Unsupervised Learning based Element Resource Allocation for Reconfigurable Intelligent Surfaces in mmWave Network

• URL: http://arxiv.org/abs/2509.03241v1
• Date: Wed, 03 Sep 2025 11:56:27 GMT
• Title: Unsupervised Learning based Element Resource Allocation for Reconfigurable Intelligent Surfaces in mmWave Network
• Authors: Pujitha Mamillapalli, Yoghitha Ramamoorthi, Abhinav Kumar, Tomoki Murakami, Tomoaki Ogawa, Yasushi Takatori,
• Abstract summary: We formulate a joint optimization problem that optimize the RIS phase configuration and resource allocation under a $alpha$-fair scheduling framework.<n>We propose a five-layer fully connected neural network (FNN) combined with a preprocessing technique to significantly reduce input dimensionality, lower computational complexity, and enhance scalability.<n>The proposed system achieves better performance while reducing computational complexity, making it significantly more scalable than the iterative optimization algorithms.
• Score: 4.564546073852808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing demand for high data rates and seamless connectivity in wireless systems has sparked significant interest in reconfigurable intelligent surfaces (RIS) and artificial intelligence-based wireless applications. RIS typically comprises passive reflective antenna elements that control the wireless propagation environment by adequately tuning the phase of the reflective elements. The allocation of RIS elements to multipleuser equipment (UEs) is crucial for efficiently utilizing RIS. In this work, we formulate a joint optimization problem that optimizes the RIS phase configuration and resource allocation under an $\alpha$-fair scheduling framework and propose an efficient way of allocating RIS elements. Conventional iterative optimization methods, however, suffer from exponentially increasing computational complexity as the number of RIS elements increases and also complicate the generation of training labels for supervised learning. To overcome these challenges, we propose a five-layer fully connected neural network (FNN) combined with a preprocessing technique to significantly reduce input dimensionality, lower computational complexity, and enhance scalability. The simulation results show that our proposed NN-based solution reduces computational overhead while significantly improving system throughput by 6.8% compared to existing RIS element allocation schemes. Furthermore, the proposed system achieves better performance while reducing computational complexity, making it significantly more scalable than the iterative optimization algorithms.

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