This paper addresses the design of multiuser MIMO amplify-and-forward relaying within a cloud radio access network (C-RAN) from an energy-efficiency perspective. The aim is to jointly select remote radio heads and optimize their transceiver in order to assist the communication between multiple source-destination pairs. We formulate the design problem as an interference leakage minimization subject to per-relay power constraints along with linear signal preserving constraints at the destinations. To obtain an energy efficient relaying solution, the objective function is penalized with a regularization term which promotes group-sparsity among the resultant relaying weights. A low-complexity iterative algorithm based on the alternating direction method of multipliers (ADMM) is then proposed to solve the regularized problem. Simulation results demonstrate the explicit benefits of the proposed algorithm, which results in notably lower power consumption and computational complexity than conventional relaying design methods.

Jointly detecting the signals from the source and relay in a spatial multiplexing (SM) multiple-input multiple-output (MIMO) relaying system improves the transmit reliability significantly. However, the existing joint detection schemes for SM MIMO relaying systems, which achieve full diversity, such as the near maximum likelihood (ML) decoder, suffer from high complexity. In this paper, we propose a weighted combining (WC) algorithm, which is applied before the detector in the SM MIMO decode-and-forward relaying system. The proposed algorithm merges the received signal vectors from the source and relay into a combined signal without expanding their dimension, and formulates an equivalent MIMO channel matrix for the combined signal, resulting in a lower complexity for the subsequent detection. We analyze the performance of the proposed WC algorithm with ML detection in terms of the diversity order and computational complexity. An approximate upper bound on the symbol error probability (SEP) for the proposed algorithm is also derived. Simulation results show that in symmetric networks, the proposed WC algorithm achieves substantially lower complexity, while maintaining an SEP performance similar to that of the benchmark NML decoder. The consistency of the derived upper bound on the SEP is also verified by simulations.

In this paper, we study the problem of joint multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying and artificial noise (AN) optimization for secure communication between a source-destination pair in the presence of multiple eavesdroppers (eves). The eves' channel state information (ECSI) is subject to arbitrarily distributed random errors. Assuming that only the first and second moments of the ECSI errors are known, we introduce a probabilistically robust design method, which aims to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination while satisfying a set of robust intercept probability constraints. Since the resultant optimization problem is non-convex, we propose a solution approach by resorting to a duality-based method along with the semidefinite relaxation (SDR) technique, where a global optimal solution to our design problem can be found. Our simulation results demonstrate the improved secrecy of the proposed robust relaying design against eavesdropping and its robustness against the channel uncertainties.

Jointly detecting signals from the source and relay in a multi-input multi-output (MIMO) relaying system can achieve lower symbol error probability (SEP) and higher diversity order. In the literature, the best detector for spatial multiplexing decode-and-forward (DF) MIMO relaying systems is the near maximum likelihood (NML) decoder. However, both NML decoder and its variation are computationally intensive, especially when high-order modulations and multiple data streams are adopted. In order to develop a more efficient detection scheme, we propose a weighted combining (WC) algorithm which is applied before the final detector. The proposed algorithm merges the signal vectors from the source and relay without expanding their dimension and formulates an equivalent MIMO channel matrix for the combined signal, resulting in a much lower complexity for the subsequent detection. Simulation results show that by using the proposed WC algorithm with the ML detector, the same diversity gain as that of the more complex NML detection scheme can be achieved. In particular, in a symmetric network topology, the performance of the proposed WC algorithm is comparable to that of NML.

Multiple-input multiple-output (MIMO) amplify-and- forward (AF) relaying is designed for secure communication between a source-destination pair in the presence of multiple eavesdroppers. Assuming statistical knowledge of the eavesdroppers' channel state information (ECSI) errors, we introduce a probabilistically robust design method, which aims to optimize the source transmission power and AF relaying matrix by maximizing the received signal-to- interference-plus-noise ratio (SINR) at the destination, while satisfying a set of intercept probability constraints. The resultant optimization problem becomes nonconvex, and hence we propose a conservative two-step solution, where the source transmission power and the relaying matrix are sequentially optimized. Our simulation results demonstrate the improved secrecy of the proposed relaying design against eavesdropping and its robustness against the channel uncertainties.

This paper addresses the problem of secure amplify-and-forward (AF) relaying for multiple-input multiple output (MIMO) relaying networks in the presence of multiple eavesdroppers. Assuming practical imperfect eavesdroppers' channel state information (ECSI), we propose a robust approach to optimize the relay AF matrix, subject to power constraint, in order to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination while satisfying a set of secrecy constraints. The ECSI errors are assumed to fall within some predefined bounded sets. Since the resultant optimization problem is non-convex and semi-infinite, we transform it into a form constituted by the differences of convex functions (DC) using suitable matrix transformation techniques. Then an algorithmic solution with proven convergence is proposed by resorting to the penalty-DC algorithm (P-DCA). Experimental results show the security of the proposed transceiver design against eavesdropping and the robustness against the channel uncertainties.

This paper addresses the problem of amplify-and- forward (AF) relaying for multiple-input multiple-output (MIMO) multiuser relay networks. where each source transmits multiple data streams to its corresponding destination with the assistance of multiple relays. Assuming only imperfect channel state information (CSI) of all the source-relay and relay-destination links, we propose a robust approach to jointly design the source and relay precoders and the receive filters, in which the worst per-stream mean square error (MSE) is minimized subject to source and relay power constraints. The channel uncertainties are assumed to be Gaussian distributed and the well-known Kronecker model is employed to characterize the spatial correlations in the proposed design. The resultant optimization problem is nonconvex and therefore, an algorithmic solution with proven convergence is proposed by resorting to the iterative block coordinate update approach along with matrix transformation and convex conic optimization techniques. Simulation results show that the proposed joint transceiver design can achieve an improved robustness against the channel uncertainties when compared to the non-robust approaches.

Recent development in wireless communication-based positioning systems using time-of-arrival (TOA) methods poses a significant challenge for the estimation of signal propagation time in indoor environments. Due to the possible obstruction of the direct path, the signal component from direct propagation can be very weak and therefore, the performance of TOA estimation will be significantly degraded, especially when the received signal experiences severe multipath propagation effects. An optimal direct path detection algorithm using multipath interference cancellation is proposed in this paper to improve the accuracy of communication-based positioning systems. By maximizing the probability of correct identification between the strong interfering paths and noise-only paths, an optimal threshold is derived for interfering multipath component selection. The interference of multipath components is reconstructed and then subtracted from the received signal to enhance the accuracy of direct path detection. The performance of the proposed algorithm is verified by simulations.

A novel adaptive Orthogonal Frequency-Division Multiplexing (OFDM) system with multi-layered (ML) transmission is proposed for mobile multimedia networks to support transmission of multiple data streams with flexible Quality-of-Service (QoS) adjustment capability and therefore can accommodate users with different QoS requirements or link conditions. The enhanced layers (ELs), formulated by parallel modulated orthogonal pseudo-random sequences, are superimposed onto OFDM data in frequency domain prior to multicarrier modulation and used to transmit different data information without the requirement of additional links. In this paper, the transceiver design particularly the frequency domain correlation detector and ELs induced interference cancellation is presented. A power distribution scheme for ELs is further proposed to optimize the overall system performance. The performance of the proposed ML-OFDM system is evaluated through numerical simulations.

We propose a first arriving path (FAP) detection algorithm using multipath interference cancellation techniques for indoor positioning systems. In indoor environments, the FAP is very often less significant in magnitude as compared with later arriving paths (LAPs) and therefore the received signal component of FAP is severely interfered by those of LAPs. As a result, it is difficult to identify the FAP in the presence of strong LAPs in indoor environments. In the proposed algorithm, we reconstruct the interference of LAPs based on channel estimation and data detection results. The interference of LAPs is subsequently reconstructed and cancelled from the received signals to improve the accuracy of FAP detection. Simulation results are provided to verify the performance of the proposed algorithm.