A Robust Adaptive Objective Power Allocation in Cognitive NOMA Networks.

Area of Science:

• Wireless Communications
• Spectrum Sharing
• Cognitive Radio Networks

Background:

• Cognitive radio (CR) enables opportunistic spectrum access for secondary users (SUs) to share spectrum with primary users (PUs).
• Non-orthogonal multiple access (NOMA) is a key technology for enhancing spectral efficiency in wireless networks.
• Effective power allocation is crucial for balancing performance metrics like energy efficiency (EE) and high throughput (HT) in CR-NOMA systems.

Purpose of the Study:

• To propose a robust adaptive target power allocation strategy for cognitive NOMA (CR-NOMA) networks.
• To enhance flexibility in achieving energy efficiency (EE) or high throughput (HT) for SUs.
• To address challenges posed by imperfect channel state information (CSI) and primary user (PU) constraints.

Main Methods:

• Developed an adaptive power allocation strategy incorporating maximum transmission power and interference power thresholds.
• Introduced a signal-to-interference-plus-noise ratio (SINR) adjustment factor for flexible target setting (EE or HT).
• Transformed semi-infinite (SI) constraints under imperfect CSI into a worst-case optimization problem solved via dual decomposition.

Main Results:

• The proposed strategy demonstrates good adaptive selectivity, allowing different users to achieve tailored communication targets (e.g., QoS).
• The approach is robust to imperfect channel state information (CSI).
• Simulation results validate the effectiveness and adaptability of the power allocation strategy.

Conclusions:

• The robust adaptive power allocation strategy effectively manages power in CR-NOMA networks.
• The strategy offers flexibility in achieving diverse communication objectives, including energy efficiency and high throughput.
• The method provides a robust solution for CR-NOMA systems operating under realistic conditions with imperfect CSI.