A Unified NOMA Framework in Beam-Hopping Satellite Communication Systems

Abstract

This paper investigates the application of a unified non-orthogonal multiple access framework in beam hopping (U-NOMA-BH) based satellite communication systems. More specifically, the proposed U-NOMA-BH framework can be applied to code-domain NOMA based BH (CD-NOMA-BH) and power-domain NOMA based BH (PD-NOMA-BH) systems. To satisfy dynamic-uneven traffic demands, we formulate the optimization problem to minimize the square of discrete difference by jointly optimizing power allocation, carrier assignment and beam scheduling. The non-convexity of the objective function and the constraint condition is solved through Dinkelbach's transform and variable relaxation. As a further development, the closed-from and asymptotic expressions of outage probability are derived for CD/PD-NOMA-BH systems. Based on approximated results, the diversity orders of a pair of users are obtained in detail. In addition, the system throughput of U-NOMA-BH is discussed in delay-limited transmission mode. Numerical results verify that: i) The gap between traffic requests of CD/PD-NOMA-BH systems appears to be more closely compared with orthogonal multiple access based BH (OMA-BH); ii) The CD-NOMA-BH system is capable of providing the enhanced traffic request and capacity provision; and iii) The outage behaviors of CD/PD-NOMA-BH are better than that of OMA-BH.

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

“…Focusing on Power-Domain (PD) NOMA, which is the primary subject of this paper, there are a few recent papers addressing very similar topics. One of them is [19], where the authors consider both code-domain (CD) and PD-NOMA for a beam hopping satellite communication systems. Their analysis leads to a nonconvex objective function, which is handled by resorting to Dinkelbach's transform and variable relaxation.…”

“…The first key contribution of this paper, Theorem 1, provides necessary and sufficient conditions for the solution of the optimization problem (19). These conditions are simply amenable to a fast algorithm to find the optimum power allocation under the proportional fairness criterion, which remains numerically stable even when the number of users is very large.…”

“…Both MAX-MIN and proportional fairness power allocations require the solution of a nonconvex optimization problem, illustrated in the following eq. (19). It is well known that nonconvex optimization problems are hard to solve, in general, and prone to the existence of multiple local optima [13].…”

“…Two main contributions of this paper are Theorem 1 and Algorithm 1. Theorem 1 provides the necessary and sufficient conditions to solve the proportional fairness power allocation problem described in detail in (19). Algorithm 1 outlines a numerical procedure to determine the power allocation vector with complete Successive Interference Cancellation (SIC) and an arbitrary number of users.…”