Energy-Efficient Resource Allocation in Multicarrier NOMA Systems With Fairness

Abstract

Non-orthogonal multiple access (NOMA) has attracted both academic and industrial interest since it has been considered as one of the promising 5G technologies in order to increase connectivity and spectral efficiency. In this paper, we focus on a downlink multicarrier (MC) NOMA network, where a single base station serves a set of users through multiple subchannels. The goal is to jointly optimize energy efficiency (EE) and fairness among users with respect to the subcarrier and power allocation parameters. To achieve this with acceptable complexity, a novel greedy subcarrier assignment scheme based on the worst-user first principle is proposed. Due to the fractional form of the EE expression and the existence of interference, the power allocation problem is non-convex and NP-hard. To this end, we first transform this into an equivalent subtractive form, which is then solved by using fractional programming with sequential optimization of the inter/intra-subchannel power allocation vectors. Simulation results reveal the effectiveness of the proposed scheme in terms of EE and fairness among users compared to baseline schemes. Finally, the proposed algorithms are of fast convergence, low complexity, and insensitive to the initial values.

Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

“…required data rate for each fCUE, and the limitation of cross-tier interference caused by fCUEs to macrocell. This optimization problem can be expressed mathematically as objective function P1 with sets of formulated constraints as in (22) ws is always positive, while the constraints C6 and C7 are associated with sub-channel assignment aspects that ensure at least one fCUE user is assigned on each sub-channel. It is challenging to solve the objective function P1 because it is a non-convex and nondeterministic polynomial (NP)-hard problem [19].…”

“…Theorem-4: The iterative power allocation of algorithm-3 always converges, and with any feasible initial points, it reaches the optimal power allocation by converging to a stationary point. For the proof, refer to [22].…”

“…Fairness is an essential attribute to be considered in resource allocation to prevent resource deprivation and misuse in wireless systems [20]. The study on the fairness-based energy-efficient resource allocation in PD-NOMA-HetNets have earlier addressed in [21] by investigating the joint subcarrier and power allocation problem with the tradeoff between energy efficiency, fairness, and energy harvesting, while the authors in [22] considered the tradeoff between energy efficiency and user fairness in a multicarrier NOMA system. However, these works also did not consider the transmitter and receiver power consumption.…”

“…Therefore, the joint problem was firstly decomposed into two sub-problems. The first sub-problem is approached by deploying the Greedy Algorithm (GA) to solve the userpairing problem [22]. On the other hand, the second subproblem, which is the power allocation problem is addressed by formulating the fair energy efficiency as a maximization objective function problem, which is a mixed-integer non-convex fractional programming problem.…”

“…On the other hand, the second subproblem, which is the power allocation problem is addressed by formulating the fair energy efficiency as a maximization objective function problem, which is a mixed-integer non-convex fractional programming problem. Hence, it is then transformed into its subtractive form to obtain the optimal power allocation solutions by adopting the Sequential Convex Programming (SCP) approach [22].…”