Device to Device (D2D) Communication is expected to be a core part of the forthcoming 5G Mobile Communication Networks as it promises improvements in energy efficiency, spectral efficiency, overall system capacity, and higher data rates with the use of the same frequencies for different D2D transmissions in short communication distances within the Cell. However, in order to achieve optimum results, it is important, among others, to select wisely the Transmission Mode of the D2D Device. Towards this end, our previous work proposed an intelligent Transmission mode selection approach in a framework that is utilizing Artificial Intelligence (AI) BDIx agents to collectively satisfy the D2D challenges in a Distributed Artificial Intelligent (DAI) manner autonomously and independently. In this paper, as a first step, a literature review focused on related Transmission mode approaches, is performed. Then, our investigated Transmission mode selection approach is further explained with formulas and evaluated based on different threshold values and investigated how these can affect the overall spectral efficiency and power usage of the network in order to achieve the maximum performance. The investigated thresholds(i.e. D2D Device Weighted Data Rate (WDR) and the D2D Device Battery Power Level) and metrics(i.e. WDR) are also further analyzed and formulated. In addition, the effect the transmission power of the D2D links has on the total spectral efficiency and total power consumption of the network, is also examined. This evaluation results arise some interesting findings that can contribute in other approaches that utilized similar or same thresholds. Also, the results obtained demonstrate that with the right tuning of the thresholds and transmission power, one can achieve a significant improvement in the network power usage and total spectral efficiency.
翻译:通信预计将成为即将到来的 5G 移动通信网络的核心部分, 因为它有望提高能源效率、 光谱效率、 整个系统能力, 提高使用不同 D2D 传输频率的数据率, 使细胞内部通信距离短, 不同的 D2D 传输速度使用同一频率, 然而, 为了取得最佳效果, 除其他外, 有必要明智地选择 D2D 设备的传输模式。 为此, 我们先前的工作提议了一个智能传输模式选择方法, 这个框架将利用人工智能智能情报(AI) BDIx 代理来集体满足 D2D 挑战, 以自主和独立的方式应对分散的 人工智能(DAI) 目标。 在本文中, 作为第一步, 以相关传输模式方法为重点的文献审查。 然后, 以不同的阈值为基础, 进一步解释我们所调查的传输模式选择方法, 并调查这些方法如何影响网络的总体光谱效率和电力使用率, 以达到最大性绩效。 已调查过的 D2 的 D2 级设备总光谱 和 DRD 数据传输 也能够进一步显示 水平 。