e-space
Manchester Metropolitan University's Research Repository

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

    Enahoro, S, Ekpo, S ORCID logoORCID: https://orcid.org/0000-0001-9219-3759 and Gibson, A ORCID logoORCID: https://orcid.org/0000-0003-2874-5816 (2022) Massive Multiple-Input Multiple-Output Antenna Architecture for Multiband 5G Adaptive Beamforming Applications. In: 2022 IEEE 22nd Annual Wireless and Microwave Technology Conference (WAMICON), 27 April 2022 - 28 April 2022, Clearwater, Florida, USA.

    [img]
    Preview
    Accepted Version
    Available under License In Copyright.

    Download (1MB) | Preview

    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-2dB drop in normalized power of the sidelobe level compared to the LMS algorithm.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    349Downloads
    6 month trend
    57Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record