Optimal Power Allocation in Downlink Multicarrier NOMA Systems: Theory and Fast Algorithms

Abstract

In this work, we propose globally optimal power allocation strategies to maximize the users sum-rate (SR), and system energy efficiency (EE) in the downlink of single-cell multicarrier non-orthogonal multiple access (MC-NOMA) systems. Each NOMA cluster includes a set of users in which the wellknown superposition coding (SC) combined with successive interference cancellation (SIC) technique is applied among them. By obtaining the closed-form expression of intra-cluster power allocation, we show that MC-NOMA can be equivalently transformed to a virtual orthogonal multiple access (OMA) system, where the effective channel gain of these virtual OMA users is obtained in closed-form. Then, the SR and EE maximization problems are solved by using very fast water-filling and Dinkelbach algorithms, respectively. The equivalent transformation of MC-NOMA to the virtual OMA system brings new theoretical insights, which are discussed throughout the paper. The extensions of our analysis to other scenarios, such as considering users rate fairness, admission control, long-term performance, and a number of future nextgeneration multiple access (NGMA) schemes enabling recent advanced technologies, e.g., reconfigurable intelligent surfaces are discussed. Extensive numerical results are provided to demonstrate the performance gaps among single-carrier NOMA (SC-NOMA), OMA-NOMA, and OMA.

Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective

“…Substituting (41) back into (40b), problem P1 can be converted into the following equivalent minimization problem, denoted by P2, as follows:…”

Hybrid Precoding-Based Millimeter Wave Massive MIMO-NOMA Systems

“…In the NOMA system, the feasible set of Equation ( 15) is the intersection of closedboxes along with the affine cluster power constraint [15], i.e., q g ∈ [Q min g , P mask g ], ∀g ∈ G, the lower bound constraint is given by:…”

“…The researchers in [14] proposed an optimal power allocation scheme for a two-user NOMA system to maximize the spectral efficiency, but the work is limited to a two-user NOMA system and does not consider the minimum rate constraint of the users. The researchers in [15] proposed a global optimal power allocation scheme with minimum rate constraint in the K-user NOMA system that is further proposed to maximize the system performance, but the work is not extended to the mmWave mMIMO system. The researchers in [16] proposed a sub-optimal power allocation scheme to improve the energy efficiency in the mmWave mMIMO-NOMA system, but the research does not consider the mathematical methods to derive the closed-form solutions for the power allocation.…”

Outage and Error-Rate Analysis of NOMA-Inspired Multicarrier Underlay Cognitive Radio System

“…In a cognitive radio scenario, QoS of the primary user has to be ensured and hence transmit power of PU will be much larger than that of SU. Assuming similar channel gains for both PU and SU, we can consider that 𝑑 ≫ 𝑑 in equation (24). Hence, equation ( 26) can be approximately written as,…”

“…Majority of the existing works are focused only on one of the following aspects -power allocation, outage analysis or error rate analysis. Optimization of power allocation among different users has been carried out considering factors like interference [7], [20], throughput [14], [20]- [24], outage probability [22], BER performance [25], coverage fairness [26] and energy efficiency [20], [24]. Mostly, power allocation schemes consider the interference constraint with respect to primary system, but do not ensure minimum throughput at PU.…”