CA-FedRC: Codebook Adaptation via Federated Reservoir Computing in 5G NR

TL;DR

This paper proposes CA-FedRC, a federated reservoir computing framework for efficient 5G NR codebook adaptation that reduces feedback overhead and improves performance in resource-constrained mobile devices.

Contribution

It introduces a novel federated reservoir computing approach for 5G codebook adaptation, balancing performance and resource consumption with rapid convergence.

Findings

Reduces feedback overhead in 5G codebook adaptation

Achieves rapid convergence and lower loss compared to traditional models

Accurately identifies channel types under various conditions

Abstract

With the burgeon deployment of the fifth-generation new radio (5G NR) networks, the codebook plays a crucial role in enabling the base station (BS) to acquire the channel state information (CSI). Different 5G NR codebooks incur varying overheads and exhibit performance disparities under diverse channel conditions, necessitating codebook adaptation based on channel conditions to reduce feedback overhead while enhancing performance. However, existing methods of 5G NR codebooks adaptation require significant overhead for model training and feedback or fall short in performance. To address these limitations, this letter introduces a federated reservoir computing framework designed for efficient codebook adaptation in computationally and feedback resource-constrained mobile devices. This framework utilizes a novel series of indicators as input training data, striking an effective balance between performance and feedback overhead. Compared to conventional models, the proposed codebook adaptation via federated reservoir computing (CA-FedRC), achieves rapid convergence and significant loss reduction in both speed and accuracy. Extensive simulations under various channel conditions demonstrate that our algorithm not only reduces resource consumption of users but also accurately identifies channel types, thereby optimizing the trade-off between spectrum efficiency, computational complexity, and feedback overhead.