e-Prime: Advances in Electrical Engineering, Electronics and Energy (Dec 2024)
Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms
Abstract
Improvements in interference cancellation, energy economy, spectrum efficiency, and system security are among the most pressing needs for today's wireless networks. One effective technique for this is the use of a Beamforming Array Antennas (BAA). However, the complexity of the Beamforming (BF) network, the long convergence time, and the large number of adjustable weight coefficients, all work against full band BAA. The key innovation and contribution of this research was to use Partial Update (PU) instead of full band adaptive algorithms, as no previous attempt had been made to do so. A subset of the array's elements, rather than all of them, will be connected to by PU methods. This allows the system to reduce the number of active antennas across all cells while maintaining high efficiency, and low cost. In this research, a new architectural model was proposed that makes use of PU adaptive algorithms, to reduce the required number of phase shifters (PSs), and hence base station antennas. For the most part, we will be discussing PU Normalized Least Mean Square (PU NLMS) algorithms like M-max NLMS, Periodic-NLMS, and Stochastic-NLMS. Using a Uniform Linear Array (ULA) Antennas in a simulation environment, we find that the, in terms of Mean Square Error (MSE), convergence rate, and steady-state error, it is evident that all PU NLMS algorithms (with the exception of Periodic-NLMS) had performed close, and approximately equivalent performance to the full band NLMS algorithms. In other hands, these PU algorithms maintain the radiation pattern with as little change from the original (distortion-free) as possible and symmetrical of the array, while. fewer elements are needed to produce the same amount of radiation. Reducing the number of required coefficients N to M (M = 5; M: Number of coefficients to be update per iteration) compared to the full update method (N = 8), to obtain the Reduction ratio 38 %.
