Multi-Antenna Cognitive Radio Systems vs. Single-Antenna Cognitive Radio Systems

In this paper, we investigate a game theory-based resource allocation methodology for multi-antenna cognitive radio systems. Compared to single-antenna cognitive radio systems, multi-antenna systems possess both traditional resources (frequency, time, and code domains) and spatial domain resources. Our research focuses on allocating transmission power, spatial resources (implemented through beamforming techniques), and feedback rates. Using limited information provided by feedback channels (specifically quantized Channel State Information), we study related resource allocation problems including: joint power allocation and beamforming optimization (solvable via optimization algorithms that handle quadratic constraints), feedback rate control for secondary users (cognitive/unlicensed users) using rate adaptation algorithms, and joint power allocation with feedback rate control (addressable through multi-objective optimization frameworks). The implementation typically involves MATLAB or Python simulations with key functions handling CSI quantization, beamforming weight calculation, and iterative optimization loops. Our findings are expected to provide new perspectives and methods for the design and optimization of cognitive radio systems.