Smart antenna system management utilising multi-agent systems

Abstract : Cellular communication networks are large and distributed systems that provide billions of people around the world with means of communication. Antennas as used currently in cellular communication networks do not provide efficient resource management given the growth in the current communication network scenario. Most of the problems are related to the number of devices that can connect to an antenna, the coverage map of an antenna, and frequency management. A smart antenna grid can cover the same area as traditional cellular system towers with some enhancements. Smart antenna grids can include a device in an area that requires connectivity rather than covering of the entire area. Frequencies are handled per antenna base, with more focus on providing stable communication. The objective of the dissertation is to improve resource management of smart antenna grids by making use of a multi-agent system. The dissertation uses a simulation environment that illustrates a smart antenna grid that operates with a multi-agent system that is responsible for resource management. The simulation environment is used to execute ten scenarios that intends to place large amounts of strain on the resources of the smart antenna grid to determine the effectiveness of using a multi-agent system. The ten scenarios show that when resources deplete, the multi-agent system intervenes, and that when there are too many devices connected to one smart antenna, the devices are managed. At the same time, when there are antennas that have frequency problems, the frequencies are reassigned. One of the scenarios simulated the shutdown of antennas forcing devices to disconnect from the antenna and connect to a different antenna. The multi-agent system shows that the different agents can manage the resources in a smart grid that is related to frequencies, antennas and devices.M.Sc. (Computer Science

AIS Algorithm for Smart Antenna Application in WLAN

Increasing numbers of wireless local area networks (WLAN) replacing wired networks have an impact on wireless network systems, causing issues such as interference. The smart antenna system is a method to overcome interference issues in WLANs. This paper proposes an artificial immune system (AIS) for a switch beam smart antenna system. A directional antenna is introduced to aim the beam at the desired user. The antenna consists of 8 directional antennas, each of which covers 45 degrees, thus creating an omnidirectional configuration of which the beams cover 360 degrees. To control the beam switching, an inexpensive PIC 16F877 microchip was used. An AIS algorithm was implemented in the microcontroller, which uses the received radio signal strength of the mobile device as reference. This is compared for each of the eight beams, after which the AIS algorithm selects the strongest signal received by the system and the microcontroller will then lock to the desired beam. In the experiment a frequency of 2.4 GHz (ISM band) was used for transmitting and receiving. A test of the system was conducted in an outdoor environment. The results show that the switch beam smart antenna worked fine based on locating the mobile device

Workshop on Advanced Technologies for Planetary Instruments, part 1

This meeting was conceived in response to new challenges facing NASA's robotic solar system exploration program. This volume contains papers presented at the Workshop on Advanced Technologies for Planetary Instruments on 28-30 Apr. 1993. This meeting was conceived in response to new challenges facing NASA's robotic solar system exploration program. Over the past several years, SDIO has sponsored a significant technology development program aimed, in part, at the production of instruments with these characteristics. This workshop provided an opportunity for specialists from the planetary science and DoD communities to establish contacts, to explore common technical ground in an open forum, and more specifically, to discuss the applicability of SDIO's technology base to planetary science instruments

Inertially-Controlled Two-dimensional Phased Arrays by Exploiting Artificial Neural Networks and Ultra-Low-Power AI-based Microcontrollers

The use of Artificial Intelligence (AI) in electronics and electromagnetics is opening many attractive research opportunities related to the smart control of phased arrays. This is particularly challenging especially in some high-mobility contexts, such as drones, 5G, automotive, where the response time is crucial. In this paper a novel method combining AI with mathematical models and firmware for orientation estimation is proposed. The goal is to control two-dimensional phased arrays using an Inertial Measurement Unit (IMU) by exploiting a feed-forward neural network. The neural network takes the IMU-based beam direction as input and returns the related phase shift matrix. To make the method computationally efficient, the network structure is carefully chosen. Specific and discretized cross-section regions of the array factor (AF) main lobe are considered to compute the phase shift matrices, used in turn to train the neural network. This approach achieves a balance between the number of phase-shifting processes and spatial resolution. Without loss of generality, the proposed method has been tested and verified on 4× 4 and 6× 6 arrays of 2.4 GHz antennas. The obtained results demonstrate that reconfigurability time, easiness of use, and scalability are suitable for a wide range of high-mobility applications

Application of MIMO Technology to Systems Beyond 3G

The evolution of mobile Broadband over the years has been phenomenal and worthy of attention by academics, researchers, the corporate world and users alike. From the days of the First Generation (1G) through the Third Generation (3G) communication systems, the evolution has continued and has been largely influenced by an ever increasing demand for improved services and greater capacity evident in higher data rates, wider and improved coverage, improved spectral efficiency and lower latency. In response to these demands and to address some of the loopholes of the 3G networks, the 3rd Generation Partnership defined the Long Term Evolution (LTE). LTE though an evolving technology is widely accepted due to its unprecedented promised performance. As the evolution continues, the design of the „LTE-Advanced‟ is already in progress and has been tagged different names such as the „4G‟ and „Beyond 3G‟ (B3G). The main backbones behind these evolutions are technological developments in the underlying mobile radio technology such as multicarrier technology (majorly OFDMA), multiple-antenna technology (MIMO) and the application of packetswitching to the radio-interface through improvements in techniques like adaptive scheduling in both the frequency and spatial dimensions, link adaptation of modulation and code-rate and several modes of fast channel state reporting. This paper is set to present the multiple antenna technology and how it contributes to the delivery of the expectations of the wireless communication systems beyond 3

Neural Network Based Robust Adaptive Beamforming for Smart Antenna System

As the growing demand for mobile communications is constantly increasing, the need for better coverage, improved capacity, and higher transmission quality rises. Thus, a more efficient use of the radio spectrum is required. A smart antenna system is capable of efficiently utilizing the radio spectrum and is a promise for an effective solution to the present wireless system problems while achieving reliable and robust high-speed, high-data-rate transmission. Smart antenna technology offer significantly improved solution to reduce interference level and improve system capacity. With this technology, each user’s signal is transmitted and received by the base station only in the direction of that particular user. Smart antenna technology attempts to address this problem via advanced signal processing technology called beamforming. The adaptive algorithm used in the signal processing has a profound effect on the performance of a Smart Antenna system that is known to have resolution and interference rejection capability when array steering vector is precisely known. Adaptive beamforming is used for enhancing a desired signal while suppressing noise and interference at the output of an array of sensors. However the performance degradation of adaptive beamforming may become more pronounced than in an ideal case because some of underlying assumptions on environment, sources or sensor array can be violated and this may cause mismatch. There are several efficient approaches that provide an improved robustness against mismatch as like LSMI algorithm. Neural network is a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experimental knowledge and making it available for use. Neural network methods possess such advantages as general purpose nature, nonlinear property, passive parallelism, adaptive learning capability, generalization capability and fast convergence rates. Motivated by these inherent advantages of the neural network, in this thesis work, a robust adaptive beamforming algorithm using neural network is investigated which is effective in case of signal steering vector mismatch. This technique employs a three-layer radial basis function neural network (RBFNN), which treats the problem of computing the weights of an adaptive array antenna as a mapping problem. The robust adaptive beamforming algorithm using RBFNN, provides excellent robustness to signal steering vector mismatches, enhances the array system performance under non ideal conditions and makes the mean output array SINR (Signal-to-Interference-plus- Noise Ratio) consistently close to the optimal one

Photonic controlled metasurface for intelligent antenna beam steering applications including 6G mobile communication systems

This paper presents a novel metasurface antenna whose radiation characteristics can be remotely controlled by optical means using PIN photodiodes. The proposed reconfigurable antenna is implemented using a single radiating element to minimize the size and complexity. The antenna is shown to exhibit a large impedance bandwidth and is capable of radiating energy in a specified direction. The proposed antenna consists of a standard rectangular patch on which is embedded an H-tree shaped fractal slot of order 3. The fractal slot is used to effectively reduce the physical size of the patch by 75 % and to enhance its impedance bandwidth. A metasurface layer is strategically placed above the patch radiator with a narrow air gap between the two. The metasurface layer is a lattice pattern of square framed rhombus ring shaped unit-cells that are interconnected by PIN photodiodes. The metasurface layer essentially acts like a superstrate when exposed to RF/microwave radiation. Placed below the patch antenna is a conductive layer that acts like a reflector to enhance the front-toback ratio by blocking radiation from the backside of the patch radiator. The patch’s main beam can be precisely controlled by photonically illuminating the metasurface layer. The antenna’s performance was modelled and analyzed with a commercial 3D electromagnetic solver. The antenna was fabricated on a standard dielectric substrate FR4 and has dimensions of 0.778λo × 0.778λo × 0.25λo mm3 , where λo is the wavelength of free space centered at 1.35 GHz. Measured results confirm the antenna’s performance. The antenna exhibits a wide fractional band of 55.5 % from 0.978 to 1.73 GHz for reflection-coefficient (S11) better than − 10 dB. It has a maximum gain of 9 dBi at 1.35 GHz with a maximum front-to-back ratio (F/B) of 21 dBi. The main beam can be steered in the elevation plane from − 24◦ to +24◦. The advantage of the proposed antenna is it does not require any mechanical movements or complicated electronic systems.Dr. Mohammad Alibakhshikenari acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 801538. The authors also sincerely appreciate funding from Researchers Supporting Project number (RSP2023R58), King Saud University, Riyadh, Saudi Arabia. Additionally, this work was supported by Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional -FEDER-, European Union) under the research grant PID2021-127409OB-C31 CONDOR. Besides above, the Article Processing Charge (APC) was afforded by Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2023)