Non-Orthogonal Multiple Access (NOMA) for Downlink Multiuser MIMO Systems: User Clustering, Beamforming, and Power Allocation (1611.07425v1)

Abstract: We investigate the application of non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) in downlink multiuser multiple-input multiple-output (MIMO) cellular systems, where the total number of receive antennas at user equipment (UE) ends in a cell is more than the number of transmit antennas at the base station (BS). We first dynamically group the UE receive antennas into a number of clusters equal to or more than the number of BS transmit antennas. A single beamforming vector is then shared by all the receive antennas in a cluster. We propose a linear beamforming technique in which all the receive antennas can significantly cancel the inter-cluster interference. On the other hand, the receive antennas in each cluster are scheduled on power domain NOMA basis with SIC at the receiver ends. For inter-cluster and intra-cluster power allocation, we provide dynamic power allocation solutions with an objective to maximizing the overall cell capacity. An extensive performance evaluation is carried out for the proposed MIMO-NOMA system and the results are compared with those for conventional orthogonal multiple access (OMA)-based MIMO systems and other existing MIMO-NOMA solutions. The numerical results quantify the capacity gain of the proposed MIMO-NOMA model over MIMO-OMA and other existing MIMO-NOMA solutions.

• The paper introduces a novel MIMO-NOMA framework that uses dynamic user clustering and linear beamforming to enhance spectral efficiency compared to traditional OMA.
• It develops a dual-step power allocation strategy combined with successive interference cancellation to effectively manage inter-user interference.
• Numerical simulations confirm the system's scalability and throughput gains, underscoring its potential for future 5G and Beyond networks.

Overview of Non-Orthogonal Multiple Access (NOMA) for Downlink Multiuser MIMO Systems

The paper presents an investigation into the deployment of Non-Orthogonal Multiple Access (NOMA) techniques within downlink multiuser Multiple Input Multiple Output (MIMO) cellular systems. It puts forward a systematic paper involving user clustering, beamforming, and dynamic power allocation, all aimed at enhancing system spectral efficiency, particularly when the number of receive antennas at user equipment (UE) exceeds that of the base station (BS).

Core Innovations and Methods

The paper introduces several important components:

1. User Clustering: The researchers propose dynamic user clustering where UE antennas are grouped into clusters corresponding to the number of BS transmit antennas. This clustering allows for scalability in system design as more antennas are deployed at the UE side.
2. Beamforming: A linear beamforming technique is developed, which leverages the channel characteristics to minimize inter-cluster interference. Each cluster employs a single beamforming vector shared by all receive antennas. This choice is driven by practicality and theoretical efficiency gains, setting the stage for maximized throughput.
3. Successive Interference Cancellation (SIC): Recognizing the inherent challenge of inter-user interference, SIC is employed at the receiver's end. Intra-cluster users are distinguished based on power domains, thus allowing stronger signals to be decoded and subtracted in order of signal strength, enabling the decoding of weaker signals.
4. Dynamic Power Allocation: The authors propose a dual-step power allocation strategy—inter-cluster and intra-cluster. Inter-cluster allocation seeks to evenly distribute power proportional to the number of cluster receivers, while intra-cluster uses a specific dynamic allocation strategy targeting maximum capacity gain.

Numerical Findings and Performance Comparisons

Extensive simulations reveal the proposed MIMO-NOMA system's efficacy, outperforming traditional Orthogonal Multiple Access (OMA) schemes. Key numerical results indicate:

• Spectral Efficiency: Quantitative gains in the spectral efficiency of the proposed MIMO-NOMA system relative to MIMO-OMA. The analysis under various clustering sizes and under conditions of correlated and uncorrelated channel gains provides exhaustive insights.
• Cluster Complexity and Throughput: Simulations highlight the system’s reliance on optimal cluster formation, particularly emphasizing throughput yield with larger cluster sizes. Increased complexity aligns with favourably enhanced throughput, particularly under high channel gain disparities.

Implications

The theoretical and empirical analysis results in several implications:

• Practical Deployment: The proposed system marks potential utility in future 5G and B5G networks, where demand for increased data rates and spectral efficiency will necessitate such sophisticated access methods.
• Spectrum Efficiency: By employing NOMA in multiuser MIMO setups, this work paves a veritable pathway for achieving elevated spectral efficiency without necessitating additional spectral bandwidth.
• Scalability: The dual clustering and beamforming approach offers a foundation for scalable solutions as the density of UE antennas increases in future deployments.

Future Directions

The paper suggests further exploration into multi-cell environments to comprehensively understand the interplay of inter-cell interference and its impact on the proposed methodologies. Advances in dynamic power allocation algorithms, as well as the refinement of user clustering mechanisms, could enhance the adaptability of this system under real-world operational constraints. Integration with other advanced wireless communication paradigms, such as millimeter Wave (mmWave) technologies and massive MIMO setups, also presents a valuable future avenue of research.

In conclusion, the research contributes significantly to understanding NOMA within MIMO systems, proposing solutions that address interference management, spectral efficiency, and power distribution with demonstrable performance benefits over existing approaches.