This paper puts forth a new, reconfigurable intelligent surface (RIS)-assisted, uplink, user-centric cell-free (UCCF) system managed with the assistance of a digital twin (DT). Specifically, we propose a novel learning framework that maximizes the sum-rate by jointly optimizing the access point and user association (AUA), power control, and RIS beamforming. This problem is challenging and has never been addressed due to its prohibitively large and complex solution space. Our framework decouples the AUA from the power control and RIS beamforming (PCRB) based on the different natures of their variables, hence reducing the solution space. A new position-adaptive binary particle swarm optimization (PABPSO) method is designed for the AUA. Two twin-delayed deep deterministic policy gradient (TD3) models with new and refined state pre-processing layers are developed for the PCRB. Another important aspect is that a DT is leveraged to train the learning framework with its replay of channel estimates stored. The AUA, power control, and RIS beamforming are only tested in the physical environment at the end of selected epochs. Simulations show that using RISs contributes to considerable increases in the sum-rate of UCCF systems, and the DT dramatically reduces overhead with marginal performance loss. The proposed framework is superior to its alternatives in terms of sum-rate and convergence stability.

Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems

In this paper, we consider the robust beamforming design in a reconfigurable intelligent surface (RIS)-aided cell-free (CF) system considering the channel state information (CSI) uncertainties of both the direct channels and cascaded channels at the transmitter with capacity-limited backhaul. We jointly optimize the precoding at the access points (APs) and the phase shifts at multiple RISs to maximize the worst-case sum rate of the CF system subject to the constraints of maximum transmit power of APs, unit-modulus phase shifts, limited backhaul capacity, and bounded CSI errors. By applying a series of transformations, the non-smoothness and semi-infinite constraints are tackled in a low-complexity manner that facilitates the design of an alternating optimization (AO)-based iterative algorithm. The proposed algorithm divides the considered problem into two subproblems. For the RIS phase shifts optimization subproblem, we exploit the penalty convex-concave procedure (P-CCP) to obtain a stationary solution and achieve effective initialization. For precoding optimization subproblem, successive convex approximation (SCA) is adopted with a convergence guarantee to a Karush-Kuhn-Tucker (KKT) solution. Numerical results demonstrate the effectiveness of the proposed robust beamforming design, which achieves superior performance with low complexity. Moreover, the importance of RIS phase shift optimization for robustness and the advantages of distributed RISs in the CF system are further highlighted.

Robust Beamforming Design for RIS-aided Cell-free Systems with CSI Uncertainties and Capacity-limited Backhaul

This paper investigates the performance of two-timescale transmission design for uplink reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems. We consider the Rician channel model and design the passive beamforming of RISs based on the long-time statistical channel state information (CSI), while the maximum ratio combining (MRC) technique is utilized to design the active beamforming of base stations (BSs) based on the instantaneous overall channels, which are the superposition of the direct and RIS-reflected channels. Firstly, we derive the closed-form expressions of uplink achievable rate for arbitrary numbers of BS antennas and RIS reflecting elements. Relying on the derived expressions, we theoretically analyze the benefits of RIS-aided cell-free mMIMO systems and draw explicit insights. Then, based on closed-form expressions under statistical CSI, we maximize the sum user rate and the minimum user rate by optimizing the phase shifts of the RISs based on the genetic algorithm (GA). Finally, the numerical results demonstrate the feasibility and the benefits of deploying large-size RISs into conventional cell-free mMIMO systems. Besides, our results validate the effectiveness of the proposed two-timescale scheme in the RIS-aided cell-free mMIMO systems.

Jin Ge

Papers

Two-Timescale Transmission Design for RIS-Aided Cell-Free Massive MIMO Systems