Abstract

In wireless networks with severe blockage, path loss critically limits communication coverage. Reconfigurable Intelligent Surfaces (RIS) offer a promising remedy. However, the fine-grained control of massive reflecting elements incurs prohibitive computational overhead, which hinders real-time deployment. To address these challenges, this paper proposes a low-complexity scheme integrating RIS block selection with adaptive beamforming. The large-scale RIS is partitioned into multiple sub-arrays to enable block-wise phase control. By activating only those blocks with dominant channel gains, the system maximizes reflection gain while minimizing control overhead. To avoid the exponential complexity of exhaustive search, we develop a deep neural network (DNN)-based prediction architecture. By learning the mapping from channel states to optimal configurations, the DNN enables instantaneous selection of near-optimal RIS block combinations. Simulation results show that the proposed data-driven scheme achieves near-optimal bit error rate (BER) performance compared to exhaustive search. Notably, it avoids the exponential complexity growth typically associated with an increasing number of reflecting elements. The proposed mechanism extends reliable coverage range and improves link stability, offering an efficient solution for future wireless networks.

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