Sliding network coding (SNC) has emerged as a promising solution for ultra-reliable and low-latency communication (URLLC) scenarios. The performance of SNC, particularly in terms of developing the encoding matrix and decoding strategy, is heavily influenced by the order h of the underlying Galois field,GF(2h). In this paper, we investigate high-order SNC, whereh> 1, to enhance transmission efficiency by employing a Vandermonde-based encoding matrix that ensures linear independence among coded packets. To maximize decoding efficiency, we design a structure-aware decoding strategy (SA-DS), which not only dynamically exploits the relationships between successfully decoded (SD) packets and the currently decoded (CD) packet, but also utilizes the first-packet deterministic decoding (FPDD) property of the Vandermonde matrix. Additionally, we develop a Markov chain-based performance analysis framework in terms of retransmission probability, packet error rate, and expected decoding delay. Numerical results demonstrate that in the evaluated settings, the proposed scheme outperforms several traditional schemes in the moderate-erasure region. In the low-erasure region, its advantage becomes particularly pronounced (reaching one to three orders of magnitude in both PER and retransmission probability while maintaining a comparable decoding delay), making it particularly suitable for URLLC applications.

Motivated by the need for low-latency and high-throughput in the low-altitude economy scenarios with multiple hovering uncrewed aerial vehicles and a single base station, we investigate a multiple-input multiple-output (MIMO) multi-pleaccess channel (MAC) with arbitrary additive noise distribution. To characterize its finite blocklength performance, we propose mismatched coding schemes based on spherical codebooks, employing either joint nearest neighbor (JNN) or successive interference cancellation (SIC) decoders. We derive second-order asymptotics for these schemes, extending single-antenna MAC analyses to the MIMO setting and highlighting the impact of antenna number on performance. While JNN and SIC decodings yield identical first-order asymptotics, JNN decoding exhibits superior performance in low-latency communication due to a larger second-order rate region, with this performance gap increasing as the number of antennas grows.

We study the two-user broadcast channel with degraded message sets and derive second-order achievability rate regions. Specifically, the channel noises are not necessarily Gaussian and we use spherical codebooks for both users. The weak user with worse channel quality applies nearest neighbor decoding by treating the signal of the other user as interference. For the strong user with better channel quality, we consider two decoding schemes: successive interference cancellation (SIC) decoding and joint nearest neighbor (JNN) decoding. We adopt two performance criteria: separate error probabilities (SEP) and joint error probability (JEP). Under our analysis, SIC and JNN decoding share the same second-order achievable rate region despite the fact that JNN decoding often yields better performance in other multiterminal problems. Furthermore, we generalize our results to the case with quasi-static fading and show that the asymptotic notion of outage capacity region is an accurate performance measure even at finite blocklengths.

Owing to the advantage of flexible deployment of uncrewed aerial vehicles (UAVs), the problem of long-distance transmission of sensor devices in Internet of Things (IoT) networks can be effectively addressed. However, the line-of-sight (LoS) channels of UAVs also make the transmitted data vulnerable to interception by eavesdroppers. In this article, a security transmission scheme is proposed for UAV-aided IoT networks, in which a UAV with variable transmission power is deployed between the sensor device and the monitor. In addition, the concept of Age of Information (AoI) is introduced to measure the freshness of information, while the difference between the AoI of the eavesdropper and the monitor is considered as the metric for the security transmission performance. To enhance system security, we derive a closed-form solution for the AoI difference and propose a UAV deployment algorithm to maximize the gap. The simulation results demonstrate a strong agreement with the theoretical analysis, and the proposed method can significantly improve the security communication performance of the system compared with the traditional method.

We first study the two-user additive noise multiple access channel (MAC) where the noise distribution is arbitrary. For such a MAC, we use spherical codebooks and either joint nearest neighbor (JNN) or successive interference cancellation (SIC) decoding. Under both decoding methods, we derive second-order achievable rate regions and compare the finite blocklength performance between JNN and SIC decoding. Our results indicate that although the first-order rate regions of JNN and SIC decoding are identical, JNN decoding has better second-order asymptotic performance. When specialized to the Gaussian noise, we provide an alternative achievability proof to the result by MolavianJazi and Laneman (T-IT, 2015). Furthermore, we generalize our results to the random access channel (RAC) where neither the transmitters nor the receiver knows the user activity pattern. We use spherical-type codebooks and a rateless transmission scheme combining JNN/SIC decoding and derive second-order achievability bounds. Comparing second-order achievability results of JNN and SIC decoding in a RAC, we show that JNN decoding achieves a strictly larger first-order asymptotic rate. When specialized to Gaussian noise, our second-order asymptotic results recover the corresponding results of Yavas, Kostina, and Effros (T-IT, 2021) up to second-order.

We study the successive refinement setting of Shannon cipher system (SCS) under the maximal leakage secrecy metric for discrete memoryless sources under bounded distortion measures. Specifically, we generalize the threat model for the point-to-point rate-distortion setting of Issa, Wagner and Kamath (T-IT 2020) to the multiterminal successive refinement setting. Under mild conditions that correspond to partial secrecy, we characterize the asymptotically optimal normalized maximal leakage region for both the joint excess-distortion probability (JEP) and the expected distortion reliability constraints. Under JEP, in the achievability part, we propose a type-based coding scheme, analyze the reliability guarantee for JEP and bound the leakage of the information source through compressed messages. In the converse part, by analyzing a guessing scheme of the eavesdropper, we prove the optimality of our achievability result. Under expected distortion, the achievability part is established similarly to the JEP counterpart. The converse proof proceeds by generalizing the corresponding results for the rate-distortion setting of SCS by Schieler and Cuff (T-IT 2014) to the successive refinement setting. Somewhat surprisingly, the normalized maximal leakage regions under both JEP and expected distortion constraints are identical under certain conditions, although JEP appears to be a stronger reliability constraint.

Cell-free technology is considered a pivotal advancement for next-generation mobile communications, which can effectively enhance the quality of service for user equipments (UEs) located at the cell edge. For cell-free systems, in this paper, we propose a hybrid downlink transmission method that combines non-orthogonal multiple access (NOMA) and multiuser diversity (MUD). To evaluate the communication performance of the system, we derive closed-form expressions for both instantaneous and average sum rates of UEs using the NOMA and MUD transmission methods. Furthermore, we comprehensively investigate the spectrum efficiency of the NOMA and MUD transmission methods to provide a basis for selecting the hybrid transmission strategy. On the basis of the proposed hybrid transmission strategy, we can derive an optimal hybrid transmission strategy for the scenarios with two access points (APs) and two UEs. Particularly, we extend the aforementioned strategy to the scenarios with multiple UEs, and formulate an optimization problem to maximize the system spectrum efficiency subject to the transmission strategy and power allocation. Furthermore, we propose a low-complexity user selection strategy and power allocation algorithm to solve the problem. Numerical results demonstrate that the hybrid transmission method and power allocation strategy can achieve higher system spectrum efficiency. Our results reveal the influence of key parameters on the downlink spectrum efficiency, analytically and numerically.

Thanks to the Internet of Things (IoT), there has been explosive growth in edge devices, which generate a tremendous amount of data that holds invaluable potential. However, conventional data mining and machine learning (ML) paradigms require transmitting raw data to data centers for further use, which puts a heavy burden on communication networks and is exposed to high privacy risks. Federated learning allows for the training of ML models using distributed datasets, which can be applied to protect data privacy and alleviate transmission burdens. Meanwhile, the technique of over-the-air (OTA) computation can be utilized to exploit the superposition property of wireless communication channels. Motivated by this, in this paper, we propose a co-phase OTA approach for communication-efficient uploading in multi-server federated learning, which does not require expansion of the uplink channel bandwidth when the numbers of users and models increase. Besides, the digital OTA with randomized transmission is proposed to overcome the disadvantages of analog OTA, where the performance analyses of analog OTA and digital OTA are deduced, respectively. Simulation results show that a lower cost function can be obtained by digital OTA while requiring fewer iterations for convergence than that in analog OTA as more users can upload.

In the Internet of Things (IoT), numerous devices and sensors are deployed to collect data sets. Although some IoT devices can process data locally, most devices may have limited power and computational capability. Since mobile edge computing (MEC) is a new paradigm to provide strong computing capability at the edge of networks close to users, these devices can offload their tasks to MEC servers. Therefore, designing an efficient computation offloading strategy to decide whether the tasks to be offloaded to MEC servers becomes crucial. In this paper, we study the computation offloading for IoT devices based on a non-cooperative game with one-shot random access, where users’ offloading decisions can be made independently to realize distributed offloading. In particular, we discuss the offloading game with and without sharing information among devices and find the Nash equilibrium (NE). Besides, we analyze the effective bandwidth as a performance metric from a device perspective, which considering the Quality of Service (QoS) of network layer while analyzing users’ offloading strategies. Simulation results show the effectiveness of proposed strategies and the impact of offloading tasks to users’ strategies in time-varying channel based on effective bandwidth.

In the Internet of Things (IoT) networks, monitoring information collection is critical for intelligent decision-making, which is a significant challenge for the sensors deployed at remote locations. Relay can effectively improve the transmission quality and transmission range of sensors by means of multi-hop transmission. It is an effective method for remote data collection in IoT networks. However, the lifetime of the relay may be dramatically reduced due to the heavy resource overhead for frequent short packet delivery. In this paper, we present an efficient relay transmission scheme for IoT networks, in which the frame aggregation technology is employed at the relay to reduce the resource overhead by sharing a common frame header and tail. Meanwhile, for the delay caused by frame aggregation, we analyze the freshness of the sensing data in terms of age of information (AoI) and take it as a constraint for the frame aggregation system. Besides, the optimal frame aggregation period is determined based on the closed-form expressions derived for the average AoI and transmission efficiency. Simulation results show that the theoretical analysis closely matches the simulations, and the proposed method significantly improves transmission efficiency compared to the traditional decode-and-forward method.

In multiple-input multiple-output (MIMO) communication systems, signal detection plays a crucial role in achieving reliable and high-performance wireless communication. However, the complexity of optimal detection methods, such as maximum likelihood (ML) detection, grows exponentially with the number of transmit antennas when exhaustive search is used, hindering practical implementation. To address this challenge, suboptimal algorithms such as successive interference cancellation (SIC)-based detection have been developed, but they suffer from error propagation. To mitigate error propagation in SIC detectors, a soft-decision based partial maximum a posteriori (MAP) method has been derived to enhance performance. Since the partial MAP method allows MIMO detection to be divided into multiple stages, detection of each layer can be approached as a regression problem, and can be carried out by deep learning (DL)-based method to reduce computational overhead. Therefore, in this paper, we propose PMAP-Net, which integrates deep neural networks (DNNs) into partial MAP method for MIMO systems. We derive the soft log-likelihood ratios (LLRs) for single and multiple signals and design the input sets of DNNs. To further reduce the number of inputs in DNNs, we decrease input dimensionality by deriving extended input sets, which alleviates computational burden to be linear with respect to the number of antennas. Simulation results demonstrate that our proposed DL-based detection algorithm can provide near-optimal performance with relatively low complexity and outperforms other DL-based detectors in various MIMO scenarios.

Motivated by diverse secure requirements of multi-user in uncrewed aerial vehicle (UAV) systems, we propose a collaborative secret and covert transmission method for multi-antenna ground users to UAV communications. Specifically, based on the power domain non-orthogonal multiple access (NOMA), two ground users with distinct security requirements, named Bob and Carlo, superimpose their signals and transmit the combined signal to the UAV named Alice. An adversary Willie attempts to simultaneously eavesdrop Bob’s confidential message and detect whether Carlo is transmitting or not. We derive close-form expressions of the secrecy connection probability (SCP) and the covert connection probability (CCP) to evaluate the link reliability for wiretap and covert transmissions, respectively. Furthermore, we bound the secrecy outage probability (SOP) from Bob to Alice and the detection error probability (DEP) of Willie to evaluate the link security for wiretap and covert transmissions, respectively. To characterize the theoretical benchmark of the above model, we formulate a weighted multi-objective optimization problem to maximize the average of secret and covert transmission rates subject to constraints SOP, DEP, the beamformers of Bob and Carlo, and UAV trajectory parameters. To solve the optimization problem, we propose an iterative optimization algorithm using successive convex approximation and block coordinate descent (SCA-BCD) methods. Our results reveal the influence of design parameters of the system on the wiretap and covert rates, analytically and numerically. In summary, our study fills the gaps in collaborative secret and covert transmission for multi-user multi-antenna uplink UAV communications and provides insights to construct such systems.

We revisit the problem of statistical sequence matching between two databases of sequences initiated by Unnikrishnan (TIT 2015) and derive achievable theoretical performance guar-antees for a generalized likelihood ratio test (G LRT) in the large deviations regime, when the number of matched pairs of sequences between two databases is unknown. In this case, the task is to accurately estimate the number of matched pairs and identify the matched pairs of sequences among all possible matches between the sequences in the two databases. We generalize the GLRT by Unnikrishnan and explicitly characterize the tradeoff among the exponential decay rates for probabilities of mismatch, false reject and false alarm. When one of the two databases contains a single sequence, the problem of statistical sequence matching specializes to the problem of multiple classification introduced by Gutman (TIT 1989). For this special case, our result strengthens previous result of Gutman (TIT 1989) and Zhou, Tan and Motani (Information and Inference 2020) by allowing the testing sequence to be generated from a distribution that is different from generating distributions of all training sequences.

Wireless ad-hoc networks play a significant role in environments without fixed infrastructure, especially in military and emergency situations. Due to the openness and wide coverage of wireless channel, it is necessary to establish a secure transmission mechanism against potential eavesdroppers. Traditional secure transmission schemes almost rely on single path transmission and physical layer security techniques which may not provide enough secrecy when eavesdroppers are widely distributed. To address this, in this paper, we propose a multipath secure transmission mechanism for the sake of both guaranteeing the transmission security and maximizing the end-to-end throughput. Furthermore, we give the approximated closed-form expressions of multipath secrecy connection probability (SCP) relying on secret sharing, and a novel bilevel optimization problem is formulated with the constraints of the end-to-end delay and the derived SCP. Finally, simulation results show that our proposed mechanism is beneficial concerning secrecy performance in comparison to traditional mechanisms. Also, a near-optimal throughput is obtained.

With the booming development of smart devices, mobile videos have drawn broad interest when humans surf social media. Different from traditional long-form videos, mobile videos are featured with uncertain human attention behavior so far owing to the specific displaying mode, thus promoting the research on saliency prediction for mobile videos. Unfortunately, the current eye-tracking experiments are not applicable for mobile videos, since the stationary eye-tracker and eye fixation acquisition are dedicated to the videos presented on computers. To tackle this issue, we propose performing the wearable eye-tracker to record viewers’ egocentric fixations and then devising a fixation mapping technique to project the eye fixations from egocentric videos onto mobile videos. Resorting to this technique, the large-scale mobile video saliency (MVS) dataset is established, including 1,007 mobile videos and 5,935,927 fixations. Given this dataset, we exhaustively analyze the characteristics of subjects’ fixations and obtain two findings. Based on the MVS dataset and these findings, we propose a saliency prediction approach on mobile videos upon Video Swin Transformer (MVFormer), wherein long-range spatio-temporal dependency is captured to derive the human attention mechanism on mobile videos. In MVFormer, we develop the selective feature fusion module to balance multi-scale features, and the progressive saliency prediction module to generate saliency maps via progressive aggregation of multi-scale features. Extensive experiments show that our MVFormer approach significantly outperforms other state-of-the-art saliency prediction approaches. Finally, we demonstrate the potential application of our MVFormer approach in the H.265 video coding standard by embedding it into the rate control scheme, such that the perceptual quality of compressed mobile videos can be significantly improved. The dataset and code will be available at https://github.com/wenshijie110/MVFormer.

We revisit the problem of statistical sequence matching between two databases of sequences initiated by Unnikrishnan, (2015) and derive theoretical performance guarantees for the generalized likelihood ratio test (GLRT). We first consider the case where the number of matched pairs of sequences between the databases is known. In this case, the task is to accurately find the matched pairs of sequences among all possible matches between the sequences in the two databases. We analyze the performance of the GLRT by Unnikrishnan and explicitly characterize the tradeoff between the mismatch and false reject probabilities under each hypothesis in both large and small deviations regimes. Furthermore, we demonstrate the optimality of Unnikrishnan’s GLRT test under the generalized Neyman-Person criterion for both regimes and illustrate our theoretical results via numerical examples. Subsequently, we generalize our achievability analyses to the case where the number of matched pairs is unknown, and an additional error probability needs to be considered. When one of the two databases contains a single sequence, the problem of statistical sequence matching specializes to the problem of multiple classification introduced by Gutman, (1989). For this special case, our result for the small deviations regime strengthens previous result of Zhou et al., (2020) by removing unnecessary conditions on the generating distributions.

Federated learning (FL) offers a promising framework for obtaining a global model by aggregating trained parameters from participating clients without transmitting their local private data. To further enhance privacy, differential privacy (DP)-based FL can be considered, wherein certain amounts of noise are added to the transmitting parameters, inevitably leading to a deterioration in communication efficiency. In this paper, we propose a novel Privacy-Preserving and Communication Efficient Federated Learning (P2CEFL) algorithm to reduce communication overhead under DP guarantee, utilizing sparse gradient and dithering quantization. Through gradient sparsification, the upload overhead for clients decreases considerably. Additionally, a subtractive dithering approach is employed to quantize sparse gradient, further reducing the bits for communication. We conduct theoretical analysis on privacy protection and convergence to verify the effectiveness of the proposed algorithm. Extensive numerical simulations show that the P2CEFL algorithm can achieve a similar level of model accuracy and significantly reduce communication costs compared to existing conventional DP-based FL methods.

Broadcast plays a vital role in wireless ad hoc networks for information dissemination, while one of the key system parameters for maximizing broadcast performance is the transmission rate. However, due to the entangled impact of various factors on the broadcast performance such as node distribution, interference, transmission rate, and multi-hop links, theoretically modeling of the impact of different parameters on the broadcast performance is intractable, and hence the optimal transmission rate is still unknown. Inspired by the similarity between the process of packet broadcasting and the spreading of contagious diseases, we resort to epidemic theory to propose a tractable approach for modeling the dynamics of message broadcast process in wireless ad hoc networks. To characterize the impact of transmission rate on the broadcast performance, a novel utility function is proposed by jointly considering the packet delivery delay and packet size. Then, we derive the dynamics of packet broadcasting analytically and obtain the approximated expression for the proposed utility function in closed-form. Furthermore, the optimization of the transmission rate is carried out based on our proposed analytical model. Simulation results show that our analytical model can accurately characterize the process of broadcasting under a wide range of system parameters and that optimizing the transmission rate can improve broadcast performance significantly.

In physical layer for non-orthogonal multiple access (NOMA), most existing studies focus on the non-delay-sensitive metrics such as the spectral efficiency. In order to improve user’s quality of service (QoS) in delay-sensitive communications, however, effective capacity can be adopted to the NOMA system to consider the QoS metric while analyzing capacity. In this paper, we deduce the closed form expression for effective capacity in downlink NOMA and propose three optimization problems with delay and effective capacity constraints. Firstly, a joint rate and power allocation scheme is proposed to maximize the total effective capacity. Secondly, we deduce the optimal solution of the problem for maximizing the minimum delay QoS exponent. Thirdly, a minimum total transmit power allocation scheme is proposed with the effective capacity constraint. Since the problems of maximizing effective capacity and minimizing total transmit power are non-convex, the particle swarm optimization (PSO) algorithm is used to find global optimization solutions. Simulation results show our proposed power and rate allocation scheme maximizes the effective capacity, which is better than orthogonal multiple access (OMA). Meanwhile, the optimal minimum delay QoS exponent and minimum total transmit power with effective capacity constraint have been achieved.

We derive a second-order achievability bound for the two-user multiple access channel with additive non-Gaussian noise. In our setting, both users use spherical codebooks and the decoder uses the nearest neighbor decoding. Our result generalizes the dispersion analysis for point-to-point mismatched channel coding by Scarlett, Tan and Durisi (TIT 2017) to the multiple user setting. When specialized to the Gaussian noise, our proof provides an alternative second-order achievability analysis of MolavianJazi and Laneman (TIT, 2015). Furthermore, we generalize our results to the random access channel with additive non-Gaussian noise, where the number of active users in each time slot are unknown, and derive a second-order achievability bound. When specialized to the Gaussian noise, our second-order asymptotic results are consistent with the Gaussian noise case recently derived by Yavas, Kostina and Effros (TIT, 2021).

We revisit multiple hypothesis testing and propose a two-phase test, where each phase is a fixed-length test and the second-phase proceeds only if a reject option is decided in the first phase. We derive achievable error exponents of error probabilities under each hypothesis and show that our two-phase test bridges over fixed-length and sequential tests in both Neyman-Pearson and Bayesian settings in the similar spirit of Lalitha and Javidi [1] for binary hypothesis testing. Specifically, our test may achieve the performance close to a sequential test with the asymptotic complexity of a fixed-length test and such test is named the almost fixed-length test. Our results generalize the design and analysis of the almost fixed-length test for binary hypothesis testing to account for more than two outcomes.

We revisit the multiple classification problem and propose a two-phase test, where each phase is a fixed-length test and the second-phase proceeds only if a reject option is decided in the first phase. We derive the achievable error exponent under each hypothesis and show that our two-phase test bridges over the fixed-length test of Gutman (TIT, 1989) and the sequential test of Haghifam, Tan, and Khisti (TIT 2021). In contrast to the fixed-length test of Gutman that requires an additional reject option, with proper choices of test parameters, our test achieves error exponents close to the sequential test of Haghifam, Tan, and Khisti without a reject option. We generalize the result of Lalitha and Javidi (ISIT 2016) for binary hypothesis testing to the more practical families of M-ary statistical classification, where the test outcome is more than two and the generating distribution under each hypothesis is unknown.

We study the successive refinement problem of Shannon cipher system under maximal leakage for a discrete memoryless source with arbitrary bounded distortion measures. Specifically, we generalize the threat model described by Issa, Wagner and Kamath (T-IT, 2020) to the successive refinement setting and derive the optimal asymptotic normalized maximal leakage region under a joint excess-distortion probability constraint. In the achievability part, we propose a type-based coding scheme and derive the asymptotic achievable normalized maximal leakage region. In the converse part, by analyzing the guessing scheme of the eavesdropper, we manage to show the above normalized maximal leakage region is optimal. Our results reveal the fundamental tradeoff between reliability and secrecy. Furthermore, for a successively refinable source-distortion measure triplet, we find that our coding scheme satisfies the successive refinability under the maximal leakage metric.

As for the time-division communications system, the pilot spoofing attack (PSA) technique is maliciously utilized by active eavesdroppers during the uplink training phase, for contaminating the legitimate channel estimation and thus altering the beamforming design towards the eavesdroppers. Nonorthogonal multiple access (NOMA) has been recognized as the key technology for the envisioned Internet of Things (IoT) networks. In order to prevent the aforementioned information leakage in NOMA-IoT systems, we develop a novel two-way training scheme to detect PSA and a robust secure beamforming design for providing secure transmission, by utilizing the emerging technique of reconfigurable intelligent surface (RIS), which is turned off during the uplink training phase and turned on during the downlink training phase, respectively. Considering that the perfect channel state information related to the eavesdropping channel is typically difficult to obtain, a secrecy outage probability-constrained robust secure beamforming design is proposed to maximize the achievable sum secrecy rate of the legitimate users, by alternatively optimizing the active beamforming and RIS passive beamforming, while satisfying the requirements of the NOMA transmission. Elaborate simulation results reveal that the proposed detection method attains a super PSA detection performance and the proposed robust secure beamforming design is capable of efficiently enhancing the achievable sum secrecy rate, compared with various benchmark schemes.

Recently, the satellite network is emerged to guarantee the demand of seamless connectivity of Internet of Things (IoT) devices, which can provide services for IoT devices at anytime and anywhere. However, the satellite suffers from jamming attack due to its highly exposed satellite-ground links and spot-beams, which may cause severe security problems. In order to combat the jamming attack, we first analyze the performance of Satellite IoT (SIoT) in terms of the transmission rate. Then, we propose an anti-jamming strategy for SIoT by using the technique of beam switching, where a suitable satellite that offers sufficient spatial diversity can be chosen to swap the coverage with the attacked satellite. To this end, the coverage relationship between satellites and ground cells is investigated using the game theory and the satellite beam angle is further optimized to maximize the sum transmission rate of satellite clusters. Simulation results show that the proposed strategy can provide high achievable transmission rate for SIoT networks when jamming attacks happen.

The unmanned aerial vehicles (UAVs) are envisioned as promising aerial facilities for providing advanced communication services as well as sensing functionalities in the next-generation wireless system. This article considers a UAV-enabled integrated sensing and communications (ISACs) system, where a moving ground user (GU) is simultaneously tracked by multiple UAVs and receives the downlink communication information transmitted from the UAV. In particular, to jointly enhance the sensing and communication (S&C) performance, optimizing the UAV moving trajectory is demanded. To achieve this goal, we first harness the extended Kalman filtering (EKF) method for predicting and tracking the motion parameters of GU at each time slot, which relies on the range measurements extracted from the sensing echoes at the base station (BS). Afterward, we formulate a weighted optimization problem that addresses the design of UAV trajectories and GU-UAV association simultaneously, incorporating the consideration of real-time downlink communication rates and the Cramér–Rao bound (CRB) for GU tracking. The problem further is constrained by the maximum consumed power, maximum traveling distance, and minimum collision avoidance distance. As a step forward, to address the resultant nonconvex problem, we develop an efficient iterative algorithm to obtain a near-optimal solution by utilizing the successive convex approximate (SCA) technique. Specifically, we alternately solve the GU-UAV association and the real-time trajectory design problem at each time slot. Finally, our numerical simulations illustrate that our proposed algorithm can track the GU accurately while meeting the sensing-centric and/or communication-centric requirements.

Covert communication, also known as communication with low probability of detection, aims to provide reliable communication for legal users and prevent any other user from detecting the occurrence of legal communication. Motivated by the strong need of security links of the next generation communication systems, we study covert communication with millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) hybrid beamforming. Consistent with existing studies on covert communication, we use the Kullback-Leibler (KL) divergence and the total variation (TV) distance as the covertness measure. Under both covertness measures, for block fading channels, we derive the covert transmission rate with and without artificial noise. These results are obtained by optimizing the transmit power and the jamming power to satisfy the covertness constraints and to maximize the transmission rate. Specifically, when artificial noise is allowed, we show that there exists an optimal jamming power to achieve the covert transmission rate given the transmit signal power. Furthermore, we propose a metric to measure the inherent sparsity of the mmWave massive MIMO channel in the spatial domain, and study its effect on the covertness measures and the corresponding covert transmission rates. Our results provide insights and benchmarks for the design of practical covert communication systems with mmWave massive MIMO.

We study the mismatched successive refinement problem where one uses Gaussian codebooks to compress an arbitrary memoryless source with successive minimum Euclidean distance encoding under the quadratic distortion measure. Specifically, we derive achievable refined asymptotics under both the joint excess-distortion probability (JEP) and the separate excess-distortion probabilities (SEP) criteria. For both second-order and moderate deviations asymptotics, we consider two types of codebooks: the spherical codebook where each codeword is drawn independently and uniformly from the surface of a sphere and the i.i.d. Gaussian codebook where each component of each codeword is drawn independently from a Gaussian distribution. We establish the achievable second-order rate-region under JEP and we show that under SEP any memoryless source satisfying mild moment conditions is strongly successively refinable. When specialized to a Gaussian memoryless source (GMS), our results provide an alternative achievability proof with specific code design. We show that under JEP and SEP, the same moderate deviations constant is achievable. For large deviations asymptotics, we only consider the i.i.d. Gaussian codebook since the i.i.d. Gaussian codebook has better performance than the spherical codebook in this regime for the one layer mismatched rate-distortion problem (Zhou et al., 2019). We derive achievable exponents of both JEP and SEP and specialize our results to a GMS, which appears to be a novel result of independent interest.

Due to increasing demands of seamless connection and massive information exchange across the world, the integrated satellite-terrestrial communication systems develop rapidly. To shed lights on the design of this system, we consider an uplink communication model consisting of a single satellite, a single terrestrial station and multiple ground users. The terrestrial station uses decode-and-forward (DF) to facilitate the communication between ground users and the satellite. The channel between the satellite and the terrestrial station is assumed to be a quasi-static shadowed Rician fading channel, while the channels between the terrestrial station and ground users are assumed to experience independent quasi-static Rayleigh fading. We consider two cases of channel state information (CSI) availability. When perfect CSI is available, we derive the instantaneous achievable sum rate of all ground users and formulate an optimization problem to maximize the sum rate. When only channel distribution information (CDI) is available, we derive a closed-form expression for the outage probability and formulate another optimization problem to minimize the outage probability. Both optimization problems correspond to scheduling algorithms for ground users. For both cases, we propose low-complexity user scheduling algorithms and demonstrate the efficiency of our scheduling algorithms via numerical simulations.

We revisit the binary classification problem where the generating distribution under each hypothesis is unknown and propose a two-phase test, where each phase is a fixed-length test and the second-phase proceeds only if a reject option is decided in the first phase. We derive the achievable error exponents of both type-I and type-II error probabilities. Furthermore, we illustrate our results via numerical examples and show that the performance close to sequential test can be achieved with the much simpler and less complex almost fixed-length test. Our results generalize the design and analysis of the almost fixed-length test for binary hypothesis testing (Lalitha and Javidi, ISIT 2016) to the more practical setting of binary classification.

This paper is eligible for the Jack Keil Wolf ISIT Student Paper Award. We derive achievability results on large deviations for mismatched successive refinement where one uses random i.i.d. Gaussian codebooks and minimum Euclidean distance encoding to compress an arbitrary memoryless source. Specifically, we consider both separate and joint excess-distortion criterion and derive achievable error exponents for both cases. Under the mismatched coding scheme, we show that the exponent of the joint excess-distortion probability equals the exponent of one of the separate excess-distortion probabilities, depending on the compression rate of the second encoder only. When specialized to a Gaussian memoryless source (GMS), we obtain the first achievable error exponent region. However, in contrast to the second-order asymptotics and to the large deviations for mismatched rate-distortion, the specialized result for GMS is not optimal. Further investigations are required to close the gap.

Recently, the Internet of Vehicles (IoV) has been employed as an enabling technology for smart transportation, which can be further enhanced by integrating space–air–ground-integrated networks (SAGIN). Since the data packets of vehicular user equipments (VUEs) are generally short, random access is usually considered for VUEs to connect to the network. However, collisions caused by the multiple VUEs initiating random access simultaneously are inevitable. To relieve the performance degradation by collisions, data aggregation can be carried out in IoV, where aggregated packets can be relayed to a base station. In this article, we first propose an aggregators-aided random access scheme for IoV, where unmanned aerial vehicles (UAVs), as one of the key components in SAGIN, are deployed as data aggregators to help transmissions of VUEs. Then, a semi-Markov chain is used to analyze the average number of aggregated packets, and the metric of the average data to overhead ratio (ADOR) is presented to evaluate the efficiency of aggregation. Finally, the altitude of UAVs and the duration of data aggregation are optimized to maximize ADOR. By numerical simulations, the accuracy of the analysis as well as the effectiveness of the proposed scheme are validated.

Unmanned aerial vehicle (UAV)-enabled backscatter communications (BackComm) is deemed to be a vital technique enabling the data transmission over massive battery-less devices for Internet of Things (IoT). However, the UAV-enabled Backcomm suffers from information leakage due to the broadcasting nature of wireless channels. To cope with the security issue, in this paper, a UAV-enabled multi-user secure BackComm system is developed using analog beamforming (ABF) and randomized continuous wave (RCW) techniques, where the multiple users are supported by the multi-carrier RCW over a single low-complexity radio frequency (RF) chain. By exploiting the RCW transmitted towards backscatter, the eavesdropping link can be eroded without any specific jamming signals. The closed-form of the secrecy rate is studied with the approximations, which is then maximized by jointly optimizing the beamforming together with the UAV’s location and the RCW settings. Simulation results are carried out to confirm the accuracy of the proposed approximation, while the convergence behavior of the optimization algorithm is analyzed. As a result, it can be shown that the secrecy rate can be significantly improved compared with the benchmark schemes.

To extend the conventional wireless sensor networks (WSNs) to support wider applications such as intruder detection and border security monitoring, active radar sensors are introduced into WSNs to further enhance their capability, thus forming wireless radar sensor networks (WRSNs). To improve the network efficiency, the technology of integrated sensing and communication (ISAC) can be applied to co-design the sensing and communication functionalities of radar sensors. Since the cooperative operations of WRSNs require effective information interaction among radar sensors, data dissemination techniques need to be investigated, which become even more critical in desolate areas without the coverage of the base stations (BSs). Therefore, in this paper, a duty cycling mechanism is applied to the network to enhance the usage of WRSNs and support data dissemination, where a storage node is deployed to store the data spreading from radar sensors and a mobile data collector is employed to collect the data from the storage node periodically. Then, the epidemic theory, as an innovative tool for modeling data dissemination, is adopted to analyze the performance of WRSNs. After epidemiological modeling, the density of radar sensors is optimized by the epidemiological analytical method to maximize the throughput of the storage node by jointly considering the functions of radar detection and communication. Simulation results validate the accuracy of analysis, which also show the efficiency of the optimization for data dissemination.

The next generation multiple access (NGMA) schemes are considered to support massive access for a large number of devices, which motivates us to develop a low-complexity approach for next generation systems. Since the generalized spatial modulation (SM) can be adopted to the system, a number of compressive sensing (CS) reconstruction algorithms are deployed for the detection of sparse signals, while the complexity of CS-based approaches is proportional to the number of antennas. In order to decrease the complexity, we propose a two-stage approach to detect sparse signals, where the received signals are divided into groups. Then, the activity variables of aggregated signals are decided and the sparse signal detection is carried out at the signals belonging to active groups. During the activity variable detection, the variational inference algorithm is applied to determine the activity variables. Moreover, in order to analyze the performance of activity variable detection, the$J$-divergence is proposed to measure the distance between the distributions, while the approximate expression of$J$-divergence is derived. Simulation results show that the proposed approach is able to provide good detection performance with low complexity. In addition, the$J$-divergence is confirmed to be useful as an evaluation metric to measure the detection performance.

Owing to their intrinsic advantages of seamless coverage and of high data rate, space-air-ground communications networks (SAGCN) are recognized as one of the emerging technologies for the future wireless communications systems. However, the broadcasting nature of wireless communications inevitably imposes security issues on SAGCN. In this paper, we consider the uplink of the full-duplex unmanned aerial vehicle (UAV)-aided three-layer SAGCN, comprising of ground Internet of Remote Things (IoRT) terminals, an unmanned aerial vehicle, and a low-earth orbit (LEO) satellite, where eavesdroppers are intercepting the information transmitted. In order to ensure a secure uplink transmission, a joint UAV deployment and power allocation scheme is conceived for maximizing the secrecy rate of the SAGCN, subject to the following constraints: i) UAV’s power, ii) the UAV deployment area, and iii) the secrecy rate, which are imposed on the different layers. More explicitly, once we formulate a joint optimization problem to maximize the secrecy rate, we decouple the variables and decompose the original problem into multiple subproblems in a tractable manner. Then, we simplify the subproblems with the aid of slack variables and solve them relying on the successive convex approximation method. Following this, initialization schemes are designed to exploit the one-direction greedy method for diverse environment settings, for speeding up the convergence of the iterative algorithm proposed. Finally, simulation results reveal that the convergence can be achieved within a small number of iterations by the proposed initialization scheme, while the algorithm conceived is capable of attaining a substantial improvement of the secrecy rate for the SAGCN.

We study the problem of simultaneous search for multiple targets over a multidimensional unit cube and derive fundamental resolution limits of non-adaptive querying procedures using the 20 questions estimation framework. The performance criterion that we consider is the achievable resolution, which is defined as the maximal$L_\infty $norm between the location vector and its estimated version where the maximization is over all target location vectors. The fundamental resolution limit is defined as the minimal achievable resolution of any non-adaptive query procedure, where each query has binary yes/no answers. We drive non-asymptotic and second-order asymptotic bounds on the minimal achievable resolution, using tools from finite blocklength information theory. Specifically, in the achievability part, we relate the 20 questions problem to data transmission over a multiple access channel, use the information spectrum method by Han and borrow results from finite blocklength analysis for random access channel coding. In the converse part, we relate the 20 questions problem to data transmission over a point-to-point channel and adapt finite blocklength converse results for channel coding. Our results extend the purely first-order asymptotic analyses of Kaspiet al.(ISIT 2015) for the one-dimensional case: we consider channels beyond the binary symmetric channel and derive non-asymptotic and second-order asymptotic bounds on the performance of optimal non-adaptive query procedures.

The intelligent transportation has been extensively investigated as an enabling technology for ubiquitous data processing and content sharing among vehicles and terrestrial infrastructures. In intelligent transportation systems, numerous vehicles and infrastructures are connected for information and data sharing to enable different operations. Since there are some urban areas that face the traffic congestion or cannot be well served, space-air-ground integrated networks (SAGIN) can be carried out to provide continuous network connectivity for vehicles. In particular, unmanned aerial vehicles (UAVs) are deployed as data collectors to receive data packets from vehicles due to the advantages of high mobility and low operating cost. It is noteworthy that the information freshness is critical to enable services for timely decision, e.g., autonomous driving and accident prevention. In this paper, we develop UAV-aided intelligent transportation systems to enhance the usage of vehicular networks and support low latency vehicular services, where the concept of age-of-information (AoI) is adopted to measure the freshness of data packets of vehicles. Then, the performance of UAV-aided intelligent transportation systems is analyzed in terms of the average AoI. In addition, the deployment of multiple UAVs is optimized to minimize the average peak AoI according to the traffic intensity of vehicles under seamless coverage, finite queue, and coverage probability constraints. To this end, the deployment optimization problem is formulated as a multi-constrained non-convex optimization problem and solved by considering each soft constraint separately. Simulation results show that our proposed system can provide timely data transmission.

We study the mismatched successive refinement problem where one uses a fixed code to compress an arbitrary source with random Gaussian codebooks and minimum Euclidean distance encoding in a successive manner. Specifically, we generalize the mismatched rate-distortion framework by Lapidoth (T-IT, 1997) to the successive refinement setting and derive the achievable second-order asymptotics. Our result implies that any source that satisfies a mild moment constraint is successive refinable under our code. Furthermore, our proof, when specialized to a Gaussian memoryless source, provides an alternative achievability proof with structured codebooks for the successive refinement problem, which was studied by Zhou, Tan, Motani (T-IT, 2018) where a covering lemma without specifying the locations of codewords was used.

With the emergence of computationally intensive and delay sensitive applications, mobile edge computing(MEC) has become more and more popular. Simultaneously, MEC paradigm is faced with security challenges, the most harmful of which is DDoS attack. In this paper, we focus on the resource orchestration algorithm in MEC scenario to mitigate DDoS attack. Most of existing works on resource orchestration algorithm barely take into account DDoS attack. Moreover, they assume that MEC nodes are unselfish, while in practice MEC nodes are selfish and try to maximize their individual utility only, as they usually belong to different network operators. To solve such problems, we propose a price-based resource orchestration algorithm(PROA) using game theory and convex optimization, which aims at mitigating DDoS attack while maximizing the utility of each participant. Pricing resources to simulate market mechanisms, which is national to make rational decisions for all participants. Finally, we conduct experiment using Matlab and show that the proposed PROA can effectively mitigate DDoS attack on the attacked MEC node.

In the Internet of Things (IoT), numerous IoT devices are deployed for environment sensing, information collecting, and data transmitting to enable different operations, including patrol monitor, industrial automation, and system control. Considering the limited power and computation capability of IoT devices, mobile-edge computing (MEC) is applied to enhance the usage of IoT. Since unmanned aerial vehicles (UAVs) can be used as MEC servers, they become an efficient means to collect data packets and assist computation. In this article, we develop UAV-aided IoT systems, where the performance of data collection is analyzed in terms of packet loss rate and data quantity using a Markov chain. Then, in order to meet the diverse service requirements, the computation frequency of UAV is designed according to the preference coefficients of the cost on energy and time consumption. Finally, the system Age of Information (AoI) is considered to define the freshness of data packets, where the models of single-IoT device and multi-IoT devices with first-come–first-served (FCFS) principle and M/M/1 queuing are analyzed. The simulation results show that the proposed system is able to provide robust data collection and efficient computation for IoT devices.

In 5G and beyond wireless scenarios, it is expected to support tremendous amount of communicating machines. With an exponential increase of machine-to-machine (M2M) system deployments, efficient approaches to massive access by a large number of devices are to be studied. In this paper, we propose a massive multiple-input multiple-output (MIMO) based grant-free random access (RA) with resolution of preamble collision for massive access. Based on the channel hardening and favorable propagation characteristics of massive MIMO, collided signals processed at the base station (BS) can be viewed as a variation of superposition modulation, and are to be recovered by successive interference cancellation (SIC) techniques. Besides, taking into consideration the effect of pass loss and fractional power control (FPC), analytic expressions of success probability of the proposed collision resolution with conjugate beamforming (CB) and zero-forcing beamforming (ZFB) are derived. With simulation results, we verify the analyses and show that the proposed protocol can resolve most preamble collisions.

In 5G massive machine-type communication (mMTC), power-limited or battery-free parasite devices such as radio frequency identification (RFID) tags, can use the transmitted signals from host devices as ambient signals for backscatter communications to send information to a base station (BS). Unmanned aerial vehicles (UAVs) can be employed as host devices to help transmissions of parasite devices due to the advantages of high mobility and low operating cost. In this paper, we propose a signal detection approach based on the central limit theorem to detect the presence of parasite devices and separate parasite signals from host signals. Then, closed-form expressions for the probability of error detection and the bit error rate (BER) are derived. Moreover, the trajectory planning of multiple UAVs is optimized with the consideration of minimizing the energy consumption of UAV swarms to serve parasite devices. Theoretical and simulation results show that our proposed method provides good detection performance for parasite devices. It also shows that the trajectory planning of multiple UAVs is optimized.

Security becomes increasingly important due to various attacks from adversaries in wireless sensor networks. This work considers a resilient distributed estimation of an unknown parameter with a cluster‐based approach when some agents are adversarial. A two‐phase algorithm is adopted to perform parameter estimation and detect attacks. First, a cluster scheme is proposed to make sure that each cluster is connected. Then, the attack is detected and estimation is achieved with a consensus+innovation estimator in each cluster. Finally, the cluster heads combine the consensus estimates in each cluster and exchange with other cluster heads to achieve unknown parameter estimation. In addition, the detection sensitivity under different cluster schemes is also compared. Numerical examples illustrate that the proposed cluster‐based approach can improve the convergence rate and detection sensitivity.

Over the last decade, the Internet of Things (IoT) has been employed as an enabling technology for the smart ocean. As one of the key technologies in the IoT, machine-type communication (MTC) has been considered to support devices' connectivity. In the MTC, random access is introduced for devices to share a common access channel during the packet transmission with low signaling overhead. However, the collision caused by the presence of multiple devices is inevitable. Since maritime sensors have limited energy sources, in this article, we propose a relay-aided random access (RARA) scheme for the smart ocean, where retransmissions are carried out by maritime buoys with the relay function, to deal with collisions. In the RARA scheme, a base station (BS) is able to recover multiple collided signal packets simultaneously by using multiuser detection with multiple copies of collided signals forwarded by buoy nodes. As a result, our proposed scheme becomes energy efficient and reliable to be suitable for the smart ocean. Theoretical and simulation results show that a high throughput and a low outage probability can be achieved with a large number of buoy nodes.

Unmanned aerial vehicle (UAV) with flexible mobility and low cost has been a promising technology for wireless communication. Thus, it can be used for wireless data collection in Internet of Things (IoT). In this article, we consider millimeter-wave (mmWave) communication on a UAV platform, where the UAV base station (UAV-BS) serves multiple ground users, which generate big sensor data. Both the deployment of the UAV-BS and the beamforming design have essential impact on the throughput of the system. Thus, we formulate a problem to maximize the achievable sum rate of all the users, subject to a minimum rate constraint for each user, a position constraint of the UAV-BS, and a constant-modulus (CM) constraint for the beamforming vector. We solve the nonconvex problem with two steps. First, by introducing the approximate beam pattern, we solve the deployment and beam gain allocation subproblem. Then, we utilize the artificial bee colony (ABC) algorithm to solve the beamforming subproblem. For the global optimization problem, we find the near-optimal position of the UAV-BS and the beamforming vector to steer toward each user, subject to an analog beamforming structure. The simulation results demonstrate that the proposed solution can achieve a more superior performance than the present random steering beamforming strategy in terms of achievable sum rate.

The ever-increasing growth in maritime activities with large amounts of Maritime Internet-of-Things (M-IoT) devices and the exploration of ocean network leads to a great challenge for dealing with a massive amount of maritime data in a cost-effective and energy-efficient way. However, the resources-constrained maritime users cannot meet the high requirements of transmission delay and energy consumption, due to the excessive traffic and limited resources in maritime networks. To solve this problem, mobile edge computing is taken as a promising paradigm to help mobile devices from edge servers via computation offloading considering the different quality of service (QoS) with the complex ocean environments, resulting in energy saving and increased transmission latency. To investigate the tradeoff between latency and energy consumption in low-cost large-scale maritime communication, we formulate the offloading optimization problem and propose a two-stage joint optimal offloading algorithm, optimizing computation and communication resource allocation under limited energy and sensitive latency. At the first stage, the maritime users make the decision on whether to offload a computation considering their demands and environments. Then, the channel allocation and power allocation problems were proposed to optimize the offloading policy which coordinates with the center cloud servers at the second stage, considering the dynamic tradeoff of latency and energy consumption. Finally, numerical simulation results show the effectiveness of the proposed algorithm.

As unmanned aerial vehicles (UAVs) are becoming more popular and the demand for wireless links for UAVs is increasing, it is crucial to develop air-to-ground (A2G) wireless links for high-speed UAVs. Suffering from the high mobility and limitation of transmission power of UAVs, A2G wireless links become unstable to provide high quality communication services. In this paper, we design robust A2G wireless links for high speed UAVs, where conjunct power control is developed together with switched beamforming to maximize the power efficiency and minimize the fluctuation of A2G wireless links of millimeter wave (mmWave) signal transmission. We first present channel models for A2G wireless links of high-speed UAVs, which can be virtually seen as multiple-input multiple-output (MIMO) channels. To maximize the power efficiency, a conjunct power control problem is formulated to allocate powers for wireless links between antenna arrays on UAVs and access points (APs). For switched beamforming, beamformers are designed to provide a certain time-invariant signal-to-interference-plus-noise ratio (SINR) to minimize the SINR fluctuation of A2G wireless links. From theoretical analysis and numerical results, it is shown that the proposed architecture is able to provide robust and high quality A2G wireless links for high-speed UAV communication systems.

In the last few years, unmanned aerial vehicles (UAVs) have developed rapidly and the applications of UAVs have been expanded in wide areas, including photography, cargo delivery, inspection, and communications. Conventionally, UAVs are controlled and operated by a ground station using specific radio transmission modules, where the line-of-sight signal transmission is preferred and the operation range is limited, especially in urban areas. Using the Internet of Things (IoT) technologies, a UAV can be regarded as a terminal device connected in the ubiquitous network, where many other UAVs are communicated, navigated, controlled, and surveilled in real time and beyond line-of-sight.

Non-orthogonal multiple access (NOMA) is one of the potential multiuser supporting techniques in the fifth generation (5G) cellular systems due to its higher spectrum efficiency (SE) and cell-edge throughput. Vector-perturbation (VP) is widely known as one of the nonlinear precoding schemes that achieves near-capacity performance in practical wireless multi-input-multi-output (MIMO) communication systems. In this paper, we propose a hybrid transmission strategy based on VP and NOMA (VP-NOMA) by designing a beamforming matrix with the power allocation strategy to minimize total transmit power for certain quality of service (QoS) requirements. Rather than searching for the optimal beamforming matrix, we propose a more intuitive sub-optimal algorithm, called iteration beamforming for VP-NOMA systems (IBVP-NOMA), to find beamforming vectors. Further, different user clustering strategies are considered and compared to enhance the performance of the VP-NOMA systems. The simulation results demonstrate that the proposed method requires lower transmit power than the NOMA system without VP.

In this study, the authors take into account the imperfect channel state information for beamforming (BF) scheme in a dual‐hop multiple‐input multiple‐output (MIMO) amplify‐and‐forward (AF) relay network. Since the overall performance has been decided by the signal‐to‐interference‐plus‐noise ratio (SINR) at the destination, the authors derive the optimal BF weights that maximise the SINR at the desination. To evaluate the performance of the relay network, the authors also derive closed‐form expressions for the outage probability and probability density function of the received SINR. Computer simulations are carried out, which show the superiority of the designed optimal BF scheme, the impacts of channel errors and antenna configurations on the performance of the MIMO AF relay systems, and the validity of the analytical expressions. It is shown that due to the channel errors, the performance of the relay network is bounded no matter how much power is injected into the system.

To enhance the spectral efficiency of geostationary Earth orbit (GEO) satellite communication systems with scarce GEO resources, cooperative transmission is widely used in GEO satellite collocation (GEOSC) systems. Current analysis on GEOSC channels is usually based on the hypothesis that channels are line‐of‐sight (LOS) ones, while multipath components are ignored. In this study, a more realistic cooperative transmission method is studied for GEOSC systems over Rician fading channels, where multipath components are taken into consideration in conjunction with LOS components. On the basis of this model, a practical user selection strategy with opportunistic beamforming is studied to optimise the capacity of GEOSC systems. Simulation results show that the GEOSC system using the techniques developed in this study has better performance comparing with the ones using existing approaches.

In this study, the authors investigate the resource allocation (RA) problem for the downlink orthogonal frequency division multiplexing based non‐orthogonal multiple access (OFDM‐NOMA) system. The RA problem is decomposed into two subproblems of subcarrier allocation (SA) and power allocation (PA). For the SA, a user grouping based greedy algorithm is proposed under the assumption that power is uniformly distributed among all the selected users. For the PA, the authors propose the iterative water‐filling and specific user rate maximising criterion with minimum rate constraints (iterative WF + SURMC‐MRC) scheme to jointly consider the PA problem among the selected users on one subcarrier and the PA problem among subcarriers. The simulation results show that the spectral efficiency performance of the proposed iterative WF + SURMC‐MRC scheme outperforms those of the (non‐iterative) WF + SURMC‐MRC scheme and the uniform distribution (UD) + SURMC‐MRC scheme. Moreover, the iterative WF + SURMC‐MRC scheme has advantages in resisting against the user overloading compared with the (non‐iterative) WF + SURMC‐MRC scheme.

In this study, the authors investigate the power allocation (PA) problem and spectral efficiency (SE) analysis for the single antenna downlink non‐orthogonal multiple access (NOMA) system under the sum rate maximising criteria with minimum rate constraints (SRMC‐MRC). For the PA problem, they propose the duality scheme for SRMC‐MRC which is considered as the optimal solution for the PA problem on one orthogonal subband in the single antenna NOMA system. They propose the specific user rate maximising criteria with minimum rate constraints (SURMC‐MRC) scheme to decrease the computational complexity of SRMC‐MRC. The PA problem on one subband under SURMC‐MRC is proved to be equivalent to SRMC‐MRC. Numerical results show that both the SE and fairness performance for SURMC‐MRC can strictly approach to those of the duality scheme in the whole signal‐to‐noise ratio (SNR) region. They prove that NOMA under SRMC‐MRC can obtain SE performance advantage in the high SNR region but accompany with some disadvantages in the low SNR region over the orthogonal multiple access system. This conclusion is verified through numerical results under different parameter conditions.

In this article, the authors focus on a correlated channel model for secure relay beamforming in the relayeavesdropper network. In this network, a single-antenna sourcedestination pair transmits secure information with the help of a amplify-and-forward (AF) relay equipped with multiple antennas. The relay cannot obtain the instantaneous channel state information (CSI) of the eavesdropper. The relay only have the knowledge of correlation information between the legitimate and eavesdropping channels. Depending on this information, we derived the conditional distribution of the eavesdropping channel. Three beamformers at the relay are studied: the zero-forcing (ZF) beamformer, the generalized match-forward (GMF) beamformer and the general-rank beamformer (GRBF). The authors found that the ZF beamformer is invalid in this system, and the GMF beamformer is the optimal rank-1 beamformer, and the GRBF is the iteratively optimal beamformer. Numerical results are presented to illustrate three beamformers' performance, and the impacts of different parameters, especially the channel correlation, on the system performance are analyzed.

In this paper, we investigate the resource allocation problem for the downlink non-orthogonal multiple access (NOMA) system with imperfect channel state information (CSI). We first derive closed-form expressions for the outage probabilities with and without the estimated instantaneous channel fading coefficient for each user. Upper bounds on both of the probability expressions above are proposed. Based on the outage probability bounds, we propose the power allocation scheme which is combined with a user selection step to minimize the maximum of the outage probability bounds of all the users. Simulation results show that the maximum outage probability can be significantly decreased by exploiting the priori knowledge of the estimated instantaneous channel fading coefficient for each user.

In this paper, we investigate energy-efficient resource management for downlink OFDMA systems with feedback channel capacity constraints. Compared with previous works, quantized channel state information (CSI) is studied by using rate-distortion theory since it can establish an information-theoretic lower bound on the capacity of the feedback channel. Based on the quantized CSI, an effective resource allocation algorithm is proposed to maximize the system energy efficiency employing the generalized fractional programming theory and the Lagrange dual decomposition method. Numerical results show that our proposed algorithm can nearly achieve the optimal solution and imply that the energy efficiency with a limited feedback rate could be close to that with perfect CSI.

Radio resource allocation for multiuser multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems is an important issue to improve overall system performance. Although the achievable rate has been adopted for a performance indicator in most resource allocation schemes, it may not be practical if a nonideal receiver including a suboptimal detector is used instead of optimal one. Under this practical circumstance, we study the subcarrier and power allocation to minimize the average bit error rate (BER) subject to a total power constraint. Different allocation algorithms are proposed for various MIMO detectors such as the maximum likelihood (ML) detector, linear detectors and the successive interference cancellation (SIC) detector. We also propose suboptimal algorithms to reduce the complexity. Based on the simulation results, we can confirm that the proposed suboptimal algorithm for each detector can achieve a comparable performance with the optimal allocation with a much lower complexity.

For iterative detection and decoding (IDD) in multiple‐input multiple‐output systems, the maximum a posteriori probability (MAP) detector would be ideal in terms of the performance. However, due to its high computational complexity, various suboptimal low‐complexity approximate MAP detectors have been studied. In this study, a lattice reduction (LR)‐based detector is considered for a near‐optimal performance for IDD. The authors improve further the performance by employing a partial bit‐wise minimum mean square error (MMSE) approach with randomised sampling, which has a lower complexity than that of the full bit‐wise MMSE method. Moreover, the list of candidate vectors obtained by randomised sampling is extended using a MAP‐aided integer perturbation algorithm for a better performance with low additional complexity. Through simulation results, it is shown that a near‐optimal performance can be obtained which is better than that of the LR‐based randomised successive interference cancellation and the full bit‐wise MMSE methods.

In this study, the authors propose a lattice reduction (LR)‐based doubly iterative receiver for joint channel estimation and detection in multiple‐input–multiple‐output (MIMO) bit‐interleaved coded modulation systems. For the inner iteration loop of the receiver, LR‐based randomised sampling detection is employed to enjoy the tradeoff between performance and complexity while for the outer iteration loop, the expectation–maximisation (EM)‐based iterative channel estimation using sampling results is proposed to achieve the maximum likelihood channel estimation performance. Besides, a modified computational efficient EM‐based channel estimation approach is also derived to reduce the complexity further. Simulation results demonstrate that the proposed doubly MIMO iterative receiver can have comparable bit‐error rate performance with a reasonable computational complexity.

In this paper, a low-complexity approach for the large-scale (underdetermined) multiple-input multiple-output (MIMO) detection is proposed using the Markov chain Monte Carlo (MCMC) algorithm in conjunction with blockwise sampling. Klein's algorithm is employed in each sub-system to draw multidimensional samples for an MCMC detector in iterative detection and decoding (IDD). From analysis, we find that the lattice reduction (LR) technique cannot improve the performance of the proposed MCMC-based approach under low-correlated channel environment. In addition, due to blockwise sampling, the proposed method exhibits a faster convergence speed when running a Markov chain and provides a near-optimal performance for the detection of underdetermined MIMO systems. Complexity analysis and simulation results show that the proposed approach outperforms the conventional LR-based Klein randomized successive interference cancellation (SIC) detection with a relatively low complexity.

Most previous researches on the rate analysis of the power allocation (PA) schemes in two-way decode-and- forward (DF) relay systems typically focus on maximizing the instantaneous objective rates. However, the ergodic rate analysis of the optimized rates is not well considered. In this paper, we investigate the ergodic rates of the PA schemes in a two-way DF relay system, where the physical-layer network coding (PNC) protocol is adopted. Specifically, under a total power constraint, we consider three heuristic PA schemes, in which the optimization objectives are: 1) maximizing the sum rate; 2) maximizing the sum rate with an additional rate fairness constraint; and 3) maximizing the minor rate of the two directions of the system. The cumulative distribution functions (CDFs) of the equivalent system SNR are approximately obtained and the analytical expressions for the calculations of the ergodic rates are theoretically derived. Numerical results show that the derived analytical rates are converged to the simulated results in high SNR regions.

In this paper, we investigate the power allocation (PA) schemes in a two-way relay system, where the relay adopts physical layer network coding (PNC) based on decode-and-forward (DF) protocol. The objective is to determine the optimal power allocation (OPA) scheme at both the source nodes and at the relay that minimizes the end-to-end symbol error probability (SEP) performance under a total power constraint. The perfect channel state information (CSI) is assumed to be available at both the source nodes and at the relay. Since numerical methods (e.g., exhaustive search) are required to find the solution of the OPA scheme, which might be infeasible for practical systems, based on upper and lower bounds on the optimal solution, we propose a near optimal power allocation (NOPA) scheme. The asymptotic SEP expression of the proposed NOPA scheme is also derived. By both the analytical and simulated results, it is shown that the proposed NOPA scheme provides the asymptotic SNR gains of about 1.76dB and 2.22dB over two well-known schemes in high SNR. Furthermore, simulation results show that the performance of proposed NOPA scheme is quite close to that of the OPA scheme.

In this study, the authors examine the power allocation (PA) in a two‐way relay system, where the relay adopts physical layer network coding based on decode‐and‐forward protocol. In the PA, the optimisation objective is to minimise the end‐to‐end symbol error probability (SEP) under a total power constraint. Since the optimal power allocation (OPA) scheme requires numerical methods (for exhaustive search) to find the solution, which might be infeasible for practical systems, the authors propose a near optimal power allocation (NOPA) scheme that has a closed‐form solution by studying the properties of the OPA scheme. The asymptotic SEP expression of the proposed NOPA scheme is also derived. Simulation results show that the performance of proposed NOPA scheme is quite close to that of the OPA scheme. Furthermore, it is shown that the NOPA scheme can provide asymptotic SNR gains of 1.76 dB and 2.22 dB over two well‐known schemes.

In this paper, we study the impact of outdated channel state information (CSI) on the error rate performance in multiple-antenna two-way decode-and-forward (DF) relay systems, where physical-layer network coding (PNC) is adopted. We consider two variations of relay selection, namely, single best relay selection (S-RS) and multiple successfully-participated relay selection (MSP-RS). Under perfect and outdated CSI assumptions, closed-form expressions for the end-to-end system symbol error probability (SEP) are derived for the S-RS scheme, along with the asymptotic SEP expressions in high SNR regions for the MSP-RS scheme. By both analytical and simulation results, it is clearly shown that a full cooperative diversity gain can be achieved with perfect CSI. The results also manifest that the S-RS scheme can achieve only the transmit diversity gain with outdated CSI. On the other hand, the MSP-RS scheme can achieve a full cooperative diversity gain even if the CSI is outdated at the expense of high complexity.

In order to improve the capacity and spectral efficiency of geostationary orbiting satellite collocation system, a cooperative transmission scheme is proposed in this paper. With the cooperative beamforming at the ground control station, a satellite spot beam can be split into virtual beams, which can support radio resources multiplexing for selected users. A cooperative gain is defined and further studied to evaluate the enhancement of cooperative and non-cooperative system. Then, according to this cooperative gain, a ground user selection criterion is derived, in which equivalent distance mismatch can be tolerated under the constraint of capacity degradation. Furthermore, the expectation of selectable ground users is analyzed to show the feasibility of our proposed scheme. Simulation results demonstrate that with the proposed cooperative transmission scheme, a high cooperative gain can be achieved.

In this paper, we consider the collaborative use of amplify-and-forward relays to form a beamforming system and provide physical layer security for a wireless network with multiple eavesdroppers. In this paper, we investigate the relay transmit power minimization under a secrecy rate constraint via secure beamforming. To minimize the relay transmit power, we design an approximate relay power minimization (RPM) beam-forming scheme, in which an iterative algorithm combining the semidefinite relaxation (SDR) technology and the gradient-based method is devised by studying the convexity of the RPM problem. By relaxing the constraints of the RPM problem, we propose a virtual eavesdropper based RPM (VE-RPM) beamforming scheme, which transforms the multivariate RPM problem into a problem of a single variable, and thus obtain an analytical solution. Our proposed beamforming designs can work well even if the number of eavesdroppers is larger than that of relays, while the existing schemes, e.g., the null-space beamforming approaches, can not work under this condition. Simulation results are presented to demonstrate the performance improvement with the RPM beamforming schemes. It is also showed that the virtual eavesdropper approaches significantly reduce the complexity with acceptable performance degradation.

Iterative channel estimation and detection (ICED) can provide a better performance as the channel estimation can be improved through iterations. For a multiple‐input multiple‐output channel, owing to a large size of the signal alphabet, ICED becomes less practical unless a low‐complexity detector such as a lattice reduction‐based detector is employed. However, since the lattice basis reduction has to be carried out for each iteration, the resulting complexity can be still high. In this study, a computationally efficient technique was proposed to perform the lattice basis reduction within ICED over both static and slowly time‐varying block‐fading channels, where orthogonal defect of basis, as well as error probability are considered to decide whether or not basis reduction is needed for each iteration.

For iterative detection and decoding (IDD) in multiple‐input–multiple‐output (MIMO) systems, although the maximum a posteriori (MAP) detector can achieve an optimal performance, because of its prohibitively high computational complexity, various low‐complexity approximate MAP detectors are studied. Among the existing MIMO detectors for the non‐IDD receivers, lattice reduction (LR)‐aided detectors can provide a near maximum‐likelihood (ML) detector's performance with reasonably low complexity, and they could be modified to be used in the IDD receivers. In this study, the authors propose a bit‐level LR‐aided MIMO detector whose performance can approach that of the MAP detector, where a priori information is taken into account for soft‐decisions. Furthermore, the proposed method can be extended to large dimensional MIMO systems by channel matrix decomposition and successive interference cancellation, by which a significant complexity reduction can be achieved. Through simulations and complexity analysis, it is shown that a near‐optimal performance is obtained by the authors proposed low‐complexity bit‐level LR‐aided detector for the IDD in the MIMO systems.

Hierarchical modulation (HM) enables unequal priority transmissions using a signal constellation of non-uniformly spaced constellation points, which is an important property for broadcast systems. Using bit interleaved coded modulation (BICM) and iterative decoding (ID), the performance of HM systems can be further improved. In this paper, we study HM system with BICM-ID or HM-BICM-ID. Since the performance of BICM-ID depends on signal mapping rules, we derive a mapping rule based on distance properties to minimize the bit error rate (BER). Furthermore, we perform the BER performance analysis for HM-BICM-ID based on binary erasure channel (BEC) modeling, which provides BER prediction without time-consuming simulations. Based on the derived BER prediction scheme, we can also perform system design and optimization for applications of HM-BICM-ID. For example, we are able to optimize the constellation priority parameter in a broadcast system to maximize the throughput.

With careful calculation of signal forwarding weights, relay nodes can be used to work collaboratively to enhance downlink transmission performance by forming a virtual multiple-input multiple-output beamforming system. Although collaborative relay beamforming schemes for single user have been widely investigated for cellular systems in previous literatures, there are few studies on the relay beamforming for multiusers. In this paper, we study the collaborative downlink signal transmission with multiple amplify-and-forward relay nodes for multiusers in cellular systems. We propose two new algorithms to determine the beamforming weights with the same objective of minimizing power consumption of the relay nodes. In the first algorithm, we aim to guarantee the received signal-to-noise ratio at multiusers for the relay beamforming with orthogonal channels. We prove that the solution obtained by a semidefinite relaxation technology is optimal. In the second algorithm, we propose an iterative algorithm that jointly selects the base station antennas and optimizes the relay beamforming weights to reach the target signal-to-interference-and-noise ratio at multiusers with nonorthogonal channels. Numerical results validate our theoretical analysis and demonstrate that the proposed optimal schemes can effectively reduce the relay power consumption compared with several other beamforming approaches.

In a multicell scenario, two competitive beamforming design schemes are proposed to maximise the worst signal‐to‐interference‐plus‐noise ratio (SINR) in each cell for downlink transmissions. The first design adopts interference control. The authors use different interference control weighting factors to suppress the inter‐cell interference effectively as each cell may experience different level of inter‐cell interference. The other design is based on power control, where the authors can adjust the power of each base station to improve the system performance. Both the two improved designs keep the distributive nature of the competitive design as well as improve the worst SINR of the system. The existence of Nash equilibrium of the proposed designs is proved and the system overhead is analysed for the comparison with other distributive algorithms. Simulation results show that both the proposed competitive designs have improved performances, and the competitive design with power control outperforms that with the interference control as the transmission power increases.

This study considers successive orthogonal beamforming (SOBF) for multicell downlink transmissions. A group of base stations (BSs) are in cooperation for effective downlink transmissions to cell‐edge users, which is usually referred to as cooperative multi‐point. As the number of cell‐edge users is small and varying, conventional beamforming schemes, for example, zero‐forcing beamforming (ZFBF), are not suitable for the multicell scenario. In the SOBF scheme considered here, the beamforming vectors are found in a successive manner when a new user comes in, so that the beamforming vectors of the existing users remain unchanged. With limited channel state information at the group of BSs, a new successive beam allocation (SBA) scheme is proposed for SOBF. In SBA, the candidate beams are broadcasted to all the users through downlink before the index of the best beam is sent back to the BS by each user. The sum rate performance of a system with two users is studied analytically. The numerical results are presented to show that the proposed SOBF with SBA outperforms ZFBF with conventional feedback strategy.

Recent studies suggest that relay communications can be a major application to extend the range of wireless communications by forwarding the signal from the sender to the receiver. Although fixed or robust distributed relay schemes for single receiver have been previously investigated, the distributed relay beamforming (DRBF) for multiple receivers is studied here. The authors propose an algorithm to design the DRBF weights for maximising the worst received signal‐to‐noise ratio subject to two different types of relay power constraints, which are the total relay power constraints and individual relay power constraints. The authors also prove that the proposed algorithm with relay power constraints results in a quadratic programming optimisation problem, which can be optimally solved by using semidefinite relaxation technology. The simulation results demonstrate an effective gain of the proposed optimal scheme over other intuition schemes.

For iterative detection and decoding (IDD) in multiple-input multiple-output (MIMO) systems, the log-likelihood ratio (LLR) of each coded bit can be found by an optimal bit-wise maximum a posteriori probability (MAP) detector. However, since this MAP detector requires a prohibitively high computational complexity, low-complexity suboptimal detectors are desirable. In this paper, lattice reduction (LR)-based MIMO detection is investigated to derive a low-complexity detector that can achieve near MAP performance for IDD. In order to approximate LLR values incorporating the extrinsic information provided by a soft-input soft-output (SISO) decoder, bit-wise LR-based minimum mean square error (MMSE) filters are derived. Furthermore, in order to minimize the performance degradation due to quantization (or rounding) errors in the LR-based detection, a low-complexity integer perturbed list generation method is proposed, where no tree search is used by taking advantage of a near orthogonal channel basis obtained by LR. Through a complexity analysis and simulations, it is shown that the proposed approach achieves near optimal performance, while the complexity is comparable with that of the MMSE soft cancellation method, which is known to be computationally efficient. As a bit-wise detector, a parallel implementation of the proposed method would be straightforward, which lowers the detection delay.

For iterative detection and decoding (IDD) in multiple-input multiple-output (MIMO) systems, although the maximum a posteriori probability (MAP) detector is desirable in terms of performance, it is difficult to be employed due to its prohibitively high complexity as an exhaustive search is used. In this paper, a lattice reduction (LR)-based MIMO detection method is studied to achieve near MAP performance with reasonably low complexity for IDD. The a priori information (API), which is available from a soft-input soft-output (SISO) decoder, is taken into account to generate a list with a randomized successive interference cancellation (SIC) method. More specifically, a joint Gaussian distribution is used to convert the API into the LR domain and a modified sampling distribution, which was originally adopted for near optimal LR-based detection in non-IDD MIMO systems, is derived for random sampling to build a list of candidate vectors of high a posteriori probability (APP) with low complexity. It is shown that the IDD receiver with the proposed method outperforms those with the conventional LR-based methods, where no API is taken into account to build a list. Furthermore, the trade-off between performance and complexity is exploited with varying list length.

ABSTRACT With the reformation of spectrum policy and the development of cognitive radio, secondary users will be allowed to access spectrums licensed to primary users. Spectrum auctions can facilitate this secondary spectrum access in a market‐driven way. To design an efficient auction framework, we first study the supply and demand pressures and the competitive equilibrium of the secondary spectrum market, considering the spectrum reusability. In well‐designed auctions, competition among participants should lead to the competitive equilibrium according to the traditional economic point of view. Then, a discriminatory price spectrum double auction framework is proposed for this market. In this framework, rational participants compete with each other by using bidding prices, and their profits are guaranteed to be non‐negative. A near‐optimal heuristic algorithm is also proposed to solve the auction clearing problem of the proposed framework efficiently. Experimental results verify the efficiency of the proposed auction clearing algorithm and demonstrate that competition among secondary users and primary users can lead to the competitive equilibrium during auction iterations using the proposed auction framework. Copyright © 2011 John Wiley & Sons, Ltd.

Recently collaborative relay systems have been studied to increase the spectral efficiency and extend the range of wireless communications. In this paper, we consider the collaborative relay beamforming (CRBF) for multiuser systems. In the proposed method, we select base station (BS) antennas for each user and optimize their CRBF weights under two different types of power constraints, which are the total relay power constraint and individual relay power constraints, by exploiting the notion of the maximization of the worst-case received signal-to-interference-and-noise ratio (SINR). The optimization problem of the CRBF weights can be efficiently solved using a semidefinite relaxation (SDR) technique.We derive an iterative CRBF scheme to jointly select the BS antennas and optimize the CRBF weights to maximize the worst SINR among all users. Simulation results validate our theoretical analysis and demonstrate the tradeoffs between the number of BS antennas and the number of relays in the CRBF system.

This paper studies the coordinated multicell downlink transmission to cope with the severe inter-cell interference (ICI) in future cellular systems. We propose an iterative scheme to jointly optimize the base station (BS) and relay beamforming weights to maximize the worst-case signal-to-interference-and-noise ratio (SINR) with perfect channel state information (CSI), under two different types of BS and relay power constraints, respectively, which are the total power constraints and individual power constraints. Using the semidefinite relaxation (SDR) technology, the optimization problems for BS and relay beamforming weights can be converted into semidefinite programming (SDP) problems, which can be effectively solved by interior-point methods. Simulation results demonstrate that the proposed iterative scheme can achieve near-optimal performance within a few iterations and provide a good tradeoff between performance and cost (i.e. the number of BS antennas and relays) in the multicell systems.

The performance of downlink transmissions can be improved by using relays that can form a distributed virtual multiple-input multiple-output (MIMO) beamforming system. In this paper, we study a collaborative downlink transmission scheme for a cellular system which consists of a base station (BS), multiple amplify-and-forward (AF) relay nodes, and multiple users. We propose a new algorithm to determine the signal forwarding weights for both transmit power and signal phase with an objective of minimizing power consumption of the relay nodes while guaranteeing the receive signal-to-noise ratio (SNR) at multiple users for the collaborative relay beamforming. We prove that the beamforming solution obtained by a semidefinite relaxation (SDR) technique is optimal with the proposed algorithm. Numerical results validate our theoretical analysis and demonstrate that the proposed optimal scheme can effectively reduce the power consumption of relay nodes compared with other beamforming approaches.

In the future cellular communication systems, multiple relay nodes can be used for distributed virtual multiple-input multiple-output (MIMO) beamforming to improve the performance of downlink transmissions. In this paper, we study a distributed relay scheme in cellular systems which consists of a base station (BS), multiple amplify-and-forward (AF) relay nodes, and multiple users. We propose an optimal distributed relay beamforming scheme that maximizes the worst-case received signal-to-noise ratio (SNR) under two different types of power constraints, which are the total relay transmit power constraint and individual relay transmit power constraints. We show that the distributed relay beamforming solution for multiusers can be obtained optimally by solving second-order cone programming (SOCP) problems. Numerical results validate our theoretical analysis and demonstrate an effective gain of the proposed scheme over other conventional schemes.

Congestion control is critical for the provisioning of quality of services (QoS) over dedicated short range communications (DSRC) vehicle networks for road safety applications. In this paper we propose a congestion control method for DSRC vehicle networks at road intersection, with the aims of providing high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method a offline simulation based approach is used to find out the best possible configurations of message rate and MAC layer backoff exponent (BE) for a given number of vehicles equipped with DSRC radios. The identified best configurations are then used online by an roadside access point (AP) for system operation. Simulation results demonstrated that this adaptive method significantly outperforms the fixed control method under varying number of vehicles. The impact of estimation error on the number of vehicles in the network on system level performance is also investigated.

Dedicated Short Range Communication (DSRC) is a promising technique for vehicle ad-hoc network (VANET) and collaborative road safety applications. As road safety applications require strict quality of services (QoS) from the VANET, it is crucial for DSRC to provide timely and reliable communications to make safety applications successful. In this paper we propose two adaptive message rate control algorithms for low priority safety messages, in order to provide highly available channel for high priority emergency messages while improve channel utilization. In the algorithms each vehicle monitors channel loads and independently controls message rate by a modified additive increase and multiplicative decrease (AIMD) method. Simulation results demonstrated the effectiveness of the proposed rate control algorithms in adapting to dynamic traffic load.

Application of network coding in wireless communications has enabled the development of physical layer network coding (PLNC). Applying the principle of PLNC to wireless cooperative networks for spectral efficiency improvement has recently received tremendous attention from the research community. The best relay selection is extensively investigated in the literature for PLNC cooperative networks with multiple relays. However, the authors study the problem of signal beamforming and relay selection for a cooperative bi-directional relay network using PLNC and consider signal beamforming and relay selection for (i) multiple relays each equipped with single antenna and (ii) multiple relays with multiple antennas on uncoded amplify-and-forward scheme for PLNC transmission. The authors derive the optimal design criteria and provide low-complexity sub-optimal solutions for relay selection and beamforming to minimise the symbol error probability.

In this paper, we derive user selection criteria based on the error probability for an actually employed detector for multiuser multiple-input multiple-output (MIMO) systems. We propose a low complexity sequential user selection scheme when a lattice reduction (LR) based MIMO detector is used. We also analyze the diversity gain for combinatorial user selection approaches with a given LR-based detector. From simulation results, we can confirm that the proposed sequential user selection approach can provide a comparable performance to the combinatorial ones with much lower complexity.

We investigate the maximum outage throughput for Orthogonal Frequency Division Multiple Access (OFDMA) systems in the presence of feedback channel capacity constraints. We attempt to establish an information-theoretic lower bound on the capacity of feedback channel and build the corresponding test channel that achieves this lower bound. Based on the derived test channel, we formulate the outage throughput maximization problem with the quantized CSI. Since the optimal solutions require an exponential time complexity, we propose a low complexity suboptimal approach that separately performs subcarrier and power allocation. Numerical results show that the proposed suboptimal approach can achieve a near optimal performance, and the outage throughput with a limited feedback of CSI can be close to that with perfect CSI by exploiting correlation properties of downlink CSI for quantization.

This paper attempts to address the effectiveness of physical-layer network coding (PNC) on the capacity improvement for multi-hop multicast in random wireless ad hoc networks (WAHNs). While it can be shown that there is a capacity gain by PNC, we can prove that the per session throughput capacity with PNC is θ (nR(n))-1), where n is the total number of nodes, R(n) is the communication range, and each multicast session consists of a constant number of sinks. The result implies that PNC cannot improve the capacity order of multicast in random WAHNs, which is different from the intuition that PNC may improve the capacity order as it allows simultaneous signal reception and combination.

In this paper, we propose a resource allocation scheme to maximize users' minimum rate with a guaranteed outage probability for Orthogonal Frequency Division Multiple Access (OFDMA) systems with imperfect CSI. To avoid the system degradation caused by the noisy and outdated CSI, we consider the minimum mean square error (MMSE) channel prediction scheme at the base station (BS). We derive the parameter, namely, the equivalent channel gain, which is determined by the requirement of the outage probability and the channel estimates at the BS. With this parameter, we can maximizes users' minimum rates under a transmit power constraint and given outage probabilities. To reduce the complexity, we propose a two-step suboptimal approach that separately performs subcarrier and power allocation. Simulation results show that the performance of the resource allocation scheme is robust against channel estimation errors and feedback delays in OFDMA systems.

Lattice reduction (LR) based detectors has been investigated for multiple input multiple output (MIMO) systems. For most LR aided detectors, it is assumed that the channel matrix is a square or tall matrix. However, practically there are many cases that the channel matrix is fat which is referred to as the underdetermined/rank-deficient MIMO system. In this paper, we employ the pre-voting cancellation approach and propose the LR-based detectors for underdetermined MIMO systems. It can be shown that the proposed detectors can exploit a full receive diversity. Furthermore, the pre-voting vector selection criteria for the proposed detectors are taken into account to improve performance further.

Abstract The performance of wireless networks is limited by multiple access interference (MAI) in the traditional communication approach where the interfered signals of the concurrent transmissions are treated as noise. In this paper, we treat the interfered signals from a new perspective on the basis of additive electromagnetic (EM) waves and propose a network coding based interference cancelation (NCIC) scheme. In the proposed scheme, adjacent nodes can transmit simultaneously with careful scheduling; therefore, network performance will not be limited by the MAI. Additionally we design a space segmentation method for general wireless ad hoc networks, which organizes network into clusters with regular shapes (e.g., square and hexagon) to reduce the number of relay nodes. The segmentation method works with the scheduling scheme and can help achieve better scalability and reduced complexity. We derive accurate analytic models for the probability of connectivity between two adjacent cluster heads which is important for successful information relay. We proved that with the proposed NCIC scheme, the transmission efficiency can be improved by at least 50% for general wireless networks as compared to the traditional interference avoidance schemes. Numeric results also show the space segmentation is feasible and effective. Finally we propose and discuss a method to implement the NCIC scheme in a practical orthogonal frequency division multiplexing (OFDM) communications networks. Copyright © 2009 John Wiley & Sons, Ltd.