基于定价的OFDM下行网络跨层调度与能量管理

Yong Yang, Bo Yang, X. Guan
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引用次数: 0

摘要

研究正交频分复用(OFDM)无线网络的下行链路调度和资源分配问题。从网络运营商的角度来看,基站(BS)将向下行用户收费以赚取收入并支付电费。网络运营商的目标是在保证网络稳定性的同时获得更好的利润。在智能电网的背景下,BS可以收集可再生能源并储存能量,以降低电力成本。面临的挑战是很难获得电价、随机费率请求和可再生能源的准确统计知识。此外,我们还必须处理具有异构服务质量(QoS)需求的下行用户的动态数据速率请求。为了解决拥塞控制、能源消耗和无线电资源分配之间的耦合问题,提出了一种基于Lyapunov优化的基于定价的跨层调度和能量管理算法PCSM:能量管理可以很好地调节下行业务中断情况下的充电率;基于数据速率定价的拥塞控制方案通过设定数据速率价格来收取适当的收入,实现流量控制和利润最大化;无线资源分配可以利用网络层的跨层信息和能量存储条件实现自适应分配。理论分析表明,在稳定网络的情况下,该方法能达到接近最优的性能,仿真结果也验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pricing-based cross-layer scheduling and energy management over OFDM downlink networks
We focus on downlink scheduling and resource allocation for orthogonal frequency division multiplexing (OFDM) wireless networks. From a perspective of network operators, the base station (BS) will charge the downlink users to earn revenue and pay for electricity consumption. The aim of network operators is to achieve better profits while ensuring network stability. In the context of smart grid, the BS can harvest renewable energy and store energy in order to cut down electricity cost. The challenge is that it is hard to obtain the accurate statistic knowledge of electricity price, random rate requests and renewable energy. Furthermore, we have to deal with the dynamic data rate requests from downlink users with heterogeneous quality of service (QoS) requirements. To handle the coupling between congestion control, energy consumption and radio resource allocation, we propose a pricing-based crosslayer scheduling and energy management algorithm PCSM based on Lyapunov optimization: the energy management can well regulate the electricity charging rate in case of interrupting the downlink service; the data rate pricing-based congestion control scheme collects proper revenue by setting data rate price to carry out flow control and profit maximization; the radio resource allocation can achieve adaptive allocation by using cross-layer information from the network layer as well as energy storage condition. Theoretical analysis shows that it can achieve a near-optimal performance when stabilizing the network and that its effectiveness is also validated through simulation results.
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