协同网络中正交随机波束形成的节能波束调度

Jae-Ho Jeong, Jeongho Kwak, S. Chong
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引用次数: 1

摘要

本文利用正交随机波束形成技术研究了协同蜂窝网络中的联合波束和用户调度问题。本文的目标是最小化基站的总平均能量消耗,同时保证给定集合中所有到达的通信量的服务时间是有限的。我们利用Lyapunov优化技术,在不知道未来网络状态(如流量到达)的情况下,将原来的长期问题转化为短期修正的最大权问题。我们还引入了一个参数来控制系统的能量延迟权衡。由于给定的短期问题是组合非线性优化问题,我们受到贪心算法的启发,设计出近最优的联合梁和用户调度策略,即BEANS。我们证明了所提出的bean (i)由于目标函数的子模块特性,确保了接近1/2容量区域内所有交通到达率和所有(能量延迟)权衡参数的有限服务时间,并且(ii)在接近1/4容量区域内所有交通到达率和所有权衡参数的平均能量消耗和平均队列积压的有限上界。最后,通过广泛的仿真,我们比较了bean与最优算法和现有算法的容量区域和能量-队列积压权衡,并表明与不考虑交通动态和能耗的算法相比,对于相同的平均队列积压,bean实现了43%的节能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy-efficient beam scheduling for orthogonal random beamforming in cooperative networks
In this paper, we study a joint beam and user scheduling problem in a cooperative cellular network utilizing orthogonal random beamforming technique. This paper aims to minimize total base stations' average energy expenditure while ensuring finite service time for all traffic arrivals in a given set. We leverage Lyapunov optimization technique to transform original long-term problem into short-term modified max-weight problem without knowledge of future network states such as traffic arrivals. We introduce a parameter which manipulates energy-delay tradeoff in our system as well. Since provided short-term problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design near-optimal joint beam and user scheduling policy, namely BEANS. We prove that proposed BEANS (i) ensures finite service time for all traffic arrival rates within close to 1/2 capacity region and all (energy-delay) tradeoff parameters thanks to submodular characteristics of the objective function, and (ii) attains finite upper bounds of average energy consumption and average queue backlog for all traffic arrival rates within close to 1/4 capacity region and all tradeoff parameters. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains 43% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations.
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