Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications

Abstract

Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction perspective in a terahertz (THz) communication system. The proposed framework achieves a two-step enhancement over the similar learning-driven counterparts. Specifically, in the first step, we train a liquid state machine (LSM) to track the historical RIS reflection coefficients at prior time steps (known as a time-series sequence) and predict their upcoming time steps. We also fine-tune the trained LSM through Xavier initialization technique to decrease the prediction variance, thus resulting in a higher prediction accuracy. In the second step, we use ensemble learning technique which leverages on the prediction power of multiple LSMs to minimize the prediction variance and improve the precision of the first step. It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11×11 is deployed. In the second step, under the same computational complexity of training a single LSM, the ensemble learning with multiple LSMs degrades the prediction variance of a single LSM up to 66% and improves the system achievable SE at most 54%.

Max-Min Fair Energy-Efficient Beamforming Design for Intelligent Reflecting Surface-Aided SWIPT Systems With Non-Linear Energy Harvesting Model

“…The signals communicate through quasi-static flat-fading channels. Similar to [8], the perfect channel state information (CSI) is assumed to be available at the transmitter (i.e., the BS), by leveraging efficient channel estimation mechanisms [27], [28], [29]. Accordingly, the transmit signal from the BS can be expressed as…”

“…In this context, a reconfigurable intelligent surface (RIS) is able to bypass the environmental obstacles and initiate virtual line-ofsight (LoS) links [5], which remarkably expands the coverage area and thus the number of supported IoT devices [6], [7]. A RIS architecturally incorporates passive metasurfaces capable of reflecting incoming signals without relying on a dedicated power source [8]. Due to the multiplicative fading effect, conventional passive beamforming RISs suffer from severe signal strength attenuation and possible detection problems at the receiver [9].…”