5G D2D Transmission Mode Selection Performance & Cluster Limits Evaluation of Distributed AI and ML Techniques

Abstract

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.

Dynamic D2D Communication in 5G/6G Using a Distributed AI Framework

“…From the open literature, the types of control that are used for the implementation of D2D communication and the establishment of links are the following (see [15], [18], [19]):…”

“…Moreover, to set a benchmark and allow for a fairer comparison, we also enhanced the DSR approach [15], [18], [19], so as to make it competitive and aligned with DAIS in a dynamic environment (see Section IV-B). DSR is a distributed algorithmic approach that focuses on maximising the aggregated data rate of all the links established in the network by using global network knowledge 2 .…”

“…Also, a short description of the implementation of the DAIS Plan, the Sum Rate Approach, the Distributed Random and the non-D2D UEs algorithms is provided. Further details appear in [15], [18], [19]. The DAI framework is realised in an efficient, distributed, autonomous and flexible way, and it focuses on the local environment rather than the global environment.…”

Performance Evaluation of Transmission Mode Selection in D2D communication

“…The problem that this paper tries to tackle is to maximize the total SE (i.e, sum rate) and as a site effect to reduce the total PC of the DAIS algorithm in the case of N number of devices under a BS, as shown in the [28]. Therefore, this paper have the following constrains/assumptions about the physical link:…”

Mobile network connectivity analysis for device to device communication in 5G network

“…Cheng et al [20] present a congestion reduction mechanism using a D2D cooperative relay in the LTE Advanced system and propose their algorithm to improve performance and reduce network congestion problems. Ioannou et al [21] present a D2D clustering solution in the 5G network based on distributed artificial intelligence (DAI) and innovative in D2D, and machine learning (ML). Zafar et al [22] propose an algorithm to solve some problems related to energy efficiency and co-channel interference in the 5G network.…”