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Letteratura scientifica selezionata sul tema "MIMO systems"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "MIMO systems"

Akhilseh, Kumar Verma, Manish, and Rajesh Kumar Dr. "OVERVIEW OF RF COMMUNICATION FROM SISO SYSTEMS TO MIMO SYSTEMS." International Journal of Advances in Engineering & Scientific Research 3, no. 3 (July 30, 2016): 13–18. https://doi.org/10.5281/zenodo.10752074.

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Abstract:  The wireless communication has derived to the present scenario after passing through number of stages and generation. From analog communication of 1st generation today we talk about digital communication in 4th generation, from the generation of single transmitting antenna and single receive antenna known as SISO systems, we reached the systems with multiple transmitting and multiple receiving antennas known as MIMO systems going through the systems of Multiple Transmit and single receive antenna systems known as MISO systems and Single transmit and multiple receive systems known as SIMO systems. This paper will present a study of generation from SISO systems to MIMO systems for digital communication under the research work going at Banasthali University, Rajsthan, India.Index terms- MIMO, SISO, SIMO, MISO, MMSE, ZFE.

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Joshi, Dr Shreedhar A. "Multi-User MIMO Systems." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 193–96. http://dx.doi.org/10.9756/bijsesc.8275.

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Swapna, Sonti. "Channel Estimation for MIMO Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 201–4. http://dx.doi.org/10.22214/ijraset.2022.39776.

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Abstract: A combination of multiple-input multiple-output (MIMO) systems and orthogonal frequency division multiplexing (OFDM) technologies can be employed in modern wireless communication systems to achieve high data rates and improved spectrum efficiency. For multiple input multiple output (MIMO) systems, this paper provides a Rayleigh fading channel estimation technique based on pilot carriers. The channel is estimated using traditional Least Square (LS) and Minimum Mean Square (MMSE) estimation techniques. The MIMO-OFDM system's performance is measured using the Bit Error Rate (BER) and Mean Square Error (MSE) levels. Keywords: MIMO, MMSE, Channel estimation, BER, OFDM

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Akhilseh, Kumar Verma, Manish, and Rajesh Kumar Dr. "EQUALIZATION SCHEMES IN MIMO RF COMMUNICATION: A PERFORMANCE ANALYSIS." International Journal of Advances in Engineering & Scientific Research 3, no. 3 (July 30, 2016): 44–50. https://doi.org/10.5281/zenodo.10752554.

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Abstract:  The wireless communication has derived to the present scenario after passing through number of stages and generation. From analog communication of 1st generation today we talk about digital communication in 4th generation, from the generation of single transmitting antenna and single receive antenna known as SISO systems, we reached the systems with multiple transmitting and multiple receiving antennas known as MIMO systems going through the systems of Multiple Transmit and single receive antenna systems known as MISO systems and Single transmit and multiple receive systems known as SIMO systems. This paper will present a study of generation from SISO systems to MIMO systems for digital communication under the research work going at Banasthali University, Rajsthan, India. Index terms- MIMO, SISO, SIMO, MISO, MMSE, ZFE.

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Akhilseh, Kumar Verma, Manish, and Rajesh Kumar Dr. "ZFE AND MMSE SCHEMES FOR MIMO COMMUNICATION SYSTEMS : AN ANALYSIS." International Journal of Advances in Engineering & Scientific Research 3, no. 3 (July 30, 2016): 37–43. https://doi.org/10.5281/zenodo.10752418.

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Abstract:  MIMO systems are very important technological advancements with respect to the third generation and fourth generation’s wireless networks for RF communication. There are multiple equalization schemes for MIMO systems and two very important of them are ZFE and MMSE. In this paper we are analyzing the performance of both the schemes in different conditions. Index terms- MIMO, SISO, SIMO, MISO, MMSE, ZFE.

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Remlein, Piotr. "Multiuser cpm transmission for mimo systems." Image Processing & Communications 18, no. 4 (December 1, 2013): 59–65. http://dx.doi.org/10.2478/v10248-012-0093-0.

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Abstract Frequency-Division Multiplexed Continuous Phase Modulation (FDM-CPM) systems with Multiple Input Multiple Output (MIMO) transmission are investigated. In this paper, a multiuser reception scheme for CPM MIMO transmission is presented. The analyzed system is designed to achieve high spectral efficiency by exploiting the multiplexing gain of MIMO techniques. To take advantage of the multiplexing gain of MIMO systems, a Zero Forcing (ZF) MIMO detector and a low-complexity iterative algorithm for Inter- Carrier Interference (ICI) cancellation are considered. Numerical simulations have been performed to assess the performance improvement achieved with the proposed frequency-division multiplexed CPM multiuser MIMO system

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Kumar Sarangi, Ashish, Amrit Mukherjee, and Amlan Datta. "Capacity comparison of MIMO and cooperative MIMO." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 638. http://dx.doi.org/10.14419/ijet.v7i1.1.10794.

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To achieve high capacity and high data rates is the main requirement for today’s generation. This paper studies about the performance and capacity comparison of MIMO and cooperative MIMO systems. The comparison of capacity between multiple- input- multiple- output (MIMO) and cooperative MIMO systems helps us to know that which system have better performance and better capacity. The simulation results shows that among SISO, SIMO, MISO and MIMO system the capacity of MIMO will be better but in between MIMO and cooperative MIMO, Cooperative MIMO system have high capacity than MIMO systems.

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Mohammed A. Ali, Musaab, and Volodymyr Vodichev. "MODELING OF MIMO SYSTEMS WITH UNIVERSAL CONTROLLER." ELECTRICAL AND COMPUTER SYSTEMS 37, no. 113 (June 30, 2023): 26–32. http://dx.doi.org/10.15276/eltecs.37.113.2023.03.

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Abstract. The control of multi-input multi-output (MIMO) systems poses significant challenges due to the intricate dynamics and interactions between multiple input and output channels. This paper presents a simulation study on MIMO systems control using the universal control technique. The proposed method offers a versatile framework for regulating MIMO systems with varying dynamics, utilizing a single control algorithm. The underlying concept of the universal control law is to devise a set of principles or strategies that can be universally applied to any system, regardless of its specific characteristics or parameters. To achieve this, the control algorithm is designed based on the derivatives balance of the system's input-output dynamics, granting the controller adaptability to both single-input single-output (SISO) and MIMO systems. Numerical examples are provided to demonstrate the effectiveness of the proposed method, involving a MIMO system comprising four inputs in a two-coupled distillation column process and two inputs in a twin rotor setup. The challenges associated with distillation column control, such as nonlinearity, disturbances, and interaction between multiple inputs and outputs, are considered. Twin rotor systems, consisting of two interconnected rotors, pose unique control difficulties, primarily arising from the coupling between the two rotors and their nonlinear behavior. The simulation results show the capability of the proposed universal controller to achieve precise and robust control over the MIMO system. Keywords: MIMO, Control, Simulation, Distillation Column Process, Twin Rotor.

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Zhang, Donghui. "Signal Processing Methods in MIMO Communication Systems." Highlights in Science, Engineering and Technology 124 (February 18, 2025): 326–31. https://doi.org/10.54097/dy7y1t24.

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This paper delves into the latest advancements and hurdles in Multi-Input Multi-Output (MIMO) technology, particularly in the realm of 5G and future networks. Initially, it examines the fusion of Non-Orthogonal Multiple Access (NOMA) with MIMO, emphasizing its crucial role in user grouping, beam steering, and power distribution strategies. Subsequently, the research explores progress in massive MIMO systems, concentrating on signal processing methods that improve spectral efficiency and system performance in sparse scattering contexts. The paper also reviews sophisticated signal processing approaches for millimeter-wave (mmWave) MIMO systems, with a particular focus on techniques like compressed sensing, which tackle the challenges associated with high-frequency bands and extensive antenna arrays. Furthermore, it investigates MIMO signal processing within Mode-Division Multiplexing (MDM) systems, suggesting an adaptive frequency-domain equalization combined with chromatic dispersion compensation to enhance performance. The discussion extends to Extremely Large-Scale MIMO (XL-MIMO) technology for 6G networks, highlighting the significance of deep learning in signal processing tasks. In conclusion, the paper summarizes the benefits of MIMO-Orthogonal Frequency Division Multiplexing (OFDM) systems, emphasizing their pivotal role in shaping the future landscape of wireless communications.

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Hwang, Inho, Han Park, and Jeong Lee. "LDPC Coded Massive MIMO Systems." Entropy 21, no. 3 (February 27, 2019): 231. http://dx.doi.org/10.3390/e21030231.

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We design a coded massive multiple-input multiple-output (MIMO) system using low-density parity-check (LDPC) codes and iterative joint detection and decoding (JDD) algorithm employing a low complexity detection. We introduce the factor graph representation of the LDPC coded massive MIMO system, based on which the message updating rule in the JDD is defined. We devise a tool for analyzing extrinsic information transfer (EXIT) characteristics of messages flowing in the JDD and the three-dimensional (3-D) EXIT chart provides a visualization of the JDD behavior. Based on the proposed 3-D EXIT analysis, we design jointly the degree distribution of irregular LDPC codes and the JDD strategy for the coded massive MIMO system. The JDD strategy was determined to achieve a higher error correction capability with a given amount of computational complexity. It was observed that the coded massive MIMO system equipped with the proposed LDPC codes and the proposed JDD strategy has lower bit error rate than conventional LDPC coded massive MIMO systems.

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Tesi sul tema "MIMO systems"

Sörman, Simon. "System Information Distribution in Massive MIMO Systems." Thesis, Linköpings universitet, Kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129294.

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The 5th generation mobile telecommunication system (5G) is currently being specified and developed, with large expectations on throughput and efficiency. While 4G and more specifically LTE might constitute a basis of the design of the network, there are some parts that should be improved. One thing to improve is the static signalling that occurs very frequently in a 4G network, of which system information such as synchronization signals, detection of network frequencies, operators, configurations etc. is a part. It has been shown that the static signalling requires both much energy and time-frequency resources. Since the system information is not intended for a single user it is always broadcast so that any user, and any amount of users can read it when needed. 5G will use a technique called massive MIMO, where the base station is equipped with a large number of antennas which can be used to direct signals in space, called beamforming. This thesis presents a new method for distribution of system information that can utilize the beamforming capabilities of massive MIMO. A simple model together with simulated user channel statistics from urban 4G scenarios are used to show that the new method outperforms the classical method of only broadcasting the information, with respect to time-frequency resources. Especially if there are high requirements on the latency of the system information, the new method results in a large gain.

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Chen, Danshan. "Precoding for MIMO systems." Thesis, University of York, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535046.

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Bennia, Abdelhak. "Mimo systems parameters identification." Thesis, Virginia Tech, 1986. http://hdl.handle.net/10919/41579.

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In this thesis, a presentation of a new canonical representation of multi-input multioutput systems is given. The new characterization covers the full range of practical situations in linear systems according to the structural properties and model of the perturbations which are known. Its direct link to ARMA processes as well as to classical state space representation ls also given. The importance of the new representation lies in the fact that all unknown parameters and state variables appear linearly multlplied by either external variables (inputs and outputs) that appear in the data record, or by matrices that are only composed of ieroes and ones. This property enables us to perform a joint state and parameters estimation. Moreover, if the noises are gaussian and their statistics are known, an on-line algorithm that involves a standard dlscrete-time time-varying Kalman filter is proposed and used successfully in the estimation of unknown parameters for simulated examples. Master of Science

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Rashid, Imran. "MIMO multi-hop relay systems." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/mimo-multihop-relay-systems(f8d60fed-c19c-419d-b14f-7defc4721bc3).html.

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Multiple Input Multiple Output (MIMO) systems use multiple transmit and receive antennas to achieve higher data rates by transmitting multiple independent data systems. Transmission errors can be reduced by using Hybrid Automatic Repeat request (HARQ) combining techniques with MIMO systems. In this thesis, the use of HARQ for MIMO multi-hop communication is studied. We propose two MIMO HARQ combining methods which are based on using pre-combiningonly and a joint pre and post combining techniques. In addition to conventional single-hop transmission, HARQ schemes for MIMO multi-hop relay systems are also investigated. A novel approach is proposed to deal with the parallel HARQ processes in MIMO relay scenario. An information theoretic throughput analysis is performed to evaluate the performance of the relay system by employing various transmission techniques for relay-destination link. Evaluation is carried out on the delay involved while employing the relay systems as compared to single hop systems. Simulation results show that the proposed system can enhance the overall throughput performance of MIMO single-hop and multi-hop relay systems. Considering the recent research interest in green radio and requirements of reduced energy consumption by the wireless networks, we evaluated the energy efficiency of existing and proposed MIMO HARQ techniques for sensor and cellular networks. The results show that the proposed scheme is more energy efficient compared to other schemes in single-hop as well as multi-hop scenarios.

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Ma, Shaodan. "Semi-blind signal detection for MIMO and MIMO-OFDM systems." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36846569.

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Ma, Shaodan, and 馬少丹. "Semi-blind signal detection for MIMO and MIMO-OFDM systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36846569.

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Negrão, João Lucas. "Efficient detection : from conventional Mimo to massive Mimo communication systems." Universidade Estadual de Londrina. Centro de Tecnologia e Urbanismo. Programa de Pós-Graduação em Engenharia Elétrica, 2018. http://www.bibliotecadigital.uel.br/document/?code=vtls000218370.

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Ao longo deste trabalho, problemas relacionados aos sistemas de comunicação equipados com múltiplas antenas no transmissor e receptor (MIMO - Multiple- Input Multiple-Output) são analisados sob o ponto de vista de detecção clássica, da otimização não-linear, bem como da pré-codificação linear, desde MIMO convencional (algumas antenas no Tx e Rx) até sistemas MIMO de larga-escala (massivo). Inicialmente, a eficiência de detecção de vários detectores MIMO foi analisada sob a prerrogativa de canais altamente correlacionados, situação em que sistemas MIMO apresentam elevada perda de desempenho, além de, em alguns casos, uma crescente complexidade. Diante deste cenário, foi estudado especificamente o comportamento em termos do compromisso complexidade x taxa de erro de bits (BER - Bit Error Rate), para diferentes técnicas de detecção, como o cancelamento de interferências sucessivo (SIC), redução treliça (LR), bem como a combinação de cada uma destas às técnicas lineares de detecção. Nessa análise, também foram considerados diferentes estruturas de antenas uniformes com arranjos geométricos lineares (ULA - uniform linear array) e de arranjo planar (UPA - uniform planar array) em ambos transmissor e receptor. Além disso, também foram considerados diferentes número de antenas e ordem de modulação. Em seguida, o problema de detecção MIMO foi estudado sob uma perspectiva de otimização não-linear, visando especificamente alcançar o desempenho ótimo. Foi analisada a solução de detecção com relaxação semi-definida (SDR - semi- definite relaxation). O detector SDR-MIMO é uma abordagem eficiente capaz de atingir o desempenho muito próximo ao ótimo, especialmente para baixas e médias ordens de modulação. Concentramos nossos esforços no desenvolvimento de uma aproximação computacionalmente eficiente para o algoritmo de detecção de máxima verossimilhança (ML - Maximum Likelihood) MIMO baseado na programação semi-definida (SDP - Semidefinite Programming) para as constelações M-QAM. Finalmente, estuda-se um problema de alocação de potência com o objetivo de maximizar a capacidade de um canal de broadcasting MIMO massivo em uma única célula equipada com pré-codificação forçagem à zero (ZFBF - zero-forcing beamforming) e inversão de canal regularizado (RCI - regularized channel inversion) na estação rádio base (BS). Nosso objetivo é investigar esse problema considerando um sistema massivo no limite, ou seja, quando o número de usuários, K, e antenas na BS, M, tendem ao infinito porém com uma razão constante, β = K M . Primeiramente deriva-se a relação sinal-interferência mais ruído (SINR) para ambos os pré-codificadores escolhidos. Em seguida, investiga-se um esquemas de alocação de potência ótimo que maximiza a soma das capacidades por antena sob uma restrição de potência máxima disponível, conclui-se que o problema é convexo e que a alocação de potência ótima segue a estratégia de watter-filling (WF). Também estudou-se o problema relacionado à alocação de potência em um grupo finito de usuários separados em grupos e determinou-se o impacto desse esquema na capacidade total do sistema. Throughout this work, problems related to communication systems equipped with multiple antennas in the transmitter and receiver (MIMO - Multiple-Input Multiple-Output) are analyzed from the point of view of classical detection, nonlinear optimization, as well as linear pre-coding, from conventional MIMO (some Tx and Rx antennas) to large-scale (massive) MIMO systems. Initially, the detection efficiency of several MIMO detectors were analyzed under the prerogative of highly correlated channels, in which situation, MIMO systems present a high loss of performance, and, in some cases, an increasing complexity. Considering this scenario, we have specifically studied the behavior in terms of compromise complexity x bit error rate (BER), for different detection techniques, such as the successive interference cancellation (SIC), lattice reduction (LR), as well as the combination of each of these with linear detection techniques. In this analysis, different uniform antenna structures with uniform linear array (ULA) and planar array array (UPA) were also considered in both transmitter and receiver side. In addition, different number of antennas and order of modulation were also considered. Next, the MIMO detection problem was studied from a nonlinear optimization perspective, specifically aiming to achieve optimum performance. The detection solution with semi-defined relaxation (SDR - it semidefinite relaxation) were analyzed. The SDR-MIMO detector is an efficient approach capable of achieving near-optimal performance, especially for low and medium modulation orders. We focused our efforts on developing a computationally efficient approach for the maximum likelihood (ML) MIMO detection algorithm based on semi-definite programming (SDP) for M-QAM constellations. Finally, we study an optimal power allocation problem aiming to maximizes the sum-rate capacity of a single cell massive MIMO broadcast channel equipped with zero-forcing beamforming (ZFBF) and regularized channel inversion (RCI) precoding at the base station (BS). Our purpose is to investigate this problem in the large-scale system limit, i.e, when the number of users, K, and antennas at the BS, M, tend to infinity with a ratio β = K/M being held constant. We first derive the signal to interference plus noise (SINR) ratio for both chosen precoders. Then we investigate optimal power allocation schemes that maximize the sum-rate per antenna under an average power constraint and we show that the problem is convex and the power allocation follows the well-known Water-Filling strategy. We also studied a problem related to an optimal power allocation at a finite group of clustered users and determine the impact of this scheme in the ergodic sum-rate capacity.

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Gonzalez, Perez Miryam Guadalupe. "Scaling up virtual MIMO systems." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31321.

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Multiple-input multiple-output (MIMO) systems are a mature technology that has been incorporated into current wireless broadband standards to improve the channel capacity and link reliability. Nevertheless, due to the continuous increasing demand for wireless data traffic new strategies are to be adopted. Very large MIMO antenna arrays represents a paradigm shift in terms of theory and implementation, where the use of tens or hundreds of antennas provides significant improvements in throughput and radiated energy efficiency compared to single antennas setups. Since design constraints limit the number of usable antennas, virtual systems can be seen as a promising technique due to their ability to mimic and exploit the gains of multi-antenna systems by means of wireless cooperation. Considering these arguments, in this work, energy efficient coding and network design for large virtual MIMO systems are presented. Firstly, a cooperative virtual MIMO (V-MIMO) system that uses a large multi-antenna transmitter and implements compress-and-forward (CF) relay cooperation is investigated. Since constructing a reliable codebook is the most computationally complex task performed by the relay nodes in CF cooperation, reduced complexity quantisation techniques are introduced. The analysis is focused on the block error probability (BLER) and the computational complexity for the uniform scalar quantiser (U-SQ) and the Lloyd-Max algorithm (LM-SQ). Numerical results show that the LM-SQ is simpler to design and can achieve a BLER performance comparable to the optimal vector quantiser. Furthermore, due to its low complexity, U-SQ could be consider particularly suitable for very large wireless systems. Even though very large MIMO systems enhance the spectral efficiency of wireless networks, this comes at the expense of linearly increasing the power consumption due to the use of multiple radio frequency chains to support the antennas. Thus, the energy efficiency and throughput of the cooperative V-MIMO system are analysed and the impact of the imperfect channel state information (CSI) on the system's performance is studied. Finally, a power allocation algorithm is implemented to reduce the total power consumption. Simulation results show that wireless cooperation between users is more energy efficient than using a high modulation order transmission and that the larger the number of transmit antennas the lower the impact of the imperfect CSI on the system's performance. Finally, the application of cooperative systems is extended to wireless self-backhauling heterogeneous networks, where the decode-and-forward (DF) protocol is employed to provide a cost-effective and reliable backhaul. The associated trade-offs for a heterogeneous network with inhomogeneous user distributions are investigated through the use of sleeping strategies. Three different policies for switching-off base stations are considered: random, load-based and greedy algorithms. The probability of coverage for the random and load-based sleeping policies is derived. Moreover, an energy efficient base station deployment and operation approach is presented. Numerical results show that the average number of base stations required to support the traffic load at peak-time can be reduced by using the greedy algorithm for base station deployment and that highly clustered networks exhibit a smaller average serving distance and thus, a better probability of coverage.

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Liu, Di. "Tensor precoding for MIMO systems." Thesis, University of York, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546819.

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Wu, Zhuo. "MIMO techniques in cellular systems." Thesis, University of York, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423603.

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Libri sul tema "MIMO systems"

Bessai, Horst J. MIMO Signals and Systems. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/b139100.

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Ezio, Biglieri, ed. MIMO wireless communications. Cambridge: Cambridge University Press, 2007.

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Kaiser, Thomas. Ultra wideband systems with MIMO. Chichester, West Sussex, U.K: Wiley, 2010.

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Duman, Tolga M. Coding for MIMO communication systems. Hoboken, NJ: J. Wiley & Sons, 2007.

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CEng, Azlina Idris, Aidatul Julia Abd Jabar, and Wan Norsyafizan W. Muhamad. MIMO-OFDM Systems with Diversity Technique. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1001-3.

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Costa, Nelson. Multiple-input multiple-output channel models: Theory and practice. Hoboken, N.J: Wiley, 2010.

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Kalis, Antonis, Athanasios G. Kanatas, and Constantinos B. Papadias, eds. Parasitic Antenna Arrays for Wireless MIMO Systems. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7999-4.

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1968-, Tsoulos George V., ed. MIMO system technology for wireless communications. Boca Raton [Fla.]: Taylor & Francis, 2006.

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Li, Jian. MIMO radar signal processing. Hoboken, NJ: J. Wiley & Sons, 2008.

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Li, Jian. MIMO radar signal processing. Hoboken, NJ: J. Wiley & Sons, 2008.

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Capitoli di libri sul tema "MIMO systems"

Fang, Song, Jie Chen, and Hideaki Ishii. "MIMO Systems." In Towards Integrating Control and Information Theories, 113–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49289-6_7.

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Tölli, Antti, Petri Komulainen, Federico Boccardi, Mats Bengtsson, and Afif Osseiran. "Multiuser MIMO Systems." In Mobile and Wireless Communications for IMT-Advanced and Beyond, 89–120. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119976431.ch5.

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Sato, Motoyuki, Weike Feng, Yuta Izumi, and Amila Karunathilake. "MIMO Radar Systems." In Ground Based Synthetic Aperture Radar, 220–90. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003316312-6.

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Chen, Shun-Ping, and Heinz Schmiedel. "MIMO Antenna Systems." In RF Antenna Beam Forming, 103–15. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21765-4_6.

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Gregorio, Fernando, Gustavo González, Christian Schmidt, and Juan Cousseau. "Massive MIMO Systems." In Signal Processing Techniques for Power Efficient Wireless Communication Systems, 193–216. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32437-7_8.

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Chen, Shun-Ping, and Heinz Schmiedel. "MIMO Antenna Systems." In RF Antenna Beam Forming, 105–17. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-67081-7_6.

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Terré, Michel, Mylène Pischella, and Emmanuelle Vivier. "MIMO and Beamforming." In Wireless Telecommunication Systems, 129–53. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118625422.ch5.

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Song, Yongduan, Kai Zhao, and Hefu Ye. "Control of MIMO Systems." In Control of Nonlinear Systems, 127–52. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003474364-7.

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Speidel, Joachim. "MIMO Systems with Precoding." In Signals and Communication Technology, 233–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00548-1_22.

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Speidel, Joachim. "MIMO Systems with Precoding." In Signals and Communication Technology, 269–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67357-4_18.

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Atti di convegni sul tema "MIMO systems"

Melchior, P., C. Inarn, and A. Oustaloup. "Path Tracking Design by Fractional Prefilter Extension to Square MIMO Systems." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87550.

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Abstract (sommario):

The aim of this paper concerns motion control and robust path tracking. An approach based on fractional prefilter synthesis was already developed. It allows tracking optimization according to the fractional derivation order, the actuators physical constraints and the control loop frequency bandwidth. The purpose of this paper is the extension of this approach to multivariable systems. A non integer prefilter synthesis methodology for square MIMO systems (Multi-Input, Multi-Output) is presented. It is based on the MIMO-QFT robust synthesis methodology, taking into account of the plant uncertainties. MIMO-QFT robust synthesis methodology is based on multiple SISO (MISO systems) synthesis by considering the loop couplings. The SISO-QFT synthesis methodology can be then used for each SISO synthesis. Then the prefilters are synthesized. The prefilter parameter optimization is founded on the prefilter output error integral minimization, taking into account of the actuators physical constraints and the tracking performance specifications. An application example is given.

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"Resource Allocation in SVD-assisted Broadband MIMO Systems Using Polynomial Matrix Factorization." In Special Session on Advances in MIMO Communication. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005265403170324.

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Pupolin, Silvano. "MIMO systems." In 2012 IEEE First AESS European Conference on Satellite Telecommunications (ESTEL). IEEE, 2012. http://dx.doi.org/10.1109/estel.2012.6400139.

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Ait-Idir, Tarik. "Session details: MIMO systems III (MIMO Systems symposium)." In IWCMC '09: 2009 International Wireless Communications and Mobile Computing Conference. New York, NY, USA: ACM, 2009. http://dx.doi.org/10.1145/3251290.

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Wang, Cheng-Xiang. "Session details: MIMO systems I (MIMO Systems symposium)." In IWCMC '09: 2009 International Wireless Communications and Mobile Computing Conference. New York, NY, USA: ACM, 2009. http://dx.doi.org/10.1145/3251268.

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Vasilakos, Athanasios (Thanos). "Session details: MIMO systems II (MIMO Systems symposium)." In IWCMC '09: 2009 International Wireless Communications and Mobile Computing Conference. New York, NY, USA: ACM, 2009. http://dx.doi.org/10.1145/3251278.

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Thoma, R. S. "MIMO measurement for double-directional channel modelling." In IEE Seminar MIMO: Communications Systems from Concept to Implementation. IEE, 2001. http://dx.doi.org/10.1049/ic:20010191.

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Burr, A. "Capacity of adaptive MIMO systems in a finite scattering channel." In IEE Seminar MIMO: Communications Systems from Concept to Implementation. IEE, 2001. http://dx.doi.org/10.1049/ic:20010214.

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Wales, S. W. "A MIMO technique within the UTRA TDD standard." In IEE Seminar MIMO: Communications Systems from Concept to Implementation. IEE, 2001. http://dx.doi.org/10.1049/ic:20010200.

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Lohse, N. "MIMO signal description for spatial-variant filter generation." In IEE Seminar MIMO: Communications Systems from Concept to Implementation. IEE, 2001. http://dx.doi.org/10.1049/ic:20010206.

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Rapporti di organizzazioni sul tema "MIMO systems"

Badiey, Mohsen, Aijun Song, and Arthur Trembanis. MIMO Transceiver Systems on AUVs. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada532962.

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Saeed, Muhammad Kamran. Pilot Contamination and Channel Estimation In Massive MIMO Systems. Ames (Iowa): Iowa State University, May 2024. http://dx.doi.org/10.31274/cc-20240624-1126.

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Tatlicioglu, Enver, Bin Xian, Darren M. Dawson, and Timothy Burg. Adaptive Control of Flat MIMO Nonlinear Systems with Additive Disturbance. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada462618.

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Garcia-Sanz, Mario, and Carlos Molins. Advanced Quantitative Robust Control Engineering: New Solutions for Automatic Loop-Shaping for SISO and MIMO Systems. Part 1: SISO Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada521387.

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Wen, Qingsong, Minzhen Ren, and Xiaoli Ma. Fixed-point Design of the Lattice-reduction-aided Iterative Detection and Decoding Receiver for Coded MIMO Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada586964.

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Bernhard, Jennifer T. Enabling Technology for Multiple Input Multiple Output (MIMO) Systems on Mobile Military Platforms: Antennas, Switches, and Packaging. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada544844.

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Anderson, James H. Mechanisms for Scalable Object Sharing in MIMD Multiprocessing Systems. Fort Belvoir, VA: Defense Technical Information Center, July 1998. http://dx.doi.org/10.21236/ada357996.

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Kovitz, B. MIME Media Type for the Systems Biology Markup Language (SBML). RFC Editor, June 2004. http://dx.doi.org/10.17487/rfc3823.

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Allen, S., and D. Wells. MIME Sub-type Registrations for Flexible Image Transport System (FITS). RFC Editor, April 2005. http://dx.doi.org/10.17487/rfc4047.

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Seel, Joachim, Joseph Rand, Will Gorman, Dev Millstein, Ryan Wiser, Will Cotton, Nicholas DiSanti, and Kevin Porter. Generator Interconnection Cost Analysis in the Midcontinent Independent System Operator (MISO) territory. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1891311.

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