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Energy-Aware Scheduling for Multi-Hop Cognitive Radio Networks

机译:多跳认知无线电网络的能量感知调度

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Cognitive radio (CR) technology, which enables unlicensed secondary users to opportunistically access the unused licensed spectrum, has attracted more and more attention from both academia and industry due to its potential to significantly improve the spectrum utilization. Considering both temporal and spatial variations of spectrum availability, this paper focuses on improving the energy efficiency in CR networks by opportunistically serving the delay-tolerant data only when enough spectrum is available. Based on this idea, a stochastic optimization problem is formulated to integrate the power control, link scheduling, and routing, which minimizes the expected power consumption while guaranteeing the system stability. To obtain the solution, we use the Lyapunov optimization technique and design an online algorithm, which solves a sub-problem without future knowledge of the related stochastic models (e.g., random data arrival and spectrum supply). Besides, in view of the NP-hardness of the sub-problem, we also develop a heuristic algorithm based on branch-and-bound framework to obtain the approximate solution with low computing complexity. Theoretical analysis shows that our algorithm offers an explicit tradeoff between energy consumption and delay performance. Numerical results also confirm the effectiveness of our solutions.
机译:认知无线电(CR)技术使无执照的二级用户能够机会性地访问未使用的许可频谱,由于其具有显着提高频谱利用率的潜力,因此引起了学术界和工业界的越来越多的关注。考虑到频谱可用性的时空变化,本文着重于通过仅在有足够频谱可用时机会性地提供延迟容忍数据来提高CR网络的能效。基于此思想,提出了一种随机优化问题,以集成电源控制,链路调度和路由,从而在保证系统稳定性的同时将预期功耗降至最低。为了获得解决方案,我们使用Lyapunov优化技术并设计了一种在线算法,该算法无需进一步了解相关随机模型(例如,随机数据到达和频谱供应)就可以解决子问题。此外,针对子问题的NP难点,我们还开发了一种基于分支定界框架的启发式算法,以降低计算复杂度。理论分析表明,我们的算法在能耗和延迟性能之间进行了明确的权衡。数值结果也证实了我们解决方案的有效性。

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