Fair Power Allocation Policies for Power-Domain Non-Orthogonal Multiple Access Transmission With Complete or Limited Successive Interference Cancellation

Abstract

Power-Domain Non-Orthogonal Multiple Access (NOMA) transmission has been addressed in this paper with a proportional fairness optimization criterion (which includes MAX-MIN fairness as a special case) and an arbitrary number of users. The optimization of the power allocation coefficients required to achieve the optimum proportional fairness objective leads to a nonconvex optimization problem, which is generally hard to solve and may lead to multiple local optima. However, a simple optimality condition is characterized in the paper, leading to the solution of a nonlinear equation in a single variable. This equation reduces to polynomial form in the case of MAX-MIN fairness. Departing from the complete Successive Interference Cancellation (SIC) paradigm, typical of NOMA systems, a limited SIC technique is discussed and the relevant power allocation coefficients are obtained with the same optimization criterion. This approach eases the implementation of downlink NOMA when a large number of low-complexity handheld terminals cannot sustain the computationally intensive task of complete SIC, at the cost of reduced their achievable rates. Numerical results are presented to illustrate the impact of complete and limited SIC, with power allocation optimization and two proportional fairness criteria. Among these results, the sum-rate loss due to proportional fairness and the impact of limited SIC on the system performance are illustrated.

Optimum Power Allocation for HARQ-Aided NOMA With Proportional Fairness on Fading Channels

“…Section II introduces the system model, the concepts of CSIT and CDIT, the requirement for SIC, and the outage achievable rates derived in the literature. Section III recalls the PF approach developed in [26] in the framework of outage achievable rate analysis. Section IV provides a description of the ARQ and HARQ protocols through a Markov chain model and the corresponding transition probabilities.…”

“…This is considered unfair since fairness corresponds to the equitable allocation of resources among the users (or devices) that share a common resource, like a wireless communication channel [29]. Different types of fairness for NOMA communication systems have been proposed in the literature (see [26], [29], [30] and references therein). One of the most flexible is proportional fairness (PF) [30], where the users with better channel conditions are allowed to transmit more data, but not to the extent that it completely starves the other users.…”

“…This is a nonconvex optimization problem which has been addressed, for the deterministic case, in [26]. By [26, Th.1], the solution is equivalent to solving the following set of equations:…”

“…If T k = 1 (MAX-MIN fairness), these equations are equivalent to a polynomial equation of degree K in α 1 [26]. More generally, an efficient iterative algorithm is proposed in [26, Algorithm 1], which allows to solve the optimal power allocation problem for any combination of target rates.…”

Differential Evolution Optimized Non-Orthogonal Multiple Access for Sum Rate Maximization

“…The authors of [13] provide the finding for the non-convex problem resulting from the enhancement of the proportionate offset objective. A dynamic methodology is utilized to obtain the optimal PA according to the irrational proportionate offset condition, without any constraint on the number of users.…”