Joint Beamforming Design and User Clustering Algorithm in NOMA-Assisted ISAC Systems

Area of Science:

• Wireless communication systems
• Signal processing
• Information theory

Background:

• Non-orthogonal multiple access (NOMA) enhances spectral efficiency in wireless systems.
• Integrated sensing and communication (ISAC) systems combine sensing and communication functionalities.
• Distributed multi-user scenarios present challenges in optimizing NOMA-ISAC performance.

Purpose of the Study:

• To develop an improved Gaussian Mixture Model (GMM)-based user clustering algorithm for NOMA-ISAC systems.
• To enhance bandwidth reuse and reduce interference in distributed NOMA-ISAC scenarios.
• To co-optimize sensing and communication signal performance.

Main Methods:

• An improved GMM user clustering algorithm incorporating Sum of Squared Errors (SSE) for cluster accuracy.
• Introduction of direction weight factors and a penalty function based on user position and sensing power.
• Modifications to the Expectation-Maximization (EM) algorithm for improved posterior probability and covariance matrix calculations.
• Application of fractional programming (FP) to address non-convex joint beamforming design.

Main Results:

• The improved GMM algorithm enhances clustering accuracy and reduces sensitivity to initial cluster centers.
• User clusters are better aligned with NOMA-ISAC system characteristics, improving interference resistance and decoding.
• The FP approach optimizes power and channel gains for co-optimized sensing and communication.
• Simulation shows a 4.3% improvement in user spectral efficiency and a 5.4% increase in base station beam intensity.

Conclusions:

• The proposed GMM-based user clustering and FP optimization significantly enhance NOMA-ISAC system performance.
• The algorithm effectively addresses challenges in distributed multi-user environments.
• This work provides a robust framework for advancing NOMA-ISAC technology.