Millimeter-Wave NOMA with User Grouping, Power Allocation and Hybrid Beamforming

Abstract: This paper investigates the application of non-orthogonal multiple access in millimeter-Wave communications (mmWave-NOMA). Particularly, we consider downlink transmission with a hybrid beamforming structure. A user grouping algorithm is first proposed according to the channel correlations of the users. Whereafter, a joint hybrid beamforming and power allocation problem is formulated to maximize the achievable sum rate, subject to a minimum rate constraint for each user. To solve this non-convex problem with high-dimensional variables, we first obtain the solution of power allocation under arbitrary fixed hybrid beamforming, which is divided into intra-group power allocation and inter-group power allocation. Then, given arbitrary fixed analog beamforming, we utilize the approximate zero-forcing method to design the digital beamforming to minimize the inter-group interference. Finally, the analog beamforming problem with the constant-modulus constraint is solved with a proposed boundary-compressed particle swarm optimization algorithm. Simulation results show that the proposed joint approach, including user grouping, hybrid beamforming and power allocation, outperforms the state-of-the-art schemes and the conventional mmWave orthogonal multiple access system in terms of achievable sum rate and energy efficiency.

Analysis of Millimeter-Wave NOMA with User Grouping, Power Allocation, and Hybrid Beamforming

This paper explores Non-Orthogonal Multiple Access (NOMA) in millimeter-wave communications, focusing on downlink transmission within a hybrid beamforming architecture. It addresses challenges in maximizing achievable sum rates while satisfying minimum rate constraints for individual users, leveraging hybrid beamforming alongside user grouping and power allocation strategies.

Highlights and Contributions

1. User Grouping Algorithm: The proposal utilizes a K-means clustering algorithm to form user groups based on channel correlations, mitigating inter-group interference by aligning users with correlated channels.

2. Joint Optimization Problem: A non-convex optimization problem is formulated to enhance achievable sum rates, incorporating user grouping, hybrid beamforming, and power allocation.

3. Power Allocation Strategy: The authors propose a division into intra-group and inter-group power allocation. Intra-group allocation leverages successive interference cancellation (SIC) principles, ensuring minimal rate requirements. Inter-group power allocation optimizes sum rates by balancing power across groups, leveraging a sequential approach informed by the zero-forcing method.

4. Hybrid Beamforming Design: Digital beamforming vector designs target minimized inter-group interference, while an innovative analog beamforming solution employs a boundary-compressed particle swarm optimization (BC-PSO) algorithm to address constant-modulus constraints.

Numerical Results and Performance

Simulation results illustrate the superiority of the proposed joint approach over traditional schemes, such as orthogonal mmWave systems, in terms of achievable sum rates and energy efficiency across diverse network configurations and channel conditions (both Line-Of-Sight and Non-Line-Of-Sight scenarios). Notably, the paper demonstrates that the performance improvement is robust against changes in minimal rate requirements, power levels, and numbers of RF chains.

Implications and Future Directions

The research contributes both practically and theoretically to the domain of mmWave communications and NOMA technology. Practically, it provides a competitive edge in scenarios requiring high connectivity density and complex interference environments, pertinent for 5G networks and beyond. Theoretically, the presented joint optimization involving user grouping and hybrid beamforming offers insights into balancing multiplexing gains with complexity constraints, hinting at future directions in evolving NOMA systems.

Furthermore, the work speculates potential developments in AI, particularly in refining hybrid beamforming algorithms and user-grouping strategies using advanced learning techniques like neural networks or reinforcement learning, potentially enhancing real-time adaptability and optimization precision under dynamic network conditions.

In summary, this paper offers an intricate blend of advanced algorithmic solutions and rigorous performance analysis to further the applicability of NOMA in modern communication frameworks, suggesting a sustainable path towards the integrated, high-efficiency communication systems of the future.