5/6G, Smart Antennas and Coding the Algorithms: Linear ANN, Non-linear ANN, and LMS

• Published in: Smart Antennas and Electromagnetic Signal Processing for Advanced Wireless Technology pp. 361 - 383
• Main Authors: Herath, H.M.C.J., Herath, H.M.G.G.J.G., Ranaweera, K.M.U.I., Uduwawala, D.N., Hoole, P.R.P.
• Format: Book Chapter
• Language: English
• Published: Denmark Routledge 2020; River Publishers
• Edition: 1
• Subjects: Aerospace & Radar Technology; Electronic Devices; Electronics & Semiconductors; Radar & Navigation Systems; Adaptive Beamformer; ANN; Beam Patterns; perceptron learning; 6G Network; Inter-element Spacing; Omnidirectional Antennas; Fast Convergence Time; 6G; Smart Antenna Systems; Extremely High Frequency; Adaptive Array; MIMO; smart antennas; Omni-directional Antennas; NFV; 6G Communications; Perceptron Ann; Pulsed Electromagnetic Fields; Cos; End Fire Direction; LMS Algorithm; 5G; Smart Antenna; Radiation Pattern; Hyperbolic Tangent Activation Function; COFDM; Visible Light Communications; Steering Vector; beamforming; Artificial intelligence
• ISBN: 9788770222068; 8770222061
• DOI: 10.1201/9781003339564-12

The demand for mobile communication is exponentially increasing in the 5/6G era with many novel technologies such as the Internet of Things (IoT), Industry 4.0, autonomous vehicles, smart cities, and smart healthcare. The need for better coverage, improved capacity, and higher transmission speeds is on the rise. The efficient usage of the radio frequency spectrum is one of the solutions to handle the increasing demand and technical constraints. Adaptive beamforming array antenna is a vital component of the above solution and it plays a key role in the 5G (2020) implementation and in the future 6G (2030) wireless system. This chapter presents both linear and non-linear antennas using perceptron ANN learning algorithms and its coding for adaptive beamforming. The perceptron ANN algorithm calculates the optimum weights of both linear and non-linear antenna arrays to steer the radiation pattern by directing multiple narrow beams toward the desired users and creating nulls toward interferers. The single-layer perceptron ANN is shown to give accurate beamforming with both minimum computational time and electronic memory. Four major types of activation functions are commonly used to steer the beam toward the desired direction. These are implemented in the algorithm for both linear and non-linear smart antenna 362arrays with different physical configurations. The MATLABTM codes for perceptron ANN algorithm to drive the smart antenna and that of the traditional Least Mean Square (LMS) algorithm driven antenna are given. The perceptron ANN driven smart antenna can perform adaptive beamforming at low computational cost while demonstrating good accuracy and fast convergence time. This chapter presents both linear and non-linear antennas using perceptron ANN learning algorithms and its coding for adaptive beamforming. The perceptron ANN algorithm calculates the optimum weights of both linear and non-linear antenna arrays to steer the radiation pattern by directing multiple narrow beams toward the desired users and creating nulls toward interferers. The demand for mobile communication is exponentially increasing in the 5/6G era with many novel technologies such as the Internet of Things (IoT), Industry 4.0, autonomous vehicles, smart cities, and smart healthcare. The wireless telecommunication system has been through an exponential improvement from 1G to 5G. A new generation is named when it denotes a significant forward leap in wireless mobile technologies. The 4G wireless system works like the 3G and may be regarded as an extension of 3G but with faster internet connection, more bandwidth, and lower latency, and the 4G is about five times faster than 3G services.