The direction of arrival (DOA) estimation and beamforming are effective methods for spatial diversity realization. Various algorithm already exists for implementing these methods. This paper explore the performance of least mean square algorithm (LMS) beamforming algorithm. This adaptive beamforming algorithm investigates receiver signal processing method that continuously monitor, calculate and update the weights in a continuously changing electromagnetic environment. Several optimization algorithms are studied, and a comparison of the least mean-squared algorithm and the minimum variance distortionless response is investigated with varying parameters (i.e. number of antenna element, element spacing etc.) using analytical method and Matlab simulation. It would be demonstrated through simulation that LMS algorithm increases signal quality by elimination interfering signals and noise by nulling them, while sending maximum signal (beams) to the desired direction.

One major challenge to full 5G systems deployment especially in mm-Wave band is the poor signal propagation. One approach to mitigate this effect is the use of new 5G technologies such as massive MIMO, adaptive beamforming, reconfigurable antennas etc. which can enhance the performance of the system. Adaptive beamforming algorithm uses advance digital signal processing techniques to generate main beams in the direction of interest while placing nulls in interfering signals direction to reduce interference. The beams are formed in the receiver rather in free space. It is therefore very crucial to develop an algorithm that can optimize the system to improve performance by generating signals at a faster convergence rate.In this paper, the performance analysis of various adaptive beamforming systems for 5G applications are presented using various LMS algorithms including a novel sign-leaky LMS algorithm. A uniform linear array antenna of varying element configurations, inter-element spacing, varying step-size, direction of arrival angles of the desired signals are analysed using various algorithms to determine the optimum performance of the systems. Simulation result shows that the convergence rate is highly enhanced, with the proposed algorithm converging with at least 5 iterations less than conventional LMS algorithm, while reducing interference effects by placing deeper nulls in interfering signal direction of arrivals using the proposed beamforming algorithm. There is also at least -2 dB drop in normalized power of the sidelobe level compared to the LMS algorithm.

Critical care has frequently been fatal for trauma patients suffering from hemorrhage. The pre-hospital communication gap between the paramedics and the doctors contributes most towards this. This paper discusses a system model of a 5G-enabled communication architecture among the major trauma centres in the Greater Manchester. An Internet of sensors acquires and wirelessly communicates biosignals from the patient in real time, using 5G. These signals are then displayed as parameters to the closest trauma care management centres. This paper proposes a connectivity model that supports such a system by assessing and identifying the most optimal path for signal transmittance. A system-level 5G network modelling and simulation findings reveal that a signal-to-noise ratio of over 2dB is achieved for two base stations between the incident site and the nearest emergency medical centre. This value decreases by over 5 dB as the number of base station doubles. Hence, reconfigurable 5G base stations connectivity subsystems are required for critical vertical use cases of the radio standard.