Fairness for Non-Orthogonal Multiple Access in 5G Systems (1504.02300v1)

Abstract: In non-orthogonal multiple access (NOMA) downlink, multiple data flows are superimposed in the power domain and user decoding is based on successive interference cancellation. NOMA's performance highly depends on the power split among the data flows and the associated power allocation (PA) problem. In this letter, we study NOMA from a fairness standpoint and we investigate PA techniques that ensure fairness for the downlink users under i) instantaneous channel state information (CSI) at the transmitter, and ii) average CSI. Although the formulated problems are non-convex, we have developed low-complexity polynomial algorithms that yield the optimal solution in both cases considered.

• The paper introduces a max-min fairness optimization strategy using a low-complexity polynomial algorithm under perfect CSI for NOMA downlink scenarios.
• The paper tackles average CSI by minimizing the maximum outage probability through an innovative decomposition into one-dimensional problems.
• The paper’s numerical results demonstrate that NOMA significantly outperforms TDMA in both fairness and outage performance, validating its practical applicability in 5G networks.

Fairness for Non-Orthogonal Multiple Access in 5G Systems

The paper "Fairness for Non-Orthogonal Multiple Access in 5G Systems" by Stelios Timotheou and Ioannis Krikidis presents an in-depth exploration of power allocation (PA) strategies to ensure fairness among users in Non-Orthogonal Multiple Access (NOMA) downlink scenarios, a crucial aspect in the evolution toward 5G communication systems. The authors tackle the inherent challenge posed by NOMA: while it promises higher spectral efficiency through power domain user multiplexing and successive interference cancellation (SIC), it also introduces potential fairness issues in user data rates.

Key Contributions

The paper's primary contributions are twofold:

1. Instantaneous Channel State Information (CSI): The authors address the fairness problem under perfect CSI at the transmitter. They propose a max-min fairness optimization strategy that ensures the minimum achievable rate among users is maximized. Recognizing the non-convexity of this problem, the authors develop a low-complexity polynomial algorithm leveraging bisection techniques and linear programming to find globally optimal solutions.
2. Average CSI: For scenarios where only average CSI is available, the authors consider the outage probability as the fairness criterion. The optimization formulation targets minimizing the maximum outage probability across users. Despite the non-convex nature of this problem, an innovative approach decomposing it into a sequence of one-dimensional problems solves it efficiently.

Numerical Results and Insights

The numerical simulations highlight the robustness and efficacy of the proposed algorithms:

• Fairness Rate Performance: The results, illustrated for varying numbers of users and total transmit power, demonstrate that the NOMA-based approaches significantly outperform traditional orthogonal multiple access (OMA) techniques like Time-Division Multiple Access (TDMA). The gains increase proportionally with the number of users.
• Outage Probability: Under average CSI scenarios, NOMA exhibits superior performance compared to TDMA and fixed PA schemes, showing an order of magnitude improvement in outage probabilities.

Implications

These findings underscore the potential of NOMA to ensure equitable resource distribution in 5G networks:

• Practical Relevance: The proposed PA algorithms are highly relevant for real-world applications where fairness and efficiency must be balanced, particularly in heterogeneous networks with varied user demands.
• Theoretical Impact: By addressing the non-convex nature of the fairness optimization problems, the authors contribute valuable methods that could be extended to other wireless communication scenarios.

Future Directions

The research opens avenues for further exploration:

• Algorithm Generalization: Extending the proposed algorithms to more complex network configurations and other forms of non-ideal CSI would be an intriguing direction.
• Integration with Machine Learning: Investigating how machine learning techniques can dynamically adjust PA based on real-time network conditions could enhance the adaptability and efficiency of NOMA.
• Cross-Layer Optimization: Analyzing how upper-layer protocols can be optimized in conjunction with physical-layer NOMA strategies to further improve overall system performance.

Conclusion

The paper by Timotheou and Krikidis makes significant advancements in ensuring fairness in NOMA-based 5G systems through innovative power allocation strategies. By providing both theoretical and practical solutions to complex non-convex optimization problems, this research stands to play a pivotal role in the deployment of fair, efficient, and high-performance 5G networks.