An Iterative Weighted MMSE Approach for MIMO Interference Broadcast Channel Distribution and Utility Maximization

In wireless communication systems, MIMO (Multiple-Input Multiple-Output) technology enhances channel capacity and spectral efficiency by utilizing multiple antennas. However, in multi-user scenarios, signal interference in interference broadcast channels significantly impacts system performance. To address this challenge, an iterative approach based on weighted MMSE (Minimum Mean Square Error) has been developed for optimizing resource allocation and achieving utility maximization.

The methodology proceeds as follows: First, the MIMO interference broadcast channel is modeled, transforming the utility maximization problem into an optimizable mathematical formulation. By introducing the weighted MMSE criterion, the original non-convex problem is converted into a series of more tractable subproblems. The algorithm then iteratively optimizes the precoding matrices and receive filters through sequential updates. During each iteration, the weight matrix is dynamically adjusted based on current channel state information, thereby improving convergence and performance. From an implementation perspective, this typically involves solving convex optimization problems using quadratic programming or Lagrangian methods at each iteration step.

Compared to conventional approaches, this iterative weighted MMSE strategy demonstrates superior capability in suppressing multi-user interference while balancing fairness and system throughput. Its key advantages lie in flexibility and adaptability, making it particularly suitable for dynamically changing wireless communication environments. The algorithm implementation may incorporate adaptive step-size control and convergence checks to ensure robust performance under varying channel conditions.