Neural Codebook Design for Network Beam Management

• URL: http://arxiv.org/abs/2403.03053v1
• Date: Tue, 5 Mar 2024 15:37:06 GMT
• Title: Neural Codebook Design for Network Beam Management
• Authors: Ryan M. Dreifuerst and Robert W. Heath Jr
• Abstract summary: Mobile systems like 5G use a beam management framework that joins the initial acquisition, access, CSINB, beamforming and data transmission. In this paper, we propose an hybrid-to-end learned codebook design algorithm that captures and optimize codebooks to mitigate interference. The proposed algorithm requires limited shared information yet designs codebooks that outperform traditional codebooks by over 10dB in alignment and achieve more than 25% improvements in network spectral efficiency.
• Score: 37.51593770637367
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Obtaining accurate and timely channel state information (CSI) is a fundamental challenge for large antenna systems. Mobile systems like 5G use a beam management framework that joins the initial access, beamforming, CSI acquisition, and data transmission. The design of codebooks for these stages, however, is challenging due to their interrelationships, varying array sizes, and site-specific channel and user distributions. Furthermore, beam management is often focused on single-sector operations while ignoring the overarching network- and system-level optimization. In this paper, we proposed an end-to-end learned codebook design algorithm, network beamspace learning (NBL), that captures and optimizes codebooks to mitigate interference while maximizing the achievable performance with extremely large hybrid arrays. The proposed algorithm requires limited shared information yet designs codebooks that outperform traditional codebooks by over 10dB in beam alignment and achieve more than 25% improvements in network spectral efficiency.

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