Power Allocation Strategies in Multi-User MIMO Systems with Implementation Examples

This code implements power allocation strategies for multi-user MIMO systems, featuring both water-filling and equal power allocation methods, along with their corresponding channel capacity calculations. In multi-user MIMO systems, power allocation is a critical strategy for efficiently distributing system power resources. The code provides two commonly used power allocation approaches: the water-filling algorithm and equal power allocation method. The water-filling algorithm is a channel state information (CSI)-based power allocation strategy that distributes power according to channel quality. The implementation involves sorting users by channel conditions and allocating more power to users with better channel quality, thereby improving their transmission rates. The algorithm typically uses iterative optimization to maximize the sum capacity under total power constraints. Equal power allocation is a simple and fair strategy that distributes the total system power equally among all users. The code implementation calculates the equal power share for each user by dividing the total available power by the number of active users, ensuring each user receives equal power resources regardless of channel conditions. By implementing these two power allocation strategies and computing the corresponding channel capacities, we can evaluate system performance and determine the optimal power allocation approach for different scenarios. The channel capacity calculation involves matrix operations and eigenvalue decomposition of the channel matrix to determine the maximum achievable data rates. In summary, this code provides solutions for power allocation problems in multi-user MIMO systems and evaluates system performance through channel capacity computations. The implementation includes proper matrix handling for MIMO channels and optimization routines for the water-filing algorithm. Whether using water-filling or equal power allocation, the appropriate strategy can be selected based on specific requirements and system constraints, with the code providing performance metrics to guide this selection.