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Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management

Özlem Tuğfe Demir, Lianet Méndez-Monsanto, Nicola Bastianello,Emma Fitzgerald,Gilles Callebaut

CoRR(2024)

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Abstract
The physical layer foundations of cell-free massive MIMO (CF-mMIMO) have been well-established. As a next step, researchers are investigating practical and energy-efficient network implementations. This paper focuses on multiple sets of access points (APs) where user equipments (UEs) are served in each set, termed a federation, without inter-federation interference. The combination of federations and CF-mMIMO shows promise for highly-loaded scenarios. Our aim is to minimize the total energy consumption while adhering to UE downlink data rate constraints. The energy expenditure of the full system is modelled using a detailed hardware model of the APs. We jointly design the AP-UE association variables, determine active APs, and assign APs and UEs to federations. To solve this highly combinatorial problem, we develop a novel alternating optimization algorithm. Simulation results for an indoor factory demonstrate the advantages of considering multiple federations, particularly when facing large data rate requirements. Furthermore, we show that adopting a more distributed CF-mMIMO architecture is necessary to meet the data rate requirements. Conversely, if feasible, using a less distributed system with more antennas at each AP is more advantageous from an energy savings perspective.
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Key words
Cell-free Massive MIMO,Energy Consumption,Data Rate,Optimization Algorithm,Energy Conservation,User Equipment,Data Rate Requirements,Downlink Data,Alternating Optimization Algorithm,Optimization Problem,Energy Efficiency,Power Consumption,Analog-to-digital Converter,Energy Model,White Rabbits,Mixed-integer Programming,Power Amplifier,Industrial Environment,Fading Channel,Spectral Efficiency,Static Random Access Memory,Minimum Mean Square Error,Large-scale Fading,Random Access Memory
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