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Learning Decentralized Power Control in Cell-Free Massive MIMO Networks
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- Authors
- Daesung Yu, Hoon Lee, Seung-Eun Hong, Seok-Hwan Park
- Issue Date
- 2023-07
- Citation
- IEEE Transactions on Vehicular Technology, v.72, no.7, pp.9653-9658
- ISSN
- 0018-9545
- Publisher
- Institute of Electrical and Electronics Engineers
- Language
- English
- Type
- Journal Article
- Abstract
- This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination. To determine the transmission policy of distributed APs, it is essential to develop a network-wide collaborative optimization mechanism. To address this challenge, we design a cooperative learning (CL) framework which manages computation and coordination strategies of the CP and APs using dedicated deep neural network (DNN) modules. To build a versatile learning structure, the proposed CL is carefully designed such that its forward pass calculations are independent of the number of APs. To this end, we adopt a parameter reuse concept which installs an identical DNN module at all APs. Consequently, the proposed CL trained at a particular configuration can be readily applied to arbitrary AP populations. Numerical results validate the advantages of the proposed CL over conventional non-cooperative approaches.
- KSP Keywords
- Access point, Collaborative Optimization(CO), Cooperative Approaches, Cooperative Learning, Deep neural network(DNN), Learning-based, MIMO networks, Massive multiple-input multiple-output (MIMO) systems, Numerical results, Optimization mechanism, Power control(PC)