Intelligent Reflecting Surface Assisted Non-Orthogonal Multiple Access (1907.03133v2)

Abstract: Intelligent reflecting surface (IRS) is a new and disruptive technology to achieve spectrum- and energy-efficient as well as cost-efficient wireless networks. This paper considers an IRS-assisted downlink non-orthogonal-multiple-access (NOMA) system. To optimize the rate performance and ensure user fairness, we maximize the minimum decoding signal-to-interference-plus-noise-ratio (i.e., equivalently the rate) of all users, by jointly optimizing the (active) transmit beamforming at the base station (BS) and the phase shifts (i.e., passive beamforming) at the IRS. A combined-channel-strength based user ordering scheme is first proposed to decouple the user-ordering design and the joint beamforming design. Efficient algorithms are further proposed to solve the formulated non-convex problem for the cases of a single-antenna BS and a multi-antenna BS, respectively, by leveraging the block coordinated decent and semidefinite relaxation (SDR) techniques. For the single-antenna BS case, the optimal solution for the power allocation at the BS and the asymptotically optimal solution for the phase shifts at the IRS are obtained in closed forms. For the multi-antenna BS case, it is shown that the rank of the SDR solution to the transmit beamforming design is upper bounded by two. Also, the convergence proof and the complexity analysis are given for the proposed algorithms. Simulation results show that the IRS-assisted downlink NOMA system can enhance the rate performance significantly, compared to traditional NOMA without IRS and traditional orthogonal multiple access with/without IRS. In addition, numerical results demonstrate that the rate degradation due to the IRS's finite phase resolution is slight, and good rate fairness among users can be always guaranteed.

• The paper proposes jointly optimizing base station transmit beamforming and intelligent reflecting surface phase shifts to maximize the minimum user rate in downlink IRS-assisted NOMA systems.
• It introduces a user ordering method based on combined channel strength and an iterative optimization algorithm using BCD and SDR techniques to solve the non-convex problem.
• Numerical results demonstrate that integrating IRS with NOMA significantly enhances user fairness and throughput compared to traditional NOMA and OMA benchmarks, especially in low phase resolution scenarios.

Intelligent Reflecting Surface Assisted Non-Orthogonal Multiple Access

The paper "Intelligent Reflecting Surface Assisted Non-Orthogonal Multiple Access" explores the integration of Intelligent Reflecting Surfaces (IRS) with Non-Orthogonal Multiple Access (NOMA) systems in downlink wireless communications. The authors focus on maximizing the minimum achievable rate among users, subject to fairness constraints, by jointly optimizing transmit beamforming at the base station (BS) and phase shifts at the IRS.

NOMA is recognized for its ability to serve multiple users within the same time-frequency-code resource, offering benefits like improved spectrum efficiency and reduced latency. However, traditional NOMA systems often require significant differences in user channel strengths to outperform Orthogonal Multiple Access (OMA) techniques. IRS represents a promising technology for enhancing wireless networks by configuring the radio environment through controllable phase shifts, which could reshape user channels to optimize NOMA performance.

Key Insights and Contributions

1. Problem Formulation: The research formulates a challenge of jointly optimizing transmit power at the BS and phase shifts at the IRS to maximize the minimum decoding rate, with constraints on power budgets and ensure sequential decoding via SIC. The problem is non-convex due to coupled variables and non-linear constraints.
2. Optimized User Ordering: The authors propose a user ordering approach based on Combined Channel Strength (CCS), which simplifies user decoding in NOMA systems while preserving performance close to exhaustive search strategies.
3. Iterative Optimization: An iterative optimization method leveraging Block Coordinate Descent (BCD) and Semidefinite Relaxation (SDR) techniques is developed. For single-antenna BS scenarios, closed-form solutions are derived for IRS phase shifts in two-user systems, significantly reducing computational complexity compared to general SDR approaches.
4. Performance Evaluation: Numerical simulations validate that IRS-assisted NOMA enhances both user fairness and throughput compared to benchmark traditional NOMA and OMA systems. The integration of IRS enables substantial rate improvements especially under low phase resolution conditions, showing robustness across various IRS configurations.

Practical and Theoretical Implications

The implications of this research are significant in both theory and practice. The integration of IRS in NOMA systems can lead to improved performance in cellular networks, particularly in ensuring equal rate fairness among users while maximizing resource utilization. The methodological advancements offer a compelling framework for future studies on IRS-assisted communication systems, highlighting the need for efficient algorithms that can operate under practical hardware constraints like finite phase resolution.

Future Considerations

Future research directions could focus on enhancing the robustness of IRS-assisted NOMA under practical constraints like imperfect CSI and studying the impact of IRS deployment in dynamic network conditions. Exploration of alternative optimization techniques and algorithmic improvements to further reduce computational overhead and increase the scalability of these systems in real-world applications would be beneficial.

In conclusion, this paper provides a comprehensive approach to IRS-assisted NOMA systems, paving the way for next-generation wireless communication strategies aimed at optimal resource allocation and enhanced performance.