5G D2D传输模式选择模式 D2D 分配的人工情报和机器学习技术选择性绩效和群集限制评价 (5G D2D Transmission Mode Selection Performance & Cluster Limits Evaluation of Distributed Artificial Intelligence and Machine Learning Techniques)

5G D2D Communication promises improvements in energy and spectral efficiency, overall system capacity, and higher data rates. However, to achieve optimum results it is important to select wisely the Transmission mode of the D2D Device to form clusters in the most fruitful positions in terms of Sum Rate and Power Consumption. Towards this end, this paper investigates the use of Distributed Artificial Intelligence (DAI) and innovative to D2D, Machine Learning (ML) approaches to achieve satisfactory results in terms of Spectral Efficiency (SE), Power Consumption (PC) and execution time, with the creation of clusters and backhauling D2D network under existing Base Station/Small Cell. Additionally, one of the major factors that affect the creation of high-quality clusters under a D2D network is the number of the Devices. Therefore, this paper focuses on a small (<=200) number of Devices, with the purpose to identify the limits of each approach in terms of number of devices. Specifically, to identify where it is beneficial to form a cluster, investigate the critical point that gains increases rapidly and at the end examine the applicability of 5G requirements. Additionally, prior work presented a Distributed Artificial Intelligence (DAI) Solution/Framework in D2D and a DAIS Transmission Mode Selection (TMS) plan was proposed. In this paper DAIS is further examined, improved in terms of thresholds evaluation, evaluated, and compared with other approaches (AI/ML). The results obtained demonstrate the exceptional performance of DAIS, compared to all other related approaches in terms of SE, PC, execution time and cluster formation efficiency. Also, results show that the investigated AI/ML approaches are also beneficial for Transmission Mode Selection (TMS) in 5G D2D communication, even with a smaller (i.e., >=5 D2D Relay,>=50 D2D Multi Hop Relay) numbers of devices as a lower limits.

翻译：5GDD 5GD2 通信承诺提高能源和光谱效率、总体系统能力以及更高的数据率。然而,为了取得最佳结果,必须明智地选择D2D设备的传输模式,以在总速率和电耗方面最有成果的位置形成集群。为此,本文件调查了使用分布式人工智能(DAI)和D2D创新型、机器学习(ML)方法,以便在光谱效率(SE)、电力消耗(PC)和执行时间方面取得令人满意的结果,在现有的基地站/Small 单元格下创建集群和回推 D2D网络。此外,影响D2D网络下高质量集群创建的主要因素之一是设备的数量。因此,本文件侧重于使用少量( ⁇ 200)的D2D、机器学习(MLM)方法在设备数量方面的各种限制。具体地说,在集群方面,调查在5GS台站/S 5D 的增益方面,在5GDMLD 5的交付方式上展示了一种特殊性能评估。在DAIS 5D 5D 中, 5D 5D IM IM IM IM IM 5 数据分析中还展示了一种拟议中, 数据分析中, 数据分析中显示一个额外的性能数据表现。DBDDD 数据数据数据分析数据分析数据分析数据分析数据分析数据数据数据数据数据数据数据数据数据数据数据, 显示数据数据显示数据数据数据数据数据数据数据数据数据数据数据分析数据分析分析数据数据数据分析数据数据数据数据分析数据数据数据数据数据数据分析分析分析分析分析分析分析数据分析分析数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据数据