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In this letter, we investigate a robust zero-forcing (ZF) Tomlinson-Harashima Precoding (THP) scheme for multibeam satellite downlink systems to improve the energy efficiency (EE) when the channel state information (CSI) at the transmitter is imperfect. By using a norm-bounded error (NBE) model with unknown statistical characterization, the worst case EE optimization problem under transmit power constraint is solved with two steps. Firstly, the optimal CSI uncertainty parameter minimizing the EE is obtained, then sequential convex approximation (SCA) is used to obtain the solution via a nested iteration method. The proposed robust algorithm achieves better EE performance in comparison with the nonrobust one in the literature in the presence of CSI error, which can be shown from simulation results.

In this letter, we propose a distributed decoding algorithm for the coded slotted ALOHA multiple access scheme. To recover the transmitted packets, the proposed algorithm applies a swap and a back substitution operation on each received packet. As a result, the hidden information in the collided packets can be drawn out immediately to help the decoding, and a high throughput can be achieved. The decoding error probability of the proposed algorithm is analyzed, and the corresponding lower bound is derived. It is shown that compared with the successive interference cancellation decoding algorithm, the proposed algorithm is able to significantly improve the throughput with nearly the same recover time.

We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closed-form expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems. Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.

This paper deals with low-complexity joint channel estimation and decoding for faster-than-Nyquist (FTN) signaling over frequency selective fading channels. The inter-symbol interference (ISI) imposed by FTN signaling and the frequency selective channel are intentionally separated to fully exploit the known structure of the FTN-induced ISI. Colored noise due to the faster sampling rate than that of the Nyquist signaling system is approximated by autoregressive process. A Forney style factor graph representation of the FTN system is developed and Gaussian message passing is performed on the graph. Expectation propagation (EP) is employed to approximate the message from channel decoder to Gaussian distribution. Since the inner product between FTN symbols and channel coefficients is infeasible by belief propagation (BP), we propose to perform variational message passing (VMP) on an equivalent soft node in factor graph to tackle this problem. Simulation results demonstrate that the proposed low-complexity hybrid BP-EP-VMP algorithm outperforms the existing methods in FTN system. Compared with the Nyquist counterpart, FTN signaling with the proposed algorithm is able to increase the transmission rate by over 40%, with only negligible BER performance loss.

In this letter, we study the receiver design for a coded faster-than-Nyquist signaling system in time-variant intersymbol interference channels, also known as doubly selective channels. The conventional minimum mean squared error (MMSE) equalizer results in high complexity. We develop a low-complexity iterative message passing algorithm toward this end. By reformulating the problem into a linear state-space model, Gaussian message passing is performed on a vector-form factor graph. Furthermore, we consider the imperfect channel information scenario to improve the robustness of the proposed algorithm. Simulation results show that the proposed algorithm outperforms the variational inference method, and it performs close to the frequency-domain MMSE equalizer with much lower computational complexity.

This work deals with frequency-domain equalization for faster-than-Nyquist (FTN) signaling in doubly selective channels (DSCs). To handle the interference of frequency-domain symbols, the minimum mean square error (MMSE) equalizer involves high complexity in DSCs. To overcome the problem, we propose low-complexity receivers based on two variational methods, i.e., mean field (MF) and Bethe approximations. Compared with the MF method, the Bethe approximation takes into account the conditional dependencies of pairwise symbols. By only considering a small set of the frequency-domain symbols that have strong interference to each other, the complexity of the proposed algorithms increases linearly with the block length. Simulation results demonstrate that the proposed algorithms for FTN signaling are able to perform close to the MMSE equalizer in DSCs while with significantly reduced computational complexity.

In this paper, we analyze the ergodic achievable rate of an uplink massive multi-user multipleinput multiple-output (MIMO) system with a large and Poisson distributed number of users. In the considered scenario, multiple user equipments (UEs) transmit their information to a base station equipped with a very large number of antennas. New asymptotic expressions for the ergodic achievable rate for large and deterministic number of users are derived for both maximum ratio combining (MRC) detector and zero-forcing (ZF) detector, as well as the ergodic achievable rate for large and random number of users. Simulation results assess the accuracy of these analytical expressions. It is shown that compared with the MRC detector, ZF detector can achieve much higher spectrum efficiency. Also, the results provide a meaningful fact that with different settings the randomness of the number of users will result in different extents of impact on the performance of massive MIMO.

In this correspondence, we analyze the ergodic capacity of a large uplink multi-user multiple-input multiple-output (MU-MIMO) system over generalized-$K$ fading channels. In the considered scenario, multiple users transmit their information to a base station equipped with a very large number of antennas. Since the effect of fast fading asymptotically disappears in massive MIMO systems, large-scale fading becomes the most dominant factor for the ergodic capacity of massive MIMO systems. Regarding this fact, in our work we concentrate our attention on the effects of large-scale fading for massive MIMO systems. Specifically, some interesting and novel lower bounds of the ergodic capacity have been derived with both perfect channel state information (CSI) and imperfect CSI. Simulation results assess the accuracy of these analytical expressions., Comment: Submitted to IEEE Transactions on Vehicular Technology as a correspondence

Tomlinson-Harashima precoding (THP) is a popular nonlinear interference mitigation technique employed at the transmitter side which can provide better performance in comparison with linear precoding technique with a slight increase in complexity. It can be considered as a dual to the successive interference cancellation (SIC). In this paper, we focus on a new THP scheme for the downlink of multiuser multiple-input multiple-output (MIMO) systems with decentralized receivers. In contrast to previous approaches, the proposed algorithm allows the provision of transmitting different types of information simultaneously subjected to different Quality of Service (QoS) requirements of each active user. This algorithm is based on minimum mean square error (MMSE) criterion and the closed-form solution of the processing matrices is found by applying Cholesky factorization with symmetric permutation. For this THP algorithm, by suitably ordering the rows of channel matrix we can obtain better performance. Moreover, it significantly improves the bit error rate (BER) performance as well as the achievable rate as compared with zero-forcing (ZF) THP available in the literature, which can be shown from simulation results.

The nonlinear distortion of wideband signal due to the filtering and efficiently operated high power amplifiers limits the performance of satellite communications. Volterra series can be used to describe the nonlinear satellite channels effectively. Most existing equalizers simply ignore the nonlinear terms or treat all the nonlinear combinations of symbols as interference. In this study, by properly exploiting information from nonlinear terms, we propose three turbo equalizers for nonlinear satellite channels, namely, joint Gaussian (JG), soft interference cancellation-minimum mean square error (SIC-MMSE) and linear minimum mean square error (LMMSE) equalizers. In JG and SIC-MMSE-based equalizers, both the linear and nonlinear terms that contain the symbol of interest are considered as desired signals. Accordingly, the required statistics are calculated based on the a priori probabilities of coded bits from output of channel decoder. For LMMSE-based equalizer, we propose to calculate the extrinsic information from output of equalizer by excluding the prior information in both the linear and nonlinear terms. Simulation results demonstrate that the proposed equalizers significantly outperform the method which ignores the presence of nonlinear interferences. Moreover, the nonlinear terms that contain the symbol of interest can be exploited to further improve the performance of turbo equalization.

As the mobile communications develop rapidly, ever higher spectral efficiency is required. The sparse code multiple access (SCMA) has been recognized as a promising technology to further increase the access efficiency of wireless networks. In this paper, we consider the receiver design problem for SCMA system over frequency selective channels. The conventional minimum mean squared error (MMSE) detection method suffers from huge complexity due to the the multi-user and inter-symbol interferences. To this end, we propose a near optimal low complexity message passing receiver. By approximating the discrete log-likelihood ratio as Gaussian random variable, all messages on factor graph can be obtained as Gaussian distributions. Furthermore, we propose to introduce auxiliary variables to the factor graph and develop a novel hybrid belief propagation (BP) and expectation propagation (EP) receiver. Simulation results show that the proposed hybrid BP-EP method performs close to the MMSE-based receiver with reduced complexity. Also, compared to the orthogonal multiple access scheme, the considered SCMA system with the proposed receiver is able to support 50% more users.

In this paper, a code-aided maximum-likelihood and moment-based joint SNR estimator is proposed for M- ary amplitude phase shift keying (APSK) signals over AWGN channels. The proposed estimator significantly improves the performance at low SNRs by utilizing the syndrome in the LDPC codes to act as a reference measurement of estimation performance. Moreover, a methodology to measure the performance of the estimation by the syndrome of the LDPC codes is derived and the simulation results reveal that the number of 0s in the syndrome presents a positive correlation with the SNR. Compared with code-aided maximum-likelihood (ML) estimators and momentbased estimators for M-APSK signals, it is validated that the proposed joint SNR estimator has integrated the advantages of the classical approaches and simulation results also show that the proposed estimator exploiting the decision metric of the selector performs better at the SNR estimation range.

This paper considers a two-user multiple-input single-output (MISO) interference channel with confidential messages (IFC-CM), where the beamforming vectors at the two transmitters are jointly optimized using the closed-form Pareto-optimal parameterization. We prove that artificial noise cannot improve the secrecy rate performance, and coordinated beamforming is secrecy-rate optimal which is achieved by agreeing on the parameters between the two transmitters. We analyze the feasible set of the beamforming parameters that guarantees positiveness of the secrecy rates when the transmitters know only statistical CSI, and then analysis in the ergodic secrecy rate region is discussed. More importantly, we derive the closed form of the ergodic secrecy rates and illustrate the Pareto-optimal structure of the beamforming vectors.

We study multiuser multiway relay networks where N user nodes exchange their information through a single-relay node. We use network coding (NC) in the relay to increase the throughput. Due to the limitation of complexity, we only consider the binary multiuser NC (BMNC) in the relay. We study the BMNC matrix [in GF(2)] and propose several design criteria on the BMNC matrix to improve the symbol error probability (SEP) performance. Closed-form expressions of the SEP of the system are provided. Moreover, an upper bound of the SEP is also proposed to provide further insights into system performance. Then, BMNC matrices are designed to minimize error probabilities.

In this study, a unified linear minimum mean-square-error (LMMSE) transceiver design framework is investigated, which is suitable for a wide range of wireless systems. The unified design is based on an elegant and powerful mathematical programming technology termed as quadratic matrix programming (QMP). Based on QMP it can be observed that for different wireless systems, there are certain common characteristics which can be exploited to design LMMSE transceivers, for example, the quadratic forms. It is also discovered that evolving from a point-to-point multiple-input–multiple-output (MIMO) system to various advanced wireless systems such as multi-cell coordinated systems, multi-user MIMO systems, MIMO cognitive radio systems, amplify-and-forward MIMO relaying systems and so on, the quadratic nature is always kept and the LMMSE transceiver designs can always be carried out via iteratively solving a number of QMP problems. A comprehensive framework on how to solve QMP problems is also given. The work presented in this study is likely to be the first shot for the transceiver design for the future ever-changing wireless systems.

We investigate the multiple-input multiple-output (MIMO) transceiver design using a weighted mean-square-error (MSE) minimization approach. A novel weighted MSE model is proposed, and it is defined as a linear matrix function with respect to the traditional data detection MSE matrix. The new model can be interpreted an extension of the weighting operation from vector field to matrix field. Based on the proposed weighting operation, a general transceiver design is proposed, which aims at minimizing an increasing matrix-monotone function of the output of the previous linear matrix function. The structure of the optimal solutions is also derived. Furthermore, two important special cases of the matrix-monotone functions are discussed in detail. It is revealed that these two problems are equivalent to the transceiver design of sum MSE minimization and capacity maximization for dual-hop amplify-and-forward (AF) MIMO relaying systems, respectively. Finally, it is concluded that the AF relaying can be interpreted as a specific application of the proposed weighting operation.

This letter addresses the robust optimization problem of downlink coordinated beamforming over the multiple-input single-output (MISO) interference channel with confidential messages (IFC-CM) exploiting imperfect channel state information (CSI) at the transmitters. Our aim is to minimize the total transmitted power subject to the worst-case individual signal-to-interference-plus-noise-ratio (SINR) and equivocation rates. To model the imperfect CSI, bounded ellipsoidal uncertainties for channel vector and covariance are both considered. By deriving the equivalent forms for the SINR and secrecy constraints, the worst-case robust problem can be reformulated as a semi-definite programming (SDP) with the help of rank relaxation. Simulation results show the effectiveness of the proposed algorithm.

In this paper, we analyze the symbol error rate (SER) of space-time network coding (STNC) in a distributed cooperative network over independent but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading channels. In this network, multiple sources communicate with a single destination with the assistance of multiple decode-and-forward (DF) relays. We first derive new exact closed-form expressions for the SER with M-ary phase-shift keying modulation (M-PSK) and M-ary quadrature-amplitude modulation (M-QAM). We then derive new compact expressions for the asymptotic SER to offer valuable insights into the network behavior in the high signal-to-noise ratio (SNR) regime. Importantly, we demonstrate that STNC guarantees full diversity order, which is determined by the Nakagami-m fading parameters of all the channels but independent of the number of sources. Based on the new expressions, we examine the impact of the number of relays, relay location, Nakagami-m fading parameters, power allocation, and nonorthogonal codes on the SER.

In this paper, robust transceiver designs for point-to-point MIMO-OFDM systems are investigated. A general performance metric of weighted mean-square-error (MSE) minimization is adopted as the optimization objective function. Exploiting the signal structure of point-to-point MIMO-OFDM systems, a simple and readily comprehensive derivation procedure for the optimal solution is given. It is revealed that for the optimal robust transceiver design the solution is an interesting variant of the famous water-filling solution, which is named as cluster water-filling. This solution is significantly different from that of the robust transceiver design over flat fading channels and its counterpart with perfect channel state information (CSI) as the water-filling bottoms are also variables to be optimized among different subcarriers. Finally, the simulation result demonstrates the performance advantages of the proposed robust design.

In this letter, we investigate multiple-input multiple-output (MIMO) communications under energy harvesting (EH) constraints. In our considered EH system, there is one information transmitting (ITx) node, one traditional information receiving (IRx) node and multiple EH nodes. EH nodes can transform the received electromagnetic waves into energy to enlarge the network operation life. When the ITx node sends signals to the destination, it should also optimize the beamforming/precoder matrix to charge the EH nodes efficiently simultaneously. Additionally, the charged energy should be larger than a predefined threshold. Under the EH constraints, in our work both minimum mean-square-error (MMSE) and mutual information are taken as the performance metrics for the beamforming designs at the ITx node. In order to make the proposed algorithms suitable for practical implementation and have affordable overhead, our work focuses on the beamforming designs with partial CSI and this is the distinct contribution of our work. Finally, numerical results are given to show the performance advantages of the proposed algorithms.

Superposition coded symmetric relaying is a bandwidth-efficient cooperative scheme where each source node simultaneously transmits both its own “local” packet and “relay” packet that originated at its partner by adding the modulated local and relay signals in Euclidean space. This paper investigates the power allocation and outage probability of a superposition coded symmetric relaying system with finite-constellation signaling. We first derive the mutual information (MI) metrics for the system. The derived MI metrics consist of two parts: one represents the MI conveyed by the modulated signal corresponding to its own data, and the other represents the MI conveyed by the modulated signal corresponding to its partner’s data. Using MI based effective signal-to-noise ratio mapping technique, we attain expressions for the outage probability. Furthermore, we discuss power allocation policies that minimize the outage probability. Simulation results are presented to verify the correctness of the outage probability analysis and the benefits of the power allocation.

In this paper, we investigate joint subcarrier and power allocation scheme for distributed multicarrier cooperative networks with imperfect channel state information. Using practical channel estimation algorithms, the statistic model of the channel estimation error is first derived. Then based on the channel error model, the resource allocation scheme aims at maximizing the sum rate of the overall network. Specifically, both subcarrier allocation and power allocation are taken into account. In the subcarrier allocation, a realistic problem of the power leakage between neighboring subcarriers is also addressed. Furthermore, based on Lagrange dual-decomposition algorithm, a practical power allocation algorithm is proposed. Finally, the simulation results demonstrate the performance advantages of the proposed robust design.

In this paper, we investigate a robust filter-and-forward (FF) beamforming design for two-waymulti-antenna relaying networks, where multiple relays assist two terminals to exchange information. With Gaussian distributed channel errors, the proposed robust beamforming design aims at maximizing the signal-to-interference-plus-noise-ratio (SINR) under individual transmit power constraints at each relay. Exploiting the elegant convex optimization mathematical tools, the optimization problem can be efficiently solved. Finally, simulation results demonstrate that the proposed robust beamformer reduces the sensitivity of the two-way multi-antenna relay networks to channel estimation errors, and outperforms the algorithm with estimated channels only. Moreover, the length of robust beamformer also influences the system performance.

Owing to the wireless signal power received by the energy harvesting (EH) node is generally higher than that of the information decoding (ID) node in simultaneous wireless information and power transfer (SWIPT) system, the confidential information becomes vulnerable to be wiretapped. Motivated, in this paper, we aim at realizing secure communications for two-user MIMO interference channel with SWIPT. Unfortunately, the formulated secrecy rate maximization problem is non-convex with respect to the covariance matrices of two transmitters, thus an alternative algorithm is proposed to solve the nonconvex optimization problem. Firstly, the orthogonal-projection-based optimization algorithm is performed to completely suppress the interference to ID receiver, then by applying the Taylor series expansion, the covariance matrix of information transmitter is optimized based on the dual optimization. Finally, numerical experiments are conducted to validate the security performance of the proposed algorithm.

In this paper, we propose a novel transceiver architecture at relay for multiple input, multiple output two-way relay channel (TWRC). This transceiver architecture consists of the analog beamforming in RF domain and the MMSE based digital processing at baseband, which is simplified as RF-MDP-MIMO architecture. Under the RF-MDP-MIMO architecture, the objective of this paper is to find the optimal relay transmit and receive beamformings that can achieve the boundary of the optimal capacity region with limited relay power. Specifically, by formulating the original optimization problem into two semidefinite programing (SDP) problems with rank-one constraints which can be effectively solved by the penalty function method, we can optimize relay transmit and receive beamformings alternatively. Then, the maximum achievable sum rate of TWRC can be obtained with the bisection method. Further, the achievable capacity region is realized. Numerical experiments are conducted to show that our proposed RF-MDP-MIMO architecture outperforms the RF-MIMO architecture in terms of the optimal achievable capacity region.

The security issue is critically important for wireless communications, especially for multi-hop communications. In this paper, we propose a cooperative secrecy beamforming scheme for a multi-hop MIMO communication network, in which there is a single-antenna source, multiple multi-antenna relays and a single-antenna destination. Moreover, two types of eavesdroppers exist in the network, one of which has known channel state information (CSI), while the other not. To achieve security communications, in our work null-space beamforming and artificial noise beamforming are jointly optimized to improve the secrecy rate of the multi-hop network. In nature, the joint optimization is nonconvex and challenging. Exploiting its structure, the considered optimization problem is decoupled into a series of subproblems that can be efficiently solved based on convex optimization theory. Finally, some numerical experiment results are provided to assess the performance of the proposed secrecy beamforming design.

This paper has proposed an iterative eigen-direction alignment (EDA) algorithm for an uplink multi-cell multiple-input-multiple-output (MIMO) system. With a small amount of computation cost, this algorithm has decreased both inter-cell and intra-cell interference. According to semi-orthogonal user selection (SUS) scheme which achieves more multiuser diversity and higher system capacity, the EDA-based average cell capacity maximization problem is resolved iteratively, which is based on semidefinite relaxation (SDR) algorithm. The simulation results illustrate that the proposed alternating EDA-SUS algorithm with acceptable computation has a better capacity performance than the zero-forcing (ZF)-SUS algorithm. In addition, with the increasing number of transmit antennas, the advantage of EDA-SUS algorithm becomes significantly more obvious.

In this paper, we analyze the performance of distributed antenna systems (DASs) using random matrix theory. We consider the scenarios that the antennas of the terminals are distributed geographically in the coverage instead of being installed nearby each other, e.g. the high-speed railway networks and the high-altitude platform networks. Thus the variances of the entries in the considered channel matrix, named as unequal-covariance Wishart matrix, are different from each other. To the best of the authors' knowledge, the work on random matrices with elements having different variances is largely open, which is of great importance for DASs. In our work, some results on the eigenvalue distributions of the unequal-covariance Wishart matrix are derived. Then based on these conclusions the outage probability of the considered DAS over Rayleigh fading channels is derived. Finally, the accuracy of our analytical results is assessed by numerical results.

In this paper, we analyze the achievable rate of the uplink of a single-cell multi-user distributed massive multiple-input-multiple-output (MIMO) system. The multiple users are equipped with single antenna and the base station (BS) is equipped with a large number of distributed antennas. We derive an analytical expression for the asymptotic ergodic achievable rate of the system under zero-forcing (ZF) detector. In particular, we consider circular antenna array, where the distributed BS antennas are located evenly on a circle, and derive an analytical expression and closed-form tight bounds for the achievable rate of an arbitrarily located user. Subsequently, closed-form bounds on the average achievable rate per user are obtained under the assumption that the users are uniformly located in the cell. Based on the bounds, we can understand the behavior of the system rate with respect to different parameters and find the optimal location of the circular BS antenna array that maximizes the average rate. Numerical results are provided to assess our analytical results and examine the impact of the number and the location of the BS antennas, the transmit power, and the path-loss exponent on system performance. It is shown that circularly distributed massive MIMO system largely outperforms centralized massive MIMO system., Single column, 30 pages, 8 figures. Submitted to IEEE Transactions on Wireless Communications

In this paper, we address the optimization problems of downlink coordinated beamforming over the K-user multiple-input single-output interference channel with confidential messages. Our design objective is to minimise the total transmit power subject to the constraints of individual signal-to-interference plus noise ratio and equivocation rates. For the case with perfect channel state information CSI, by deriving the equivalent forms for the signal-to-interference plus noise ratio and the secrecy constraints, the optimization problem is reformulated as a second-order cone programme problem. Then, an optimal distributed algorithm using the partial Lagrangian duality is discussed. In addition we extend the analysis to the imperfect CSI scenario with bounded ellipsoidal errors, the centralised algorithm has been proposed by solving a semi-definite programme with the help of rank relaxation, whereas the distributed robust algorithm is devised by using ADMM. Finally, the simulation results have shown that with perfect CSI, the centralised and distributed algorithms have nearly the same performance, but in the case of imperfect CSI, the centralised algorithm shows a slightly better performance. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, an accurate maximum likelihood (ML) signal-to-noise ratio (SNR) estimator is proposed for coded M-ary amplitude phase shift keying (APSK) signals over a slowly time-varying fading channel. The proposed estimator significantly improves the estimation precision at low SNRs by utilizing a posteriori probabilities of coded bits provided by channel decoder. Moreover, a methodology to calculate the Cramer-Rao bound (CRB) of the proposed code-aided (CA) ML SNR estimator for coded M-APSK signals is derived. Compared with moments-based estimators and the non-data-aided (NDA) Expectation Maximization (EM) based estimator for M-APSK signals, simulation results show that the proposed estimator exploiting a posteriori information out of low-density parity-check (LDPC) decoder or Turbo decoder performs better, especially at low SNRs. It is also validated that the performances of the proposed CA-ML SNR estimator for coded 16- and 32-APSK signals can closely achieve the derived CRB.

A code-aided (CA) iterative signal-to-noise ratio (SNR) estimator based on Expectation Maximization (EM) algorithm is proposed for M-ary amplitude phase shift keying (APSK) signals. The estimation algorithm utilizes a posteriori probabilities of coded bits obtained from channel decoder to improve estimation precision at low SNRs. Furthermore, Cramer-Rao bound (CRB) of the proposed CA iterative SNR estimator for M-APSK is derived and simulated numerically. Compared with the non-data-aided (NDA) EM-based estimator and moments-based estimators for M-APSK signals, computer simulation results show that the proposed estimator exploiting a posteriori information has more excellent performance, especially at low SNRs. It is also demon-strated that the performances of the proposed CA SNR estimator for 16- and 32-APSK signals are very close to the derived CRB.

A low complexity non-data-aided (NDA) signal-to-noise ratio (SNR) estimator is proposed for quadrature phase shift keying (QPSK) signals in complex additive white Gaussian noise (AWGN) channel. The novel SNR estimator utilizes the constant amplitude characteristics of the in-phase and quadrature components of QPSK modulation to evaluate the signal power and the noise power separately. The estimation mean and normalized mean square error (NMSE) of the proposed SNR estimator are investigated by Monte Carlo simulations. Results demonstrate that, compared with M2M4 algorithm and Ijaz's method, the proposed estimator saves about 80% and 50% multiplications, respectively. Furthermore, the proposed method performs better than other NDA SNR estimators at the same computational complexity.

This paper presents a message passing approach to joint channel estimation, data detection and decoding over time-selective Rayleigh fading channel with residual carrier frequency offset (CFO). The proposed algorithm utilizes the sum product algorithm (SPA) implemented on a factor graph (FG) representing the joint a posteriori probability distribution of the unknown CFO, information bits and channel coefficients vector given the channel output. A combination of particle filtering and Gaussian parameterization is employed to approximate the exact probability density function in message passing for CFO and channel estimation. Computer simulations demonstrate the effectiveness of the proposed algorithm in combating the CFO over unknown Rayleigh fading channels.

In this paper, we investigate a unified linear transceiver design with mean-square-error (MSE) as the objective function for a wide range of wireless systems. The unified design is based on an elegant mathematical programming technology namely quadratic matrix programming (QMP). It is revealed that for different wireless systems such as multi-cell coordination systems, multi-user MIMO systems, MIMO cognitive radio systems, amplify-and-forward MIMO relaying systems, the MSE minimization beamforming design problems can always be solved by solving a number of QMP problems. A comprehensive framework on how to solve QMP problems is also given., 6 Pages WCSP 2012 Final Version

In this paper, we investigate robust beamforming design for two-way amplify-and-forward (AF) MIMO relaying system with Gaussian distributed channel errors. Take mean-square-error (MSE) of the detected data as the design criteria, beamforming matrix at relay is optimized under imperfect channel state information (CSI). Both frequency-division-duplex (FDD) system and time-division-duplex (TDD) system are considered. Then two algorithms are proposed based on a framework of quadratic programming. The effectiveness of the proposed robust design for different channel mode are finally demonstrated by the simulation results.

An echo channel estimation method in the multi-input multi-output(MIMO) system under the frequency-flat fading channel is presented. The key idea of echo MIMO estimation method is to reflect, or “echo”, back the received training signal to the transmitter. But the noise reflected back to the BS together with the training signal will critically decrease the accuracy of channel estimation. In this paper, we propose a preprocessing method, which can eliminate the impact of noise, before reflecting back the training signal to solve this problem. Moreover, based on Kronecker channel model, different designs of preprocessing matrix are proposed under different antenna configuration, which will result in different estimation performance. Simulation results show that the enhanced echo MIMO channel estimation method could effectively decrease the mean square errors (MSEs) of channel estimation.

In this paper, we propose an improved iterative receiver scheme for low density parity check (LDPC) codes transmitted over unknown channels affected by a strong phase noise. For achieving joint channel parameter estimation, data detection and decoding, the proposed algorithm utilizes the sum product algorithm (SPA) implemented on the factor graph (FG) that represents the joint a posteriori probability of information symbols and channel parameters given the channel output. Through the iterative use of the soft information on coded symbols from channel decoder, the proposed algorithm employs forward-backward recursions for message passing on the graph. Numerical results for binary LDPC codes show that, the proposed algorithm can be able to cope with a strong phase noise under unknown channel information and achieve nearly the same performance as the optimal coherent receiver under DVB-S2 compliant ESA phase noise model, and only a slightly decrease under strong Wiener model.

In this paper, we consider robust distributed filterand-forward beamforming design for two-way relaying networks over the frequency selective fading channels, in which two users exchange information with the aid of multiple single-antenna relay nodes. In contrast to the prior works in which the channel state information (CSI) is available, in our work CSI for all relevant links between the users and relay nodes is not perfectly known with stochastic channel errors. Under stochastic channel errors, a robust beamforming design aiming at maximizing the total system signal-to-interference-plus-noise-ratio (SINR) under individual relay power constraints is proposed. Simulation results show that the proposed robust beamformer reduces the sensitivity of the two-way relaying systems to channel estimation errors, and performs better than the algorithm using estimated channels only.

In this paper, we propose a novel signal-to-noise ratio (SNR) estimation method for linear modulations on additive white Gaussian noise (AWGN) channel. It estimates noise power directly without estimating the received total power. The estimation mean and mean square error (MSE) are analyzed theoretically and verified by simulations. Results show that the proposed SNR estimator performs better at low SNR than the conventional estimators while it suffers a little degradation at high SNR. Moreover, the proposed estimator can be implemented with less real multiplications than the conventional ones.

In this paper, we investigate maximum mutual information design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with imperfect channel state information, i.e., Gaussian distributed channel estimation errors. The robust design is formulated as a matrix-variate optimization problem. Exploiting the elegant properties of Majorization theory and matrix-variate functions, the optimal structures of the forwarding matrices at the relays and precoding matrix at the source are derived. Based on the derived structures, a water-filling solution is proposed to solve the remaining unknown variables.

In this paper, we investigate the robust transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel estimation errors. Aiming at maximizing the mutual information under imperfect channel state information (CSI), source precoder at source and forwarding matrix at the relay are jointly optimized. Using some elegant attributes of matrix-monotone functions, the structures of the optimal solutions are derived first. Then based on the derived structure an iterative waterfilling solution is proposed. Several existing algorithms are shown to be special cases of the proposed solution. Finally, the effectiveness of the proposed robust design is demonstrated by simulation results., 6 Pages, 1 Figure

In this paper, joint transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel estimation errors in both two hops is investigated. Due to the fact that various linear transceiver designs can be transformed to a weighted linear minimum mean-square-error (LMMSE) transceiver design with specific weighting matrices, weighted mean square error (MSE) is chosen as the performance metric. Precoder matrix at source, forwarding matrix at relay and equalizer matrix at destination are jointly designed with channel estimation errors taken care of by Bayesian philosophy. Several existing algorithms are found to be special cases of the proposed solution. The performance advantage of the proposed robust design is demonstrated by the simulation results., Comment: 5 Pages, 1 Figure

In this paper, we present two construction schemes for LDPC codes in coded cooperative communication. The scheme which uses rate compatible LDPC codes based on puncturing parity bits is presented first. Then the paper proposes the scheme which uses QC-LDPC codes based on the multiplicative inverses in finite fields. Detailed implementations of two schemes are presented. Simulation results show that the LDPC codes constructed by these two schemes are valid in coded cooperation.

In this paper, we consider a multi-relay Demodulate-and-Forward (DMF) cooperation system, in which the high order superposition modulation (SM), superposition 16-QAM modulation (16-QAM SM), is employed. For the given system, a new relay selection scheme with constellation rearrangement (CR) technique is proposed, which is based on the optimal total mutual information (MI) criterion. Firstly we find the optimal bit-symbol mappings for all the relays. And then the source chooses the best relay, which maximizes the MI, to cooperate with. We assume that all the channel state information (CSI) is available. A variety of simulations are performed and show that they match exactly with analytic ones. In addition, the result shows that our new strategy utilizing high order superposition modulation with CR outperforms the classical SM cooperation scheme and traditional DMF scheme for about 1.5 dB and 2.5 dB, respectively. Meanwhile, it improves spectrum efficiency with the same complexity.

In this paper, beamforming design for multi-pair two-way amplify-and-forward (AF) MIMO relaying networks is considered, in which there are multiple users using one multi-antenna relay to exchange information. As there are multiple user-pairs, beamforming matrix at relay should be carefully designed to eliminate the mutual interference among different user-pairs. Both signal-to-interference-plus-noise ratio (SINR) at the receiver and mean-square-error (MSE) of the detected data are taken as the design criteria. Therefore, two different algorithms are proposed based on a framework of quadratic programming. The performance of the two proposed algorithms are finally demonstrated by the simulation results.

This paper considers coordinated precoding design in multi-cell scenarios where transmitters and receivers are both equipped with multiple antennas. To suppress the co-channel interference, much channel state information (CSI) should be sent back to the transmitters. Most previous work in the area has assumed that transmitters have common knowledge of both data symbols of all users and full or local CSI. Herein, we propose coordinated precoding scheme with interference CSI only. Note that interference CSI denotes the channel state information from the transmitter to the interference user particularly. "Zero-Forcing" (ZF) method can be easily adopted to minimize the co-channel interference. However, coordinated ZF precoding imposes a condition that each BS should have more antennas than receive antennas combined by all users. Therefore, a coordinated precoding scheme is presented by minimizing leakage-plus-noise (MLN) which is similar to maximizing signal-to-leakage-plus-noise ratio (SLNR) criterion. Simulation results show that the proposed precoding scheme endures an acceptable performance loss compared to traditional schemes while much feedback overload is reduced.

In this paper, we focus on a two-hop amplify-and-forward (AF) cooperative network, in which one source node equipped with multiple antennas communicates with a single-antenna destination, relying on a single-antenna relay. In order to implement beamforming, channel state information (CSI) is feeded back to the source from relay using random vector quantization (RVQ) codebook. Furthermore, one relay with the best CSI is selected in each communication. An upper bound of the outage probability of the whole system is derived and the simulation results show that this bound is very tight.

In this paper, optimal linear transceiver designs for multi-hop amplify-and-forward (AF) Multiple-input Multiple-out (MIMO) relaying systems with Gaussian distributed channel estimation errors are investigated. Some commonly used transceiver design criteria are unified into a single matrix-variate optimization problem. With novel applications of majorization theory and properties of matrix-variate function, the optimal structure of robust transceiver is first derived. Based on the optimal structure, the original transceiver design problems are reduced to much simpler problems with only scalar variables whose solutions are readily obtained by iterative water-filling algorithms. The performance advantages of the proposed robust designs are demonstrated by the simulation results., Comment: Accepted by WCSP2011

In this paper, we propose a novel protocol for cooperative diversity based on decode-and-forward (DF) with iterative detection and decoding (IDD) receiver. Closed-form expression of outage probability is derived under quasi-static fading channels. Numerical results show that the proposed scheme achieves higher diversity order than traditional DF scheme without additional transmitter power.