Radio resource allocation for heterogeneous traffic in GFDM‐NOMA heterogeneous cellular networks. Issue 12 (1st August 2016)

• Record Type: Journal Article
• Authors: Mokdad, Ali; Azmi, Paeiz; Mokari, Nader
• Is Part Of: IET communications. Volume 10:Issue 12(2016)
• Page Start: 1444
• Page End: 1455
• Publication Date: 2016-08-01
• Subjects: resource allocation -- telecommunication traffic -- cellular radio -- frequency division multiplexing -- concave programming -- convex programming -- computational complexity -- iterative methods -- approximation theory -- geometry -- integer programming -- linear programming; downlink radio resource allocation -- heterogeneous traffic -- GFDM‐NOMA heterogeneous cellular networks -- generalised frequency division multiplexing -- nonorthogonal multiple access -- elastic type -- streaming type -- weighted elastic user sum‐rate maximisation -- subcarrier power constraint -- transmit power constraint -- nonconvex NP‐hard optimisation problem -- subcarrier allocation -- power allocation -- iterative algorithm -- integer linear program -- successive convex approximation approach -- arithmetic‐geometric mean approximation -- AGMA -- concave functions -- computational time; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2016.0011
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : In this study, the authors consider the downlink radio resource allocation for heterogeneous traffic in generalised frequency division multiplexing (GFDM)‐non‐orthogonal multiple access (NOMA) based heterogeneous cellular networks. In this scheme, multiple number of users can be allocated on each subcarrier. Two types of traffic are considered, elastic and streaming. The problem of maximising the weighted sum‐rate of elastic users is addressed subject to streaming users minimum rate in addition to subcarrier and transmit power constraints. This problem is a non‐convex NP‐hard optimisation problem. To solve this problem, the authors divide it into two subproblems, subcarrier allocation and power allocation then an iterative algorithm is proposed. Subcarrier allocation is updated by solving an integer linear program, where a successive convex approximation approach is adopted to transform the power allocation subproblem to a sequence of convex subproblems, using one of the three methods, successive convex approximation for low ComplExity, arithmetic‐geometric mean approximation (AGMA) and difference of two concave functions to find the power allocation optimal solutions. Numerical experiments show that the proposed algorithms can improve the system performance. Furthermore, they show that AGMA can achieve a sum‐rate near to the global optimal solution, at the expense of more computational time.