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Achievable Computation Rate in NOMA-Based Wireless-Powered Networks Assisted by Multiple Fog Servers

机译:基于NOMA的无线动力网络中可实现的计算速率由多个雾服务器辅助

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This article investigates a multifog server (FS)-assisted nonorthogonal multiple access (NOMA)-based wireless powered network, where an energy-limited wireless device (WD) first harvests energy from a power transmitter (PT) and multiple helping FSs and then uses the harvested energy to partially offload its computing task to the FSs with NOMA for computing. To explore the WD's performance limit in terms of achievable computation rate, an optimization problem is formulated by jointly optimizing the time assignment, the power allocation, and the computation frequency under multiple system constraints. Since the problem is nonconvex with no known solution, an efficient solution approach is designed to achieve the epsilon-optimal solution, in which the transmit power vector and the computation frequency are jointly optimized with fixed-time assignment, and then, a golden section search (GSS)-based algorithm is designed to find the optimal time assignment. For the case when the FS is with sufficiently strong computation capability, some semiclosed-form results are derived. Numerous results show that our proposed design achieves much higher computation rate than benchmark schemes. Moreover, with the increment of the helping FSs, the achievable computation rate increases while the increasing rate is decreased. Besides, by employing NOMA, the WD's computation rate is also improved compared with orthogonal multiple access (OMA)-based scheme. Additionally, in such a system, with nonlinear energy harvesting (EH) model adopted, the more the helping FSs are deployed, the more the performance loss caused by the traditional linear EH model can be reduced.
机译:本文调查多因素服务器(FS) - 基于非正交多访问(NOMA)的无线供电网络,其中能量有限无线设备(WD)首先收集来自电力发射器(PT)的能量和多个帮助FSS,然后使用收获的能量将其计算任务部分卸载到具有NOMA的FSS以进行计算。为了在可实现的计算速率方面探讨WD的性能限制,通过在多个系统约束下联合优化时间分配,功率分配和计算频率来制定优化问题。由于该问题是没有已知解决方案的非耦合,因此设计了一种有效的解决方案方法,以实现epsilon - 最佳解决方案,其中发射动力矢量和计算频率是用定时分配共同优化的,然后是金部分搜索(GSS)基于算法旨在找到最佳时间分配。对于FS具有足够强的计算能力的情况,派生了一些半导体结果。众多结果表明,我们的拟议设计达到了比基准计划更高的计算率。此外,随着帮助FSS的增量,可实现的计算速率在增加速率下降时增加。此外,通过使用NOMM,与基于正交多次访问(OMA)的方案相比,WD的计算速率也得到改善。另外,在这种系统中,通过采用非线性能量收集(EH)模型,可以降低由传统线性EH模型引起的性能损耗越多。

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