2011IT-Secrecy throughput of MANETs under passive and active attacks
Secrecy Throughput of MANETs Under Passive and Active Attacks
Yingbin Liang,Member,IEEE,H.Vincent Poor,Fellow,IEEE,and Lei Ying,Member,IEEE
Abstract—The secrecy throughput of mobile ad hoc networks (MANETs)with malicious nodes is investigated.The MANET consists of legitimate mobile nodes and malicious nodes. Transmissions between legitimate nodes are subject to a delay constraint.A model under passive attack is?rst studied,in which the malicious nodes are assumed to be eavesdroppers that only listen to transmission without actively injecting signals.An information-theoretic approach for security is applied to achieve secure communication among legitimate nodes in MANETs with transmissions being kept perfectly secure from eavesdroppers.A critical threshold on the number of malicious nodes is iden-ti?ed such that when,i.e.,, the optimal secrecy that of without malicious nodes,i.e.,the impact of the presence of malicious nodes on the network throughput is negligible;and when
,i.e.,for a positive constant,the optimal secrecy throughput is limited by the number of malicious nodes.A model under active attack is further studied,in which the malicious nodes actively attack the network by transmitting modi?ed packets to the destination nodes.It is shown that to guarantee the same throughput as the model under passive attack,the model under active attack needs to satisfy more stringent condition on the number of malicious nodes.
Index Terms—Erasure channel,mobile ad hoc network (MANET),mobility model,secrecy,throughput scaling,wiretap channel.
I.I NTRODUCTION
M OBILE ad hoc networks(MANETs)represent one of the most innovative emerging networking technologies, with broad potential applications in personal area networks,
Manuscript received May20,2010;revised December21,2010;accepted May26,2011.Date of current version October07,2011.The work of Y.Liang was supported by a National Science Foundation CAREER Award under Grant CCF-10-26565and by the National Science Foundation under Grant CCF-10-26566.The work of H.V.Poor was supported by the National Science Foun-dation under Grant CNS-09-05398.The work of L.Ying was supported by the National Science Foundation under Grant CNS-09-53165,and by the De-fense Threat Reduction Agency(DTRA)under Grants HDTRA1-08-1-0016and HDTRA1-09-1-0055.The material in this paper was presented in part at the IEEE International Symposium on Information Theory,Seoul,South Korea, June–July2009.
Y.Liang is with the Department of Electrical Engineering and Computer Sci-ence,Syracuse University,Syracuse,NY13244USA(e-mail:yliang06@syr. edu).
H.V.Poor is with the Department of Electrical Engineering,Princeton Uni-versity,Princeton,NJ08544USA(e-mail:poor@https://www.wendangku.net/doc/d48906205.html,).
L.Ying is with the Department of Electrical and Computer Engineering,Iowa State University,Ames,IA50011USA(e-mail:leiying@https://www.wendangku.net/doc/d48906205.html,). Communicated by R. A.Berry,Associate Editor for Communication Networks.
Color versions of one or more of the?gures in this paper are available online at https://www.wendangku.net/doc/d48906205.html,.
Digital Object Identi?er10.1109/TIT.2011.2165800emergency and rescue operations,military applications,etc.The unique features of MANETs,such as mobility and peer-to-peer connectivity,make MANETs a very?exible technology for es-tablishing communication in areas with limited infrastructure. However,providing secure communication over MANETs using traditional cryptographic methods presents signi?cant challenges due to:1)the open nature of the wireless medium, which allows eavesdroppers and attackers to intercept informa-tion transmission(in particular,transmission of secret keys)or to degrade transmission quality;2)the lack of infrastructure, which makes key distribution and management required for traditional symmetric-key cryptographic approaches dif?cult; and3)energy and complexity limitations at terminals that may prohibit the use of alternative cryptographic methods,such as public key cryptography.New approaches to achieving security in MANETs are thus of considerable interest.
In this paper,we propose to achieve secure communication over MANETs via an approach developed based on informa-tion-theoretic security.The idea is to apply the powerful secure coding developed in information-theoretic security to prepro-cess messages being transmitted through the network to guar-antee secure communication in the presence of malicious nodes. The contributions of this paper are summarized below.
?We identify equivalent wiretap models for MANETs with malicious nodes,which facilitate the application of the information-theoretic security approach for securing MANETs,and the corresponding theoretical analysis of fundamental secrecy rate limits.
?The messages transmitted securely between legitimate nodes can be viewed as secret keys,and hence sym-metric keys are established between legitimate nodes over MANETs.This solves the open problem of key distribution for MANETs under a two-dimensional(2-D) independent and identically distributed(i.i.d.)mobility model[1],[9].
?The fundamental limits of the secrecy rate can be charac-terized in terms of the order of the numbers of legitimate and malicious nodes in networks.These limits apply to all possible secure transmission schemes,including those im-plemented via cryptographic approaches.
?The information-theoretic approach we proposed provides provable secure transmission(or key distribution)over MANETs.
More speci?cally,the MANET model we consider consists of a number of legitimate nodes transmitting information among themselves,and also a number malicious nodes,which can receive information that is transmitted between the legit-imate nodes.We assume that the malicious nodes follow the same mobility behavior as legitimate nodes.For MANETs,a virtual(or an equivalent)channel representation was developed
0018-9448/$26.00?2011IEEE
in[1]to model the impact of mobility on packet delivery via an erasure channel,in which the erasure probability at the receiver corresponds to the probability that a packet could not get close enough to its destination before its deadline.The virtual channel representation enables a holistic information-theoretic view for the design of capacity-achieving algorithms in MANETs[1]. In this paper,we show that the behavior of malicious nodes can also be included in the virtual channel representation by introducing an additional eavesdropper,and hence the entire system of MANETs with malicious nodes is modeled by a wiretap channel with a destination(legitimate)receiver and an eavesdropper as studied in[2]and[3].A review of informa-tion-theoretic security can be found in[4].
We?rst consider the passive attack model,in which the mali-cious nodes are assumed to be passive eavesdroppers,which do not send signals over the communication channels.In this case, the equivalent model is an erasure wiretap channel,in which both the channels to the destination and to the eavesdropper are erasure channels,i.e.,each bit is successfully transmitted with a certain probability and otherwise gets erased(lost).Using information-theoretic approaches,the best secrecy rate(i.e., the secrecy capacity)at which information can be transmitted successfully while being kept secret from an eavesdropper in the basic wiretap channel is characterized in[2],and coding schemes designed to achieve this rate are developed in[5] and[6].Thus,these coding schemes can be applied to achieve secure communication in MANETs,and the secrecy capacity of the wiretap channel provides a way to characterize the fundamental limits on the secrecy throughput in MANETs. The goal of this paper is to explore these information-theoretic approaches to investigate MANETs.
Although the performance limits of MANETs in terms of performance bounds on throughput and delay have been ex-tensively studied(e.g.,in[1]and[7]–[17]),performance limits of MANETs under secrecy constraints have not been studied much before although with exceptions[18]–[21].This is in gen-eral a challenging problem,because traditional cryptographic approaches are not easy to quantify for optimality analysis.In this paper,we?rst explore information-theoretic approaches to provide an upper bound on the secrecy throughput,which is the largest throughput possible over the network under secrecy con-straints no matter what kind of schemes are used for achieving security.Hence,this upper bound also provides a fundamental performance limit for approaches based on encryption.We then propose joint coding,scheduling,and routing schemes to achieve this upper bound.Our results demonstrate that the scaling of throughput is separated into two regimes charac-terized by how the number of legitimate nodes compares with the number of malicious nodes,and correspondingly two different transmission schemes need to be implemented for these two regimes.The two regimes are separated by a threshold on,1where denotes the delay constraint and scales with.In particular,we show that when 1We adopt the following notation in the paper.For nonnegative functions and,means there exist positive constants and such that for all means there exist pos-itive constants and such that for all
means that both and hold;
means that means that
and means that is a polynomial.
,the secrecy throughput equals the throughput of MANETs without malicious nodes and can be achieved by a multihop secrecy scheme;and when, the secrecy throughput is limited by the number of malicious nodes,and can be achieved by a single-hop scheme.
We then extend our approach to study the active attack model, in which the malicious nodes can transmit modi?ed packets to the destination nodes in addition to eavesdropping.We?rst show that this model is equivalent to a wiretap channel with the channel to the legitimate receiver being a binary symmetric erasure channel(i.e.,each bit may be successfully received with a certain probability,modi?ed with a certain probability and erased otherwise)and the channel to the eavesdropper being an erasure channel.Hence,the active attack is charac-terized by the properties of the legitimate receiver’s channel in the equivalent wiretap channel,while the passive attack is characterized by the eavesdropper’s channel.By applying the secrecy rate and achievable secrecy schemes for the wiretap channel,we also characterize the secrecy throughput for the active attack model for MANETs in two https://www.wendangku.net/doc/d48906205.html,pared to the passive attack model,the difference lies in that,to guarantee the same throughput,the model under active attack needs to satisfy more stringent condition on the number of malicious nodes.However,when holds,the same secrecy throughput is achieved as the passive model because the single-hop scheme dominates the contribution to the secrecy throughput.
We would like to comment that the secrecy throughput of static ad hoc wireless networks has recently been studied in [18]–[21].In particular,Koyluoglu et al.[18]show that if the eavesdropper density is on the order of,then the secrecy rate scales as.The network model we consider as-sumes the nodes are mobile,and hence the network has dynamic structure.In a static ad hoc wireless network,multihop trans-missions(routing)are needed to deliver packets from sources to their corresponding destinations;while in mobile ad hoc net-works,the mobiles can physically carry the packets to their des-tinations instead of using routing.Therefore,both the transmis-sions strategies and throughput scaling of MANETs are funda-mentally different from those of static ad hoc networks.The re-sults and analysis of this paper,hence,are different from those in[18]–[21].For example,compared to[18],our model allows the density of eavesdroppers to be larger than,and the secrecy throughput depends on not only and,but also,the delay constraint.
The paper is organized as follows.In Section II,we introduce the MANET model with the secrecy constraint on the system. In Section III,we introduce the basic concepts and de?nitions of information-theoretic security,and provide the main result on the secrecy capacity of the block erasure wiretap channel,which is very useful for analyzing the secrecy throughput of MANETs. In Sections IV and V,we provide the main results on MANETs under passive attacks.In Section VI,we provide the results for MANETs under active attacks.Finally,in Section VII,we give a few concluding remarks.
Fig.1.An example MANET with eavesdroppers.
II.MANET M ODEL
In this section,we describe our models for network con?g-uration,communications,and security attacks.We consider a wireless MANET that consists of legitimate wireless nodes and malicious nodes positioned in a unit square(see Fig.1 for an illustration).We assume the legitimate nodes know the value of or at least the order of.We adopt the2-D i.i.d.mo-bility model[1],[9].As such,each node is uniformly,randomly positioned in the unit square,and the node position changes in-dependently across time slots.The positions of different nodes are independent.That the mobility behavior of malicious nodes is the same as that of the legitimate nodes is justi?ed by the fact that the malicious nodes can be easily detected if they be-have differently.We assume that there are source–destination (S–D)pairs in the network,and each legitimate node is both a source and a destination.Without loss of generality,we assume that the destination of node is node,and the destination of node is node.
We adopt the well-known protocol model[22]to model trans-missions between nodes in the network.We assume that all mo-bile nodes use a common transmission radius.Let
denote the Euclidean distance between node and node.Node can successfully transmit to node if and
for each node which transmits at the same time,where is a protocol-speci?ed guard zone whose purpose is to prevent interference.We further assume a fast mobility model[1]in which only one-hop transmissions are feasible and each transmission can send bits,which is inde-pendent of.
We study both passive and active attacks in this paper.The ?rst model considers passive attacks,in which the malicious nodes do not transmit in the network,but can receive packets transmitted between legitimate nodes.A malicious node can successfully receive a packet from a transmitter if it is within the transmitter’s transmission radius.We consider the worst case scenario,in which all malicious nodes collaborate to decode messages transmitted in the network by exchanging their re-ceived outputs.Hence,in this case,the malicious nodes can be viewed as one super-eavesdropper,which receives a packet as long as one of the malicious nodes receives this packet.The second model considers active attacks,in which a malicious node not only can receive packets as assumed for passive at-tacks,but also can modify and deliver the packets to a destina-tion if the destination is within transmission radius of this ma-licious node.We note that the secrecy capacity of static ad hoc networks with colluding eavesdroppers has been studied in[19] and[20],which,however,is fundamentally different from the problem considered in this paper.
Given a delay constraint,a packet is said to be successfully delivered if the destination obtains the packet within time slots after it is sent out from the source.Let denote the number of information bits being successfully delivered to node in time interval and being kept perfectly secret from the malicious nodes(the de?nition of perfect secrecy will be given in Section III).A secrecy throughput per S–D pair is said to be achievable under the delay constraint and loss probability constraint if there exists such that for every, there exists a joint coding,scheduling,and routing algorithm such that
The goal of this paper is to characterize how the secrecy throughput scales with the numbers of legitimate nodes and malicious nodes.
III.I NFORMATION-T HEORETIC S ECURITY
In this section,we provide some basic background on infor-mation-theoretic security including the basic wiretap channel model,de?nitions,and information-theoretic characterization of secrecy capacity,which are useful in our study of MANETs. The basic model to study information-theoretic security is the wiretap channel introduced and studied by Wyner[2].This channel includes a source node that wishes to transmit a mes-sage to a destination node(legitimate receiver)and wishes to keep this message as secret as possible from an eavesdropper (see Fig.2for an illustration).The channel is characterized by a transition probability distribution,where denotes the channel input,and and denote channel outputs at the legitimate receiver and the eavesdropper.The secrecy level of the message at the eavesdropper is measured by the equiv-ocation rate de?ned as
(1) where denotes the outputs at the eavesdropper for codeword length.The equivocation rate indicates the eavesdropper’s un-certainty about the message given the information available to it.Hence,the larger is the equivocation rate,the higher is the level of secrecy.2
A rate is achievable with perfect secrecy if there exists a block coding and decoding scheme such that the average error 2We note that the secrecy de?ned based on the equivocation rate in(1)is referred to as weak secrecy in the sense that information is secure at the level of the(encoding)block,i.e.,encoded messages.There is also a notation of strong secrecy[23]that concerns security at the level of the transmission bit.This paper focuses only on weak secrecy.The problems considered in this paper can also be studied in the sense of strong secrecy.
Fig.2.The wiretap
channel.
Fig.3.A block transmission of the block erasure wiretap channel.
probability converges to zero as the codeword length goes to in ?nity and
(2)
The secrecy capacity is the largest rate achievable with per-fect secrecy.
The general form of the secrecy capacity for the wiretap channel is characterized by Csiszár and K?rner [3],and is given by
(3)
where the maximization is taken over all joint distributions
between the channel input and an auxiliary random variable satisfying the Markov chain condition .Based on the result (3),we now study the secrecy capacity of the block erasure wiretap channel,which will be useful for studying MANETs.For the block erasure wiretap channel,each channel input symbol takes values in .A block of input
may be successfully received at the re-ceiver with probability ,and may be erased completely with probability ,where .Hence,the distribution of a block channel output for any given input is
with probability
with probability
where denotes erased bits.From one block to another,channel inputs are erased independently with the same param-eter .The channel to an eavesdropper is assumed to be the same as that to the legitimate receiver,but with a different era-sure parameter .An illustration of this channel for one block is given in Fig.3.This channel models packet transmission in practice with each packet contains a block of coded information bits.
Theorem 1:The secrecy capacity of the block erasure wiretap channel with block length is given by
(4)
where equals if and equals otherwise.
Proof:We view a block of transmission as one channel use,and hence the input alphabet takes values .Assume we choose the input probability distribution be
for ,where .
We ?rst that if ,the receiver’s channel is stochasti-cally degraded with respect to the eavesdropper’s channel,and hence the secrecy capacity is zero.If ,the eavesdropper’s channel is stochastically degraded with respect to the receiver’s channel.In this case,choosing in (3)is optimal.We hence compute
(5)
The above rate is maximized by choosing
for ,i.e.,the uniform input distribution.We nor-malize the rate computed above and obtain the desired secrecy capacity given by
which concludes the proof.
It is clear that the block erasure wiretap channel has the same secrecy capacity as the erasure wiretap channel (with block-length ).Hence,correlation between bits within blocks does not affect the secrecy capacity of the erasure wiretap channel.Thus,in this paper,we do not speci ?cally distinguish between the two channels in terms of the secrecy capacity.One way to achieve the secrecy capacity of the block erasure channel is to apply interleaving,i.e.,assigning symbols within each block to different codewords so that each bit in one codeword sees an in-dependent erasure channel.In this way,secure coding design for the erasure wiretap channel (with the blocklength as the channel parameter )can be applied.
Secure coding design to achieve the secrecy capacity for the binary erasure wiretap channel with was ?rst studied by Ozarow and Wyner [5],in which a nested code structure was proposed.Based on this structure,Thangaraj et al.[6]provided an explicit code design to achieve the secrecy capacity for the binary erasure wiretap channel.For the passive attack model,we will explore the secrecy capacity given in (4)to study the se-crecy throughput for MANETs,and we will also propose strate-gies to apply the secure codes given in [6]to achieve the secrecy throughput.
IV .MANET S U NDER P ASSIVE A TTACKS
In this section,we study MANETs under passive attacks.We ?rst characterize the secrecy throughput of MANETs in this case,and then present a heuristic argument to illustrate the intu-ition of our result.We delegate the rigorous proof to Section V.Theorem 2:For the MANET model under passive at-tacks described in Section II,if and
Fig.4.An equivalent wiretap channel representation.
,then the optimal secrecy throughput of MANETs is,and if,then the optimal secrecy throughput of MANETs is. Remark1:From this theorem,it can be seen that the behavior of the secrecy throughput of MANETs falls into two different cases.i)When the number of malicious nodes is,the secrecy throughput is a function of the number of nodes and the delay constraint,which is at the same order as the one without malicious nodes.Thus,the presence of malicious nodes has negligible impact on the network throughput.ii)When the number of malicious nodes is,the secrecy throughput is limited by the number of malicious nodes. Remark2:The additional constraint on achievability is to guarantee that,i.e.,the number of bits that can be transmitted within time slots(the delay constraint)is at least a constant number,so that the throughput has a practical meaning.
A.A Heuristic Argument
In this section,we provide a heuristic argument to demon-strate the main idea of achieving secure communication and an-alyzing secrecy throughput for MANETs,which provides key intuition for Theorem2.We also demonstrate the interplay of security,throughput,and delay in MANETs.
Consider a packet sent out by its source node.With some probability,say probability,the packet is delivered to its destination.At the same time,the packet may also be heard by the eavesdroppers with a certain probability,say probability .Thus,we model each S–D pair as a virtual system(see Fig.4),in which is the rate at which a source can send out packets.The system also includes two erasure channels,one to the destination with erasure probability,and the other to a super-eavesdropper with erasure probability.The erasure channel at the bit level is shown in Fig.5.As we mentioned earlier,the super-eavesdropper sees outputs of all eavesdroppers since all eavesdroppers collaborate.Hence,a packet is erased at the super-eavesdropper only when none of the eavesdroppers receive the packet.Clearly the two erasure channels form an erasure wiretap channel.From Section III,it is clear that the secrecy capacity of the erasure wiretap channel is the largest communication rate achievable
with perfect secrecy,and hence can be applied to derive the fundamental secrecy throughput for the corresponding MANET.
To derive the secrecy throughput,we classify the packets sent out from a source into the following two types,respectively corresponding to single-hop and multihop transmissions,and hence respectively corresponding to two virtual erasure wiretap Fig.5.An erasure channel.
channels.Furthermore,these two type of packets correspond to major contributions to secrecy throughput in two network regimes,respectively.
?Type-I packets:packets that are directly sent to their destinations;
?Type-II packets:packets that are sent to their destinations via relay nodes.
We next heuristically compute the erasure probabilities and for the above two types of packets,and analyze the corre-sponding secrecy throughputs.
1)Secrecy Throughput of Type-I Packets:According to the de?nition of Type-I packet,the source node sends out a Type-I packet only when the corresponding destination is in the com-munication range of the source node.Hence,.Such a packet is obtained by the super-eavesdropper if the packet is heard by at least one of the malicious nodes.The probability of this event is given by.Furthermore,the probability that an S–D pair is within the communication range is,which implies that the rate at which the source can send out a Type-I packet is given by.
We let denote the secrecy throughput of Type-I packets. Based on the secrecy capacity of the erasure wiretap channel given in(4),we obtain
2)Secrecy Throughput of Type-II Packets:The delivery of a Type-II packet contains three phases:
?the packet is transmitted from the source to one or multiple relays;
?the mobile relays physically carry the packet near the destination;
?some mobile relay transmits the packet to the destination. We consider a super-time-slot consisting of time slots,and assume that each source sends out packets over the super-time-slot.We note that each broadcast generates relay copies in the network with a high probability.We say a packet is deliverable if it is within distance from the destination.We have the following observations.
?Assume that there are relay copies for each packet.
Each copy becomes deliverable at time with probability .Thus,the probability that the packet is deliverable in one of the time slots is at most
?Each packet has to be transmitted at least once before being delivered,so the erasure probability of the super-eaves-dropper is upper bounded by
?At each time slot,the network can support at most simultaneous transmissions.Thus,during one
slot,at most packets can be sent out from the sources.
Then,the rate for each S–D pair is upper bounded by Based on the secrecy capacity of the erasure wiretap channel given in(4),we obtain the following approximate(in fact,upper bound on)secrecy throughput of Type-II packets:
(6) By further analyzing(6),we obtain the following lemma on the secrecy throughput of Type-II packets.
Lemma1:If,the secrecy throughput is given by
otherwise,if,then the secrecy throughput is given by
Proof:See the Appendix.
3)Total Secrecy Throughput:To combine the secrecy throughputs of Type-I and Type-II packets,we note that the total throughput satis?es
and we need to guarantee that the throughput is achievable for Type-II packets.Thus,we conclude that if
,then
otherwise
where denotes equality in the sense of being of the same order.Otherwise,if,we have that
Hence,we conclude that if,then
;otherwise,if,then .
We note that the above argument is heuristic.For example, is the probability that a packet becomes deliverable. However,it is not equal to the probability that the packet is actually delivered because when multiple packets to the same destination become deliverable at the same time,one packet is delivered.Nevertheless,the heuristic argument still reveals some important information that will guide our mathematical proofs given in the next section,in which we will?rst prove that the above heuristic results are upper bounds on the secrecy throughput,and we will then present algorithms that achieve the upper bounds and hence achieve the optimal secrecy throughput under certain conditions.
V.P ROOF OF T HEOREM2
The proof of Theorem2consists of three parts:an upper bound and two achievable algorithms for two network regimes with and,respectively.
A.Upper Bound
We provide an upper bound on the secrecy throughput in the following lemma.
Lemma2(Upper Bound):If,then the secrecy throughput of MANETs is and if
,then the secrecy throughput of MANETs is .
Proof:It follows from[1]that is the maximum throughput for MANETs without nodes, and hence without secrecy constraints.It thus serves as an upper bound on the secrecy throughput.For the case when
,we separately bound the throughputs of Type-I and Type-II packets.The details are as follows.
We?rst consider Type-I packets transmitted between a spe-ci?c S–D pair.Assume that the source sends out Type-I packets during a time period of time slots such that .We also note that the probability that a S–D pair is within the communication range at a given time slot is,and the source can send packets in one time slot,where is the packet size.Without loss of generality,we assume that, so the source can send one packet per time slot.Hence,by the Chernoff bound,we obtain that
which converges to as goes to in?nity.Thus,we have that
(7) We next consider Type-II packets.We further classify Type-II packets into the following two subtypes:
1)Type-II-1packets have more than relay
copies;
2)Type-II-2packets have no more than relay
copies.
Note that to generate a Type-II-1packet,the source needs to have more than relays in its communication range when the source sends out the packet.This event occurs with probability no more than.Thus,the probability that there are more than Type-II-1packets for a speci?c source over time slots is lower bounded by
The probability that there are no more than
Type-II-1packets is lower bounded by
which converges to one as goes to in?nity.We note that each Type-II-1packet has at most relay copies,and the probability that a Type-II-1packet becomes deliverable before its deadline is no more than,and each packet will be heard with probability by one of the malicious nodes (i.e.,the super-eavesdropper).Thus,de?ning to be the secrecy throughput per S–D pair contributed from Type-II-1 packets,we have
(8) To guarantee that the equation above is positive,, and hence we have
(9) We next consider Type-II-2packets.The probability that a Type-II-2packet becomes deliverable before its deadline is no more than.With transmission radius ,at most packets can be transmitted sources during one time slot.the number of Type-II-2packets in the network is upper bounded by.Therefore,de?ning to be the secrecy throughput S–D pair contributed from Type-II-2 packets,we have
(10) To guarantee that the right-hand side of the equation above is positive,we require,and hence we obtain
(11) We also note that we need for
to guarantee that the secrecy throughput is https://www.wendangku.net/doc/d48906205.html,-paring(9)and(11)with(7),we conclude that(7)provides the dominant term for the secrecy throughput for the case when
,which concludes the proof.
B.Achievable Algorithm I for
In this section,we describe secure communication algorithms for the case in which.To create an equivalent dis-crete memoryless erasure wiretap channel,we need to guarantee that symbols in one codeword(that encodes one message)see independent erasure channels.This requires:1)symbols in one codeword must be in different packets;and2)relay copies that contain symbols from one codeword do not collide to transmit to the same destination.The?rst condition is guaranteed by message interleaving and the second condition is guaranteed by scheduling relay copies that contain symbols from one code-word to different super-time-slots.We outline our algorithm as follows.
1)Stochastic secure coding and message interleaving:We apply the secure codes[6]and stochastic encoding schemes proposed in[2]for the erasure wiretap channel to encode each message.In particular,each message corresponds to
a set of codewords.If a message is chosen to be sent to its destination,one of the codewords in the set is randomly selected to be sent.Such a stochastic encoding process is implemented to confuse the eavesdropper.We let the codeword length be bits.Group codewords (corresponding to messages)for interleaving,i.e.,generate super-packets with each super-packet consisting of one symbol from each codeword.Hence,each super-packet contains bits.We then break each super-packet into packets,each with bits.An illustration is depicted in Fig.6.We note that each codeword in the?gure is the randomly selected one under the stochastic encoding schemes.
2)Cell scheduling:We set the transmission radius of each node to be.The unit torus is divided into cells such that the side length of each cell is.We group every set of cells into a super-cell,and index the cells from to.We divide each time slot into minislots,and at minislot,the cells with index are chosen to be active.If a cell is active,one mobile in the cell is selected to transmit.
It is easy to verify that under this cell scheduling algorithm, simultaneous transmissions do not cause interference under the protocol model.
3)Two-hop transmission scheme:Consider time-slots, where we group every set of time slots into a super-time-slot. Thus,we have super-time-slots.At the th super-time-slot, the packets belonging to the th super-packet are transmitted using the following scheme:
a)Broadcasting:This step consists of time slots.
At each time slot,in each cell,we randomly choose
a mobile.The mobile checks other mobiles within its
transmission radius.If there are more than
mobiles in the cell,and the selected mobile has not
broadcast all packets belonging to the th super-packet,
Fig.6.An illustration of message interleaving.
then a packet that was not previously sent is broadcast
in the cell.Recall that our choice of packet size and cell
scheduling allow one node in each cell to transmit during each time slot.
b)Receiving:This step consists of the remaining
time slots.At each time slot,each destination checks
whether there are deliverable packets within its cell.
During the minislot allocated to a certain cell,if there
is only one deliverable packet in the cell,then the
packet is transmitted to the destination using a one-hop
transmission.At the end of this step,all undelivered
packets are dropped.
4)Decoding:Each destination decodes the super-packets. Namely,the destination groups the th bit from all
super-packets,and then decodes the th source message.
The probability of packet loss for the legitimate destination can be computed,which corresponds to the erasure probability for the channel from the source to the destination.The super-eavesdropper(all malicious nodes)may get hold of packets in the broadcasting and receiving steps in the two-hop transmis-sion scheme.The probability of the packet loss for the super-eavesdropper based on the above scheme can be computed as well.These two erasure probability parameters are used to de-sign the secure code to encode the messages so that perfect se-crecy can be guaranteed.The following lemma speci?es the se-crecy throughput achieved by the above scheme.
Lemma3(Lower Bound I):If and ,then there exist and
such that each S–D pair can communicate messages within time slots with perfect secrecy.
Proof:We consider the probability that a packet is received by one of the malicious nodes(the super-eavesdropper).Under the secure communication algorithm,each packet will be trans-mitted at most twice,and each transmission will be heard by a malicious node with probability.Thus,the probability that a packet is not received by the super-eavesdropper,i.e.,the era-sure probability of the virtual channel between the source and malicious nodes,is given by where we have used the assumption that and
.As shown in[1],is achiev-able and is a constant when.Hence,the existence of the malicious nodes does not change the order of the throughput.
C.Achievable Algorithm II for
In this section,we consider the case in which
.We consider only Type-I packets,which dominate the secrecy throughput.We describe our algorithm as follows.
1)Stochastic secure coding and message interleaving:Each message is coded into bits using secure codes and is transmitted via stochastic encoding.Group coded messages and generate super-packets(each with bits) similar to the procedures in step1)of Algorithm I.
2)Cell scheduling:We set the transmission radius of each node to be.The unit torus is divided into cells such that the side of each cell is.The cell scheduling is the same as that in Algorithm I.
3)One-hop transmission scheme:At each time slot,each destination checks whether its source is within its cell.During the minislot allocated to a certain cell,if there is only one
S–D pair within the cell,then the packet is transmitted to the destination directly from the source.
4)Decoding:Each destination groups the th bit of all super-packets and decodes the th source messages.
We specify the secrecy throughput achieved by the above al-gorithm in the following lemma.
Lemma4(Lower Bound II):If,then there exists such that each S–D pair can commu-nicate messages within time slots with perfect se-crecy.
Proof:We choose and
.We consider a speci?c S–D pair within a?xed super-time-slot.
It is clear that based on Algorithm II,the corresponding era-sure probability of the channel to the destination is zero.We also note that the eavesdropper can obtain a packet during the broad-cast phase or the delivery phase.The probability that packet is obtained by the eavesdroppers is lower bounded by Thus,the secrecy throughput is given by
We comment that due to the i.i.d.mobility assumption,the probability of successful delivery of a packet depends on the number of relays that carry the packet,and is independent of
which nodes are the relays.Therefore,it is suf?cient to con-sider single-hop or two-hop transmissions because a source can broadcast a packet to suf?ciently many relays using one broad-cast instead of using multiple-hop transmissions.
VI.MANET S U NDER A CTIVE A TTACKS
We now consider the MANET model under active attacks,in which the malicious nodes not only can receive the packets that are transmitted in their reception range,but also can modify and deliver the packets to the destination if the destination is within its transmission range.We assume that the malicious nodes modify every bit in the codewords,which can be argued to be the best active attack strategy,given that message inter-leaving is adopted by the transmitters.This is because different bits in a packet belong to different codewords,and hence are independent.Therefore,modifying every bit in a packet maxi-mally reduces decodability at the destination.
In the rest of this section,we will?rst provide a heuristic ar-gument on the secrecy throughput for the model under active attacks.We will then present the main theorem on the secrecy throughput followed by a proof.Similar to the passive attack model,we identify an equivalent wiretap channel for the active attack model.We?rst note that bit modi?cation affects only des-tinations,and hence packet reception of malicious nodes(i.e., the super-eavesdropper)is the same as that for the passive attack model and can be modeled as an erasure channel with erasure probability.To model packet delivery at legitimate destina-tions,we assume that when multiple copies of a coded packet are received by a destination,the destination keeps only the?rst one and drops the others.Similarly to the passive attack model,we consider Type-I and Type-II packets.For Type-I packets,which are directly sent to their destinations,it is clear that active at-tacks do not affect packet delivery,because these packets are directly sent from source nodes and are the?rst copies received by the corresponding destinations.Hence,the packet delivery at destinations can be modeled as a perfect channel,i.e.,an erasure channel with erasure probability zero,which is the same as the passive attack model.
We next focus on Type-II packets,which are?rst sent to relay nodes,and are then delivered to their destinations via relay nodes.Unlike the passive attack model,the malicious nodes can modify received packets and deliver them to destinations.We consider a super-time-slot consisting of time slots,and as-sume that each source sends out packets over the super-time-slot.We note that each broadcast generates with a high proba-bility relay(legitimate)copies and modi?ed(by malicious nodes)copies in the network.We have the following (heuristic)observations.
?For the relay(legitimate)copies of a packet,each copy becomes deliverable at time with probability.
Thus,the probability that the packet is delivered correctly in one of the time slots is at most
?For the modi?ed copies of
a packet,each copy be-comes deliverable at time with
probability.Thus,the Fig.7.An equivalent wiretap channel representation for active attacks.
Fig.8.A binary symmetric erasure channel.
probability that the modi?ed packet is delivered in one of the time slots is at most
?If none of relay(legitimate)and modi?ed copies of a packet are delivered at the destination within time slots, the packet is erased with probability.?At each time slot,the network can support at most simultaneous transmissions.Thus,during one
slot,at most packets can be sent out from the sources.
Then,the rate for each S–D pair is upper bounded by ?Each packet has to be transmitted at least once before being delivered,so the erasure probability of the super-eaves-dropper is upper bounded by
In summary,the MANET under active attack can be mod-eled as an equivalent wiretap channel as depicted in Fig.7.The channel to the super-eavesdropper(all malicious nodes)can be modeled as an erasure channel with erasure probability,and the channel to the legitimate receiver can be modeled as a binary symmetric erasure channel(BSEC),in which an input symbol may be correctly received with probability,modi?ed with probability,and erased with probability.The BSEC at the bit level is depicted in Fig.8.We note that although malicious nodes can perform active attacks by modifying the packets,the equivalent model has only a passive eavesdropper, in which the active attack is modeled into the statistics of the channel to the legitimate receiver.
The secrecy capacity of the above wiretap channel can be de-rived by using(3).The channel input is binary,and the auxil-iary random variable has a cardinality constraint.Hence,the optimal joint input distribution can be obtained numeri-cally to compute the secrecy capacity.However,it is dif?cult to obtain any analytical properties from such numerical results. We will hence approach this in a different https://www.wendangku.net/doc/d48906205.html,ly,we will ?rst compute an achievable secrecy rate obtained by choosing
and a uniform distribution for,and then show that this special choice of the input distribution does not affect the optimality in terms of the order of nodes.
Based on(3),an achievable secrecy rate is given by
(12) where is the binary entropy function.We then have the following theorem based on (12).
Theorem3:If and, then the optimal secrecy throughput of MANETs is
,and if,then the optimal secrecy throughput of MANETs is.
Proof:We?rst note that the upper bound obtained in Lemma2continues to be an upper bound.
We now consider achievable schemes.If
and,then and hence
.Thus,applying an algorithm similar to the achievable scheme proposed in Section V-B(with the secure coding scheme designed for the BSEC wiretap channel), it can be shown that as,which implies that
(13) Now if,then we apply the algorithm proposed in Section V-C,in which all packets are directly transmitted from sources to destinations.Since each destinations accepts only the ?rst copy when multiple copies of a packet are received,the network is immune to active attacks under this scheme.The secrecy throughput is hence achievable.
If we compare our results for the models under passive and active attacks,it is clear that the secrecy throughput is the same when the number of malicious nodes is large.This is a little counterintuitive,because having a large number of malicious nodes would seem to strengthen active attacks.However,this is not true,because in this case,the dominant contribution to the secrecy throughput is via single-hop transmissions from sources to destinations directly,which avoid active attacks in the?rst place.The difference between the models under passive and ac-tive attacks lies in the case when the number of malicious nodes is small.In this case,the dominant contribution to the secrecy throughput is via two-hop transmissions,during which mali-cious nodes may send modi?ed packets to destinations.There-fore,to guarantee the same throughput as the model under pas-sive attacks,the model under active attacks needs to satisfy a more stringent condition on the number of malicious nodes.
VII.C ONCLUSION
In this paper,we have studied the secrecy throughput of MANETs with malicious nodes under both passive attacks and active attacks.For the model with passive attacks,we have modeled communication in MANETs by the erasure wiretap channel,and have applied the secrecy capacity of the wiretap channel to characterize the secrecy throughput for MANETs. We have then explored secure coding design for the erasure wiretap channel to construct secure communication algorithms to achieve the optimal secrecy throughput.For the model under active attacks in addition to passive attacks,we have modeled communication in MANETs by the BSEC wiretap channel, and obtained the secrecy throughput via an approach similar to the model under passive attacks.We have also compared the secrecy throughput of the two models,and discussed the connections and differences between the two.
In this paper,we have considered a simple mobility model: the2-D i.i.d.mobility model,in which the nodes are indepen-dently reshuf?ed at the beginning of each time slot.This mo-bility model assumes that the mobiles move fast enough such that they can move from one location to any other location in one time slot.This simple mobility model enables us to connect net-work transmissions under security constraints to an equivalent discrete memoryless wiretap channel in order to quantify the scaling behavior of the secrecy throughput.One future research problem of interest is to investigate the secrecy throughput of more realistic mobility models such as the random walk model and the random waypoint model.The approach adopted in this paper may be applicable for these models if correlation in mo-bility models decays as time increases so that a block-memo-ryless wiretap channel may be a good approximation.Alterna-tively,more complicated wiretap models may need to be devel-oped for studying these more realistic mobility models.
A PPENDIX
P ROOF OF L EMMA1
To guarantee must satisfy the following condition:
(14) To further analyze,we?rst note that
if
if
where is a positive constant.
Assuming inequality(14)holds and de?ning
we evaluate for the following three cases:
Case1),which implies that
due to(14).We then obtain
Case2)and.We obtain
Case3),which implies that
.We obtain
It can be shown that if,then all of the above three cases are feasible and the secrecy throughput is given by Otherwise,if,then only Case3is feasible,and the secrecy throughput is given by
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Yingbin Liang(S’01–M’05)received the Ph.D.degree in electrical engineering from the University of Illinois at Urbana-Champaign,Urbana,in2005.
In2005–2007,she was working as a Postdoctoral Research Associate at Princeton University,Princeton,NJ.In2008–2009,she was an Assistant Professor at the Department of Electrical Engineering,University of Hawaii. Since December2009,she has been an Assistant Professor at the Department of Electrical Engineering and Computer Science,Syracuse University,Syra-cuse,NY.Her research interests include communications,wireless networks, information theory,and machine learning.
Dr.Liang was a Vodafone Fellow at the University of Illinois at Urbana-Champaign during2003–2005,and received the V odafone-U.S.Foundation Fel-lows Initiative Research Merit Award in2005.She also received the M.E.Van Valkenburg Graduate Research Award from the Department of Electrical and Computer Engineering,University of Illinois at Urbana-Champaign,in2005. In2009,she received the National Science Foundation CAREER Award,and the State of Hawaii Governor Innovation Award.
H.Vincent Poor(S’72–M’77–SM’82–F’87)received the Ph.D.degree in elec-trical engineering and computer science from Princeton University,Princeton, NJ,in1977.
From1977until1990,he was on the faculty of the University of Illinois at Ur-bana-Champaign,Urbana.Since1990,he has been on the faculty at Princeton, where he is the Dean of Engineering and Applied Science,and the Michael Henry Strater University Professor of Electrical Engineering.His research in-terests are in the areas of stochastic analysis,statistical signal processing and in-formation theory,and their applications in wireless networks and related?elds. Among his publications in these areas are Quickest Detection(Cambridge Univ. Press,2009),coauthored with O.Hadjiliadis,and Information Theoretic Secu-rity(Now Publishers,2009),coauthored with Y.Liang and S.Shamai.
Dr.Poor is a member of the National Academy of Engineering and the Na-tional Academy of Sciences,a Fellow of the American Academy of Arts and Sciences,and an International Fellow of the Royal Academy of Engineering (U.K.).He is also a Fellow of the Institute of Mathematical Statistics,the Optical Society of America,and other organizations.In1990,he served as President of the IEEE Information Theory Society,in2004–2007as the Editor-in-Chief of the these T RANSACTIONS,and in2009as General Co-Chair of the IEEE Inter-national Symposium on Information Theory,held in Seoul,South Korea.He received a Guggenheim Fellowship in2002and the IEEE Education Medal in 2005.Recent recognition of his work includes the2010IET Ambrose Fleming Medal for Achievement in Communications,the2011IEEE Eric E.Sumner Award,the2011IEEE Information Theory Paper Award,and an honorary D.Sc. from the University of Edinburgh,conferred in2011.
Lei Ying(M’08)received the B.E.degree from Tsinghua University,Beijing, China,in2001and the M.S.and Ph.D.degrees in electrical engineering from the University of Illinois at Urbana-Champaign,Urbana,in2003and2007, respectively.
During fall2007,he worked as a Postdoctoral Fellow at the University of Texas at Austin,Austin.He is currently an Assistant Professor at the Depart-ment of Electrical and Computer Engineering,Iowa State University,Ames. His research interest is broadly in the area of information networks,including wireless networks,mobile ad hoc networks,P2P networks,and social networks. Dr.Ying received a Young Investigator Award from the Defense Threat Re-duction Agency(DTRA)in2009,NSF CAREER Award in2010,and is named The Northrop Grumman Assistant Professor(formerly the Litton Industries As-sistant Professor)at the Department of Electrical and Computer Engineering at Iowa State University for2010–2012.
航空售票管理系统
摘要 伴随着经济的不断发展,必然带动交通业和旅游业务的不断扩大, 特别是航空售票和订票的信息管理日异复杂, 传统的售票方式已经难以满足快节奏, 高效率的现代生活需求,这就要求航空公司要有一套好的售票数据库系统。 一个正常营运的航空公司需要管理所拥有的飞机、航线的设置、客户的信息等，但更重要的还要提供票务管理。面对各种不同种类的信息，需要合理的数据库结构来保存数据信息以及有效的程序结构支持各种数据操作的执行。对数据的添加、修改、删除及查询等方面的操作应简单易行，并且能够具有较好的稳定性。航空售票管理系统主要采用Delphi 7.0做为开发工具，进行开发与设计的。本系统的使用界面具有十分人性化的特征，具有方便的查询功能，对售票、网上订票等方面的操作应简单易行，并且能够具有较好的稳定性。 关键词: 航空；售票；网上订票；管理系统；数据库；SQL语言。
目录 1.开发一个航空售票管理系统的背景和意义 (1) 1.1.传统售票方式的回顾和特点分析 (1) 1.2.航空售票管理系统的应用现状和前景展望 (1) 2.用计算机开发一个航空售票管理系统的可行性分析 (1) 2.1.技术可行性 (1) 2.2.经济可行性 (2) 2.3.法律可行性 (2) 3.开发环境的选择 (3) 3.1.Delphi 7.0简介 (3) 3.2.开发工具的选择 (3) 4.航空售票管理系统的需求分析 (3) 4.1.系统分析 (4) 4.2.系统功能模块设计 (4) 4.3.功能子模块分析 (5) 4.3.1.网上订票模块 (5) 4.3.2.用户查询模块 (5) 4.3.3.用户订票模 (5) 4.4.后台管理系统 (6) 4.4.1.后台管理系统子模块 (6) 4.5. 民航售票管理系统的顶级数据流程图 (8) 4.6. 民航售票管理系统一级数据流图 (9) 4.7. 数据字典定义 (10) 4.7.1.数据项定义 (10) 4.8.E/R模型 (13) 5.详细设计 (14) 5.1.系统的总体流程图 (14) 5.2.系统各模块的实现 (15) 5.2.1.系统登录窗口 (15) 5.2.2.主界面窗口 (16) 5.2.3.信息操作模块 (17) 5.2.4.送票员模块 (22) 5.2.5.员工管理模块 (23) 5.2.6.系统模块 (24) 5.2.7.售票员模块 (25) 5.2.8.前台订票模块 (26)
(完整版)LTE系统峰值速率的计算
LTE系统峰值速率的计算 我们常听到” LT网络可达到峰值速率100M、150M、300M ,发展到LTE-A更是可以达到 1Gbps “等说法，但是这些速率的达成究竟受哪些因素的影响且如何计算呢？为了更好的学习峰值速率计算，我们可以带着下面的问题来一起阅读： 1、LTE系统中，峰值速率受哪些因素影响？ 2、FDD-LTE系统中，Cat3和Cat4,上下行峰值速率各为多少？ 3、T D-LTE系统中，以时隙配比3:1、特殊子帧配比10:2:2为例，Cat3、Cat4上下行峰值速率各为多少？ 3、LTE-A （ LTE Advaneed要实现IGbps的目标峰值速率，需要采用哪些技术？ 影响峰值速率的因素有哪些？ 影响峰值速率的因素有很多，包括： 1. 双工方式——FDD、TDD FDD-LTE为频分双工，即上、下行采用不同的频率发送；而TD-LTE采用时分双工，上、下行 共享频率，采用不同的时隙发送。 因此如果采用相同的带宽和同样的终端类型，FDD-LTE能达到更高的峰值速率。 2. 载波带宽 LTE网络采用5MHz、10MHz、15MHz、20MHz等不同的频率资源，能达到的峰值速率不同。 3. 上行/ 下行 上行的业务需求本就不及下行，因此系统设计的时候也考虑“下行速率高些、上行速率低些” 的原则，实际达到的效果也是这样的。 4. UE能力级 即终端类型的影响，Cat3和Cat4是常见的终端类型，FDD-LTE系统中，下行峰值速率分别能达到100Mbps和150Mbps，上行都只能支持最高16QAM的调制方式，上行最高速率50Mbps。 5. TD-LTE系统中的上下行时隙配比、特殊子帧配比 不同的上下行时隙配比以及特殊时隙配比，会影响TD-LTE系统中的峰值速率水平。 上下行时隙配比有1:3和2:2等方式，特殊时隙配比也有3:9:2和10:2:2等方式。考虑尽量提升下行速率，国内外目前最常用的是DL:UL=3:1、特殊时隙配比10:2:2这种配置。 6. 天线数、MIMO 配置 Cat4 支持2*2MIMO ，最高支持双流空间复用，下行峰值速率可达150Mbps；Cat5 支持 4*4MIMO ，最高支持四层空间复用，下行峰值速率可达300Mbps。 7. 控制信道开销 计算峰值速率还要考虑系统开销，即控制信道资源占比。实际系统中，控制信道开销在20~30% 的水平内波动。 总之，有很多因素影响所谓的“峰值速率”，所以提到峰值速率的时候，要说明是在什么制式下、采用了多少带宽、在什么终端、什么方向、什么配置情况下达到的速率。 下行峰值速率的计算： 计算峰值速率一般采用两种方法： 第一种：是从物理资源微观入手，计算多少时间内（一般采用一个TTI或者一个无线帧）传 多少比特流量，得到速率； 另一种：是直接查某种UE类型在一个TTI （LTE系统为1ms）内能够传输的最大传输块，得到速率。
LTE计算汇总
如对你有帮助，请购买下载打赏，谢谢！ 1.RSRP及RSRQ计算 RSRP=-140+RsrpResult（dBm）； ●-44<=RSRP<-140dbm ●0<= RsrpResult<=97 下行解调门限：18.2dBm来计算的话，下行支持的最小RSRP为18.2-130.8= -112.6 下行解调门限：上行支持的最小RSRP为23-126.44= -103.44dBm RSRQ=-20+1/2RsrqResult（dB） RSRQ=N×RSRP/(E-UTRA carrier RSSI)，即RSRQ = 10log10(N) + UE所处位置接收到主服务小区的RSRP – RSSI。 RSRQ=20+RSRP – RSSI 2.W及dBm换算 “1个基准”：30dBm=1W “2个原则”： 1）+3dBm，功率乘2倍；-3dBm，功率乘1/2 33dBm=30dBm+3dBm=1W× 2=2W 27dBm=30dBm-3dBm=1W× 1/2=0.5W 2）+10dBm，功率乘10倍；-10dBm，功率乘1/10 40dBm=30dBm+10dBm=1W× 10=10W 20dBm=30dBm-10dBm=1W× 0.1=0.1W 3.功率计算 其中max transmissionpower = 43dBm 等效于20W Partofsectorpower=100(%) ; confOutputpower=20(W) Sectorpower=20(W) 需确保Sectorpower=confOutputpower*Partofsectorpower*% 如Partofsectorpower=50(%) ; confOutputpower=40(W) Sectorpower(20W)=confOutputpower(40W) *Partofsectorpower(50%) 4.参考信号接收功率计算 RSRP功率=RU输出总功率-10lg(12*RB个数) , 如果是单端口20W的RU,那么可以推算出 RSRP功率为43-10lg1200=12.2dBm. 1)A类符号指整个OFDM符号子载波上没有RS符号，位于时隙的索引为1、2、3、5、6
中国民航机场航空收费管理系统
中国民航机场航空收费管理系统 、系统设计背景 航空服务费是在航空运输过程中，一方（航空公司、机场或其他航空运输服务企业）为另一方（航空公司的班机）提供导航、起降、地面服务、机务、商务等各项服务，需向对方收取的费用或收回为对方垫付的费用等形成的债权、债务。 随着机场的新建、扩建以及航空公司的纷纷成立，带来了航空服务费种类和数量 的增加，这些费用是机场、航管等航空企业实现收入成果的主要手段。由于航空服务费的收取、分配、审核牵涉到多家单位，因此提供一个快捷、合理的服务收费管理系统是民航机场提高服务的有力保障。 以前的服务收费管理系统只局限于中国航空结算中心，由结算中心代理各单位进行服务费的结算。虽然航空收费是机场收入来源的重要组成部分，各机场现有的系统只是完成简单的计算，信息处理效能较低，外场与空管等部门的起降费用处理互不集成，缺少与其他相关系统的连接，给分析管理工作带来一定的困难，使收入结算基本处于手工状态，为了提高航空服务费结算效率，加速资金周转，增加航空服务费收费透明度，提高经济效益，所以开发机场航空收费管理系统势在必行。 新开发的中国民航机场航空收费管理系统是涵盖机场航空服务费业务的管理信息系统。该系统从航管中心、机场外场系统、地面服务公司和候机楼等处采集航班信息，根据相应的航班信息，计算费用，自动形成清算数据和帐务数据，然后对收入进行分配，分配信息经财务接口入机场财务系统，实现帐务数据的自动传递，同时可以生成相应报表，使机场可以直接开帐，实现了服务费当月转账。 、系统总体设计方案 1、系统目标和主要功能 1）自动接收航管系统和机场外场的飞行动态信息，并同时具备手工录入
民航订票系统
1 绪论 1.1 系统设计的目的 为了方便机场工作人员对机票的管理，提高飞机票的管理效率，降低飞机票管理中的出错率，减少信息交流及其带来不必要的开销。需要设计一个民航订票系统，该系统能及时反映航班及机票的剩余数量等信息，可快速的帮乘客订到想要的机票，而乘客也可以通过该系统实现自助退票的功能。 1.2 实现的目标 该系统是由两个人共同设计实现功能，由于一个人的能力有限，该系统仅能实现录入航班信息、查询航班信息、订购飞机票、退订飞机票等一部分功能（航班的信息存储在文件夹中）。 1.3 课题的研究方法 机票管理系统是实现机场机票的预定、统计和分析，提高机票运用的科学管理水平，充分发挥机票利用率，节省订退票时间。系统的主要设计思想是实现对机票的查询、退订等功能。航班的信息以文件的形式存储，用头文件包含、全局变量定义和结构体定义源代码，用结构体数组来存放顾客名、航班号、座位号等信息，用链式结构存储信息。系统的人机接口采用简单的字符界面进行交互。系统的设计方法是结构化设计方法，采用C语言进行开发。软件开发平台采用Visual C++6.0[1]。
2 软件模块结构图 民航订票系统可以分为四个部分，订票、退票、查询信息、信息载入。 图2.1 民航订票系统模块结构图
3 详细设计 3.1 数据结构设计 用printf()函数输出界面，typedef struct airline{}定义航班的信息，如航班号、飞机号、目的地、座位总数和剩余座位数等。typedef struct customer{}定义顾客信息，如顾客名、所订航班号和座位号等。初始化链表，并在airline、customer 链表中操作，实现系统的功能[1]。 3.2 模块设计 3.2.1 界面设计 用printf()函数输出界面，提示用户选择需要的功能，用条件语句捕捉用户输入的选项，并执行代码，进入用户所选的操作界面。若用户输入非选项的字符，则提示输入错误，提醒用户重新选择。 3.2.2 订票模块设计 进入订票模块后，进行订票。若票已经售完，提示用户不能预订该航班的机票。若还有余票，则提示用户输入个人信息订票。订票成功后，系统中该航班的余票减少相应的数量，并将顾客信息存入文件[2]。代码实现： status book(airline *l,char *flight_num,customer *c,char *name) { airline *p=l; customer *q=c->next ; p=l->next ; for(;q->next !=NULL;q=q->next){} for(;p!=NULL;p=p->next ) { if(strcmp(flight_num,p->flight_num )==0) { if(p->left >0)
通信人才网-LTE峰值速率的计算详解
LTE系统峰值速率的计算 我们常听到”LTE网络可达到峰值速率100M、150M、300M，发展到LTE-A 更是可以达到1Gbps“等说法，但是这些速率的达成究竟受哪些因素的影响且如何计算呢？ 为了更好的学习峰值速率计算，我们可以带着下面的问题来一起阅读： 1、LTE系统中，峰值速率受哪些因素影响？ 2、FDD-LTE系统中，Cat3和Cat4，上下行峰值速率各为多少？ 3、TD-LTE系统中，以时隙配比3:1、特殊子帧配比10:2:2为例，Cat3、Cat4上下行峰值速率各为多少？ 3、LTE-A（LTE Advanced）要实现1Gbps的目标峰值速率，需要采用哪些技术？ 影响峰值速率的因素有哪些？ 影响峰值速率的因素有很多，包括： 1. 双工方式——FDD、TDD FDD-LTE为频分双工，即上、下行采用不同的频率发送；而TD-LTE采用时 分双工，上、下行共享频率，采用不同的时隙发送。 因此如果采用相同的带宽和同样的终端类型，FDD-LTE能达到更高的峰值速率。 2. 载波带宽 LTE网络采用5MHz、10MHz、15MHz、20MHz等不同的频率资源，能达到的峰值速率不同。 3. 上行/下行 上行的业务需求本就不及下行，因此系统设计的时候也考虑“下行速率高些、上行速率低些”的原则，实际达到的效果也是这样的。 4. UE能力级 即终端类型的影响，Cat3和Cat4是常见的终端类型，FDD-LTE系统中，下行峰值速率分别能达到100Mbps和150Mbps，上行都只能支持最高16QAM的调制方式，上行最高速率50Mbps。 5. TD-LTE系统中的上下行时隙配比、特殊子帧配比
民航旅客管理系统
目录 摘要 (1) 一、项目开发背景 (1) 二、系统分析 (2) （一）可行性分析 (2) （二）组织结构调查，管理功能分析 (2) （三）E-R图 (6) 三、系统设计 (7) （一）概要设计 (7) （二）系统功能结构设计 (8) （三）数据库设计 (8) 四、系统实施 (9) （一）系统运行时的环境 (9) （二）主要的界面及脚本介绍 (9) 小结 (22) 参考文献 (22)
不死鸟航空公司客户管理系统 （计算机应用专业） 摘要：不死鸟，又叫做菲尼克司。是一种神话中的鸟类，它与埃及神话中 的太阳神和希腊神话中的阿波罗有着密切的关系。不死鸟航空公司以不死鸟为吉祥物，以安全的飞行，热情的服务，美好的经历致力打造航空中的“不死鸟”。近年来，随着计算机技术的发展和互联网时代的到来，当今社会已经进入了信息时代，也有人称为数字化时代，在这数字化的时代里，传统的机票预定形式已经跟不上历史的潮流。电子机票预定系统就是为满足各种用户，公司企业的需求，而开发的一套实用的系统。通过互联网创建网络机票预定系统，可以宣传航班的线路和其他产品，招揽更多的旅客购买机票，从而为航空公司带来更多的经济效益。使用网络机票预定系统还可以为航空公司节省人力成本，提高工作效率，从而增强企业的竞争力。 因此基于以上的考虑在开发网络订票系统—中采用目前比较流行并且技术已经十分成熟的三层构架技术来实现航班管理对航班信息、机票信息、用户信息、订单信息的便捷管理，而数据库则采用轻量级的数据库MySql不但可是对系统数据更高效的管理而且便于系统的移植和跨平台操作，实现了航班管理的数字化、信息化，减少了人力，节省了财力，提高了企业运作的效率对有效控制机票销售提供了必要的信息情报为企业节省了不必要的浪费。因此网络机票预定系统---实现了对航班信息、机票信息、用户信息、订单信息的查询、录入、修改等基本操作。但还有待于进一步发掘深层次的用户需求进行二次开发完善其功能性，使该系统在操作方面更方便、操作界面更加友好。 关键字：航班订票；数据库；Microsoft Visual Studio 2008 一、项目开发背景 目前，国内的上网人数急剧倍增，以及随着人们生活水平的提高，选择航空出行的人们越来越多，这对航空公司来说是个好消息，但是，航空公司间的竞争也日趋激烈，如果航空公司不能做到定退票的方便服务，很可能会被淘汰，所以不死鸟航空公司紧跟时代潮流，开发网上订票系统方便旅客轻轻松松实现定退票。既节省了用户的时间和金钱也简化了机票销售人员的工作。以高效化、系统化、规范化、科学化的网络机票预定模式是顺历史潮流而动，是大势所趋。 今天已经步入了网络时代。互联网的普及为网络服务和电子商务注入了新的活力，网络服务成为增长最快、最具活力的领域。因此，本系统的目标是一个可以面向网络交互的真正意义上的网络服务，让用户体会到网络的方便与快捷。在计算机网络，数据库和先进的开发平台上，利用现有的软件，配置一定的硬件，开发一个具有开放体系结构的、易扩充的、易维护的、具有良好人机交互界面的机票预定系统。实现航空公司的机票销售的自动化的计算机系统，为企业的决策层提供准确、精细、迅速的机票销售信息。最终使本系统可以面向一切网络用户。
2016 年民航机场生产统计公报
名次 本期完成上年同期同比增速%名次本期完成 上年同期同比增速%名次本期完成上年同期同比增速%合计1,016,357,068914,773,31111.115,104,056.714,094,002.77.29,238,2918,565,5267.9北京/首都194,393,45489,939,049 5.021,943,159.71,889,439.5 2.81606,081590,199 2.7上海/浦东266,002,41460,098,0739.813,440,279.73,275,231.1 5.02479,902449,171 6.8广州/白云359,732,14755,201,9158.231,652,214.91,537,758.97.43435,231409,679 6.2成都/双流446,039,03742,239,4689.05611,590.7556,552.19.95319,382293,6438.8昆明/长水541,980,33937,523,09811.99382,854.3355,422.87.74325,934300,4068.5深圳/宝安641,975,09039,721,619 5.741,125,984.61,013,690.511.16318,582305,461 4.3上海/虹桥740,460,13539,090,865 3.58428,907.5433,600.1-1.19261,981256,603 2.1西安/咸阳836,994,50632,970,21512.214233,779.0211,591.510.57291,027267,1029.0重庆/江北935,888,81932,402,19610.810361,091.0318,781.513.38276,807255,4148.4杭州/萧山1031,594,95928,354,43511.46487,984.2424,932.714.810251,048232,0798.2厦门/高崎1122,737,61021,814,244 4.212328,419.5310,606.6 5.713183,546180,112 1.9南京/禄口1222,357,99819,163,76816.711341,267.1326,026.5 4.712187,968166,85812.7长沙/黄花1321,296,67518,715,27813.821130,276.1122,022.1 6.818167,910153,3679.5武汉/天河1420,771,56418,942,0389.716175,294.8154,656.213.316175,669164,524 6.8郑州/新郑1520,763,21717,297,38520.07456,708.8403,339.013.214178,054154,46815.3青岛/流亭1620,505,03818,202,08512.715230,747.8208,064.010.917168,537155,4838.4乌鲁木齐/地窝堡 1720,200,76718,506,4639.217157,508.7156,469.80.719162,265153,097 6.0海口/美兰1818,803,84816,167,00416.320148,814.2135,944.69.521135,523121,82511.2三亚/凤凰1917,369,55016,191,9307.32986,846.885,369.3 1.726114,581108,532 5.6天津/滨海2016,871,88914,314,32217.913237,085.2217,279.29.120143,822125,69314.4哈尔滨/太平2116,267,13014,054,35715.722124,794.7116,103.87.524122,282108,42812.8大连/周水子2215,258,20914,154,1307.819149,008.0137,048.18.723127,680117,7948.4贵阳/龙洞堡2315,105,22513,244,98214.02895,898.687,207.010.022129,001116,91410.3沈阳/桃仙2414,967,22812,680,11818.018155,769.4142,069.69.625115,16499,56315.7济南/遥墙2511,616,9149,520,88722.026100,013.286,336.815.827100,15286,15816.2福州/长乐2611,606,44610,887,292 6.623121,657.5116,497.5 4.42897,60696,127 1.5南宁/吴圩2711,559,86010,393,72811.225104,618.195,710.39.33094,06586,8738.3兰州/中川2810,897,0258,009,04036.13259,455.250,093.818.73191,09167,83534.3太原/武宿 29 9,847,840 8,842,98711.436 49,103.8 45,463.68.035 82,64179,376 4.1 2016年民航机场吞吐量排名 2016-01-01到2016-12-31 机场旅客吞吐量（人次） 货邮吞吐量（吨） 起降架次（架次）
机票购票和售票管理系统设计
随着国家经济的不断发展，人们生活水平的不断提高，互连网已经成为人们日常生活，成为办公学习中不可缺少的组成部分。而随着互连网的不断普及，网络技术也得到了快速的发展，特别是在网络销售，办公管理方面发展尤为迅速。机票在线订购管理系统正是在这种环境之下制作完成的，随着网络技术的飞速发展和人民生活水平的不断提高，航空公司已不再满足于独立、零散的办公自动化应用和机票销售管理，航空公司需要的是协同工作、综合、集成化的解决方案。而网络是解决由于物理距离造成的信息交流不畅、协商沟通不便的管理瓶颈问题的最佳方式。机票在线订购管理系统是通过对机票在线预订销售管理各要素的闭环整合，实现了工作流、信息流、和办公自动化的整合管理，提供了一个科学、开放、先进的信息化机票在线预订平台，实现了航班信息管理、机票信息管理、机票预订管理等管理内容的高度继成。机票在线订购管理系统将航空公司机票销售管理人员从繁琐、无序、低序、低端的工作中解放出来从事核心事务，整体提高了航空公司机票预订销售的工作效率、提高了机票预订管理的可控性，降低了管理成本，提高执行力，使机票在线预订销售信息管理趋于完善。 以往的传统购票和售票管理模式（即手工管理模式）下，各方面的数据采集和反馈都是需要一定的时间传递的，因此耗时多、速度慢，还同时存在易出错、易失真、易丢失等问题，信息在传递中发生错误甚被遗失的情况严重。而且传统的手工管理模式并没有有效利用先进的现代化通讯技术，远程订票业务无法实现，那么地处偏远的顾客就会存在购票不便的困难，同时，手工管理模式会导致公司的各项服务衔接不利，为顾客的购票甚至出行带来诸多不便，不能使顾客有很好的服务体验，顾客满意度不高会影响了公司的名誉，而且公司自身的经营效率也很低。航空订票系统应运而生，它的目标就是提升航空公司的经营效率、为顾客出行提供便利条件，采用各种先进的现代化技术，结合优良的组织管理方式，对航空公司的订票业务全过程进行有效的管理。 网络技术的不断发展为很多传统行业提供了改革的契机，机票在线订购管理系统必将在未来的航空公司票务销售管理工作中发挥越来越重要的作用。“机票在线订购管理系统”的设计采用当今最为流行的网络编程语言之一的JA V A制作，数据库采用MYSQL，提高了数据的存储安全性，另外采用tomcat服务器加快了系统的整体访问速度，利于系统和用户之间的交互，“机票在线订购管理系统”
民航售票管理系统实验报告
[键入文档标题] [键入文档副标题] 安徽大学 计算机科学与技术1班 陈斌E 陈柚霖E 刘昊霖E 2016-9-20
目录
一、目的及要求 1.实验目的 (1)通过本次课程设计，熟练掌握一种开发语言（如C#）和一种数据库系统软件 （如SQL?server?2014）的使用。 (2)加深对软件工程的理解，训练编写程序的良好习惯。包括：认真编写需求分 析文档、做好系统功能和数据库设计、学会自己进行程序的算法、数据结构设计。 (3)培养良好的程序设计风格（模块划分、接口设计、程序界面、应用系统设计） 和习惯（程序备份、版本更新与控制），提高软件测试、调试的能力与技巧。 (4)通过本次课程设计，应该达到具有独立完成小型应用系统设计的能力，具备 编写较为规范的软件设计文档的能力。 2.实验要求 民航售票管理系统主要分为机场、航空公司和客户三方的服务。航空公司提供航线和飞机的资料，机场则对在本机场起飞和降落的航班和机票进行管理，而客户能得到的服务应该有航班线路和剩余票数的查询，以及网上订票等功能。客户又可以分为两类，一类是普通客户，对于普通客户只有普通的查询功能和订票功能，没有相应的机票优惠，另一种是经常旅客，需要办理注册手续，但增加了里程积分功能和积分优惠政策。机场还要有紧急应对措施，在航班出现延误时，要发送相应的信息。 本系统至少能完成如下查询功能： (1)查某代售地某月售出的票数和金额。 (2)查航空公司拥有多少航班。 (3)查某天某航空还剩多少票或座位。 (4)查某天某航空还剩商务舱座位以及经济舱座位票价。 (5)查某航空公司拥有多少售票点、某月售出总金额以及某航线售出票数。
LTE计算汇总
1.RSRP及RSRQ计算 RSRP=-140+RsrpResult（dBm）； ●-44<=RSRP<-140dbm ●0<= RsrpResult<=97 下行解调门限：18.2dBm来计算的话，下行支持的最小RSRP为18.2-130.8= -112.6下行解调门限：上行支持的最小RSRP为23-126.44= -103.44dBm RSRQ=-20+1/2RsrqResult（dB） RSRQ=N×RSRP/(E-UTRA carrier RSSI)，即RSRQ = 10log10(N) + UE所处位置接收到主服务小区的RSRP – RSSI。 RSRQ=20+RSRP – RSSI 2.W及dBm换算 “1个基准”：30dBm=1W “2个原则”： 1）+3dBm，功率乘2倍；-3dBm，功率乘1/2 33dBm=30dBm+3dBm=1W× 2=2W 27dBm=30dBm-3dBm=1W× 1/2=0.5W 2）+10dBm，功率乘10倍；-10dBm，功率乘1/10 40dBm=30dBm+10dBm=1W× 10=10W 20dBm=30dBm-10dBm=1W× 0.1=0.1W 3.功率计算 其中max transmissionpower = 43dBm 等效于20W Partofsectorpower=100(%) ; confOutputpower=20(W) Sectorpower=20(W) 需确保Sectorpower=confOutputpower*Partofsectorpower*% 如Partofsectorpower=50(%) ; confOutputpower=40(W) Sectorpower(20W)=confOutputpower(40W) *Partofsectorpower(50%)
民航票务管理系统分析和设计
数据库原理及应用课程设计任务书 指导教师（签章）： 2008 年 1 月 3 日
计算机工程系 数据库原理及应用课程设计报告 选题名称:民航票务管理系统 系（院）:计算机工程系 专业:计算机科学与技术（信息安全方向） 班级:信息 1 0 5 1 姓名:高博学号: 1051303116 指导教师:冯万利王红艳 学年学期:2007 ~ 2008 学年第 1 学期 2008 年 1 月 3 日
摘要： 随着信息技术在管理上越来越深入而广泛的应用以及信息的不断海量化，在很多行业对信息的管理不得不依赖计算机，而不是使用比较原始的纯人力管理方法。在当今，各行各业都有很多计算机管理的系统，特别是民航方面，每天，有上千万的人次订票、买票或者乘坐飞机，如果采取传统的人工售票或登记的方式会大大影响机场运作的效率，并且也无法避免一些人为的错误。所以一个好的票务管理系统由为重要，特别是现在很多人喜欢提前订票或上网订票，自然，好的票务管理系统不仅需要快捷方便的操作、优秀的保存和统计功能、还要应付大容量数据的快速查找和保存及应付长时间的工作需要，必须保证系统的稳定性和安全性，更重要的是，安全的票务管理系统不仅需要有能够防护各种病毒和黑客恶意攻击的能，还需要有能够应付突发状况的能力，比如突然断电之后，系统的售票信息和当前正在进行的进程、操作应该怎么处理，或者，系统突然死机之后，所有数据又应该怎样处理等等。这些问题都是我们在实际生活中经常会遇到的，所以，能不能解决上述问题就成了评价一个好的票务管理系统的主要依据。而这次的课程设计则是根据票务管理系统中的最主要的功能，结合数据库中所学的知识，来实现一个简单的民航票务管理系统，供学习和研究之用！ 关键词：民航票务管理系统；数据库；查询；修改；https://www.wendangku.net/doc/d48906205.html,
LTE速率计算
TD-LTE的最高下行速率计算LTE TDD帧结构
在TDD帧结构中，一个特殊子帧的大小是1ms，就是两个资源模块RB，一个
RB占7个OFDM符号，所以一个特殊子帧占14个OFDM符号，但是不管特 殊子帧内部结构如何变换，其大小都是1ms。 1、计算方法: 根据TD-LTE的帧结构，采用5ms的周期，最大是3个下行子帧+1个上行子帧，另外DwPTS也可以承载下行数据，最多是12个符号。 因此，5ms周期最多可以传3*14+12=54个符号，当使用20M带宽时，有1200个子载波，以最高效的64QAM计算，5ms周期内可传 54*1200*6=0.388 8M比特的数据，也就是最高下行速率为77.76Mbps。注意，这是没有使用MI MO。使用MIMO后，最高下行速率为 155.52Mbps。 当然，大家都知道每个子帧控制信息都占用至少一个符号，因此业务数据最多可占用50个符号，也就是不使用MIMO，最高下行速率为72Mbps；使用MI MO后，最高下行速率为144Mbps。 这还只是粗略计算，因为参考信号以及同步信号都会占用符号的部分或全部，因此最终的最高下行速率低于144Mbps。据中兴宣称，其最高速率为130Mbps。 2 参考信号的占用情况与MIMO是否使用有关。 a. 没有MIMO，每个RB中会分布有8个参考信号，因为第一个符号已经用于控制部分，不用重复计算，因此会占用6个调制符号的位置，也就是每个子帧占用的比特数为： 6*6（64QAM）*4（3下+DwPTS）*100（RB数量）=14.4kb 而1秒有200个子帧，对应速率为2.88Mbps b. 有MIMO，每个RB中会分布有16个参考信号，因为第一个符号已经用于控制部分，不用重复计算，因此会占用12个调制符号的位置，也就是每个子帧占用的比特数为： 12*6（64QAM）*2（MIMO）*4（3下+DwPTS）*100=57.6kb 对应速率为11.52Mbps。 这里有个地方不是很确定，就是DwPTS中参考信号的分布情况，但影响的数量应该不会很大。 3 考虑同步信号信道占用情况 同步信号只占用6个RB，因此每个子帧占用的比特数为： 2（主、从）*12（每RB子载波数）*6（64QAM）*4（3下+DwPTS）*6（R B数量）=3456b 对应速率为0.6912Mbps，如果采用MIMO，对应速率为1.3824Mbps
LTE速率计算
下行峰值速率的计算： 计算峰值速率一般采用两种方法： 第一种：是从物理资源微观入手，计算多少时间内（一般采用一个TTI或者一个无线帧）传多少比特流量，得到速率； 另一种：是直接查某种UE类型在一个TTI（LTE系统为1ms）内能够传输的最大传输块，得到速率。 下面以FDD-LTE为例，分别给出两种方法的举例。 【方法一】 首先给出计算结果： 20MHz带宽情况下，一个TTI内，可以算得最高速率为： 总速率=， 业务信道的速率=201.6*75%≈150Mbps 数字含义： 6：下行最高调制方式为64QAM，1个符号包含6bit信息； 2和7：LTE系统的TTI为1个子帧（时长1ms），包含2个时隙，常规CP下，1个时隙包含7个符号；因此：在一个TTI内，单天线情况下，一个子载波下行最多传输数据6×7×2bit；2：下行采用2×2MIMO，两层空分复用，双流可以传输两路数据； 1200：20MHz带宽包含1200个子载波（100个RB，每个RB含12个子载波） 75%：下行系统开销一般取25%（下行开销包含RS信号(2/21)、 PDCCH/PCFICH/PHICH(4/21)、SCH、BCH等），即下行有效传输数据速率的比例为75%。如果是TD-LTE系统，还要考虑上下行的时隙配比和特殊时隙配比，对下行流量对总流量占比的影响。 如在时隙配比3:1/特殊子帧配比10:2:2的情况下： 一个无线帧内，各子帧依次为DSUDD DSUDD，其中D为下行子帧U为上行子帧，每个子帧包含2个时隙共14个符号，S为特殊子帧，10:2:2的配置，表示DwPTS(Downlink Pilot TimeSlot)、GP(Guard Period)和UpPTS(Uplink Pilot TimeSlot)各占10个、2个和2个符号。那么所有下行符号等效在一个TTI内占的比例为(6*14+2*10)/14*10=74%，如果也粗略考虑75%的控制信道开销，那么TD-LTE系统在3:1/10:2:2的配置下，下行峰值速率可达：201.6*75%*74%≈112Mbps 其他的时隙配比、特殊子帧配比，都可以参考这个方法来计算。 【方法二】 这个方法简单直观很多，如下表，第一列是终端类型1~8(常用3、4) 第二列为一个TTI内传输的最大传输块bit数，那么峰值速率就等于最大传输块大小/传输时间间隔，以Cat3和Cat4为例，峰值吞吐率分别为102048/0.001=102Mbps和 150752/0.001=150Mbps。Cat5因为可以采用了4*4高阶MIMO，4层空分复用在一个TTI 内传299552bit，因此能达到300Mbps的下行峰值速率。 FDD-LTE系统，计算可到此为止，TD-LTE系统需要再根据时隙配比/特殊子帧配比乘上比例，Cat3和Cat4的下行峰值吞吐率分别为75Mbps和111Mbps。 超级啰嗦： 1、Cat3因为最大传输块为102048，所以FDD-LTE中峰值速率最高只能到100Mbps。
民航订票管理系统
《软件工程实习》 说明 题目: 民航订票管理系统 别: 计算机科学与工程指导教师: 年月曰
15 软件工程实习成绩评定表 项目测试 100 个人综合成绩二小组总成绩*30%+个人实际得分*70% 用户安装及使用手册 项目成果提交 小组总成绩 评分项目 需求分析 数据库设计 系统设计 项目管理 代码质量 小组评分标准 实际得分 分值 10 15 10 10 10 25
目录 1. 项目管理 2. 需求分析 3. 数据库设计 4. 概要设计 5. 详细设计13 6. 项目测试29 7. 安装手册32 8. 使用手册34 9. 总结35 参考文献36
项目甘特图如图 1.项目管理 该部分文档编写负责人 该部分文档复核人 最终版本 1.1甘特图 1.1.1项目名称及起止时间 1.1.2 项目甘特图 任务名称 工期 开始时间 芫咸时间 前置任雰 1 进行需求片析 2工性日 2010年8月2汨 2010年8月2汨 E 参老资料 厂工作日 却10年8月2汨 RID 年8月烈日 3 :圍 墅数据库 1工作日 加10年&月￡汨 201。年S 月25日 L,2 4 编宜mailt 代码 1亍作日 ZOiCi 年 8^26 0 2010^3^250 3 5 编写Cg t Din erf 3工作日 2CI1CI 年 8^26 0 2010年8月羽日 3 G 编写 LingF on* 3工作日 药10 年 8^26 0 2010^8^230 3 7 编宜］hM 代码 3工住日 2010 年 8^28 0 aoiQ 年￡月如日3 …才 编 ^SellT 3工作日 如10年8月茨日 2010年3月如日3 ■ 9' 1 编写Form 1代码 1工柞日 2010^8^310 2010^3^315 %瓦6」尼 3 1Q 编写文档 1工住日 2010 年 8^310 药1仃年E 月31日 11 1 握交非业 1工作日 EDici 年g 月1日 201。年g 月1日 L ,乙 3, 4, 5, S,兀 图1-1 甘特图 项目名称及起止时间由图 1-1给出。 图1-2项目甘特图
LTE速率计算
1、FDD理论计算公式： 一个时隙（0.5ms）内传输7个OFDM符号，即在1ms内传输14个OFDM符号，一个资源块（RB）有12个子载波（即每个OFDM在频域上也就是 15KHZ），所以1ms内（2个RB）的OFDM个数为168个（14*12），它下行采用OFDM技术，每个OFDM包含6个bits，则20M带宽时下行速速为：
民航售票管理系统(课程设计、C)
课程设计报告书 民航售票管理系统 1.选择题目 题目：民航售票管理系统 2.内容摘要 摘要：对民航的运营而言，售票是民航最关键的部分之一，也是民航的“生命线”。而售票管理又是售票的核心技术。实行电子化的售票管理，可以让人力资源管理人员从繁重琐碎的案头工作解脱出来，去完成更重要的工作，更重要的是用最短的时间能非常精确地完成工作，达到公司和顾客利益的最大化。本文介绍课程设计课题的选题意义，说明了本系统提供的主要功能，并画出功能框图，对设计思路、研究开发的过程、实现细节、开发工作进行了比较完整的综述，最后给出了作者在小学期设计过程的体会。 3.索引关键字 关键词：民航售票管理，功能框图，动态链表，文件保存
4.目录 1.题目要求---------------------------------4 2.设计思想---------------------------------5 3.系统完成功能及框图-----------------------6 4.界面设计---------------------------------6 5.核心代码及说明---------------------------8 6.结论-------------------------------------10 7.后记-------------------------------------15 8.参考资料----------------------------------17 9.附录-------------------------------------18
5.正文 5.1课程设计题目要求 5.1.1内容要求 本系统要求实现如下功能： 1. 航班信息录入功能。航班信息用文件保存。 2. 航班信息的维护功能。航班信息增加、删除、修改功能。 3. 浏览航班信息功能。 4. 航班信息查询功能。根据航班号、终点站、飞行时间等条件查询。 5.订票功能。对订票信息应该存储在一个数组或链表中，每次进行订票时应该先统计该 天该航班的已定人数，如果超过成员定额，则不能订票。 6. 统计每个航班某一天的已售（订）出座位数、剩余座位数。 7. 退票功能。输入用户名或订票编号，在订票信息链或数组中删除该条信息。 5.1.2设计要求 ①建立两个二进制文件：其中之一，用来存放航班信息；另一个用来存放客户订票信息。 ②结构体采用动态链表形式，用动态链表存放数据，及实现数据的存储与读取。 ③在两个动态链表间，用指针连接起来。 ④设计一个漂亮的欢迎界面和友好的系统界面。 ⑤要求系统能够根据系统菜单相应的功能执行相应的部分。 ⑥要求能够实现录入、查询等相应的功能。
民航机场售票管理系统
青岛理工大学 数据库系统课程设计 院（系）：计算机工程学院 专业： XXXXXXXXX 姓名: XXXXXXX 班级： XXXXXXXXX 学号: XXXXXXXXXX 题目：民航机场售票管理系统 起迄日期:＿ 2011.1.3 ~ 2011.1.14 ＿ 设计地点: 2号实验楼402 指导教师: XXXXXX
目录 第1章前言 (4) 1.1. 课题背景 (4) 1.2. 开发工具 (4) 1.2.1 Windows SDK (4) 1.2.2 SQL Server 2005数据库 (4) 1.2.3 ODBC API数据库连接技术 (5) 第2章需求分析 (6) 2.1. 任务概述 (6) 2.1.1 系统目标 (6) 2.1.2 用户特点 (6) 2.2. 系统的功能需求 (6) 2.2.1 系统角色功能需求 (6) 2.2.2 功能模块 (6) 2.3. 系统的性能需求 (7) 2.4. 系统的数据需求 (7) 第3章系统总体设计 (8) 3.1. 系统总体设计 (8) 第4章数据库设计 (9) 4.1. 数据库概念设计 (9) 4.1.1 订票信息实体E-R图 (9) 4.1.2 客户信息实体E-R图 (9) 4.1.3 航线信息实体E-R图 (10) 4.1.4 客机信息实体E-R图 (10) 4.1.5 舱位等级实体E-R图 (10) 4.1.6 实体间关系E-R图 (11) 4.2. 数据库逻辑设计 (11) 4.2.1 AIR_BOOK_TABLE（订票管理表） (11) 4.2.2 AIR_PLANE_TABLE(飞机管理表) (11) 4.2.3 AIR_SEAT_TABLE(舱位管理表) (12) 4.2.4 AIR_GUEST_TABLE(客户管理表) (12) 4.2.5 AIR_LINE_TABLE(航线管理表) (12) 第5章详细设计 (13) 5.1. 页面设计 (13) 5.1.1 “登陆”界面 (13) 5.1.2 程序主界面 (13) 5.1.3 “机票预订信息管理”界面 (14) 5.1.4 “客户信息管理”界面 (14) 5.1.5 “航线信息管理”界面 (15) 5.1.6 “客机信息管理”界面 (15) 5.1.7 “舱位信息管理”界面 (16) 5.1.8 “关于”界面 (16) 5.2. 编码设计 (17) 5.2.1 与数据库建立的链接 (17) 5.2.2 获取数据库中特定表的某元组 (17)
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