There is a compelling need to address the problem of accommodating the rapidly growing number of machine type devices engaging in communication within the scarce radio spectrum, in current and forthcoming generations of wireless networks. The coming decades will also witness various novel applications, demanding wireless communication at very high data rate and energy efficiency, which currently favored technologies and techniques are inadequate to support. Because of these reasons, it seems inevitable that wireless communication will have to be facilitated by the efficient utilization of larger bandwidth at higher frequencies. Non-Orthogonal Multiple Access (NOMA) and shifting to higher frequency ranges such as the lower terahertz band, have been considered as solutions to provide space to the increasing number of communicating devices while meeting performance requirements. In this paper a machine type communication system model, utilizing the promising technique NOMA and operating in the sub-terahertz band, is developed and analyzed for its performances of data rate, spectral efficiency and energy efficiency. Thereafter the energy efficiency of the proposed model is optimized by using unconstrained optimization and then constrained (by threshold of data rate) optimization, the latter solving a multi-objective problem by the $ϵ$-constraint method. The optimal transmission power is also derived for both optimization techniques. Finally, simulation results demonstrate that the energy efficiency can be maximized by utilizing unconstrained optimization and the spectral efficiency can be maximized by utilizing constrained optimization for both machine-to-machine and base-to-machine communications.

在当前和即将来临的无线网络世代中，迫切需要解决适应在稀缺的无线电频谱内进行通信的机器类型设备的数量迅速增长的问题。未来几十年还将见证各种新颖的应用，这些应用要求以很高的数据速率和能效进行无线通信，而目前受青睐的技术和技术尚不足以提供支持。由于这些原因，似乎不可避免地必须通过在较高频率下有效利用较大带宽来促进无线通信。非正交多址（NOMA）并转移到较高的频率范围，例如较低的太赫兹频段，已经被认为是在满足性能要求的同时为越来越多的通信设备提供空间的解决方案。本文利用有前景的技术NOMA并在亚太赫兹频段工作，开发了一种机器类型的通信系统模型，并对其数据速率，频谱效率和能量效率的性能进行了分析。此后，通过使用无约束优化来优化所提出模型的能量效率，然后再约束（以数据速率阈值为准）优化，后者通过以下方法解决了多目标问题：频谱效率和能量效率。此后，通过使用无约束优化来优化所提出模型的能量效率，然后再约束（以数据速率阈值为准）优化，后者通过以下方法解决了多目标问题：频谱效率和能量效率。此后，通过使用无约束优化来优化所提出模型的能量效率，然后再约束（以数据速率阈值为准）优化，后者通过以下方法解决了多目标问题： $ϵ$-约束方法。还针对两种优化技术得出了最佳发射功率。最后，仿真结果表明，可以通过使用无约束优化来最大化能量效率，并且可以通过使用约束优化来使机器对机器通信和基础对机器通信两者最大化频谱效率。