1 research outputs found
An Efficient Hybrid Beamforming Design for Massive MIMO Receive Systems via SINR Maximization Based on an Improved Bat Algorithm
Hybrid analog and digital (HAD) beamforming has been recently receiving
considerable deserved attention for a practical implementation on the
large-scale antenna systems. As compared to full digital beamforming,
partial-connected HAD beamforming can significantly reduce the hardware cost,
complexity, and power consumption. In this paper, in order to mitigate the
jamming along with lowering the hardware complexity and cost by reducing the
number of RF chains needed, a novel hybrid analog and digital receive
beamformer based on an improved bat algorithm (I-BA) and the phase-only is
proposed. Our proposed beamformer is compared with robust adaptive beamformers
(RABs) methods proposed by us, which are considered in the digital beamforming
part. The evolutionary optimization algorithm is proposed since most of the RAB
methods are sensitive to the DOA mismatch, and depending on the complex
weights, resulting in an expensive receiver. In the analog part, analog phase
alignment by linear searching (APALS) with a sufficiently fine grid of points
is employed to optimize the analog beamformer matrix. The performance of the
proposed I-BA is revealed using MATLAB simulation and compared with BA, and
Particle swarm optimization (PSO) algorithms, which shows a better performance
in terms of convergence speed, stability, and the ability to jump from the
local minima.Comment: 11 pages, 9 figure