Machine Learning-Assisted Design and Performance Optimization of Smart Antennas for Beyond-5G Networks
Keywords:
Smart antennas, Beyond-5G, Machine learning, Beamforming, Antenna optimization, Massive MIMO, Deep Reinforcement Learning.Abstract
The Beyond-5G (B5G) wireless networks needs smart smart antennas systems with
adaptive beamforming, interference cancellation and energy efficient performances in
a highly dynamic propagation conditions. Traditional antenna design and optimization
methods are based on repeated full-wave electromagnetic (EM) simulations and trial
and-error searching methods, leading to multiple complexities in computations and
scala-lack of these methods to real-time reconfiguring. This paper will resolve these
issues with a new total machine learning-aided scheme to design and optimize the
performance of smart antennas in B5G networks. The suggested approach combines
both types of supervised learning-based surrogate models with EM simulation data in
such a way that it will be possible to predict the antenna parameters and geometry
optimise within a short period of time, hugely cutting down the time spent on the design.
Moreover, a dynamic reinforcement learning (DRL)-assisted beamforming algorithm is
designed, which means that the excitation weights are adjusted dynamically to different
channel state information to maximize the link reliability and spectral efficiency. The
framework focuses on optimizing the main performance measures of the antennas
which are the gain, radiation efficiency, sidelobe level and spectral efficiency of the
antennas under real channel conditions. These 25 percent gains in antenna gain, 18
percent gains in spectral efficiency, and 3040 percent decreases in computational
cost, attainable by the proposed ML-assisted method over the traditional EM-driven and
heuristic optimization methods, have been made possible through extensive simulation
results. These findings validate the use of machine learning as an effective way of
modeling complex interactions between antennas and channels, and optimizing them
fast and adaptively. The suggested framework has a scalable and smart solution to the
next-generation smart antenna systems and is, therefore, best fit in B5G and initial 6G
wireless communication.
