In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform framework by capitalizing on the benefits of maximum distance separable (MDS) code and the reconfigurable intelligent surface (RIS). The proposed scheme is referred to as MDS-OFDM-RIS. The proposed design scheme consists of (i) an MDS code based amplitude and phase modulation scheme for OFDM transmission, which helps increase the minimum Hamming distance among symbols and improve on the error detection capabilities, (ii) a RIS that is placed near the radio frequency (RF) source, (iii) as well as a reduced-complexity maximum likelihood (RC-ML) detection algorithm at the receiver by utilizing the error detection ability of the MDS codes. We derive an upper bound for the bit error rate (BER) and a closed-form expression of the mutual information. Using the obtained analytical expressions, we formulate two optimization problems and derive the corresponding optimal solutions for RIS phase shifts. It is found that the two optimization problems share the same optimal solution, which indicates that the obtained RIS phase shifts optimize the system BER and channel capacity simultaneously. Simulation results show that compared with conventional OFDM systems, the proposed system can better combat multipath fading and provide higher channel capacity, especially when the RIS phase shifts are optimal. Moreover, the accuracy and low complexity of the proposed RC-ML detection scheme are demonstrated by numerical results.

In this paper, we propose a novel maximum distance separable (MDS) code based and reconfigurable intelligent surface (RIS) assisted wireless communication system with orthogonal frequency division multiplexing (OFDM). Specifically, input bits are firstly divided into groups and their MDS codes are utilized to decide the amplitudes and phases of subcarriers. The introduction of the MDS code helps to increase the minimum Hamming distance between symbols and improve on the capability of error detection. Besides, the RIS is adopted to create additional paths between the radio frequency (RF) and the receiver as well as alter the signal phases with derived optimal solution. Benefiting from the strength of the RIS, the proposed system can better overcome multipath fading compared with conventional systems. Simulation results are presented to demonstrate the efficacy of the proposed system in terms of reducing bit error rate (BER) through multipath channels.

Highly directional beams in millimeter wave (mmWave) communications necessitate beam training or alignment between the access point (AP) and the user equipment (UE), and exhausted beam search (EBS) method is suggested in the current 3GPP standard. Nevertheless, EBS suffers from high overheads and inevitably lowers the throughput, especially when the beam space is large. In this paper, we utilize the spatial correlation among different beams as well as the strong feature extraction/representation capability of the deep convolutional neural network (CNN), and propose an intelligent beam training algorithm. With the proposed method, the AP can probe only a fixed subset of the whole beam space and identify the optimal beam intelligently. Simulation results show that, the proposed method can largely reduce the overheads for the beam training meanwhile boost the throughput performance compared with the state of the arts.

Orthogonal time frequency space (OTFS) is a new multi-carrier modulation technology emerging in recent years. Like orthogonal frequency division multiplexing (OFDM), OTFS also has the problem of high peak to average power ratio (PAPR). Because of the high PAPR, OTFS signals are easy to enter the nonlinear region of the power amplifier (PA), and result in nonlinear distortion. In this paper, we study the PAPR problem for OTFS system and propose an improved algorithm by jointly exploiting the traditional selective mapping (SLM) scheme and the imperialist competition algorithm (ICA), namely ICA-SLM. The simulation results shown that the proposed novel PAPR reduction method is capable of achieving better performance compared to conventional SLM for OTFS systems.

High speed, high maneuverability, and weak space-moving targets (SMTs) are major threats for space-borne radar (SBR) systems. First, the severe range migration (RM) and Doppler extension are induced by complex relative motion between the radar platform and non-cooperative moving targets, making moving target detection (MTD), and parameter estimation particularly difficult. Apart from this challenge, Doppler ambiguity and Doppler aliasing arise from the limited pulse repetition frequency (PRF) of a SBR system to ensure an adequate coverage rate, which may make the existing MTD algorithms deteriorate dramatically. To address these issues, we focus on the detection of high speed and high maneuverability targets based on the modified keystone transform matched filtering (MKTMF), whose Doppler frequency exceeds PRF as well as spans multiple PRFs. The proposed method is suitable for the weak MTD under a low signal-to-noise ratio (SNR) case since the nonlinear operation is not involved. Finally, some numerical results and real data results are provided to validate the superiority of the proposed method.

The communication topologies between vehicles in a platoon substantially impact the platoon’s stability. This study provides a distributed linear feedback control law that considers time-varying delay with guaranteed internal and string stability of the platoon system under various communication topologies. Firstly, the vehicle dynamics linearized model was derived using the precise feedback linearization technology. Different types of communication topologies, such as vehicle-to-vehicle communication and sensor-based communication, were described using directed graphs. Secondly, the linear feedback control law was designed to establish the stable zone of the linear controller gain under the effect of different communication topologies using directed graphs and the Routh-Hurwitz stability theorem. The Lyapunov-Razumikhin theorem determines the upper bound of the time-varying delay of various communication topologies. Additionally, the string stability of leader-predecessor following topology was studied, and the results were combined with the internal stability to determine the upper bound of time-varying delay. Finally, the results were verified by conducting two numerical simulations.

Terahertz (THz) communication is considered as an attractive way to overcome the bandwidth bottleneck and satisfy the ever-increasing capacity demand in the future. Due to the high directivity and propagation loss of THz waves, a massive MIMO system using beamforming is envisioned as a promising technology in THz communication to realize high-gain and directional transmission. However, pilots, which are the fundamentals for many beamforming schemes, are challenging to be accurately detected in the THz band owing to the severe propagation loss. In this paper, a unified 3D beam training and tracking procedure is proposed to effectively realize the beamforming in THz communications, by considering the line-of-sight (LoS) propagation. In particular, a novel quadruple-uniform planar array (QUPA) architecture is analyzed to enlarge the signal coverage, increase the beam gain, and reduce the beam squint loss. Then, a new 3D grid-based (GB) beam training is developed with low complexity, including the design of the 3D codebook and training protocol. Finally, a simple yet effective grid-based hybrid (GBH) beam tracking is investigated to support THz beamforming in an efficient manner. The communication framework based on this procedure can dynamically trigger beam training/tracking depending on the real-time quality of service. Numerical results are presented to demonstrate the superiority of our proposed beam training and tracking over the benchmark methods.

In this paper, we propose to apply intelligent reflecting surface (IRS) to the physical-layer service integration (PHY-SI) system, where a single-antenna access point (AP) integrates two sorts of service messages, i.e., multicast message and confidential message, via superposition coding to serve multiple single-antenna users. Our goal is to optimize the power allocation (for transmitting different messages) at the AP and the passive beamforming at the IRS to maximize the achievable secrecy rate region. To this end, we formulate this problem as a bi-objective optimization problem. To tackle the non-convexity of this problem, we propose a Charnes-Cooper transformation (CCT)-based algorithm to obtain its high-quality suboptimal solutions, thereby approximately characterizing the secrecy rate region. Numerical results demonstrate the advantages of leveraging IRS in improving the performance of PHY-SI.

With the development of software-defined radio (SDR) and artificial intelligence (AI), smart jammers are becoming more and more powerful, and have cognitive and intelligent capacities such as spectrum sensing, learning and reconfigurability. To cope with the AI-enabled smart jammers, this paper investigates the naive weakness of smart jamming strategies and proposes a deceptively adversarial attack (DAA) based proactively intelligent anti-jamming algorithm to ensure reliable communication for wireless communication networks (WCNs). In contrast to the existing intelligent schemes, the proposed DAA-based anti-jamming algorithm focus on minimizing total reward of the smart jammer and destroy their spectrum sensing and learning abilities by delivering a carefully designed deceptively adversarial signal. We implement the DAA-based anti-jamming algorithm in both white-box and black-box settings according to the degree of information acquisition from the smart jammers, respectively. Simulation results show that system performance in terms of anti-jamming reward, computational complexity and anti-jamming efficiency can be significantly improved compared with the existing intelligent schemes.

Spectrum cartography aims to estimate multidimensional radio map from limited samples taken over a geographical region. The radio map, formulated as a third-order tensor, admits an approximated low rank CANDECOMP/PARAFAC (CP) decomposition (CPD). We formulate the spectrum cartography problem as a low-CP-rank tensor completion problem. To handle the sequential spectrum observations which have not been addressed in existing research, the time-varying spectrum cartography problem is handled via online tensor completion based on incremental CPD and then solved by block coordinate descent approach. In addition, we show that the incremental CPD generates a sequence of latent factors estimates converging to a stationary point. Numerical simulations show that our proposed algorithm has better performance than the baseline methods and is suitable for realtime radio map estimation.

In this paper, a precise beam position design algorithm for space-based early warning radar (SEWR) based on the actual antenna pattern search is proposed. In the proposed algorithm, the beam positions along elevation and azimuth dimensions are filled according to the main lobe widths of elevation and azimuth antenna patterns, respectively, and thus the beam position designment is more accurate since the range and azimuth angle dependences are considered. Based on the designed beam positions, the precise dwell time can be obtained according to the coverage rata of a SEWR system. The beam position designment results by using the proposed method provide a reference for the beam filling designment of a space-based radar.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In the massive machine-type communications (mMTCs) scenario for Internet-of-Things (IoTs) applications, though a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission simultaneously, which requires a novel design of protocols and receivers to enable efficient data transmission and accurate multiuser detection (MUD). Aiming at this problem, grant-free nonorthogonal multiple access (GF-NOMA) is proposed, which enables active devices to directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for nonorthogonal preambles (NOPs)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this article, from an aspect of network deployment, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG). Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. We optimize the energy efficiency and AP coverage efficiency for GF-NOMA via numerical methods to meet the low-energy-consumption and low-infrastructure-cost demands of IoT applications. Our analysis results are verified with Monte Carlo simulations, which show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and grant-free orthogonal multiple access.

In this paper, we investigate the uplink communication of a reconfigurable intelligent surface (RIS) assisted system, in which an user equipment (UE) with single radio frequency (RF) chain delivers information to an access point (AP) by adopting the spatial modulation (SM). Specifically, we first investigate the transmit SM (TSM) scheme and jointly optimize the UE’s power allocation matrix and the RIS reflection coefficients to enhance the system reliability. We formulate a non-convex optimization problem to reduce the system symbol-error-rate (SER) and propose a novel penalty-alternative optimizing algorithm to obtain a near-optimal solution. Following this, we show that with the assistant of RIS, receive SM (RSM) scheme can also be performed even if the UE has only one RF chain. Based on this observation, a novel RIS-assisted RSM scheme is proposed, which can provide a low cost and complexity solution for system realization. The reflection coefficients of the RIS are also optimized for the proposed RSM. Numerical results show that the RIS-assisted TSM can achieve a lower SER than the conventional communication scheme (CTS) without SM and the proposed RSM scheme has lower detection complexity than that of CTS. It is also shown that the performance of the TSM and RSM schemes is more sensitive to the quantization accuracy of the phase of the RIS coefficients than that of the amplitude.

This paper focuses on a novel orthogonal frequency division multiplexing (OFDM) receiver architecture, which uses a low-resolution analog-to-digital converter (ADC) to oversample the received signal in time domain. Although attractive in terms of power consumption and hardware cost, low-resolution ADC incurs severe nonlinear quantization distortion and thus destroys the orthogonality between the OFDM subcarriers. The loss of orthogonality, together with the oversampling-caused correlation in noise samples, poses a great challenge to the OFDM receiver design. This paper aims to tackle this challenge. For the proposed receiver, we consider an often-used two-phase transmission protocol. In the first phase, training OFDM symbols are transmitted for channel estimation. In the second phase, information-conveying OFDM symbols are transmitted and detected using the previously obtained channel estimate. Therefore, the proposed receiver consists of two key components: channel estimator and data detector. These components are elaborately derived in the framework of Bayesian inference. Due to the diversity gain resulting from the oversampling operation, the proposed receiver can remarkably outperform the counterparts, including the conventional OFDM receiver with perfect infinite-precision quantization.

Intelligent reflecting surface (IRS) has emerged as an appealing solution to enhance wireless communication performance by reconfiguring the wireless propagation environment. In this paper, we propose to apply IRS to the physical-layer service integration (PHY-SI) system, where a single-antenna access point (AP) integrates two sorts of service messages, i.e., multicast message and confidential message, via superposition coding to serve multiple single-antenna users. Our goal is to optimize the power allocation (for transmitting different messages) at the AP and the passive beamforming at the IRS to maximize the achievable secrecy rate region. To this end, we formulate this problem as a bi-objective optimization problem, which is shown equivalent to a secrecy rate maximization problem subject to the constraints on the quality of multicast service. Due to the non-convexity of this problem, we propose two customized algorithms to obtain its high-quality suboptimal solutions, thereby approximately characterizing the secrecy rate region. The resulting performance gap with the globally optimal solution is analyzed. Furthermore, we provide theoretical analysis to unveil the impact of IRS beamforming on the performance of PHY-SI. Numerical results demonstrate the advantages of leveraging IRS in improving the performance of PHY-SI and also validate our theoretical analysis.

Massive multiple-input and multiple-output (MIMO), especially in the millimeter-wave (mmWave) frequency band, has been recognized as a promising technology for future wireless communications. However, most previous research in mmWave massive MIMO only uses conventional MIMO channel model without taking into account the spatial-wideband effect in mmWave massive MIMO systems, where the number of antennas is very large and the transmission bandwidth is very wide. In this paper, a novel mmWave massive MIMO channel model is introduced which embraces the spatial-wideband effect. Then, a joint parameter learning algorithm is proposed to extract different channel parameters from the received data. Furthermore, a low-complexity spatial wideband channel estimation scheme is proposed for the new channel model. Simulation results and complexity analysis show that the proposed scheme are capable of achieving a considerable reduction in complexity compared with the recently spatial-wideband channel estimation algorithm with a small performance loss. Moreover, our scheme can effectively estimate the novel channel even part of the antenna data is received.

This paper addresses the issue of anti-jamming communication in dynamic and unknown environment for ultradense network (UDN). A deep reinforcement learning based antijamming algorithm is proposed by exploiting the frequency-hopping technology to cope with the jamming attack without estimating the mode and parameters of the jamming. In contrast to existing learning based schemes, in which the anti-jamming action, e.g., frequency hopping and transmit power adjustment, is taken from a pre-defined discrete anti-jamming strategy space, the proposed anti-jamming algorithm takes anti-jamming action from the continuous action space based on deterministic policy gradient. We represent this anti-jamming algorithm in an actor-critic framework, for which a convolution neural network (CNN) is adopted as the actor for anti-jamming action selection, and a deep neural network (DNN) is used as the critic for value estimation. We have implemented the proposed algorithm based on Google TensorFlow. Simulation results show that the system performance can be significantly improved by the proposed algorithm.

In some communication scenarios, non-Gaussian impulsive noise is dominant, for which conventional Gaussian noise based signal detection algorithm cannot achieve desirable performance due to the mismatch of noise model. Therefore, it is necessary to develop novel signal detection algorithm for communication receiver in non-Gaussian impulsive noise. In this paper, we focus on the detection of continuous phase frequency shift keying (CPFSK) signals for bursty impulsive noise, which is modeled as the stationary m-order α-sub-Gaussian (αSG(m)) process. For coherent detection, a sequence detection algorithm is proposed based on Viterbi algorithm by utilizing the Markov property of αSG(m) noise. To reduce the computational complexity, a multidimensional myriad branch measure is proposed to replace the complicated Maximum Likelihood(ML) branch measure. For non-coherent detection, a multiple symbols aided method is applied to minimize the detection error. The multidimensional myriad measure is also used. For the sake of comparison, the Gaussian measure derived from Gaussian distributed noise is also considered in both coherent and non-coherent detection. The simulation results show that the performance of the myriad measure based algorithm can closely approach that of the ML measure based algorithm, and is much better than that of the Gaussian measure based algorithm.

The acquisition of channel state information is crucial in massive multiple-input multiple-output (MIMO) systems when large scale antenna array is used. For Millimeter Wave (mmWave) wideband massive MIMO-OFDM systems, both the number of antennas and subcarriers are very large, so it is very difficult for us to get accurate channel information. In this paper, we propose a tensor-based channel estimation scheme for mmWave wideband channels. Firstly, we transform the two- dimensional mmWave wideband channel matrix into a three-dimensional tensor due to the large number of antennas and subcarriers. Secondly, by utilizing the PARAFAC decomposition of the tensor, the estimated three factor matrices can be obtained. Then, channel parameters can be extracted from the estimated factor matrices. Simulation results show that our proposed method outperforms the conventional low complexity compressed sensing based method, such as orthogonal matching pursuit (OMP) method, while maintaining the same complexity order.

In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multiple-input multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the K -nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS and PA parameter evaluation. Furthermore, we investigate the design of feature vectors for the SLC and DNN approaches and propose a novel feature vector generator to match the specific transmission mode of SM. As a further advance, our proposed approaches are extended to other adaptive index modulation (IM) schemes, e.g., adaptive modulation (AM) aided orthogonal frequency division multiplexing with IM (OFDM-IM). Our simulation results show that the SLC and DNN-based adaptive SM-MIMO systems outperform many conventional optimization-driven designs and are capable of achieving a near-optimal performance with a significantly lower complexity.

Linear frequency modulated (LFM) signal is widely applied in many fields for its excellent characteristic of long time interval and wide frequency band. The chirp rate is the key parameter in LFM signal. The traditional search-based chirp rate estimation algorithms have the contradiction between estimation performance and complexity. The phase-based algorithms have good estimation performance in the case of high SNR, but the complexity is high. In order to solve these problems, a new chirp rate estimation algorithm by multiple discrete polynomial phase transform (DPT) and weighted combination is proposed. In this algorithm, the chirp rate estimation is simplified to multiple frequency estimations by multiple DPT. Then, in frequency estimation, a new unbiased interpolator is proposed to eliminate the contradiction of search-based algorithms. Finally, the multiple estimates are combined through the optimal weighting factors which are obtained by theoretical derivation to minimize the root mean square error. Both theoretical analysis and simulation results demonstrate that the proposed algorithm bears a relatively low complexity and its estimation performance basically coincides with the Cramer-Rao lower bounds and has no error floor, which is much better than the existing algorithms.

Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless multicast networks, on the premise that a receiver is able to collect a full set of network-coded packets. However, in resource (time, bandwidth, and so on) constrained communications, a receiver may only receive a partial set of network coded packets, leading to poor system performance. On the other hand, device-to-device (D2D) communications utilize user proximity and spatial diversity to improve the communication efficiency. In this paper, we propose a hybrid multicast and D2D transmission scheme based on adaptive RNC (ARNC) to increase network throughput under packet erasure channels. In the proposed scheme, the packet encoding structure is optimized adaptively according to the network status, such that even if only a partial set of the coded packets are received, the user equipments (UEs) can still decode useful information and regenerate new encoded packet for D2D communications. In particular, the multicast mode and D2D mode are switched dynamically during a scheduling session according to the status of each UE. Under this hybrid mode, we can effectively overcome the effect of erasure channel and improve the overall network throughput. By comparing our scheme with other scheduling methods, we provide simulation results to corroborate the effectiveness of the proposed techniques.

Due to the imperfection of practical oscillators, phase noise (PHN) inevitably occurs in the phase of desired signals, and may incur significant degradation in the performance of wireless communications. This paper addresses the PHN compensation in uplink massive multiple-input multiple-output orthogonal frequency division multiplexing systems, where multiple single-antenna users simultaneously communicate with a base station equipped with a large antenna array. The proposed compensation scheme considers a two-stage transmission protocol. In the first stage, training symbols are transmitted, and the channels of multiple users are estimated along with the PHN sequences involved. In the second stage, information-conveying data symbols are transmitted, and the channel estimates previously obtained are used to estimate the data symbols in the presence of unknown PHN. For both stages, the estimation methods are elaborately derived based on the variational expectation maximization methodology. Moreover, some simplifications are conducted to significantly reduce the computational complexity of the proposed scheme. The effectiveness of the proposed PHN compensation scheme is corroborated through extensive simulations.

This paper addresses wideband channel estimation in an uplink massive multiple-input multiple-output (MIMO) system, which consists of multiple single-antenna users and a base station (BS) equipped with a large number of antennas. Due to the spatially non-stationary characteristics in massive MIMO channels, the channel vectors corresponding to different BS antennas may take on different sparsity patterns in delay domain. Taking this into account, we propose an adaptive grouping sparse Bayesian learning (AGSBL) scheme for uplink channel estimation. Specifically, for the channel vectors to be estimated, we judiciously design a hierarchical prior model controlled by a set of tunable hyperparameters. Then, we develop a variational Bayesian algorithm to infer the posterior probability of the channel vectors and the hyperparameters associated with the prior, thereby obtaining the channel estimates. Due to the designed prior, we are able to adaptively divide the channel vectors involved into several groups, each group bearing a similar sparsity pattern. In this way, the AGSBL can efficiently exploit the partial joint sparsity shared by each group of channel vectors, and therefore significantly improves the channel estimation performance. The simulation results are provided to verify the advantages of the proposed AGSBL over the related counterparts.

In this paper, we study the resource allocation problem for a cooperative device-to-device (D2D)- enabled wireless caching network, where each user randomly caches popular contents to its memory and shares the contents with nearby users through D2D links. In order to enhance the throughput of spectrum-sharing D2D links, which may be severely limited by the interference among D2D links, we enable the cooperation among some of the D2D links to eliminate the interference among them. We formulate a joint link scheduling and power allocation problem to maximize the overall throughput of cooperative D2D links (CDLs) and non- cooperative D2D links (NDLs), which is NP-hard. To solve the problem, we decompose it into two sub- problems, which maximize the sum rates of the CDLs and the NDLs, respectively. For CDL optimization, we propose a semi-orthogonal-based algorithm for joint user scheduling and power allocation. For NDL optimization, we propose a novel low-complexity algorithm to perform link scheduling and develop a Difference of Convex functions (D.C.) programming method to solve the non-convex power allocation problem. Simulation results show that cooperative transmission can significantly improve both the number of served users and the overall system throughput.

In many communication scenarios, the signal is corrupted by impulsive noise, which is modeled as symmetric α- stable (SαS) distribution. In this paper, we investigate the signal pre-processing and detection techniques in SαS noise. Due to its near-optimal performance, the myriad filter is a commonly used pre-processing method in SαS nolse. However, due to its memory, the myriad filter has high computation complexity. Hence, we propose an optimal memoryless pre-processing in this paper. We provide the criteria of optimizing the memoryless pre-processing function and propose a feasible optimization procedure. Based on the obtained pre-processing method, the signal detection method for SαS noise is further investigated. In order to evaluate the performance of the pre-processing based signal detection, the optimal signal detection in SαS noise is also studied. In our work, it is assumed that the pulse shape of the transmitted signal occupies multiple symbol periods. In this case, each transmitted symbol will have influence on detecting its following symbols. Thus, a multiple symbols aided maximum likelihood detection (MLD) is proposed, and its theoretical symbol error rate (SER) is also analyzed. Simulation results verify that the proposed memoryless pre-processing method can achieve nearly the same waveform recovery performance as myriad filter. Moreover, the SER performance of the proposed memoryless pre-processing based signal detection can approach that of optimal MLD.

Grant-free non-orthogonal multiple access (NOMA) has recently gained significant attention for reducing signaling overhead in machine-type communications (MTC). In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame based multi-user uplink scenarios where users are (in)active for the duration of a frame, namely frame-wise joint sparsity model. Firstly, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and consider that the user sparsity level should be unknown for multiuser detection, two enhanced BCS- based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross validation aided block sparsity adaptive subspace pursuit (CVA- BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance, by reasonably setting the threshold based on the AWGN noise floor. And the proposed CVA-BSASP algorithm is a highly practical algorithm design that does not require any prior knowledge, by adopting the statistical and machine learning mechanism cross validation (CV) to determine the stopping condition of the algorithm. Superior performance of the proposed algorithms is demonstrated by numerical experiments.

In this paper, we investigate mobile association and power allocation in device-to-device (D2D)- enabled heterogeneous networks with non-orthogonal multiple access (NOMA) protocol. We formulate two optimization problems to maximize the minimum rate and the sum rate of the network, respectively. Each problem includes power allocation, access point selection and transmission mode switching. To solve the problems, we develop a two-step method, which can always reach the closed-form solution. Simulation results show that the proposed method can significantly improve the overall throughput of the system, compared with the traditional solutions without D2D communications. In addition, we also investigate the tradeoff between overall throughput and fairness.

This paper considers a new frequency-domain artificial noise (AN)-aided transmit design for service integration in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. In this system, two sorts of service messages are combined and provided simultaneously at each frequency subchannel: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the other receivers. To characterize the tradeoff between the secrecy rate and multicast rate, our goal is to maximize the weighted sum of the two rates over all subchannels by jointly designing the input covariances for the multicast message, confidential message and AN at each subcarrier. This problem is nonconvex by nature and challenging to solve. To make it tractable, we recast this weighted sum rate maximization (WSRM) problem into a primal decomposable form, which is amenable to alternating optimization (AO). By this means, we can obtain a locally optimal solution to the WSRM problem. Numerical results are finally presented to show the efficacy of our proposed method and reveal some insights into our considered scheme.

A new radio frequency fingerprint that incorporates the channel impulse response and Angle of Arrival was introduced to enhance the accuracy of the indoor positioning. To use the new fingerprint. We use the generalized distance metric, i.e., Jensen-Shannon Divergence and cluster center to compare the difference between data collected at the test point and given reference point. Based on the new RF fingerprint, we proposed a device free passive localization system that does not require the user to have any measurement devices. The user location is estimated by searching the reference points with the the smallest distance between the test data and the database. The experimental result shows that the proposed system has better location accuracy method comparing with the conventional fingerprint algorithms.

In this paper, we consider the channel estimation and multi-user detection problems in fog radio access networks (F-RANs). Based on block coordinate descent algorithm, we propose two methods to solve a mixed ℓ2,1-regularization functional which exploits both the sparsity of user activities and the spatial sparsity of user signals in F-RAN. Both of our methods split the computation and corresponding data into multiple units of a cluster and solve the problem in a distributed manner. Hence they can be deployed flexibly at the distributed logical edges as well as the cloud baseband unit pool in F-RAN. The differences between the two methods are that the first one operates in a serial manner and is guaranteed to converge, while the second one works in parallel and under empirical guidance. Deployment details are also provided. Numerical results demonstrate the effectiveness of the proposed methods.

In this letter, the optimal and suboptimal nonlinear processing for correlation-based signal detection is addressed in symmetric alpha-stable noise. By maximizing the correlator output signal-to-noise ratio, a constrained functional optimization problem is established. As this optimization problem is hard to get analytical solution, we apply finite discretization to it and prove that the resulting approximation problem is a convex quadratic programming problem. This optimal nonlinear processing provides performance bound and design criteria for correlator detection. Based on the noise parameter α and the order statistic of received data, we further propose an adaptive method to determine the optimal threshold of the commonly used soft limiter. Simulation results show that the proposed method achieves near-optimal performance.

It is known that random network coding (RNC) can be used to improve the performance of wireless multicast networks. However, in the delay sensitive applications, no useful information can be recovered if a user cannot collect a full set of the encoded packets. This becomes more severe for multicast hard deadline constrained prioritized data because of the delivery time limitation and packet interdependency. Meanwhile, the performance of a multicast network is also limited by its bottleneck user(s) due to the heterogeneity of the underlying physical channels. Accordingly, we propose a cross-layer transmission scheme that utilizes beamforming at physical layer and adaptive RNC (ARNC) at network layer to maximize the overall network throughput. Under this joint-design, a smart antennas array that operates the beamforming is used to dynamically allocate the transmitting power among users, and the ARNC is adopted to achieve the network coding gain. In this way, the performance of each user is well balanced and the overall network throughput is increased observably. Furthermore, we propose a sample-based feedback scheme to reduce the system overhead. The analytical and simulation results are shown that the throughput of the network has increased by 30%~40% under our schemes compared with other RNC-based schemes.

Grant-free non-orthogonal multiple access has recently gained significant attention for reducing signaling overhead in machine-type communications. In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame-based multi-user uplink scenarios where users are (in)-active for the duration of a frame, namely, the frame-wise joint sparsity model. First, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and considering the user sparsity level to be unknown for multiuser detection, two enhanced BCS-based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross-validation aided block sparsity adaptive subspace pursuit (CVA-BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance by reasonably setting the threshold based on the additive white Gaussian noise floor. Moreover, the proposed CVA-BSASP algorithm is a highly practical algorithm design that adopts the statistical and machine learning mechanism cross-validation to determine the stopping condition of the algorithm and this does not require prior knowledge. Furthermore, the convergence and the computational complexity of the proposed algorithms are derived and the superior performance of the proposed algorithms is demonstrated by numerical experiments.

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). By taking into account of the sparsity of user activities in C-RAN, we solve the CE and MUD problems with compressed sensing to greatly reduce the large pilot overhead. A mixed ℓ2,1-regularization penalty functional is proposed to exploit the inherent sparsity existing in both the user activities and remote radio heads with which active users are associated. An iteratively re-weighted strategy is adopted to further enhance the estimation accuracy, and empirical and theoretical guidelines are also provided to assist in choosing tuning parameters. To speed up the optimization procedure, three low-complexity methods under different computing setups are proposed to provide differentiated services. With a centralized setting at the baseband unit pool, we propose a sequential method based on block coordinate descent (BCD). With a modern distributed computing setup, we propose two parallel methods based on alternating direction method of multipliers (ADMM) and hybrid BCD (HBCD), respectively. Specifically, the ADMM is guaranteed to converge but has a high computational complexity, while the HBCD has low complexity but works under empirical guidance. Numerical results are provided to verify the effectiveness of the proposed functional and methods.

Spatial-multiplexing aided spatial modulation (SMx-SM) is proposed, which intrinsically amalgamates the concept of vertical bell labs space-time (V-BLAST) and SM to attain a high transmission rate, despite its low number of radio frequency (RF) chains at the transmitter. Specifically, in the SMx-SM scheme, the transmit antennas are partitioned into groups and the SM technique is applied individually to each group. Furthermore, low-complexity threshold-aided compressive sensing-based and message passing-based detectors are derived for our SMx-SM system. Our simulation results show that the proposed SMx-SM system exhibits a better performance despite its lower complexity than the conventional generalized spatial modulation system. More importantly, the proposed SMx-SM system is capable of providing considerable performance gains over the V-BLAST system at the same number of RF chains and throughput. Finally, an upper bound is derived for the average bit error probability, which is confirmed by our simulation results.

Energy efficiency has now become a key pillar in the design of communication networks. With millions more base stations and billions of connected devices, the demand for energy-efficient system design and operation will be even more compelling in the fifth generation (5G) communication network.In this paper, we focus on maximizing the downlink system energy efficiency (EE) of the 5G ultra dense network through efficient resource allocation algorithms. First, a constrained EE maximize problem is formulated. However, the resulting optimization problem is challenging due to its non-convex nature. To overcome this issue, the transformations based on fractional programming theory are applied, so that the primal problem can be converted into a convex form. Then, we adopt a centralized algorithm to solve the optimization problem and get the global optimal EE. In order to reduce the complexity, an efficient distributed algorithm based on alternating direction method of multipliers (ADMM) is further proposed. The simulation results show that both the centralized algorithm and the distributed algorithm converge to the same EE, but the latter one has lower computational complexity.

Impulsive noise modeled as symmetric α-stable SαS distribution is commonly seen in many practical communication scenarios. In this paper, we focus on the synchronization of single-carrier signals in the mixture of SαS impulsive noise and Gaussian noise. We first derive the Cramér-Rao lower bound (CRLB) of the joint estimation of carrier phase and timing offsets for both liner modulation (LM) signals and continuous phase modulation (CPM) signals. In order to minimize the CRLB, the optimal training sequence (TS) is then designed for the synchronization of the LM signals and the CPM signals, respectively. We further propose a practical low-order cross-correlation based synchronization algorithm, which can be used for both LM and CPM signals. Numerical simulations show that our designed TS can outperform the pseudo-random (PN) sequence. By taking minimum shift keying (MSK) signals for instance, simulation results show that the performance of our proposed synchronization algorithm has negligible gap with the CRLB.

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). Assuming that active users are sparse in the network, we solve CE and MUD problems with compressed sensing (CS) technology to greatly reduce the long identification pilot overhead. A mixed ℓ2.1-regularization functional for extended sparse group-sparsity recovery is proposed to exploit the inherently sparse property existing both in user activities and remote radio heads (RRHs) that active users are attached to. Empirical and theoretical guidelines are provided to help choosing tuning parameters which have critical effect on the performance of the penalty functional. To speed up the processing procedure, based on alternating direction method of multipliers and variable splitting strategy, an efficient algorithm is formulated which is guaranteed to be convergent. Numerical results are provided to illustrate the effectiveness of the proposed functional and efficient algorithm.

The existing solutions for multi-user detection in uplink (UL) grant-free sparse code multiple access (SCMA) rely on the prior knowledge of user sparsity, i.e., the number of active users. An alternative solution, which sets the sparsity as a statistically empirical value to get a rough active user set and then eliminates the false detected inactive users with joint message passing algorithm (JMPA), leads to either increasing computation complexity of JMPA or high missed detection probability. In this paper, we propose a receiver for UL grant-free SCMA which relies on no prior knowledge of user sparsity. We propose a detection-based group orthogonal matching pursuit (DGOMP) active user detector to get an accurate active user set rather than a rough active user set. Then we modify the JMPA by taking the channel gain and noise power into consideration when calculating the prior information of the zero codeword. The modified JMPA helps to further eliminate the false detections caused by noise, channel fading and non-orthogonality of pilot sequences. Simulation results show that our proposed receiver without prior knowledge of user sparsity has acceptable performance degradation compared with currently existing solution with ideal, however unable to get in practice, prior knowledge of user sparsity.

We study a downlink non-orthogonal multiple access (NOMA) system with cooperative full-duplex relaying, where the near user in terms of the base station (BS) is enabled to act as a full-duplex relay for the far user. In particular, we first derive the outage probability with closed-form expressions when the power allocations at the BS and relay (or the near user) are fixed. Then, we analytically obtain the optimal power allocations with closed-form expressions at the BS and relay to minimize the outage probability. Numerical results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over the state of arts.

This paper studies the throughput maximization problem for a three-node relay channel with direct link and non-ideal circuit power. The relay operates in a half- duplex manner, and the decode-and-forward (DF) relaying is adopted. Considering the extra power consumption by the circuits, the optimal power allocations over infinite time horizon are investigated. First, two special scenarios, i.e., the direct link transmission (only use the direct link to transmit) and the relay assisted transmission (the source and the relay transmit with equal probability), are studied. By solving two non-convex optimization problems, the solutions show that the source and the relay transmit with certain probability, which is determined by the average power budgets, circuit power consumptions, and channel gains. Then, based on the above results, the optimal power allocation for the original throughput maximization problem is investigated, which is shown to be a mixed transmission scheme between the direct link transmission and the relay assisted transmission.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. In particular, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the instantaneous cross-channel gain by observing the power adaption. Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of art, we show the advantages of the proposed proactive estimator.

Centralized spectrum management is one of the key dynamic spectrum access (DSA) mechanisms proposed to govern the spectrum sharing between government incumbent users (IUs) and commercial secondary users (SUs). In the current centralized DSA designs, the operation data of both government IUs and commercial SUs need to be shared with a central server. However, the operation data of government IUs are often classified information and the SU operation data may also be commercial secrets. The current system design dissatisfies the privacy requirement of both IUs and SUs, since the central server is not necessarily trustworthy for holding such sensitive operation data. To address the privacy issue, this paper presents a privacy-preserving centralized DSA system (P2-SAS), which realizes the complex spectrum allocation process of DSA through efficient secure multi-party computation. In P2-SAS, none of the IU or SU operation data would be exposed to any snooping party, including the central server itself. We formally prove the correctness and privacy-preserving property of P2-SAS and evaluate its scalability and practicality using experiments based on real-world data. Experiment results show that P2-SAS can respond an SU's spectrum request in 6.96 s with communication overhead of less than 4 MB.

Non-orthogonal multiple access (NOMA) is an emerging research topic in the future fifth generation wireless communication networks, which is expected to support massive connectivity for massive machine-type communications (mMTC). Due to the sporadic communication nature of mMTC, the grant-free transmission methodology is highly expected in uplink NOMA systems, to drastically reduce the transmission latency and signaling overhead. Exploiting the inherent sparsity nature of user activity, compressive sensing (CS) techniques have been applied for efficient multi-user detection in the uplink grant-free NOMA. In this paper, we propose a prior-information-aided adaptive subspace pursuit (PIA-ASP) algorithm to improve the multi-user detection performance. In this algorithm, a parameter evaluating the quality of the prior-information support set is introduced, in order to exploit the intrinsically temporal correlation of active user support sets in several continuous time slots adaptively. Then, to mitigate the incorrect estimation effect of the prior support quality information, a robust PIA-ASP algorithm is further proposed, which adaptively exploits the prior support based on the corresponding support quality information in a conservative way. It is noted that both of the two proposed algorithms do not require the knowledge of the user sparsity level, while most of the state-of-the-art CS-based multi-user detection algorithms usually need. Moreover, for the two proposed algorithms, the upper bound of the signal detection error and the computational complexity is derived. Simulation results demonstrate that the two proposed algorithms are capable of achieving much better performance than that of the existing CS-based multi-user detection algorithms with a similar computational complexity.

Millimeter-wave (mmWave) communication is envisioned to provide orders of magnitude capacity improvement. However, it is challenging to realize a sufficient link margin due to high path loss and blockages. To address this difficulty, in this paper, we explore the potential gain of ultra-densification for enhancing mmWave communications from a network-level perspective. By deploying the mmWave base stations (BSs) in an extremely dense and amorphous fashion, the access distance is reduced and the choice of serving BSs is enriched for each user, which are intuitively effective for mitigating the propagation loss and blockages. Nevertheless, co-channel interference under this model will become a performance-limiting factor. To solve this problem, we propose a large-scale channel state information (CSI)-based interference coordination approach. Note that the large-scale CSI is highly location-dependent, and can be obtained with a quite low cost. Thus, the scalability of the proposed coordination framework can be guaranteed. Particularly, using only the large-scale CSI of interference links, a coordinated frequency resource block allocation problem is formulated for maximizing the minimum achievable rate of the users, which is uncovered to be an NP-hard integer programming problem. To circumvent this difficulty, a greedy scheme with polynomial-time complexity is proposed by adopting the bisection method and linear integer programming tools. Simulation results demonstrate that the proposed coordination scheme based on large-scale CSI only can still offer substantial gains over the existing methods. Moreover, although the proposed scheme is only guaranteed to converge to a local optimum, it performs well in terms of both user fairness and system efficiency.

In this paper, a novel multiple-input multiple-output (MIMO) transmission scheme, termed as receive antenna selection (RAS)-aided spatial modulation MIMO (SM-MIMO), is proposed for millimeter-wave (mmWave) communications. It employs the spatial modulation (SM) concept and the RAS technique to tackle the costs of the multiple radio frequency (RF) chains at both link ends. Moreover, we develop a pair of RAS algorithms for the proposed mmWave RAS-SM scheme based on the capacity maximization (max-capacity) and the bit-error rate (BER) minimization criteria, which are formulated as two combinatorial optimization problems. The theoretical gradients of the capacity and the BER with respect to RAS variables are derived and the convexities of these problems are discussed. Furthermore, a novel iterative algorithm through jointly designing the log-barrier algorithm (LbA) and the simplified conjugate gradient method is proposed for RAS optimization. Our simulation results show that the proposed RAS-SM schemes are capable of achieving considerable performance gains over conventional norm-based and eigenvalue-based schemes in mmWave MIMO channels, while avoiding an overwhelming complexity imposed by exhaustive search.

We study a downlink non-orthogonal multiple access system with cooperative full-duplex relaying, where the near user in terms of the base station (BS) is enabled to act as a full-duplex relay for the far user. In particular, we first derive the outage probability and ergodic sum rate with closed-form expressions when the power allocations at the BS and relay (or the near user) are fixed. Then, we analytically obtain the optimal power allocations with closed-form expressions at the BS and relay to minimize the outage probability. Furthermore, by taking the fairness between the near user and far user into account, we characterize the optimal power allocations with closed-form expressions at the BS and relay to maximize the minimum achievable rate of users. Simulation results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over the state of the art.

In this paper, we address codebook-based beamforming for multi-antenna orthogonal frequency division multiplexing (OFDM) systems operating in 60-GHz millimeter-wave band. Given a pre-specified codebook, the task of the beamforming process is to identify the best transmit and receive beam vectors to maximize the spectral efficiency. By properly modeling the multipath structure in 60-GHz channels, we transform the above task into the estimation of several sparse signal vectors. Furthermore, we tailor compressive sensing (CS) technology to efficiently estimate such sparse signals, wherein the CS measurement matrix is specifically designed by minimizing the inner products between different columns of the CS effective dictionary. As a result, CS-based iterative and non-iterative beamforming schemes are proposed. Due to the ability to judiciously exploit multipath sparsity inherent in 60-GHz channels, the proposed schemes significantly outperform the existing counterparts in terms of training overhead and success probability, which is confirmed by theoretical analysis and extensive simulations.

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary channel gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

To realize high beamforming gain and thus sufficient link budget, millimeter wave (MMW) multiple-input multiple-output (MIMO) systems employ large antenna arrays at both the transmitter and receiver. However, due to the power and cost limitation, only a limited number of radio frequency (RF) chains are available for MMW antenna arrays. In other words, the number of RF chains is far smaller than that of antenna elements. This MMW MIMO setup poses a formidable challenge for channel estimation, which is conventionally required to realize the optimal singular value decomposition (SVD) beamforming. To circumvent the formidable channel estimation, this paper proposes two iterative antenna training schemes for the SVD beamforming in MMW MIMO systems. Relying on the channel reciprocity in time division duplex, the proposed schemes employ power iteration and Lanczos iteration, respectively, to gradually approach the SVD beamforming. During iterations, we only need to estimate several channel-related vectors instead of the MIMO channel matrix. Thanks to the spatial sparsity in MMW channels, the training overheads required by the proposed schemes are moderate. Simulations demonstrate that the proposed schemes outperform the counterpart in terms of performance and training overhead, and can achieve the performance very close to that of the perfect SVD beamforming.

This paper addresses downlink channel estimation for frequency division duplex multi-user massive multiple-input multiple-output systems. Suppose that a base station communicates with K mobile users, then the task is to estimate K channel matrices, each corresponding to one user. Due to the limited scattering in physical propagation, each channel matrix is sparse in the virtual angular domain. Besides, different user links tend to share some common scatterers. As such, different channel matrices may have a partially common sparsity pattern. These observations motivate us to take a variational Bayesian inference based approach for channel estimation. Specifically, we design a Gaussian mixture prior model, which can efficiently capture the individual sparsity in each channel matrix and the partially joint sparsity shared by different channel matrices. Furthermore, we develop a variational expectation maximization strategy to estimate the hyperparameters associated with the prior model and the channel matrices. Compared with the existing counterparts, the proposed approach achieves much better performance in terms of the channel estimation accuracy, while maintaining a low computational complexity.

This paper studies the throughput maximization problem for a three-node relay channel with non-ideal circuit power. In particular, the relay operates in a half-duplex manner, and the decode-and-forward (DF) relaying scheme is adopted. Considering the extra power consumption by the circuits, the optimal power allocation to maximize the throughput of the considered system over an infinite time horizon is investigated. First, two special scenarios, i.e., the direct link transmission (only use the direct link to transmit) and the relay assisted transmission (the source and the relay transmit with equal probability) are studied, and the corresponding optimal power allocations are obtained. By transforming two non-convex problems into quasi-concave ones, the closed-form solutions show that the source and the relay transmit with certain probability, which is determined by the average power budgets, circuit power consumptions, and channel gains. Next, based on the above-mentioned results, the optimal power allocation for both the cases with and without direct link is derived, which is shown to be a mixed transmission scheme between the direct link transmission and the relay assisted transmission.

In this paper, low-complexity time-domain turbo equalization (TDTE) detectors based upon soft-interference-cancellation (SIC)-aided minimum mean-square error (MMSE) criterion are proposed for single carrier generalized spatial modulation (SC-GSM) systems. First, a symbol-by-symbol-aided TDTE detector for application to the small-scale GSM systems is proposed, where the zero symbols are considered as constellation points when performing SIC. Then, vector-by-vector-aided TDTE (VV-TDTE) detectors for application to larger-scale antenna systems are introduced, where the GSM symbol is treated as an entire vector when performing SIC. As for the proposed VV-TDTE detectors, in addition, different time-varying filter coefficients are designed, in order to strike a flexible tradeoff between complexity and performance. By relying upon extrinsic information transfer chart analysis, we show that the proposed TDTE detectors are capable of providing considerable bit error rate performance gains over existing TDTE detectors and over the classic frequency-domain equalization-based MMSE detector, especially for the unbalanced antenna configurations.

Differential space time block coding (STBC) achieves full spatial diversity and avoids channel estimation overhead. Over highly frequency-selective channels, STBC is integrated with orthogonal frequency division multiplexing (OFDM) to achieve high performance. However, low-cost implementation of differential STBC- OFDM using direct-conversion transceivers is sensitive to In-phase/Quadrature-phase imbalance (IQI). In this paper, we quantify the performance impact of IQI at the receiver front-end on differential STBC-OFDM systems and propose a compensation algorithm to mitigate its effect. The proposed receiver IQI compensation works in an adaptive decision-directed manner without using known pilots or training sequences, which reduces the rate loss due to training overhead. Our numerical results show that our proposed compensation algorithm can effectively mitigate receive IQI in differential STBC-OFDM.

Device-to-Device (D2D) communications are considered as one of the key technologies for 5G wireless communication systems. In this paper, a frequency reuse scheme, in which multiple D2D links share spectrum resources with multiple cellular links simultaneously, is studied. Two novel concepts are introduced, namely the D2D pool and the cellular pool. D2D links in the D2D pool reuse uplink resource blocks occupied by the cellular links in the corresponding cellular pool. To maximize the reuse of spectrum, an algorithm for searching maximum D2D pool is proposed. Moreover, an algorithm for solving the problem of resource competition between different D2D pools is proposed. Simulation results are provided to show that the proposed scheme can well support D2D communications in cellular networks and significantly improve the data rate of D2D links.

In this paper, a new iteratively reweighted least squares method is proposed for recovery of block-sparse signals with unknown cluster patterns. In many practical applications, sparse signals have block-sparse structures with nonzero coefficients occurring in clusters, while the prior information of the cluster pattern is usually unavailable. To address this issue, we propose an element-overlapping log-sum functional to encourage the sparseness and the cluster pattern simultaneously. The algorithm is developed by iteratively minimizing a convex surrogate function that majorizes the original objective function, which results in an iteratively reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. Convergence of the iterations to a local minimum of the penalty function is also guaranteed. Numerical results are provided to illustrate the effectiveness of the proposed method.

In this paper, we design the low-complexity tree search-based detectors for generalized spatial modulation (GSM) aided single carrier (SC) systems over dispersive channels. Specifically, we commence with a brief review of the existing detection algorithms and then extend the sphere decoding-aided (SD) tree search algorithms designed for flat fading channels to GSM-aided SC systems. Moreover, a pair of reduced-complexity tree search algorithms are proposed by employing a hybrid concept of SD and M-algorithm to balance a tradeoff between the performance and complexity. Our proposed detectors are capable of recovering the transmit signal for both the overdetermine and underdetermine antenna configurations. Simulation results show that: 1) the extended SD-aided tree search algorithms are capable of providing the same performance as the maximum likelihood (ML) algorithm with reduced complexity; 2) the proposed novel algorithms exhibit lower complexity with negligible performance loss, compared with other tree search algorithms.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access the licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the EE maximization as a joint optimization problem of the spectrum sensing duration and the transmit power of the cognitive transmitter. Specially, we consider the constraints of both the collision probability between the primary and cognitive transmission and the outage probability of the cognitive transmission. Since the joint optimization problem of the sensing duration and the transmit power is hard to be solved directly, we decompose it into two sub-problems with the spectrum sensing duration and the transmit power as variables, respectively. Based on the analytical solvers of the two sub-problems, we propose an iterative-based algorithm to solve the joint optimization problem. Finally, numerical results are provided to validate the analysis and performance of our proposed algorithms.

Centralized spectrum management is one of the key dynamic spectrum access (DSA) mechanisms proposed to govern the spectrum sharing between government incumbent users (IUs) and commercial secondary users (SUs). In the current centralized DSA designs, the operation data of both government IUs and commercial SUs needs to be shared with a central server. However, the operation data of government IUs is often classified information and the SU operation data may also be commercial secret. The current system design dissatisfies the privacy requirement of both IUs and SUs since the central server is not necessarily trust-worthy for holding such sensitive operation data. To address the privacy issue, this paper presents a privacy-preserving centralized DSA system (P2-SAS), which realizes the complex spectrum allocation process of DSA through efficient secure multi-party computation. In P2-SAS, none of the IU or SU operation data would be exposed to any snooping party, including the central server itself. We formally prove the correctness and privacy-preserving property of P2-SAS and evaluate its scalability and practicality using experiments based on real-world data. Experiment results show that P2-SAS can respond an SU's spectrum request in 6.96 seconds with communication overhead of less than 4 MB.

Conventional energy harvesting (EH) communications are limited by energy unsteadiness and causality. In this paper, a novel technique namely energy borrowing (EB) is specifically designed as an efficient way to bridge energy harvesting and power grid in wireless communications. The EH nodes are capable of adaptively borrowing and returning energy from the power grid, at the cost of paying extra energy interest. In order to maximize the transmission throughput, the packets scheduling problem in the proposed EB-aided EH systems is investigated and adaptive solutions are developed. Simulation results show that the proposed EB-aided EH system provides throughput improvement over the conventional one without EB, while achieving extra energy benefits for the power grid.

Symmetric α-stable (SαS) distribution noise is widely used to model co-channel and network interference in wireless communication systems. As robust and adaptive techniques, myriad filtering (MyF) and spherically symmetric vector MyF have been applied to suppress univariate and spherically symmetric multivariate SαS distribution noise, respectively. At a communication receiver, the received band-pass noisy signals are usually down-converted to complex baseband, and the resulted complex baseband SαS noise has been demonstrated not to be circularly symmetric. In this paper, we proposed a complex baseband MyF (CBMyF) to suppress the non- circularly symmetric complex baseband SαS noise. Besides, there are few researches with respect to the demodulation of memory modulation signals, e.g., minimum shift keying (MSK) signal, under SαS noise. Thus, based on CBMyF, we proposed coherent and non-coherent MSK demodulation algorithms under SαS noise in this paper. Furthermore, maximum likelihood (ML) MSK demodulation under SαS noise also been proposed. Simulation results show that the bit error rate (BER) performance of the proposed CBMyF based MSK demodulation can closely approach that of ML demodulation. Meanwhile, the proposed CBMyF is compared with the common used clipper, and the results validate its advantage of robustness and adaptivity.

The problem of the centralized caching is studied in a two-layer network. The first layer of the network is constructed by a server and K1helpers, and the second layer consists of K1orthogonal sub-networks, each of which contains a helper and K2users. The pioneer caching design in the two-layer network is to directly apply the Maddah-Ali & Niesen (MAU) centralized caching [1] into individual layers, such that single-layer multicast opportunities (SMO) are deployed. In this paper, a joint caching (JC) algorithm is developed by exploiting both the SMO and the correlations of caching contents across two layers, namely, cross-layer storage correlations (CSC). Furthermore, cross-layer multicast opportunities (CMO) can also be created by applying the MAU scheme between the server and users. In order to simultaneously obtain the caching gains from SMO, CSC, and CMO, a hybrid caching scheme is proposed and demonstrated to be order-optimal when the storage sizes at both helpers and users are limited. In other words, the achievable rate region lies within a constant multiplicative and additive gap of the information-theoretic bounds. In particular, the multiplicative and additive factors can be carefully characterized to be 1/48 and 4, respectively.

Conventionally, fast frequency hopping (FFH) is regarded as a non‐coherent system, which has inevitable shortcomings in low spectral efficiency. To achieve a high spectral efficiency while maintaining a favourable bit error ratio (BER) performance in FFH systems, in this study, the authors extend their previously proposed subset‐based coherent FFH (S‐CFFH) scheme with the aid of spatial modulation (SM). Furthermore, they derive the closed‐form expressions of the pairwise error probability and asymptotically BER bound for the S‐CFFH/SM maximum‐likelihood receivers in the presence of multitone jamming and highly frequency‐selective Rayleigh fading channels, where the perfect and imperfect channel state information are both considered. The authors’ analysis and simulation results show that, the proposed S‐CFFH/SM scheme outperforms both the conventional non‐coherent FFH and the S‐CFFH schemes, in terms of spectral efficiency and BER, respectively.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access a licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the average EE maximization problem in fading channels as a joint optimization problem of the spectrum sensing duration and the transmit power of cognitive users. Meanwhile, we consider the constraints of both the collision probability between the primary and cognitive transmissions and the outage probability of cognitive transmissions. However, the joint optimization problem subject to the constraints is complicated and it is computationally hard to obtain the optimal solution. Alternatively, we develop two algorithms, i.e., a linear search algorithm and an iterative-based algorithm, with considerable complexity to solve the problem. Numerical results verify the correctness of both algorithms and show that the proposed algorithms can achieve the performance close to that of the exhaustive search algorithm and outperform the state of arts.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. Specifically, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the cross-channel gain by observing the power adaption. To demonstrate the accuracy of the estimation, we analytically characterize both an upper bound and a lower bound of the estimation performance. Furthermore, we study the impact of CT's relaying on the primary transmission and observe that the impact is related to the CT's location. By introducing a factor φ (0 ≤ φ ≤ 1) to denote the probability that the CT's relaying improves the primary transmission instead of causes interference, we design the CT location as a function of φ. Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of the art, we show the advantages of the proposed estimator.

Generalized spatial modulation (GSM) is a recently proposed appealing multi-input multi-output (MIMO) transmission technique, which is capable of striking a tradeoff between the achievable transmission rate and the cost of radio frequency (RF) chains. In this letter, a novel low-complexity near-optimal soft decision (SoD)-aided detector is proposed for GSM, which considerably reduces the search space by employing a block minimum mean-squared error (B-MMSE) algorithm. Our simulation results show that the proposed detector is capable of achieving a better tradeoff between bit-error-rate (BER) performance and computational complexity compared with the existing matched filter (MF)-based SoD algorithms.

Generalized frequency division multiplexing (GFDM) is a recent multicarrier 5G waveform candidate with the flexibility of pulse shaping filters. However, the flexibility of choosing a pulse shaping filter may result in intercarrier interference (ICI) and intersymbol interference (ISI), which becomes more severe in a broadband channel. In order to eliminate the ISI and ICI, based on discrete Gabor transform (DGT), in this paper, a transmit GFDM signal is first treated as an inverse DGT, and then a frequency-domain DGT is formulated to recover (as a receiver) the GFDM signal. Furthermore, to reduce the complexity, a suboptimal frequency-domain DGT called local DGT is developed. Some analyses are also given for the proposed DGT-based receivers.

The benefits of transmit antenna selection (TAS) invoked for spatial modulation (SM) aided multiple-input multiple-output (MIMO) systems are investigated. Specifically, we commence with a brief review of the existing TAS algorithms and focus on the recently proposed Euclidean distance-based TAS (ED-TAS) schemes due to their high diversity gain. Then, a pair of novel ED-TAS algorithms, termed as the improved QR decomposition (QRD)-based TAS (QRD-TAS) and the error-vector magnitude-based TAS (EVM-TAS) are proposed, which exhibit an attractive system performance at low complexity. Moreover, the proposed ED-TAS algorithms are amalgamated with the low-complexity yet efficient power allocation (PA) technique, termed as TAS-PA, for the sake of further improving the system's performance. Our simulation results show that the proposed TAS-PA algorithms achieve signal-to-noise ratio (SNR) gains of up to 9 dB over the conventional TAS algorithms and up to 6 dB over the TAS-PA algorithm designed for spatial multiplexing systems.

We study a one-hop device-to-device (D2D)-assisted wireless caching network, where popular files are randomly and independently cached in the memory of end users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through a one-hop D2D transmission, or from the base station. We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex, and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link-scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link-scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link-scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of the arts.

We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems. Specifically, given a set of symbol vectors to be transmitted to K users, the problem is to find an OFDM-modulated signal that has a low PAPR and meanwhile enables multiuser interference (MUI) cancellation. Unlike previous works that tackled the problem using convex optimization, we take a Bayesian approach and develop an efficient PAPR reduction method by exploiting the redundant degrees of freedom of the transmit array. The sought-after signal is treated as a random vector with a hierarchical truncated Gaussian mixture prior, which has the potential to encourage a low PAPR signal with most of its samples concentrated on the boundaries. A variational expectation-maximization (EM) strategy is developed to obtain estimates of the hyperparameters associated with the prior model, along with the signal. In addition, the generalized approximate message passing (GAMP) is embedded into the variational EM framework, which results in a significant reduction in computational complexity of the proposed algorithm. Simulation results show our proposed algorithm achieves a substantial performance improvement over existing methods in terms of both the PAPR reduction and computational complexity.

In this paper, we investigate a pair of transmit precoding (TPC) algorithms conceived for spatial modulation (SM) systems communicating over flat-fading multiple-input multiple-output (MIMO) channels. In order to retain all the benefits of conventional SM, we design the TPC matrix to be diagonal and introduce two design criteria for optimizing the elements of the TPC matrix. Specifically, we first investigate a TPC design based on maximizing the minimum Euclidean distance dmin(max-dmin) between the SM signal points at the receiver side. A closed-form solution of the optimal max-dmin-based TPC matrix is derived. Then, another TPC design algorithm is proposed for directly minimizing the bit error ratio (BER) upper bound of SM, which is capable of jointly optimizing the overall Euclidean distance between all received signal points. In the minimum BER (min-BER)-based TPC algorithm, the theoretical gradient of the BER with respect to the diagonal TPC matrix is derived and a simplified iterative conjugate gradient (SCG) algorithm is invoked for TPC optimization. Our simulation results demonstrate that the proposed max-dmin-based TPC algorithm is optimal in terms of the minimum distance. However, increasing dmindoes not achieve a further BER improvement. We also confirm that the min-BER-based TPC outperforms the max-dmin-based TPC schemes in terms of the achievable BER performance.

We consider the problem of uplink channel estimation for millimeter wave (mmWave) systems, where the base station (BS) and mobile stations (MSs) are equipped with large antenna arrays to provide sufficient beamforming gain for outdoor wireless communications. Hybrid analog and digital beamforming structures are employed by both the BS and the MS due to hardware constraints. We propose a layered pilot transmission scheme and a CANDECOMP/PARAFAC (CP) decomposition-based method for joint estimation of the channels from multiple users (i.e., MSs) to the BS. The proposed method exploits the intrinsic low-rank structure of the multiway data collected from multiple modes, where the low-rank structure is a result of the sparse scattering nature of the mmWave channel. The uniqueness of the CP decomposition is studied, and the sufficient conditions for essential uniqueness are obtained. The conditions shed light on the design of the beamforming matrix, the combining matrix, and the pilot sequences, and meanwhile provide general guidelines for choosing system parameters. Our analysis reveals that our proposed method can achieve a substantial training overhead reduction by leveraging the low-rank structure of the received signal. Simulation results show that the proposed method presents a clear advantage over a compressed sensing-based method in terms of both estimation accuracy and computational complexity.

The subset-based coherent fast frequency hopping (S-CFFH), was recently proposed to enable reliable channel estimation and coherent schemes for FFH over fading channels. In this paper, we analyze the maximum-likelihood (ML) receiver for the S-CFFH/binary phase shift keying (BPSK), with partial band noise jamming (PBNJ), frequency selective Rayleigh fading, and imperfect channel state information (CSI). The expressions of closed-form bit error ratio (BER) are presented, and the results are validated by simulations. It is shown that the proposed ML receiver significantly outperforms some conventional diversity combining receivers in terms of BER. The ML receiver also greatly reduces the jammer's efficiency.

With device-to-device (D2D) communications, a user terminal(UT) can naturally be used as a relay node (RN) and thus inherently support multi-hop transmission. Thus, cell-edge or deeply faded users can obtain a more uniform connectivity experience. In this paper, we investigate mobile association for the UT with the capability of D2D communications in heterogeneous networks (HetNets). We will develop a framework on joint mobile association and transmission mode switching between the direct and the D2D relay modes to improve the system spectrum efficiency (SE) and energy efficiency (EE). We first formulate the optimization problems, and then obtain closed-form solutions. Simulation results show that with the proposed schemes, both SE and EE of the network can be significantly improved compared to the traditional solutions without D2D communications. We also discuss the trade-off between the minimum rate requirement and EE of a network.

Carrier Interferometry Orthogonal Frequency Division Multiplexing (CI/OFDM) can mitigate the problem of high peak-to-average power ratio (PAPR) of OFDM signal. In practice, some subcarriers have to be deactivated in order to avoid harmful interference to licensed system in cognitive radio (CR) context. For these cases, Non-Continuous CI/OFDM (NC-CI/OFDM) has recently been proposed, for which some subcarriers are unused intentionally. Unfortunately, the PAPR performance of current NC-CI/OFDM schemes is sensitive to the distribution of unused subcarriers. In this paper, we propose a new spectrum adaptive NC-CI/OFDM system with improved CI spreading scheme to achieve desired PAPR performance. A novel iterative method is proposed herein to design pilot symbols so that desirable balance between the channel estimation performance and the PAPR can be obtained. We further propose a minimum mean square error (MMSE) based soft signal detection and its complexity-reduced implementation. Simulation results show that our proposed new NC-CI/OFDM scheme can achieve significantly better PAPR performance than that of existing schemes, while its bit-error-rate (BER) performance close approaches that of the existing schemes with negligible performance gap.

In this study, the authors investigate the benefits of phase‐rotation‐assisted precoding (PRP) technique in spatial modulation (SM) systems, which are based on maximum free distance d min . First, a closed‐form solution of the maximum‐ d min PRP matrix is derived for the scenario of having two transmit antennas ( N t = 2). Moreover, two numerical methods are proposed for dealing with the case of N t > 2. The complexity of the proposed algorithms is presented. The authors simulation results show that the proposed PRP algorithms provide beneficial bit error ratio performance improvements compared with both the conventional SM and with the existing adaptive SM.

This paper investigates energy-efficient device-to-device (D2D) communications in cellular networks. We aim to maximize the overall energy-efficiency (EE) of D2D users and regular cellular users (RCUs) while considering the circuit power consumption and the quality-of-service (QoS) requirements for both types of users as well as power constraints. Three transmission modes, namely, dedicated mode, reusing mode, and cellular mode, are considered for D2D users to share spectrum with RCUs. Parametric Dinkelbach method and concave-convex procedure (CCCP) are adopted to transform the original optimization problems into more tractable forms through sequential convex approximations. Then, interior point method is exploited to obtain the optimal solution. Simulation results show that system EE can be improved significantly with the proposed mode switching algorithm compared with the single mode transmission. Besides, it is also shown that the reusing mode is more preferred in the EE based mode switching while it is the dedicated mode in the spectrum-efficiency (SE) based mode switching in most situations.

A time-domain golay complementary sequences based frequency-dependent I/Q imbalance compensation scheme for receiver is presented. By utilizing property of golay sequences, the signal and its conjugate interference in preamble are separated by correlation and used to estimate the equivalent channel of I branch and Q branch. After that, a filter which contains the difference of those equivalent channels is estimated by Least Square Error (LSE) method and adopted to compensate the imbalance. The compensator structure is designed to fit the scheme so that the phase imbalance is not needed to estimate separately during the compensation. The provided scheme could be effectively applied in standards like 802.15.3c or 802.11ad where golay sequence is adopted as preamble of a frame so that there is no cost in frame structure. All the estimation and compensation are operated in time-domain thus no FFT operation is needed. Also, no further information about the channel is required, which ensures the imbalance distortion could be eliminated right after synchronization in the receiver before channel estimation and equalization. Thus, there is no strict on the algorithms of channel estimation and equalization. The performance of the scheme is evaluated by computer simulation and compared with existed algorithms. The results show that it has effectively eliminated the influence of the imbalance at a low complexity.

Recently, a proactive estimation method has been proposed to obtain the cross-channel gain from cognitive transmitter (CT) to primary receiver (PR), where CT acts as a full-duplex amplified-and-forward (AF) relay for primary users. However, since the cognitive and primary users usually have no cooperation, it has no guarantee that the direct and relay signals are synchronized, i.e, the time delay between direct and relay signals may be randomly large or small. This may cause interference to PR. In this paper, we analyze the impacts of the small and large time delays on proactive estimation, and propose an interference-free method to estimate the cross-channel gain. Simulation results demonstrate that the proposed method obtains about 5% estimation error with about 99% interference-free probability.

Most existing studies on two-way relay networks (TWRNs) typically assume that the channels are reciprocal. However, in time-frequency dispersive channels, the channels between the multiple access channel phase and broadcast channel phase become highly non-reciprocal, which significantly complicates the indispensable channel estimation for TWRNs employing coherent detection. In this paper, the challenging problem of channel estimation and data detection (CE-DD) is investigated for amplify-and-forward (AF) orthogonal frequency division multiplexing (OFDM)-based TWRNs over unknown non-reciprocal time-frequency dispersive channels. An independent CE-DD algorithm according to the minimum mean-square error (MMSE) criterion is firstly proposed. To further improve system performance, an iterative joint CE-DD algorithm employing the variational Bayesian inference (VBI) framework is then developed. Simulation results show that initialized by the proposed MMSE-based algorithm, the proposed VBI-based iterative algorithm converges in a few iterations and after convergence, its performance approaches the ideal case which supposes perfect knowledge of channel state informations.

Most existing works on two‐way relay systems (TWRSs) are based on the assumption that the channels are reciprocal. However, in high‐speed moving scenarios, the channels between the multiple access channel phase and broadcast channel phase become non‐reciprocal, which significantly complicates the indispensable channel estimation for TWRSs employing coherent detection. In this study, the challenging problem of channel estimation and data detection (CEaDD) is investigated for amplify‐and‐forward orthogonal frequency division multiplexing‐based TWRSs under unknown non‐reciprocal doubly selective fading channels. First, an independent CEaDD algorithm according to the minimum mean‐square error (MMSE) criterion is proposed. To further improve system performance, an iterative joint CEaDD algorithm employing the variational Bayesian inference (VBI) framework is developed. It is shown by simulations that initialised by the proposed MMSE‐based algorithm, the proposed VBI‐based iterative algorithm converges in a few iterations and after convergence, its performance approaches the ideal case which assumes perfect knowledge of channel state information.

To support interference-free quasi-synchronous code-division multiple-access (QS-CDMA) communication with low spectral density profile in a cognitive radio (CR) network, it is desirable to design a set of CDMA spreading sequences with zero-correlation zone (ZCZ) property. However, traditional ZCZ sequences (which assume the availability of the entire spectral band) cannot be used because their orthogonality will be destroyed by the spectrum hole constraint in a CR channel. To date, analytical construction of ZCZ CR sequences remains open. Taking advantage of the Kronecker sequence property, a novel family of sequences (called "quasi-ZCZ" CR sequences) which displays zero cross-correlation and near-zero auto-correlation zone property under arbitrary spectrum hole constraint is presented in this paper. Furthermore, a novel algorithm is proposed to jointly optimize the peak-to-average power ratio (PAPR) and the periodic auto-correlations of the proposed quasi-ZCZ CR sequences. Simulations show that they give rise to single-user bit-error-rate performance in CR-CDMA systems which outperform traditional non-contiguous multicarrier CDMA and transform domain communication systems; they also lead to CR-CDMA systems which are more resilient than non-contiguous OFDM systems to spectrum sensing mismatch, due to the wideband spreading.

In many practical applications such as direction-of- arrival (DOA) estimation and line spectral estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a continuous domain. To apply the conventional compressed sensing to such applications, the continuous parameter space has to be discretized to a finite set of grid points. Discretization, however, incurs errors and leads to deteriorated recovery performance. To address this issue, we propose an iterative reweighted method which jointly estimates the unknown parameters and the sparse signals. Specifically, the proposed algorithm is developed by iteratively decreasing a surrogate function majorizing a given objective function, which results in a gradual and interweaved iterative process to refine the unknown parameters and the sparse signal. Numerical results show that the algorithm provides superior performance in resolving closely-spaced frequency components.

In this letter, we propose a passive detection method to enable cognitive user to detect active primary receiver. With our method, cognitive user may conduct underlay spectrum sharing without the need of channel state information between primary and cognitive users. Simulation results show that our detector can provide about 100% to 300% more access probabilities in average than conventional energy detector.

This letter develops a novel iterative joint channel state information estimation (CSIE), jammer estimation (JE) and data detection (DD) algorithm for orthogonal frequency division multiplexing (OFDM)-based distributed antenna systems in the presence of multiple unknown jammers. In contrast to existing methods that only heuristically combine CSIE, JE and DD, the proposed algorithm rigorously casts the three problems under a unified variational Bayesian inference framework. Simulation results demonstrate that the proposed algorithm converges rapidly, and after convergence, the bit-error-rate performance of the proposed algorithm is very close to that of the ideal case which supposes perfect knowledge of CSI and jammer. In addition, it is shown that the proposed algorithm significantly outperforms other state-of-the-art methods.

As a fundamental technology of cognitive radio, the spectrum sensing scheme is required to perform well in low signal-to-noise ratio (SNR) environments. In this paper, we propose a novel spectrum sensing method based on adaptive bistable stochastic resonance (A-BSR). By maximizing the SNR gain introduced by the BSR system, we first present an A-BSR system, of which the parameters can be adaptively adjusted based on the background noise. Then, we propose an A-BSR aided spectrum sensing scheme by passing the received signal through an A-BSR system to improve SNR. Based on the characteristics of A-BSR system output in frequency and time domains, we further propose two energy detection (ED)-based spectrum sensing algorithms. As the output of an A-BSR system given a noise input is concentrated around frequency zero, we propose a modified ED based on periodogram (P-ED) in frequency domain. Moreover, as the A-BSR system output for noise input has approximate constant amplitude in time domain, a novel deviation-based ED (D-ED) is proposed. Extensive simulation results show that the proposed A-BSR aided spectrum sensing scheme can achieve much better performance than the existing ED and BSR aided spectrum sensing schemes with fixed parameters, especially under very low SNR region.

In this paper, we investigate optimal resource allocation for device-to-device (D2D) communication underlaying cellular network in fading channels. We consider a scenario that the instantaneous channel power gain of interference links from regular cellular users (CUs) to D2D users are unknown at base station (BS) since obtaining the channel-state-information (CSI) in this case is difficult and requires high overhead. We assume that BS provides guaranteed quality-of-service (QoS) in terms of signal-to-interference-plus-noise-ratio (SINR) for CUs and outage probability for D2D pairs, respectively. Based on the assumptions, we first propose a probabilistic access control for D2D pairs to satisfy all the QoS requirements and power constraints. We then derive joint power and channel allocation to maximize the overall throughput of the CUs and admissible D2D pairs. Through simulation, we show the effectiveness of the proposed probabilistic strategy and there exists an optimal threshold of the targeted outage probability with respect to D2D access rate and overall network throughput.

N-continuous orthogonal frequency division multiplexing (OFDM) is a promising sidelobe suppression technique by enhancing the continuity between adjacent symbols with higher-order derivatives. However, it has high computational complexity due to the large-scale matrix operations in frequency domain. In this paper, a low-complexity time-domain signal processing (TDSP) algorithm for N-continuous OFDM is proposed. Based on linear combination of basis vectors, it utilizes small-scale matrix operations to generate the coordinates to construct N-continuous OFDM signal. Simulation results approve that the proposed algorithm achieves dramatic complexity reduction with identical sidelobe suppression and BER performance compared to conventional frequency-domain processed N-continuous OFDM.

Spectrum sensing is a fundamental technology to detect the presence of primary users (PU) in cognitive radio. Usually, the spectrum sensing scheme is required to have good performance even in extremely low signal-to-noise ratio (SNR) environments. In this paper, we proposed a novel spectrum sensing method based on adaptive bistable stochastic resonance (A-BSR) to meet this requirement. For the proposed A-BSR aided spectrum sensing scheme, the received signal is first passed through an A-BSR system to improve SNR. Furthermore, we proposed a novel simple but efficient energy detection (ED) scheme to make decision on the existence of PU's signal. Extensive simulation results show that the proposed A-BSR aided novel ED based spectrum sensing can achieve much better performance than exiting ED and bistable stochastic resonance aided ED based spectrum sensing schemes.

In device-to-device (D2D) communications underlaying uplink (UP) cellular networks, the channel state information (CSI) of interference links between regular cellular users (CUs) and D2D receivers is necessary to provide guaranteed quality-of-service (QoS) to D2D users. However, getting the CSI is very difficult and requires high overhead. In this paper, we propose a selected-K maximum distance ratio (MDR) feedback scheme (KMDR) to reduce feedback overhead, in which each D2D receivers only needs to feedback CSI of K CUs with the largest MDR metric. Simulation results show that up to 80% feedback can be reduced at D2D receivers by KMDR while still providing a near optimal performance. We also study the effect of side information at the D2D receivers. It is shown that it is possible to further reduce the feedback information when full side information is known at the D2D receivers.

In this paper, we investigated the cooperative sensing in a heterogeneous cognitive radio (CR) network scenario, where each secondary user (SU) may be equipped with different number of receive antennas and have different signal processing capacity, e.g., sampling rate. By considering the discrepancy in sensing reliability of different SUs, we extended the existing researches to propose an optimal cooperative sensing (OCS) scheme based on normalized energy detection (NED) by considering reporting errors, i.e., the fusion center (FC) may receive erroneous results from some SUs due to channel fading. It is derived that the proposed OCS scheme is the linear combination of modified local detection statistics. The optimal combining coefficient is just a function of the numbers of the antennas and the received signal samples, the signal to noise ratio (SNR) and the variance of the reporting errors at each SU. Meanwhile, the performance of the proposed OCS scheme, the well-known equal gain combination (EGC) method and the maximum normalized energy (MNE) detector with reporting errors is analytically derived, respectively. Furthermore, to avoid the prior information of each SU's SNR and the variance of the reporting errors, and simplify the decision-making as well as threshold setting, a sub-optimal cooperative spectrum sensing (SOCS) is proposed. Numerical results show that the proposed OCS and SOCS schemes all perform much better than the existing EGC and MNE methods.

As a cognitive radio (CR) modulation technique, transform domain communication system (TDCS) has been proposed by utilizing white space spectrum for the network access and achieving low probability of interception (LPI). In the previously reported TDCS- based cognitive radio networks (CRN), however, the non-zero periodic correlation function between any pair of users results in multiuser interference (MUI). In this paper, a novel framework under the CR constraints is presented by employing a two- dimension spreading scheme (i.e., time and frequency domains). We first obtain a class of spreading sequences with almost perfect periodic correlation function for arbitrary spectrum utilization pattern. Then, an universal criteria is presented for synchronous MUI-free CRNs when employing TDCS. Simulation results demonstrate that the proposed TDCS-based CRN architecture is a preferable candidate for distribution wireless networks, such as CR Ad-hoc wireless sensor networks.

SUMMARY In cognitive radio networks (CRNs), considering the randomness of primary users' (PUs) arrival and the nonideality of spectrum sensing performed by secondary users (SUs), the collisions between PUs and SUs are unavoidable. Frequent occurrences of collisions will strongly degrade PUs' and SUs' QoS. Therefore, collision statistics, such as the average number of collisions, is a very important performance metric in CRNs. If collision statistics are not considered in the scheduling scheme of the CRNs, some SUs will meet more collisions than the others, which cause unfair experiences among the SUs. Therefore, a fair scheduling scheme based on collision statistics is proposed in this paper, which can improve fairness across all SUs. First, the number of collisions is defined as an important fairness metric for each SU, and the scheduler dynamically adjusts the priorities of the SUs by periodically counting each SU's collision number. Then, a prediction algorithm, based on the continuous‐time Markov chain model, is proposed to predict the idle probabilities of the available channels in the next slot. Considering both the priorities of the SUs and the idle probabilities of the available channels, a rational scheduling scheme will be achieved finally. Assuming the ordered hunt scheduling scheme applied by the primary system, the simulation results show that the proposed scheme can significantly improve the fairness across all SUs with little impact on spectrum utilization. Copyright © 2012 John Wiley & Sons, Ltd.

Transform domain communication system (TDCS), as an overlay cognitive radio communication system, has been proposed to obtain a low probability of interception by using spectrum bin nulling to synthesise an adaptive waveform corresponding to the spectrum sensing output. However, its low spectral efficiency limits potential practical applications. In this study, an efficient modulation scheme, namely quadrature cyclic code shift keying (Q‐CCSK), is proposed for TDCSs by generating another fundamental modulation waveform as the data‐bearing quadrature branch. Although using Q‐CCSK doubles the spectral efficiency for TDCSs, the instinct inter‐branch interference arises from the added quadrature branch. Through the orthogonality analysis, it is proven that the two branches (in‐phase and quadrature‐ branches) are still satisfied the property of quasi‐orthogonality. Moreover, the performances of TDCSs employing Q‐CCSK in additive white Gaussian noise (AWGN) and multipath fading channels are discussed, respectively. Analytical and simulation results demonstrate that, compared with conventional schemes, the TDCS employing Q‐CCSK can double the spectral efficiency with comparable systematic performance.

In this letter, we consider a decentralized detection problem in which a number of sensor nodes collaborate to detect the presence of an unknown deterministic signal. Due to stringent power/bandwidth constraints, each sensor quantizes its local observation into one bit of information. The binary data are then sent to the fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to make a global decision. In this context, we study one-bit quantizer design and analyze the asymptotic performance of the one-bit GLRT detector for cases where the quantized data are sent to the FC via perfect or imperfect channels. Simulation results are carried out to corroborate our theoretical analysis and to illustrate the performance of the proposed scheme.

In cellular networks, proximity users may communicate directly without going through the base station, which is called Device-to-device (D2D) communications and it can improve spectral efficiency. However, D2D communications may generate interference to the existing cellular networks if not designed properly. In this paper, we study a resource allocation problem to maximize the overall network throughput while guaranteeing the quality-of-service (QoS) requirements for both D2D users and regular cellular users (CUs). A three-step scheme is proposed. It first performs admission control and then allocates powers for each admissible D2D pair and its potential CU partners. Next, a maximum weight bipartite matching based scheme is developed to select a suitable CU partner for each admissible D2D pair to maximize the overall network throughput. Numerical results show that the proposed scheme can significantly improve the performance of the hybrid system in terms of D2D access rate and the overall network throughput. The performance of D2D communications depends on D2D user locations, cell radius, the numbers of active CUs and D2D pairs, and the maximum power constraint for the D2D pairs.

For overlay cognitive radio networks (CRNs), transform domain communication system (TDCS) has been proposed to support multiuser communications through spectrum bin nulling and frequency domain spreading. In TDCS-based CRNs, each user is assigned a specific pseudorandom spreading sequence. However, the existence of multiuser interference (MUI) is one of main concerns, due to the non-zero cross-correlations between any pair of TDCS signals. In this paper, a novel framework of TDCS-based CRNs with the joint design of sequences and modulation schemes is presented to realize MUI avoidance. With the uncertainty of spectrum sensing results in CRNs, we first introduce a unique sequence design through two-dimensional time-frequency synthesis and obtain a class of almost perfect sequences whose periodic auto-correlation and cross-correlations are identically zero for most circular shifts. These correlation properties are further exploited in conjunction with a specially-designed cyclic code shift keying in order to achieve the advantage of MUI avoidance. Numerical results demonstrate that the proposed TDCS-based CRNs are well suited for decentralized networks against the near-far problem.

A novel detect-and-forward (DeF) relaying aided cooperative SM scheme is proposed, which is capable of striking a flexible tradeoff in terms of the achievable bit error ratio (BER), complexity and unequal error protection (UEP). More specifically, SM is invoked at the source node (SN) and the information bit stream is divided into two different sets: the antenna index-bits (AI-bits) as well as the amplitude and phase modulation-bits (APM-bits). By exploiting the different importance of the AI-bits and the APM-bits in SM detection, we propose three low-complexity, yet powerful relay protocols, namely the partial, the hybrid and the hierarchical modulation (HM) based DeF relaying schemes. These schemes determine the most appropriate number of bits to be re-modulated by carefully considering their potential benefits and then assigning a specific modulation scheme for relaying the message. As a further benefit, the employment of multiple radio frequency (RF) chains and the requirement of tight inter-relay synchronization (IRS) can be avoided. Moreover, by exploiting the benefits of our low-complexity relaying protocols and our inter-element interference (IEI) model, a low-complexity maximum-likelihood (ML) detector is proposed for jointly detecting the signal received both via the source-destination (SD) and relay-destination (RD) links. Additionally, an upper bound of the BER is derived for our DeF-SM scheme. Our numerical results show that the bound is asymptotically tight in the high-SNR region and the proposed schemes provide beneficial system performance improvements compared to the conventional MIMO schemes in an identical cooperative scenario.

In this paper, the challenging problem of signal detection under severe communication environment that plagued by unknown doubly selective channels (DSCs), multiple narrowband interferences (NBIs) and phase noises (PNs) is investigated for orthogonal frequency division multiplexing based virtual multiple-input multiple-output (OFDM-V-MIMO) systems. Based on the Variational Bayesian Inference framework, a novel iterative algorithm for joint signal detection, DSC, NBI and PN estimations is proposed. Simulation results demonstrate quick convergence of the proposed algorithm, and after convergence, the bit-error-rate performance of the proposed signal detection algorithm is very close to that of the ideal case which assumes perfect channel state information, no PN, and known positions and powers of NBIs plus additive white Gaussian noise. Furthermore, simulation results show that the proposed signal detection algorithm outperforms other state-of-the-art methods.

This paper addresses the application of the emerging compressive sensing (CS) technology to the detection of ultra-wideband (UWB) signals. Capitalizing on the sparseness of random UWB signals in the basis of eigen-functions, we develop a new CS dictionary called eigen- dictionary. Coupled with this eigen-dictionary, an enhanced Bayesian learning procedure is proposed to reconstruct the sparse UWB signal from a small collection of random projection measurements. Furthermore, by utilizing a common sparsity profile inherent in UWB signals, the proposed Bayesian algorithm naturally lends itself to multi-task CS for simultaneously recovering multiple UWB signals. Since the statistical inter-relationships between different CS tasks are exploited, the multi-task (MT) Bayesian CS can efficiently improve the reconstruction accuracy and thus the performance of UWB communications. Simulations based on real UWB data demonstrate the advantages of the proposed approach over its counterparts.

In this paper, we investigate the joint optimization of sensing and power allocation for cooperative relay in cognitive radio networks (CRNs). Specifically, in the sensing time slot, the source secondary user (SU) and the relay SUs individually sense the channel, and then the relay SUs send their sensing results to the source SU, in which a fusion rule is employed to determine whether the primary user (PU) is idle or not on the channel. In the data transmission slot, the selective amplify-and-forward (S-AF) cooperative relay scheme is considered to assist the source SU for data transmission. By jointly considering the two slots, the optimization problems are respectively formulated to maximize the CRN's average throughput and minimize the outage probability of secondary transmission under the average transmit power constraint of SUs and the average interference power constraint of PU. The optimal algorithms are developed to acquire the optimal sensing time and power allocation. Moreover, in the S-AF scheme, a relay selection scheme is also considered in the optimization problem. Finally, we provide simulation results to validate our proposed algorithms.

SUMMARY In an opportunistic spectrum sharing system, secondary users (SUs) opportunistically access the white space spectrum that is not occupied by the primary user (PU). Some analytical performance models have been available based on Markov chain modeling. In these models, SUs and PUs access the spectrum by randomly selecting the available channels with equal probability. However, how SUs and PUs use the spectrum are controlled by the access strategy designed in their MAC layer in an ad‐hoc network or centralized radio resource management layer in an infrastructure‐based network. To analyze the grade of service (GoS) of the secondary system under consideration for the access strategy, we propose an access rule transition matrix to model the access behavior of radio resource management, and apply it into the continuous‐time Markov chain model. It was proved that the proposed model is equivalent to the original models assuming the random access strategy by simulation. Moreover, we analyzed the GoS performance of the secondary system by assuming an ordered hunt access strategy. The results showed that the GoS performance of secondary systems can be improved greatly if it knows the spectrum access strategy of the primary system. Copyright © 2011 John Wiley & Sons, Ltd.

This study presents a cluster-based transform domain communication system (TDCS) to improve spectrum efficiency. Unlike the utilities of clusters in orthogonal frequency division multiplex systems, the cluster-based TDCS framework divides entire unoccupied spectrum bins into L clusters, where each one represents a data stream independently, to achieve L times of spectrum efficiency compared to that of the traditional one. Among various schemes of spectrum bin spacing and allocation, the TDCS with random allocation scheme appears to be an ideal candidate to significantly improve spectrum efficiency without seriously degrading power efficiency. In multipath fading channel, the coded TDCS with random allocation scheme achieves robust bit error rate (BER) performance owing to a large degree of frequency diversity. Furthermore, this study shows that the smaller spectrum bin spacing should be configured for the cluster-based TDCS to achieve higher spectrum efficiency and more robust BER performance.

In this letter, an extension of the near-optimal matched-filter (NMF) detector that provides improvement of the performance with negligible extra complexity is proposed for space-time shift keying (STSK) systems. In contrast to the NMF which only utilizes the index of the most probable active dispersion-matrix (DM) for detection, the proposed method sorts the DM index set and achieves the final results from theKmost probable indices, so as to alleviate the error propagation caused by DM misdetection. Simulation results show that the proposed algorithm provides considerable performance improvement for both hard and soft output detection as compared to NMF. Furthermore, it is also shown that the proposed detector is capable of achieving the same performance as that of the exhaustive-search MF (EMF) detector at reduced complexity, especially for high data rates.

ABSTRACT For wireless multiple‐input multiple‐output (MIMO) communications systems, both channel estimation error and spatial channel correlation should be considered when designing an effective signal detection system. In this paper, we propose a new soft‐output MMSE based Vertical Bell Laboratories Layered Space‐Time (V‐BLAST) receiver for spatially‐correlated Rician fading MIMO channels. In this novel receiver, not only the channel estimation errors and channel correlation but also the residual interference cancellation errors are taken into consideration in the computation of the MMSE filter and the log‐likelihood ratio (LLR) of each coded bit. More importantly, our proposed receiver generalizes all existing soft‐output MMSE V‐BLAST receivers, in the sense that, previously proposed soft‐output MMSE V‐BLAST receivers can be derived as the reduced forms of our receiver when the above three considered factors are partially or fully simplified. Simulation results show that the proposed soft‐output MMSE V‐BLAST receiver outperforms the existing receivers with a considerable gain in terms of bit‐error‐rate (BER) performance. Copyright © 2011 John Wiley & Sons, Ltd.

Cooperative spectrum sensing (CSS) is a promising technology in cognitive radio (CR) networks. Among the many existing CSS methods, the cooperation in a single channel is studied extensively. However, CR networks usually deal with multiple channels, therefore multi-channel CSS needs be studied. In this paper, we investigate the problems that how to optimally assign secondary users (SU) to cooperatively sense multiple channels and how to optimally set the sensing time and sensing thresholds. An optimization problem of multi-channel CSS is formulated to maximize the average throughput of CR networks subject to the constraints of probability of detection for each channel. An exhaustive algorithm and a greedy algorithm are proposed to obtain the optimal solutions of the optimization problem. Finally, both analytical and numerical results are presented to demonstrate the effectiveness of our proposed algorithms. It is also shown that the greedy algorithm with a low complexity achieves the same performance as the exhaustive algorithm.

In this paper, we develop a downlink dynamic soft frequency reuse (SFR) scheme with inter-cell coordination in orthogonal frequency division multiple access (OFDMA) cellular networks. A clustered base station (BS) coordination strategy is employed to facilitate frequency resource allocation among coordinated sectors. Compared to the traditional static SFR scheme, the proposed scheme can exploit frequency selectivity of wireless channels and multi-user diversity through inter-cell coordination and achieve better performance. Since the proposed approach performs resource allocation in a distributed way, only limited information needs to be shared or exchanged even within a cluster. As shown by computer simulation, compared with the traditional scheme, the proposed one can improve the throughput for those users at the edge of a sector by 41.9% while maintaining the same throughput for those users at the center of a sector.

In this paper we propose a novel linear interpolated channel estimation method in non-continuous subcarrier mapping between contiguous pilot symbols. The proposed method completes channel interpolation in the time domain instead of in the frequency domain to avoid the discontinuity of the channel frequency response when non-continuous subcarrier mapping is adopted. The simulation results show that our proposed method can overcome the drawbacks of interpolation in the frequency domain in non-continuous subcarrier mapping and can significantly improve the system performance in various mobile environments.

Spectrum sensing is a fundamental technology to detect the presence of primary user in cognitive radio networks. Usually, the requirements for spectrum sensing are very stringent. It requires that the spectrum sensing scheme has a good performance even in extremely low signal-to-noise ratio (SNR) environments. In this paper, we propose a novel spectrum sensing method based on stochastic resonance (SR) noise enhance detection (NED) to meet the requirement. For the proposed SR NED based spectrum sensing scheme, a specific signal-independent SR noise is added to the received signal so that the probability distribution of the detection statistics is modified to match the applied nonlinear suboptimal detector better. The performance of the applied detector can then be significantly improved according to the basic principle of SR NED. Under the constraint of the false alarm probability, the method to find the optimal SR noise is provided for both single node sensing and multiple nodes cooperative sensing by maximizing the probability of detection. For single node sensing, the popular energy detector is considered. For cooperative sensing, both maximal ratio combination (MRC) and equal gain combination (EGC) based energy detectors are investigated. Theoretical analysis and simulations results show that the proposed SR NED based spectrum sensing schemes can achieve much better performance than that of the applied original detectors.

In cooperative relay systems, relayed signals propagate through different channels and are received at the destination node with distinct timing and carrier frequency offsets (CFOs). This feature makes synchronization a rather challenging task as compared with centralized multiple-input multiple-output systems. Unlike conventional preamble designs with good correlation properties only in time domain or frequency domain, a new preamble that has good correlation properties in both time domain and frequency domain is proposed for synchronization in orthogonal frequency-division multiplexing (OFDM) based cooperative relay systems. According to the proposed preamble, a synchronization scheme is developed and a practical threshold is derived that jointly considers the statistic distribution of the correlation function output and the inevitable interference originated from residual CFOs to assist fine timing synchronization. Simulation results verify that the proposed scheme provides notable performance improvement as compared with a previous related work.

We investigate the design and use of systematic binary deterministic rateless (BDR) codes for information transmission over block-erasure broadcast channels. BDR codes are designed to obtain a level of maximal distance separable (MDS) properties, making these codes ideal for the considered broadcast scenario. For a certain number of encoded redundancy blocks, we derive an expression for the probability that the MDS properties are maintained. Moreover, if limited feedback is available, we extend the BDR coding protocol to further improve the system performance. Numerical results show that for a finite number of source blocks and as the number of users grows the proposed systematic BDR codes performs significantly better than LT codes. The proposed schemes with feedback have better performance than traditional ARQ schemes.

For multiple-input multiple-output (MIMO) wireless communication system with minimum mean square error (MMSE) detection, power allocation between pilot and data symbols is investigated under MMSE channel estimation in this paper. We first propose a novel soft-output MMSE MIMO detector by taking the channel estimation error into consideration. Then, by random matrix theorem, we propose an effective power allocation scheme between pilot and data symbols to maximize the average lower bound of minimum post-processing signal-to-interference and noise ratio (SINR) for the MIMO system, which has equal number of transmitter and receiver antennas. Simulation results validate our power allocation scheme.

It has been shown in the literature that network coding can improve the transmission efficiency of wireless broadcasting as compared to traditional ARQ schemes. In this paper, we propose an improved network coding scheme that can asymptotically achieve the theoretical lower bound on transmission overhead for a sufficiently large number of information blocks. The proposed scheme makes use of an index allocation algorithm that distributes information blocks that have been erased during transmission into a minimum number of encoding sets, where each set represents the erased blocks to be jointly network encoded and retransmitted. Numerical results show that the proposed scheme enables higher transmission efficiencies than traditional ARQ, and previously proposed networks coding schemes for wireless broadcasting.

Peak-cancellation is a pre-distortion technique for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. The distortion introduced to the transmitted signals can be mitigated on the condition that the signal attenuation factor is known at the receiver. In this paper, to alleviate the distortion and disclose the signal feature of peak-cancellation combined OFDM signals, the signal attenuation factor is analyzed and hence evaluated, which is supported by simulation results.

In this letter, a low-complexity structure of PAPR reduction scheme, user grouping, in multicarrier code-division multiple access (MC-CDMA) is introduced. The proposed structure is based on joint spreading and inverse fast Fourier transform (S-IFFT) processing, and a class of low-complexity structure of user grouping scheme is proposed on the tradeoff between computational complexity and storage size. Comparison with the conventional PTS schemes, it shows that the proposed low-complexity scheme can achieve similar PAPR reduction with lower complexity.

Power allocation between pilot and data symbols is investigated for multiple-input and multiple-output (MIMO) spatial multiplexing (SM) system with zero-forcing receiver and minimum mean square error (MMSE) channel estimation under flat block-fading channel. Based on a modified zero-forcing receiver, which takes channel estimation error into account, a new power allocation scheme to maximize the average post-processing signal-to-noise-ratio (SNR) is proposed. This scheme only requires the computation of scalars and does not need any form of channel coefficients feedback. Simulation results show that the proposed scheme outperforms simple equal power allocation with an improvement of around 3 dB and 2 dB for QPSK and 16-QAM modulations when FER = 10-2, respectively.

As channel estimation errors are not taken into account by existing soft-output minimum mean square error (MMSE) multiple-input multiple-output (MIMO) detector, its performance can therefore be degraded under imperfect channel estimation. In this letter, we propose a novel soft-output MMSE MIMO detector under maximum likelihood (ML) channel estimation for a MIMO system with spatially correlated receiver and transmitter antennas. Our proposed detector takes both channel estimation errors and spatial correlation of antennas into account when constructing MMSE filter and computing log-likelihood ratio (LLR) of each coded bit. Simulation results show the proposed novel detector outperforms existing soft-output MMSE MIMO detector with considerable improvement.

Multiple-input multiple-output (MIMO) bit-interleaved coded modulation (BICM) has been proposed to apply in fast-fading environment. In this paper, we investigated the link-level capacity (LLC) and bit-error-rate (BER) performance of MIMO BICM system with soft-output MMSE soft interference cancellation (SIC) detection. We compared two SIC schemes in this paper. The first one is ordering successive SIC, in which the transmitted symbols are detected sequentially with post-detection signal-to-noise ratio (SNR) based ordering and interference cancellation error compensation. In the second scheme, we proposed to perform parallel interference cancellation (PIC) and detect the transmitted symbols simultaneously. We compared the LLC and BER performance of different detection schemes through extensive simulation.

Cooperative diversity has recently emerged as novel communication scheme for future wireless communications because of diversity gain, which is achieved by sharing each cooperative user's antenna to form a virtual antenna array in the wireless network. Previous works on analysis of system performance of cooperative networks mainly focused on static scenarios. In this paper, we derive exact average symbol error rate (ASER) of a cooperative wireless transmission with adaptive decode-and-forward (DF) protocol [6], where the number of relays or cooperative user- nodes, n, which can correctly decode the received signal, is a random variable subject to the Poisson distribution, the normal distribution and the uniform distribution, respectively. Both the analytical and the numerical results corroborate that both diversity and coding gain will be affected by the relay- or user-node distribution. For the Poisson distribution and the normal distribution, the diversity gain is affected much than the coding gain. For the uniform distribution, the coding gain is affected whereas the diversity gain keeps fixed.

In certain multiple-input multiple-output (MIMO) fading environments, the offered channel capacity can be very low even when the MIMO channels are uncorrelated; this effect has been termed as keyhole or pinhole effect. In this paper, we present rigorous error rate analysis of orthogonal space time block codes in Nakagami-m keyhole channels with arbitrary fading parameters. Using a moment-generation-function based approach, we provide the exact expressions of symbol error rate. Furthermore, the closed-form asymptotic expressions are derived, based on which one can easily quantify the diversity gain of the considered system with arbitrary m-fading parameters while existing methods in literature fail to do so. Simulation results are also provided to verify our analytical results.

We consider a cognitive network which is operating with licensed networks simultaneously. One of the major concerns lies in the fact that cognitive networks may cause harmful interference to licensed users. This paper focuses on a joint channel and power allocation scheme that can both protect licensed users and meet the quality of service (QoS) of cognitive radio users with fairness. The problem can be formulated as a nonlinear programming. For reducing complexity in obtaining optimal channel and power allocation scheme, we propose a two-step suboptimal scheme that can achieve good performance with lower complexity. In the first step, resources, such as channels and powers, are allocated by cognitive radio base station (CRBS) based on fairness and QoS requirements to get the maximum available resources of cognitive radio customer premises equipment (CRCPE). In the second step, to get the final resource and reduce algorithm complexity, the allocation task is accomplished by each CRCPE simultaneously, rather than accomplished by CRBS in a serial order. Theoretical analysis and simulation results show that our scheme can support the QoS of CRCPEs with both lower power consumption and fair resource allocation.

In this paper, we address the problem of channel estimation in time-variant multi-path environment in Orthogonal frequency division multiplexing systems(OFDM). Based on the assumption that the channel varies in a linear fashion during an OFDM block duration, a novel iterative channel estimation arithmetic with noise and interference suppression using pilot tones is investigated. Through iterative noise and interference suppression to determine the positions of channel taps, then we compute the time averages and slopes of that to get the time domain channel matrix, so such method can improve the precision of the estimation. To increase the spectral efficiency and reduce the computational complexity, assuming the positions of channel taps are not changed in one frame duration, a frame based on such estimation scheme is presented. Along with the channel estimation technique, we also analyze the optimum pilot tones placement. Theoretical analysis and simulation results show that our assumption is reasonable, and the proposed channel estimation arithmetic has a good performance with low computational complexity and high spectral efficiency.

In this paper we show that mutual exchange of independent information between multiple nodes in a wireless network can be efficiently performed by using network coding. Furthering this conclusion, we propose a mechanism for network coding based multi-node information exchange in large wireless mesh networks. We also provide a series of techniques for applying this mechanism in practical environment, and run a group of simulations to evaluate the performance of our design. The simulation results show that our mechanism can substantially improve network throughput.

In practical wireless multiple-input multiple-output (MIMO) communications system, the channel estimation must be imperfect. Unfortunately, the channel estimation errors have not been taken into account by existing soft-output MIMO minimum mean square error (MMSE) vertical Bell lab space time (V- BLAST) detectors when calculating the log-likelihood ratio of each coded bit, i.e., the soft information. As a result, the system performance can be significantly degraded. In this paper, we propose a novel soft-output MIMO MMSE V-BLAST detector based on random vector theorem, which takes the channel estimation error into account in the computation of the MMSE filter and LLR of each coded bit. Furthermore, a simplified implementation of the proposed detector is given so that the variance of channel fading coefficient is not needed. When compared with existing MIMO MMSE V-BLAST detectors, our simulation results show that the proposed novel detector can obtain significant performance gain at the cost of negligible increase of complexity.

For any practical wireless multiple-input multiple-output (MIMO) communication systems, the channel estimation can never be perfect, i.e., it is always imperfect. Unfortunately, the channel estimation errors have not been taken into account by existing soft-output two-stage parallel minimum mean square error (MMSE) detector when calculating the log-likelihood of each coded bit, i.e., the soft information. As a result, the system performance can be drastically degraded. In this paper, we propose a novel soft-output two-stage MMSE MIMO detector based on random vector theorem, which takes the channel estimation error into account in the computation of the MMSE filter and LLR of each coded bit. When compared with existing two-stage MMSE MIMO detector, our simulation results show that the proposed novel detector can remarkably reduce the residual error floor and obtain significant performance gain at the cost of negligible increase of complexity. Furthermore, it is observed from our simulation results that the proposed detector is not sensitive to the inaccuracy of variance of the channel estimation error. Therefore, it can be a promising candidate to be applied in practical systems.

Partial transmit sequence (PTS) is an attractive distortless peak-to-average power ratio (PAPR) reduction technique for orthogonal frequency division multiplexing (OFDM) system. However, the complexity of PTS increases exponentially with the number of sub-blocks as it requires an exhaustive searching over all phase factor combinations. In this paper, we explored the correlation among the multiple candidate signals of PTS and proposed a modified scheme with lower complexity. The main idea is based on selecting a subset from the whole set of candidate signals by analyzing the correlation of candidate signals. Simulation results show that the performance of the modified scheme is close to the theoretical lower bound and outperforms selected mapping (SLM) scheme with the same computational complexity.

There are two important constraints for cooperative spectrum sensing in cognitive radio (CR) context. Firstly, as the CR system can only tolerate low transmitting overhead, the local sensing data must be compressed before transmitting. Secondly, many sophisticated data fusion techniques cannot be used in CR system because of the lack of the signal's prior knowledge. In this paper, we proposed a novel cooperative spectrum sensing scheme which adapts to different overhead tolerance and does not need any prior knowledge. This scheme consists of two main parts: the quantization schemes and the data fusion rule. Two different quantization schemes, according to whether or not the distribution functions of test statistic are known, are proposed. Correspondingly, the optimal data fusion rule of multi-bits decisions is derived. Furthermore, to make the optimal fusion rule more practical, an iterative scheme, which does not need any prior knowledge of the signal, is proposed to estimate the likelihood ratio of local decisions. Simulation results show that our scheme achieves better performance than the schemes with "OR" and "AND" combinations. Furthermore, it is also shown that the proposed scheme could achieve the theoretically optimal performance by only two or three bits quantization.

In reality, this is no such thing as perfect channel estimation. But unfortunately, channel estimation errors have never been taken into account by conventional soft-output minimum mean square error (MMSE) multiple-input multiple-output (MIMO) detector when calculating the log-likelihood ratio (LLR) for each coded bit, i.e., the soft information. As a result, its system performance can be significantly degraded. In this paper, we propose a novel soft-output MMSE MIMO detector by the random vector theorem, which takes channel estimation errors into account in the computation of the MMSE filter and LLR of each coded bit. When compared with conventional soft-output MMSE MIMO detector, our simulation results show that the proposed novel detector can remarkably reduce the residual error floor due to the channel estimation errors, while achieving significant performance gain at the cost of negligible increase of complexity. Furthermore, it is observed from our simulation results that the proposed detector is not sensitive to the inaccuracy of channel estimation error statistics. Therefore, it can be a promising candidate to be applied in practical systems.

Cognitive radio networks need to continuously monitor spectrum to detect the presence of the licensed users. In this paper, we have exploited spatial diversity in multiuser networks to improve the spectrum sensing capabilities of centralized cognitive radio (CR) networks. We develop a fixed and a variable relay sensing scheme. The fixed relay scheme employs a relay that has a fixed location to help the cognitive network base station detect the presence of the primary user. The variable relay sensing scheme employs cognitive users distributed at various locations as relays to sense data and to improve the detection capabilities. This effectively reduces the average detection time by exploiting spatial diversity inherent in multiuser networks. Finally, we study the network outage probabilities to compare the performances of the fixed and variable relay schemes.

This paper focuses on a modified Vertical-Bell Labs Layered Space-Time (V-BLAST) system, where different delay offsets are intentionally applied to the spatially multiplexed data streams. We consider a multiuser MIMO uplink environment, where each user carries Mttransmit antennas, but is allocated only one signature sequence. Compared with the conventional V-BLAST, the modified scheme gains advantage mainly in two aspects: firstly, it is able to recover the transmitted information from only one receive antenna, whereas the conventional V- BLAST requires that Mrges Mt(Mris the number of receive antenna); secondly, the modified system could achieve the maximum possible diversity gain Mrof V-BLAST at the cost of limited multiplexing gain, whereas the diversity gain is only Mr- Mt+ 1 in conventional V-BLAST with ZF detector. The exact closed-form expression of bit error rate (BER) is derived for an Mttimes Mrmodified system with ZF strategy. And further explanation for the performance difference between the modified and conventional scheme is given from the spatial correlation point of view. Finally, we compare the performance of our proposed system with the conventional scheme by computer simulations which validate the analytical results.

Selected mapping (SLM) is a promising peak-to-average power ratio (PAPR) reduction technique for orthogonal frequency division multiplexing (OFDM) system. In SLM, phase sequences are combined with the data for generating alternative signals, so as to reduce the PAPR. In this paper, a new criterion is developed to examine the effects of different phase sequence sets in SLM-OFDM based on the mathematical correlation analysis among the alternative signals. Furthermore, according to the proposed criteria, the chaotic phase sequence set is introduced for improving the PAPR reduction performance in SLM. Simulation results show that the improved SLM outperforms conventional methods such as proposed in [6].

Cognitive radio networks need to continuously monitor spectrum to detect the presence of the licensed users. In this paper, we have exploited spatial diversity in multiuser networks to improve the spectrum sensing capabilities of centralized cognitive radio (CR) networks. We develop a fixed and a variable relay sensing scheme. The fixed relay scheme employs a relay that is fixed in location to help the cognitive network base station detect the presence of the primary user. The variable relay sensing scheme employs cognitive users distributed at various locations as relays to sense data and to improve the detection capabilities. We theoretically prove that the proposed variable relay sensing scheme effectively reduces the average detection time which is also illustrated by an insightful example. Finally, we introduce a useful metric to measure the performance of fixed relay and variable relay schemes.

This paper proposes a novel method to reduce the peak-to- average power ratio (PAPR) of multicarrier signals in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems by frequency block exploitations, named cross-frequency permutation and inversion (CFPI). Our design improves the performance and framework flexibility (compatible with both space-time and space-frequency patterns) of PAPR reduction in MIMO-OFDM system, which utilizes additional degrees of frequency freedom. Especially, this proposed design can be freely combined with many good-featured space-time or space-frequency coding to achieve better whole system performance, which is unlike the previous typical methods strictly based on space-time block coding structure with relative low data rate. Simulation results show that the good performance is achieved by our novel design.

A reduced-complexity minimum mean square error (MMSE) soft interference cancellation (SIC) based multiple-input multiple-output (MIMO) turbo receiver is proposed. In this new proposal, the computational complexity intensive part of the MMSE SIC detector is ignored if the reliability of the a priori information is larger than a specific threshold. As the co-antenna interference (CAI) of MIMO system can be cancelled out with very high probability at higher iteration numbers, the computational complexity can be effectively reduced. Compared with those existing schemes which simply replace the MMSE filter with match filter (MF) at iteration stages, this new proposal takes the reliability of the a priori information into account and thus can reduce the computation complexity at the cost of negligible performance loss. Meanwhile, it is very flexible for this proposal to make a tradeoff between the performance and the computational complexity through changing the threshold of reliability. Simulation results confirm the advantages of this scheme.

While most current researches on opportunistic beamforming assume that users are uniformly distributed around BS, no attention has been paid to address more pratical scenarios where spatial user density is heterogeneous. In this paper, we propose a novel opportunistic beamforming scheme to better exploit the multiuser diversity in case of heterogeneous user density. The key idea is to grant the highest service priority to users in the area with highest spatial user density. It is implemented by an improved selection of the weight vector at each time slot without explicit knowledge of the spatial user density. To address the fairness issue, the joint area-user proportional fair scheduling (JAUPFS) is proposed, which simultaneously guarantees the fairness between areas with different spatial user densities and different users in the same area. Simulations are conducted to demonstrate that our proposed scheme has a better rate performance than that of the conventional opportunistic beamforming.

The high peak-to-average power ratio (PAPR) is one of the main drawbacks of multicarrier code division multiple access (MC-CDMA) system. To alleviate this problem, in this paper, we propose a novel user grouping scheme for PAPR reduction in the downlink of MC-CDMA. The basic principle is to partition the users into subgroups, and generate multiple alternatives to achieve considerable PAPR reduction. The proposed scheme can be regarded as a modified version of partial transmit sequence (PTS), but with improved performance. And simulation results show that the user grouping scheme is more effective than the conventional PTS in MC-CDMA system.

High peak-to-average power ratio (PAPR) is one of the main drawbacks of MC-CDMA system. Among current methods that aim at reducing PAPR, partial transmit sequence (PTS) and variable code sets (VCS) are the kind of schemes that can improve PAPR statistics effectively but with large search complexity. To overcome this disadvantage, in this paper, we investigate a PAPR reduction scheme which employs recursion algorithm to decrease the computational complexity and a cyclical-shift technique to reduce the signals' PAPR effectively. Theoretical analysis and simulation results show that the proposed scheme can achieve better PAPR reduction performance than VCS but with lower computational complexity.

In this paper, we analyze the sensitivity of MAP algorithm and its reduced ones, as max-log-MAP and scaled max-log-map, to SNR mismatch in turbo detection systems. And then, we propose a new reliability-based SNR estimation method using the soft extrinsic information output from SMLM decoder. Next, a new turbo equalization scheme is also provided with the new SNR estimation algorithm. Finally, the performance of turbo equalization systems are evaluated using our new scheme. The simulation results show that our equalization scheme performs very close to the LM algorithm with known channel SNR, although it does not need pilot symbols.

Partial transmit sequence (PTS) with pseudo-random sub-block partition can achieve lower Peak-to-Average Power Ratio (PAPR) performance. Based on the correlation of the candidate signals, we can prove that the performance is uncorrelated with the input signal. In general, the correlation of the candidate signals will increase with the number of candidate signals. As a result, the performance improvement will decrease evidently. In this paper, we provide a novel method to design the phase factor for PTS scheme, so as to achieve quasi orthogonal candidates to get better PAPR reduction performance. Simulation results prove it is more efficient than the traditional one.

In opportunistic beamforming, the throughput can be improved by broadcasting multiple weight vectors in one time slot and selecting the best one. However, broadcasting too many weight vectors will consume the time for data transmission and thus bring down the throughput. In this paper, we investigate two multiple weight vector(MWV) opportunistic beamforming schemes tailored for fast fading and slow fading scenarios respectively and we derive tight upper bounds of the data rates for both schemes. To maximize the upper bounds, we obtain the optimal numbers of weight vectors to be broadcast. Simulation results demonstrate the validity of our theoretical analysis. Based on these results, we obtain some basic guidelines for MWV design problem. For the MWV scheme of fast Rayleigh fading scenario, we claim that (1) the faster the fading is, the less weight vectors are desired; (2) the more users there are, the less weight vectors are desired. For the MWV scheme of slow Rayleigh fading scenario, we have the conclusion that a small number of weight vectors are good enough to achieve a desirable performance.

In this paper, we propose a new list-based detection algorithm to achieve near maximum likelihood (ML) performance with low computational complexity for multiple-input multiple-output (MIMO) systems. First, we use the minimum mean square error (MMSE) detection to yield an initial bit decision and the corresponding bit log-likelihood ratio (LLR) for a specified layer. Next we construct a list of LLR-based candidate error vectors for the specified layer, and then apply independent reduced-complexity sub-detectors to the remaining layers after cancelling the contribution of each candidate vector of the list. Finally the most likely vector is returned among all visited decisions. Simulation results show that the proposed method can approach identical performance to that of ML detection but only 0.15% complexity for (4,4) system using uncoded 16-QAM.

In this paper, we address the problem of carrier frequency offset estimation for multiple-input multiple-output (MIMO) correlated fading channels. A maximum likelihood (ML) carrier frequency offset estimator based on the average likelihood function is proposed, which can make use of the channel correlation information by averaging the conditional likelihood function over all realizations of the channel and can exploit both transmit and receive spatial diversity as well. The Cramer-Rao bound (CRB) for the problem is also derived, and simulation results are given to illustrate the performance of the proposed estimator and compare it with the CRB. It is shown that the proposed estimator can provide satisfactory frequency offset estimates in MIMO correlated fading channels.

This paper proposed a modified vertical-Bell Labs layered space-time (V-BLAST) system, where different delay offsets are intentionally applied to the spatially multiplexed data streams in order to improve bit error rate (BER) performance. The proposed system can use only one receive antenna to recover the transmitted information by zero forcing (ZF) detection, whereas the conventional synchronous V-BLAST system requires that Mrges Mt(where Mtand Mrare the number of transmit and receive antennas, respectively). Compared with the conventional synchronous V-BLAST system, the modified system can achieve Mrth-order diversity using ZF detection. Although delay offsets in the system introduce an attenuation factor which decreases the instantaneous signal-to-noise ratio (SNR), the BER performance of the modified system with proper system parameters demonstrates a performance improvement over the conventional synchronous V-BLAST system.

This letter presents a class of low complexity partial transmit sequence (LC-PTS) techniques for reducing the peak-to- average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. The basic principle is to analyze and utilize the correlation among the candidate signals generated in PTS, so as to simplify the computational complexity. Furthermore, the proposed technique focuses on simplifying the computation for each candidate signal, instead of reducing the total number of candidate signals such as in (Jayalath and Tellambura, 2000). Thus, it can be easily combined with other simplified techniques. Simulation results show that the new technique can effectively reduce the complexity compared with the conventional PTS scheme.

Differential Frequency Hopping (DFH) is a novel technique for the frequency-hopping system in HF (High Frequency) bands. The frequency transition function (FTF) and the signal detection methods are the key issues for DFH system. Symbol-by-symbol detection and sequence detection are adopted in DFH system. With these two detection methods, the symbol error rate (SER) performances of DFH system are analyzed over a Rayleigh-fading channel with multi-tone jamming (MTJ) and additive white Gaussian noise (AWGN). Simulations validate the analyses. The results of analyses and simulations prove that the DFH system with sequence detection can achieve better anti-jam (AJ) performance than that of conventional frequency-hopping (FH) system.

This paper presents a type of frequency-hopping system in HF (High Frequency) bands, which is called Differential Frequency Hopping (DFH) system. The frequency transition function (FTF) and the methods of signal detection are the key technologies for DFH system. The error-rate performances of DFH with Partial Band Jamming (PBJ) and additive white Gaussian noise (AWGN) over a Rayleigh-fading channel by using product-combining receiver (PCR) are studied analytically, the performances are validated by simulation results, performance comparisons among the various receivers show that the anti-jam (AJ) performance of DFH system with PCR outperforms that with linear-combining receiver (LCR).

Alamouti Code makes use of a rich algebraic structure to provide diversity gains with small decoding complexity. In this work, we investigate the problem of co-channel interference (CCI) mitigation for two users applying AC in flat fading and quasistatic wireless channels. By exploring the algebraic properties of Alamouti matrix, we develop a selective CCI mitigation method at the receiver. Assuming that zero-forcing (ZF) or minimum mean-squared error (MMSE) interference suppression techniques are used to separate two users and according to post-filtering instantaneous signal-to-noise-ratio (ISNR) or instantaneous signal-to-interference-plus-noise ratio (ISINR), we derive a very simple unified selection formula, avoiding computing the inverse (pseudoinverse) of channel matrix. Through the selection, we detect the user with higher post-filtering ISNR or ISINR, and then subtract its contribution from the received signals and detect another user. The complexity analysis and simulation results show that our method demonstrates better performance versus complexity tradeoff compared to the existing methods.

Differential frequency hopping (DFH) is a novel technique for the frequency-hopping system in HF (high frequency) bands. The frequency transition function (FTF) and the signal detection methods are the key issues for DFH system. A novel non-coherent detection scheme is proposed in asynchronous multi-user environments and additive white Gaussian noise (AWGN) channels. The multi-user performance of DFH with this novel receiver is studied analytically and validated by simulation results. By comparing of synchronous multi-user system and asynchronous multi-user system with conventional non-coherent detection receiver, it is shown that the performance of asynchronous multi-user system outperform that of synchronous multi-user system, and the novel detection scheme can achieve better performance than the conventional non-coherent detection scheme in asynchronous multi-user environments

In cognitive radio (CR) contexts, the available spectrum holes between the CR transmitter and receiver may be dynamically varying. Therefore, an important issue for CR application is the coordination of available spectrum at the transmitter and receiver before and during the process of information transmission. In this paper, we address this problem by proposing a signaling mechanism. Our scheme includes both the initial link establishment algorithm and dynamic link maintenance mechanism. We also propose the interleaved OFDM-based transform domain communication system as a promising candidate for the initial access signaling transmission by comparing with currently available modulation schemes. Simulation results of the proposed access link establishment mechanism are also presented to evaluate its feasibility

Reliable detection of available spectrum is the foundation of cognitive radio technology. To improve the detection probability under sustainable false alarm rate, a distributed spectrum sensing scheme has been proposed. In this paper, we propose a new decision combination scheme, in which the credibility of local spectrum sensing is taken into account in the final decision at central access point and Dempster-Shafer's evidence theory is adopted to combine different sensing decisions from each cognitive user. Simulation results show that significant improvement in detection probability as well as reduction in false alarm rate is achieved by our proposal

A type of frequency-hopping system in HF (high frequency) bands, differential frequency hopping (DFH) system, is presented in this paper. The frequency transition function (FTF) and the methods of signal detection are the key issues for DFH system. Symbol-by-symbol detection and sequence detection are adopted in DFH system. With these two detection methods, the symbol error rate (SER) performances of DFH system are analyzed over a Rayleigh-fading channel with partial band noise jamming (PBNJ) and additive white Gaussian noise (AWGN). Simulations validate the analyses. The results of analyses and simulations prove that the DFH system with sequence detection can achieve better anti-jam (AJ) performance than that of conventional frequency-hopping (FH) system.

The orthogonal frequency division multiplexing (OFDM) systems that exploit Alamouti transmit diversity technique can be divided into two categories, i.e. Alamouti space-time block-coded OFDM (STBC-OFDM) and space-frequency block-coded OFDM (SFBC-OFDM). In time-varying multipath Rayleigh fading channels, these systems suffer from both time and frequency selectivity of channels. Recently, several detection schemes have been proposed to improve the bit error rate (BER) performance of these systems. In this paper, the achievable average capacities of different detectors are investigated. Based on the probability density function (pdf) of the effective instantaneous signal-to-noise ratio (SNR) obtained by each detector, the analytical expressions of achievable average capacities for those systems with different detectors are derived. Numerical results are also presented to give some insights

In this letter, we show how to compute the asymptotic growth rate of input-output weight enumerator (AGR-IOWE) for some accumulate-based codes by using the sharp tools already developed. Numerical results on the AGR-IOWE for irregular repeat-accumulate (IRA) codes, systematic regular RA (SRA) codes, and concatenated zigzag codes are reported. It is observed that the SRA code has the same AGR-IOWE as a comparable concatenated zigzag code. For both SRA and concatenated zigzag codes, if keeping the code rate fixed, the increase of the grouping factor for the component punctured accumulate code may result in better asymptotic performance under maximum-likelihood decoding, but often worse performance under iterative sum-product decoding.

We derive upper bounds on the decoding error probability of binary block codes over noncoherent block additive white Gaussian noise (AWGN) and fading channels, with applications to turbo codes. By a block AWGN (or fading) channel, we mean that the carrier phase (or fading) is assumed to be constant over each block but independently varying from one block to another. The union bounds are derived for both noncoherent block AWGN and fading channels. For the block fading channel with a small number of fading blocks, we further derive an improved bound by employing Gallager's first bounding technique. The analytical bounds are compared to the simulation results for a coded block-based differential phase shift keying (B-DPSK) system under a practical noncoherent iterative decoding scheme proposed by Chen et al. We show that the proposed Gallager bound is very tight for the block fading channel with a small number of fading blocks, and the practical noncoherent receiver performs well for a wide range of block fading channels

This paper is concerned with the design and performance evaluation of a time and frequency domain chip-level differential detection (TF-CLDD) technique for two-dimension spread spectrum system. Unlike the conventional chip-level differential encoding/detection performed either time domain or frequency domain, such a technique envisages differential detection of the time domain and frequency domain spreading code chips to counteract both the time-selective and frequency-selective fading at the same time. The bit error rate performance of the receiver is analytically evaluated as a function of the relevant system parameters.

Traditional irregular low-density parity-check (IR-LDPC) codes are constructed based on the irregular bipartite graphs with carefully chosen degree patterns on both sides and computer searching is usually applied, which leads complexity to be relatively high. In this paper, we introduce the concept of V-matrix which is a matrix composed of vectors and propose a new approach to construct the IR-LDPC codes. By introducing some special operations, we can obtain the desired sparse parity-check matrix for IR-LDPC codes design. Because computer searching is avoided, the complexity is efficiently reduced. Computer simulation results show that our approach can achieve good tradeoff between complexity and performance

A novel differential detection method in frequency domain spread spectrum systems, called multicarrier spread spectrum chip-level differential detection (MC-SS-CLDD), is proposed. The moment generating function (MGF) of the decision variable and a saddlepoint approximation method are exploited to analyze the BER performance of MC-SS-CLDD. The results of analysis and simulation indicate that, if the coherent bandwidth of the channel does not exceed the occupied bandwidth of two subcarriers, MC-SS-CLDD exhibits a better performance in making the system resistant to frequency-selective fading in the case of narrower channel coherent bandwidth.

Abstract Orthogonal frequency division multiplexing (OFDM) is very attractive for next generation high‐speed wireless communication because of its various advantages in lessening the severe effects of frequency‐selective fading. When using pilot symbol assisted (PSA) M‐ary quadrature amplitude modulation (M‐QAM) in OFDM systems, if the power of pilot is too low, the channel parameters cannot be accurately estimated, whereas, high pilot power will lead to less power left for data symbols under the condition that the total transmitted power is constrained to be a constant. In this paper, we present how pilot power affects the demodulation performance in Rayleigh fading propagation environments. Consequently, the pilot‐to‐data power ratio (PDR) is optimized analytically, and degradation of system performance due to inaccurate channel estimation is also evaluated. The theoretical and simulation results show that the optimum PDR is affected by the signal‐to‐noise ratio (SNR), Doppler frequency and interpolation size etc. When the optimum PDR is adopted for an OFDM system, the total transmission power will be minimal for certain bit error rate (BER) target. Copyright © 2005 John Wiley & Sons, Ltd.

The TDD pre-equalized OFDM systems have a mighty significant advantage of saving system resource by omitting the pilot symbols. Along with that, there is a limitation that the timing error can not be revised in the absence of pilot symbols. In this paper we investigate the impact of timing error on the receiver's BER performance of TDD pre-equalized OFDM systems. We obtain the analytic BPSK BER expressions of ideally and nonideally pre-equalized OFDM systems in multipath Rayleigh fading environment. The corresponding simulations verify the analytic expressions. Compared with the theoretical system performance bound, the performance of ideal and nonideal pre-equalization entail loss of 1db and 3db respectively when the normalized timing error is 1/8. When it is 1/4, both of the two systems' performances degrade severely. At the same time, the gap between them diminishes. In that case, timing error becomes dominant among those elements which would affect system performance.

The paper proposes a new method, minimum clipping power loss scheme (MCPLS), to control the clipping noise based on the relationship between the power of clipped portion and the clipping noise power of OFDM signals. First, the transmitter generates multi-route signals that carry the same information then estimates the clipping noise power of every route under the same clipping threshold. The transmitter then selects the signal with the lowest clipping noise power to clip and transmits the signal. The simulation results proved that this method can mitigate clipping noise and improve the performance of the system. The signal-to-noise ratio is gained 8 dB to achieve 10/sub -3/ symbol-error-rate when the number of subcarriers is 256, the modulation mode is 16-QAM, the clipping threshold is 1.5 and the redundancy of system is 0.59%. The floor of symbol-error-rate is reduced from 10/sub -3/ to 3/spl middot/10/sub -5/. The arithmetic achieves better effect if the threshold is lower.

In this paper, the bit error rate (BER) performance of orthogonal frequency division multiplexing (OFDM) system in multipath fading channels using the frequency domain differential demodulation (FDDD) and time domain differential demodulation (TDDD) is analyzed. The results indicate that in frequency-nonselective fading channel, the BER performance of FDDD is better than that of TDDD. In frequency-selective fading channel, if the time delay doesn't exceed the guard time, the BER performance of TDDD is not affected by the multipath and time delay, but it is affected by the Doppler frequency; the BER performance of FDDD is affected by the time delay and the Doppler frequency at the same time. With the increase of Doppler frequency, the BER performance of FDDD is better than that of TDDD demodulation.

In this paper, the current analysis focuses on the influence of demodulation performance in multipath fading propagation environments, which results from the pilot power variation of pilot symbol assisted modulation (PSAM) M-QAM in orthogonal frequency division multiplexing (OFDM) systems. The pilot-to-data power ratio (PDR) is optimized analytically, and the loss due to imperfect channel estimation is calculated. Theoretical and simulation results show that the optimum PDR is affected by the signal-to-noise ratio (SNR), Doppler frequency and interpolation size etc. When the optimization parameter of PDR is used, the power of the system transmission is minimum while still meeting a fixed bit error rate (BER) target.

In this paper, we derive closed-form expressions of the optimal interference cancellation weight (OICW) and evaluate bit error rate (BER) performance for multistage partial parallel interference cancellation (PPIC) over multipath fading channels in the case of perfect and imperfect channel estimation. The channel model assumes independent path with Rayleigh fading statistics. The effects of receiving BER of last stage, power ratio of pilot to date channels, multiple access and multi-path interference, additive white Gaussian noise (AWGN) and the factors affecting channel estimation error are analyzed. Then, the BER performance for multistage PPIC with complex spreading is evaluated by characteristic function method, indicating that all factors determining the optimal weight certainly affect the BER performance of PPIC. Performance of optimal weight PPIC (OW-PPIC), constant weight PPIC (CW-PPIC) and PPIC with perfect channel estimation (PCE-PPIC) is compared by analysis and simulation, indicating that OW-PPIC employing two stages achieves the performance near to PCE-PPIC with one stage.

This paper introduces an improved multistage parallel interference cancellation (PIC) whose weights are optimized by processing information from channel estimators. An expression determining the optimal interference cancellation weight (OICW) for partial parallel interference cancellation (PPIC) in multipath fading channels is derived and then a new weight-optimizing scheme for PPIC is proposed. Stage by stage, the proposed scheme can make the variance of residual interference less and less and is applicable to multi-rate CDMA systems. The simulation results in single-rate and multi-rate CDMA systems validate our reasonable assumption and show that the proposed scheme makes bit error rate (BER) at the following stage less than that at the previous stage. It is indicated that the proposed PPIC performs better than CW-PPIC with best interference cancellation weight (ICW), still more conventional PIC. Moreover, at a BER of 0.01. the performance loss of the proposed PPIC stage 2 is only 1.5dB, compared to PPIC with channel-known interference of one stage.

The performance of the system with partial transmit sequences is dominated by the side-information. On the premise of that the total transmitting power is limited, the authors investigate the effect of different power allocation schemes between the data and side-information. The arithmetic to obtain the optimal power allocation coefficient is proposed. Besides, the authors propose a new simple arithmetic to obtain the sub-optimal coefficient. The simulation results prove that when the number of carriers is large enough, reducing a little power on every data carrier will obtain large gain on the side-information. So the system is apt to averagely allocate the power to all carriers. Increasing signal noise ratio, the required additional power on the side-information decreases. The bit error rate with the optimal or sub-optimal coefficient is close to the ideal value.

Correct time and frequency synchronization is important for the performance of orthogonal frequency-division multiplexing (OFDM) system. While using the partial power training sequences to synchronize in orthogonal frequency-division multiplexing (OFDM) system, the received data signals will be interfered by the training sequences. In this paper, we give a method for suppressing the interference caused by the training sequence, and introduce an optimized formula for the partial interference canceling factor of partial power training sequence. The above technique is evaluated by means of pilot assistant coherent OFDM system under time-varying multipath Rayleigh fading channels, and the linear interpolation channel estimator is employed to exploit the frequency domain correlation. Especially we focus on only one OFDM symbol in a packet transmission, that is, both the OFDM data and the training sequences are included in one OFDM symbol, and there are no other symbols before or after it. Both theoretical and simulated results of the above technique are presented. It has been shown that the better BER performance can be achieved as compared with no separation, and only the one OFDM symbol can accomplish user data communication assignment.

We explore in this paper one novel deterministic simulation model for downlink multiple-input multiple-output (MIMO) fading channel in wireless communication. The von Mises function, which characterizes accurately the distribution of non-isotropic scattering around mobile station, is used to deterministic modeling process for both space-time codes (STC) scenario and downlink beam-forming (DBF) scenario. Statistical fading characteristics of the simulated MIMO channel, including level-crossing rate, average duration of fades and envelope cross-correlation, are studied. The impact of non-isotropic scattering on MIMO-link capacity is also investigated. The numerical results show that existence of non-isotropic scattering degraded ergodic capacity of MIMO channel by 1 bit/Hz/s in the case of STC scenario. And the maximum achievable capacity in the case of DBF scenario is always lower than that of STC scenario because transmit steering vector affected the transmit antenna correlation. The proposed MIMO channel simulation model has a universal structure and requires less parameter measurements. Hence, it can be used effectively for link-level simulation and capacity evaluation in MIMO wireless communication systems.

In this paper, we propose a novel method for orthogonal frequency-division multiplexing (OFDM) time and frequency synchronization by using PN sequence and barker code, time synchronization performance is improved by barker code, and we also propose a simple but effective method by setting the threshold during time synchronization. Improved frequency synchronization algorithm is presented in this paper too. The idea of multipath utilizing is the core of the frequency synchronization algorithm. The performance of the synchronization proposed here is better than that of conventional methods. Estimation precision is boosted from 10/sup -3/ to 10/sup -4/ for moderate SNR. The requirements of time and frequency synchronization for beyond 3G system based on OFDM can be well satisfied while using the proposed method.

The ultra wideband (UWB) interference to code division multiple access (CDMA) system is analyzed. Two popular UWB modulation schemes, the time hopping pulse position modulation (TH-PPM) and time hopping pulse amplitude modulation (TH-PAM), are considered. The pulse repetition rate (PRR) of UWB impulse radio signal and the frequency band of the CDMA system play the primary roles in the BER performance of the CDMA system receiver in the presence of UWB interference. The characteristics of an aggregation of multiple UWB signals are somehow different from that of a single UWB device, especially for the TH-PPM scheme, where discrete spectral lines maybe exist in the band of CDMA system receiver. The system parameters of TH-PPM such as the modulation index and maximum shift incurred by the time hopping code determine the amplitude of the discrete spectral lines. In multiuser case of TH-PPM, sometimes one discrete spectral line in the band of CDMA system receiver should degrade the BER performance dramatically. It is observed by theoretical analysis and simulations that the interference to CDMA system receiver from single UWB device is very little in common case.