Efficient radio resource allocation scheme for 5G networks with device-to-device communication

Abstract

<p>A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.</p>

A Survey of Resource Management in D2D Communication for B5G Networks

“…Then, in [154], the authors investigate the interference management problem of D2D communication underlying cellular networks in the uplink scenario. The authors aim to mitigate the interferences between CUs and DUs.…”

“…The techniques, advantages, and limitations of the current research are highlighted. In addition, the recent works are categorized based on the performance metrics that have been considered such as sum rate [151], [152], [153], [154], [155], network capacity [156], [157], [158], [159], and coverage probability [160], [161], [162] as explained in fig. 8.…”

Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

“…It is part of the 5G network, officially from the latest addition in the 3rd Generation Partnership Project (3GPP) Release 18 specifications [35], which is expected to benefit the telecommunications sector with the development of various notable applications [70][71][72][73]. As part of the new 6G network, AI plays an essential role in revolutionizing communication and automation, such as in handover process [74][75][76][77][78], resources allocation [79,80], and network selection. Additionally, integrating AI to the 6G networks leads to higher performance, particularly when delay-sensitive applications are involved.…”

Radio frequency receiver of long-term evolution system design by MATLAB Simulink

“…Where loss directly by the decibels mode or indirectly by the distance and frequency mode as illustrated in Figure 3. This Bloch added free space path loss to filterd transmitted signal [21], [22]. AWGN channel adding white Gaussian noise to the input signal.…”