Future 5G Network Based Smart Hospitals: Hybrid Detection Technique for Latency Improvement

Abstract

With the rapid increase in the development of a cellular communication system, remote health monitoring and smart health care are improving and getting through a swift transformation. Currently, we are utilizing the advance long term evolution (A-LTE) network to support the modern health care. Nevertheless, smart hospital/health concern is not fully evolved all around the world. The rollout of the fifth generation (5G) will improve the standard of the smart health care. However, requirements of a smart hospital will be different as compared to other applications such as education, industries, and the public. The smart hospital will be connected 24/7, with several small devices integrated with the sensors. In simple words, the future smart hospital will be based on the 5G and the internet of things (IoT), expected to augment the system coverage, effectiveness, and throughput of the system. Further, high speed, low latency, spectral efficiency, and low energy consumption are the requirements of the 5G based modern hospital. In this correspondence, we focused to improve the latency, spectrum, and throughput of the 5G network by implementing a hybrid detection technique based on the QR decomposition and the M algorithm-maximum likelihood detection (QRM-MLD) and beamforming (BF) for massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) system. In addition, a comparison between the proposed and conventional detection techniques is presented. The proposed hybrid detection technique improves the throughput of the system and reduces the computational complexity as compared to the conventional QRM-MLD algorithm, conventional BF and zero-forcing (ZF) techniques on the platform of several parameters i.e. complexity, bit error rate (BER), peak power, etc.