Implementation of Best Hybrid Adaptive and Intelligent MIMO Detector on Reconfigurable Architecture for 5G LTE/IoT Environment

Abstract

Internet of things (IoT) in wearable health care is the major area facilitates the usage of numerous transmitters on board, leading to the employment of multiple-input–multiple-output (MIMO) system (with high-performance decoders) for an effective communication. Employing MIMO system suitable for IoT applications with quality of performance (QoP), low power, and minimum computational complexity with reconfigurable architectures is a challenge. This paper presents a new MIMO detector called best hybrid adaptive and intelligent (B-HAI) detector which consists of various combinations of conventional MIMO detectors, such as zero forcing (ZF) with fuzzy K-best and minimum mean squared error (MMSE) with fuzzy K-best. One of these combinations of detectors will be chosen using cognitive selective permutation theory, which is adaptive to the spatially multiplexed (SM) input parameters signal-to-noise (S/N) ratio, to achieve high QoP parameters. In the proposed algorithm, intelligence (fuzzy based) has been incorporated to dynamically upgrade the value of K and to reduce complexity as well as power. Simulations were performed in the reconfigurable programmable architecture, employing a MIMO system suitable for IoT applications. The proposed detector offers better performance compared to other detectors.