Optimizing IRS placement and element configuration in B5G: A novel cooperative hybrid communication system

Intelligent reflecting surface (IRS) is a key enabling technology to reshape the electromagnetic propagation environment and enhance the communication performance. Current single IRS-aided or multiple distributed IRSs-aided wireless communication systems leave inter-IRSs collaboration out of consideration, and as a result, the system performance may be severely restricted. For cooperative double IRSs-aided wireless communication systems, dyadic backscatter channel model is widely used in the performance analysis and optimization. However, the impact of factors such as the size and gain of IRS elements is omitted. As a result, the performance quantification and evaluation are inaccurate. In order to avoid the above limitations, spatial scattering channel model is leveraged to quantify the path loss of the double reflection link in typical application scenarios of double IRSs-aided wireless communication systems. When the near-field condition is satisfied, the electromagnetic wave signal transmitted between IRSs is a spherical wave, which leads to high-rank channel and a lower signal to noise ratio. This paper considers the rank-1 inter-IRSs equivalent channel and derives the closed-form received signal power which reveals its relationship with the deployment of IRSs and the physical and electromagnetic properties of IRSs. Taking the impact of near/far-field effects of IRS on signal propagation further into consideration, the network configurations under which double cooperative IRSs can enhance the system performance are recognized. Simulation results show that whether double IRSs should be selected to assist in the communication between the transmitter and the receiver depends on practical network configurations, and the same number of elements should be assigned to the two IRSs to maximize the system performance if they are adopted.

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.

On security performance analysis of IRS-aided VLC/RF hybrid system

Intelligent reflecting surface (IRS) is a promising new technology for achieving spectrum and energy efficient wireless communication systems in the future. By adaptively varying the incident signals' phases/amplitudes and thereby establishing favorable channel responses through a large number of reconfigurable passive reflecting elements, IRS is able to enhance the communication performance of mobile users in its vicinity cost-effectively. In this paper, we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system in which an IRS is deployed to assist the communication between a nearby user and its associated base station (BS). We aim to maximize the downlink achievable rate for the user by jointly optimizing the transmit power allocation at the BS and the passive array reflection coefficients at the IRS. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the BS's power allocation and the IRS's passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the cases without the IRS or with other heuristic IRS designs.

5G and beyond 5G (B5G) wireless systems promise to support services with different requirements in the same network, as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communication (mMTC). One alternative is to consider the network slicing paradigm, where the wireless network resources are shared (or sliced) among active services with different requirements. In addition, another emerging technology, that is considered as a key enabler for B5G wireless systems, is the intelligent reflecting surfaces (IRS). From the deployment of an IRS, it is possible to improve the received signal quality and consequently increase the overall network capacity. Therefore, in this paper, we investigate the use of IRS to support simultaneous eMBB and URLLC services. We evaluate the achievable rate of an IRS-aided radio access network, where the uplink resources are shared between eMBB and URLLC users either under heterogeneous orthogonal multiple access (H-OMA) or heterogeneous non-orthogonal multiple access (H-NOMA) techniques. Results show that exploiting an IRS can considerably increase the eMBB rate and the URLLC reliability simultaneously, regardless of whether operating under H-OMA or H-NOMA. Moreover, we also provide some insights on the best user pairing strategy, showing that higher rates are achieved by matching many eMBB users near to the IRS with a URLLC user close to the base station.

Currently, the increasing energy consumption is a global issue. Information technology and telecommunication industry is one of the areas with the largest energy consumption. The growth of mobile data users is an issue and the biggest challenge for the future. The cooperative wireless communication system has been the focus of research as one of the information delivery strategy with more efficient energy consumption. Energy efficiency in the cooperative wireless communication system can be improved by using a relay between base station and user device, where the distance between base station and user can be shortened, thus, the energy transmission can be minimized. Relay mechanism can be built by utilizing the protocol in the cooperative communication system, such as amplify and forward (AF), decode and forward (DF), and quantize and forward (QF). Relay-selection is an important issue in a cooperative wireless communication system that can reduce energy consumption at the system level. This study analyzes energy efficiency of multi-relay QF cooperative communication for line-of-sight (LOS) and non-line-of-sight (NLOS) environment based on relay selection strategies: reactive and proactive relay selection. A computer simulation is conducted based on a system model and mathematical analysis. Energy efficiency is calculated based on power consumption of signal transmission and observed in the distance between the source, relay, and destination. Simulation result shows that multi-relay QF networks with relay selection consume lower energy than without relay selection, hence, the energy usage in the relay selection networks is more efficient. Moreover, the strategy of proactive relay selection provides low energy consumption and high energy efficiency compared to the reactive relay selection strategy.

Intelligent reflecting surface (IRS), with a large number of reflective elements, is a promising technology to achieve both spectrum and energy efficient wireless communication. The IRS can reflect the incident electromagnetic wave passively and steer it to the desirable way before reaching the intended receiver by adjusting the phase shift on the reflective elements. In order to better improve communication quality, the beamforming vector at the base station (BS) and the phase shift induced by the IRS should be jointly designed carefully. However, thus far, previous works on IRS have assumed that the channel state information (CSI) is perfectly known at the BS, which is not available in the practical systems. In this paper, we study an IRS-enhanced multi-user multiple-input single-output (MISO) downlink communication system assuming imperfect CSI. An optimization problem is formulated to jointly optimize the beamforming vector at the BS and the phase shift at the IRS such that the total transmit power is minimized under the individual outage probability constraints. An algorithm based on alternating optimization (AO) and semi-definite relaxation (SDR) is proposed to solve this challenging non-convex problem. Finally, numerical results have validated the effectiveness of the proposed algorithm.

Intelligent reflecting surface (IRS) is a promising new technology for achieving both spectrum and energy efficient wireless communication systems in the future. However, existing works on IRS mainly consider frequency-flat channels and assume perfect knowledge of channel state information (CSI) at the transmitter. Motivated by the above, in this paper we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system under frequency-selective channels and propose a practical transmission protocol with channel estimation. First, to reduce the overhead in channel training as well as exploit the channel spatial correlation, we propose a novel IRS elements grouping method, where each group consists of a set of adjacent IRS elements that share a common reflection coefficient. Based on this method, we propose a practical transmission protocol where only the combined channel of each group needs to be estimated, thus substantially reducing the training overhead. Next, with any given grouping and estimated CSI, we formulate the problem to maximize the achievable rate by jointly optimizing the transmit power allocation and the IRS passive array reflection coefficients. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the power allocation and the passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the case without using IRS. Moreover, it is shown that there exists an optimal size for IRS elements grouping which achieves the maximum achievable rate due to the practical trade-off between the training overhead and IRS passive beamforming flexibility.

This paper presents a comparative analysis of intelligent reflecting surface (IRS) technology versus conventional amplify-and-forward (AF) and decode-and-forward (DF) relay schemes in wireless communication. By focusing on transmit power, energy efficiency, and the minimum IRS elements required to outperform traditional relaying, we explore IRS’s potential as an energy efficient alternative. Our analysis shows that IRS configurations, especially with optimal phase shifts, achieve substantial power savings and superior energy efficiency over to AF and DF relays. Moreover, IRS requires fewer elements to meet or exceed relaying performance under higher data rate demands, making it an ideal choice for energy conscious, high performance network designs. These results highlight IRS technology as a promising solution for sustainable, next-generation communication networks.

An optimized scheme for energy efficient wireless communication via intelligent reflecting surfaces

Intelligent reflecting surface (IRS) is envisioned to be a new and revolutionizing technology for achieving spectrum and energy efficient wireless communication networks cost-effectively in the future. Specifically, an IRS consists of a large number of low-cost passive elements each reflecting the incident signal with a certain phase shift to collaboratively achieve beamforming and/or interference suppression at designated receivers. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system where one IRS is deployed to assist in the communication from a multi-antenna access point (AP) to multiple single-antenna users. As such, each user receives the superposed signals from the AP as well as the IRS via its reflection. We aim to minimize the total transmit power at the AP by jointly optimizing the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the IRS, subject to users' individual signal-to-interference-plus-noise ratio (SINR) constraints. However, the formulated problem is non-convex and difficult to be solved optimally.

ABSTRACTThis paper proposes a controlled signal technique for visible light non‐orthogonal multiple access (VL‐NOMA) communication in an interference‐controlled environment with intelligent reflecting surfaces (IRS) for beyond 5G (B5G) and 6G communication networks. The light‐emitting diode (LED) is used for carrier signal generation to transmit signals to the two users (photodiodes, PDs) due to its advantages, such as its programmable nature and flexibility. The potential challenge is how the signals could be controlled with an IRS approach, which prompted this research. We have used IRS, which is a cutting‐edge enabling technology that modifies the signal's reflection by utilizing numerous inexpensive passive reflecting elements to improve the signal's performance. Furthermore, deep reinforcement learning (DRL) is deployed to control the reflected signals, simulate, make decisions, and link LED‐IRS‐PDs, redirecting the signals. The entire system is successfully synchronized, and then the bit error rate (BER), line of sight (LOS), and non‐line of sight (NLOS) performances are investigated. Furthermore, we place a blocker at the center of the model as a NLOS to check how the transmitted signals will perform. We observed that the propagated signal improved the BER as per LOS, hence, the NLOS blocker reduced the signal's performance. Furthermore, we optimized the signals to investigate BER, LOS, and NLOS signal performance. We observed that LOS signals performed better than NLOS signals.

Intelligent reflecting surface (IRS)-assisted multiple-input multiple-output (MIMO) systems are foreseen as key enablers of beyond 5G (B5G) and 6G wireless communications. By properly designing the MIMO precoding matrices and the IRS phase-shift matrix, the system performance significantly improves in terms of higher transmission rates, lower power consumption and delays, and improved communication security. To overcome the high dimensionality of the joint optimization of the precoders and the IRS phase shift matrix, we propose an innovative deep reinforcement learning (DRL)-based approach. We aim at maximizing the system sum-rate by considering an adaptation of the deep deterministic policy gradient (DDPG) framework, namely twin delayed DDPG (TD3). Hence, the optimization problem is formulated in terms of continuous action and state spaces, while artificial neural networks (ANNs) are used for the function approximations. The simulation results show that the proposed solution reaches a competitive performance when compared with other state-of-the-art algorithms.

Non-Orthogonal Multiple Access (NOMA) has already proven to be an effective multiple access scheme for 5th Generation (5G) wireless networks. It provides improved performance in terms of system throughput, spectral efficiency, fairness, and energy efficiency (EE). However, in conventional NOMA networks, performance degradation still exists because of the stochastic behavior of wireless channels. To combat this challenge, the concept of Intelligent Reflecting Surface (IRS) has risen to prominence as a low-cost intelligent solution for Beyond 5G (B5G) networks. In this paper, a modeling primer based on the integration of these two cutting-edge technologies, i.e., IRS and NOMA, for B5G wireless networks is presented. An in-depth comparative analysis of IRS-assisted Power Domain (PD)-NOMA networks is provided through 3-fold investigations. First, a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems, and parallels are drawn with conventional network configurations, i.e., conventional NOMA, Orthogonal Multiple Access (OMA), and IRS-assisted OMA networks. Followed by this, a comparative analysis of these network configurations is showcased in terms of significant performance metrics, namely, individual users' achievable rate, sum rate, ergodic rate, EE, and outage probability. Moreover, for multi-antenna IRS-enabled NOMA networks, we exploit the active Beamforming (BF) technique by employing a greedy algorithm using a state-of-the-art branch-reduce-and-bound (BRB) method. The optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques, i.e., minimum-mean-square-error, zero-forcing-BF, and maximum-ratio-transmission. Furthermore, we present an outlook on future envisioned NOMA networks, aided by IRSs, i.e., with a variety of potential applications for 6G wireless networks. This work presents a generic performance assessment toolkit for wireless networks, focusing on IRS-assisted NOMA networks. This comparative analysis provides a solid foundation for the development of future IRS-enabled, energy-efficient wireless communication systems.

This paper proposes an drone-assisted intelligent reflecting surface (IRS) for device-to-device (D2D) communication in infrastructure-less scenarios. The aim of this paper is to enhance the reliability among D2D ground users (GUs) and extend the lifespan of 5G and beyond 5G (B5G) wireless communication system. This work may be applicable for packet delivery in bustling urban areas, especially where ground-to-ground (G2G) links are in deep fade. For modeling air-to-ground (A2G) links among GUs to IRS/drone and IRS/drone to GUs, we consider a height-dependent Nakagami-<inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> channel model for small-scale fading and height-dependent path-loss exponent for modeling large-scale fading. The lifespan of the network is improved by proposing height-dependent energy harvesting (EH) at drone. We derive the cumulative distribution function (CDF) of the signal-to-noise ratio (SNR) whenever the signal reaches the receiving node, either via drone or via IRS. We also develop the expression of spectral efficiency and derive a closed-form expression of an outage probability by taking the combined effect of the signal for the proposed scenario using decode-and-forward (DF) and amplify-and-forward (AF) relaying at the drone. Additionally, the statistical parameters such as mean, variance, and probability density function (PDF) of total noise are derived, which is useful at the receiver node for estimating the bit error rate (BER). The analytical result is validated with simulation results, and the work is compared with the existing state-of-the-art.