desired directions In fulfill all the issues of smart antenna
(2015) Smart Antenna Design for Tracking Multiple Users Using FPGA Virtex-5. MTech thesis.
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(2004)Application of smart antennas to wideband code division multiple access : the uplink performance. Research Master thesis, Victoria University of Technology.
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.
Are there matlab system level Simulators for smart antennas?
The beam forming of the smart antennae will be optimized to have the highest signal to noise ratio (SNR) at the receivers output in presence of fading.
Using the graphical user interfaces provided in this thesis, it is able toconfigure the beam steering of the smart antenna array, which allows the user to analyse andoptimise the signal strength of the received WiFi signals around the mobile device.
Are there matlab system level Simulators for smart antennas
Smart Antenna systems have received increasing interest recently as the de- mands for good quality and latest value added services on the available wire- less communication systems are increasing. There is an increase in demand on mobile wireless operators to provide voice and high-speed data services and to support more users in a single base station in order to reduce costs for overall network and make the services affordable to subscribers. Smart antenna technology offers a significant improved solution to reduce interfer- ence levels and improve the capacity of the system. Although lot of study has been done on smart antennas, adaptive methods are not specially em- phasized and developed. Adaptive beamforming is used for desired signal enhancement while suppressing interference at the output of an antenna ar- ray. In this work, different types of optimization techniques are used to form an adaptive system. Tuned LMS, Hybrid learning, RLS, harmony search and modified harmony search are the algorithms used to detect the angle of arrival and interference for multiple users. Comparative analysis of these techniques is also performed. A very high performance Xilinx FPGA device Virtex-5 XC5VSX50T is used. It generates multiple partial beams simulta- neously using the offline generated optimized weights from a 5 element and 10 element array and their summation gives the full beam. Using FPGA virtex-5, optimized parameters from various adaptive algorithms are used to generate output signals and to track multiple users at the same instant and the results are observed in chipscope.
The second stage of the study presents several feeding network structures, which controlthe amplitude and phase excitations of the smart antenna elements.
The diversity gain of a smart antenna system with ..
Autonomous smart antenna systems for future mobile …
The final stage of the study investigates hardware and software automatic control systemsfor the smart antenna array.
Simulation of a smart antenna system - CORE
MacSphere: Multibeam smart antenna systems for …
Smart Antenna systems have received increasing interest recently as the de ..
Multibeam smart antenna systems for wireless communications: ..
The performance of smart antenna system greatly relies on the beam forming technique that forming the main lobe beam pattern to the desired user direction and place null in the direction of undesired interference source. This paper investigates the implementation of Minimum Variance Distortionless Response (MVDR) Adaptive Beam-Forming (ABF) algorithm on Rectangular Antenna Array (RAA) is discussed and analyzed. The MVDR ABF technique performance is studied in accordance with varying the number of array elements, spacing between the array elements, the number of interference sources, noise power label, and the number of snapshots. The MVDR performance is compared on the basis of output radiation pattern and SINR. Computer simulation results show that the performance of the MVDR improved as the number of elements get more. This mean MVDR strongly depends on the number of the element. 0.5λ is considered the best spacing between adjacent antenna elements, the performance degraded as the noise power label increased, and more accurately resolution occurred when the number of snapshots increased
Introduction Antenna System Smart Antenna System Enhancing Network ..
The design and characterisation of a novel antenna structure and four-element liner arraygeometry for smart antenna systems are proposed in the first stage of this study.
Networks Using Smart Antennas” MS Thesis ..
As a further improvement to the Outdoor Distributed Antenna System the antennas could be replaced with smart antennas which have their own benefits.
Smart Antenna Grid for Outdoor Distributed System
Smart antennas have emerged as one of the key technologies in wireless communications. This thesis focuses on developing algorithms and structures that can be applied to one of the most important types of smart antennas--the multibeam smart antenna--in order to improve the quality and capacity of both existing and future wireless networks. A large database, consisting of vector channel measurement data using an 8-element circular antenna array is analyzed to investigate the underlying characteristics of the multibeam smart antenna in practical propagation environments. Extensive vector channel simulations are also conducted, including the innovative work on the simulation of smart antennas in Frequency Division Duplexing (FDD) systems. Based on the data analysis and simulation results, two multibeam smart antenna algorithms, one for reception and one for transmission, are proposed. Significant performance and capacity improvements can be achieved using these two algorithms. The proposed multibeam smart antenna reception algorithm for DS-CDMA system is expected to have important application in both existing and the third generation CDMA systems, while the proposed multibeam smart antenna transmission algorithm can be used in FDD wireless networks to solve the downlink problem.
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