Cloud-assisted distributed edge brains for multi-cell joint beamforming optimization for 6G

Abstract

In 5G networks, optimization of antenna beam weights of base stations has become the key application of AI for network optimization. For 6G, higher frequency bands and much denser cells are expected, and the importance of automatic and accurate beamforming assisted by AI will become more prominent. In existing network, servers are "patched" to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead, large inference latency and potential risks of data security. Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response, but it is inefficient in collecting and sharing global information among base stations. In this paper, we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization. We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation. Compared with the centralized solution, our solution improves prediction accuracy by 24.66%, and reduces storage cost by 83.82%. Compared with the decentralized solution, our solution improves prediction accuracy by 68.26%, and improves coverage performance by 0.4dB. At last, the future research work is prospected.