8 research outputs found
Deep Reinforcement Learning for Practical Phase Shift Optimization in RIS-aided MISO URLLC Systems
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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Optimization of RIS-aided Integrated Localization and Communication
Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost
communication performance, especially when the line-of-sight (LOS) path between
the user equipment (UE) and base station (BS) is blocked. To control the RIS,
channel state information (CSI) is needed, which entails significant pilot
overhead. To reduce this overhead and the need for frequent RIS
reconfiguration, we propose a novel framework for integrated localization and
communication, where RIS configurations are fixed during location coherence
intervals, while BS precoders are optimized every channel coherence interval.
This framework leverages accurate location information obtained with the aid of
several RISs as well as novel RIS optimization and channel estimation methods.
Performance in terms of localization accuracy, channel estimation error, and
achievable rate demonstrates the efficacy of the proposed approach.Comment: 30 pages, 8 figure
Power Scaling Law Analysis and Phase Shift Optimization of RIS-aided Massive MIMO Systems with Statistical CSI
This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI). Considering the complex environment, the general Rician channel model is adopted for both the users-RIS links and RIS-BS links. We first derive the closed-form approximate expressions for the achievable rate which holds for arbitrary numbers of base station (BS) antennas and RIS elements. Then, we utilize the derived expressions to provide some insights, including the asymptotic rate performance, the power scaling laws, and the impacts of various system parameters on the achievable rate. We also tackle the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS based on genetic algorithm (GA). Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Our simulations also demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems
Analysis and Optimization of A Double-IRS Cooperatively Assisted System with A Quasi-Static Phase Shift Design
The analysis and optimization of single intelligent reflecting surface
(IRS)-assisted systems have been extensively studied, whereas little is known
regarding multiple-IRS-assisted systems. This paper investigates the analysis
and optimization of a double-IRS cooperatively assisted downlink system, where
a multi-antenna base station (BS) serves a single-antenna user with the help of
two multi-element IRSs, connected by an inter-IRS channel. The channel between
any two nodes is modeled with Rician fading. The BS adopts the instantaneous
CSI-adaptive maximum-ratio transmission (MRT) beamformer, and the two IRSs
adopt a cooperative quasi-static phase shift design. The goal is to maximize
the average achievable rate, which can be reflected by the average channel
power of the equivalent channel between the BS and user, at a low phase
adjustment cost and computational complexity. First, we obtain tractable
expressions of the average channel power of the equivalent channel in the
general Rician factor, pure line of sight (LoS), and pure non-line of sight
(NLoS) regimes, respectively. Then, we jointly optimize the phase shifts of the
two IRSs to maximize the average channel power of the equivalent channel in
these regimes. The optimization problems are challenging non-convex problems.
We obtain globally optimal closed-form solutions for some cases and propose
computationally efficient iterative algorithms to obtain stationary points for
the other cases. Next, we compare the computational complexity for optimizing
the phase shifts and the optimal average channel power of the double-IRS
cooperatively assisted system with those of a counterpart single-IRS-assisted
system at a large number of reflecting elements in the three regimes. Finally,
we numerically demonstrate notable gains of the proposed solutions over the
existing solutions at different system parameters.Comment: 40 pages, 7 figures. This work is submitted to IEEE Trans.Wireless
Commun. (under major revision