access icon free Fully distributed joint resource allocation in ultra-dense D2D networks: a utility-based learning approach

© The Institution of Engineering and Technology
Received 27/03/2018, Accepted 03/08/2018, Revised 04/06/2018, Published 16/08/2018

In this study, the authors address the problem of joint resource block and power allocation in ultra-dense device-to-device (D2D) networks underlaying cellular networks. They propose a fully distributed scalable scheme that ensures quality-of-service (QoS) of the cellular user equipments (CUEs) while maximising the sum rate of the D2D tier. The resource allocation problem is modelled as a Stackelberg game with pricing. The base station as the leader decides prices to be paid by the D2D users for reusing the resources of the CUEs, based on the QoS requirements of the CUEs, and broadcasts the prices. D2D pairs as followers use the pricing information and a completely uncoupled learning algorithm that guarantees Pareto optimal solution. Analytical proof is provided to establish the convergence of the learning algorithm. Simulation results show superior performance compared to two other existing schemes.

Inspec keywords: telecommunication computing; mobile handsets; communication complexity; game theory; Pareto optimisation; learning (artificial intelligence); resource allocation; quality of service; cellular radio; telecommunication network reliability

Other keywords: cellular networks; power allocation; fully distributed scalable scheme; cellular user equipments; utility-based learning approach; fully distributed joint resource allocation problem; ultradense D2D networks; Pareto optimal solution; ultradense device-to-device networks; Stackelberg game; uncoupled learning algorithm; base station; pricing information; quality-of-service; CUE; QoS requirements

Subjects: Mobile radio systems; Computational complexity; Optimisation techniques; Game theory; Optimisation techniques; Reliability; Communications computing; Knowledge engineering techniques; Game theory

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