Deep Learning-Based Modulation Detector for an MIMO System

Deep learning approaches applied in diverse fields further the expectations and demands of 5G wireless networks and necessitates leveraging the intelligence to network with widespread renewed interest of big data. The integration of machine learning (ML) notion across core and edge infrastructure of wireless networks imparts intelligence. ML can enable mobile devices to intelligently monitor their environment by learning the features of a wireless channel—its dynamics, traffic pattern, requests, and user context, among other aspects. ML enables the infrastructure to learn from the wireless networking environment and adaptively makes decision, which provides optimized solutions for best connectivity, resource allocation, quality-of-service parameters, etc. ML can redefine how the physical layer functions, namely, coding/decoding, channel estimation, channel equalization, and modulation/demodulation at the transmitter and receiver sides. The majority of the communication system is modeled as a linear system, but practically, ML captures the nonlinear effects of hardware and quantization and provides a better solution. A combined approach is possible through ML for the solutions provided in the literature for individual blocks, say, coding, resource allocation block. Also parallel processing ability and distributed memory aid to complexity reduction at the respective block. The proliferation of graphics processing unit–enabled hardware provides speedy action. These are the benefits of deep learning in physical layer design. This chapter intends to provide a deep learning model addressing the physical layer of the network. The authors propose an intelligent ML detector at the multiple-input multiple-output receiver able to classify a signal constellation and the transmitted antenna.