Relevant bibliographies by topics / Antenna Selection

Academic literature on the topic 'Antenna Selection'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Antenna Selection"

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Li, Jie, Shuang Zhi Li, Xiao Min Mu, and Jian Kang Zhang. "Joint Transmit Antenna Selection and User Selection in Cognitive Radio MIMO Systems." Applied Mechanics and Materials 548-549 (April 2014): 1355–58. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1355.

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Using multiple antennas in coexisting radio systems can cancel or control the co-channel interference and hence improves the overall spectrum efficiency. However, the hardware complexity and costs limit the usage of multiple-antenna technology. Antenna selection may reduce such costs while partly remaining the advantage of the multiple-antenna technology. In this paper, a fixed power cognitive radio system model jointly combined with antenna selection and users selection is set up. And the mathematical closed-form expressions of the channel capacity and bit error rate (BER) are obtained through mathematical derivation. Simulation verifies the correctness of theoretical results and shows that the system exists an optimal transmit power which optimizes the system performance. Furthermore, the influences of users number and antennas number on the system performance have been studied.
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Ou, Yang, and Yi Ming Wang. "Spectrum Sensing Optimization Based on Multiple Antennas for Cognitive Radio Networks." Applied Mechanics and Materials 443 (October 2013): 363–67. http://dx.doi.org/10.4028/www.scientific.net/amm.443.363.

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To protect the primary user and improve the credibility of spectrum sensing, a spectrum sensing optimization algorithm based on antenna selection is proposed in this paper. In the case where the channel coefficient and signal-to-noise ratio are not known, one antenna weighting and selection algorithm based on auto-correlation is proposed. This algorithm can also be used to distinguish whether it is necessary for antennas selection so as to optimize spectrum sensing performance. Based on auto-correlation ratio, selecting parts of the antennas to cooperatively sense spectrum can maximize the detection probability. Simulations are used to verify the method. The results indicate that the proposed antenna weighting and selection algorithm can be able to optimize network performance.
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Saini, Mehak, and Surender K. Grewal. "Transmit Antenna Selection Methods For Mimo Systems In Wireless Communications." Journal of University of Shanghai for Science and Technology 23, no. 08 (August 16, 2021): 523–31. http://dx.doi.org/10.51201/jusst/21/08424.

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Though MIMO systems improve performance of a wireless communication network by the usage of multiple antennas, demand of distinct set of RF chain (i.e., electronic components required for antenna transmission and reception, in wireless communication) for all the antennas leads to an increase in complexity and cost. Antenna selection technique of MIMO has proved to be a good means to solve this issue. Antenna Selection methods find optimal number of antennas required out of the total antennas present in the MIMO (Multiple Input Multiple Output) system. The selection of antenna can be performed at both ends of the communication network i.e., transmitter or receiver. In this paper, an overview of various Transmit Antenna Selection techniques for various MIMO systems is presented.
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Wang, Chaowei, Weidong Wang, Cheng Wang, Shuai Wang, and Yang Yu. "A Fast Adaptive Receive Antenna Selection Method in MIMO System." International Journal of Antennas and Propagation 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/175783.

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Antenna selection has been regarded as an effective method to acquire the diversity benefits of multiple antennas while potentially reduce hardware costs. This paper focuses on receive antenna selection. According to the proportion between the numbers of total receive antennas and selected antennas and the influence of each antenna on system capacity, we propose a fast adaptive antenna selection algorithm for wireless multiple-input multiple-output (MIMO) systems. Mathematical analysis and numerical results show that our algorithm significantly reduces the computational complexity and memory requirement and achieves considerable system capacity gain compared with the optimal selection technique in the same time.
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Taluja, Pawandeep S., and Brian L. Hughes. "Optimal Antenna Selection Designs for Coupled MIMO Systems." International Journal of Antennas and Propagation 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/694627.

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We consider the impact of antenna mutual coupling on receive antenna selection systems. Prior work on selection with mutual coupling has not considered the effects of the inactive (i.e., unselected) antenna terminations and spatial noise correlation. In this work, we show that the presence of inactive antennas can profoundly alter system performance when the antennas are strongly coupled. We also propose a new antenna selection technique that seeks to exploit coupling to improve performance. Simulations suggest that the new technique can significantly outperform traditional selection when coupling is present.
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Zheng, Nae, Xiu Kun Ren, Peng Dong, and Shi Lei Zhu. "A Novel Antenna Selection Algorithm Based on Port Selection in Distributed MIMO Systems." Applied Mechanics and Materials 687-691 (November 2014): 3956–62. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3956.

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The antenna number in distributed MIMO system is much larger than that in distributed antenna system (DAS) and traditional centralized MIMO system. Therefore adopting the existing antenna selection algorithms with excellent performance will make it difficult to realize the system due to the complexity of the algorithms. In order to solve the problem, a novel antenna selection algorithm performed at the base station (BS) is proposed according to the structural characteristics of the system. In the proposed algorithm, the antenna search scope is narrowed down by port selection based on the trace of the sub-channel matrices, and antennas with little contributions to the system capacity are removed gradually by iteratively updating the optimization parameter, which further reduces the complexity. When this algorithm is treated as the transmit antenna selection algorithm, its port selection process is performed by the user equipment, which can reduce the feedback overhead. Simulation results show that the proposed algorithm possesses the similar system capacity with the optimal algorithm.
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Legkiy, N. M., and N. V. Mikheev. "Selection of location of radiators in a non-equivident antenna array." Russian Technological Journal 8, no. 6 (December 18, 2020): 54–62. http://dx.doi.org/10.32362/2500-316x-2020-8-6-54-62.

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Antennas are one of the main elements of radio engineering systems. Phased antenna arrays (PAR), which make it possible to regulate the direction of radiation due to the ability to control the phases or phase differences of the emitted signal, are the most effective types of antennas. The size, design and shape of the PAR depend on the tasks to be solved, the type of emitters and the nature of their location. The article discusses the transformation of an equidistant PAR into a non-equidistant antenna array in order to reduce the level of side lobes and suppress diffraction maxima with a given minimum distance between the emitters. A model of a non-equidistant antenna array and calculation formulas for its analysis are presented. The method presented in the work based on iterative calculation methods makes it possible to select the main parameters of a non-equidistant PAR taking into account the bonds formed between neighboring radiating elements. The coordinates of the emitter elements of the non-equidistant PAR were calculated in a program using the MATLAB language. At the same time, a method was implemented to search for the optimal arrangement of emitters relative to each other, in which the directional pattern of the antenna array will have a minimum level of diffraction maxima and the required level of side lobe. According to the results of the program execution, the coordinates of the new non-equidistant PAR were obtained. The non-equidistant phased array antenna simulated according to the calculation results showed a complete absence of diffraction maxima, in contrast to the equidistant array, but it was not possible to sufficiently obtain the required level of side lobes. The calculated antenna radiation patterns presented for comparison showed the advantages of a non-equidistant antenn array.
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Chien-Hsiang Wu, Chien-Hsiang Wu, and Chin-Feng Lai Chien-Hsiang Wu. "Data-driven Diversity Antenna Selection for MIMO Communication using Machine Learning." 網際網路技術學刊 23, no. 1 (January 2022): 001–9. http://dx.doi.org/10.53106/160792642022012301001.

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<p>With the popularity of wireless application environments, smart antenna technology has completely changed the communication system. In order to improve the quality of wireless transmission, smart antennas have been widely used in wireless devices. Wireless signal modeling and prediction machine learning gradually replaced the traditional smart antenna selection method in the antenna selection solution. This article utilizes mobile devices to adjust the diversity antenna pattern for test verification in a MIMO wireless communication environment. The proposed method manipulates signal parameters through error vector magnitude (EVM) and adds data-driven training data. The results show that the SVM and NN methods proposed in this paper are 10.5% and 14% higher than the traditional EVM calculation methods, respectively. Thereby, realize precise antenna adjustment of mobile devices and improving wireless transmission quality.</p> <p>&nbsp;</p>
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Qian, Kun, Wen-Qin Wang, and Huaizong Shao. "Low-Complexity Transmit Antenna Selection and Beamforming for Large-Scale MIMO Communications." International Journal of Antennas and Propagation 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/159375.

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Transmit antenna selection plays an important role in large-scale multiple-input multiple-output (MIMO) communications, but optimal large-scale MIMO antenna selection is a technical challenge. Exhaustive search is often employed in antenna selection, but it cannot be efficiently implemented in large-scale MIMO communication systems due to its prohibitive high computation complexity. This paper proposes a low-complexity interactive multiple-parameter optimization method for joint transmit antenna selection and beamforming in large-scale MIMO communication systems. The objective is to jointly maximize the channel outrage capacity and signal-to-noise (SNR) performance and minimize the mean square error in transmit antenna selection and minimum variance distortionless response (MVDR) beamforming without exhaustive search. The effectiveness of all the proposed methods is verified by extensive simulation results. It is shown that the required antenna selection processing time of the proposed method does not increase along with the increase of selected antennas, but the computation complexity of conventional exhaustive search method will significantly increase when large-scale antennas are employed in the system. This is particularly useful in antenna selection for large-scale MIMO communication systems.
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Gaikwad, Snehal, and P. Malathi. "A Performance Analysis of Massive MIMO System using Antenna Selection Algorithms." International Journal of Electrical and Electronics Research 11, no. 1 (March 30, 2023): 126–31. http://dx.doi.org/10.37391/ijeer.110117.

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A large number of transmitting components makes Massive Multiple-Input Multiple-Output (MIMO) one of the most hopeful solution for the 5G technology. However, a large antenna system boosts the hardware intricacy and cost of the system because of RF transceivers used at the base station for every antenna element. Hence, antenna selection is one of the most effective schemes to select a good subset of antennas with the finest channel circumstances and contribute maximum to the channel capacity. This paper presents Branch and Bound (BAB) algorithm for efficient antenna selection in Massive MIMO technology. The effectiveness of the simulated BAB algorithm is evaluated based on channel capacity and compared with the traditional state of arts such as fast antenna selection algorithm, Exhaustive Search, Fast antenna selection, CBF, CBW, Random antenna selection, etc. Sunflower Optimization-based antenna selection has been shown to provide improved results in terms of channel capacity when compared to the traditional Branch and Bound algorithm. The results indicate that the Sunflower Optimization technique is a promising alternative for antenna selection in Massive MIMO systems, especially in cases where a large number of antennas are present at the transmitter and receiver ends. The proposed solution provides significant improvements over the traditional methods, making it an attractive option for optimizing MIMO performance in future wireless communication systems.
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Dissertations / Theses on the topic "Antenna Selection"

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Al-Shuraifi, Mohammed. "Transmit antenna selection and user selection in multiuser MIMO downlink systems." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13271.

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Multiuser multiple input multiple output (MU-MIMO) systems play essential role in improving throughput performance and link reliability in wireless communications. This improvement can be achieved by exploiting the spatial domain and without the need of additional power and bandwidth. In this thesis, three main issues which are of importance to the data rate transmission have been investigated. Firstly, antenna selection in MU-MIMO downlink systems has been considered, where this technique can be e fficiently used to reduce the complexity and cost caused by radio frequency chains, associated with antennas, while keeping most of the diversity advantages of the system. We proposed a transmit antenna selection algorithm which can select an optimal set of antennas for transmission in descending order depending on the product of eigenvalues of users' effective channels. The capacity achieved by the proposed algorithm is about 99:6% of the capacity of the optimum search method, with much lower complexity. Secondly, user selection technology in MU-MIMO downlink systems has been studied. Based on the QR decomposition, we proposed a greedy suboptimal user selection algorithm which adopts the product of singular values of users' effective channels as a selection metric. The performance achieved by the proposed algorithm is identical to that of the capacity-based algorithm, with significant reduction in complexity. Finally, a proportional fairness scheduling algorithm for MU-MIMO downlink systems has been proposed. By utilising the upper triangular matrix obtained by applying the QRD on the users' effective channel matrices, two selection metrics have been proposed to achieve the scheduling process. The first metric is based on the maximum entry of the upper triangular matrix, while the second metric is designed using the ratio between the maximum and minimum entries of the triangular matrix multiplied by the product of singular values of effective channels. The two metric provide significant degrees of fairness. For each of these three issues, a different precoding method has been used in order to cancel the interuser interference before starting the selection process. This allows to investigate each precoding design separately and to evaluate the computational burden required for each design.
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Husbands, Ryan R. "Transmit antenna selection for multiuser massive MIMO." Thesis, University of Kent, 2018. https://kar.kent.ac.uk/69467/.

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In massive multiple input multiple output (MIMO) systems, major challenges are present due to the large number of active antennas and radio frequency (RF) chains,suchasincreasedpowerconsumptionandcomputationcomplexity. Transmitantennaselection(TAS)isbeinginvestigatedasasolutiontotacklethesechallenges. In this thesis, a dynamic transmit antenna selection technique is proposed whichcanmaximizethesumrateofamultiuser(MU)-MIMOcommunicationsystem. In order to satisfy the objective, the number of transmit antennas required is determined by remodeling it as a binary Knapsack Problem (KP) and then extending to a Multiple KP (MKP) for MU-MIMO. Furthermore, an improvement in the decision making is also proposed with the introduction of a flexible decision criteria, whilst reducing the structure of the MKP to resemble that of a single binary KP. Additionally, comparisons of the KP based algorithms are done with two low complexity techniques, which are the sequential selection algorithm and random selection algorithm. Results show that the KP based techniques outperform these low complexity techniques. The modified binary KP algorithm is also superior to that of the MKP, as it is not sensitive to solving as binary knapsack sub-problems. The proposed technique has good performance for different antenna selection measures and is suitable to ensure communication efficiency in future wireless communication systems.
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Hugine, Akilah L. "Antenna Selection for a Public Safety Cognitive Radio." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/32577.

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Ever since the dawn of radio communication systems, the antenna has been the key component in the construction and performance of every wireless system. With the proliferation of new radio systems, a cognitive radio is a radio that has the capability to sense, learn, and autonomously adapt to its environment. The hardware components are essential to optimizing performance. Antenna hardware for cognitive radio applications presents distinctive problems, since in theoretical terms, a cognitive radio can operate anywhere in the spectrum. The purpose of this thesis is to investigate a particular type of cognitive radio system and examine the potential affects the antenna will have on the system. The thesis will provide an overview of fundamental antenna properties, the performance characteristics of the particular antenna used in this research, and the system characteristics when the antenna is integrated. This thesis will also illustrate how the antenna and its properties affect the overall public safety cognitive radio performance. This information can be used to establish antenna selection criteria for optimum system performance.
Master of Science
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Lupupa, Mzabalazo. "Transmit antenna selection in fading wireless communication systems." Master's thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/12110.

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Includes abstract.
Includes bibliographical references (leaves 79-83).
To address the drawbacks associated with multiple-input multiple-output (MIMO) systems, we propose the use of the transmit antenna selection technique. In transmit antenna selection, the best performing antenna(s) is selected from all the available transmit antennas for transmission. Transmit antenna selection reduces the number of radio frequency chains, and the system complexity while still achieving the goals of multiple antenna systems. In this thesis the performance of a MIMO system employing transmit antenna selection and maximal-ratiocombining is studied.
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Lu, Di. "Antenna selection schemes for multiple-input multiple out systems." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493529.

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Theoretical research show that wireless communication systems using multiple-input multiple-output (MIMO) antennas can provide a very high spectral efficiency in rich scattering environments. This thesis investigates issues of antenna selection in MIMO systems with an emphasis on the criteria and algorithms of receive antenna selection in layered spatial multiplexing architectures.
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PEREZ, DAILYS ARRONDE. "ANTENNA SELECTION IN THE DOWNLINK OF PRECODED MULTIUSER MIMO SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36056@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Esta dissertação enfoca o enlace direto de sistemas MIMO multiusuário com pré-codificação onde a estação base e os terminais dos usuários possuem múltiplas antenas mas transmitem e recebem, respectivamente, símbolos de informação através de subconjuntos selecionados de seus conjuntos de antenas. O trabalho considera sistemas que utilizam técnicas de précodificação linear como Zero Forcing (ZF) e Minimum Mean Square Error (MMSE). Expressões gerais que descrevem os sistemas e relacionam a energia gasta na transmissão com a energia disponível para a detecção em cada usuário são apresentadas. Com base nestas relações, um procedimento para seleção de antenas na transmissão é proposto visando a minimização da probabilidade de erro. Um algoritmo de busca não exaustiva denominado ITES (Iterative Search) foi desenvolvido e testado e mostrou-se capaz de, com apenas uma pequena fração do esforço computacional, fornecer um desempenho próximo ao da seleção ótima, que demanda uma busca exaustiva. A seleção de antenas na recepção é também efetuada usando um critério de otimização semelhante. O caso geral da seleção conjunta de antenas na transmissão e na recepção contempla a combinação de ambas estratégias, resultando na redução da complexidade tanto na estação base, quanto nos terminais dos usuários. Os resultados de desempenho em termos da taxa de erro de bit, obtidos por meio de simulações e abordagem semianalítica, são apresentados para diferentes cenários.
This thesis focuses on the downlink of a multiuser multiple-input multiple-output (MU-MIMO) systems where the Base Station (BS) and the users stations (UEs) transmit and receive information symbols, respectively, by selected subset of their antennas. The performance of the system is evaluated employing linear precoding techniques as Zero Forcing (ZF) and Minimum Mean Square Error (MMSE). A general model to describe the system and expressions that relate the energy spent in transmission with the energy available for detection at each user are presented. A transmit antenna selection procedure is proposed aiming at the minimization of the detection error probability. A suboptimal search algorithm, called ITES (Iterative Search), able to deliver a performance close to the one resulting from the optimal exhaustive search selection is also proposed. The receive antenna selection is also performed using a similar optimization criterion. Joint antennas selection at the transmitter and receiver contemplates the efficient combination of both strategies, leading to a complexity reduction in BS and UEs. BER performance results, obtained via simulation and semi-analytical approaches, are presented for different scenarios.
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Bahceci, Israfil. "Multiple-Input Multiple-Output Wireless Systems: Coding, Distributed Detection and Antenna Selection." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-08262005-022321/.

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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006.
Altunbasak, Yucel, Committee Chair ; Mersereau, Russell M., Committee Member ; Fekri, Faramarz, Committee Member ; Smith, Glenn, Committee Member ; Huo, Xiaoming, Committee Member. Includes bibliographical references.
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Morris, Matthew Leon. "The Impact of Antenna and RF System Characteristics on MIMO System Capacity." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd978.pdf.

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Zhou, Zhenxiang. "Performance analysis and tracking for multibeam selection antenna array system in mobile communication." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0022/MQ51520.pdf.

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Jang, Dhong Woon. "A 3D LINK ANALYSIS AND SELECTION OF A RECEIVE ANTENNA ANGLE IN TELEMETRY SYSTEMS." International Foundation for Telemetering, 2002. http://hdl.handle.net/10150/606362.

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International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California
A three dimension (3D) link analysis is performed considering multipath effects caused by a reflected signal and the difference angle between the antenna bore-sight and Line-Of-Sight (LOS). In addition, a direction of a receive antenna is determined for a receiver to get maximum signal strength in a telemetry situation. For a fixed receive antenna, the angle is determined to maximize the average Carrier to Noise Ratio (CNR) over the interested part of a trajectory. For a tracking antenna, the angle at every position is selected to give maximum CNR or to direct the boresight to the flying projectile.
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Books on the topic "Antenna Selection"

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Evans, Alvis J. Antennas: Selection and installation. 2nd ed. Ft. Worth, Tex: Radio Shack, 1989.

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Munk, Ben. Finite antenna arrays and FSS. Hoboken, NJ: John Wiley, 2003.

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Evans, Alvis J. Antennas: Selection, installation, and projects. 4th ed. Lincolnwood, Ill: Master Pub., 1998.

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Evans, Alvis J. Antennas: Selection, installation, and projects. 3rd ed. Ft. Worth, Tex: Radio Shack, 1994.

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Narayan, Shiv, B. Sangeetha, and Rakesh Mohan Jha. Frequency Selective Surfaces based High Performance Microstrip Antenna. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-775-8.

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Optimizing the antenna system of a microwave space power station: Implications for the selection of operating power, frequency, and antenna size. [Washington, D.C.]: National Aeronautics and Space Administration, 1987.

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Nurie, Nashat Shawkat. Microstrip array antennas for land mobile/satellite system communications: Selection of microstrip radiating elements for optimal performance of a shaped beam antenna array.... Bradford, 1986.

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ANTENNAS SELECTION, INSTALLATION AND PROJECTS RADIOSHACK. Master Publishing, Inc., 1998.

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Narayan, Shiv, Rakesh Mohan Jha, and B. Sangeetha. Frequency Selective Surfaces based High Performance Microstrip Antenna. Springer, 2015.

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Narayan, Shiv, Rakesh Mohan Jha, and B. Sangeetha. Frequency Selective Surfaces Based High Performance Microstrip Antenna. Springer London, Limited, 2015.

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Book chapters on the topic "Antenna Selection"

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García-Naya, José Antonio. "HSDPA Antenna Selection Techniques." In Evaluation of HSDPA and LTE, 139–52. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119954705.ch6.

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Zhao, Zhongyuan, Mugen Peng, Li Wang, Wenqi Cai, Yong Li, and Hsiao-Hwa Chen. "Antenna Selection in Large-Scale Multiple Antenna Systems." In Wireless Algorithms, Systems, and Applications, 756–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21837-3_74.

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Ding, Ming, and Hanwen Luo. "The Sixth Category: Antenna Selection Technologies." In Signals and Communication Technology, 85–114. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34949-2_3.

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Mohanty, Mihir Narayan, Sarmistha Satrusallya, and Takialddin Al Smadi. "Antenna selection criteria and parameters for IoT application." In Printed Antennas, 283–95. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003347057-18.

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Shi, Zhiyuan, Changzheng Zhang, Yifeng Zhao, Lianfen Huang, and Yao-Chung Chang. "A New Antenna Selection Algorithm for Indoor Distributed Antenna Systems Environment." In Computer Science and its Applications, 1395–400. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45402-2_193.

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Gorokhov, Alexei, Dhananjay A. Gore, and Arogyaswami J. Paulraj. "Antenna Subset Selection in MIMO Communication Systems." In Space-Time Processing for MIMO Communications, 245–68. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470010045.ch7.

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An, Jianping, Kai Yang, Xiaozheng Gao, and Neng Ye. "Space-Time Network Coding with Antenna Selection." In Sustainable Wireless Communications, 57–76. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0448-6_5.

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An, Jianping, Kai Yang, Xiaozheng Gao, and Neng Ye. "Space-Time Network Coding with Antenna Selection." In Sustainable Wireless Communications, 57–76. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0448-6_5.

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Chouhan, Sonali, and Tinamoni Taye. "Low Complexity Antenna Selection Scheme for Spatially Correlated Multiple Antenna Cognitive Radios." In Advances in Intelligent Systems and Computing, 275–83. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0132-6_19.

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Garrouani, Yassine, Fatiha Mrabti, and Aicha Alami Hassani. "An Antenna Selection Algorithm for Massive MIMO Systems." In Lecture Notes in Electrical Engineering, 89–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6259-4_7.

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Conference papers on the topic "Antenna Selection"

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Wang Jun-ling, Gao Mei-guo, Fu Tuo, and Xu Cheng-fa. "Capacity degradation of representative antenna selection algorithms and an antenna selection scheme." In 4th IET International Conference on Wireless, Mobile & Multimedia Networks (ICWMMN 2011). IET, 2011. http://dx.doi.org/10.1049/cp.2011.0972.

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Bansal, Dipali, Malay Ranjan Tripathy, and Shantanu Padhi. "Bilateral Antenna Pattern Selection (BAPS) based Optimal Smart Antenna Selection in 5G system." In 2022 IEEE Wireless Antenna and Microwave Symposium (WAMS). IEEE, 2022. http://dx.doi.org/10.1109/wams54719.2022.9847700.

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Moritz, J. "Measurement of HF antenna impedances." In IEE Colloquium. Frequency Selection and Management Techniques for HF Communications. IEE, 1999. http://dx.doi.org/10.1049/ic:19990081.

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Garcia-Naya, Jose A., Christian Mehlfuhrer, Sebastian Caban, Markus Rupp, and Luis Castedo. "Throughput-Based Antenna Selection Measurements." In 2009 IEEE Vehicular Technology Conference (VTC 2009-Fall). IEEE, 2009. http://dx.doi.org/10.1109/vetecf.2009.5378992.

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Mehanna, Omar, Nicholas D. Sidiropoulos, and Georgias B. Giannakis. "Multicast beamforming with antenna selection." In 2012 IEEE 13th Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2012). IEEE, 2012. http://dx.doi.org/10.1109/spawc.2012.6292982.

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Gao, Yuan, and Thomas Kaiser. "Antenna selection in massive MIMO systems: Full-array selection or subarray selection?" In 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2016. http://dx.doi.org/10.1109/sam.2016.7569725.

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Tarkhan, Aliasghar. "Receive Antenna Selection in Uplink Smart Antenna MIMO Systems." In 2020 IEEE International Conference on Communication, Networks and Satellite (Comnetsat). IEEE, 2020. http://dx.doi.org/10.1109/comnetsat50391.2020.9328785.

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Kurniawan, Ernest, A. S. Madhukumar, and Francois Chin. "Heuristic Antenna Selection Algorithm for Multiuser Multi-Antenna Downlink." In 2009 IEEE 69th Vehicular Technology Conference Spring. IEEE, 2009. http://dx.doi.org/10.1109/vetecs.2009.5073576.

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Mehrpouyan, Hani, Steven D. Blostein, and Edmund C. Y. Tam. "Random Antenna Selection & Antenna Swapping Combined with OSTBCs." In 2007 International Symposium on Signals, Systems and Electronics. IEEE, 2007. http://dx.doi.org/10.1109/issse.2007.4294442.

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Asaad, Saba, Ali Bereyhi, Ralf R. Muller, and Amir M. Rabiei. "Asymptotics of transmit antenna selection: Impact of multiple receive antennas." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7997122.

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Reports on the topic "Antenna Selection"

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Wendt, Joel Robert, G. Ronald Hadley, Sally Samora, Hung Loui, Alvaro Augusto Cruz-Cabrera, Paul Davids, Shanalyn A. Kemme, Lorena I. Basilio, William Arthur Johnson, and David William Peters. Transmissive infrared frequency selective surfaces and infrared antennas : final report for LDRD 105749. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/974868.

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