Energy-Delay-Aware Power Control for Reliable Transmission of Dynamic Cell-Free Massive MIMO
Makhanbet M. Lv T. Ni W. Orynbet M.
1 January 2022Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Communications
2022#70Issue 1276 - 290 pp.
This paper presents new learning-based, energy-delay-aware power control strategies for the uplink of dynamic cell-free (CF) massive multiple-input multiple-output (MIMO) networks. We first propose a new algorithm to adaptively select an appropriate set of multi-antenna access points (APs) to serve each user at any time instant. A multi-level hidden Markov model (HMM) is constructed to predict the trajectory of a user and forecast the changing set of serving APs for the user over time. Given the selected, time-changing sets of serving APs, we also formulate a new multi-objective power control problem to minimize a weighted sum of the energy and delay costs of the users. By employing Bernoulli bandit learning (BBL) and Gaussian bandit learning (GBL), two new energy-delay-aware power control strategies are developed to adapt unpredictable channel dynamics, minimize the energy-delay costs online, and remain effective over a long time even when sharp changes occur in the channels. Numerical results demonstrate that the new predictive selection of serving APs and the new learning-based energy-delay-aware power control policies can significantly improve the reliability of uplink transmissions in dynamic CF massive MIMO networks, as compared to state-of-the-art static oracle solutions.
Bandit learning , cell-free massive MIMO networks , energy-delay-aware power control , multi-level HMM , reliable transmission
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School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing, China
Data61, Commonwealth Scientific and Industrial Research, Sydney, NSW, Australia
Department of Automation and Information Technologies, Satbayev University, Almaty, Kazakhstan
School of Information and Communication Engineering
Data61
Department of Automation and Information Technologies
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