Joint Optimization Algorithm Outperforms Feedback-Rate-Only Approaches

In limited feedback scenarios, traditional algorithms typically focus solely on optimizing feedback rates while neglecting the impact of power allocation. This paper proposes a game-theory based joint optimization method that integrates power allocation and feedback rate control within a unified analytical framework. By establishing a novel game model, the study proves the existence of Nash equilibrium solutions, thereby ensuring the solvability of the optimization problem. The implementation employs iterative best-response updates where each player (power allocator and feedback controller) sequentially optimizes their strategy based on opponents' current decisions.

Furthermore, addressing the interdependence between power and feedback rates, the article designs an efficient iterative algorithm. This algorithm achieves rapid convergence to the game's fixed point within limited iterations, effectively reducing computational complexity through a convergence-checking mechanism that monitors strategy updates until deviation thresholds are met. Simulation results demonstrate that the joint optimization algorithm significantly outperforms feedback-rate-only optimization schemes, with performance metrics showing 15-30% improvement in system throughput and resource utilization efficiency, validating the method's practical effectiveness in real-world systems.

This research provides novel solutions for resource allocation in limited feedback environments. Through game-theoretic joint optimization, it achieves comprehensive system performance enhancement by implementing a coordinated optimization loop where power allocation parameters and feedback rates are jointly adapted using gradient-based update rules and equilibrium verification procedures.