Adaptive Learning Precoder for Channel Aging in MIMO Systems

The system performs preliminary actions by training the deep neural network offline using historical CSI data and channel statistics before actual precoder selection. The DNN is pre-trained with various channel conditions and aging scenarios, enabling it to rapidly adapt to current channel conditions without requiring extensive real-time computation. This preliminary training phase separates the computationally intensive learning process from the time-critical precoder selection process.

The deep neural network acts as an intermediary between the outdated CSI feedback and the actual channel conditions. Instead of directly using outdated CSI for precoder selection, the system feeds the outdated CSI into the DNN, which then predicts the current channel state and selects the appropriate precoder. This intermediary DNN layer bridges the gap between delayed feedback and real-time channel conditions, improving reliability without requiring direct access to current channel state.

The deep neural network is trained offline in advance using extensive channel data and simulations. This preliminary training phase captures the complex relationships between channel conditions, aging effects, and optimal precoder selection. During actual operation, the pre-trained DNN requires only inference-time computation, which is significantly faster than real-time training, thus maintaining low feedback delay while achieving high data rates through accurate CSI prediction.

The system uses a simplified DNN architecture that focuses on the most critical features for CSI prediction rather than implementing a complete, exhaustive model. By selecting only the essential neural network layers and parameters needed for accurate precoder selection, the system achieves sufficient prediction accuracy with reduced computational complexity and faster inference time, thereby minimizing feedback delay while maintaining high productivity.

The deep neural network serves as an intermediary that processes and enhances the information contained in infrequent CSI feedback. Instead of requiring frequent feedback to maintain accuracy, the DNN takes the limited outdated CSI feedback and predicts the current channel state by learning from historical patterns and channel statistics. This intermediary processing extracts maximum useful information from minimal feedback, achieving high measurement precision without increasing feedback overhead.

The system creates a virtual copy of the current channel state by using the DNN to predict CSI based on outdated feedback and historical data. Rather than requiring frequent physical measurements and feedback transmissions, the DNN generates a predicted copy of the channel state that is sufficiently accurate for precoder selection. This copying approach reduces the need for frequent actual feedback transmissions while maintaining channel accuracy.