Massive Multiple-Input Multiple-Output Antenna Architecture for Multiband 5G Adaptive Beamforming Applications

Abstract

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.

Hybrid mmWave-Li-Fi 5G Architecture for Reconfigurable Variable Latency and Data Rate Communications

“…1) [5] is proposed in this paper. The generic mmWave-FSO architecture of a hybrid broadband communication system comprises an array of multi-radio standards sensors in tandem with multimedia tapped-delay lines and finite impulse response filter networks [5][6][7]. The hybrid mmWave-Li-Fi MIMO beamformer enables reliable, power-efficient, low-cost, and scalable over-the-air multi-radio access technology solutions implementations.…”

“…The deterministic use case and/or application data rate and latency requirements determine the choice of the radio communication medium technology. Depending on the chosen radio transmission and reception technology (mmWave or Li-Fi), the noise condition of each sensor node, and the channel state, the received input and the signal filter outputs, selectively yield the feedback outputs [5][6][7]. The REF beamformer architecture (Fig.…”

“…Despite the increase in the capabilities of the autonomous functions of vehicles in the last decade, it is more apparent than ever that a more connected approachin which data are shared effectively between vehicles and/or infrastructureis needed. By conglomerating raw compute performance and massive information available among individual vehicles and infrastructure, a true capability-based autonomous operation of vehicles will be achieved [5][6][7][8][9][10].…”

“…However, it has a promising potential as a stop-gap solution for sub-optimal conditions for RF-based technologies. Hence, a hybrid multiple input multiple output (MIMO) beamformed mmWave-Li-Fi 5G communication is a holy grail for autonomous vehicular communication use case and/or applications [4][5][6][7][8][9].…”