[Paper Review] Dynamic Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) in 5G Wireless Networks

Mina Baghani, Saeedeh Parsaeefard+2 more|arXiv (Cornell University)|Nov 21, 2018

Advanced Wireless Communication Technologies4 citations

TL;DR

This paper proposes a dynamic multiple access technology selection framework in 5G networks that jointly optimizes subcarrier, power, and access mode (OMA or NOMA) per user based on channel state information. Using a utility function balancing spectral efficiency and processing cost, the authors develop a two-step iterative algorithm combining linear integer programming and DC programming to solve the non-convex optimization problem, achieving superior performance over static OMA or NOMA schemes in diverse network conditions.

ABSTRACT

In this paper, facilitated via the flexible software defined structure of the radio access units in 5G, we propose a novel dynamic multiple access technology selection among orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques for each subcarrier. For this setup, we formulate a joint resource allocation problem where a new set of access technology selection parameters along with power and subcarrier are allocated for each user based on each user's channel state information. Here, we define a novel utility function taking into account the rate and costs of access technologies. This cost reflects both the complexity of performing successive interference cancellation and the complexity incurred to guarantee a desired bit error rate. This utility function can inherently demonstrate the trade-off between OMA and NOMA. Due to non-convexity of our proposed resource allocation problem, we resort to successive convex approximation where a two-step iterative algorithm is applied in which a problem of the first step, called access technology selection, is transformed into a linear integer programming problem, and the nonconvex problem of the second step, referred to power allocation problem, is solved via the difference-of-convex-functions (DC) programming. Moreover, the closed-form solution for power allocation in the second step is derived. For diverse network performance criteria such as rate, simulation results show that the proposed new dynamic access technology selection outperforms single-technology OMA or NOMA multiple access solutions.

Motivation & Objective

• To address the limitations of static OMA and NOMA in 5G networks, particularly NOMA's sensitivity to CSI accuracy and high receiver complexity.
• To enable flexible, software-defined dynamic selection between OMA and NOMA per subcarrier based on user channel conditions.
• To formulate a joint resource allocation problem that optimizes subcarrier, power, and access technology selection under QoS constraints.
• To introduce a novel utility function that balances spectral rate gains against the processing cost of NOMA, including SIC and BER requirements.
• To develop a low-complexity, iterative solution via successive convex approximation for the non-convex optimization problem.

Proposed method

• Formulates a joint resource allocation problem with access technology selection, power allocation, and subcarrier assignment as decision variables.
• Introduces a utility function combining sum rate and a cost term representing NOMA processing complexity and BER constraints.
• Applies a two-step successive convex approximation algorithm: first solving access mode selection via linear integer programming, then solving power allocation via difference-of-convex (DC) programming.
• Derives a closed-form solution for power allocation in the second step, enabling efficient computation.
• Uses Lagrangian relaxation and Karush-Kuhn-Tucker (KKT) conditions to derive optimality conditions for power allocation.
• Models interference and QoS constraints per service provider (SP) using dual variables and rate constraints in the Lagrangian function.

Experimental results

Research questions

• RQ1 How can dynamic access mode selection between OMA and NOMA improve spectral efficiency and reduce complexity in 5G networks?
• RQ2 What is the optimal trade-off between spectral rate gains and processing costs in NOMA, and how can it be modeled in a utility function?
• RQ3 How can joint subcarrier, power, and access mode allocation be optimized under QoS constraints for multiple service providers?
• RQ4 Can a two-step iterative algorithm effectively solve the non-convex optimization problem arising from dynamic OMA/NOMA selection?
• RQ5 What is the impact of channel state information accuracy and user channel gain differences on the performance of dynamic OMA/NOMA selection?

Key findings

• The proposed dynamic OMA/NOMA selection framework outperforms both pure OMA and pure NOMA in terms of spectral efficiency across diverse network conditions.
• The utility-based approach effectively captures the trade-off between rate gains and NOMA processing costs, enabling intelligent access mode selection.
• The two-step iterative algorithm converges to a near-optimal solution, with the power allocation step achieving a closed-form solution for computational efficiency.
• Simulation results confirm that dynamic selection significantly improves system performance when channel gain differences among users are small, where NOMA alone degrades.
• The algorithm maintains QoS requirements for multiple service providers by incorporating SP-specific rate constraints in the optimization.
• The derived power allocation formula (Equation 38) enables efficient computation of power levels for NOMA users under interference and SIC constraints.