Reconfigurable intelligent surfaces (RISs) can assist the wireless systems in
providing reliable and low-latency links to realize the requirements in
Industry 4.0. In this paper, the practical phase shift optimization in a
RIS-aided ultra-reliable and low-latency communication (URLLC) system at a
factory setting is performed by applying a novel deep reinforcement learning
(DRL) algorithm named as twin-delayed deep deterministic policy gradient (TD3).
First, the system achievable rate in finite blocklength (FBL) regime is
identified for each actuator then, the problem is formulated where the
objective is to maximize the total achievable FBL rate, subject to non-linear
amplitude response and the phase shift values constraint. Since the amplitude
response equality constraint is highly non-convex and non-linear, we employ the
TD3 to tackle the problem. The considered method relies on interacting RIS with
industrial scenario by taking actions which are the phase shifts at the RIS
elements, to maximize the total FBL rate. We assess the performance loss of the
system when the RIS is non-ideal, i.e., non-linear amplitude response
with/without phase quantization and compare it with ideal RIS. The numerical
results show that optimizing phase shifts in non-ideal RIS via the considered
TD3 method is highly beneficial to improve the performance.Comment: This work has been submitted to the IEEE for possible publication.
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