Deep Learning-based Joint Pilot and Data Power Control in Cell-Free Massive MIMO Networks
Rajapaksha, Nuwanthika; Rajatheva, Nandana; Latva-Aho, Matti (2024-11-28)
IEEE
28.11.2024
N. Rajapaksha, N. Rajatheva and M. Latva-Aho, "Deep Learning-based Joint Pilot and Data Power Control in Cell-Free Massive MIMO Networks," 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), Washington, DC, USA, 2024, pp. 1-6, doi: 10.1109/VTC2024-Fall63153.2024.10757576
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503252191
https://urn.fi/URN:NBN:fi:oulu-202503252191
Tiivistelmä
Abstract
A deep learning (DL)-based joint pilot and data power control algorithm that solves the sum rate maximization problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. The sum rate optimization problem for the uplink is formulated subject to per-user total transmit energy budget constraints, where user pilot and data power allocations are optimized to maximize the system sum rate. Instead of solving the non-convex problem using mathematical optimization theory, we utilize a data-driven solution approach to learn the optimal solutions. Specifically, we model a deep neural network (DNN) and train it via unsupervised learning using a custom loss function that captures the sum rate optimization objective and transmit energy constraints in the optimization problem. This unsupervised learning approach has a simpler and more flexible model training stage since it does not require labeled data for model training as in supervised learning. Simulation results show that the proposed DNN-based joint pilot and data power control algorithm improves the system sum rate compared to equal power and equal energy allocation heuristics and data power control-only approach. Furthermore, the joint power allocation results in significant energy savings (around 60 %) compared to fixed power allocation schemes.
A deep learning (DL)-based joint pilot and data power control algorithm that solves the sum rate maximization problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. The sum rate optimization problem for the uplink is formulated subject to per-user total transmit energy budget constraints, where user pilot and data power allocations are optimized to maximize the system sum rate. Instead of solving the non-convex problem using mathematical optimization theory, we utilize a data-driven solution approach to learn the optimal solutions. Specifically, we model a deep neural network (DNN) and train it via unsupervised learning using a custom loss function that captures the sum rate optimization objective and transmit energy constraints in the optimization problem. This unsupervised learning approach has a simpler and more flexible model training stage since it does not require labeled data for model training as in supervised learning. Simulation results show that the proposed DNN-based joint pilot and data power control algorithm improves the system sum rate compared to equal power and equal energy allocation heuristics and data power control-only approach. Furthermore, the joint power allocation results in significant energy savings (around 60 %) compared to fixed power allocation schemes.
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