Blind Packet-Based Receiver Chain Optimization Using Machine Learning

The selection of the most appropriate equalization-detection-decoding algorithms in wireless receivers is a challenging task due to the diversity of application requirements, algorithm performance-complexity trade-offs, numerous transmission modes, and channel properties. Typically, the fixed receiver-chain is employed for specific application scenario that may support iterative processing for better adaptation to variable channel conditions. We propose a novel method for optimizing receiver efficiency in the sense of maximizing packet transmission reliability while minimizing receiver processing complexity. We achieve this by packet-wise dynamic selection of the least complex receiver that enables error-free packet reception out of set of available receivers. The scheme employs convolutional neural network (CNN) and supervised deep learning approach for packet classification and subsequent prediction of the optimum receiver using raw baseband signals. The proposed scheme aims to approach a packet error rate close to the rate of the most complex receiver architecture while using a combination of both low and high complexity architectures. This is achieved by employing the neural network based classifier to dynamically select packet-specific optimum architecture; i.e. instead of using the most complex receiver for all packets, the approach dynamically assigns the packet to the most appropriate receiver in terms of equalization-detection-decoding capability and the least possible complexity. We analyze the performance of the proposed scheme considering various channel scenarios. The system demonstrates excellent packet classification performance resulting in the significant performance increase and the reduction of the usage of the functional blocks that can go up to 96% of the time in different scenarios.