This paper investigates the design of integrated detection and communication waveforms with a particular focus on the exploitation of constructive interference (CI) at both the system and symbol levels. We formulate a waveform design problem with the objective of optimizing the detection performance which is characterized by the Kullback Leibler distance (KLD) between the probability density functions (PDFs) under two hypotheses, subject to the constraints of the CI condition and constant modulus. To address the challenging non-convex problem, a low-complexity recursive penalty-based Riemannian conjugate gradient (RE-PRCG) algorithm is proposed. The numerical results demonstrate the effectiveness of the proposed algorithm in improving the detection performance and reducing the computational burden over the benchmark.

In this paper, we for the first time demonstrate how to enable OFDMA in Wi-Fi backscatter for capacity and concurrency enhancement. With our approach, the excitation signal is reflected, modulated and shifted to lie in the frequency band of the OFDM subcarrier by the tag; OFDMA is realized by coordinating tags to convey information to the receiver with orthogonal subcarriers concurrently through backscatter. The crux of the design is to achieve strict synchronization among communication components, which is more challenging than in regular OFDMA systems due to the more prominent hardware diversity and uncertainty for backscattering. We reveal how the subtle asychnronization scenarios particularly for backscattering can incur system offsets, and present a series of novel designs for the excitation signal transmitter, tag, and receiver to address the issue. We build a prototype in 802.11g OFDM framework to validate our design. Experimental results show that our system can achieve 5.2-16Mbps aggregate throughput by allowing 48 tags to transmit concurrently, which is 1.45-5x capacity and 48x concurrency compared with the existing design respectively. We also design an OFDMA tag IC, and the simulation and numerical analysis results show that the tag's power consumption is in tens of μW.

Periodic broadcasting has achieved prominent performance in VoD (Video on Demand) service in wired network. However, the development of wireless VoD service still has gone on hard and slowly in comparison to the rapid growth of mobile video service. In this paper, we propose a scalable video streaming method HQOBA (Heterogeneous Quality-Oriented Bandwidth Allocation) to address the problems in wireless network. To be concrete, in order to tackle the heterogeneous client type problem, we employ variable bandwidth allocation, instead of fixed bandwidth value for each channel. Moreover, to handle the problem of scarce server bandwidth and unpredictable wireless channel conditions, the proposed method takes the advantage of FGS (Fine Granular Scalable) video coding and Periodic Broadcasting to successfully transmit video segment. Further-more, we develop a bandwidth allocation algorithm to maximize client perceptual visual quality for the proposed transmit scheme. The experimental results of HQOBA scheme also validate the effectiveness.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology for improving network resource utilization, and cooperative communication technology is usually used to mitigate the interference caused by D2D communication. Due to the additional signal processing cost introduced by cooperative communication, the cellular links who have the exclusive usage right of the network spectrum can charge the D2D links a fee for spectrum usage to enhance their profit. In this paper, we propose a contract-based cooperative spectrum sharing mechanism to exploit transmission opportunities for the D2D links and meanwhile achieve the maximum profit of the cellular links. We first design a cooperative relaying scheme that employs superposition coding at both the cellular transmitters and D2D transmitters. The cooperative relaying scheme can maximize the data rate of the D2D links without deteriorating the performance of the cellular links. Then, we employ a contract-theoretic framework to model the spectrum trading process based on the cooperative relaying scheme, and derive the optimal power-payment contracts for the cellular links under both the cases that the private information (i.e., channel quality) of the D2D links is continuous and discrete using tools from continuous-and discrete-time optimal control theories, respectively. Analytic and numerical results confirm the efficiency of the proposed spectrum sharing mechanism.

Delayed traffic offloading is a promising paradigm to alleviate the cellular network congestion caused by explosive traffic demands. As we all know, in mobile networks, the delay profile for traffic is remarkable due to users’ mobility. How to exploit user delay tolerance to improve the profit of operator as well as mobile users becomes a big challenge. In this paper, we model this delayed offloading process as a monopoly market based on contract theory, where operator acts as the monopolist setting up the optimal contract by statistical information on user satisfaction. We propose an incentive mechanism to motivate users to leverage their delay and price sensitivity in exchange for service cost. To capture the heterogeneity of user satisfaction, we classify users into different types. Each user chooses a proper quality–price contract item according to its type. More specifically, we investigate this delayed offloading scheme under strongly incomplete information scenario, where user type is private information. We derive an optimal contract, which maximizes operator’s profit for both the continuous-user-type model and the discrete-user-type model. Numerical results validate the effectiveness of our incentive mechanism for delayed traffic offloading in cellular networks.

Traffic offloading over Delay Tolerant Networks (DTNs) is a promising paradigm to alleviate the network congestion caused by explosive traffic demands. As we all know, in mobile networks, the delay profile for traffic is remarkable due to user's mobility. How to exploit delay tolerance to improve the profit of the operator as well as mobile users becomes a big challenge. In this paper, we investigate the problem of the interrelation of delay and user QoS. Inspired by contract theory, we model the delayed offloading process as a monopoly market where the operator makes pricing by considering statistical information about user satisfaction. In addition, we propose an incentive framework to motivate users to leverage their delay and price sensitivity in exchange for service cost. To capture the heterogeneity of user satisfaction, we classify users into different types. Each user chooses an appropriate quality-price contract item according to its type. Moreover, we derive an optimal contract which is feasible and maximizes the operator's profit as well. Numerical results validate the effectiveness of our incentive framework for traffic offloading over DTNs.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, interference generated by D2D communications is usually viewed as an obstacle to cellular communications. However, in this paper, we present a new perspective on the role of D2D interference by taking security issues into consideration. We consider a large-scale D2D-enabled cellular network with eavesdroppers overhearing cellular communications. Using stochastic geometry, we model such a network and analyze the signal-to-interference-plus-noise ratio (SINR) distributions, connection probabilities and secrecy probabilities of both the cellular and D2D links. We propose two criteria for guaranteeing performances of secure cellular communications, namely the strong and weak performance guarantee criteria. Based on the obtained analytical results of link characteristics, we design optimal D2D link scheduling schemes under these two criteria respectively. Both analytical and numerical results show that the interference from D2D communications can enhance physical layer security of cellular communications and at the same time create extra transmission opportunities for D2D users.

This paper studies the beamforming design problem of a multiuser downlink network, assuming imperfect channel state information known to the base station. In this scenario, the base station is equipped with multiple antennas, and each user is wiretapped by a specific eavesdropper where each user or eavesdropper is equipped with one antenna. It is supposed that the base station employs transmit beamforming with a given requirement on sum transmitting power. The objective is to maximize the sum secrecy rate of the network. Due to the uncertainty of the channel, it is difficult to calculate the exact sum secrecy rate of the system. Thus, the maximum of lower bound of sum secrecy rate is considered. The optimization of the lower bound of sum secrecy rate still makes the considered beamforming design problem difficult to handle. To solve this problem, a beamforming design scheme is proposed to transform the original problem into a convex approximation problem, by employing semidefinite relaxation and first-order approximation technique based on Taylor expansion. In addition, with the advantage of low complexity, a zero-forcing-based beamforming method is presented in the case that base station is able to nullify the eavesdroppers' rate. When the base station does not have the ability, user selection algorithm would be in use. Numerical results show that the former strategy achieves better performance than the latter one, which is mainly due to the ability of optimizing beamforming direction, and both outperform the signal-to-leakage-and-noise ratio-based algorithm.

Intra-cell interference management is one of the technical challenges for device-to-device (D2D) communication underlaying cellular networks. In this paper, we propose two superposition coding-based cooperative relaying schemes to exploit the transmission opportunities for the D2D users without deteriorating the performance of the cellular users. In the first scheme, the D2D transmitter (DT) is enabled to decode and regenerate the cellular signal, and transmit the cellular signal by superposing it with its own signal. In this way, the interference from the D2D pair to the cellular pair can be canceled by properly allocating time and power. To further exploit the transmission opportunity for the D2D pair, in the second scheme the cellular transmitter splits its signal into two parts and broadcasts these two parts in a superposition signal. DT relays only one part of the cellular signal. Analytic and numerical results confirm the efficiency of the proposed schemes.

Mobile and wireless communication has been evolved from the basic model for providing point-to-point voice centric services into more complicated service provision technologies with point-to-multipoint transmission mode, such as MBMS in 3GPP and BCMCS in 3GPP2. However, the group member with the worst channel condition becomes the bottleneck since the base station or access point is supposed to take care of all the users in a multicast group whose members would like to receive a same service. On the other hand, delay is one of the most important QoS criteria along with throughput especially for realtime services such as mobile TV and IP conference. In this paper, we propose a framework for performance analysis of multicast service with delay constraints. Additionally, an admission control policy for real-time multicast scheduling is demonstrated.

This paper studies the precoder design of a multi-relay multi-user bidirectional networks in which each relay is equipped with multiple antennas. We study the relay precoder design in the case of imperfect channel state information (CSI) and a norm-bounded error model is adopted. Then, we solve the power minimization problem under this scenario. Numerical results demonstrate that the proposed robust precoding algorithm outperforms the non-robust solution.

In this paper, we will consider a coordinated multipoint (CoMP) transmission system which accords with the current Long Term Evolution-Advanced (LTE-A) system and the 5th generation wireless system (5G). This paper will consider two different circumstances, one concerning the condition that transmitters knows exactly which user will be wiretapped; the other concerning the condition that transmitters have no knowledge about the wiretapped user's index. Accordingly, we provide the corresponding precoding strategies and simulation results to verify the effectiveness of our proposed algorithms.

The heterogeneous multicast networks are the networks with several wireless technologies that provide multicast service. Due to the high data rate of the service, point-to-point (P2P) transmission mode, which is employed by unicast channels, is no longer the best mode because of its inefficiency in power consumption and spectrum utilization. Compared with P2P, point-to-multipoint (P2M) transmission mode is recognized as a more promising mode for multicast service. But since P2M mode is provided by mulitcast channels who have turn-on thresholds, the P2M link of a transmission point (TP) is not always available for User Equipments (UEs). To deal with the UEs' cell/transmission mode selection problem in heterogeneous multicast networks, this paper proposes a novel auction mechanism. In the auction process, TPs act as auctioneers who own channel resource and UEs act as bidders who wish to buy these channels for multicast service. Different from many auction based mechanism, we design two kinds of prices, one for unicast channels providing P2P links and the other for multicast channels providing P2M links. In auction, the equilibrium is defined as the state that no UE wishes to change it decision. Through simulation, we show that the auction process converges to a equilibrium state, and the mechanism achieves about 40%-50% power efficiency compared with that of random access.

This paper considers the precoder design issue of bidirectional networks in which each node is equipped with multi-antenna. In contrast to the conventional research of two way relay network, this paper adopts a more general scenario, a multi-relay multi-user network is considered. Firstly, assuming perfect channel state information is available, this paper investigates the precoding design for the relays and users with aim of minimizing the sum minimum square error. Then, this paper takes a more practical hypothesis into account by analyzing the channel estimation error. Utilizing the alternative optimization method, the main problem is divided into three decoupled subproblems and solved respectively. It is shown that in both cases, the proposed precoding algorithms outperform the existing schemes.

We consider how to efficiently employ D2D communications for secondary users (SUs) in a cognitive cellular network. In this network, primary users (PUs) transmit via base station normally, while SUs can employ multiple transmission modes. One is to transmit via base station (BS mode), and the other is to employ D2D communication (D2D mode) due to the scarce idle spectrum. The SUs who have the potential to transmit to each other using D2D mode form a group. Within this group, they can transmit to each other via BS mode or using D2D mode directly. Outside this group, only BS mode is available. To investigate how to employ D2D mode into this network, first we define the utilities of SUs employing BS mode and D2D mode respectively considering achieved data rate, power consumption, price of unit bandwidth and the impact of interference. Then we analyze the optimal power allocation for each mode. To optimize SUs' strategies of mode selection, we adopt replicator dynamics in evolution theory to model the behaviors of SUs. Furthermore, we prove the existence of SUs' evolutionary stable strategy (ESS) of the mode selection process. Based on our model, we finally propose a distributed protocol for SUs within a D2D group to converge to ESS automatically. Numerical results show that our proposed protocol is not only efficient to achieve ESS with improved network performance, but also robust in ESS.

Vehicular Ad Hoc Networks (VANETs) with base stations are called hybrid VANET, where base stations are deployed to improve the throughput capacity. In this paper, we study the multicast throughput capacity for hybrid wireless VANET with a directional antenna on each vehicle and the end-to-end delay is constrained. In the hybrid VANET, there are n mobile vehicles (or nodes) distributed in a unit area with m strategically deployed base stations connected using high-bandwidth wire links. There are n_s multicast sessions and each multicast session has one source which transmits identical data to its associated p destinations. We investigate the multicast throughput capacity for two mobility models with two mobility scales, respectively, while each vehicular node is equipped with a directional antenna and with a tolerant delay D. That is, a source node transmits to its p destinations only with the help of normal nodes within D consecutive time slots. Otherwise, the transmission will be performed with in the infrastructure mode, i.e., with the help of base stations. We demonstrate that the one dimensional i.i.d. slow mobility pattern catch the main feature of VANETs. And we find that the multicast throughput capacity of the hybrid wireless VANET greatly depends on the delay constraint D, the number of base stations m, and the beamwidth of directional antenna θ. In the order of magnitude, we obtain the closed form of the multicast throughput capacity of the hybrid directional VANET, where the impact of D, m and θ on the multicast throughput capacity is analyzed. Moreover, we derive the lower bound of the muticast throughput using a similar raptor coding approach.

In this paper, we define an ad hoc network where multiple sources transmit packets to one destination as Converge-Cast network. We will study the capacity delay tradeoffs assuming that n wireless nodes are deployed in a unit square. For each session (the session is a dataflow from k different source nodes to 1 destination node), k nodes are randomly selected as active sources and each transmits one packet to a particular destination node, which is also randomly selected. We first consider the stationary case, where capacity is mainly discussed and delay is entirely dependent on the average number of hops. We find that the per-node capacity is Θ (1/√(n log n)) (given nonnegative functions f(n) and g(n): f(n) = O(g(n)) means there exist positive constants c and m such that f(n) ≤ cg(n) for all n ≥ m; f(n)= Ω (g(n)) means there exist positive constants c and m such that f(n) ≥ cg(n) for all n ≥ m; f(n) = Θ (g(n)) means that both f(n) = Ω (g(n)) and f(n) = O(g(n)) hold), which is the same as that of unicast, presented in (Gupta and Kumar, 2000). Then, node mobility is introduced to increase network capacity, for which our study is performed in two steps. The first step is to establish the delay in single-session transmission. We find that the delay is Θ (n log k) under 1-hop strategy, and Θ (n log k/m) under 2-hop redundant strategy, where m denotes the number of replicas for each packet. The second step is to find delay and capacity in multisession transmission. We reveal that the per-node capacity and delay for 2-hop nonredundancy strategy are Θ (1) and Θ (n log k), respectively. The optimal delay is Θ (√(n log k)+k) with redundancy, corresponding to a capacity of Θ (√((1/n log k) + (k/n log k)). Therefore, we obtain that the capacity delay tradeoff satisfies delay/rate ≥ Θ (n log k) for both strategies.

Abstract Most of analysis so far for IEEE 802.11 wireless local area networks (WLANs) focuses on saturated condition. However, it is of practical value to take into account the unsaturation case. In this paper, we modified Bianchi's Markov back‐off model to make it applicable to unsaturated condition and the analytic results are provided by employing the renewal‐reward theorem. Under our proposed model, we study the fixed‐point solution of the system and provide a condition to guarantee both the uniqueness and balance of the fixed point. From the fixed point, we find that under unsaturated condition, network parameters should be adjusted according to the traffic load. Then, we study the system throughput. In the case where there are a large number of nodes, we provide closed‐form formulas for the collision probability, the aggregate attempt rate, and the throughput. We find that in such a scenario, the system yields similar performance as that under saturated situation. Moreover, we compare all the results with those under saturated condition and find the latter is a special case of our results. Hence, all of our analysis based on unsaturated condition well covers saturated condition. Our analytical results are validated through ns2 simulations. Copyright © 2010 John Wiley & Sons, Ltd.

Mobile and wireless communication has been evolved from the basic model for providing point-to-point (PtP) voice centric services into more complicated service provision technologies with point-to- multipoint (PtM) transmission mode, such as MBMS in 3GPP and BCMCS in 3GPP2. On the other hand, the evolution of mobile and wireless communication has also resulted in a large number of wireless technologies such as UMTS cellular network, WiMAX and WLAN. As a result, issues related to multicast/broadcast service over heterogeneous networks will be more complex, interesting and valuable. In this paper, we propose a solution aimed to provide seamless and ubiquitous support for multicast/broadcast service on heterogeneous accesses. Additionally, a vertical handover policy named PtM First for multicast resource management in heterogeneous networks is demonstrated.

We address the transceiver design problem for amplify-and-forward (AF) MIMO two-way relay systems with imperfect channel estimation. Since only estimated channel state information (CSI) is available, the self-interference (SI) cannot be completely canceled at destination nodes. With both channel estimation errors and residual self-interference considered, we propose two robust schemes to minimize the average sum mean squared error (MSE). % averaged over channel uncertainties., In the iterative scheme, the relay precoder and destination receivers are alternately optimized until convergence. In the constrained structure scheme, a specific structure is imposed on the relay precoder to reduce complexity. By solving a relaxed optimization problem, we derive a closed-form solution for the constrained structure precoder. Simulation results show that the proposed iterative scheme provides better robustness against channel uncertainties than the non-robust iterative scheme, and the constrained structure scheme achieves close performance with the proposed iterative scheme.

This paper presents an evolution framework of resource allocation by infection among secondary users (SUs) in an OFDMA-based cognitive radio cellular networks. Each primary user (PU) sells his extra sub-channels to SUs in his sensing range to achieve the highest payoff and each SU may come across another in his sensing range to make infection. Two different infection processes among SUs, the infection with and without local knowledge respectively, are considered. We prove the existence and convergence of evolutionary equilibrium (EE) for both cases, and show some interesting properties such as the impact of cheating of SUs in the above infection processes. Besides, we proposed two algorithms for the infection processes to converge to EE in a distributed manner. Simulation results show that the algorithms with local knowledge can equally share the extra resources among SUs efficiently, which is actually the overall optimal solution (OOS). While for the algorithm without local knowledge, we find that though EE exists, OOS cannot always be achieved. Furthermore, we optimize the second algorithm to make EE approximate to OOS.

Multicast is emerging as an important means of transmitting the same content to multiple receivers so as to efficiently utilize the overall network resource. We propose an opportunistic multicast scheduling algorithm that exploits time-varying channel conditions in time division multiplexing (TDM) cellular networks and maximizes the overall network throughput under certain resource fairness constraints among all groups in the system. The optimum transmission rate for each group is selected by taking into account the current channel conditions and average received throughput of each user to achieve the best tradeoff between throughput and fairness. We then prove the optimality of the multicast scheduling algorithm and show that our algorithm does not deteriorate any group's performance to improve the overall network throughput compared with the non-opportunistic scheduling algorithm. Through extensive simulations we demonstrate that our opportunistic multicast scheduling algorithm improves network resource efficiency under fairness constraints.

Cooperative sharing of spectrum among one primary user (PU) and multiple secondary users (SUs) helps to enhance the throughput of the whole system. In this paper, we propose a two-tier game in the sharing of spectrum in which SUs make their decisions on whether to cooperate under replicator dynamics and the PU adjusts its strategy to allocate time slots for the cooperative SUs' transmission. Furthermore, we develop the distributed algorithm to describe the learning process of SUs, and prove that the dynamics can converge to the evolutionary stable strategy (ESS) efficiently, which is actually the optimal strategies of both PU and SUs. Simulation results show that our proposed mechanism converges to ESS automatically, at which point all the SUs will remain their strategies. In addition, it is shown that this mechanism helps the SU to share information and gain higher transmission rate than fully cooperative or non-cooperatively scenario.

This paper proposes a novel quasi-cyclic low-density parity-check convolutional code, and a two-stage construction algorithm with modified progressive edge growth (PEG) method is provided. We propose both encoder and decoder implementation architecture for this code. The quasi-cyclic form provides the parallelism for encoder and decoder, which can increase the throughput and decrease the delay significantly. The proposed modified min-sum decoding algorithm can speed up the process of convergence and reduce the hardware complexity. We also designed a GPU based simulation platform to speed up about 200 times against CPU to verify the code performance. Simulation results show the proposed code can get 0.5~1dB coding gain and lower error floor compared with the LDPC codes in WiMAX standard with the same code length, while the decoder only has 20 iterators.

In this paper, we propose an agent-based spectrum trading model, where agent plays a third-party role in the trading process. Providing service to secondary users (SUs) with spectrum bought from primary users (PUs), agent makes profit in the spectrum trading process. We address the challenge of finding the most profitable strategy of agent(s) when spectrum demand is uncertain. We first address this challenge for the secondary network where single agent operates, and extend it to a multiple-agent system. To our best knowledge, this is the first solution to agent-based spectrum trading considering demand uncertainty.

Cognitive Radio is a promising technology to solve the problem of increasing spectrum shortage in wireless communication. It allows secondary users(SUs) to opportunistically access idle spectrum of primary users(PUs) while ensuring collision perceived by PUs under a specific requirement. Testbed is essential to advance the development of cognitive radio networks. This paper presents a real time testbed for the evaluation of the cognitive radio MAC algorithm. The testbed supports the necessary components of cognitive radio including programmable RF transceiver, software-defined MAC layer and adaptive network layer to support multi-PU multi-SU cognitive radio networks. It is much easier to configure and control than the traditional FPGA-based testbed by using system-on-chip(SoC) programmable processors. We present the main features and the implementation details of the testbed in this paper. We also introduce an experiment using the testbed to evaluate the performance of an adaptive CR MAC algorithm proposed by us.

Recently, there has been a great interest in cognitive radio networks. In addition, the use of subchannel allocation and power distribution in OFDM systems have been discussed in the literature and proven to help improve the capacity performance in cooperative networks. Therefore, in this paper we propose to add a primary source-destination pair to the secondary system in a cognitive radio network. We also provide the analysis of a subchannel and power allocation in cognitive networks. The optimal solution for such a problem is shown to be exponentially complex computationally. Therefore, we propose a suboptimal algorithm to reduce the complexity for which the subchannel allocation and power distribution among transmitters in the secondary system are performed in two steps. Simulation results are presented showing that the performance of the proposed suboptimal algorithm approach performs asymptotically to the optimal algorithm solution.

In this paper, we consider a scenario where multiple secondary users search for idle frequency bands in a spectrum consisting of multiple channels. The state of each channel is modeled as a discrete-time Markov process. In each time slot, one secondary user(SU) chooses one channel to sense and decides whether to use the channel based on the sensing result. When there is only one SU, former work has proved that a myopic policy maximizing the current reward is optimal when the channel state transitions are positively correlated over time. In this paper, we extend the myopic policy to multiple-SU scenario. We propose and analyze two approaches that make use of the myopic policy more efficiently when multiple SUs exist.

The recent emergence of indoor wireless communication applications, e.g. WLAN, requires cost effective channel measurement equipments. In this contribution, we present a detailed design of an indoor 2x2 MIMO channel measurement system of 2.4/5.8GHz, using FPGA, PC and some RFICs without any specific instruments. The semi-sequential scheme and sliding correlation method is used for MIMO channel measurement. The design of signal processing algorithms, FPGA firmware and graphical user interface is described.

The poor performance of TCP in multihop ad hoc networks is mainly attributed to the inefficient interaction among different protocol layers in previous literature, while the heavy load caused by end-to-end TCP acknowledgements (ACKs) with limited information is usually ignored. In this paper, we propose a novel incorporating ACK transfer scheme, IACK, to alleviate its impact. In IACK, TCP acknowledgements are incorporated in the control packets at the MAC layer and are transferred hop by hop from the sink node to the source node. To meet the requirement of IACK, we enhance the packet queuing policy at the routing layer, and propose a new rate-based TCP transfer scheme, TCP-AP+. Then, we implement IACK in ns-2, evaluate it over comprehensive scenarios and compare it with TCP-AP and TCP-Newreno. Simulation results show that IACK improves both the TCP throughput and goodput significantly.

Spectrum under-utilization is one of the bottlenecks of the development of wireless communication, and dynamic spectrum access (DSA) is envisioned as a novel mechanism to solve the problem of spectrum scarcity. Spectrum auction has been recognized as an effective way to achieve DSA, wherein the primary spectrum owner (PO) acts as an auctioneer who has free channels and is willing to sell them for additional revenue, and the secondary user (SU) acts as a bidder who is willing to buy a channel from POs for its service. In this paper, we adopt a progressive spectrum auction named MAP, which has been proved optimal and incentive compatible in DSA networks with distributed POs and SUs. However, in MAP, the profit of POs is not maximized under the equilibrium point due to the scarcity of SUs' private information known by POs. We propose an information sharing mechanism, in which the POs exchange their local information with each other. We show analytically that, allowing information sharing, each PO is able to learn the private information of SUs and increase its profit accordingly. Long term profit acts as the incentive for information sharing that all the POs automatically reveal the true information when they are aware of this. It is notable that information sharing doesn't affect social optimality. Simulation shows the increase of POs' profits in the sense of long term interests.

Capacity and power consumption are two main bottlenecks in the wide deployment of Voice-over-IP (VoIP) over IEEE 802.11 wireless LAN, when mobile terminals are used, the impact on the Voice-over-IP (VoIP) by power consumption becomes more critical. In this paper, we analyze the capacity of VoIP traffic in 802.11 WLAN and do the relevant simulations with and without sleep strategy. We find that capacity decreases when the network adopts sleep strategy. After a detailed analysis by p-persistent model of the IEEE 802.11 protocol, we provide a dynamic sleep strategy, namely Collision Detective Dynamic Sleep Strategy (CDDSS) to improve both the capacity and energy saving rate of the VoIP over 802.11 WLANs. In our CDDSS, mobile station (MS) adjusts sleep interval and packetization interval adaptively according to the average collision probability pc of the network. Our simulation results show that CDDSS cannot only improve the capacity of VoIP in 802.11 WLANs, but also reduce considerable energy consumption.

By using cognitive radio technology, opportunistic spectrum access has the potential to solve the underused spectrum problem. In this paper, by first introducing a specific cognitive radio scheme, we analyze the secondary user's capacity and the collision probability. Employing this scheme, we build two call admission control models for three classes of service, namely, handoff voice calls, new voice calls and data calls. We show that they achieve improved blocking probability and throughput by exploiting the cognitive radio scheme. Further, we show a tradeoff between collision probability and blocking/throughput in the call admission control.

Cooperative diversity is a transmission technique, where multiple terminals forms a virtual antenna array that realizes spatial diversity gain in a distributed fashion. The concept of cooperation has already been introduced to MAC layer to design MAC protocol. However, it's much different with that at physical layer. In this paper, we present a new MAC protocol based on IEEE 802.11, called C-MAC, that can support the basic building block of cooperative system. That is, in C-MAC, source would invite a relay node into data transmission if there exits an available one. During data transmission, source sends the signal to destination at first. The relay node will retransmit the overheard information to the destination at the second time slot. The destination combines two signals from source and helper, thus creating spatial diversity and robustness against channel fading. The C-MAC is backward compatible with legacy IEEE 802.11 system. The performance of C-MAC mainly depends on the physical layer's performance as it just provides the support for cooperation at MAC layer. If the physical layer works well, C-MAC would outperform IEEE 802.11 considering packet error rate. We also do the simulation using ns-2 with assumptive physical parameters. The result shows C-MAC would outperform 802.11 if packet error rate is a little high, and C-MAC would lead to some unfairness to nodes without relay.

This paper focuses on subchannel and power allocation problem in OFDMA-based decode-and-forward (DF) cooperative relay networks. The objective of this problem is to maximize the total data rate under the constraints of joint total transmission power and subchannels occupation, while maintaining the maximum fairness among multiple relay nodes. We transform this mixed integer programming problem to a standard convex optimization one through Lagrangian relaxation method. Accordingly, the optimal solution can be achieved by using Karush-Kuhn-Tucker (KKT) conditions. In addition, we propose two suboptimal subchannel allocation schemes with and without subchannel permutation (SP), which reduce significantly the computational complexity with small performance loss. The simulation results show that the performance of the proposed suboptimal algorithms with SP approaches asymptotically that of the optimal algorithm and with the increment of the number of relay nodes.