Low-Complexity Power Allocation in NOMA Systems With Imperfect SIC for Maximizing Weighted Sum-Rate

Abstract

In this paper, we investigate the power allocation for maximizing weighted sum rate (WSR) in downlink multiple carriers non-orthogonal multiple access (MC-NOMA) systems with imperfect successive interference cancellation (SIC). We formulate the power allocation problem as a non-convex optimization problem with the total power constraint of all sub-channels while considering often-neglected issues of SIC error and power order constraints at users. First, we discuss that the optimization problem assuming receivers can perform perfect SIC, and we provide a concavity condition of the WSR maximization problem for the MC-NOMA system. When the concavity condition is not satisfied, a fractional quadratic transformation is used to overcome the difficulty of problem non-convexity. Based on the transformation, we propose an iterative power allocation algorithm. Then, we consider the SIC error and the power order constraints in the optimization problem and present a power allocation method with imperfect SIC. Moreover, for both the perfect and imperfect SIC, we derive some propositions of the optimal power allocation solution to the WSR maximization problem and propose a low-complexity power allocation algorithm based on these propositions. Finally, we provide a joint user scheduling and power allocation algorithm for maximizing the WSR. The simulation results illustrate that the proposed resource allocation methods have a better performance than the existing schemes.

Probability-Based User Management Algorithm for the Hybrid NOMA System

“…In addition, the SINR γ u k s k has an identical form with γ uj s k , thus the probability Pr(γ u k s k ≥ γ th. s k ) can be further attained as (19) for w ∈ R + and as (23) for w = 2 by using the results of ( 17) and (21) and switching the subscript of m, L, and γ from 1 to 2. Similarly, the SNR γ u k s k has an identical form with γ uj sj , thus the probability Pr( γ u k s k ≥ γ th.…”

“…The first is the user management for transmission mode selection and user pairing matters, and the second is the power allocation for the paired users with the NOMA. It is worth mentioning that the second issue can be executed by using the existing power allocation techniques for the NOMA [19]- [23]. Consequently, the first issue, the user management for the hybrid NOMA systems, becomes a core module of the 5G communications since it influences the eMBB and mMTC services, which is also the main target of this paper.…”

Fairness‐aware power allocation in downlink MIMO‐NOMA systems

“…), which is a boundary case of the optimal solution to the sum-rate maximization problem for a given (≥ 1) in [28]. is the scaling factor that can guarantee SIC and user fairness.…”

Power allocation with fairness for non‐orthogonal multiple access systems

“…This approach mainly benefits users with low‐channel gains. PA for maximising the weighted sum rate is investigated in [16]. Optimisation problems for single and multi‐carrier NOMA are formulated and an iterative solution is proposed.…”