Extremely large-scale arrays and high frequencies are vital technologies for enhancing the capacity in wireless communications. The employment of these technologies makes the near-field propagation become dominant, where the achievable rate is reduced due to the misfocus effect caused by the beam mismatch. To mitigate it, beamforming schemes assuming spherical wavefront propagation must be investigated. However, the integration of angle and distance information in the beam domain, alongside the impacts of molecular absorption and spherical wave reflection coefficients, has been scarcely explored. We derive the expression of the molecular absorption at high frequencies and formulate the reflection coefficient expression under the spherical wave assumption, and embed them into the design of the beamforming. We then analyze these characteristics and propose a near-field beamforming scheme utilizing the Gauss-Legendre quadrature and design a minimum difference mapping strategy for phase mapping. Based on this scheme, we propose two beamforming algorithms for line-of-sight and non-line-of-sight scenarios, respectively. Numerical results demonstrate that the proposed algorithms are robust against the beam misfocus effect and achieve a higher achievable transmission rate.

Extremely large antenna array (ELAA) is able to significantly improve the spectral efficiency, so it is regarded as one of the most important technologies for the next-generation networks. However, larger antenna aperture and higher frequency make the Rayleigh distances dramatically increased, so in sixth-generation (6G) networks, more and more communication scenarios will take place in the near-field region. Different from traditional far-field communication, electromagnetic waves transmitted in the near-field communication is widely considered to be spherical rather than planar, so techniques designed for far-field scenarios are no longer applicable. In this paper, we study the near-field communication scenario in which the base station is equipped with a cylindrical antenna array (CLA). Specifically, by exploiting the geometrical relationship between CLA elements and the near-field user, codebook design for beamforming in near-field CLA systems is studied for the first time. We first derive the beamforming gain in the elevation angle domain, the azimuth angle domain and the distance domain respectively. Then a 3-D near-field CLA codebook is proposed to make beamforming more effectively in near-field CLA systems. We obtain the sampling method in each domain by controlling the correlation between different codewords. Simulation results prove the effectiveness of the proposed codebook.

Abstract In order to balance accuracy and real‐time performance in semantic segmentation, this paper proposes a real‐time semantic segmentation algorithm model based on attention mechanism and multi‐branch feature fusion using Fast convolutional neural network model (Fast‐SCNN). In this method, the spatial detail feature enhancement branch is introduced to enhance spatial detail features firstly. Then, through rational design of fusion module, the feature information of each branch is optimized to achieve better fusion of deep and shallow features. At the end of the feature fusion module, an adaptive feature enhancement focus module is introduced to capture the interdependence between remote pixels. The experimental results show that the proposed algorithm achieves 71.55% segmentation accuracy on Cityscapes dataset, the reasoning speed FPS is 97.6 frames/s, and the number of parameters is 1.39 M, which verifies the effectiveness of the network model constructed by the algorithm. Code is available at https://github.com/ccchhheeennn/model .

Extremely large antenna array (ELAA) is regarded as one of the most crucial technologies for the next-generation communications due to its ability to significantly improve spectral efficiency. However, larger antenna aperture and higher frequency make the Rayleigh distances dramatically increased, resulting in more and more communications taking place in the near-field region. Different from traditional far-field communications, near-field communications are widely considered to be spherical wavefront-based rather than planar wavefront based, thus techniques designed for far-field scenarios might be no longer applicable. In this paper, we study the near-field communication system with an extremely large cylindrical antenna array (CLA). Under such a setup, we study the near-field beamforming, exploiting the geometrical relationship between CLA and user with the spherical-wavefront model. Specifically, we analyze the beamforming gain in the elevation angle, azimuth angle domain and distance domains, respectively. We then study the beam focusing properties in near-field CLA systems, namely the asymptotic orthogonality and the depth of focused beams. Moreover, a three-dimensional (3-D) near-field CLA codebook is proposed to make beam focusing more effective. Simulation results demonstrate that the proposed near-field codebook can effectively focus the beam to a certain location and thus improve the system achievable rate.

Orthogonal time frequency space (OTFS) modulation has been advocated as a promising waveform for achieving integrated sensing and communication (ISAC) due to its superiority in high-mobility adaptability and spectral efficiency. Despite its advantages, due to the complex input-output relation of the OTFS processing, the current OTFS based ISAC schemes require high computation complexity/latency to achieve satisfactory sensing accuracy. In this paper, a concisely fast/slow-time representation for the OTFS input-output relation is derived, which can clearly reveal the physical interactions between the OTFS signals and sensing channels. Under this basis, a novel delay-Doppler spectrum matching assisted active sensing framework is proposed for the OTFS based ISAC systems. The framework consists of three sequentially operated modules, i.e., a coarse searching and triangular spectrum matching module, a local fine tune and quadratic spectrum matching module, and a constant false alarm rate (CFAR) target detection module. The three modules work coordinately to realize joint target detection and high-accuracy target range/velocity estimation. Simulation results show that the proposed framework achieves better detection performance and range/velocity estimation accuracy with much less computations when compared with the reference schemes.

Integrated sensing and communication (ISAC) has been seen as a promising technology to satisfy the dual requirements of communication and sensing for the emerging applications in the next-generation wireless networks. In this paper, we research one down-link multi-cell orthogonal frequency division multiple access (OFDMA) ISAC system, in which a group of collaborative ISAC base stations send signals to their corresponding communication users, and concurrently work with multiple sensing receivers to estimate locations of multiple targets. Specifically, we investigate the joint sub-channel assignment and power allocation for users and targets to maximize the sum-rate, while ensuring the minimal signal-to-interference-plus-noise ratio (SINR) constraint for each user and the maximal Cramer-Rao lower bound (CRLB) requirement for each target. We propose a deep reinforcement learning (DRL) approach to address the above sub-channel assignment and power allocation problems. In our approach, we adopt the dueling deep Q network (DDQN) and the deep deterministic policy gradient (DDPG) network to output the sub-channel assignment policy and power allocation policy separately. Simulation results aim to prove the effectiveness of our proposed algorithm.

In order to meet the needs of some scenes with unknown and rapidly changing noise power, a spectrum sensing method based on deep reinforcement learning is proposed in this paper to improve the traditional energy detection method. A complex reward function is designed in the deep Q network (DQN) algorithm, which can make the agent adjust the decision threshold of energy detection more intelligently. In addition, combined with the clustered cooperative spectrum sensing architecture, the performance of spectrum sensing is further improved through twice decision fusion based on cumulative accuracy. In this paper, five other common spectrum sensing methods are compared. The simulation results show that the proposed method converges on datasets, and its sensing performance is superior to the other methods. Its detection accuracy rates are 87.88%, 90.18%, 88.13%, 84.74%, 82.43% respectively. Thus, limitations of traditional energy detection methods are broken through this method and the whole system is more intelligent and stable.

The utilization of the aerial base station (ABS) has been treated as a promising solution to the coverage, deployment and cost savings problems in the wireless networks. Different from the terrestrial networks, in the ABS-assisted air-to-ground network, the service positions of ABSs and user association should be jointly optimized, which is challenging due to the unknown propagation environment in the task area. In this article, a binary radio map (BRM) is constructed to grant the ABSs the location-specific channel knowledge within the entire task area. With the assistance of the BRM, a multiple ABS deployment and user association framework is proposed. The framework consists of an offline optimization stage during which the initial ABS deployment position and user association are jointly optimized based on the BRM, and an online refinement stage during which the ABS positions and user association are refined in real time according to both the observed Quality of Service (QoS) and virtual QoS emulated by the BRM. The performance of the proposed framework is examined in a complex urban scenario with mobile users. The results show that higher achievable rate performance and lower on-board energy consumption can be realized when compared with the reference schemes.

We propose a learned factor graph based on convolutional neural network (CNN) and Bi-directional Long Short Term Memory (BiLSTM) to realize signal detection under the scenario of no channel model. It can solve the inevitable over-reliance on channel state information (CSI) of model-based signal detection methods and avoid the shortcomings of large training scale of general data-driven methods by using relatively small training samples. The proposed method uses a network of CNN-BiLSTM structure with strong learning capabilities to determine the statistical relationship of the channel model which is what traditional model-based methods rely on. Based on above, the parameter estimation (Gaussian mixture model considering Akaike information criterion) and non-parametric estimation (adaptive kernel density) are adopted to learn a factor node together. The simulations show that, the proposed method can guarantee the accuracy of signal detection and robustness to the training of imperfect CSI.

In this paper, a model-driven signal detection method with deep transfer learning (DTL) is proposed for downlink multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Specifically, we first introduce some learnable parameters to an unfolded iterative algorithm for MIMO detection and improve it through a preconditioned process to speed up its convergence. Then we combine this modified algorithm with the successive interference cancellation (SIC) structure in NOMA detection to propose our learned preconditioned conjugate gradient descent network with SIC (LPCG-SIC). Furthermore, to improve the reusability of the trained network, a DTL-based detection algorithm and three model-driven transfer strategies are proposed for our LPCG-SIC detector. Simulation results show that the proposed detection network outperforms conventional detectors, and the transfer strategies can obtain significant performance gain compared to no-transfer methods.

In this paper, we study joint beamforming and resource allocation in downlink multi-cell orthogonal frequency division multiple access (OFDMA) systems. We design a multi-agent deep Q-network (MADQN) algorithm to solve this problem. Furthermore, in order to improve the adaptability of neural networks for different wireless environment, we propose a transfer learning framework based on MADQN called TL-MADQN to dynamically output optimal beamforming and resource allocation policy. Finally, we adjust the allocation policy to maximize the sum-rate of all users by updating the weights of each neural network. Simulation results illustrate that the proposed TL-MADQN algorithm has higher sum-rate and faster convergence speed compared with the baseline algorithms.

In order to reduce the consumption cost for successive interference cancellation in non-orthogonal multiple access(NOMA), we propose a resource allocation scheme that involves both orthogonal multiple access and NOMA technologies. The scheme uses deep learning to choose the appropriate access according to the communication environment. Moreover, the scheme jointly allocates subcarrier and power resources for users by utilizing a deep Q network and a multi-agent deep deterministic policy gradient network. Meanwhile, an adaptive mechanism combining online learning and offline learning is introduced into allocation scheme to flexibly adapt to the communication environment. Results show that the proposed scheme can achieve better system performance in sum-rate. In order to better cope with changes in the environment and make the resource allocation strategy more robust, we propose a novel resource allocation algorithm combining transfer learning and deep reinforcement learning. The algorithm can effectively improve the model convergence speed when changing the communication environment. Furthermore, the algorithm allows us to transfer the subcarrier allocation network and the power allocation network simultaneously or separately depending on the environment.

This paper investigates coverage analysis and performance optimization for a high-speed railway (HSR) communication system with carrier aggregation (CA). Initially, the system model is established. By using CA technology, the statistical characteristic of the received signal-to-noise ratio (SNR) is obtained. Then, for the systems with or without CA, unified theoretical expressions for the edge coverage probability (ECP) and the percentage of cell coverage area (CCA) are obtained, respectively. After that, a chance-constrained coverage optimization is proposed to improve the coverage performance, which is effectively solved by using a heuristic algorithm. Numerical results show that the derived expressions of the ECP and the percentage of CCA are accurate and can be directly used to evaluate the coverage performance without time-intensive simulations. Moreover, the impacts of CA, transmit power, cell radius, received SNR threshold, HSR scenario on coverage performance have been discussed. Furthermore, the effectiveness of the proposed coverage optimization scheme is also verified.

Elevating base stations (BSs) in the air, forming what are called aerial platforms (APs), has recently been treated as a promising solution to the problems of coverage, deployment and cost savings in mobile networks. Unlike terrestrial transmission with fixed BSs, AP-assisted air-to-ground (A2G) communications require joint optimization of the transmit parameters and AP service position, which is challenging due to the unknown propagation environment in the overall flying area. In this paper, a novel binary channel feature map (CFM) concept is proposed to grant the AP knowledge of the global propagation environment. With the assistance of the binary CFM, a unified A2G information and energy delivery framework is proposed, which includes three modules, i.e., a transmit optimization and user satisfaction evaluation module, an offline AP position planning module and an online AP position adjustment module. These modules operate coordinately to jointly perform beamforming matrix optimization for information delivery, transmit covariance optimization for energy delivery, and three-dimension AP service position planning and real-time adjustment. The simulation results reveal that the proposed framework can achieve higher user satisfaction level and lower outage probability, and can result in great savings of battery power or fuel in the AP when compared with reference schemes.

This paper proposes a deep learning-based receiver scheme with soft information for uplink multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) systems, named by DLSI. In the proposed DLSI, we first perform QL-decomposition for channel matrix and iteratively construct the solution of the objective function for signal detection. Then deep neural networks (DNNs) are used to realize iteration steps and restore the symbols of the transmitted signals one by one. After all symbols of a user are detected, the successive interference cancellation (SIC) algorithm is used to detect the symbols of the next user. The output of each DNN is added with a soft decision layer, and the soft information will be input to the next DNN to improve the accuracy of training. Soft information contains more signal knowledge than hard decision, so it is also used in the SIC step, which reduces error propagation to a certain extent. Simulation results show that the performance of the DLSI is better than other deep learning methods.

Non-orthogonal multiple access (NOMA) has been recently considered in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. Massive connectivity and low latency are two important challenges for the Internet of Things (IoT) to achieve the quality of service provisions required by the numerous devices. Motivated by these challenges, we propose a power domain mmWave NOMA scheme based on user clustering. In particular, considering the quality of service (QoS) requirements, the machine type communication (MTC) devices are assigned to different ranks in the NOMA cluster, where they are transmitted through the same frequency resource. In this paper, investigate the power allocation problem as a weighted sum rate maximization problem. To solve this non-convex problem with high-dimensional variables, we transform the problem into a convex form by introducing two sets of auxiliary variables. In addition, we propose an iterative algorithm to obtain the optimal solution of power allocation by updating the weight factors and the auxiliary variables. Finally, we give the simulation results to verify the effectiveness of our scheme.

Non-orthogonal multiple access (NOMA) technology can meet user access requirements and improve system capacity. In this paper, we investigate the joint subcarrier assignment and power allocation problem in an uplink multi-cell NOMA system to maximize the energy efficiency (EE) while ensuring the minimum data rate of all users. We propose a multi-agent deep reinforcement learning (MADRL) method with centralized training and distributed execution to solve this dynamic optimization problem. In our method, we design a deep q-network (DQN) with parameter sharing to generate the subcarrier assignment policy, and use multi-agent deep deterministic policy gradient (MADDPG) network for power allocation of NOMA user. Finally, we adjust the entire resource allocation policy by updating the parameters of neural networks according to the reward. The simulation shows that our method has better and more stable sum EE than centralized and distributed methods.

Abstract Non‐orthogonal multiple access (NOMA) has attracted great attention due to its potential of providing high spectral efficiency and massive connectivity. Combining it with multiple‐input multiple‐output (MIMO) technology can further improve the spectrum efficiency. In this paper, the power allocation problem in downlink multi‐cluster MIMO‐NOMA systems is investigated for maximizing the fairness utility function. First, a long‐term fairness function is considered and the optimization problem is formulated as a weighted sum‐rate maximization problem. The problem is transformed into a convex form by introducing two sets of auxiliary variables, and propose an iterative algorithm to update the weight factors and the auxiliary variables. Then, an instantaneous fairness is considered and the optimization problem is formulated as a minimum data‐rate maximization problem. It is transfiormed into a one‐dimensional optimization problem based on an iterative algorithm and a closed‐form power allocation expression is deduced. Simulation results illustrate that the proposed two fairness power allocation schemes have better performance of edge‐user rate than the comparable schemes.

In this paper, we introduce a full‐duplex backscatter‐assisted wireless powered communication network (FDBA‐WPCN) with a full‐duplex access point (FAP) and multiple energy harvesting wireless devices (WDs). The communication mode is a combination of backscatter communication (BC) and harvest‐then‐transmit (HTT). The entire time period of network is divided into energy harvesting/backscattering (EHB) period and information transmission (IT) period. In the EHB period, each WD either reflects information to the FAP by backscatter or harvests energy to prepare for the IT period. In the IT period, the WDs use their harvested energy to transmit information to FAP in time division multiple access (TDMA). However, under the setting, WDs with different distances from FAP will encounter unfairness in throughput due to the round‐trip path loss in backscatter and the doubly near‐far problem in HTT. To overcome the drawback, an optimization problem is considered to maximize the sum throughput under the condition of ensuring throughput fairness. By using convex optimization techniques, we obtain the optimal time allocation and the maximum same throughput of each WD. Comparing to the other two benchmark schemes, the simulation results prove the superiority of our proposed method.

In this article, we study the edge caching problem by considering the heterogeneous context with unknown users' preferences. The cache provider (CP) can personalize the users' storage based on available data to maximize the overall cache hit rate, accounting for the dynamic natures of both mobile edge cache scenarios and the users' preferences. Toward this end, we introduce an online Bayesian clustering caching algorithm for the CP to autonomously learn the users' interactive cache hit data in a collaborative way while maintaining sustainable scalability. Specifically, a Bayesian generative framework called the Dirichlet multinomial mixture (DMM) model is used to describe the uncertainty about the latent number of users' clusters, each of which consists of the users with the same preference. Then, a dynamic clustering policy is proposed to obtain both the underlying mapping of users to clusters and the preferences of each cluster by using a collapsed Gibbs sampling algorithm. Subsequently, cache decisions are made according to the generated mappings by extending the traditional cache bandit algorithm to a new bandit mechanism with clusters of arms, capable of expediting the learning process between the exploitation and exploration. We theoretically characterize the value of dynamic Bayesian clustering for the long-term edge caching scenario with respect to the regret incurred by the noncluster schemes. Finally, using a real-world data set, our numerical results show that the proposed scheme outperforms the caching algorithms without clustering in the uncertain network scenario.

Nonorthogonal multiple access (NOMA) is one of the promising technologies to meet the huge access demand and high data-rate requirements of the next-generation networks. In this article, we investigate the joint subchannel assignment and power allocation problem in an uplink multiuser NOMA system to maximize the energy efficiency (EE). Different from conventional model-based resource allocation methods, we propose three deep-reinforcement-learning (DRL)-based frameworks to solve this nonconvex optimization problem, referred to as the discrete DRL-based resource allocation (DDRA) framework, continuous DRL-based resource allocation (CDRA) framework, and joint DRL and optimization resource allocation (DORA) framework. Specifically, for the DDRA framework, a multi-DQN-based network is designed to dynamically allocate resources discretely, which can reduce the output dimension and improve the learning efficiency. To overcome the loss of power discretization in DDRA, a joint DQN and deep deterministic policy-gradient (DDPG)-based network (CDRA framework) is designed to generate the resource allocation policy. The DORA framework is then proposed as a performance boundary. Finally, an event-triggered learning method is combined with all three frameworks to further reduce the computational consumption. The numerical results show that the proposed frameworks can improve the EE performance of the uplink NOMA system and reduce the computation time.

With the certainty of the high-speed railway(HSR) route, high-speed train(HST) is always driving periodically, and it is quite meaningful to assist HSR wireless signal detection through historical big data. One key challenge in this detection is that the HSR wireless channel is varying when the HST drives to different places, thus the data under various channel environment needs to be analyzed separately. In this paper, we propose a deep learning algorithm to detect the multiple input multiple output(MIMO) signal for HSR scenarios, and the entire algorithm framework is divided into two phases: offline training phase and online detection phase. At the offline training phase, we first analyze the data of HSR at each location, and explore a division scheme to further divide each scene into multiple smaller regions so that data in each divided region can share the same network. Then, the deep neural network(DNN) is constructed and trained for each divided region. At the online detection phase, the HST locates the current region according to the location information achieved by GPS, and selects the corresponding DNN model to detect the signal in real time. In addition, this DNN structure combines channel estimation and signal detection. Thus, the HSR detection system can detect the MIMO signal directly without the step of channel estimation. Finally, the simulation results show that the deep learning detection algorithm has better accuracy than those traditional detection algorithms, such as the least square(LS) algorithm and the minimum mean-square error(MMSE) algorithm.

Mobility and green are of great significance to develop in the future wireless communications. In this paper, a joint transmission mode selection and power optimization scheme is researched to maximize the energy efficiency (EE) for distributed antenna system (DAS) in high-speed railway (HSR) communications. This work can be divided into two aspects: power optimization and transmission mode selection. For the power optimization, we first formulate a non-convex optimization problem and convert it into an equivalent convex form. Then, the optimization problem is solved by Lagrange method. With regard to the transmission mode selection, it can be switched between multiple-input multiple-output (MIMO) and single-input multiple-output (SIMO) freely, and the transmission mode with higher EE is selected to transmit signals. Simulation results show that our proposed scheme outperforms that without transmission mode selection, and it can significantly improve the system EE compared with the scheme without power optimization.

In this paper, we investigate a power allocation scheme in downlink multi-carrier non-orthogonal multiple access (MC-NOMA) systems for maximizing weighted sum-rate. Taking user-priority into account, we add the weighting factors into the formulated optimization problem. Firstly, we give sufficient and necessary concavity conditions that should be satisfied by the proposed weighted sum-rate maximization problem. Then, for more generally non-concave case, we use a first order approximation to convert the optimization problem and provide an iterative power allocation algorithm to find the globally optimal solution. Simulation results show that our proposed algorithm is superior to orthogonal frequency division multiple access (OFDMA) scheme and average power allocation scheme.

This study investigates the spectral efficiency (SE) and energy efficiency (EE) of a multipair two‐way massive multiple‐input multiple‐output amplify‐and‐forward relaying system over Ricean fading channels. Both maximum‐ratio combining/maximum ratio transmission and zero‐forcing transmission/zero‐forcing reception beamforming matrices are considered at the relay with imperfect channel state information (CSI). The asymptotic signal‐to‐interference‐plus‐noise ratio expressions (in the number of relay antennas M ) are derived. Moreover, four power scaling schemes are proposed and the asymptotic SE and EE based on the proposed power scaling schemes are obtained analytically. Theoretical analyses and simulation results show that with imperfect CSI when M tends to infinity, the transmit power should be scaled down to different proportions for the Ricean channel with and without line‐of‐sight components to maintain a desirable rate. However, when M tends to infinity, SE is independent of the Ricean K ‐factor with perfect CSI.

In traditional automatic retransmission request (ARQ) based wireless broadcasting (WBC) systems, one lost packet is retransmitted per slot to guarantee the correct reception of each packet, which leads to low spectrum efficiency. In this paper, we propose an Ave-Max-Min Network Coding (NC) based WBC protocol. Specifically, to reduce the overall number of retransmissions, lost packets of different user equipments (UEs) are combined by performing Ave-Max-Min NC at the base station. Then, NC combined packets are broadcasted to all the UEs. At each UE, lost packets can be recovered by using the proposed joint network recursive systematic convolution (RSC) decoder (JNRD). Theoretical analyses and simulation results show that the proposed Ave-Max-Min NC based WBC protocol outperforms traditional NC based WBC protocol on the average number of transmissions performance.

In this paper, we study the integer carrier frequency offset (ICFO) estimation problem for dynamic lattice multicarrier transmission (DLMT) system over time-varying rayleigh fading channel. Firstly, a novel preamble structure based on two constant amplitude zero auto-correlation (CAZAC) sequences is designed for DLMT system. The proposed preamble structure is named as dual-CAZAC preamble. Then, by using the designed dual-CAZAC preamble, a weighted cross ambiguity function (WCAF) based ICFO estimation algorithm is proposed. Simulation results show that the proposed WCAF based ICFO estimation algorithm can mitigate the impact of time-varying multipath rayleigh fading channel and outperforms traditional ICFO estimator on the correct estimation probability performance.

In this study, a hybrid user association scheme for maximising energy efficiency is proposed in a wireless uplink heterogeneous network with human‐to‐human and machine‐to‐machine communications coexistence. Different from conventional user association algorithms, the authors consider access control protocol for both the human‐type‐communications (HTCs) users equipments’ (UEs) and machine‐type‐communications (MTCs) UEs according to their characteristics during user association phase, i.e. a contention‐free access mechanism for HTC UEs and a random access based on access class barring mechanism for MTC UEs. They formulate the user association problem as a maximisation of the overall UEs’ energy efficiency in consideration of both the HTC and MTC UEs’ quality of service (QoS) guarantees and load balance among base stations simultaneously. To solve the problem, they propose a distributed iterative algorithm which adopts both the matching theory and the dual decomposition theory, and prove its convergence. Finally, simulation results show that their proposed user association scheme not only outperforms existing schemes in terms of the load balance and the overall energy efficiency but also achieves the same performance as exhaustive search in the case of less number of UEs, while satisfying both the HTC and MTC UEs’ QoS requirements.

In this study, the authors consider the direct link aware relay selection schemes for full‐duplex relaying (FDR) protocol. Different from traditional scheme, the direct link between source and destination is exploited to improve the performance of considered schemes. By investigating the signal‐to‐interference‐plus‐noise ratios at the relay and the destination, the exact expressions of outage probabilities for FDR protocol with proposed relay selection schemes are derived in closed‐form over Nakagami‐ m fading channels. To get more insight about the effect of system parameters, they develop an asymptotic analysis on the outage performance. The authors’ results show that the achievable diversity of the schemes depends on the shape factors of fading channels and the power scaling scheme of the relays. Moreover, they show that the proposed relay selection schemes achieve better outage performance when compared with traditional schemes and are robust to the residual self‐interference caused by the full‐duplex operation.

Subcarrier suppression (SS), a recently proposed technique for orthogonal frequency division multiplexing (OFDM) transmissions that suppresses subcarriers with bad channel condition, has been known as an efficient technique to improve bit error rate (BER) performance. In this paper, we consider a two way relay network (TWRN) applying SS with decode-and-forward physical layer network coding (DF-PLNC). According to the asymmetry nature of SS, some subcarriers are suppressed on one side. For these subcarriers, one way relay (OWR) is adopted, which reduces the performance of system since the channel condition of the second hop for OWR is bad. To solve this problem, we propose a modified SS scheme combined with subcarrier pairing (SP), called SP based SS (SPSS) where subcarriers that adopt OWR in opposite directions are paired. As a result, BER performance could be improved. Numerical results show that SPSS outperforms traditional SS schemes without pairing in terms of BER performance. Simulations together with analysis verify the potential benefits for combination of SP and SS.

This paper considers the multi-pair full-duplex massive MIMO relaying, where multiple source-destination pairs communicate simultaneously with a common full-duplex amplify-and-forward relay. The low-complexity transceiver design at the relay based on zero-forcing (ZF) processing is presented. The asymptotic expression of the end-to-end signal-to-interference-plus-noise power (SINR) is derived under the general power scaling scheme. Through the theoretical results, we show that the deployment of very large antenna arrays at the relay has potential to eliminate the effect of echo interference due to the full-duplex operation, if the power scaling scheme is properly selected. Moreover, it is shown that the multi-pair full-duplex relaying outperforms the multi-pair half-duplex relaying in the spectral and energy efficiencies when the number of relay receive/transmit antennas is large.

The opportunistic full‐/half‐duplex relaying schemes are studied where the effects of imperfect channel state information (CSI) and transmit imperfection are taken into account. The opportunistic schemes with different levels of channel knowledge, that is, the scheme with CSI at the transmitter (CSIT), the scheme with CSI at the receiver (CSIR) and the scheme with only statistical CSI, are proposed. Power adaption (PA) schemes with different levels of channel knowledge are presented and combined with the opportunistic schemes to further improve the system performance. The opportunistic schemes are evaluated in the term of average capacity. The author's results show that the opportunistic scheme with CSIR achieves comparative performance to the scheme with CSIT when the PA is not used. Moreover, it is shown that the choice of full‐duplex relaying becomes more attractive when the quality of estimation for desired channels degrades. However, the converse is true as the quality of estimation for echo interference channel degrading or the power of transmit noise increasing.

Abstract As a powerful economic theory, auction mechanism has been extensively studied in dynamic spectrum allocation for cognitive radio networks (CRNs) recently. Different from most of existing works that focused on the mechanism design from the spectrum owner's side, we study from a new perspective on profit maximization of the secondary users (SUs). Because the spectrum auction mechanism has already been designed by the spectrum owner, we derive SUs' optimal bid strategies, which maximize their profits. First, we relax the limitation of SU's value on spectrum band, which is formerly defined as the transmission rate on channel, and introduce the affiliated value considering the impacts from other SUs. Further, the optimal value determination function is derived, which maximizes SU's expected profit; second, we analyze the auctioneer cheating issue, which has great influence on SU's profit, and the Nash equilibrium strategies for both spectrum owner and SUs are derived. Moreover, the repeated auction game mechanism is proposed that resists the auctioneer cheating effectively. Copyright © 2013 John Wiley & Sons, Ltd.

This paper considers the transceiver design and antenna subset selection for cooperative half-duplex relaying (HDR) and full-duplex relaying (FDR) protocols. We first present the low-complexity transceiver design for given antenna subset at each node. Then the antenna subset selection schemes for both FDR and HDR protocols are proposed and evaluated in the terms of outage probability and achievable diversity. For HDR protocol, we show that the increase of antenna subset size, although cannot increase the achievable diversity, does improve the outage performance of the protocol by increasing the coding gain. Moreover, we show that the antenna configuration at the relay can impact the achievable diversity of FDR protocol significantly and the antenna configuration that optimizes the achievable diversity is analyzed.

Recently, many dynamic spectrum allocation schemes based on economics are proposed to improve spectrum utilization in cognitive radio networks (CRNs). However, existing mechanisms do not take into account the economic efficiency and the spatial reusability simultaneously, which leaves room to further enhance the spectrum efficiency. In this paper, we introduce the coalition double auction for efficient spectrum allocation in CRNs, where secondary users (SUs) are partitioned into several coalitions and the spectrum reusability can be executed within each coalition. The partition formation process is not only related to the interference condition between SUs, but also the expected economic goals. Therefore, we propose a fully-economic spatial spectrum allocation mechanism by incorporating the coalition formation approach with auction theory. With the proposed scheme, the primary operator acts as an auctioneer, who performs multiple virtual auctions to form a stable partition of SUs and conducts a final auction to decide the winning SUs. Moreover, we propose a possible operation rules for the primary operator to iteratively change the partition, and prove that the virtual auctions could converge in finite time. Comprehensive theoretical analysis and simulation results are presented to show that our scheme can satisfy the crucial economic robustness properties of double auction, and outperform existing mechanisms.

In this paper, we analyze the performance of two-way relaying (TWR) protocol in Rayleigh fading channels, where the terminals and relay are interfered by a finite number of co-channel interferers. The relay is assumed to operate in the decode-and-forward mode. The average symbol error rate (SER) performance for binary phase shift keying (BPSK) is analyzed. To make the analysis mathematically tractable, two approximations are adopted to deal with the problem of correlations between the received SINRs of the relay and terminal and a tight approximate expression of the average SER is derived in closed-form. Moreover, it can be shown that the result can be simply applied in straightforward network coding protocol with co-channel interference. Based on the analytic results, we study the impacts of system parameters, such as interference power, number of interferers and relay placement, on the average SER performance. Finally, the correctness of our analytic results is validated through Monte Carlo simulations.

Spectrum sensing is one of the most important components of cognitive radio (CR) technology. In this paper, we investigate the design of the sensing time to minimize the secondary user (SU) transmission delay under the condition of sufficient protection to primary users (PUs). It has been proven that there exists one optimal sensing time which yields the minimum transmission delay. Then, a novel cooperative spectrum sensing (CSS) framework is proposed. In the novel CSS framework, one SU's reporting time is also used for other SUs' sensing. For time varying channels, the novel multi-slot CSS is derived. In order to minimize the transmission delay, some algorithms are derived to obtain the optimal fusion scheme. Computer simulations show that fundamental improvement of delay performance can be achieved by the optimal fusion scheme. In addition, the novel multi-slot CSS scheme shows a much lower transmission delay than CSS based on general frame structure.

In this paper, we investigate the outage performance of time division broadcasting (TDBC) protocol in independent but non-identical Rayleigh flat-fading channels, where all nodes are interfered by a finite number of co-channel interferers. We assume that the relay operates in the amplified-and-forward mode. A tight lower bound as well as the asymptotic expression of the outage probability is obtained in closed-form. Through both theoretic analyses and simulation results, we show that the achievable diversity of TDBC protocol is zero in the interference-limited scenario. Moreover, we study the impacts of interference power, number of interferers and relay placement on the outage probability. Finally, the correctness of our analytic results is validated via computer simulations.

In this paper, a novel carrier frequency offset estimation approach, including preamble structure, carrier frequency offset estimation algorithm, is proposed for hexagonal multi-carrier transmission (HMCT) system. The closed-form Cramer-Rao lower bound of the proposed carrier frequency offset estimation scheme is given. Theoretical analyses and simulation results show that the proposed preamble structure and carrier frequency offset estimation algorithm for HMCT system obtains an approximation to the Cramer-Rao lower bound mean square error (MSE) performance over the doubly dispersive (DD) propagation channel.

Opportunistic relay selection (RS) is an efficient method to obtain diversity gain in analogue network coding (ANC) protocol with multiple relays. However, in networks with mobile relays, the channel state information (CSI) used in the RS procedure becomes outdated because of the time‐varying nature of fading channels, which severely deteriorates the system performance. In this study, the authors study the RS strategy which aims to optimise the outage performance of the ANC protocol with multiple mobile relays. RS schemes for two different cases are designed, that is, (i) the scheme with only the outdated CSI during the RS procedure, and (ii) the scheme with both the outdated CSI and the statistical knowledge of channels during the RS procedure. The closed‐form expressions of the outage probabilities as well as the asymptotic expressions are analytically derived for the proposed schemes, and moreover, the achievable diversities are analysed based on the asymptotic expressions. Simulation results are presented to evaluate the performances while validate the theoretical analyses for the proposed schemes.

In a recent paper (IEEE Trans. Wireless Commun., vol.8, no.9, 2009), Yi and Kim presented an optimum power allocation scheme for bidirectional cooperative networks employing opportunistic source selection (OSS) protocol. However, the optimum power allocation expressions in [1] are proved to be incorrect. We present correct optimum power allocation expressions, which are validated by the simulation results.

In cognitive radio networks, secondary users (SUs) are considered as selfish spectrum users, thus how to maximize the spectrum efficiency by these selfish users becomes an endless research topic. Recent studies mostly focus on the competition analysis between SUs using economic mechanism, such as game theory and auction, but the spectrum owner can hardly increase the spectrum efficiency directly when SUs apply the distributed manner. In this paper, we consider the slotted uplink scenario where several SUs have data transmitted to secondary access point (AP) under distributed random access manner. The AP decides how to divide its spectrum which maximizes the whole throughput, then SUs select the channels which would maximize their own profit. Our results show that SUs' channel selection process leads to a Nash Equilibrium, and the AP derives the proper number of channels based on the properties of NE. Moreover, we derive a rule for AP to decide which SUs should access the spectrum and lead to the increment in the whole throughput.

In this paper, a novel receiver for Hexagonal Multicarrier Transmission (HMT) system based on the maximizing Signal-to-Interference-plus-Noise Ratio (Max-SINR) criterion is proposed. Theoretical analysis shows that the prototype pulse of the proposed Max-SINR receiver should adapt to the root mean square (RMS) delay spread of the doubly dispersive (DD) channel with exponential power delay profile and U-shape Doppler spectrum. Simulation results show that the proposed Max-SINR receiver outperforms traditional projection scheme and obtains an approximation to the theoretical upper bound SINR performance within the full range of channel spread factor. Meanwhile, the SINR performance of the proposed prototype pulse is robust to the estimation error between the estimated value and the real value of time delay spread.

This paper investigates the capacity, delay and energy consumption for MotionCast (a multicast mechanism for MANETs) with general Markovian mobility. We consider MotionCast in an extended cell partitioned network under a Markovian node mobility model and exactly compute the pernode throughput capacity. A Two hop relay algorithm is proposed to guarantee such capacity, which also achieves a better delay-capacity tradeoff, i.e., Θ(N log k). Considering that redundancy can significantly improve network delay, we present a Two hop relay algorithm with redundancy and study the general influence of redundancy on the capacity and delay of MotionCast network. Moreover, we use the minimum energy function to characterize the energy consumption for the MotionCast network. An accurate piecewise minimum energy function to keep network stable is derived. Furthermore, a simple Minimum energy algorithm is designed, which reduces actual energy consumption arbitrarily close to the minimum energy function at the cost of increasing delay. Our result shows that the proposed algorithm achieves the optimal energy-delay tradeoff.

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.

Partial decoding has the potential to achieve a larger capacity region than full decoding in two-way relay (TWR) channels. Existing partial decoding realizations are however designed for Gaussian channels and with a static physical layer network coding (PLNC). In this paper, we propose a new solution for joint network coding and channel decoding at the relay, called pairwise check decoding (PCD), for low-density parity-check (LDPC) coded TWR system over block fading channels. The main idea is to form a check relationship table (check-relation-tab) for the superimposed LDPC coded packet pair in the multiple access (MA) phase in conjunction with an adaptive PLNC mapping in the broadcast (BC) phase. Using PCD, we then present a partial decoding method, two-stage closest-neighbor clustering with PCD (TS-CNC-PCD), with the aim of minimizing the worst pairwise error probability. Moreover, we propose the minimum correlation optimization (MCO) for selecting the better check-relation-tabs. Simulation results confirm that the proposed TS-CNC-PCD offers a sizable gain over the conventional XOR with belief propagation (BP) in fading channels.

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.

Relay stations are introduced into cellular systems to extend the coverage of the cell, improve the throughput and outage probability of the system. However, present resource-allocation algorithms for traditional cellular system are not directly applicable to the system with relay stations. In this paper, we propose an adaptive resource-allocation algorithm for orthogonal frequency-division multiple-access (OFDMA) cellular system in which relay stations participate in the channel allocation. This algorithm adjusts the frame structure in time domain adaptively according to the channel state and the real-time system throughput. Simulation results show that the proposed algorithm has higher throughput and lower outage probability.

We study the multicast throughput capacity for hybrid wireless mobile ad hoc networks (MANETs) with a directional antenna and delay constraint. The hybrid wireless network consists of a mobile ad hoc network with $n$ nodes and $m$ regularly placed base stations connected by high-bandwidth wired links. For the MANET, there are $n_s$ multicast sessions and each multicast session has one source which transmits identical informations to its associated $p$ destinations. Assuming that the mobile nodes adopt 2D-i.i.d. mobility model, we investigate the ad hoc mode multicast throughput capacity when each node is equipped with a directional antenna along 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 switched to the infrastructure mode, where the base stations serve as relays. We find that the multicast throughput capacity of a hybrid wireless MANETs greatly depends on the delay constraints $D$, the number of base stations $m$, and the beamwidth of directional antenna $\theta$. We show that the multicast throughput capacity of the hybrid directional wireless network is $\Theta(\sqrt{\theta})+\Theta(\frac{m}{n_sp}W_2)$ bits/sec, if $D=\Omega(\frac{n_s}{(\log p)^2(\log(\theta n_sp))^2})$; $\Theta(\frac{m}{n_sp}W_2)$ bits/sec, if $D=O(\sqrt[3]{\frac{n_s}{(\log p)^2(\log(\theta n_sp))^2}})$; and $O((\log p)(\log(\theta n_sp))\sqrt{\frac{\theta D}{n_s}})+\Theta(\frac{m}{n_sp}W_2)$ bits/sec, otherwise. We analyze the impact of $D$, $m$ and $\theta$ on the multicast throughput capacity of the hybrid MANET. Finally, we derive lower bound of the muticast throughput using a similar raptor coding approach as in\cite{ZY10}.

We present a novel adaptive physical layer network coding (PLNC) at the relay, called pseudo exclusive-or (PXOR), for LDPC coded two-way relay (TWR) block fading channels. Based on the pairwise check decoding (PCD), the check relationship table generated by the proposed PXOR mapping obtains the same Hamming distances of the PLNC mapped codewords as that of conventional XOR mapping. In the meantime, the PXOR mapping optimizes the Euclidean distances by adjusting the symbol distances dynamically as far as possible in order to compensate the amplitude fading and phase deviation of the TWR block fading channels. For the system end-to-end error probability, simulation results show that the proposed coded PXOR considerably outperforms the coded conventional XOR and achieves the same performance as the complicated coded CNC for considered two TWR block fading channels.

In this paper, we deal with the scenario that a single secondary user (SU) attempts to opportunistically access the 3G wireless CDMA networks licensed to primary users (PUs). Different to earlier studies in which consider the channel as two state Markov process, here, we expand the channel's availability into a multiple-state Markov process according to the transmission power of the SU. We propose a cyclical, but not periodic sensing and transmission strategy, which can be divided into two steps: 1) the SU acquires the estimation about how many PUs are in this network during a sensing period Ts; 2) Based on the estimation, the SU determines how much transmission power and how long it can transmit. Further, we derive the optimal transmission strategy which consists of different transmission power and time corresponding to different number of PUs in service. Finally, simulation results indicate that our optimal strategy is superior to the empirical strategy such as the maximum power transmission strategy and the maximum time transmission strategy, and achieves the highest throughput of SUs in the long run.

Providing physical-layer security for mobile users in future broadband wireless networks is of both theoretical and practical importance. In this paper, we formulate an analytical framework for resource allocation in a downlink OFDMA-based broadband network with coexistence of secure users (SU) and normal users (NU). The problem is formulated as joint power and subcarrier allocation with the objective of maximizing average aggregate information rate of all NU's while maintaining an average secrecy rate for each individual SU under a total transmit power constraint for the base station. We solve this problem in an asymptotically optimal manner using dual decomposition. Our analysis shows that an SU becomes a candidate competing for a subcarrier only if its channel gain on this subcarrier is the largest among all and exceeds the second largest by a certain threshold. Furthermore, while the power allocation for NU's follows the conventional water-filling principle, the power allocation for SU's depends on both its own channel gain and the largest channel gain among others. We also design a suboptimal algorithm to reduce the computational cost. Numerical studies are conducted to evaluate the performance of the proposed algorithms in terms of the achievable pair of information rate for NU's and secrecy rate for SU at different power consumptions.

Cell selection and resource allocation (CS-RA) are processes of determining cell and radio resource which provide service to mobile station (MS). Optimizing these processes is an important step towards maximizing the utilization of current and future networks. In this paper, we investigate the problem of CS-RA in heterogeneous wireless networks. Specifically, we propose a distributed cell selection and resource allocation mechanism, in which the CS-RA processes are performed by MSs independently. We formulate the problem as a two-tier game named as inter-cell game and intra-cell game, respectively. In the first tier, i.e. the inter-cell game, MSs select the best cell according to an optimal cell selection strategy derived from the expected payoff. In the second tier, i.e., the intra-cell game, MSs choose the proper radio resource in the serving cell to achieve maximum payoff. We analyze the existence of Nash equilibria of both games, the structure of which suggests the interesting property that we can achieve automatic load balance through the two-tier games. Furthermore, we propose distributed algorithms named as CS-Algorithm and RA-Algorithm to enable the independent MSs converge to Nash equilibria. Simulation results show that the proposed algorithms converge effectively to Nash equilibria and that the proposed CS-RA mechanism achieves better performance in terms of throughput and payoff compared to conventional mechanisms.

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.

Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unlicensed users (i.e., secondary users, SUs) to access the licensed spectrum. Market-driven spectrum trading is an efficient way to achieve dynamic spectrum accessing/sharing. In this paper, we consider the problem of spectrum trading with single primary spectrum owner (or primary user, PO) selling his idle spectrum to multiple SUs. We model the trading process as a monopoly market, in which the PO acts as monopolist who sets the qualities and prices for the spectrum he sells, and the SUs act as consumers who choose the spectrum with appropriate quality and price for purchasing. We design a monopolist-dominated quality-price contract, which is offered by the PO and contains a set of quality-price combinations each intended for a consumer type. A contract is feasible if it is incentive compatible (IC) and individually rational (IR) for each SU to purchase the spectrum with the quality-price intended for his type. We propose the necessary and sufficient conditions for the contract to be feasible. We further derive the optimal contract, which is feasible and maximizes the utility of the PO, for both discrete-consumer-type model and continuous-consumer-type model. Moreover, we analyze the social surplus, i.e., the aggregate utility of both PO and SUs, and we find that, depending on the distribution of consumer types, the social surplus under the optimal contract may be less than or close to the maximum social surplus.

Providing physical-layer security for mobile users in future broadband wireless networks is of both theoretical and practical importance. In this paper, we formulate an analytical framework for resource allocation in a downlink orthogonal frequency-division multiple access (OFDMA)-based broadband network with coexistence of secure users (SUs) and normal users (NUs). The SUs require secure data transmission at the physical layer while the NUs are served with conventional best-effort data traffic. The problem is formulated as joint power and subcarrier allocation with the objective of maximizing average aggregate information rate of all NUs while maintaining an average secrecy rate for each individual SU under a total transmit power constraint for the base station. We solve this problem in an asymptotically optimal manner using dual decomposition. Our analysis shows that an SU becomes a candidate competing for a subcarrier only if its channel gain on this subcarrier is the largest among all and exceeds the second largest by a certain threshold. Furthermore, while the power allocation for NUs follows the conventional water-filling principle, the power allocation for SUs depends on both its own channel gain and the largest channel gain among others. We also develop a suboptimal algorithm to reduce the computational cost. Numerical studies are conducted to evaluate the performance of the proposed algorithms in terms of the achievable pair of information rate for NU and secrecy rate for SU at different power consumptions.

Cognitive radio (CR) is a promising paradigm to achieve efficient utilization of the limited spectrum resource by allowing the unlicensed users to access the licensed spectrum, and dynamic spectrum access (DSA) is one of the fundamental functions of CR networks. Market-driven spectrum auction has been recognized as an effective way to achieve DSA. In spectrum auction, the primary spectrum owners (POs) act as auctioneers who are willing to sell idle spectrum bands for additional revenue, and the secondary users (SUs) act as bidders who are willing to buy spectrum bands from POs for their services. However, conventional spectrum auction designs are restricted within the scenario of single auctioneer. In this paper, we study the spectrum auction with multiple auctioneers and multiple bidders, which is more realistic for practical CR networks. We propose MAP, a Multiauctioneer Progressive auction mechanism, in which each auctioneer systematically raises the trading price and each bidder subsequently chooses one auctioneer for bidding. The equilibrium is defined as the state that no auctioneer and bidder would like to change his decision. We show analytically that MAP converges to the equilibrium with maximum spectrum utilization of the whole system. We further analyze the incentive for POs and SUs joining the auction and accepting the auction result. Simulation results show that MAP well converges to the equilibrium, and the spectrum utilization is arbitrary closed to the global optimal solution according to the length of step.

In this paper, we exploit a novel setting for Cognitive Radio (CR) networks to enable multiple operators to involve secondary users (SUs) as cooperative relays for their primary users. In return, SUs get an opportunity to access spare channels for their own data transmission. Initially, we assume that the CR network supports payment transfer. Then, we formulate the system as a transferable utility coalitional game. We show that there is an operating point that maximizes the sum utility over all operators and SUs while providing each player a share such that no subset of operators and SUs has an incentive to break away from the grand coalition. Such operating points exist when the solution set of the game, the core, is nonempty. Subsequently, we examine an interesting scenario where there is no payment mechanism in the network. This scenario can be investigated by using a nontransferable utility coalitional game model. We show that there exists a joint action to make the core nonempty. A general method with exponential computational complexity to get such a joint action is discussed. Then, we relate the core of this game to a competitive equilibrium of an exchange economy setting under special situations. As a result, several available efficient centralized or distributed algorithms in economics can be employed to compute a member in the core. In a nutshell, this paper constitutes the design of new coalition based dynamics that could be used in future CR networks.

This paper studies the problem of determining locations of Secondary Access Points (SAPs) which belong to two competitive service providers in a certain region. SUs connect to the SAP according to their preferences. A key feature of our modeling approach is that it permits SAPs to set discriminatory powers for SUs, that is, SUs in different locations will be scheduled with different transmission powers. The profit maximizing SAPs compete with each other by setting locations and delivered power schedules to attract SUs, taking into account the impact of the revenue obtained and the power cost incurred. To study this competitive situation, an appropriate extensive form game is defined. The game can be viewed as two-staged. The selection of transmission powers by the SAPs can be investigated for each pair of locations. Then, the problem of location choice can be analyzed, anticipating what transmission powers will be chosen. We make an important observation: the Nash equilibrium of the game exists and the global revenue maximum strategy is a Nash equilibrium strategy. An example demonstrating how to place SAPs in a linear region is presented.

In this paper, we study the diversity-multiplexing tradeoff (DMT) of two-way relay channels (TWRC) with multi-antenna relay at finite signal-to-noise ratio (SNR). A two-phase decode-and-forward (DF) relay protocol and Rayleigh fading environment are considered. We first derive upper and lower bounds on the outage probability. These bounds are very tight for all practical SNR regions. Based on these bounds, the estimates of finite-SNR DMT (f-DMT) are then obtained. Our analysis shows that the multiplexing gain approaches one when SNR decreases to zero. On the other hand, the diversity gain approaches the number of relay antennas when SNR increases to infinity. Furthermore, the impact of time sharing, rate allocation and relay location on the outage and f-DMT is also discussed through numerical examples.

In this paper, we study the distributed energy efficient spectrum sharing strategy selection (i.e., joint discrete power control and multimode precoding strategy selection) with limited feedback in MIMO interference channels. We assume that the users are selfish and noncooperative. The goal of each user is to maximize its individual energy efficiency under the constraints of the minimum data rate and the interference temperature. Game theory is used to model the spectrum sharing strategy selection. We design a payoff function to guarantee the feasibility of the pure Nash equilibrium of the game without knowing the infeasible strategy profiles in advance. Then we propose a distributed game-theoretic spectrum strategy selection algorithm and prove that this algorithm always attains the feasible strategy profiles. Numerical results show that the proposed algorithm can enhance the energy efficiency of the users and significantly outperforms the random selection algorithm.

The traditional spectrum auctions require a central auctioneer. Then, the secondary users (SUs) can bid for spectrum in multiple auction or sealed auction way. In this paper, we address the problem of distributed spectrum sharing in the cognitive networks where multiple owners sell their spare bands to multiple SUs. Each SU equips multi-interface/multi-radio, so that SU can buy spare bands from multiple owners. On the other hand, each owner can sell its spare bands to several SUs. There are two questions to be addressed for such an environment: the first one is how to select bands/the owners for each SU; the second one is how to decide the competitive prices for the multiple owners and multiple SUs. We propose a two-sided multi-band market game theoretic framework to jointly consider the benefits of all SUs and owners. The equilibrium concept in such games named core. The outcomes in the core of the game cannot be improved upon by any subset of players. These outcomes correspond exactly to the price-lists that competitively balance the benefits of all SUs and owners. We show that the core in our model is always non-empty. The measurement of price is set to discrete value. Subsequently, the core of the game is defined as discrete core. The Dynamic Multi-band Sharing (DMS) Algorithm is proposed to converge to the discrete core of the game. With small measurement of price, the algorithm can achieve the optimal performance compared with centralized one in terms of total profit of the system.

This paper studies the performance limits of two-way relay channel (TWRC) at finite signal-to-noise ratio (SNR) in Rayleigh fading environment. A two-phase decode-and-forward (DF) protocol is considered. We first derive closed-form expressions for both outage probability and diversity-multiplexing tradeoff (DMT). Our results are general and suitable for any time sharing and any rate allocation in the two-way relay protocol. It is found that DF outperforms amplify-and-forward (AF) when either multiplexing gain or SNR is small enough, otherwise, DF is inferior to AF in term of outage probability. Meanwhile, finite-SNR DMT of DF is always lower than that of AF regardless of SNR due to the additional sum-rate constraint imposed on the relay node for full decoding. Furthermore, the optimum relay location for any given combination of time sharing and rate allocation is presented.

We present a novel partial decoding method at the relay, called pairwise check decoding (PCD), for two-way relay fading channels. The proposed PCD method forms a so-called check-relationship table for the superimposed Low-Density Parity-Check (LDPC)-coded packet pair during the multiple access phase. Meanwhile, it incorporates adaptive network coding by using closest-neighbor clustering mapping (CNCM) to compensate the phase deviation of the fading channels. The proposed PCD method is a practical and efficient realization of the promising denoise-and-forward relay strategy with advanced channel coding and non-linear network coding. Simulation results show that under the same LDPC-coded two-way relay system, our proposed PCD considerably outperforms the case where the relay performs only adaptive network coding without channel decoding. It also performs better than the case where the relay adopts the belief propagation decoding along with conventional XOR-based network coding under certain regions.

In this paper, we study the quantized precoding strategy selection for multiple-input multiple-output (MIMO) multiple access channels (MAC) in cognitive radio (CR) networks through a game-theoretic perspective. Since the secondary users in such system are difficult to be coordinated by a centralized authority, they are noncooperative and attempt to maximize their own payoffs selfishly in a distributed method. We propose a noncooperative precoding strategy selection game and find that it is a potential game which possesses at least one pure strategy Nash equilibrium. A decentralized learning algorithm with a small amount of feedback is proposed to obtain Nash equilibrium. We prove that the proposed algorithm can converge to a pure strategy Nash equilibrium. Simulation results are provided to verify our analysis.

In this paper we exploit a setting for cognitive radio networks by utilizing cooperation from secondary users (SUs) to assist the transmissions of operators' primary users (PUs). On the other hand, SUs can share the spare spectrum of operators. Such a scenario can be viewed as a market where multiple operators trade their spare spectrum for the assistance of SUs, and multiple SUs trade the transmission energy for access opportunities from operators. We model the system using transferable payoff coalitional game theory. An outcome of a coalitional game is a specification of the coalition that forms and the joint action it takes. We show that the optimum joint action strategy can be obtained as a solution of convex optimization problem. Then, based on dual technique, we show that there is an operating point that maximizes the sum utility over the operators and SUs while providing each player a share such that no subset of operators and SUs has an incentive to break away from the brand coalition.

In this paper, the problem of optimal power allocation for OFDM based cognitive radio (CR) system is studied. The maximum transmit rate of cognitive user (CU) is expressed as a convex problem with both total transmit power constraint and interference power constraint. Using dual decomposition method, the linear coupled constraints are decoupled one by one, and the original optimization problem is transferred to several sub-problems which are easy to be solved. A two level dual variable iterative (TLDVI) algorithm is proposed to obtain the final optimal power allocation with lower complexity. Simulation results show that the proposed algorithm convergence fast and can achieve the same maximum transmit rate performance of CU as the standard interior point algorithm.

In this paper, we study the relay assignment problem in cooperative wireless networks with self-interested nodes. Such systems should be organized from the point of view of efficiency, stable and providing consistent incentives to all nodes. We propose a cooperation mechanism which includes the cooperative relationship formation stage and cooperation maintenance stage. The cooperative relationship among the nodes can be modeled as an exchange market game (a special coalitional game) where nodes trade transmission power between each other to get diversity gain. The exchange games have a basic assumption that each node conforms to trade agreement. So that, each agent has the option to trade its good in order to get a better one. In such game, strict core is considered as individual rational, Pareto optimal and relationship-stable solution. A Cooperation Cycle Formation (CCF) algorithm is proposed to get the strict core solution. But, in networks, some deviated nodes may break the cooperation agreement to get more utility gain. Such deviated behaviors in the cooperation cycles can totally destroyed the cooperation relationship. However, the date transmissions in networks have the repeated element (e.g., the data of each user are transmitted in many time slots). Hence, based on the cooperative cycle formed by CCF, we introduce a repeated game model for cooperation maintenance in the second stage. A Dynamic Punishment and Recover (DPR) mechanism is proposed to punish the deviated behaviors and recover cooperation.

In this paper, a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is discussed. An AIC RF front-end sampling structure is proposed requiring only low rate ADCs and few storage units for spectrum sampling. Multiple CRs collect compressed samples through AICs and recover spectrum jointly. A novel joint sparsity model is defined in this scenario, along with a universal recovery algorithm based on S-OMP. Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.

Peer-to-peer (P2P) networks are very popular for large-scale data sharing in today's internet. It is naturally envisioned that the file sharing would also be an important application for civil mobile ad hoc networks in the future. The authors propose the cooperative diversity download methods utilising the wireless medium and common file sources in wireless P2P networks. The designed protocols including multi-source opportunistic direct downloading (MODD) and multi-source opportunistic decode-and-forward downloading (MODFD) can adaptively work in different file distribution scenarios. Both of them rely on the cooperation between the sources or receivers and the proposed distributed source-selection schemes. For the situation with one receiver and multi-source, MODD selects ‘best’ source with most favourite channel condition from available m sources. In the situation with multiple receivers and sources, the multiple receivers could form the cooperative pairs to receive the data from the selected source based on decode-and-forward transmission strategy. Compared with MODD, MODFD saves the source-selection time and provides incentives for the cooperation between the peers. A distributed collision solving scheme for the selection schemes is also proposed. The information theoretic analysis of outage probability shows that the proposed downloading methods can both obtain m times diversity gain.

This paper is concerned with decentralized selection of multimode precoding strategy for multiple-input multiple-output (MIMO) multiple access channels. We formulate it as a discrete noncooperative game. This game is shown to possess at least one pure strategy Nash equilibrium (NE) and the optimal strategy profile which maximizes the sum rate constitutes a pure strategy NE. Then we propose a decentralized algorithm based on learning automata to achieve the NE. A repeated mechanism is introduced to improve the sum rate performance and a mechanism for adapting step size is designed to control the convergence speed. Simulation results show that the proposed algorithm, which only requires limited feedback, can achieve near optimal or optimal sum rate performance.

We study the effect of various geometries on the capacity of wireless networks via percolation, which was not considered much before. Percolation theory was first applied to derive an achievable rate 1/√n in [5] by constructing a highway system, in contrast with the previous result Θ(1/√n log n) in [6], where n is the number of the nodes. While a highway system that consists of both horizontal and vertical edge-disjoint paths exists in a square network, B. Liu et al. in [1] pointed out that the horizontal paths will disappear if the width of a strip network is increasing more slowly than \log n. In this paper, first we take a deeper look at the percolation in a strip network. We discover that when a highway system exists, the capacity is restricted by the maximum length of the sides. Moreover, a sub-highway system is still in presence when the highway system disappears. Secondly, we consider the situations in a triangle network. Conditions that percolation highway exists in it, and the achievable rate for a triangle network are discussed. We find that corner effect can be a bottleneck of the capacity. By combining the achievable rate of the former networks, we attribute the variance between them to their symmetry discrepancies. Finally, we turn to the capacity of three dimensional (3D) networks via percolation. The whole study shows that geometric symmetry plays a significant role in the percolation and the capacity, thereby shedding a light on the network design and the scheduling.

We study the throughput capacity of hybrid wireless networks with a directional antenna. The hybrid wireless network consists of n randomly distributed nodes equipped with a directional antenna, and m regularly placed base stations connected by optical links. We investigate the ad hoc mode throughput capacity when each node is equipped with a directional antenna under an L-maximum-hop resource allocation. That is, a source node transmits to its destination only with the help of normal nodes within L hops. Otherwise, the transmission will be carried out in the infrastructure mode, i.e., with the help of base stations. We find that the throughput capacity of a hybrid wireless network greatly depends on the maximum hop L, the number of base stations m, and the beamwidth of directional antenna \theta. Assuming the total bandwidth W bits/sec of the network is split into three parts, i.e., W_1 for ad hoc mode, W_2 for uplink in the infrastructure mode, and W_3 for downlink in the infrastructure mode. We show that the throughput capacity of the hybrid directional wireless network is \Theta(\frac{nW_1}{\theta^2L\log n})+\Theta(mW_2), if L=\Omega(\frac{n^{1/3}}{\theta^{4/3}\log^{2/3} n}); and \Theta((\theta^2L^2\log n)W_1)+\Theta(m W_2), if L=o(\frac{n^{1/3}}{\theta^{4/3}\log^{2/3} n}), respectively. Finally, we analyze the impact of L, m and \theta on the throughput capacity of the hybrid networks.

Cognitive radio is emerging as a promising technique to improve the utilization of the radio frequency spectrum. In this paper, we consider the problem of spectrum sharing among primary (or "licensed") users (PUs) and secondary (or "unlicensed") users (SUs). We formulate the problem based on bandwidth auction, in which each SU makes a bid for the amount of spectrum and each PU may assign the spectrum among the SUs by itself according to the information from the SUs without degrading its own performance. We show that the auction is a noncooperative game and that Nash equilibrium (NE) can be its solution. We first consider a single-PU network to investigate the existence and uniqueness of the NE and further discuss the fairness among the SUs under given conditions. Then, we present a dynamic updating algorithm in which each SU achieves NE in a distributed manner. The stability condition of the dynamic behavior for this spectrum-sharing scheme is studied. The discussion is generalized to the case in which there are multiple PUs in the network, where the properties of the NE are shown under appropriate conditions. Simulations were used to evaluate the system performance and verify the effectiveness of the proposed algorithm.

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.

In this paper, we address the incentive-based relay-selection problem over multi-source and multi-relay wireless networks. A two-side market game approach is employed to jointly consider the benefits of all sources and relays. The equilibrium concept in such games is called core. The outcomes in the core of the game cannot be improved upon by any subset of players. These outcomes correspond exactly to the price-lists that competitively balance the benefits of all sources and relays. When the price assumes only discrete values, the core of the game is defined as discrete core. The Distributed Source-Relay Assignment (DSRA) algorithm is proposed for competitive price adjustment and converges to the discrete core of the game. With small enough measurement of price, the algorithm can achieve the optimal performance compared with centralized one in terms of total profit of the system.

We present a new physical layer network coding (PLNC) scheme, called superimposed XOR, for two-way relay channels. The new scheme specifically takes into account the channel as well as information asymmetry in the broadcast phase of two-way relaying. It is based upon both bitwise XOR and symbol-level superposition coding. We first derive its achievable rate regions when integrated with two known time control protocols over Gaussian channels. We then demonstrate its average maximum sum-rate and service delay performance over fading channels. Compared with the existing bitwise XOR and symbol-level superposition coding, the proposed superimposed XOR scheme achieves larger rate region in asymmetric channels. As a result, it performs much better in terms of averaged maximum sum-rate and service delay over fading channels. Numerical results also show that the proposed practical PLNC closely approaches the capacity bound given by the information-theoretic random binning.

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.

It is conventionally supposed that the periodicity of superimposed training (ST) sequence designed for channel estimation has no impact on the ST system's performance, as long as the channel identification condition is satisfied. Accordingly the shortest sequence period equal to channel length is always preferred in ST-OFDM systems for its computational tractability. However, with additional consideration on extraction of the transmitted information, not only focusing on the accuracy of channel estimation, analysis and simulation indicate that the period length of ST sequence may have an unignorable influence on BER performance of ST-OFDM systems. As a special case, we show that ST with training sequence period equal to channel length can bring OFDM no benefit but BER performance degradation compared with its frequency-divided pilot counterpart.

This paper investigates the joint user and transmit antenna selection for uplink multi-user MIMO systems. Our goal is to maximize the sum-rate capacity. The naive exhaustive search algorithm which can get the optimal sum rate performance is impractical in the large system. To tradeoff the complexity and performance, two suboptimal low-complexity joint user and transmit antenna selection algorithms (called CB-JUTAS and NB-JUTAS) are proposed. Numerical results show that the sum rate performance can be improved significantly by implementing joint user and transmit antenna selection. And the proposed CB-JUTAS may reach about 98% of the optimal sum rate performance with the complexity increasing linearly rather than exceptionally with the number of users and antennas. The NB-JUTAS with an extreme low-complexity has a reduced performance when SNR grows large, especially when the number of the transmit antennas increases.

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.

Channel allocation was extensively investigated in the framework of cellular networks, but it was rarely studied in the wireless ad hoc networks, especially in the multihop networks. In this paper, we study the competitive multiradio multichannel allocation problem in multihop wireless networks in detail. We first analyze that the static noncooperative game and Nash equilibrium (NE) channel allocation scheme are not suitable for the multihop wireless networks. Thus, we model the channel allocation problem as a hybrid game involving both cooperative game and noncooperative game. Within a communication session, it is cooperative; and among sessions, it is noncooperative. We propose the min-max coalition-proof Nash equilibrium (MMCPNE) channel allocation scheme in the game, which aims to maximize the achieved data rates of communication sessions. We analyze the existence of MMCPNE and prove the necessary conditions for MMCPNE. Furthermore, we propose several algorithms that enable the selfish players to converge to MMCPNE. Simulation results show that MMCPNE outperforms NE and coalition-proof Nash equilibrium (CPNE) schemes in terms of the achieved data rates of multihop sessions and the throughput of whole networks due to cooperation gain.

Abstract An improved channel estimation technique based on the Steiner low‐cost channel estimator is proposed, which is widely used in TD‐SCDMA (Time Division‐Synchronous Code Division Multiple Access) cellular mobile radio systems. TD‐SCDMA is also known as third‐generation mobile systems where adaptive antennas are employed. As additive noise has a great adverse effect on the performance of the Steiner estimator, the proposed method employs time‐correlated post‐processing with a threshold filter to reduce channel noise and compensate channel variations. Furthermore, channel estimation combining direction‐of‐arrivals (DOAs) is performed, which can reduce channel interferences without adding computational complexity, for the information of DOAs has been obtained by the inherent adaptive antenna system. The performance of the improved channel estimator is compared with conventional channel estimation approaches, and numerical results show that the new approach can lead to considerable performance enhancement even in high‐speed vehicle propagation environments. Copyright © 2008 John Wiley & Sons, Ltd.

Extensive studies on supporting voice traffic over wireless 802.11 networks have been carried out in the literature. Most of them were focused only on one hop infrastructure mode. This paper addresses the issue of voice over multihop wireless networks. Through simulation considering a simple topology, we identify the voice traffic bottleneck in 802.11 MAC and thus propose a burst queue (BQ) scheme to cooperate with 802.11b and 802.11e MAC protocols. Extensive simulation results under variety of circumstances show that BQ scheme can support 50%~100% more voice calls and obtain 20%~50% lower delay at the cost of a little higher loss ratio which is tolerable to voice flows. Even for the grid and random topology, our BQ scheme can provide smaller packet loss ratio, and shorter end-to-end delay.

The performance of TCP-Vegas is not satisfactory in multihop ad hoc networks over IEEE 802.11 MAC protocol. We analyze the problem with a unified network model and simulation results. We observe that the aggregate throughput of all traffics decreases as the load of the network increases. The main reasons lie in Vegas's large minimum congestion window, large reset slow start threshold and aggressive window increase policy. To fix these problems, we propose a modified TCP protocol based on TCP-Vegas for multihop ad hoc networks, called Vegas- W. We extend the congestion window to fraction; change the probing mechanisms of legacy TCP-Vegas in both slow start and congestion avoidance and update slow start threshold tracking the stable window. We evaluate the performance of Vegas-W through ns-2. Extensive simulation results under a variety of scenarios show that Vegas-W can improve the throughput up to 87% over legacy TCP-Vegas and up to 27% over FeW, which is another improved algorithm based on TCP-Newreno scenarios.

Channel allocation was extensively researched in the framework of cellular networks, but it was rarely studied in the ad-hoc wireless networks, especially in the multi-hop ad-hoc networks. In this paper, we study the problem of competitive multi-radio multi-channel allocation in multi-hop wireless networks in detail. We model the channel allocation problem as a static cooperative game, and then derive a min-max coalition-proof Nash equilibrium (MMCPNE) in this game. We study the existence of MMCPNE in the static game and prove the necessary and sufficient conditions for MMCPNE. Finally, we propose a two-step distributed algorithm that enable the selfish players to converge to MMCPNE.

This paper proposes a novel channel estimation method to further improve the performance of the optimal pilot sequences in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We first assume that the virtual pilot tones superimposed at the data locations over the specific sub-carriers are transmitted from all transmit antennas. We then obtain the virtual received pilot signals at the corresponding locations at the receive antennas by interpolation in the time domain. Finally, the channel parameters are obtained from the combination of the virtual and real received pilot signals over one OFDM symbol based on the least squares (LS) channel estimation. Simulation results show that the proposed channel estimation method provides much better performance than the previous method for the optimal pilot sequences over multiple OFDM symbols, especially in fast time- varying channels.

TCP is a reliable transport protocol tuned to perform well over traditional wired networks. Although it performs well for wired networks, TCP's implicit assumption that any packet loss is due to congestion is not valid any longer in mobile ad hoc networks. It is observed that TCP induces the over-action of routing protocol and reduces the performance of the connection. Fraction window increment (FeW) scheme for TCP improves the connection performance by limiting TCP's aggressiveness. But to some extent, this limitation is too strict in that it eliminates the possibility to deliver more bytes under the same congestion window. To solve this problem, we propose an adaptive packet size (APS) scheme to work on top of FeW for TCP. The proposed scheme utilizes the advantages of both legacy TCP and FeW to achieve high performance over multihop 802.11 networks. Extensive simulation results demonstrate that APS over FeW outperforms FeW alone by 10 -25 % according to different scenarios, e.g., chain-topology, grid-topology, and random-topology with mobility.

In traditional call admission control (CAC) schemes, mobile users are always the passive roles during the admission procedures and the base station determines whether to admit or reject the call requests without the involvement of mobile users. In this paper, we propose a novel stochastic CAC framework that allows the mobile user to be an active entity during the CAC process. The objective of each mobile user is to maximize its utility function based on its own decision (i.e., to join the queue or not to join the queue). The optimal stochastic decision is in the sense that the probability for a mobile user to join the queue will maximize the expectation of the utility function. In other words, in the long run, the mobile user will benefit from the optimal joining probability p*. We further show that the optimal join probability for a mobile user could be quite different depending on the number of mobile users waiting in the queue. Finally, we illustrate the structure of the optimal join probability p* under various utility functions, which indicates that the optimal user's policy depends on the utility functions. Moreover, we find that even if the theoretical structure of service time in the system are quite different for various queue scheduling schemes, the optimal pi* are identical in most of circumstance.

The performance evaluation of delay-tolerant CFDP is seen in the literatures. In this work, we focus on experimental performance investigation of the delay-tolerant CFDP in the deferred NAK mode over a simulated low earth orbit (LEO)-satellite communication link. A rate-based transmission protocol is taken as a reference. This work is to see how the delay-tolerant CFDP protocol performs over a LEO- satellite link delay, especially when compounded with a high bit- error-rate (BER). The investigation results show that the rate- based protocol shows superior throughput performance over both symmetric and asymmetric channels, especially at higher BERs. Quantitatively, the throughput advantage of the rate- based protocol over CFDP-TCP in symmetric channel at BER=10"5 is approximately 2200 bytes/sec, and in asymmetric channel is approximately 8500 bytes/sec.

The Consultative Committee For Space Data Systems (CCSDS) file delivery protocol (CFDP) offers selectable quality of service, according to mission requirements and transmission capability, ranging from an unacknowledged option to a fully acknowledged option providing error recovery through retransmission. The performance of CFDP in the deferred NAK mode has been evaluated in a theoretical manner. In this paper, we present an experimental performance evaluation of CFDP in the deferred NAK mode over the direct, point-to-point geostationary earth orbit (GEO)-satellite links simulated using a test-bed. We compare CFDP running over TCP with the CCSDS space communication protocol standards (SCPS) protocol stack to see which one is more effective over a GEO-satellite link. The experimental results show that CFDP does not have performance advantage over other protocols over a less lossy channel with a BER less than or equal to 10-6. However, along with the increase of BER, CFDP shows significant performance advantage. Quantitatively, for a symmetric channel with BER=10-5, CFDP-TCP protocol has the highest throughput which is 2000 bytes/sec higher than SCPS-VJ and almost 3500 bytes/sec higher than SCPS-Vegas. The throughput advantage of CFDP with BER=10-5is 1500 bytes/sec over SCPS-VJ and 2800 bytes/sec over SCPS- Vegas, over asymmetric channel with BER=10-5.

Prior works of the antenna selection mainly studied the single user case. In this paper, we focus on the multi-user case which is more practical. The multi-user antenna selection problem is formulated as an N-person discrete non-zero sum finite strategy-form game. The capacity of each user is used as the utility function. The Nash equilibrium of the proposed game is studied by using stochastic learning and numerical results are given.

In this paper, we present a modified BP decoding algorithm based on select updating schedule by analyzing the properties of message-passing through cycles, we also proposed a concrete scheme to obtain the select updating schedule, the simulations prove that we could improve performance and reduce complexity at the same time for short regular LDPC codes which have a small difference between the maximal and minimal single bit node girths by select updating schedule compared with the conventional BP decoding algorithm.

We investigate the relay multiple access channels (RMAC) with two users and a destination. We determine the lower bound and upper bound on the capacity of RMAC under time-division (TD) decode-and-forward (DF) mode by using superposition modulation. A relay scheme with resource allocation is proposed to achieve the lower bound. Analytical results and simulation results show that the proposed scheme can achieve larger capacity region than that of direct transmission (DT) for the fixed channel gain case. But they provide the same maximum sum capacity. Whereas, the proposed relay scheme can provide higher outage capacity than DT scheme due to the fact that the two sources can share the resources from each other.

In this letter, we propose a novel quadratic programming model for soft-input soft-output (SISO) multiple-input multiple-output (MIMO) detection that is compact for QAM constellations and easy to analyze. A semidefinite relaxation for this model is derived that can be solved by the interior-point method. We also give the sufficient conditions and necessary conditions to speed up the interior-point method.

In this paper, the general channel estimator for MIMO OFDM systems is developed, the MSE bound of the estimator is derived to examine the issue of pilot tone placement, and the MM principle together with theory of majorization is applied to channel estimation to reduce computational complexity. It's well known that EM-type algorithms are powerful tools to estimate channel parameters through iterative calculation, actually, every EM-type algorithm is a special case of the more general class of MM algorithms. The first M of MM stands for majorize (minorize) and the second M stands for minimization (maximization). To construct an EM-type algorithm, specifying the complete data, computing the conditional expectation and maximizing the conditional expectation analytically are skillful and computationally complex. In contrast, the MM algorithms developed from the inequalities are easier to be understood and calculated. In addition to constructing algorithms based on the MM principle we analyze the convergence property of the MM algorithms. Finally, the simulation results demonstrate the performance of MM-type channel estimation algorithms

Abstract In this paper, we investigate the cooperative channels with one source‐destination pair and multiple partners (relays), where only the destination is equipped with multiple antennas. The achievable capacity with the optimal power allocation and partner selection is analyzed with the total transmit power constraints under the different cooperation modes, including amplify‐and‐forward (AF) and decode‐and‐forward (DF). With the partial channel state information (CSI) at the destination, we develop three algorithms to choose the possible best partner(s) for AF, repetition‐coded DF and Gaussian‐coded DF, which are called Selective AF (SAF), Selective repetition‐coded DF (SRDF), and Selective Gaussian‐coded DF (SGDF), respectively. In these algorithms, only one partner will be employed as relay for SAF and SRDF to maximize the capacity while multiple partners are selected for SGDF. An efficient quasi‐distributed protocol to support SAF, SRDF, and SGDF is also involved. We study the impact of the number of receive antennas and SNR on SAF, SRDF, and SGDF. Numerical results show that SAF and SRDF have higher spectral efficiency than direct transmission (DT), especially in low SNR regime and for the small number of receive antennas and large number of users cases, while SGDF outperforms DT evidently in every scenario. Copyright © 2007 John Wiley & Sons, Ltd.

All existing training signals designs for OFDM systems assume no guard subcarriers. In practice, guard subcarriers exist in almost all the OFDM systems. In this paper, we propose a Linear Programming(LP) method for optimal power distribution design and a certain matrix for optimal phase design in MIMO OFDM systems with guard subcarriers. Based on these methods, the optimal training signals for 802.11a and 802.16 are derived. Simulation results show that a significant performance improvement can be achieved with the proposed training signals.

The problem of random access (RA) in a wireless SIMO-OFDM system is addressed and a decentralized medium access control (MAC) strategy is proposed based on the so called random orthogonal frequency division multiple access (ROFDMA). By employing receive beamforming, the problem of packet collisions at the same subcarrier are solved by using random spatial division multiple access (RSDMA), which therefore provides a multipacket reception (MPR) capability. First, we study the optimal number of users allowed to transmit at the same subcarrier and the same slot to maximize the separable packets subject to their BER requirements. Then the transmission probability based on user's channel gain is designed to achieve the desired number of users with the highest probability. Finally, a MPR scheme under average transmit power constraint is proposed with modulation selection. The performance of the proposed scheme is analyzed analytically and evaluated through simulations.

In this paper, we propose a semi-blind channel estimation method based on first-order statistics of OFDM systems. The transmitter superimposes periodic pilot sequences on information sequences, where there is no loss in information rate but a controllable increase in transmission power. Employing the first-order statistics, it enables a closed-form channel estimation method to estimate the frequency-selective fading channel. Then, a mean square error (MSE) bound is derived to evaluate the proposed channel estimator. Computer simulations have been provided to compare the MSE performances of the proposed method with the LS estimation method based PSAM and the subspace method.

Some properties of permutation polynomials over integer rings, mainly the 3ncase, are studied and some conclusions are drawn. A decision theorem for 3ncase and a proof of the MCF property for size pnpermutation polynomial-based interleaver are presented. A design criterion for interleavers based on third degree permutation polynomial is proposed to select the coefficients of the polynomials. Simulation results show that these interleavers can achieve near performance of S-random interleavers, and for short frame size, they outperform S-random interleavers.

Abstract In this paper, we present a novel group space‐time block coding slow frequency‐hopping multicarrier direct‐sequence code division multiple access (GSTBC SFH/MC DS‐CDMA) system over frequency selective fading channels. The proposed scheme greatly improves the bandwidth‐efficient through assigning the users employing the same frequency‐hopping (FH) pattern. Moreover, the users employing the same FH pattern are assigned into different virtual groups, in which the users are assigned to the different spreading codes, while the users are assigned to the same spreading code in the same virtual group. Then, a novel group detection scheme that we denote by group ordered successive interference cancellation (GOSIC) is presented to suppress the interference between the different virtual groups. Our proposed scheme consists of ordering group at the receiver side in order to maximize the overall system performance, and carrying BLAST‐STBC (LSTBC) detection for the users in the same virtual group. We define and derive the optimal group order based on the post group signal to interference plus noise ratio (PGSINR). We also propose another suboptimal group order in order to overcome the complexity issues. Finally, we compare the performance of our proposed GOSIC with conventional group successive interference cancellation (GSIC), conventional STBC multiuser detection (MUD), and LSTBC‐MUD, and show that significant improvement is introduced. Finally, it is shown that the proposed scheme is robust to the imperfect channel estimation. Copyright © 2006 John Wiley & Sons, Ltd.

A time-domain based feedback reduction algorithm for orthogonal frequency division multiplexing (OFDM) transmitter power adaptation is proposed, which can significantly reduce the feedback amount during the power adaptation comparing with the conventional frequency-domain adaptation schemes. It is found that the size of cluster plays an important role on the adaptation performance, especially for the vehicular environment. According to simulation results, Lagrange interpolation is used to give an explicit curve between the Doppler frequency, cluster size and the adaptation performance, which makes a good trade-off between the resulted BER and the amount of feedback.

This paper addresses the optimal resource allocation problem for multiuser MIMO-OFDM systems. We apply an optimization algorithm to obtain a joint subcarrier and power allocation scheme based on orthogonal frequency division multiple access (OFDMA) combined with dirty paper coding (DPC) assuming instantaneous channel state information (CSI), which is called as DPC-OFDMA. The ultimate objective is to minimize the total transmit power subject to individual required data rates constraints. To reduce the complexity of the optimal solution, the analysis is considered in two stages. The first stage addresses subcarriers allocation, in which users are allowable to share subcarriers. The second stage employs DPC technology to deal with simultaneous transmissions of the users sharing the same subcarriers. An efficient algorithm to choose the best possible ordering for DPC and the optimal precoding design of each user are also involved. Simulation results show that DPC-OFDMA scheme has high spectral and power efficiency than conventional fixed schemes, where fixed power and subcarriers are allocated to each user.

In wireless communications, multiple receive antennas can be used with orthogonal frequency division multiplexing (OFDM) or single-carrier (SC), and frequency domain equalization (FDE) or frequency domain pre-equalization (pre-FDE) to significantly improve system capacity and performance. However, the complexity of such combination can be very large because multiple discrete Fourier transform (DFT) blocks, each for one receive antenna, are required to fully take advantage of the space diversity. Reference Defeng Huang et al., (2004) proposed a receive space diversity architecture for OFDM systems using orthogonal designs. Using this architecture, the number of DFT blocks can be reduced but performance degradation will be introduced. In this paper, we propose space diversity schemes for space-time block coding (STBC) based multiple-input multiple-output (MIMO) systems and frequency domain pre-equalization single-input multiple-output (SIMO) systems. These schemes can be used with OFDM and SC transmission techniques. Using these schemes, the total number of Fourier transform (FT) blocks can be further reduced and the performance can be significantly improved at higher signal-to-noise ratio. Simulation results show the benefits of the proposed schemes

This paper presents a power allocation scheme for multi-user multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with channel state information (CSI) on transmitter and receiver. In the presented scheme, multi-user power allocation can be decoupled into single user power allocation through null space mapping multi-user channel and power allocation can be performed through spatial-spectral water-filling per user. To deal with increasing number of system users and fading correlation, scheduling can be used to maintain power allocation gain. The proposed scheme has low complexity and can substantially improve system spectral efficiency. Simulation results assess our theoretic analyses.

Channel parameters in OFDM systems over fading channels are generally obtained by transmitting pilot symbols in given positions of the time-frequency grid. The pilot-based method usually involves the LS estimation step on the pilot subcarriers and the interpolation step over the entire time-frequency grid. Increasing the accuracy of the pilot estimation will accordingly increase the precision in the interpolation step. This can be achieved by subspace tracking method. We propose in this paper an improved subspace-tracking algorithm by the Givens plane rotation based delay-subspace tracking and the RLS filtering based amplitude tracking. Performed on a parameterized model of the channel, simulations prove that our channel estimation is more robust and accurate while the complexity of the algorithm is low.

We propose a novel iterative decoding algorithm based on partial combination of the parity check matrix for iterative soft decoding of Reed-Solomon product codes. It uses only the simple syndrome equations whose solutions are confined to parts of the columns of the parity check matrix. Compared to other algorithms, the proposed algorithm has lower complexity while offering better performance, which is demonstrated by simulations.