Traffic-Aware Resource Allocation for Multi-User Beamforming

Yu Hsuan Liu, Kate Ching Ju Lin

研究成果: Article同行評審


5G New Radio has been proposed to enhance the flexibility, scalability, and efficiency of 5G networks. By leveraging phased-array antennas, a BS can adaptively form multiple directional beams to serve geo-distributed UEs concurrently. However, the imperfect beam pattern of a phased-array antenna may create side lobes. While recent efforts have focused on beam selection that mitigates inter-beam interference and maximizes the sumrate, we notice that the selected beams may not be fully utilized in an OFDMA-based system. The root cause is that only a fixed set of beams can be configured at a time to serve a wide frequency band but some resource blocks may not be able to be allocated to any UEs due to limited traffic demands. To address the above problem, this paper presents traffic-aware joint beam configuration and resource allocation, which explicitly considers user traffic demands and configures beams that can optimally utilize the spectrum resources. We derive an approximation model that produces a suboptimal solution solvable by open-source solvers and further develop a low-complexity greedy algorithm for large-scale networks. Our simulation results show that the proposed traffic-aware allocation and beam configuration scheme achieves better utilization, especially when users have heterogeneous traffic demands.

期刊IEEE Transactions on Mobile Computing
出版狀態Accepted/In press - 2022


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