June2008, and real-time at-sea experiments conducted at the same location in July2011.We note that this is the?rst paper that presents adaptive modulation results for UWA links with real-time at-sea experiments.
Index Terms—Adaptive modulation,feedback,orthogonal frequency-division multiplexing(OFDM),underwater acoustic (UWA)communication.
Manuscript received February26,2012;revised October17,2012and Feb-ruary12,2013;accepted March12,2013.Date of publication May24,2013; date of current version April10,2014.This work was supported by the Multidis-ciplinary University Research Initiative(MURI)of the U.S.Of?ce of Naval Re-search(ONR)under Grants N00014-07-1-0738/0739,N00014-10-1-0576,and N00014-09-1-0700.
Associate Editor:S.Zhou.
A.Radosevic is with Qualcomm Technologies Inc.,San Diego,CA92122 USA(e-mail:radosevica@https://www.wendangku.net/doc/6a11727655.html,).
R.Ahmed and M.Stojanovic are with the Department of Electrical and Com-puter Engineering,Northeastern University,Boston,MA02115USA(e-mail: rarameez@https://www.wendangku.net/doc/6a11727655.html,;millitsa@https://www.wendangku.net/doc/6a11727655.html,).
T.M.Duman is with the Department of Electrical and Electronics En-gineering,Bilkent University,Bilkent,Ankara06800,Turkey(e-mail: duman@https://www.wendangku.net/doc/6a11727655.html,.tr).
J.G.Proakis is with the Department of Electrical and Computer Engi-neering,University of California at San Diego,La Jolla,CA92093USA (e-mail:jproakis@https://www.wendangku.net/doc/6a11727655.html,).
Color versions of one or more of the?gures in this paper are available online at https://www.wendangku.net/doc/6a11727655.html,.
Digital Object Identi?er10.1109/JOE.2013.2253212
I.I NTRODUCTION
U NDERWATER ACOUSTIC(UWA)channels are con-sidered as some of the most challenging communication media,generally characterized by low propagation speed of sound in water(nominally1500m/s),limited bandwidth,and randomly time-varying multipath propagation which results in frequency-selective fading[1].Delay spreading in an UWA channel can occur over tens of milliseconds;however,the channel impulse response often has a sparse structure,with only a few propagation paths carrying most of the channel energy.
Orthogonal frequency-division multiplexing(OFDM)has re-cently emerged as a promising alternative to single-carrier sys-tems for UWA communications because of its robustness to channels that exhibit long delay spreads and frequency selec-tivity[2]–[14].However,applying OFDM to UWA channels is a challenging task because of its sensitivity to frequency offset that arises due to motion.In particular,because of the low speed of sound and the fact that acoustic communication signals oc-cupy a bandwidth that is not negligible with respect to the center frequency,motion-induced Doppler effects result in major prob-lems such as nonuniform frequency shift across the signal band-width and intercarrier interference(ICI)[15],[16].
Time-varying multipath propagation and limited bandwidth place signi?cant constraints on the achievable throughput of UWA communication systems.To support high spectral ef?ciencies over long intervals of time in a nonstationary environment such as the UWA channel,we consider commu-nication systems employing adaptive modulation schemes. While adaptive signaling techniques have been extensively studied for radio channels[17]–[21],only preliminary results for UWA channels are reported in[22]–[26],where simulations and recorded data are used to demonstrate the effectiveness of the proposed adaptation metrics.
The performance of an adaptive system depends on the trans-mitter’s knowledge of the channel which is provided via feed-back from the receiver.Since sound propagates at a very low speed,the design and implementation of an adaptive system es-sentially relies on the ability to predict the channel at least one travel time ahead.This is a very challenging task for communi-cations in the range of several kilometers which imposes signi?-cant limitations on the use of feedback.However,our prior work has shown that channel prediction is possible over such intervals of time using a low-order predictor[27].Crucial to successful
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Fig.1.The adaptive system with the important functional blocks.
channel prediction is motion compensation that stabilizes the nonuniform Doppler shift and enables (sparse)channel estima-tion.The so-obtained channel estimates contain only a few sig-ni ?cant coef ?cients that are shown to be stable enough to sup-port prediction several seconds into the future.
In this paper,we design an adaptive OFDM system and study its performance using recorded test channels and real-time at-sea experiments.Our approach and contributions are the following.
?We estimate small Doppler rates (less than 10)that cor-respond either to drifting of the instruments,or residuals after initial resampling in mobile systems (e.g.,systems using autonomous underwater vehicles).Proper Doppler compensation ensures stability over intervals of time that are long enough to support channel prediction several sec-onds ahead.
?We exploit the sparse multipath structure of the channel impulse response to estimate the most signi ?cant channel paths and simplify the prediction problem.Speci ?cally,we estimate only a few signi ?cant paths of the channel within a possibly large overall delay spread.We treat the statistical properties of the underlying random process of the channel fading as unknown,and compute the parameters of a linear predictor adaptively,by applying a recursive least squares (RLS)algorithm [28].
?We develop two modulation schemes,distinguished by the level of adaptivity:Scheme 1adjusts only the modula-tion level and assumes a uniform power allocation,while scheme 2adjusts both the modulation level and the power allotted to each subcarrier.Both schemes are based on a greedy algorithm whose optimality was discussed in [20].?We propose a new design criterion for an adaptive OFDM system based on the information that is fed back to the transmitter.Speci ?cally,we consider two cases.In the ?rst case,the information about the modulation alphabet and the quantized power level for each subcarrier is computed at the receiver and fed back to the transmitter.In the second case,the quantized channel estimates are fed back,and the adaptive algorithm for bit loading and power allocation is implemented at the transmitter.
?We demonstrate the effectiveness of the proposed adap-tive schemes using computer simulations,test channels recorded during the Kauai Acoustic Communications Mul-tidisciplinary University Research Initiative (MURI)2008(KAM08)experiment in shallow water off the western
coast of Kauai,HI,USA,in June 2008,and real-time at-sea experiments conducted during the Kauai Acoustic Com-munications MURI 2011(KAM11)experiment at the same location in July 2011.The numerical and experimental re-sults show that the adaptive modulation scheme can pro-vide signi ?cant throughput improvements as compared to conventional,nonadaptive modulation for the same power and target bit error rate (BER).
The paper is organized as follows.In Section II,we describe the system and the channel model that characterizes an UWA channel.In Section III,we introduce a linear RLS predictor for the channel tap coef ?cients.In Section IV,we introduce the rules for selection of the modulation levels,the information that is fed back to the transmitter,and the adaptive OFDM schemes.In Section V,we demonstrate the performance of the proposed adaptive schemes using numerical and experimental results that are based on recorded test channels and real-time at-sea trials,respectively.In Section VI,we provide concluding remarks.
II.S YSTEM AND C HANNEL M ODEL
Let us consider an OFDM system with subcarriers,where the th block of the input data symbols ,
,is modulated using the inverse fast Fourier transform (IFFT).The block of input data consists of information-bearing sym-bols and pilots,with corresponding sets denoted as and ,respectively.We assume that the information symbols are inde-pendent,while candidate modulation schemes are binary phase-shift keying (BPSK),quadrature phase-shift keying (QPSK),8phase-shift keying (8PSK),and 16-quadrature amplitude mod-ulation (16QAM)with 2-D Gray mapping.In other words,for the th subcarrier,where ,and the th block,the modu-lation level ,and if no data are transmitted
.It is assumed that the pilot symbols take
values from the QPSK modulation alphabet.For each modula-tion alphabet,we assume a uniform distribution of the constel-lation points with a normalized average power.The transmitter sends frames of OFDM blocks,such that one OFDM block oc-cupies an interval ,where and are the symbol duration and the guard time interval,respectively.We denote by the total bandwidth of the system,by the fre-quency of the ?rst subcarrier,by the central frequency,and by the subcarrier separation.
In this paper,we consider an adaptive system illustrated in Fig.1.The different functional blocks of the system,such as channel and Doppler estimation,channel prediction,adaptive
RADOSEVIC et al.:ADAPTIVE OFDM MODULATION FOR UNDERWATER ACOUSTIC COMMUNICATIONS359
allocation,and feedback information,are discussed in the rest of the paper.
A.Channel Model
Let us now de?ne the impulse response of the overall channel
(1) where is the number of distinct propagation paths,is the delay variable,and is the time at which the channel is ob-served.Coef?cient represents the real-valued gain of the th path,and represents the corresponding delay.Here, we emphasize that the channel model(1)includes the initial re-sampling operation at the receiver by a common Doppler factor. Assuming a high bandwidth(suf?cient resolution in the delay variable),the set of coef?cients offers a good representation of the actual propagation paths.The re-ceived signal is given as
(2) where is the transmitted signal and represents the ad-ditive white Gaussian noise(AWGN)process with zero mean and power spectral density normalized to unity.1If we also de-?ne the equivalent baseband signals and with respect to the frequency,such that
(3) we then obtain
(4)
where
(5) and is the equivalent baseband noise.Equation(4)implies the equivalent baseband channel response
(6)
B.Modeling of the Time-Varying Path Delay
Following the approach from our previous work[27],we model the time-varying path delays as
(7)
1The AWGN assumption incurs no loss of generality of the proposed adaptive scheme even though acoustic noise is not white.where is the Doppler scaling factor,which is some func-tion of time.This model includes the?xed term,which describes the nominal propagation delay corresponding to the system geometry at the time of transmission,and an additional term that describes the effect of motion at the time of either due to drifting of the instruments(Doppler rates less than10)in stationary systems,or residuals after initial resampling in mobile systems(e.g.,systems using au-tonomous underwater vehicles).The system motion during a period of time corresponding to a few seconds(or several data packets)is modeled by velocity and acceleration terms which lead to a linear Doppler rate.A more accurate model could include higher order terms;however,experimental results con-?rm that this is not necessary.Speci?cally,we model as a piecewise linear function
(8) where,and are the Doppler scaling factors evaluated at time instances.
This channel model is deemed suitable for the time scales of interest to an adaptive UWA communication system,since pro-viding a reliable predicted channel state information(CSI)de-pends on the availability of a stable signal reference that can be obtained through accurate motion compensation.For example, for a2-km link and the center frequency20kHz,a small Doppler rate can cause the phase of in (5)to change up to radians during a time interval of1.33s that corresponds to the propagation delay of one travel time.2 Such a phase shift can considerably degrade the performance of channel prediction and the reliability of the corresponding CSI. In other words,proper Doppler compensation ensures stability over intervals of time that are long enough to support channel prediction several seconds ahead.
Model(7)allows one to decouple phase into two terms:one that is not related to motion,and another that is re-lated to motion.While the?rst term may not be predictable with suf?cient accuracy because frequency may be several orders of magnitude larger than the inverse of the path delay,the second term can be predicted using the estimates of the Doppler scaling factors.With this fact in mind,we proceed to develop a channel prediction method that focuses on two general terms:a complex-valued coef?cient and a mo-tion-induced phase.In other words, we model the baseband channel as
(9) where we treat each as an unknown complex-valued channel coef?cient,which is assumed to be stable over a prolonged period of time(tens of seconds),and as an unknown motion-induced phase,which is modeled as a second-order polynomial based on expressions(7)and(8).We 2Here we should make a distinction between making the prediction for one travel time ahead,and for the round-trip time(two travel times ahead),since the two cases correspond to different feedback implementation strategies,i.e., different functions performed by the two ends of a link.
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Fig.2.Channel estimates obtained by the RLS and the MP algorithm. emphasize that this model is valid for some interval of time,but its parameters may change from one such interval to another. Our goal is to develop a two-step procedure in which we?rst estimate the channel coef?cients at the receiver from a probe signal,and then use the so-obtained estimates to form predic-tions,which are?nally fed back to the transmitter.This CSI will be used at the receiver(or the transmitter)to perform adaptive allocation of the modulation levels and power for each subcar-rier in the current OFDM block transmission.
C.Channel Estimation
Channel estimation consists of two steps.In the?rst step, initial phase compensation is performed to produce a stable reference signal.This step includes resampling by a nominal (average)Doppler factor and removal of the phase offset. Here,we should emphasize that the process relies on the esti-mates of the Doppler scaling factors,which are assumed to be available with a certain precision(e.g.,from a dedicated synchronization preamble).In the second step,the so-obtained signal is used to estimate the path coef?cients.The Doppler factors are not needed thereafter,as we conjecture that the channel coef?cients after motion compensation exhibit suf?cient stability to allow prediction several seconds into the future.
Fig.2illustrates the channel estimates obtained from real data collected during the KAM08experiment.Speci?cally, in this section,we will focus on channel estimates obtained from a short probe signal described in[29].After the initial phase compensation where a phase-locked loop(PLL)was used,we perform channel estimation from the received signal using the matching pursuit(MP)algorithm[30].Note from Fig.2that the MP algorithm produces eight coef?cients,where neighboring coef?cients belong to the same propagation path due to the path dispersion[1].For further analysis,we weigh the adjacent coef?cients based on the channel tap power and merge them,so as to represent the channel via four propagation paths,,,and.Therefore,the MP algorithm provides estimates of the channel coef?cients,assuming that channel coef?cients are suf?cient for the description of the sparse multipath structure.These estimates are denoted
by Fig.3.(a)Magnitudes and(b)phases of the channel path coef?cients.
,and computed at time instances separated by
155ms.For comparison purposes,we also provide the channel estimate obtained using the RLS algorithm.Different peaks in the channel estimates can be associated with multiple surface and bottom re?ections calculated from the geometry of the experiment.As can be seen from the?gure,the MP algorithm successfully estimates the signi?cant channel coef?cients,and reduces the estimation error with respect to that incurred by the RLS algorithm.
We emphasize that positions of the signi?cant paths may drift on a larger time scale(tens of seconds),and,therefore,have to be updated accordingly.In Fig.3,we show the magnitudes and phases of the signi?cant paths over a time period of8s.As we initially conjectured,the phases of remain relatively stable for more than a few seconds(a propagation delay over several kilometers).
III.C HANNEL P REDICTION
As we previously reported in[27],the future values of are predicted from the estimates.In particular, if the OFDM blocks are periodically transmitted at time instances,we use observations made at times
to predict the channel at time.To account for possible correlation between the path coef?cients, we allow for their joint prediction.In other words,we use all channel coef?cients to predict each new coef?cient.The prediction is thus made as
(10) where
(11)
(12) Matrix contains prediction coef?cients that are to be determined.
RADOSEVIC et al.:ADAPTIVE OFDM MODULATION FOR UNDERWATER ACOUSTIC COMMUNICATIONS 361
Table I
P REDICTION RLS A
LGORITHM
Because the second-order statistics are not available for the random process ,we compute adaptively,by ap-plying the RLS algorithm as speci ?ed in Table I.In (14),is an matrix,which represents an estimate of the in-verse joint autocorrelation matrix and is a small constant,typically a fraction of the minimum among vari-ances of the channel coef ?cients jointly predicted by the RLS algorithm.
As discussed earlier,UWA systems suffer from inherently long propagation delays,which pose additional challenges in the design of a predictor.To counteract this problem,channel prediction one travel time ahead is achieved by using an RLS predictor of a low-order (e.g.,or )and a small forgetting factor [e.g.,,which corresponds to
an effective window of length
].Note that the forgetting factor is uniquely speci ?ed for all channel coef ?cients.With a small order and only a few sig-ni ?cant paths,i.e.,a small ,computational complexity of joint channel prediction is suf ?ciently low to allow for a practical im-plementation.
The structure of matrix is primarily driven by the ge-ometry of the propagation environment,i.e.,not all of the prop-agation paths are mutually correlated.In the present data set,the strongest arrival often exhibits more stability,and the con-tribution from the other,weaker paths in its prediction appears to be negligible.Therefore,the strongest path can be predicted independently,without loss in performance.In other words,if channel coef ?cient corresponds to the strongest path,(18)can be modi ?ed as follows:the th column of is recursively updated only for those elements that correspond to the prior ob-servations of the th coef ?cient
.In addition,exploiting the correlation among the re-maining paths may lead to a performance improvement,whose exact amount is determined by the environmental pro ?le,and accuracy of the channel and Doppler estimates.
After performing channel prediction at the receiver,the so-obtained CSI is used to initialize adaptive allocation of the modulation levels and power across the OFDM subcarriers.As we will discuss later,depending on which end of the com-munication link performs adaptive allocation,different types of information are fed back over a low-rate feedback channel.In the following,we describe the design framework,initially
proposed in [26],under which we developed two practical adaptive modulation schemes,and we also discuss the design of bandlimited feedback.
IV .A DAPTIVE M ODULATION AND P OWER A LLOCATION The system model assumes that residual Doppler effects are negligible after proper initial motion compensation [resampling by a nominal Doppler factor and removal of the phase offset
].After this initial step,it is also assumed that the channel is constant at least over the transmission interval of one OFDM block.Therefore,the received signal can be expressed as
(20)
where
(21)
and ,,and are,respectively,the received signal after fast Fourier transform (FFT)demodulation,the transmitted power,and zero-mean circularly symmetric complex AWGN
with variance
per dimension.The noise term includes the effects of ambient noise and residual ICI on the th subcarrier and the th OFDM block,which is approximated as a Gaussian random variable.
For the transmission of each OFDM block,we adaptively
compute the size of the modulation alphabet
and the transmission power .The objective of our adaptive OFDM system is to maximize the throughput by maintaining a target average BER.To maintain the BER at a ?xed value,we propose the following optimization criterion:
maximize subject to
(22)
where is the overall average power allocated to the th OFDM block,is the average BER for the th subcarrier,and is the target average BER.The average power can be
expressed as
where is a constant and is the residual power from the previous block which was not allocated (i.e.,is less than the minimum power increment required by the algorithm for a one-bit increase of the overall throughput).Here,we should emphasize the difference be-tween total power allocation and distribution of this total power among the subcarriers.In the former case,one can design
an adaptive scheme where the total power
is adaptively allocated (and uniformly distributed among the subcarriers)to achieve the prespeci ?ed performance [e.g.,the target average BER or signal-to-noise (SNR)at the receiver]for the ?xed system throughput,whereas in the latter case,the ?xed total
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power is nonuniformly distributed among the subcarriers to achieve the prespeci?ed performance,and to maximize the system throughput.For the purpose of experimental sea trials, the total power allocation is initially set to a value which is able to support the target error rate,and avoid the outage scenario(no data transmission).
To reduce the computational complexity of the adaptive algo-rithm,the subcarriers of the th OFDM block can be grouped into clusters.If we assume,we group consecutive sub-carriers into clusters,where is the size of each cluster.We denote by and,respectively,the allocated power and the level to the th cluster,.The optimal power level for each cluster depends on the transfer function of the channel.If the channel does not change much within a cluster,computation of and is performed based on the average channel gain cluster that if a cluster is affected by a deep fade, it will be dominated by the subcarrier with the lowest channel gain.Clustering reduces the computational load(see[26]for more details),but implies possible error penalization and/or a decrease in throughput as compared to the full computation of modulation levels and powers for all subcarriers.
A.Thresholds for Modulation Levels
Due to the large propagation delays,the proposed adaptive OFDM transmission relies on channel prediction.We obtain predictions of the channel gains one travel time ahead based on the time-domain predictions of the most signi?cant channel coef?cients(10).We model the prediction error on the th channel path as a complex zero-mean circularly symmetric Gaussian random variable with variance per dimension. Furthermore,based on the a priori obtained from the channel prediction,we model as a complex Gaussian random variable with mean
(23) and variance,where is the number of sig-ni?cant time-domain channel coef?cients.Assuming that the current channel gain is perfectly known,we apply max-imum likelihood symbol detection for the AWGN channel at the output of the matched?lter.Thus,the probability of bit error for the th subcarrier for M-ary phase-shift keying(MPSK)/mul-tiple quadrature amplitude modulation(MQAM)is well approx-imated by[18]
(24) where coef?cients are determined numerically for each modulation alphabet,as accurately as desired for the BER approximation and take values for
,respectively.
For transmission of the th OFDM block,the adaptive system has knowledge of the predicted values,but not of the full channel.Therefore,from(24),the average BER on the th subcarrier is obtained as[18]
(25) For a given target,we now compute the thresholds for the available modulation levels.The solution for is given by
(26) where is the principal branch of the Lambert -function,the inverse function of.Note that if,the threshold goes to zero,i.e.,
.This case corresponds either to high SNR regimes with reliable CSI,or to very high target BERs of the system.Reasonably accurate approximations for,which can be computed ef?ciently,are provided in[31].We should emphasize that different thresholds correspond to different av-erage values of,since all of the subcarriers are affected by the prediction error of the same variance.
The optimization problem(22)is hard to solve from the standpoint of a practical implementation,because it is com-putationally too intensive to be run at the receiver(or the transmitter)for every OFDM block.Therefore,we pursue suboptimal solutions which are obtained by relaxing one of the problem constraints.Speci?cally,we focus on two adaptive schemes in the rest of this section.
B.Adaptive Scheme1
The optimal solution for(22)includes a nonuniform power allocation for a maximum attainable throughput,such that the target average BER is.This causes that each subcarrier con-tributes to the average BER differently,due to the frequency se-lectivity of the channel.However,the problem can be simpli?ed if we consider adaptive allocation of the modulation levels while distributing the power uniformly among the subcarriers.Since we adaptively allocate only the modulation levels,the so-ob-tained solution for(22)will be suboptimal.Speci?cally,we apply a greedy algorithm that computes the modulation levels in a given block using the allocations from the previous block for initialization.The proposed algorithm is given in
RADOSEVIC et al.:ADAPTIVE OFDM MODULATION FOR UNDERWATER ACOUSTIC COMMUNICATIONS 363
Table II
M ODULATION L EVEL A
LLOCATION
Table II.Similar greedy algorithms have already been consid-ered in [32]and [33].
After initialization of the algorithm for each subcarrier,as given by (27)–(30),we successively increase the modulation levels for those subcarriers that require the smallest power in-crement (31)–(43),while maintaining the average BER below the target .If the set of modulation levels from the previous transmission interval is not a greedy-based solution for the cur-rently available CSI ,the algorithm greedily searches for the closest solution which is used as a new initialization point of the algorithm.Also,if the algorithm does not support the throughput from the previous transmission interval (i.e.,it fails during the initialization step),it searches for the subcarrier with the largest power decrement that is required to decrease the modulation level .The algorithm is terminated when the
prespeci ?ed
is achieved.C.Adaptive Scheme 2
In the second scheme,we consider adaptive allocation of the
modulation levels and the subcarrier powers such that for each subcarrier.
Once the thresholds are computed from (26),we apply the adaptive algorithm of Table III to generate the signal of the th
OFDM block.The algorithm is terminated when the available power is exhausted,or when all subcarriers achieve the max-imum modulation level (16QAM).Here,we emphasize that for those subcarriers that are in a deep fade no data are transmitted (zero power is allocated).In other words,the subcarrier with index is in deep fade if threshold is high enough to violate the power constraint in (22).
Because of the additional freedom to adjust the power,this scheme will achieve a higher overall throughput as compared to scheme 1.
D.Limited Feedback for Adaptive UWA Systems
We assume that a limited-feedback channel is available for conveying information from the receiver back to the transmitter.The receiver has knowledge of the channel frequency response at subcarrier frequencies and the corresponding channel im-pulse response.The transmitter needs to know the modulation levels and the power levels at the frequencies.To accomplish this,there are different feedback options.Here we consider three different alternatives.
A ?rst option is to feed back the frequency response at the subcarriers,where is typically of the order of 1000.If the channel frequency response changes slowly across frequencies,neighboring subcarriers would be allocated the same modula-tion and power levels.In such a case,it is not necessary to feed back the channel frequency response in amplitude and phase for each subcarrier.Hence,the total number of bits fed back can be reduced from a factor of to some factor,say ,where is the number of subcarriers contained in the coher-ence bandwidth of the channel.
A second option is to transmit the actual modulation levels and the power levels directly to the transmitter at the subcar-rier frequencies.bits may be used to represent the available modulation levels.For example,in our case,we used
bits.The power levels can be uniformly quantized,such that bits are used to represent each quantization level.
A third option is to feed back the values of the quantized channel impulse response.Since the impulse response is sparse,the total number of bits required to convey this information to the transmitter is ,where is the number of sig-ni ?cant coef ?cients in the channel impulse response (typically,
or less for the shallow-water channels considered),is the number of bits required to represent the quantized com-plex-valued channel coef ?cients,and is the number of bits required to represent the time delay of each dominant channel coef ?cient.
To further reduce the number of bits fed back to the trans-mitter,we applied a lossless data compression technique.In par-ticular,we employed run length encoding (RLE)[34],which is a simple coding scheme that provides good compression of data that contains many runs of zeros or ones,together with the well-known Lempel–Ziv–Welch (LZW)algorithm (used as an inner code)[35],to ef ?ciently compress the feedback informa-tion.As we will see in the following section,assuming perfect CSI at the receiver,feeding back the sparse channel impulse re-sponse and computing the modulation levels and power levels at the transmitter requires signi ?cantly fewer bits.
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Table III
M ODULATION AND P OWER L EVEL A
LLOCATION
V .N UMERICAL AND E XPERIMENTAL R ESULTS
In this section,we present numerical and experimental re-sults on the performance of the proposed adaptive schemes from Section IV.The numerical results are based on channel mea-surements recorded during the KAM08experiment,and exper-imental results from the real-time at-sea trials that were con-ducted during the KAM11experiment.Both experiments were conducted at the same location with operational areas marked in Fig.4.
A.Numerical Results From the KAM08Experiment
The KAM08experiment took place in 100-m deep water,with a communication distance of 4km.The transmitter was de-ployed at the location Sta00(see Fig.5)as a 52.5-m aperture ver-tical array of eight ITC-1001transducers (7.5-m spacing),with a sampling rate of 100kHz.The receiver was deployed at the location Sta08as a 56.25-m aperture vertical line array (VLA)of 16elements (3.75-m spacing),with a sampling rate of 50kHz.The performance results are based on the channel estimates for transmissions between the fourth trans-ducer from the bottom (49.5m deep)and the tenth hydrophone from the bottom (65.25m deep).The total bandwidth and the guard time are 7.8kHz and 100ms,respectively.We assume an OFDM transmission with subcarriers and a frequency separation of 15.25Hz.The target average BER is .We estimate the channel using the MP
algorithm,and predict the ?ve signi ?cant channel coef ?cients 2.67s ahead.
Fig.4.The KAM08and KAM11operational areas are outlined by the dashed and solid lines,respectively.
Fig.5.Mooring deployment positions during the KAM08in latitude and lon-gitude.The acoustic source array was located at Sta00,while the VLAs were located at Sta08and Sta16.
Fig.6presents achievable throughput results for the OFDM systems that employ scheme 1and scheme 2without clustering for 24dB,which is measured relative to the overall channel power.We also provide performance results for the nonadaptive scheme (with uniform power and modulation levels)and the optimal solution,which is evaluated using the interior-point method [36]to solve the nonlinear convex opti-mization problem (22).Interestingly,scheme 2shows a slight performance loss only for the high SNR regime as compared to the optimal solution,while scheme 1exhibits a performance degradation for the entire SNR region.Both schemes signi ?-cantly outperform the nonadaptive solution.
In Fig.7,we summarize the feedback requirements of scheme 2without clustering .Feeding back the power and mod-
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Fig.6.Throughput performance of the various schemes
considered.
Fig.7.Performance of limited feedback for scheme 2with ,overall
power
of 60dB,and average throughput of 3b per subcarrier.Numbers on the graph indicate the number of bits that are used to represent the quantized power levels (dashed line),and the real and imaginary parts of each quantized channel coef ?cient (solid line).
ulation level computed at the receiver clearly requires more bits than feeding back the (sparse)channel response.2,3,4,and 5b are used to represent the quantized power levels,and
3b are used to represent the ?ve modulation levels (no transmission,BPSK,QPSK,8PSK,and 16QAM),resulting in a total of 2560,3072,3584,and 4096b with and .The feedback information is then compressed,as discussed in Section IV,resulting in 201,245,294,and 350b (the values in-dicated on the -axis).If the channel response is fed back,
3,4,,10b are used to represent the real and the imaginary parts of each quantized channel coef ?cient,and 8b are used to represent the corresponding delays.The feedback infor-mation is then compressed similarly as in the previous strategy.We note that the minimum number of bits required to
maintain the target average BER at 10is 350and 120for the two cases,
Fig.8.Mooring deployment positions during the KAM11in latitude and lon-gitude.The VLAs were located at Sta05and
Sta10.The acoustic source array was located at the ship and used when the ship was stationary.
Fig.9.The geometry and the setup of the adaptive system.
i.e.,that feeding back the channel response reduces the feed-back requirements approximately threefold.When clustering is applied,the two feedback strategies require a similar number of bits to feed back;however,clustering is performed at the ex-pense of reducing the overall throughput of scheme 2.B.Experimental Results From the KAM11Experiment The KAM11experiment took place in 100–120-m-deep water,with communication distances of 1,2,and 3km.The transmitter was deployed from the ship as a 1.5-m aperture vertical array of four ITC-1032transducers (0.5-m spacing)at different locations within the operational area while the ship was stationary.The sampling rate was 100kHz.The radio-frequency (RF)-coupled receiver was deployed at loca-tions Sta05and Sta10(see Fig.8)as a 0.6-m aperture VLA of four elements (0.2-m spacing),with a sampling rate of
100kHz.Both the transmitter and the receiver were deployed in the middle of the water column.A feedback from the recorder buoy was provided using an RF link.The geometry of the experiment and the setup of the system are given in Fig.9.Due to the variations of the channel that are inherently present,and different communication distances tested in the ?eld,a typical SNR at the receiver varied between 2and 20dB.
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The OFDM frame contains four blocks with sub-carriers per block,at a central frequency of30kHz.The re-ceiver operates coherently where50%of subcarriers are used as pilots to accommodate for real-time testing of the system,since the channel multipath structure can signi?cantly change during an experimental trial(tens of minutes or even hours).Note that such a high overhead will not be required in practice when a propagation model can be run before deployment to evaluate the multipath extent for a given system geometry.The total band-width and the guard time are10kHz and100ms, respectively.Frame synchronization is performed using a PN sequence of duration25ms and the symbol rate10ksymb/s. The presented performance results are generated by employing maximal ratio combining(MRC)of signals received at four el-ements.However,we should emphasize that even though MRC is used for data detection,we use only one receive element to perform channel estimation and adaptive allocation to minimize the processing time at the receiver.
The adaptive system is initialized at the transmitter end(a terminal at the ship)by sending activation commands to the re-ceiver end(a terminal at the RF-coupled buoy)through the wire-less link.Once a con?rmation message is received from the re-ceiver terminal,the transmitter end executes a sequence of op-erations such as acquiring the ship position from a Global Po-sitioning System(GPS),gathering various environmental data, etc.This is followed by the?rst OFDM frame transmission with a uniform power allocation and QPSK modulation alphabet for all data subcarriers.Once the frame is detected at the receiver, it is stored at the local driver for further processing.In partic-ular,we perform initial synchronization using the PN preamble, which is followed by PLL-based Doppler estimation and com-pensation,as suggested in[15];we then conduct channel esti-mation over the uniformly spaced pilot grid using the orthogonal matching pursuit(OMP)algorithm[30],and perform coherent detection for each OFDM block of the received frame;?nally, using the channel estimates,we execute scheme2at the receiver to compute the power and modulation levels,which are then fed back to the transmitter and used for the next OFDM frame trans-mission.During each real-time trial,we transmitted between30 and50consecutive OFDM frames to demonstrate the perfor-mance of the proposed adaptive scheme,and the functionality of the implemented system.
Among various constraints on the real-time implementation of the system(e.g.,out-of-band interference from the other sys-tems simultaneously tested,a weak RF link for certain positions of the ship,weather conditions,etc.),the most important limita-tion is determined to be the total round-trip time of the system that was on the order of10–20s.This signi?cant delay was mainly caused by all-level processing at both sides of the link (acquiring GPS and environmental data before each transmis-sion and after each reception,frame acquisition,recording,data processing including prediction and adaptive allocation,etc.), while physical propagation contributed with delays of0.67–2s. Note that the RF feedback imposes no signi?cant delay in the system,and the total round-trip time is mainly determined by high processing delays.Since these delays were on the order of several seconds,a good performance of the proposed schemes is expected for channels whose coherence time sits within
this Fig.10.Channel estimates from initial frame synchronization preamble for three consecutive nonadaptive OFDM frame transmissions.The average time interval between two consecutive frame transmissions is(roughly)20s. interval.In contrast,for rapidly varying channels,high pro-cessing delays will result in a poor performance of channel pre-diction and outdated CSI(which can be seen as feedback error). Here,we should emphasize that the ultimate performance limi-tation of an adaptive UWA system will not be determined by the processing delay,but by the physical propagation delay,which gives a lower bound on the channel coherence time that can be supported.
As discussed in Sections II and III,some channel measure-ments indicated that the channel coherence time was3–4s(or more),which allowed us to perform channel prediction and minimize feedback errors.These conditions notably prevailed during sea trials when the channel conditions were calm(e.g., wind speed of2–8kn and Doppler rates of10),while higher wind speeds usually coincided with reduced coherence time. In the rest of this section,we will focus on the experimental results obtained from calmer sea trials with the(average) channel coherence time on the order of seconds.We note that channel conditions in general may not be so calm,resulting in a proportional reduction of coherence times that can severely limit the performance of our adaptive scheme.
In Fig.10,we show the channel estimates obtained from the frame synchronization preamble of a2-km link for three consecutive nonadaptive QPSK-modulated OFDM frame trans-missions,labeled as,,and.As mentioned earlier,the av-erage time interval between two consecutive frame transmis-sions is(roughly)20s.Note the signi?cant variations of the channel impulse response within a1-min time interval.For the given consecutive OFDM frame transmissions,in Fig.11,we provide the performance results for the receiver with four ele-ments.Note that poor performance is achieved for transmissions and,while a fair performance is obtained for transmission ,corresponding to very high SNR observed at the receiver(see Fig.10).If the target average BER for OFDM systems is set to10–10,the nonadaptive scheme should use either more power,or reduce the overall throughput by employing the BPSK modulation alphabet which is preferable for the power limited systems.
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Fig.11.Scatter plots for three consecutive nonadaptive OFDM frame transmissions,each containing four OFDM blocks.The average time interval between two
consecutive frame transmissions is(roughly)20s.The corresponding channel impulse response estimates are given in Fig.
10.
Fig.12.Channel estimates from initial frame synchronization preamble for three consecutive adaptive OFDM frame transmissions.The average time in-
terval between two consecutive frame transmissions is(roughly)20
s.
Fig.13.Scatter plot for the?rst adaptive OFDM frame transmission,each containing four OFDM blocks.The adaptive scheme2allocates only QPSK modulation alphabet to the data subcarriers.
In Fig.12,we illustrate channel estimates of a2-km link for three consecutive adaptive OFDM frame transmissions,labeled as,,and.The available adaptive modulation alphabets are BPSK,QPSK,and8PSK.As in the previous set of nonadaptive OFDM block transmissions,we note signi?cant variations in the channel impulse response within a1-min time interval.For the given consecutive OFDM frame transmissions,in Figs.13–15,we provide the performance results for the receiver with four elements.For the target average BER set to10–10,we note that a good performance is achieved for all three transmis-sions(,,and in Figs.13–15,respectively),since scheme 2successfully tracks the underlying channel variations.Due to large propagation delays and channel variations(the coherence time on the order of seconds)that impose severe limitations on channel prediction,the adaptive scheme tends to oscillate in performance around the target BER.In Figs.16–18,we illus-trate the channel frequency response,the allocated power,and modulation levels across the data subcarriers,respectively.A high attenuation in the frequency region30–35kHz is mainly due to the cutoff frequency of the hydrophones,which is lo-cated around30kHz,resulting in a severe rolloff across the upper part of the operational bandwidth.We emphasize that this system limitation was not known a priori,and the whole opera-tional bandwidth(25–35kHz)was used for OFDM transmis-sions.However,scheme2has successfully demonstrated the ability to adapt to the system limitations by allocating the power and modulation levels to the lower part of the frequency region, as illustrated in Figs.17and18.Since the transition band of the hydrophone?lter is not sharp,we can note an active tone lo-cated at30.55kHz;this artifact results from a suf?ciently high channel gain present at the given frequency.
VI.C ONCLUSION
In this paper,we explored design aspects for adaptive OFDM modulation over time-varying UWA channels.First,we investi-gated the possibility of predicting an UWA channel at least one travel time ahead.The key step in providing a stable reference for channel prediction is compensation of the motion-induced phase offset.MP algorithms are used to identify the signi?cant path coef?cients,which are then processed by a low-order adaptive RLS predictor to account for large prediction lags (long feedback delays).Second,assuming that the channel is predicted one travel time ahead with a given accuracy,approx-imate expressions for the BER of each subcarrier(or a cluster of adjacent subcarriers)are obtained.From these expressions, a set of thresholds is obtained that determine the modulation level and the power needed on each subcarrier to maximize the throughput while keeping the average BER at the target level.Third,spectrally ef?cient adaptive schemes(scheme 1and scheme2)are applied to allocate the modulation and
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Fig.14.Scatter plot for the second adaptive OFDM frame transmission,each containing four OFDM blocks.The adaptive scheme 2allocates QPSK and 8PSK modulation to the data
subcarriers.
Fig.15.Scatter plot for the third adaptive OFDM frame transmission,each containing four OFDM blocks.The adaptive scheme 2allocates QPSK and 8PSK modulation alphabets the data
subcarriers.
Fig.16.A sample
estimate of the channel frequency response for the OFDM
system with
subcarriers.Fig.17.A sample power allocation for data subcarriers based on scheme 2and the channel response from Fig.16.
the power across the OFDM subcarriers.Finally,assuming a limited feedback channel,two competitive strategies were analyzed:one that feeds back the quantized power and mod-ulation levels for each subcarrier/cluster,and another that feeds back the quantized estimate of the signi ?cant channel coef ?cients in the time
domain.The second strategy is found to offer better performance,as it requires signi ?cantly fewer
Fig.18.A sample constellation level allocation for data subcarriers based on
scheme 2and the channel response from Fig.16.
feedback bits.Numerical and experimental results that are obtained with recorded channels and real-time at-sea experi-ments,respectively,show that the adaptive modulation scheme provides signi ?cant throughput improvements as compared to conventional,nonadaptive modulation at the same power and target BER.This work leads us to conclude that adaptive modulation methods may be viable for reliable,high-rate UWA communications.To our knowledge,this is the ?rst paper that presents adaptive modulation results for UWA links with real-time at-sea experiments.
A CKNOWLEDGMENT
The authors would like to thank Dr.J.C.Preisig and K.R.Ball for their valuable comments,suggestions,and outstanding technical assistance during the KAM11experiment.
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Andreja Radosevic(S’09)received the B.S.degree
in electrical engineering from the University of Bel-
grade,Belgrade,Serbia,in2007and the M.S.and
Ph.D.degrees in electrical and computer engineering
from the University of California San Diego,La Jolla,
CA,USA,in2009and2012,respectively.
Currently,he works at Qualcomm Technologies
Inc.,San Diego,CA,USA.His research inter-
ests include channel capacity analysis,adaptive
modulation,channel estimation,and orthogonal fre-
quency-division multiplexing(OFDM)techniques, mainly in the context of underwater acoustic
communications.
Rameez Ahmed received the B.Tech.degree in
telecommunication engineering from the Vellore
Institute of Technology,Vellore,Tamil Nadu,India,
in2008and the M.S.degree in electrical and com-
puter engineering from the Northeastern University,
Boston,MA,USA,in2010,where he is currently
working toward the Ph.D.degree.
He is associated with the Communication and
Digital Signal Processing(CDSP)Lab.He conducts
research in underwater acoustic communication.His
research interests include adaptive power control, orthogonal frequency-division multiplexing(OFDM)in the physical layer, fountain codes,ARQ techniques,and their applications to underwater acoustic communication.
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Tolga M.Duman (S’97–M’98–SM’03–F’11)re-ceived the B.S.degree from Bilkent University,Bilkent,Ankara,Turkey,in 1993and the M.S.and Ph.D.degrees from Northeastern University,Boston,MA,USA,in 1995and 1998,respectively,all in electrical engineering.
He is a Professor at the Electrical and Electronics Engineering Department,Bilkent University,and is on leave from the School of Electrical,Computer and Energy Engineering,Arizona State University,Tempe,AZ,USA.Before joining Bilkent University
in August 2012,he was with the Electrical Engineering Department,Arizona State University,?rst as an Assistant Professor (1998–2004),then as an Associate Professor (2004–2008),and starting August 2008as a Professor.His publications include a book Coding for MIMO Communication Systems (New York,NY ,USA:Wiley,2007),over 50journal papers,and over 100conference papers.His current research interests are in systems,with particular focus on communication and signal processing,including wireless and mobile communications,coding/modulation,coding for wireless communications,data storage systems,and underwater acoustic communications.
Dr.Duman is a recipient of the National Science Foundation CAREER Award and the IEEE Third Millennium medal.He served as an editor for the IEEE T RANSACTIONS ON W IRELESS C OMMUNICATIONS (2003–2008),the IEEE T RANSACTIONS ON C OMMUNICATIONS (2007–2012),and the IEEE O NLINE J OURNAL OF S URVEYS AND T UTORIALS (2002–2007).He is currently the coding and communication theory area editor for the IEEE T RANSACTIONS ON C OMMUNICATIONS (2011–present)and an editor for Elsevier’s Physical Communications Journal
(2010–present).
John G.Proakis (S’58–M’62–F’84–LF’99)re-ceived the B.S.E.E.degree from the University of Cincinnati,Cincinnati,OH,USA,in 1959,the M.S.E.E.degree from the Massachusetts Institute of Technology (MIT),Cambridge,MA,USA,in 1961,and the Ph.D.degree from Harvard University,Cambridge,MA,USA,in 1967.
He is an Adjunct Professor at the University of California at San Diego,La Jolla,CA,USA,and a Professor Emeritus at Northeastern University,Boston,MA,USA.He was a faculty member at
Northeastern University from 1969through 1998and held the following aca-
demic positions:Associate Professor of Electrical Engineering (1969–1976),Professor of Electrical Engineering (1976–1998),Associate Dean of the College of Engineering and Director of the Graduate School of Engineering (1982–1984),Interim Dean of the College of Engineering (1992–1993),and Chairman of the Department of Electrical and Computer Engineering (1984–1997).Before joining Northeastern University,he worked at GTE Laboratories and the MIT Lincoln Laboratory.His professional experience and interests are in the general areas of digital communications and digital signal processing.He is the coauthor of the books Digital Communications (New York,NY ,USA:McGraw-Hill,2008,5th ed.),Introduction to Digital Signal Processing (Upper Saddle River,NJ,USA:Prentice-Hall,2007,4th ed.),Dig-ital Signal Processing Laboratory (Englewood Cliffs,NJ,USA:Prentice-Hall,1991),Advanced Digital Signal Processing (New York,NY ,USA:Macmillan,1992),Algorithms for Statistical Signal Processing (Upper Saddle River,NJ,USA:Prentice-Hall,2002),Discrete-Time Processing of Speech Signals (New York,NY,USA:Macmillan,1992,IEEE Press,2000),Communication Systems Engineering (Upper Saddle River,NJ,USA:Prentice-Hall,2002,2nd ed.),Digital Signal Processing Using MATLAB V.4(Boston,MA,USA:Brooks/Co-leThomson Learning,2007,2nd ed.),Contemporary Communication Systems Using MATLAB (Boston,MA,USA:Brooks/Cole-Thomson Learning,2004,2nd ed.),and Fundamentals of Communication Systems (Upper Saddle River,NJ,USA:Prentice-Hall,
2005).
Milica Stojanovic (SM’08–F’10)graduated from the University of Belgrade,Belgrade,Serbia,in 1988and received the M.S.and Ph.D.degrees in electrical engineering from Northeastern University,Boston,MA,USA,in 1991and 1993,respectively.After a number of years with the Massachusetts In-stitute of Technology (MIT),Cambridge,MA,USA,where she was a Principal Scientist,she joined the faculty of Electrical and Computer Engineering De-partment,Northeastern University,in 2008.She is also a Guest Investigator at the Woods Hole Oceano-graphic Institution (WHOI),Woods Hole,MA,USA,and a Visiting Scientist at MIT.Her research interests include digital communications theory,statistical signal processing and wireless networks,and their applications to underwater acoustic communication systems.
Prof.Stojanovic is an Associate Editor for the IEEE J OURNAL OF O CEANIC E NGINEERING and the IEEE T RANSACTIONS ON S IGNAL P ROCESSING .
基于matlab实现OFDM的编码.
clc; clear all; close all; fprintf('OFDM系统仿真\n'); carrier_count=input('输入系统仿真的子载波数: \n');%子载波数128,64,32,16 symbols_per_carrier=30;%每子载波含符号数 bits_per_symbol=4;%每符号含比特数,16QAM调制 IFFT_bin_length=1024;%FFT点数 PrefixRatio=1/4;%保护间隔与OFDM数据的比例1/6~1/4 GI=PrefixRatio*IFFT_bin_length ;%每一个OFDM符号添加的循环前缀长度为1/4*IFFT_bin_length ,即256 beta=1/32;%窗函数滚降系数 GIP=beta*(IFFT_bin_length+GI);%循环后缀的长度40 SNR=10; %信噪比dB %================信号产生=================================== baseband_out_length=carrier_count*symbols_per_carrier*bits_per_symbol;%所输入的比特数目 carriers=(1:carrier_count)+(floor(IFFT_bin_length/4)-floor(carrier_count/2));%共轭对称子载波映射复数数据对应的IFFT点坐标 conjugate_carriers = IFFT_bin_length - carriers + 2;%共轭对称子载波映射共轭复数对应的IFFT点坐标 rand( 'twister',0); %每次产生不相同得伪随机序列 baseband_out=round(rand(1,baseband_out_length));%产生待调制的二进制比特流figure(1); stem(baseband_out(1:50)); title('二进制比特流') axis([0, 50, 0, 1]); %==============16QAM调制==================================== complex_carrier_matrix=qam16(baseband_out);%列向量 complex_carrier_matrix=reshape(complex_carrier_matrix',carrier_count,symbols_per
OFDM技术仿真(MATLAB代码)
第一章绪论 1.1简述 OFDM是一种特殊的多载波传输方案，它可以被看作是一种调制技术，也可以被当作一种复用技术。多载波传输把数据流分解成若干子比特流，这样每个子数据流将具有低得多的比特速率，用这样的低比特率形成的低速率多状态符号再去调制相应的子载波，就构成多个低速率符号并行发送的传输系统。正交频分复用是对多载波调制（MCM，Multi-Carrier Modulation）的一种改进。它的特点是各子载波相互正交，所以扩频调制后的频谱可以相互重叠，不但减小了子载波间的干扰，还大大提高了频谱利用率。 符号间干扰是多径衰落信道宽带传输的主要问题，多载波调制技术包括正交频分复用(OFDM)是解决这一难题中最具前景的方法和技术。利用OFDM技术和IFFT方式的数字实现更适宜于多径影响较为显著的环境，如高速WLAN 和数字视频广播DVB等。OFDM作为一种高效传输技术备受关注，并已成为第4代移动通信的核心技术。如果进行OFDM系统的研究，建立一个完整的OFDM 系统是必要的。本文在简要介绍了OFDM 基本原理后，基于MATLAB构建了一个完整的OFDM动态仿真系统。 1.2 OFDM基本原理概述 1.2.1 OFDM的产生和发展 OFDM的思想早在20世纪60年代就已经提出，由于使用模拟滤波器实现起来的系统复杂度较高，所以一直没有发展起来。在20世纪70年代，提出用离散傅里叶变换（DFT）实现多载波调制，为OFDM的实用化奠定了理论基础；从此以后，OFDM在移动通信中的应用得到了迅猛的发展。 OFDM系统收发机的典型框图如图1.1所示，发送端将被传输的数字信号转换成子载波幅度和相位的映射，并进行离散傅里叶变换（IDFT）将数据的频谱表达式变换到时域上。IFFT变换与IDFT变换的作用相同，只是有更高的计算效
OFDM系统设计及其Matlab实现
课程设计 。 课程设计名称：嵌入式系统课程设计 专业班级： 07级电信1-1 学生姓名：__王红__________ 学号：_____107_____ 指导教师：李国平，陈涛，金广峰，韩琳 课程设计时间：— |
1 需求分析 运用模拟角度调制系统的分析进行频分复用通信系统设计。从OFDM系统的实现模型可以看出，输入已经过调制的复信号经过串／并变换后，进行IDFT或IFFT和并／串变换，然后插入保护间隔，再经过数／模变换后形成OFDM调制后的信号s(t)。该信号经过信道后，接收到的信号r(t)经过模／数变换，去掉保护间隔，以恢复子载波之间的正交性，再经过串／并变换和DFT或FFT后，恢复出OFDM的调制信号，再经过并／串变换后还原出输入符号 2 概要设计 1.简述OFDM通信系统的基本原理 2.简述OFDM的调制和解调方法 3.概述OFDM系统的优点和缺点 4.基于MATLAB的OFDM系统的实现代码和波形 ： 3 运行环境 硬件：Windows XP 软件:MATLAB 4 详细设计 OFDM基本原理 一个完整的OFDM系统原理如图1所示。OFDM的基本思想是将串行数据，并行地调制在多个正交的子载波上，这样可以降低每个子载波的码元速率，增大码元的符号周期，提高系统的抗衰落和干扰能力，同时由于每个子载波的正交性，大大提高了频谱的利用率，所以非常适合移动场合中的高速传输。
在发送端，输入的高比特流通过调制映射产生调制信号，经过串并转换变成N条并行的低速子数据流，每N个并行数据构成一个OFDM符号。插入导频信号后经快速傅里叶反变换(IFFT)对每个OFDM符号的N个数据进行调制，变成时域信号为： [ 式 式1中：m为频域上的离散点；n为时域上的离散点；N为载波数目。为了在接收端有效抑制码间干扰(InterSymbol Interference，ISI)，通常要在每一时域OFDM符号前加上保护间隔(Guard Interval，GI)。加保护间隔后的信号可表示为式，最后信号经并／串变换及D／A转换，由发送天线发送出去。 式 接收端将接收的信号进行处理，完成定时同步和载波同步。经A／D转换，串并转换后的信号可表示为：
无线通信原理 基于matlab的ofdm系统设计与仿真..
基于matlab的ofdm系统设计与仿真
摘要 OFDM即正交频分复用技术，实际上是多载波调制中的一种。其主要思想是将信道分成若干正交子信道，将高速数据信号转换成并行的低速子数据流，调制到相互正交且重叠的多个子载波上同时传输。该技术的应用大幅度提高无线通信系统的信道容量和传输速率，并能有效地抵抗多径衰落、抑制干扰和窄带噪声，如此良好的性能从而引起了通信界的广泛关注。 本文设计了一个基于IFFT/FFT算法与802.11a标准的OFDM系统，并在计算机上进行了仿真和结果分析。重点在OFDM系统设计与仿真，在这部分详细介绍了系统各个环节所使用的技术对系统性能的影响。在仿真过程中对OFDM信号使用QPSK调制，并在AWGN信道下传输，最后解调后得出误码率。整个过程都是在MATLAB环境下仿真实现，对ODFM系统的仿真结果及性能进行分析，通过仿真得到信噪比与误码率之间的关系，为该系统的具体实现提供了大量有用数据。
第一章 ODMF 系统基本原理 1.1多载波传输系统 多载波传输通过把数据流分解为若干个子比特流，这样每个子数据流将具有较低的比特速率。用这样的低比特率形成的低速率多状态符号去调制相应的子载波，构成了多个低速率符号并行发送的传输系统。在单载波系统中，一次衰落或者干扰就会导致整个链路失效，但是在多载波系统中，某一时刻只会有少部分的子信道会受到衰落或者干扰的影响。图1－1中给出了多载波系统的基本结构示意图。 图1-1多载波系统的基本结构 多载波传输技术有许多种提法，比如正交频分复用(OFDM)、离散多音调制(DMT)和多载波调制(MCM)，这3种方法在一般情况下可视为一样，但是在OFDM 中，各子载波必须保持相互正交，而在MCM 则不一定。 1.2正交频分复用 OFDM 就是在FDM 的原理的基础上，子载波集采用两两正交的正弦或余弦函数集。函数集{t n ωcos }， {t m ωsin } (n,m=0,1,2…)的正交性是指在区间(T t t +00,)内有正弦函数同理：)0()()(2/0cos *cos 00===≠?? ???=? +m n m n m n T T tdt m t n T t t ωω 其中ωπ2=T （1-1）
用MATLAB实现OFDM仿真分析
3.1 计算机仿真 仿真实验是掌握系统性能的一种手段。它通过对仿真模型的实验结果来确定实际系统的性能。从而为新系统的建立或系统的改进提供可靠的参考。通过仿真，可以降低新系统失败的可能性，消除系统中潜在的瓶颈。优化系统的整体性能，衡量方案的可行性。从中选择最后合理的系统配置和参数配置。然后再应用于实际系统中。因此，仿真是科学研究和工程建设中不可缺少的方法。 3.1.1 仿真平台 ●硬件 CPU：Pentium III 600MHz 内存：128M SDRAM ●软件 操作系统：Microsoft Windows2000 版本5.0 仿真软件：The Math Works Inc. Matlab 版本6.5 包括MATLAB 6.5的M文件仿真系统。 Matlab是一种强大的工程计算软件。目前最新的6.x版本 (windows环境)是一种功能强、效率高、便于进行科学和工程计算的交互式软件包。其工具箱中包括:数值分析、矩阵运算、通信、数字信号处理、建模和系统控制等应用工具程序，并集应用程序和图形于一便于使用的集成环境中。在此环境下所解问题的Matlab语言表述形式和其数学表达形式相同，不需要按传统的方法编程。Matlab的特点是编程效率高，用户使用方便，扩充能力强，语句简单，内涵丰富，高效方便的矩阵和数组运算，方便的绘图功能。 3.1.2 基于MATLAB的OFDM系统仿真链路 根据OFDM 基本原理，本文给出利用MATLAB编写OFDM系统的仿真链路流程。串行数据经串并变换后进行QDPSK数字调制，调制后的复信号通过N点IFFT变换，完成多载波调制，使信号能够在N个子载波上并行传输，中间插入10训练序列符号用于信道估计，加入循环前缀后经并串转换、D /A后进入信道,接收端经过N点FFT变换后进行信道估计，将QDPSK解调后的数据并串变换后得到原始信息比特。 本文采用MATLAB语言编写M文件来实现上述系统。M文件包括脚本M文件和函数M文件，M文件的强大功能为MATLAB的可扩展性提供了基础和保障,使MATLAB能不断完善和壮大，成为一个开放的、功能强大的实用工具。M文件通过input命令可以轻松实现用户和程序的交互，通过循环向量化、数组维数预定义等提高M文件执行速度，优化内存管理，此外，还可以通过类似C++语言的面向对象编程方法等等。
2010年本科毕业设计：基于MATLAB的OFDM系统仿真及分析
2010年本科毕业设计：基于MATLAB的OFDM系统仿真及分 析 MATLABOFDM 正交频分复用(OFDM) 是第四代移动通信的核心技术。该文首先简要介绍了OFDM的发展状况及基本原理, 文章对OFDM 系统调制与解调技术进行了解析，得 到了OFDM 符号的一般表达式，给出了OFDM 系统参数设计公式和加窗技术的原理 及基于IFFT/FFT 实现的OFDM 系统模型，阐述了运用IDFT 和DFT 实现OFDM 系统的根源所在,重点研究了理想同步情况下,保护时隙(CP)、加循环前缀前后和不同的信道内插方法在高斯信道和多径瑞利衰落信道下对OFDM系统性能的影响。在给出OFDM系统模型的基础上,用MATLAB语言实现了传输系统中的计算机仿真并给出 参考设计程序。最后给出在不同的信道条件下,研究保护时隙、循环前缀、信道 采用LS估计方法对OFDM系统误码率影响的比较曲线,得出了较理想的结论。 : 正交频分复用;仿真;循环前缀;信道估计 I Title: MATLAB Simulation and Performance Analysis of OFDM System ABSTRACT OFDM is the key technology of 4G in the field of mobile communication. In this
article OFDM basic principle is briefly introduced. This paper analyzes the modulation and demodulation of OFDM system, obtaining a general expression of OFDM mark, and giving the design formulas of system parameters, principle of windowing technique, OFDM system model based on IFFT/FFT, the origin which achieves the OFDM system by using IDFT and DFT. Then, the influence of CP and different channel estimation on the system performance is emphatically analyzed respectively in Gauss and Rayleigh fading channels in the condition of ideal synchronization. Besides, based on the given system model OFDM system is computer simulated with MATLAB language and the referential design procedure is given. Finally, the BER curves of CP and channel estimation are given and compared. The conclusion is satisfactory. KEYWORDS:OFDM; Simulation; CP; Channel estimation II
基于Matlab的OFDM系统仿真
论文题目: 基于MATLAB的OFDM系统仿真 学院： 专业年级： 学号： 姓名： 指导教师、职称： 2010 年 12 月 10 日
基于Matlab的OFDM系统仿真 摘要：正交频分复用（OFDM）是一种多载波宽带数字调制技术。相比一般的数字通信系统，它具有频带利用率高和抗多径干扰能力强等优点，因而适合于高速率的无线通信系统。正交频分复用OFDM是第四代移动通信的核心技术。论文首先简要介绍了OFDM 基本原理。在给出OFDM系统模型的基础上，用MATLAB语言实现了整个系统的计算机仿真并给出参考设计程序。最后给出在不同的信道条件下，对OFDM系统误码率影响的比较曲线，得出了较理想的结论，通过详细分析了了技术的实现原理，用软件对传输的性能进行了仿真模拟并对结果进行了分析。 介绍了OFDM技术的研究意义和背景及发展趋势,还有其主要技术和对其的仿真?具体如下:首先介绍了OFDM的历史背景?发展现状及趋势?研究意义和研究目的及研究方法和OFDM的基本原理?基本模型?OFDM的基本传输技术及其应用,然后介绍了本课题所用的仿真工具软件MATLAB,并对其将仿真的OFDM各个模块包括信道编码?交织?调制方式?快速傅立叶变换及无线信道进行介绍,最后是对于OFDM的流程框图进行分析和在不影响研究其传输性的前提下进行简化,并且对其仿真出来的数据图形进行分析理解? 关键词：OFDM；MATLAB；仿真 一、OFDM的意义及背景 现代通信的发展是爆炸式的。从电报、电话到今天的移动电话、互联网，人们从中享受了前所未有的便利和高效率。从有线到无线是一个飞跃，从完成单一的话音业务到完成视频、音频、图像和数据相结合的综合业务功能更是一个大的飞跃。在今天，人们获得了各种各样的通信服务，例如，固定电话、室外的移动电话的语音通话服务，有线网络的上百兆bit的信息交互。但是通信服务的内容和质量还远不能令人满意，现有几十Kbps传输能力的无线通信系统在承载多媒体应用和大量的数据通信方面力不从心:现有的通信标准未能全球统一，使得存在着跨区的通信障碍;另一方面，从资源角度看，现在使用的通信系统的频谱利用率较低，急需高效的新一代通信系统的进入应用。 目前，3G的通信系统己经进入商用，但是其传输速率最大只有2Mbps，仍然有多个标准，在与互联网融合方面也考虑不多。这些决定了3G通信系统只是一个对现有移动通信系统速度和能力的提高，而不是一个全球统一的无线宽带多媒体通信系统。因此，在全世界范围内，人们对宽带通信正在进行着更广泛深入的研究。 正交频分复用（OFDM, Orthogonal Frequency Division Multiplexing) 是一种特殊的多载波方案，它可以被看作一种调制技术，也可以被当作是一种复用技术。选择OFDM的一个主要原因在于该系统能够很好地对抗频率选择性衰落或窄带干扰。正交频分复用（OFDM）最早起源于20世纪50年代中期，在60年代就已经形成恶劣使用并行数据传输和频分复用的概念。1970年1月首次公开发表了有关OFDM的专利。 在传统的并行数据传输系统中，整个信号频段被划分为N个相互不重叠的频率子信道。每个子信道传输独立的调制符号，然后再将N个子信道进行频率复用。这种避免信道频谱重叠看起来有利于消除信道间的干扰，但是这样又不能有效利用宝贵频谱资源。为了解决这种低效利用频谱资源的问题，在20世纪60年代提出一种思想，即使用子信道频谱相互覆盖的频域距离也是如此，从而可以避免使用高速均衡，并且可以对抗窄带脉冲噪声和多径衰落，而且还可以充分利用可用的频谱资源。 常规的非重叠多载波技术和重叠多载波技术之间的差别在于，利用重叠多载波调制技术可以几乎节省50%的带宽。为了实现这种相互重叠的多载波技术，必须要考虑如何减少各个子信道之间的干扰，也就是要求各个调制子载波之间保持正交性。 1971年，Weinstein和Ebert把离散傅立叶变换（DFT）应用到并行传输系统中，作为调制和解调过程的一部分。这样就不再利用带通滤波器，同时经过处理就可以实现FDM。而且，这样在完成FDM的过程中，不再要求使用子载波振荡器组以及相关解调器，可以完全依靠执行快速傅立叶变换（FFT）的硬件来实施。
本科毕业设计：基于MATLAB的OFDM系统仿真及分析
摘要 正交频分复用(OFDM) 是第四代移动通信的核心技术。该文首先简要介绍了OFDM的发展状况及基本原理, 文章对OFDM 系统调制与解调技术进行了解析，得到了OFDM 符号的一般表达式，给出了OFDM 系统参数设计公式和加窗技术的原理及基于IFFT/FFT 实现的OFDM 系统模型，阐述了运用IDFT 和DFT 实现OFDM 系统的根源所在,重点研究了理想同步情况下,保护时隙(CP)、加循环前缀前后和不同的信道内插方法在高斯信道和多径瑞利衰落信道下对OFDM系统性能的影响。在给出OFDM系统模型的基础上,用MATLAB语言实现了传输系统中的计算机仿真并给出参考设计程序。最后给出在不同的信道条件下,研究保护时隙、循环前缀、信道采用LS估计方法对OFDM系统误码率影响的比较曲线,得出了较理想的结论。 关键词: 正交频分复用;仿真;循环前缀;信道估计
Title: MATLAB Simulation and Performance Analysis of OFDM System ABSTRACT OFDM is the key technology of 4G in the field of mobile communication. In this article OFDM basic principle is briefly introduced.This paper analyzes the modulation and demodulation of OFDM system, obtaining a general expression of OFDM mark, and giving the design formulas of system parameters, principle of windowing technique, OFDM system model based on IFFT/FFT, the origin which achieves the OFDM system by using IDFT and DFT. Then, the influence of CP and different channel estimation on the system performance is emphatically analyzed respectively in Gauss and Rayleigh fading channels in the condition of ideal synchronization. Besides, based on the given system model OFDM system is computer simulated with MATLAB language and the referential design procedure is given. Finally, the BER curves of CP and channel estimation are given and compared. The conclusion is satisfactory. KEYWORDS:OFDM; Simulation; CP; Channel estimation
移动通信系统OFDM系统仿真与实现基于MATLAB
OFDM系统仿真与实现 1、OFDM的应用意义 在近几年以内,无线通信技术正在以前所未有的速度向前发展。由于用户对各种实时多媒体业务需求的增加与互联网技术的迅猛发展,未来的无线通信及技术将会有更高的信息传输速率,为用户提供更大的便利,其网络结构也将发生根本的变化。随着人们对通信数据化、个人化与移动化的需求,OFDM技术在无线接入领域得到了广泛的应用。OFDM就是一种特殊的多载波传输方案,它将数字调制、数字信号处理、多载波传输技术结合在一起,就是目前已知的频谱利用率最高的一种通信系统,具有传输速率快、抗多径干扰能力强的优点。目前,OFDM技术在数字音频广播(DAB)、地面数字视频广播(DVB-T)、无线局域网等领域得到广泛应用。它将就是4G移动通信的核心技术之一。 OFDM广泛用于各种数字传输与通信中,如移动无线FM信道,高比特率数字用户线系统(HDSL),不对称数字用户线系统(ADSL),甚高比特率数字用户线系统HDSL,数字音频广播(DAB)系统,数字视频广播(DVB)与HDTV地面传播系统。1999年,IEEE802.11a通过了一个SGHz的无线局域网标准,其中OFDM调制技术被采用为物理层标准,使得传输速率可以达54MbPs。这样,可提供25MbPs的无线ATM接口与10MbPs的以太网无线帧结构接口,并支持语音、数据、图像业务。这样的速率完全能满足室内、室外的各种应用场合。 OFDM由于技术的成熟性,被选用为下行标准很快就达成了共识。而在上行技术的选择上,由于OFDM的高峰均比(PAPR)使得一些设备商认为会增加终端的功放成本与功率消耗,限制终端的使用时间,一些则认为可以通过滤波,削峰等方法限制峰均比。不过,经过讨论后,最后上行还就是采用了SC-FDMA方式。拥有我国自主知识产权的3G标准一一TD-SCDMA在LTE演进计划中也提出了TD-CDM-OFDM 的方案B3G/4G就是ITU提出的目标,并希望在2010年予以实现。B3G/4G的目标就是在高速移动环境下支持高达100Mb/S的下行数据传输速率,在室内与静止环境下支持高达IGb/S的下行数据传输速率。而OFDM技术也将扮演重要的角色。 2、OFDM的原理研究与分析 2、1OFDM的关键技术 (1) 时域与频域同步 OFDM系统对定时与频率偏移敏感,特别就是实际应用中与FDMA、TDMA与CDMA 等多址方式结合使用时,时域与频率同步显得尤为重要。 (2) 信道估计
基于MATLAB的OFDM的仿真
一、实习目的 1、熟悉通信相关方面的知识、学习并掌握OＦDM技术的原理 2、熟悉ＭＡＴLＡB语言 3、设计并实现OＦDM通信系统的建模与仿真 二、实习要求 仿真实现ＯFDM调制解调，在发射端,经串／并变换和IFFT变换，加上保护间隔(又称“循环前缀”)，形成数字信号，通过信道到达接收端，结束端实现反变换,进行误码分析三、实习内容 1.实习题目 《正交频分复用ＯFＤM系统建模与仿真》 2.原理介绍 ＯFDＭ的基本原理就是把高速的数据流通过串并变换，分配到传输速率相对较低的若干个子信道中进行传输。由于每个子信道中的符号周期会相对增加，因此可以减轻由无线信道的多径时延扩展所产生的时间弥散性对系统造成的影响。并且还可以在OFDＭ符号之间插入保护间隔,令保护间隔大于无线信道的最大时延扩展,这样就可以最大限度地消除由于多径而带来的符号间干扰（ISI)。而且,一般都采用循环前缀作为保护间隔,从而可以避免由多径带来的子载波间干扰(（IＣI) 。 3.原理框图 图1-1 OFDM 原理框图
4. 功能说明 ４.1确定参数 需要确定的参数为：子信道,子载波数,ＦF Ｔ长度,每次使用的OFDM 符号数,调制度水平，符号速率,比特率,保护间隔长度，信噪比,插入导频数,基本的仿真可以不插入导频,可以为０。 ４．2产生数据 使用个随机数产生器产生二进制数据,每次产生的数据个数为ｃａrr ｉｅr_co ｕn ｔ * sym ｂｏls_pe ｒ_car ｒier * bits_per_s ｙm ｂo ｌ。 4.3编码交织 交织编码可以有效地抗突发干扰。 4.4子载波调制 OF ＤM 采用BP ＳK 、QP ＳK 、16QAM 、64QAM ４种调制方式。按照星座图,将每个子信道上的数据,映射到星座图点的复数表示,转换为同相Ic ｈ和正交分量Qch 。 其实这是一种查表的方法,以１6QAM 星座为例,ｂits_p ｅｒ_ｓｙm ｂo ｌ=4，则每个ＯFDM 符号的每个子信道上有4个二进制数{d1,d2，d3，d4},共有16种取值，对应星座图上1６个点，每个点的实部记为Qch 。为了所有的映射点有相同高的平均功率,输出要进行归一化,所以对应BPSK,PQSK,16ＱA Ｍ,64QA Ｍ,分别乘以归一化系数系数１，21, 101, 421.输出的复数序列即为映射后的调制结果。 4.5串并转换。 将一路高速数据转换成多路低速数据 4.6 IFF Ｔ。 对上一步得到的相同分量和正交分量按照（Ic ｈ＋Qch ＊i)进行ＩＦFT 运算。并将得到的复数的实部作为新的I ｃh ，虚部作为新的Ｑch 。 在实际运用中， 信号的产生和解调都是采用数字信号处理的方法来实现的, 此时要对信号进行抽样， 形成离散时间信号。 由于O ＦDM 信号的带宽为B=Ｎ·Δｆ， 信号必须以Δt=１／B ＝1/（N ·Δf ）的时间间隔进行采样。 采样后的信号用sn ，ｉ 表示， i ＝ ０, 1, …， N-1，则有 ∑-== 1 /2j ,,e 1N k N ik k n i n S N s π 从该式可以看出，它是一个严格的离散反傅立叶变换（ID ＦT ）的表达式。ＩDFT 可以采用快速反傅立叶变换(ＩＦFT ）来实现 4.7加入保护间隔。 由ＩF ＦＴ运算后的每个符号的同相分量和正交分量分别转换为串行数据,并将符号尾部G 长度的数据加到头部，构成循环前缀。如果加入空的间隔，在多径传播的影响下，会造成载波间干扰ICI 。保护见个的长度G 应该大于多径时的扩张的最大值。
基于MATLAB的MIMO-OFDMA系统的设计与仿真
基于MATLAB的MIMO-OFDMA系统的设计与仿真 摘要 在信息时代的快速发展形势下，产生了越来越多的业务需求，用户对通信系统的性能提出了更高的要求。基于正交频分复用( Orthogonal Frequency Division Multiplexing，OFDM )技术和多输入多输出（Multiple Input Multiple Output，MIMO ）技术的无线通信系统在增加系统容量、提高频谱利用率以及对抗频率选择性衰落等方面具备优越的性能，是未来通信领域中的关键技术。 本文首先阐述了MIMO技术和OFDM技术的国内外研究概况，然后通过分析MIMO技术和OFDM技术的基本原理和系统结构，设计出简单的MIMO-OFDM系统。基于MATLAB软件对所建立的MIMO系统的信道容量进行了仿真，并对SISO-OFDM系统和MIMO-OFDM系统的性能进行了比较，仿真结果表明，本文所提出的MIMO-OFDM系统方案能够在不增加误比特率的情况下增加信道容量，最后结合空时分组码（Space Time Block Coding,STBC）对MIMO-OFDM系统进行了完善并采用MATLAB对其性能进行了仿真，结果显示，相较于未完善的系统完善后的系统的误比特率指标明显降低，传输可靠性得到了极大的提高。 关键词：无线通信；MIMO；OFDM；误比特率
Performance Evaluation of MIMO-OFDMA System using Matlab Abstract As the rapid development of information technology has resulted in more influences on people’s daily lives and businesses. Higher requirements should be provided by communication system to meet people’s needs. The communication system which based on the technology of Orthogonal Frequency Division Multiplexing (OFDM) and Multiple Input Multiple Output (MIMO) enables to not only increase the system capacity, but improve the spectrum utilization, and moreover to effectively against frequency selective fading, has become the key technologies in the field of communication in the future. This paper first gives an in-detailed survey on MIMO and OFDM technologies in academic society. After that, we designed a simple MIMO-OFDM system by means of the analysis of the basic concepts and the architecture of MIMO and OFDM technology. Followed by performance evaluation via Matlab to compare SISO-OFDM and MIMO-OFDM systems in term of channel capacity and Bit Error Rate (BER) to validate the proposed MIMO-OFDM system outperforms SISO-OFDM. Finally, we further integrated space-time block codes into the proposed MIMO-OFDM system, through simulation results, we can observe that BER can be significant reduced compared to its counterpart which without implements space-time block codes. Keywords:Wireless communication，MIMO, OFDM, Bit Error Rate (BER)
基于matlab的ofdm系统设计与仿真
基于matlab的ofdm系统设计与仿真 OFDM即正交频分复用技术，实际上是多载波调制中的一种。其主要思想是将信道分成若干正交子信道，将高速数据信号转换成并行的低速子数据流，调制到相互正交且重叠的多个子载波上同时传输。该技术的应用大幅度提高无线通信系统的信道容量和传输速率，并能有效地抵抗多径衰落、抑制干扰和窄带噪声，如此良好的性能从而引起了通信界的广泛关注。 本文设计了一个基于IFFT/FFT算法与802.11a标准的OFDM系统，并在计算机上进行了仿真和结果分析。重点在OFDM系统设计与仿真，在这部分详细介绍了系统各个环节所使用的技术对系统性能的影响。在仿真过程中对OFDM信号使用QPSK调制，并在AWGN信道下传输，最后解调后得出误码率。整个过程都是在MATLAB环境下仿真实现，对ODFM系统的仿真结果及性能进行分析，通过仿真得到信噪比与误码率之间的关系，为该系统的具体实现提供了大量有用数据。
第一章 ODMF 系统基本原理 1.1多载波传输系统 多载波传输通过把数据流分解为若干个子比特流，这样每个子数据流将具有较低的比特速率。用这样的低比特率形成的低速率多状态符号去调制相应的子载波，构成了多个低速率符号并行发送的传输系统。在单载波系统中，一次衰落或者干扰就会导致整个链路失效，但是在多载波系统中，某一时刻只会有少部分的子信道会受到衰落或者干扰的影响。图1－1中给出了多载波系统的基本结构示意图。 图1-1多载波系统的基本结构 多载波传输技术有许多种提法，比如正交频分复用(OFDM)、离散多音调制(DMT)和多载波调制(MCM)，这3种方法在一般情况下可视为一样，但是在OFDM 中，各子载波必须保持相互正交，而在MCM 则不一定。 1.2正交频分复用 OFDM 就是在FDM 的原理的基础上，子载波集采用两两正交的正弦或余弦函数集。函数集{t n ωcos }， {t m ωsin } (n,m=0,1,2…)的正交性是指在区间(T t t +00,)内有正弦函数同理：)0()()(2/0cos *cos 00===≠?????=? +m n m n m n T T tdt m t n T t t ωω 其中ω π 2=T （1-1） 根据上述理论，令N 个子信道载波频率为)(1t f ,)(2t f ,……,)(t f N ，并使其
无线通信原理基于matlab的ofdm系统设计与仿真
无线通信原理:基于matlab的ofdm系统设计与仿真 OFDM即正交频分复用技术,实际上是多载波调制中的一种。其主要思想是将信道分成若干正交子信道,将高速数据信号转换成并行的低速子数据流,调制到相互正交且重叠的多个子载波上同时传输。该技术的应用大幅度提高无线通信系统的信道容量和传输速率,并能有效地抵抗多径衰落、抑制干扰和窄带噪声,如此良好的性能从而引起了通信界的广泛关注。 本文设计了一个基于IFFT/FFT算法与802.11a标准的OFDM系统,并在计算机上进行了仿真和结果分析。重点在OFDM系统设计与仿真,在这部分详细介绍了系统各个环节所使用的技术对系统性能的影响。在仿真过程中对OFDM信号使用QPSK调制,并在AWGN信道下传输,最后解调后得出误码率。整个过程都是在MATLAB环境下仿真实现,对ODFM系统的仿真结果及性能进行分析,通过仿真得到信噪比与误码率之间的关系,为该系统的具体实现提供了大量有用数据。 第一章ODMF系统基本原理 1.1多载波传输系统 多载波传输通过把数据流分解为若干个子比特流,这样每个子数据流将具有较低的比特速率。用这样的低比特率形成的低速率多状态符号去调制相应的子载波,构成了多个低速率符号并行发送的传输系统。在单载波系统中,一次衰落或者干扰就会导致整个链路失效,但是在多载波系统中,某一时刻只会有少部分的子信道会受到衰落或者干扰的影响。图1－1中给出了多载波系统的基本结构示意图。 图1-1多载波系统的基本结构
多载波传输技术有许多种提法,比如正交频分复用(OFDM)、离散多音调制(DMT)和多载波调制(MCM),这3种方法在一般情况下可视为一样,但是在OFDM 中,各子载波必须保持相互正交,而在MCM 则不一定。 1.2正交频分复用 OFDM 就是在FDM 的原理的基础上,子载波集采用两两正交的正弦或余弦函数集。函数集{t n ωcos }, {t m ωsin } (n,m=0,1,2…)的正交性是指在区间(T t t +00,)内有正弦函数同理：)0()()(2/0cos *cos 00===≠?????=? +m n m n m n T T tdt m t n T t t ωω 其中ω π 2=T （1-1） 根据上述理论,令N 个子信道载波频率为)(1t f ,)(2t f ,……,)(t f N ,并使其满足下面的关系：),1(,/0N k T k f f N k ?=+=,其中N T 为单元码持续时间。单个子载波信号为： ? ??<≤=others T t t f t f N k k 00)2cos()(π （1-2） 由正交性可知:????≠==n m n m T dt t f t f N m n 0)(*)( （1-3） 由式（1-3）可知,子载波信号是两两正交的。这样只要信号严格同步,调制出的信号严格正交,理论上接收端就可以利用正交性进行解调。OFDM 信号表达式与FDM 的一样,区别在于信号的频谱。OFDM 信号的频谱与FDM 频谱情况对比如图1－2所示。由图1－2可以看出,由于采用的原理不一样,FDM 中接收端需要频率分割,因而需要较宽的保护间隔。OFDM 系统的接收端利用正交性解调,相邻子信道频谱在一定程度上是可以重叠的。 图1-2 FDM 与OFDM 的频谱
基于MATLAB的OFDM系统设计与仿真
基于MATLAB的OFDM系统设计与仿真 摘要：随着通信产业的逐步发展，4G时代已经来临。作为第四代移动通信技术的核心，OFDM得到了前所未有的关注。它具有频谱利用率高、抗干扰能力强等优点。本文首先简要介绍了OFDM的发展状况以及优缺点，然后详细分析了OFDM的工作原理及其相应的各个模块，并介绍了它的关键技术。最后，分别利用M函数和Simulink做了OFDM 系统的设计与仿真，并对误码率进行了分析，得到了BER性能曲线。 关键词：正交频分复用；MATLAB；仿真；BER Design and Simulation of OFDM System Based on MATLAB Abstract:With the gradual development of the communication industry, 4G era has come. As the key technology of the fourth generation mobile communications,OFDM has received unprecedented attention. It has a high spectrum utilization, strong ability of anti-interference and so on. This article describes the development of OFDM and it’s advantages and disadvantages briefly, analysis the working principles of OFDM and each module detailed,and describes it’s key tec hnology.At last, design and simulate OFDM system with the M function and Simulink separately, analysis the error rate and obtain BER performance curve . Keywords: OFDM; MATLAB; Simulation; BER
基于matlab的OFDM系统仿真毕业设计论文
毕业设计论文 基于Matlab的OFDM系统仿真及分析 Simulation and Performance Analysis of OFDM System Based on Matlab
毕业论文任务书
毕业设计开题报告
摘要 在无线通信系统中，存在着各种严重的衰落，例如频率选择性衰落、快衰落和慢衰落，以及由于各种物体对传输信号的反射引起的多径传播，而由此引起的符号间干扰是无线通信系统设计中必须考虑的问题，特别是在高速传输的环境中。而正交频分复用（OFDM）正是为了解决这些问题提出的，它是第四代移动通信的核心技术之一。 OFDM是一种特殊的多载波传输方案，它将数字调制、数字信号处理、多载波传输等技术有机结合在一起，是目前已知的频谱利用率最高的一种通信系统，具有传输速度快、抗多径干扰能力强的优点。目前，OFDM技术在数字音频广播、地面数字视频广播、无线局域网等领域得到广泛应用。 本文论述了OFDM的基本原理以及信号调制技术，给出了OFDM系统模型，并从频域的角度分析OFDM信号的性质及DFT实现，最后用MATLAB语言实现了整个系统的计算机仿真并给出参考设计程序，对OFDM调制系统中主要传输技术、基本参数的选择、同步及关键技术和仿真实现进行了相关的讨论。 关键词：OFDM多载波系统仿真MATLAB
Abstract There are some severe problems in wireless communication systems, such as frequency selective fading, fast fading and slow fading, and various objects of reflection led to the transmitted signal multipath propagation. The resulting inter-symbol interference (ISI) is a wireless communication system design issues that must be considered, especially in the high-speed transmission environment. Orthogonal frequency division multiplexing (OFDM) is proposed to solve these problems, it is the core technology of the fourth generation mobile communication. OFDM is a special multi-carrier transmission scheme, it combines some technologies such as figure modulation, digital signal processing, multi-carrier transmission. It is the maximum utilization of the spectrum communication system, with the advantages of faster transfer rates, anti-multipath interference. Currently known at present, OFDM technology is widely used in the digital audio broadcasting, terrestrial digital video broadcasting and wireless LAN. This paper introduce the orthogonal frequency division multiplexing basic principle and discusses signal modulation technology, then, given OFDM system model, and analysis the nature and DFT realization of OFDM signals from the point of view of frequency domain. Finally, based on the given system model, OFDM system is computer simulated with MATLAB language and the referential design procedure is given. Discussing in the system of OFDM modulation transmission technology, basic parameter selection, system of synchronous, key technology and OFDM system simulation. Key words：OFDM Multi-carrier System Simulation MATLAB
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