Device to Device (D2D) Communication is expected to be a one of the major contributing factors of the realisation of 5G and Beyond 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 and act 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, 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 on utilized values (i.e., D2D Device Weighted Data Rate (WDR), 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. The evaluation results revealed 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.
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 in order 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 (i.e., DAIS, FuzzyART, DBSCAN and MEC) to achieve satisfactory results in terms of Spectral Efficiency (SE), Power Consumption (PC) and execution time, with the creation of clusters and backhauling links in D2D network under existing Base Station. Additionally, this paper focuses on a small number of Devices (i.e., <=200), with the purpose to identify the limits of each approach in terms of low number of devices. More specifically, we investigate when an operator must consider implementing a D2D network (that requires extra complexity), therefore when the cluster members are sufficient enough to achieve better results than the classic mobile network. So, this research identifies 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 (i.e., Weighted Data Rate (WDR), Battery Power Level (BPL)), evaluated, and compared with other approaches (AI/ML). The results obtained demonstrate the exceptional performance of DAIS and FuzzyART, compared to all other related approaches in terms of SE, PC, execution time and cluster formation. 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 numbers of devices (i.e., >=5 for clustering, >=50 for back-hauling) as a lower limits.
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