Multiple-input Multiple-output Relay Research Articles

Overview of Tensor-Based Cooperative MIMO Communication Systems-Part 1: Tensor Modeling.

Due to increasingly strong and varied performance requirements, cooperative wireless communication systems today occupy a prominent place in both academic research and industrial development. The technological and economic challenges for future sixth-generation (6G) wireless systems are considerable, with the objectives of improving coverage, data rate, latency, reliability, mobile connectivity and energy efficiency. Over the past decade, new technologies have emerged, such as massive multiple-input multiple-output (MIMO) relay systems, intelligent reflecting surfaces (IRS), unmanned aerial vehicular (UAV)-assisted communications, dual-polarized (DP) antenna arrays, three dimensional (3D) polarized channel modeling, and millimeter-wave (mmW) communication. The objective of this paper is to provide an overview of tensor-based MIMO cooperative communication systems. Indeed, during the last two decades, tensors have been the subject of many applications in signal processing, especially for digital communications, and more broadly for big data processing. After a brief reminder of basic tensor operations and decompositions, we present the main characteristics allowing to classify cooperative systems, illustrated by means of different architectures. A review of main codings used for cooperative systems is provided before a didactic and comprehensive presentation of two-hop systems, highlighting different tensor models. In a companion paper currently in preparation, we will show how these tensor models can be exploited to develop semi-blind receivers to jointly estimate transmitted information symbols and communication channels.

Throughput maximization for downlink DF MIMO relay based SWIPT cognitive radio networks

We address the throughput maximization problem for downlink transmission in decode-and-forward (DF) relay-assisted cognitive radio networks (CRNs) based on simultaneous wireless information and power transfer (SWIPT) capability. In this assumed network, the multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to operate with the energy harvest from radio frequency (RF) signals as well as SWIPT capability. Also, the cognitive base station (CBS) communicates with SU just through a MIMO relay. Based on the considered network model, multiple combinatorial constraints in the main problem complicate the solution. So, this paper applies heuristic policies in the convex optimization framework to tackle this complexity. First, the throughput maximization problem is considered for the two sides of the relay separately. Second, we progress towards solving each side’s complicated problem optimally by recruiting subproblems-solving strategy. Finally, these optimum solutions are integrated by proposing a heuristic iterative power assignment algorithm in which combinatorial constraints are satisfied with low convergence time. The proposed algorithm performance is evaluated in comparison with benchmark algorithms via numerical outcomes in respect of optimality, convergence time, and compliance with constraints.

Semi-Blind Receivers for Two-Hop MIMO Relay Systems with a Combined TSTF-MSMKron Coding.

Due to the increase in the number of mobile stations in recent years, cooperative relaying systems have emerged as a promising technique for improving the quality of fifth-generation (5G) wireless networks with an extension of the coverage area. In this paper, we propose a two-hop orthogonal frequency division multiplexing and code-division multiple-access (OFDM-CDMA) multiple-input multiple-output (MIMO) relay system, which combines, both at the source and relay nodes, a tensor space-time-frequency (TSTF) coding with a multiple symbol matrices Kronecker product (MSMKron), called TSTF-MSMKron coding, aiming to increase the diversity gain. It is first established that the signals received at the relay and the destination satisfy generalized Tucker models whose core tensors are the coding tensors. Assuming the coding tensors are known at both nodes, tensor models are exploited to derive two semi-blind receivers, composed of two steps, to jointly estimate symbol matrices and individual channels. Necessary conditions for parameter identifiability with each receiver are established. Extensive Monte Carlo simulation results are provided to show the impact of design parameters on the symbol error rate (SER) performance, using the zero-forcing (ZF) receiver. Next, Monte Carlo simulations illustrate the effectiveness of the proposed TSTF-MSMKron coding and semi-blind receivers, highlighting the benefit of exploiting the new coding to increase the diversity gain.

Nonlinear Transceiver Design for Full-Duplex MIMO Relaying with Direct Link

This paper investigates the joint transceiver design for full-duplex (FD) multiple-input multiple-output relay systems where the direct link (DL) and nonlinear successive-interference-cancellation (SIC) detection are considered. The objective is to jointly optimize the source precoder, relay precoder, and receiver such that the resultant symbol-vector error rate (SVER) is minimized. The joint design is challenging since the coexistence of DL and FD relaying will introduce the inter symbol-vector interference. To address this problem, we first propose a novel QR-SIC receiver structure by performing the dual-filtering (DuF) and channel stacking before SIC. Then, the source and relay precoders are optimized for the proposed DuF-based QR-SIC receiver. Compared with the source precoder, optimizing the relay precoder is much more involved due to the complicated objective function. Hence, we transform the original optimization problem into a trace-minimization one, enabling a closed-form solution for the relay precoder. Furthermore, we theoretically show that the proposed design framework can be extended to the system with the DuF-based minimum mean-squared-error SIC receiver. Simulations show that our transceivers are indeed able to significantly improve the overall SVER performance.

Information Rate Optimization for the Non-Regenerative Linear MIMO Relay Channel With a Direct Link and Variable Duty Cycle

We consider the optimization of a two-hop linear relay channel based on an amplify-and-forward Multiple-Input Multiple-Output (MIMO) relay. The relay is assumed to derive the output signal by applying a Relay Transform Matrix (RTM) applied to the input signal. Assuming perfect channel state information about the channel at the relay and iid transmitted symbols, the RTM is optimized according to two different criteria: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)</i> MIMO information rate; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> information rate based on Orthogonal Space–Time Block Codes. The two assumptions have been addressed in part in the literature. The optimization problem is reduced to a manageable convex form, whose KKT equations are explicitly solved. Then, a parametric solution is given, which yields the power constraint and the information rate achieved with uncorrelated transmitted symbols as functions of a positive indeterminate. The solution for a given average power constraint at the relay is amenable to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">water-filling-like</i> algorithm, and extends earlier literature results addressing the case without the direct link. The duty cycle of the two-hop relaying process is also addressed in the general form of the achievable rate. Simulation results are reported, which are relevant to a Rayleigh fading MIMO relay channel and the role of the direct link SNR is precisely assessed. Duty cycle optimization is also considered by a numerical example.

Tensor-Based Joint Channel Estimation for Multi-Way Massive MIMO Hybrid Relay Systems

Recently, research on the design of relay structure and optimal precoding matrix in massive multiple-input multiple-output (MIMO) relay systems has been proposed in many works assuming that partial or all of the channel state information (CSI) is known. However, in the practical communication systems, the CSI is unknown and needs to be estimated. In particular, channel estimation is more difficult when the relay adopts a hybrid precoding structure. In this paper, we propose an efficient channel estimation scheme based on Tucker-2 tensor model for a multi-way massive MIMO hybrid relay system. The proposed algorithm provides all the CSI involved in the communication links at each user end. Compared with traditional channel estimation algorithms, the proposed algorithm has better estimation performance and requires few iterations to achieve convergence. In addition, we derive the analytical expression of the Cramér-Rao lower bound (CRB) for reference. Simulation results verify the effectiveness of the proposed Tucker-2-based channel estimation approach.

Mutual Information Maximization for SWIPT AF MIMO Relay Systems With Non-Linear EH Models and Imperfect Channel State Information

In this paper, we investigate a two-hop simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system with an energy-limited relay node. The relay node harvests energy based on the radio-frequency (RF) signal transmitted from the source node through the time-switching (TS) protocol and fully uses the harvested energy to precode and forward the information to the destination node. The non-linear energy harvesting models are considered at the relay node. With the consideration of the channel estimation error, the joint optimization of the TS factor, source and relay precoding matrices is proposed with robustness against the channel state information (CSI) mismatch to maximize the mutual information (MI) between the source and destination nodes. The optimal structure for the source and relay precoding matrices is derived to simplify the transceiver optimization problem. Numerical simulations show that the system performance provided by the proposed algorithms with robustness is better than the non-robust algorithm.

Transceiver Optimization for Two-Hop AF MIMO Relay Systems With DFE Receiver and Direct Link

In this paper, we consider precoding and receiving matrices optimization for a two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system with a decision feedback equalizer (DFE) at the destination node in the presence of the direct source-destination link. By adopting the minimum mean-squared error (MMSE) criterion, we develop two new transceiver design algorithms for such a system. The first one employs an iterative procedure to design the source, relay, feed-forward, and feedback matrices. The second algorithm is a non-iterative suboptimal approach which decomposes the optimization problem into two tractable subproblems and obtains the source and relay precoding matrices by solving the two subproblems sequentially. Simulation results validate the better MSE and bit-error-rate (BER) performance of the proposed algorithms and show that the non-iterative suboptimal method has a negligible performance loss when the ratio of the source node transmission power to the relay node transmission power is small. In addition, the computational complexity analysis suggests that the second algorithm and one iteration of the first algorithm have the same order of complexity. As the first algorithm typically converges within a few iterations, both proposed algorithms exhibit a low complexity order.

Performance Optimization of Polarized MIMO Relay Channels Based on the COST 2100 Channel Model

Polarization selectivity has a great impact on multiple-input–multiple-output (MIMO) relay channels. In this letter, the channel capacity of MIMO relay channels is analyzed, revealing the polarization dependency of multipath components. Polarization optimization schemes are proposed, including a regular optimization and a dynamic weight particle swarm optimization (DWPSO) for MIMO relay channels. The performance analysis shows that the DWPSO can significantly improve the link capacity of MIMO relay channels based on the COST 2100 channel model. This study indicates that it is necessary to optimize the polarization direction of MIMO relay channels.

Joint Task Offloading and Caching for Massive MIMO-Aided Multi-Tier Computing Networks

In this paper, a massive multiple-input multiple-output (MIMO) relay assisted multi-tier computing (MC) system is employed to enhance the task computation. We investigate the joint design of the task scheduling, service caching and power allocation to minimize the total task scheduling delay. To this end, we formulate a robust non-convex optimization problem taking into account the impact of imperfect channel state information (CSI). In particular, multiple task nodes (TNs) offload their computational tasks either to computing and caching nodes (CCN) constituted by nearby massive MIMO-aided relay nodes (MRN) or alternatively to the cloud constituted by nearby fog access nodes (FAN). To address the non-convexity of the optimization problem, an efficient alternating optimization algorithm is developed. First, we solve the non-convex power allocation optimization problem by transforming it into a linear optimization problem for a given task offloading and service caching result. Then, we use the classic Lagrange partial relaxation for relaxing the binary task offloading as well as caching constraints and formulate the dual problem to obtain the task allocation and software caching results. Given both the power allocation, as well as the task offloading and caching result, we propose an iterative optimization algorithm for finding the jointly optimized results. The simulation results demonstrate that the proposed scheme outperforms the benchmark schemes, where the power allocation may be controlled by the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR).

MmWave massive analog relay MIMO

Millimeter-Wave (mmWave) communications are a key technology to realize ultra-high data rate and ultra-low latency wireless communications. Compared with conventional communication systems in the microwave band such as 4G/LTE, mmWave communications employ a higher frequency band which allows a wider bandwidth and is suitable for large capacity communications. It is expected to be applied to various use cases such as mmWave cellular networks and vehicular networks. However, due to the strong diffraction loss and the path loss in the mmWave band, it is difficult or even impossible to achieve high channel capacity for User Equipment (UE) located in Non-Line-Of-Sight (NLOS) environments. To solve the problem, the deployment of relay nodes has been considered. In this paper, we consider the use of massive analog Relay Stations (RSs) to relay the transmission signals. By relaying the signals by a large number of RSs, an artificial Multiple-Input Multiple-Output (MIMO) propagation environment can be formed, which enables mmWave MIMO communications to the NLOS environment. We describe a theoretical study of a massive relay MIMO system and extend it to include multi-hop relays. Simulations are conducted, and the numerical results show that the proposed system achieves high data rates even in a grid-like urban environment.

Effective Power Allocation Using for Transmit Source In MIMO Relay Communication

The Multiple Input Multiple Output (MIMO) systems using relays are of interest for high-speed radio communication systems. Currently, most of the articles are interested in the model of three nodes with purposes such as increasing the channel capacities (mutual information) or reducing the minimum mean square of error. This paper extends to more than one relay and is concerned with the maximum channel capacity. It is assumed that the channel matrices between source and relay as well as relay and receiver are random matrices; the relay precoders are also assumed to be random and known at the receiver. The article proposes that the Lagrange multiplier finding algorithm using the Newton – Raphson optimization method is more straightforward than the traditional finding algorithm using the first and second derivatives but still gives a higher channel capacity.

Throughput maximization method for SWIPT DF multi-relaying network with low computational complexity

In this paper, maximum achievable throughput of downlink decode-and-forward (DF) multiple input multiple output (MIMO) simultaneous wireless information and power transfer (SWIPT) relaying system is investigated. In the considered network, a/two MIMO relay(s) can harvest power and transfer information simultaneously. In this paper, three possible scenarios are taken into account. In the first scenario, there is no direct link between the base station (BS) and the users; indeed, the information transfer is done between the BS and the users via a relay. In the second one, some users get service directly via the BS and other part of them receive the BS’s transmitted information via the relay. In the third scenario, there are two relays alongside the direct link. In the all scenarios, the allocated powers to the spatial sub-channels of the BS-relay, the relay-users links and the BS-users links as well as power splitting ratio (PSR) at the relay(s) are optimized. Based on the complicated form of optimality conditions in the all scenarios, two novel power allocation algorithms proposed which can find the optimal solution with a low convergence time. Indeed, the throughput maximization problem is separated into two sub-problem. A novel algorithm couples this sub-problems based on the optimal conditions, which are obtained through convex optimization framework. It should be noted that PSR optimization is carried out based on the sub-gradient method. Moreover, the power allocation and PSR optimization are successively done. Simulation results validate the efficiency of the proposed methods in terms of optimality and convergence time.

Evaluation and Analysis for Maximum Lifespan of Wireless Sensor Networks by Energy-Efficient Design

Wireless Sensor Networks (WSNs) have used worldwide in the past few years and are now being used in health monitoring ,disaster management, defense, telecommunications, etc. Such networks are used in many industrial and consumer applications such as industrial process and environment monitoring, among others. A WSN network is a collection of specialized transducers known as sensor nodes with a communication link distributed randomly in any locations to monitor environmental parameters such as water level, and temperature. Each sensor node is equipped with a transducer, a signal processor, a power unit, and a transceiver. WSNs are now being widely used to monitor environmental parameters, including the amount of gas, water, temperature, humidity, oxygen level, dust, etc. The WSN for environment monitoring can be equivalently replaced by a multiple-input multiple-output (MIMO) relay network. Multi-hop relay networks have attracted significant research interest in recent years for their capability in increasing the coverage range. The network communication link from a source to a destination is implemented using the amplify-and-forward (AF) or decode-and-forward (DF) schemes. The AF relay receives information from the previous relay and simply amplifies the received signal and then forwards it to the next relay. On the other hand, the DF relay first decodes the received signal and then forwards it to the next relay in the second stage if it can perfectly decode the incoming signal. For analytical simplicity, in this thesis, we consider the AF relaying scheme and the results of this work can also be developed for the DF relay.

Two-Timescale Uplink Channel Estimation for Dual-Hop MIMO Relay Multi-User Systems

Multiple-input multiple-output (MIMO) relay systems have received great attention for the development of future wireless networks, since they can be invoked for improving system capacity and extending the coverage area. Considering the fixed base station (BS) and relay station (RS) and the highly mobile users (UEs), the dual-hop MIMO relay channel possesses the two-timescale characteristics, i.e., the UEs-RS channel varies in a short-timescale manner whereas the RS-BS channel varies in a long-timescale manner. In order to acquire the knowledge of all individual channels at the BS directly, we propose two-timescale channel estimation algorithms with one-stage training (OST) scheme based on the above time-varying channel characteristics. Specifically, the two-timescale channel estimation problem is firstly formulated as an approximate maximum likelihood (AML) problem. Then, we design batch algorithm to solve the resultant optimization problem. Moreover, to overcome the drawbacks of batch algorithm, a low-complexity online algorithm is also proposed based on the two-stage stochastic optimization framework. Finally, simulation results are provided to verify the effectiveness of the proposed two-timescale channel estimation algorithms.

Robust Transceiver Design for Multi-Hop AF MIMO Relay Multicasting From Multiple Sources

In this article, the transceiver design optimization problem is investigated for multi-hop multicasting amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems, where multiple source nodes broadcast their message to multiple destination nodes via multiple serial relay nodes. Multiple antennas are installed at the sources, relays, and the destination nodes. In the transceiver design, we consider the mismatch between the true and the estimated channel state information (CSI), where the CSI mismatch follows the Gaussian-Kronecker model. A robust transceiver design algorithm is developed to jointly optimize the source, relay, and destination matrices to minimize the maximal weighted mean-squared error (WMSE) of the received message at all destination nodes. In particular, the WMSE is made statistically robust against the CSI mismatch by averaging through the distributions of the true CSI. Moreover, the WMSE decomposition is exploited to reduce the computational complexity of the transceiver optimization. Numerical simulations show a better performance of the proposed robust transceiver design against the channel mismatch.

Maximizing the Partial Decode-and-Forward Rate in the Gaussian MIMO Relay Channel

It is known that circularly symmetric Gaussian signals are the optimal input signals for the partial decode-and-forward (PDF) coding scheme in the Gaussian multiple-input multiple-output (MIMO) relay channel, but there is currently no method to find the optimal covariance matrices nor to compute the optimal achievable PDF rate since the optimization is a non-convex problem in its original formulation. In this paper, we show that it is possible to find a convex reformulation of the problem by means of an approximation and a primal decomposition. We derive an explicit solution for the inner problem as well as an explicit gradient for the outer problem, so that the efficient cutting-plane method can be applied for solving the outer problem. Based on these ingredients, we propose a convergent algorithm whose output is an approximation of the globally optimal solution along with a certificate of accuracy in terms of a maximum distance to the true global optimum. In numerical simulations, this distance converged to values close to zero in all considered instances of the problem, showing that the proposed method manages to find the global optimum in all these instances.

Mixed-Timescale Channel Estimation for MIMO Relay Multi-User Systems Based on the PARAFAC Decomposition

This letter presents novel mixed-timescale channel estimation algorithms for uplink multiple-input multiple-output (MIMO) relay multi-user systems, considering the mixed-timescale property of practical dual-hop channel. With the one-stage training (OST) scheme, we formulate the channel estimation problem based on the parallel factor (PARAFAC) decomposition using the received pilots at multiple time slots, where the second-hop channel is viewed as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-timescale</i> variable and the first-hop channel is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">short-timescale</i> . Then, the alternating least-square (LS) fitting method in batch mode (Batch-ALS) and online recursive alternating LS (Online-RALS) algorithms are proposed for mixed-timescale channel estimation. Simulation results show that the proposed algorithms improve the estimation performance significantly compared to the existing single-timescale channel estimation algorithms.

Минимизации вероятности ошибки на бит в многоэтапной релейной MIMO-системе

This research is devoted to the investigation of the relay multi-stage relay MIMO (Multiple-Input Multiple-Output) of cellular communication with antenna arrays at each stage of transmission, in which parallel independent subchannels for data transmission are formed, are considered. The possibility of minimizing the error probability per bit of transmitted information is investigated while using the method of separate optimization of each transmission stage and the threshold method for selecting energetically strong subchannels. The presented simulation results in the case of a multipath channel with Rayleigh signal fading confirm the high efficiency of the proposed approach.

On the Physical Layer Security of Underlay Relay-Aided Device-to-Device Communications

In this paper, we study the underlay relay-aided Device-to-Device (D2D) communications to improve the physical layer (PHY) security of the cellular network. We propose a cooperative scheme, whereby the D2D pair, in return of being allowed to share the spectrum band of the cellular network, serves as a friendly jammer, through its multiple-input multiple-output relay, to degrade the wiretapped signal at an eavesdropper. This paper aims to show that spectrum sharing is advantageous for both D2D communications and cellular networks with respect to reliability and robustness for the former, and the physical layer security enhancement for the latter. To assess the proposed cooperative system model, closed-form expressions for the D2D outage probability, the secrecy outage probability, and the probability of non-zero secrecy capacity are derived. More importantly, the benefits due to the cooperation scheme are verified through numerical and simulation results.