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In this letter, we consider a multi-reconfigurable intelligent surface(RIS)-assisted multi-cell communication system in which we jointly optimize the base station (BS)-RIS-user association and RIS deployment locations to maximize the ergodic capacity. In order to derive deterministic approximate expressions for the ergodic capacity, active and passive beamforming are first optimized to maximize the signal power of the relevant path. Then, we propose a two-level nested algorithm to address this mixed-integer nonlinear programming (MINLP) problem. Finally, the effectiveness of the proposed algorithm is validated through numerical simulations.

In recent years, deep learning (DL) has seen extensive applications in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In particular, DL has been integrated into receivers to enhance signal processing capabilities, exemplified by the DeepReceiver. However, such integrations typically train on static scenarios, resulting in suboptimal performance in dynamic real-world conditions and limiting broader applicability. To overcome these challenges, we introduce a DL-based receiver that employs transfer learning (TL) in MIMO-OFDM systems for rapid and accurate signal detection after sufficient training. Especially, we develop a novel binary classifier to accelerate training processes. Furthermore, we propose two TL strategies tailored for the DeepReceiver, designed to facilitate quick retraining under changing conditions. Simulation results confirm that our proposed receiver not only achieves superior bit error rate performance but also adapts more effectively to dynamic channels.