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arXiv 1707.01673cs.ITmath.IT
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未来信道增益下混合业务的节能资源分配

Energy Efficient Resource Allocation for Hybrid Services with Future Channel Gains

Changyang She, Chenyang Yang

摘要:

在本文中,我们提出了一个框架,以最大限度地提高能源效率(EE)的系统支持实时(RT)和非实时服务,通过利用未来的平均信道增益的移动用户,在秒的时间尺度变化,并报告可预测在一分钟长的时间窗口。为了证明通过利用未来的平均信道增益和当前的瞬时信道增益来联合优化两种服务的资源分配来提高EE的潜力,我们以服务RT和视频点播(VoD)用户的正交频分多址系统为例,优化了具有完美预测的两个时标策略。考虑到对每个用户进行细粒度预测的代价很高,本文提出了一种启发式策略,该策略只需要预测VoD用户的平均信道增益的中值。仿真结果表明,最优策略的性能优于相关的同行,表明联合优化的必要性,为两个服务和两个时标。此外,启发式策略的性能接近最优策略与完美的预测,而变得优越的预测误差大。这表明,可以通过粗粒度预测来捕获非预测策略的EE增益。

英文摘要:

In this paper, we propose a framework to maximize energy efficiency (EE) of a system supporting real-time (RT) and non-real-time services by exploiting future average channel gains of mobile users, which change in the timescale of seconds and are reported predictable within a minute-long time window. To demonstrate the potential of improving EE by jointly optimizing resource allocation for both services by harnessing both future average channel gains and current instantaneous channel gains, we optimize a two-timescale policy with perfect prediction, by taking orthogonal frequency division multiple access system serving RT and video-on-demand (VoD) users as an example. Considering that fine-grained prediction for every user is with high cost, we propose a heuristic policy that only needs to predict the median of average channel gains of VoD users. Simulation results show that the optimal policy outperforms relevant counterparts, indicating the necessity of the joint optimization for both services and for two timescales. Besides, the heuristic policy performs closely to the optimal policy with perfect prediction while becomes superior with large prediction errors. This suggests that the EE gain over non-predictive policies can be captured with coarse-grained prediction.

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