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Deep unfolding for energy-efficient resource allocation in mmWave networks with multi-connectivity

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Abstract

In millimeter-wave (mmWave) communications, multi-connectivity can enhance the communication capacity while at the cost of increased power consumption. In this paper, we investigate a deep-unfolding-based approach for joint user association and power allocation to maximize the energy efficiency of mmWave networks with multi-connectivity. The problem is formulated as a mixed integer nonlinear fractional optimization problem. First, we develop a three-stage iterative algorithm to achieve an upper bound of the original problem. Then, we unfold the iterative algorithm with a convolutional neural network (CNN)-based accelerator and trainable parameters. Moreover, we propose a CNN-aided greedy algorithm to obtain a feasible solution. The simulation results show that the proposed algorithm can achieve good performance and strong robustness but with much reduced computational complexity.

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Authors and Affiliations

  1. Department of Information Science and Electronic Engineering, Zhejiang University, 310027, Hangzhou, China

    Pan Chongrui & Yu Guanding

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  1. Pan Chongrui
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  2. Yu Guanding
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Correspondence to Yu Guanding.

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Chongrui, P., Guanding, Y. Deep unfolding for energy-efficient resource allocation in mmWave networks with multi-connectivity. Ann. Telecommun. 78, 627–639 (2023). https://doi.org/10.1007/s12243-023-00970-x

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