Energy-Efficient Transmission Design in Non-Orthogonal Multiple Access (1606.02379v1)

Abstract: Non-orthogonal multiple access (NOMA) is considered as a promising technology for improving the spectral efficiency (SE) in 5G. In this correspondence, we study the benefit of NOMA in enhancing energy efficiency (EE) for a multi-user downlink transmission, where the EE is defined as the ratio of the achievable sum rate of the users to the total power consumption. Our goal is to maximize the EE subject to a minimum required data rate for each user, which leads to a non-convex fractional programming problem. To solve it, we first establish the feasible range of the transmitting power that is able to support each user's data rate requirement. Then, we propose an EE-optimal power allocation strategy that maximizes the EE. Our numerical results show that NOMA has superior EE performance in comparison with conventional orthogonal multiple access (OMA).

• The paper presents an energy efficiency optimization in downlink SISO NOMA by developing a new power allocation strategy through fractional programming.
• It decouples a non-convex problem into closed-form power allocation for multiple users and univariate optimization to meet minimum data rate constraints.
• Simulations validate that the NOMA-based approach outperforms OMA methods, paving the way for energy-efficient designs in future wireless networks.

Energy-Efficient Transmission Design in Non-Orthogonal Multiple Access

The paper "Energy-Efficient Transmission Design in Non-Orthogonal Multiple Access" by Yi Zhang et al. addresses an essential aspect of modern communication systems: the enhancement of energy efficiency (EE) in Non-Orthogonal Multiple Access (NOMA). NOMA has emerged as a pivotal technology in 5G networks due to its capability to improve spectral efficiency (SE) by allowing multiple users to be served simultaneously. This paper extends the application of NOMA by focusing on energy efficiency, a critical challenge given the substantial energy consumption attributed to information and communication technologies globally.

Key Contributions

1. EE Optimization in NOMA Systems: The authors present an optimization problem aimed at maximizing the EE of a downlink single-input single-output (SISO) NOMA system. This involves maximizing the achievable sum rate relative to the total power consumption while ensuring each user receives a minimum required data rate.
2. Non-Convex Fractional Programming Problem: The paper introduces a novel EE-optimal power allocation strategy to solve the non-convex fractional programming problem inherent in the EE maximization with user QoS constraints. The optimization problem is decoupled into two subproblems: one focusing on a closed-form solution for the power allocation among multiple users, and the other on adjusting the total transmission power using a univariate optimization approach.
3. Numerical Validation of Superiority of NOMA: Through simulation studies, the paper validates the proposed strategy, demonstrating that NOMA outperforms traditional orthogonal multiple access (OMA) techniques, such as time-division multiple access (TDMA), in terms of energy efficiency. This is attributed to NOMA’s ability to exploit power domain separation to enhance user diversity gains.

Numerical Results and Implications

The numerical results highlight several crucial aspects:

• Energy Efficiency Enhancement: The proposed NOMA-based approach consistently shows superior EE over OMA, particularly as the number of users increases, due to enhanced spectrally efficient power usage.
• Impact of User Location and QoS: The effectiveness of the proposed EE strategy is sensitive to the minimum data rate requirements and the spatial distribution of users. The system's EE tends to be maximized when users with stronger channel conditions receive additional power, optimizing the power-to-rate ratio.

Theoretical and Practical Implications

Theoretically, the paper contributes to the mathematical underpinnings of fractional programming in the context of EE optimization, providing a tractable approach to solve non-convex allocation problems. Practically, this work opens avenues for designing NOMA-based downlink systems in future mobile networks, where energy conservation remains a high priority. Beyond 5G, such energy-efficient designs will be imperative for sustainability in dense cellular networks and IoT applications.

Future Directions

This paper lays a foundation for further exploration into:

• Dynamic Channel Conditions: Extending this framework to accommodate varying channel conditions, particularly in fast-fading environments.
• Multi-Antenna Configurations: Investigating the application of the proposed strategy in multiple-input multiple-output (MIMO) settings, which could further leverage spatial diversity gains.
• Integration with Other Technologies: Exploring how NOMA can be combined with other emerging technologies like machine learning for adaptive power management based on predictive analytics.

In summary, this paper demonstrates significant advancements in the pursuit of energy-efficient designs in multi-user communications, marking a step forward in leveraging NOMA’s full potential within modern wireless networks.