RIS-Aided E2E Multi-Path Uplink Transmission Optimization for 6G Time-Sensitive Services
Zisheng Gong, Ziyue Xiao, Liu Cao, Zhaoyu Liu, Dongyu Wei, Lyutianyang Zhang
TL;DR
The paper tackles the challenge of meeting stringent E2E latency for 6G time-sensitive services by introducing a RIS-aided E2E multi-path UL framework that accounts for both radio link and N3 backhaul delays. It develops an alternating optimization approach to jointly optimize traffic splitting, UL transmit power, receive combining, and RIS phase shifts, aiming to minimize the instantaneous E2E latency $u_{n,m,q}(t)$. The method combines MMSE-based receive combiners, SCA for the traffic/power portion, and SDR for RIS configuration within a unified AO loop, demonstrating latency reductions up to 43% for a single user and 15% system-wide in simulations. The results underscore the benefit of integrating RIS with E2E multi-path ATSSS-inspired designs to meet the QoS demands of 6G time-sensitive services in realistic multi-BS/multi-UE deployments.
Abstract
The Access Traffic Steering, Switching, and Splitting (ATSSS) defined in the 3GPP latest Release enables traffic flow over the multiple access paths to achieve the lower-latency End-to-end (E2E) delivery for 6G time-sensitive services. However, the existing E2E multi-path operation often falls short of more stringent QoS requirements for 6G time-sensitive services. This proposes a Reconfigurable Intelligent Surfaces (RIS)-aided E2E multi-path uplink(UL) transmission architecture that explicitly accounts for both radio link latency and N3 backhaul latency, via the coupled designs of the UL traffic-splitting ratio, transmit power, receive combining, and RIS phase shift under practical constraints to achieve the minimum average E2E latency. We develop an alternating optimization framework that updates the above target parameters to be optimized. The simulations were conducted to compare the effectiveness of the proposed E2E optimization framework that lowers the average E2E latency up to 43% for a single user and 15% for the whole system compared with baselines.