PROJECT TITLE :

Fairness-Aware Energy-Efficient Resource Allocation for AF Co-Operative OFDMA Networks

ABSTRACT:

In this paper, we have a tendency to adopt an energy-potency (EE) metric, named worst-EE, that is suitable for EE fairness optimization in the uplink transmission of amplify-and-forward (AF) cooperative orthogonal frequency division multiple access (OFDMA) networks. Additional specifically, we have a tendency to assign subcarriers and allocate powers for mobile and relay stations in order to maximise the worst-EE, i.e., to maximise the EE of the mobile station (MS) with rock bottom EE worth, subject to MSs transmit power, relay station (RS) transmit power, and MSs quality-of-service (QoS) constraints. The formulated primal max–min optimization downside is nonconvex fractional mixed integer nonlinear program, i.e., NP-laborious to resolve. We have a tendency to offer a completely unique optimization framework that studies the structure of the primal drawback and prove that the twin min–max optimization downside attains the same optimal solution of the primal downside. Additionally, we have a tendency to propose a changed Dinkelbach algorithm, named dual Dinkelbach, to realize the optimal resolution of the dual drawback in a very polynomial time complexity. We have a tendency to more exploit the structure of the obtained optimal solution and develop an occasional complexity suboptimal heuristic. Numerical results show the effectiveness of the proposed algorithm to boost the network performance in terms of fairness between MSs, worst-EE, and average network transmission rate in comparison to ancient schemes that maximize the EE of the full network. Presented results additionally show that the suboptimal heuristic balances the achieved performance and therefore the computational complexity.