Non-Linear Energy Harvesting in RIS-assisted URLLC Networks for Industry Automation

Abstract

A reconfigurable intelligent surface (RIS)-assisted wireless communication system with non-linear energy harvesting (EH) and ultra-reliable low-latency constraints is considered for its possible applications in industrial automation. A distant data-center (DC) communicates with the multiple destination machines with the help of a full-duplex (FD) server machine (SM) and RIS. Assuming the deficiency of enough transmission power at the FD-SM, the SM is considered in the near vicinity of the destinations in the industry to forward the data received from the distant DC. The reception at SM is assisted by the RIS and a non-linear hybrid power-time splitting (PTS) based EH receiver architecture is adopted to extend the lifespan of SM, thus increasing network lifetime. The scheduling of multiple destinations is done by SM based on the considered selection criteria namely, random (RND) scheduling, absolute (ABS) channel-power-based (CPB) scheduling and normalized (NRM) CPB scheduling. The end-to-end performance of the considered FD RIS-assisted network is analyzed, and the expressions for the block error rate (BLER) for all scheduling schemes are derived. Moreover, the effects of number of RIS elements, packet size, channel uses on the system performance are analyzed for the considered ultra-reliable and low-latency communication (URLLC) network. The scheduling fairness of all the scheduling schemes is also analyzed to study the performance-fairness trade-off. The derived analytical results are verified through Monte-Carlo simulations.

Liquid Crystals: The way to Scalable and Practical Reconfigurable Intelligent Surfaces in 6G

“…The majority of literature focuses on leveraging RISs to enhance various aspects of communications systems, including capacity [33], energy efficiency [34], and fairness [35]. The use-cases for these studies vary from the traditional cellular network to vehicular [36], aerial [37], and industrial [38] use-cases.…”

Robust Beamforming Design for IRS-Aided URLLC in D2D Networks

“…In [9], the authors presented the performance analysis of the average achievable rate and error probability over an IRS-aided URLLC transmission with/without phase noise. Considering non-linear energy harvesting, the end-to-end performance of the IRS-assisted wireless system was analyzed in [10] for industrial URLLC applications, and the approximate closedform expression of block error rate was derived. Authors in [11] studied an IRS-assisted downlink multiuser URLLC system and jointly optimized the user grouping and the blocklength allocation at the base station (BS), as well as the reflective beamforming at the IRS for latency minimization.…”

Deep Reinforcement Learning-Based Optimization for End-to-End Network Slicing With Control- and User-Plane Separation

“…In this paper, we aim to solve the CUPS-based E2E (CUPS-E2E) network slicing optimization of the four typical E2E network slices: one typical mMTC slice for CP wide-coverage, two typical eMBB slices for UP high-throughput services (such as panorama virtual reality [25]) and UP computation-intensive services (such as augmented reality [26]), respectively, and one typical URLLC slice for UP delay-sensitive services (such as immersive virtual reality [27] and industry automation [28]). We define the utilities of the four E2E network slices based on their coverage percentage, throughput, computing capability and delay requirements, respectively, while taking the relevance between the CP coverage and the UP services into consideration.…”