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Conference Paper Sequential Beam, User, and Power Allocation for Interference Management in 5G mmWave Networks
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Authors
Suyoung Ahn, Joonpyo Hong, Yunhee Choi, Jeehyeon Nal, Jeongho Kwak
Issue Date
2022-01
Citation
International Conference on Information Networking (ICOIN) 2022, pp.429-434
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICOIN53446.2022.9687107
Project Code
21HH1100, 5G Open Intelligence-Defined RAN (ID-RAN) Technique based on 5G New Radio, Jeehyeon Na
Abstract
Small cell, mmWave, and massive Multi-Input Multi-Output (MIMO) technologies in 5G cellular networks becomes inevitable trend caused by killer applications such as holographic video which is traffic-intensive. In this paper, we study a sequential activation beam selection, user scheduling, and power allocation problem in a mmWave network with massive MIMO utilizing a physical layer preceding technique. We aim to maximize the time-Averaged utility of users with a time-Averaged transmit power constraint on top of the EdgeSON architecture, which takes advantage of both centralized and distributed characteristics. We decompose the original long-Term problem into two-Time scales in which solving the problem of choosing beam pattern is run at a slower time scale than solving user scheduling and power allocation. Then, to solve user scheduling and power allocation, we leverage the Lyapunov drift-plus-penalty framework which transforms an original long-Term average problem into per-slot modified sub-problems. Since provided per-slot problem to find a set of user scheduling and power allocation is known as NP-hard, we propose a low-complex and practical interference management algorithm, namely CRIM, by introducing two critical users with the highest interference channel gain in intra-BS and inter-BS respectively. Finally, via extensive simulations, we verify and compare the performance of the proposed algorithm and comparing algorithms in a real mmWave network environment.
KSP Keywords
5G cellular networks, Beam pattern, Channel Gain, Drift-plus-penalty, Lyapunov drift, MmWave networks, Multi-input, Multi-output, Multiple input multiple output(MIMO), NP-hard, Physical Layer