Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System.

Area of Science:

• Wireless Communication Systems
• Signal Processing
• Optimization Theory

Background:

• 5G massive MIMO-NOMA systems face challenges in energy efficiency (EE) maximization due to imperfect channel state information (CSI).
• Minimizing inter-user interference and ensuring fairness are critical for system performance.

Purpose of the Study:

• To develop a robust beamforming design for maximizing EE in 5G massive MIMO-NOMA downlink systems.
• To address the challenge of imperfect CSI using an ellipsoidal uncertainty model (EUM).
• To propose a joint user pairing and dynamic power allocation (JUPDPA) algorithm.

Main Methods:

• Formulation of a fractional non-convex optimization problem for EE maximization.
• Transformation of the non-convex problem into a parametric form using fractional programming.
• Application of the constrained concave-convex procedure (CCCP) and Dinkelbach's algorithm for solving the optimization problem.

Main Results:

• The proposed JUPDPA algorithm effectively minimizes inter-user interference and enhances user fairness.
• The robust beamforming design achieves higher worst-case energy efficiency compared to existing NOMA and OMA schemes.
• Convergence to a stationary point is achieved for the formulated optimization problem.

Conclusions:

• The developed robust beamforming strategy significantly improves energy efficiency in 5G massive MIMO-NOMA systems.
• The proposed JUPDPA algorithm offers a viable solution for optimizing performance under imperfect CSI.
• The study demonstrates the superiority of the proposed scheme over conventional approaches in terms of worst-case EE.