Matching-Theory-Based Resource Allocation for Underlay Device to Multi-Device Communications

In underlay device-to-device multicast communication (D2MD), a group of users can communicate directly by reusing cellular resources blocks (RB) to share common content. This paradigm brings great benefits to cellular networks in term of energy and spectral efficiency. However, D2MD or cellular comm...

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Bibliographic Details
Published in2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) pp. 28 - 35
Main Authors Hmila, Mariem, Fernandez-Veiga, Manuel, Rodriguez-Perez, Miguel
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
5G wireless networks
D2D multi-casting
Device-to-device communication
Fractional programming
Interference
Matching theory
Optimization
Power control
Receivers
Resource allocation
Resource management
Transmitters
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DOI10.1109/WiMOB.2018.8589125

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Summary:In underlay device-to-device multicast communication (D2MD), a group of users can communicate directly by reusing cellular resources blocks (RB) to share common content. This paradigm brings great benefits to cellular networks in term of energy and spectral efficiency. However, D2MD or cellular communication quality may degrade or blocked due to harmful mutual interference between cellular, D2MD users sharing the same communication resources. Therefore, to fully achieve the advantages of D2MD communication, resource management and power control became critical. In this paper, we model the join power and resource allocation as a mixed integer nonlinear problem (MINLP) to maximize network global energy efficiency (GEE). To coup with NP-hard nature of the problem, we investigate a two stages solution. At first, we propose a scheme based on matching theory to solve resource allocation sub-problem. Here, we introduce a reuse and a split factor to control the aggregated interference on CUE from D2MD and the number of RB used by each group. Second, we apply the framework of fractional programming to optimally solve the power control sub-problem subject to QoS constraints. Finally, GEE metric is analysed via extensive numerical simulations with a spatial Poisson process for the users' locations and applying different clustering algorithms as K-nearest neighbour, distance limit.
DOI:10.1109/WiMOB.2018.8589125