N. Rajapaksha, K. B. S. Manosha, N. Rajatheva and M. Latva-aho, "Unsupervised Learning-Based Joint Power Control and Fronthaul Capacity Allocation in Cell-Free Massive MIMO With Hardware Impairments," in IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1159-1163, July 2023, doi: 10.1109/LWC.2023.3265348.
Unsupervised learning-based joint power control and fronthaul capacity allocation in cell-free massive MIMO with hardware impairments
|Author:||Rajapaksha, Nuwanthika1; Manosha, K. B. Shashika1; Rajatheva, Nandana1;|
1Centre for Wireless Communications, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023081194901
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-08-11
A deep learning-based resource allocation algorithm that maximizes the sum rate of a limited fronthaul cell-free massive MIMO network with transceiver hardware impairments is proposed in this letter. The sum rate maximization problem with user power constraints and total fronthaul capacity constraints for channel state information (CSI) and data transmission is considered. The deep neural network (DNN) PowerNet is proposed to learn solutions to the joint power control and capacity allocation problem in a low-complex, flexible, and scalable way. An unsupervised learning approach is used which eliminates the need of knowing the optimal resource allocation vectors during model training, hence having a simpler and more flexible model training stage. Numerical simulations show that PowerNet achieves close sum rate performance compared to the existing optimization-based approach, with a significantly lower time complexity which does not exponentially scale with the number of users and access points (APs) in the network. Furthermore, the addition of the online learning stage resulted in a better sum rate than the optimization-based method.
IEEE wireless communications letters
|Pages:||1159 - 1163|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Academy of Finland, 6G Flagship Program under Grant 346208, and in part by the Hexa-X Project funded by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 101015956.
|EU Grant Number:||
(101015956) Hexa-X - A flagship for B5G/6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds
|Academy of Finland Grant Number:||
346208 (Academy of Finland Funding decision)
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.