Progressive Linear Precoder Optimization for ARQ Packet Retransmissions in Nonregenerative MIMO Relay Systems

This paper investigates the precoder design for packet retransmissions in nonregenerative multi-input multi-output (MIMO) relay systems. Assuming the channel state information (CSI) only available at the relay and the destination, the precoder design performed at the relay is considered. To fully utilize the time diversity provided by automatic repeat request (ARQ), progressive precoders are designed with the objective of maximizing the mutual information delivered by multiple transmissions of the same packet. We derive the form of the optimal ARQ precoder by restating the optimization problem as a matrix diagonalization problem, then the power allocation is formulated as a convex optimization problem where the karush-kuhn-tucker conditions are used to obtain the optimal solution. Numerical results show that the proposed precoders can improve the system performance significantly.

We investigate the problem of robust joint transmitter and receiver power allocation for uplink multi-input multi-output (MIMO) transmissions. The channel model is assumed to have Rayleigh flat fading. The objective of power allocation is to minimize the total MSE each of which has limited transmit power. The problem is formulated as a non-linear optimization problem. In addition, we investigate robust joint transmitter and receiver power allocation with imperfect channel state information (CSI). The CSI error is assumed to be unknown but bounded by a constant. This problem is formulated as a semidefinite programming (SDP) problem with bilinear matrix inequality (BMI) constraints. Numerical results indicate that, with imperfect CSI, the BER performance improvement obtained by taking robustness into account in the joint power allocation process.

ABSTRACTBy combining Spatial modulation (SM) with cooperative communication, the diversity gain brought by cooperative relaying and high rate of SM can be obtained. In this paper, considering amplify-and-forward (AF) protocol, the performance of cooperative SM system with imperfect channel state information (CSI) over Rayleigh channel is analyzed. According to the performance analysis, the moment generating functions (MGFs) of effective signal-to-noise ratios (SNRs) are derived. With the MGFs, closed-form expressions of the error probability of antenna index detection and the error probability of symbol detection for imperfect CSI are achieved. Thereby, the approximate overall average bit error ratio (BER) is obtained, and these theoretical expressions include those under perfect CSI as special cases. Besides, the asymptotically approximate BER for large SNR is also derived. Based on this, a suboptimal power allocation (PA) scheme is developed, and the corresponding PA coefficients are attained by minimizing the asymptotical BER. As a result, closed-form PA can be attained for the system with multiple receive antennas. Computer simulations show that the theoretical BERs are close to the corresponding simulated results, which illustrates the effectiveness of the derived theoretical expressions. Compared with the conventional equal PA scheme, the proposed PA scheme enhances the BER performance efficiently.

In this paper, we consider a multicasting multiple-input multiple-output (MIMO) relay system where the transmitter multicasts a common message to multiple receivers with the aid of a relay node, all equipped with multiple antennas. Given the power constraints at the source and the relay nodes, we aim at minimizing the maximal mean-squared error (MSE) of the signal waveform estimation among the destination nodes through joint source, relay, and receiver matrices optimization. We provide a low complexity solution to this highly nonconvex optimization problem. In particular, we show that under the (moderately) high signal-to-noise ratio (SNR) assumption, the joint source and relay optimization problem can be solved using standard semidefinite programming (SDP) technique. Numerical simulations provided demonstrate the effectiveness of the proposed algorithm.

In this paper, a two-way relay precoder is designed for multiple-input multiple-output (MIMO) distributed cooperative relay systems. A constrained optimization problem with respect to (w.r.t.) the relay precoder is formulated for the most general relay and antenna scenario, namely, multiple relays each with multiple antennas, It is shown that due to the difficulty of two-way distributed relaying mechanism as well as the block-diagonal nature of the relay precoding matrix, this optimization problem cannot be solved by existing one-way relaying methods. It is then proved that with full channel state information available at relays, the underlying problem can be converted to a convex optimization w.r.t. the non-zero entries of the relay precoding matrix only, such that the Lagrangian multiplier method is applicable to the relay precoder design, leading to a closed-form relay precoding solution. A simulation study is conducted to justify the superior performance of the proposed two-way relaying scheme.

It is well known that traditional water-filling provides a closed form solution for capacity maximization in orthogonal frequency division multiplex (OFDM) system. In this paper, cooperative power allocation is investigated in a two-transmitter multi-receiver model for OFDM systems. The local full channel state information (CSI) is available at the two transmitters respectively, where each transmitter has an individual power constrain. The transmitters first cooperate by sharing CSI, and then jointly optimize power allocation in the metric of sum throughput, which can be modeled as a convex optimization problem. Through an application of Karush-KuhnTucker (KKT) conditions, the convex optimization problem is reformulated as a simplified convex one. Then the closed form solution is derived, which takes a form similar to classic waterfilling principle. Based on the solution, the optimal cooperative power allocation algorithm is constructed, the structure of which can be explained as a cooperative water-filling relative to the traditional water-filling. Finally, numerical simulation is given to evaluate and demonstrate the performance of the optimal cooperative water-filling scheme.

A unified approach is developed for the study of multi-input, multi-output (MIMO) systems under fast fading with quantized covariance feedback. In such systems, the receiver computes, using perfect channel state information (CSI), the covariance matrices to be adopted by the transmitter corresponding to the current channel realization, and feeds back a quantized version of this information to the transmitter using finite, say Nf, bits per channel realization. Our analysis is based on a geometric framework we developed in a companion paper for manifolds of positive semi-definite (covariance) matrices with various trace and rank constraints. That analysis applies to MIMO systems in a unified manner to optimal as well as various suboptimal precoding methods for obtaining input covariances. These include, for example, the capacity achieving spatio-temporal water-filling strategy, the incorporation of just spatial water-filling and reduced-rank beamforming with power allocation in both cases, as well as MIMO systems with antenna selection. For a given system strategy, including the one that achieves capacity, the gap between the communication rates in the perfect and quantized covariance cases is shown to be O(2-Nf/N), where N is the dimension of the particular Pn manifold used for quantization. This dimension depends on the precoding strategy adopted and strongly determines how quickly the communication rate with quantized covariance feedback approaches that with perfect CSI at the transmitter (CSIT). Our results show how the choice of covariance quantization manifold can be tailored to the available feedback rate in MIMO system design.

On the Capacity of Underlay Cognitive Radio Systems Lokman Sboui Due to the scarcity of frequency spectrum in view of the evolution of wireless communication technologies, the cognitive radio (CR) concept has been introduced to efficiently exploit the available spectrum. This concept consists in introducing unlicensed/secondary users (SU’s) in existing networks to share the spectrum of licensed/primary users (PU’s) without harming primary communications hence the name of “spectrum sharing” technique. We study in this dissertation, the capacity and the achievable rate of the secondary user within various communication settings. We, firstly, investigate the capacity of the (SU’s) at low power regime for Nakagami fading channels and present closed form of the capacity under various types of interference and/or power constraints. We explicitly characterize two regimes where either the interference constraint or the power constraint dictates the optimal power profile. Our framework also highlights the effects of different fading parameters on the secondary link ergodic capacity. Interestingly, we show that the low power regime analysis provides a specific insight on the capacity behavior of CR that has not been reported by previous studies. Next, we determine the spectral efficiency gain of an uplink CR Multi-Input MultiOutput (MIMO) system in which the SU is allowed to share the spectrum with the PU using a specific precoding scheme to communicate with a common receiver. Applied 5 to Rayleigh fading channels, we show, through numerical results, that our proposed scheme enhances considerably the cognitive achievable rate. For instance, in case of a perfect detection of the PU signal, after applying Successive Interference Cancellation (SIC), the CR rate remains non-zero for high Signal to Noise Ratio (SNR) which is usually impossible when we only use space alignment technique. In addition, we show that the rate gain is proportional to the allowed interference threshold by providing a fixed rate even in the high SNR range. Finally, we study the impact of the broadcast approach and multi-layer coding on the throughput of CR systems for general fading channels. And we found that at the absence of the channel state information(CSI), we show that this improvement could be almost reached by 2-Layers coding. Then, we introduce a quantized CSI policy and highlight its improvement in terms of throughput before we study the rate when BA with quantized CSI is adopted. Numerical results show that the improvement of the additional layers is decreasing as the number of quantized regions increases.

In this letter, energy-efficient transmission and power allocation for fixed-gain amplify-and-forward relay networks with partial channel state information (CSI) are studied. In the energy-efficiency problem, the total power consumed is minimized while keeping the signal-to-noise-ratio (SNR) above a certain threshold. In the dual problem of power allocation, the end-to-end SNR is maximized under individual and global power constraints. Closed-form expressions for the optimal source and relay powers and the Lagrangian multiplier are obtained. Numerical results show that the optimal power allocation with partial CSI provides comparable performance as optimal power allocation with full CSI at low SNR.

A joint power allocation (PA) scheme for a class of MIMO relay systems is presented in this paper. Based on the maximization of the capacity or minimization of the mean-square error (MSE), two joint PA optimization problems are first formulated. Since the cost functions thus obtained are in general not convex, a tight lower bound of the capacity and a similar upper bound of the MSE are derived, and employed to modify the two cost functions so as to obtain a convex optimization problem. It is confirmed through computer simulations that the proposed PA scheme outperforms the existing method in terms of both the capacity and the MSE of MIMO relay systems.

In this paper, optimal power allocation and relay selection strategies in energy harvesting cooperative wireless networks are studied. In particular, signal‐to‐noise ratio (SNR)‐maximizing based power allocation and relay selection without and with energy cooperation—via wireless energy transfer—are considered. Moreover, total relay power minimization subject to target end‐to‐end SNR is investigated. The different optimal strategies are formulated as optimization problems, which are non‐convex. Thus, intelligent transformations are applied to transform non‐convex problems into convex ones, and polynomial‐time solution procedures are proposed. Simulation results illustrate that power allocation strategies achieve higher end‐to‐end SNR than relay selection ones. Finally, energy cooperation is shown to be effective in improving end‐to‐end SNR, while total relay power minimization balances end‐to‐end SNR, transmit power consumption, and harvested energy. Copyright © 2016 John Wiley & Sons, Ltd.

We study the performance of multi-input multi-output (MIMO) channels with coarsely quantized outputs in the low signal-to-noise ratio (SNR) regime, where the channel is perfectly known at the receiver. This analysis is of interest in the context of ultra-wideband (UWB) communications from two aspects. First the available power is spread over such a large frequency band, that the power spectral density is extremely low and thus the SNR is low. Second the analog-to-digital converters (ADCs) for such high bandwidth signals should be low-resolution, in order to reduce their cost and power consumption. In this paper we consider the extreme case of only 1-bit ADC for each receive signal component. We compute the mutual information up to second order in the SNR and study the impact of quantization. We show that, up to first order in SNR, the mutual information of the 1-bit quantized system degrades only by a factor of 2/pi compared to the system with infinite resolution independent of the actual MIMO channel realization. With channel state information (CSI) only at receiver, we show that QPSK is, up to the second order, the best among all distributions with independent components. We also elaborate on the ergodic capacity under this scheme in a Rayleigh flat-fading environment.

The massive Multiple-input Multiple-output (MIMO) and Non-Orthogonal Multiple Access (NOMA) are considered as two key techniques for the coming 5G wireless communications. Through beam selection, the massive MIMO systems can reduce the number of required radio-frequency (RF) chains while without distinct performance degradation, which can enhance energy efficiency of the systems. In order to further improve energy efficiency, in this paper, we propose an energy efficient power allocation design for beamspace based multi-user multiple-input single-output (MISO) NOMA systems. First, according to the beamspace channel state information (CSI) matrix obtained by beam selection, we get the precoding matrix through zero-forcing (ZF) method. Then we formulate the energy efficiency maximization (EEmax) optimization problem with power allocation considerations. Through sequential convex approximation (SCA) and second-order cone programming (SOCP), the original non-convex problem can be reformulated as a convex optimization problem. Finally, we choose iterative optimization algorithm to solve the reformulated problem. Numerical results show that the proposed power allocation design is effective in achieving better energy efficiency comparing with the conventional methods.

A multihop regenerative relay system is considered. We explore adaptive power allocation (PA) under a total power constraint to minimize the system error probability when the perfect channel state information (CSI) is known at the transmitters. We propose adaptive PA schemes with limited feedback when the transmitters have no perfect knowledge of CSI. Simulation results demonstrate the good performance of the proposed PA scheme with perfect CSI. When no perfect CSI is available at the transmitters, the proposed PA schemes with a small number of feedback bits are able to achieve very close performance to the adaptive PA with perfect CSI.

In this paper, we propose a new multiple relay selection scheme with the availability of outdated channel state information (CSI) for decode and forward (DaF) relay systems, namely the $N$ plus normalized threshold opportunistic relay selection ($N$+NT-ORS). In particular, we first opportunistically select $N$ best relays out of all relays in the decoding subset with respect to CSI. Then the ratios of the instantaneous signal-to-noise ratios (SNRs) on the rest of the relays to that of the $N$-th highest instantaneous SNR are tested against a normalized threshold $\mu$$\in$$[0,1]$ and only those relays passing this test are selected. Through the derivation of the moment generation function (MGF) of SNR, together with the relationship between MGF and outage probability, we obtain a closed-form expression for the outage probability. In addition to analyzing the asymptotic diversity order for $N$+NTS-ORS, we also outline a distributed $N$+NT-ORS selection protocol without the need of global CSI at each relay. Numerical results confirm the correctness of the derivation and show how $N$+NT-ORS outperforms its existing counterparts.