Time Reversal for Multiple Access and Mobility: Algorithmic Design and Experimental Results

Abstract

Time Reversal (TR) has been proposed as a competitive precoding strategy for low-complexity devices, relying on ultra-wideband waveforms. This transmit processing paradigm can address the need for low power and low complexity receivers, which is particularly important for the Internet of Things, since it shifts most of the communications signal processing complexity to the transmitter side. Due to its spatio-temporal focusing property, TR has also been used to design multiple access schemes for multi-user communications scenarios. However, in wideband time-division multiple access schemes, the signals received by users suffer from significant levels of inter-symbol interference as well as interference from uncoordinated users, which often require additional processing at the receiver side. This paper proposes an iterative TR scheme that aims to reduce the level of interference in wideband multi-user settings, while keeping the processing complexity only at the transmitter side. The performance of the proposed TR-based protocol is evaluated using analytical derivations. In addition, its superiority over the conventional Time Reversal Division Multiple Access (TRDMA) scheme is demonstrated through simulations as well as experimental measurements at 2.5 GHz carrier frequency with variable bandwidth values.

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

“…Having conducted computer simulations for the multi-RISenabled smart wireless environment included in Section II, we present in this section the performance evaluation of the proposed DRL methodology, presented in detail in Section IV-A, which has as an orchestration objective the sum-rate maximization criterion expressed in (11). We have elaborated on a number of practical aspects for the design of the proposed (D)RL algorithms as well as the benchmark schemes, including hyperparameter selection and performance evaluation strategies.…”

“…To complement the asymptotic time complexities of the DRL algorithms discussed in Section III-E, we herein quantify the execution time of each considered method in the simulation setup presented in Section VI-B. For this evaluation, we have used a desktop computer with an Intel i5-8400 processor, 16 GB of RAM, and an NVidia GTX-1060 with a 6 GB VRAM, and the DRL algorithms were implemented in Tensorflow 11 . The mean execution time in time steps per second for all simulated methods is given in Table VIII.…”

“…Following the theoretical foundations of (D)RL, as presented in Section III, for the considered system model in Section II-B and its achievable sum-rate maximization design problem given by (11), we map the parameters of the wireless communication system with the DRL ingredients, as illustrated in Fig. 5 and discussed in the sequel:…”

“…The positions of the latter were sampled at random and kept fixed throughout the numerical evaluation process. To lay emphasis on the capability of RISs to enable wireless communications, we considered the presence of a blocker that obstructed each BS-UE k link, i.e., the respective coefficients of the direct channel As discussed in Section II, the design objective of the proposed multi-RIS-empowered smart wireless environment is to maximize the sum-rate performance, i.e., solve OP 1 in (11), by allowing the orchestration controller (i.e., the agent) to observe the channel matrices per coherent time interval, and accordingly, configure the profiles of the RISs along with the selection of the BS precoding matrix. Cases with and without the constraint in (11c) for the individual rate requests have been investigated.…”

“…To achieve the envisioned requirements enabling various immersive (e.g., virtual and augmented reality), massive connectivity [7] (e.g., Internet of Things (IoT)) in industry and smart cities), Ultra-Reliable and Low Latency Communications (URLLC) (e.g., autonomous vehicles and remote surgery), and zero-touch [8] (i.e., increased network automation) applications, 6G networks need to transform to a unified communication, sensing, and computing platform incorporating artificial intelligence and machine learning methodologies [9]. This platform will highly likely integrate spectra from higher than the 5G millimeter Wave (mmWave) band as well as the Tera-Hertz (THz) frequencies [10], requiring cost-and power-efficient Radio Frequency (RF) front-ends and multi-antenna transceiver architectures, as well as ultra-wideband waveforms and computationally efficient signal processing schemes [11]- [13].…”