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Cell-free massive MIMO (CF-mMIMO) networks leverage seamless cooperation among numerous access points to serve a large number of users over the same time/frequency resources. This paper addresses the challenges of pilot and data power control, as well as pilot assignment, in the uplink of a cell-free massive MIMO (CF-mMIMO) network, where the number of users significantly exceeds that of the available orthogonal pilots. We first derive the closed-form expression of the achievable uplink rate of a user. Subsequently, harnessing the universal function approximation capability of artificial neural networks, we introduce a novel multi-task deep learning-based approach for joint power control and pilot assignment, aiming to maximize the minimum user rate. Our proposed method entails the design and unsupervised training of a deep neural network (DNN), employing a custom loss function specifically tailored to perform joint power control and pilot assignment, while simultaneously limiting the total network power usage. Extensive simulations demonstrate that our method outperforms the existing power control and pilot assignment strategies in terms of achievable network throughput, minimum user rate, and per-user energy consumption. The model versatility and adaptability are assessed by simulating two different scenarios, namely a urban macro (UMa) and an industrial one. |
