Joint Subcarrier and Power Allocation in NOMA: Optimal and Approximate Algorithms

TL;DR

This paper introduces new algorithms for joint subcarrier and power allocation in NOMA systems, balancing optimality and computational complexity to improve spectral efficiency and fairness in 5G networks.

Contribution

It proposes a novel optimal algorithm, an approximation scheme, and a heuristic for JSPA in NOMA, advancing beyond existing sub-optimal solutions with lower complexity.

Findings

Opt-JSPA achieves optimal WSR with reduced complexity.

$oldsymbol{ ext{ extepsilon}- ext{JSPA}}$ offers a controllable trade-off between performance and complexity.

Grad-JSPA attains near-optimal WSR with significantly lower computational cost.

Abstract

Non-orthogonal multiple access (NOMA) is a promising technology to increase the spectral efficiency and enable massive connectivity in 5G and future wireless networks. In contrast to orthogonal schemes, such as OFDMA, NOMA multiplexes several users on the same frequency and time resource. Joint subcarrier and power allocation problems (JSPA) in NOMA are NP-hard to solve in general. In this family of problems, we consider the weighted sum-rate (WSR) objective function as it can achieve various tradeoffs between sum-rate performance and user fairness. Because of JSPA's intractability, a common approach in the literature is to solve separately the power control and subcarrier allocation (also known as user selection) problems, therefore achieving sub-optimal result. In this work, we first improve the computational complexity of existing single-carrier power control and user selection schemes. These improved procedures are then used as basic building blocks to design new algorithms, namely Opt-JSPA, $\varepsilon$-JSPA and Grad-JSPA. Opt-JSPA computes an optimal solution with lower complexity than current optimal schemes in the literature. It can be used as a benchmark for optimal WSR performance in simulations. However, its pseudo-polynomial time complexity remains impractical for real-world systems with low latency requirements. To further reduce the complexity, we propose a fully polynomial-time approximation scheme called $\varepsilon$-JSPA. Since, no approximation has been studied in the literature, $\varepsilon$-JSPA stands out by allowing to control a tight trade-off between performance guarantee and complexity. Finally, Grad-JSPA is a heuristic based on gradient descent. Numerical results show that it achieves near-optimal WSR with much lower complexity than existing optimal methods.