Relevant bibliographies by topics / Channel capacity

Academic literature on the topic 'Channel capacity'

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Published: 4 June 2021
Last updated: 1 June 2024

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Journal articles on the topic "Channel capacity"

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MEDEIROS, REX A. C., and FRANCISCO M. DE ASSIS. "QUANTUM ZERO-ERROR CAPACITY." International Journal of Quantum Information 03, no. 01 (March 2005): 135–39. http://dx.doi.org/10.1142/s0219749905000682.

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We define a new kind of quantum channel capacity by extending the concept of zero-error capacity for a noisy quantum channel. The necessary requirement for which a quantum channel has zero-error capacity greater than zero is given. Finally, we point out some directions on how to calculate the zero-error capacity of such channels.
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Kianvash, Farzad, Marco Fanizza, and Vittorio Giovannetti. "Bounding the quantum capacity with flagged extensions." Quantum 6 (February 9, 2022): 647. http://dx.doi.org/10.22331/q-2022-02-09-647.

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In this article we consider flagged extensions of convex combination of quantum channels, and find general sufficient conditions for the degradability of the flagged extension. An immediate application is a bound on the quantum Q and private P capacities of any channel being a mixture of a unitary map and another channel, with the probability associated to the unitary component being larger than 1/2. We then specialize our sufficient conditions to flagged Pauli channels, obtaining a family of upper bounds on quantum and private capacities of Pauli channels. In particular, we establish new state-of-the-art upper bounds on the quantum and private capacities of the depolarizing channel, BB84 channel and generalized amplitude damping channel. Moreover, the flagged construction can be naturally applied to tensor powers of channels with less restricting degradability conditions, suggesting that better upper bounds could be found by considering a larger number of channel uses.
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He, Yifeng, and Ling Guan. "Improving Streaming Capacity in Multi-Channel P2P VoD Systems via Intra-Channel and Cross-Channel Resource Allocation." International Journal of Digital Multimedia Broadcasting 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/807520.

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Multi-channel Peer-to-Peer (P2P) Video-on-Demand (VoD) systems can be categorized intoindependent-channelP2P VoD systems andcorrelated-channelP2P VoD systems. Streaming capacity for a channel is defined as the maximal streaming rate that can be received by every user of the channel. In this paper, we study the streaming capacity problem in multi-channel P2P VoD systems. In an independent-channel P2P VoD system, there is no resource correlation among channels. Therefore, we can find the average streaming capacity for the independent-channel P2P VoD system by finding the streaming capacity for each individual channel, respectively. We propose a distributed algorithm to solve the streaming capacity problem for a single channel in an independent-channel P2P VoD system. The average streaming capacity for a correlated-channel P2P VoD system depends on both the intra-channel and cross-channel resource allocation. To better utilize the cross-channel resources, we first optimize the server upload allocation among channels to maximize the average streaming capacity and then propose cross-channel helpers to enable cross-channel sharing of peer upload bandwidths. We demonstrate in the simulations that the correlated-channel P2P VoD systems with both intra-channel and cross-channel resource allocation can obtain a higher average streaming capacity compared to the independent-channel P2P VoD systems with only intra-channel resource allocation.
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Ostroumov, O. A., and A. D. Sinyuk. "BROADCAST CHANNEL TRANSMISSION CAPACITY." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 183 (September 2019): 33–42. http://dx.doi.org/10.14489/vkit.2019.09.pp.033-042.

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The most important studies of well-known Broadcast Communication Channels (BCC) models are associated with obtaining accurate information efficiency estimates(IE). Earlier, the coding problem was stated, the joint information measure (JI) of the proposed BCC model was introduced and investigated. Then the information capacity (IC) was introduced and the conditions for maximizing the average JI were defined, the uncertainty concept was defined, and an evidence-based adjustment of the Feinstein inequality for the channel model under study was made. In the present paper, the general information accurate estimate transmitted via the BCC by proving the fundamental coding theorems is obtained. On the basis of the previously obtained results, the inverse coding theorem for BCC was proved, which determines the condition for the code error average probability striving to one, which consists in choosing a code with a speed exceeding IE BCC. The Feinstein inequality role on the basis of which the direct coding theorem roof is carried out is determined. The theorem states that there are codes with a low error probability, provided that the code rate does not exceed the channel's IE. The coding theorems cumulative result proves that the IE and the throughput (BC) coincide. An accurate estimate of BC BCC is obtained. The results obtained do not contradict and extend the well-known IE studies of various BCC models and can be used by designers to assess the synthesized communication systems potential capabilities, including BCC channels. The purpose of further research is the gain estimate through IE channel transmission in comparison with the successive transmission through the component channels, which will outline the conditions for the preferred use of the BCC.
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DiVincenzo, David P., Peter W. Shor, and John A. Smolin. "Quantum-channel capacity of very noisy channels." Physical Review A 57, no. 2 (February 1, 1998): 830–39. http://dx.doi.org/10.1103/physreva.57.830.

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Pang, Arthur O. T., Noah Lupu-Gladstein, Hugo Ferretti, Y. Batuhan Yilmaz, Aharon Brodutch, and Aephraim M. Steinberg. "Experimental Communication Through Superposition of Quantum Channels." Quantum 7 (October 3, 2023): 1125. http://dx.doi.org/10.22331/q-2023-10-03-1125.

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Information capacity enhancement through the coherent control of channels has attracted much attention of late, with work exploring the effect of coherent control of channel causal orders, channel superpositions, and information encoding. Coherently controlling channels necessitates a non-trivial expansion of the channel description, which for superposing qubit channels, is equivalent to expanding the channel to act on qutrits. Here we explore the nature of this capacity enhancement for the superposition of channels by comparing the maximum coherent information through depolarizing qubit channels and relevant superposed and qutrit channels. We show that the expanded qutrit channel description in itself is sufficient to explain the capacity enhancement without any use of superposition.
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Huang, Da Zu, Zhi Gang Chen, Xin Li, and Ying Guo. "Quantum Polarization Codes for Capacity-Achieving in Discrete Memoryless Quantum Channel." Applied Mechanics and Materials 44-47 (December 2010): 2978–82. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2978.

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Quantum channel combining and splitting, called quantum channel polarization, is suggested to design qubit sequences that achieve the symmetric capacity for any given discrete memoryless quantum channels. The polarized quantum channels can be well-conditioned for quantum channel codes, through which one need to send data at rate 1 by employing quantum channels with capacity near 1 and at rate 0 by employing the remaining quantum channels.
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WU, YU-CHUN, ZHENG-WEI ZHOU, and GUANG-CAN GUO. "THE HSW CHANNEL CAPACITY FOR THE DIAGONAL UNITAL QUDIT CHANNELS." International Journal of Quantum Information 02, no. 04 (December 2004): 489–93. http://dx.doi.org/10.1142/s021974990400047x.

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The Holevo–Schumacher–Westermoreland (HSW) channel capacity for the diagonal unital qudit channels is considered. In Phys. Rev.A69, 022302, the HSW channel capacity for the diagonal unital qudit channels Φ is given as χ(Φ)=1- min ρH(Φ(ρ)), where minimization is over the input states of the channel. In this paper, using the concavity of von Neumann entropy, we show that using only pure states we can work out its HSW channel capacity. Hence, our result simplifies the computation.
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Leung, Debbie, and John Watrous. "On the complementary quantum capacity of the depolarizing channel." Quantum 1 (September 19, 2017): 28. http://dx.doi.org/10.22331/q-2017-09-19-28.

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The qubit depolarizing channel with noise parameter η transmits an input qubit perfectly with probability 1−η, and outputs the completely mixed state with probability η. We show that its complementary channel has positive quantum capacity for all η>0. Thus, we find that there exists a single parameter family of channels having the peculiar property of having positive quantum capacity even when the outputs of these channels approach a fixed state independent of the input. Comparisons with other related channels, and implications on the difficulty of studying the quantum capacity of the depolarizing channel are discussed.
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Goldsmith, A. J., and P. P. Varaiya. "Capacity of fading channels with channel side information." IEEE Transactions on Information Theory 43, no. 6 (1997): 1986–92. http://dx.doi.org/10.1109/18.641562.

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Dissertations / Theses on the topic "Channel capacity"

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VIEIRA, ROBSON DOMINGOS. "MIMO MEASURED CHANNELS: CAPACITY RESULTS AND ANALYSIS OF CHANNEL PARAMETERS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7954@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Sistemas com múltiplas antenas transmissoras e receptoras, também conhecidos como sistemas MIMO (Multiple Input-Multiple Output), têm sido apontados como uma solução para aumentar a capacidade de enlaces sem fio, permitindo aos usuários utilizar aplicações com altas taxas de dados. Isto é extremamente importante em sistemas onde a capacidade obtida com as técnicas tradicionais é bastante limitada devido às características do ambiente de propagação. Com o sistema MIMO, algumas destas características são exploradas para criar canais paralelos e obter aumento expressivo de capacidade. A análise da capacidade de sistemas MIMO se baseia em uma modelagem desenvolvida a partir do comportamento estatístico dos pares de enlaces existentes entre as múltiplas antenas transmissoras e receptoras. Existe, portanto, um grande interesse em medir este comportamento para situações típicas bem como em relacioná-lo a determinados parâmetros do sistema. Nesta tese apresentam-se os resultados de uma campanha de medidas visando caracterizar canais MIMO de faixa estreita e faixa larga em ambientes fechados (indoor) com uma freqüência de portadora de 2GHz. A partir dos dados medidos, avalia-se a capacidade e diversos parâmetros do canal espaço-temporal. Os parâmetros do canal MIMO são estimados através do algoritmo FD-SAGE e as dispersões temporal e espacial do canal são calculadas a partir dos parâmetros estimados. Uma análise dos autovalores da matriz do canal MIMO é realizada com o objetivo de relacionar os valores da capacidade ao número de canais paralelos. É analisada, ainda, a correlação entre a capacidade e os parâmetros físicos do canal, tais como espaçamento entre os elementos do arranjo, espalhamento angular, espalhamento dos retardos, número e potência dos multipercursos.
Multiple antenna systems known as MIMO (Multiple Input Multiple Output) systems have been proposed as an effective way to address the user demand for high data rate applications in wireless systems. This is especially important in systems where the capacity attained with traditional techniques is very limited due to the adverse characteristics of the propagation environment. With MIMO, some of these characteristics are used to create parallel channels producing significant increase in capacity. The analysis of MIMO capacity is based on models developed from the statistical behavior of the multiple links between the transmitting and receiving antennas, and therefore there has been large interest in measuring these characteristics in typical scenarios and in relating the data to system parameters. In this thesis the results of a MIMO wideband measurement campaign carried out in an indoor scenario with a carrier frequency of 2 GHz is presented. The wideband and narrowband channel capacity and several channel parameters are evaluated from the measured data. The channel parameters are estimated using the frequency domain Space-Alternating Generalized Expectation maximization (FD- SAGE) algorithm. Temporal and spatial dispersions of the multipath channel are calculated from the estimated parameters and an eigenvalue analysis is performed seeking to relate the capacity values to the number of parallel channels. In addition, the correlation between channel capacity and physical parameters as antenna spacing, angle spread, delay spread, number and power of multipath components is investigated.
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Abdelaziz, Amr Mohamed. "Information Theoretical Studies on MIMO Channel with Limited Channel State Information." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500592938716914.

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Reátegui, del Águila Fernando. "On the capacity of cognitive interference channel structures." Thesis, University of Surrey, 2015. http://epubs.surrey.ac.uk/807077/.

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The cognitive interference channel extends the classical two-user interference channel to have unidirectional cooperation at the transmitters. In this model, the cognitive transmitter is assumed to have noncausal knowledge of the other transmitter's current message (primary message). This a priori knowledge is used by the cognitive user to accomplish its two main purposes, i.e., to relay the primary message in order to boost the primary user's data rate and to maximise its own data rate by cancelling the interference at its receiver. The cognitive interference channel is well studied in the literature and capacity results are available for the weak and very strong interference regimes, amongst others. A general solution is still elusive. In this thesis we study the capacity region of cognitive structures that are based in their core on the cognitive interference channel but with the aggregate that an additional node is considered, e.g., an additional receiver node, an additional transmitter node or a relay node. The cognitive broadcast interference channel consists of the cognitive interference channel with an additional receiver. The cognitive side serves either one or two receivers and the interference goes from the cognitive transmitter to the primary receiver only. In this model we provide a general achievable rate region when the cognitive side serves two receivers. We analyse the discrete memoryless channel and show that the region simplifies to existing results in the literature when certain assumptions are made. An achievable rate region for the Gaussian channel is also provided for the case where the cognitive side sends common information to both receivers. When the cognitive side serves only one receiver, we provide an achievable rate region and an outer bound and show the gap graphically. The cognitive interference channel with a relay consists of the cognitive interference channel with an additional relay node. In this model we show that as in the interference channel with a relay, interference forwarding is also beneficial. We describe analytically achievable rate regions and show the benefits of interference forwarding. We also provide an achievable rate region with generalised interference forwarding, i.e., the relay forwards the intended message and the interference simultaneously, and show that allowing the relay to allocate part of its power to forward interference is beneficial when we are in the strong but not in the very strong interference regime. The cognitive interference channel with causal unidirectional destination cooperation is formed by transferring the relaying capabilities of the relay node in the previous model to the cognitive receiver and its operation is causal rather than strictly causal. In this model we show that instantaneous amplify and forward is good enough to achieve the capacity region of the Gaussian channel. We derive analytically an inner and outer bounds and show that they coincide for certain values of the antenna gain at the relay in the very strong interference regime. We also analyse the cognitive interference channel with a relay for the case where the relay operates causally. The capacity region is obtained for a special case of very strong interference. The cognitive multiple access interference channel consists of the cognitive interference channel with an additional cognitive transmitter. In this model the interference goes from the primary user to the cognitive receiver only. The cognitive users form a MAC channel. We show for this scenario that dirty paper coding achieves the capacity region in the Gaussian case. In the analysis we make use of encoding techniques first utilised for the MAC with state available noncausally at the encoder.
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Clower, Terry L. "Increasing Telecommunications Channel Capacity: Impacts on Firm Profitability." Thesis, University of North Texas, 1997. https://digital.library.unt.edu/ark:/67531/metadc279298/.

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In calling for the deployment of high-capacity telecommunications infrastructures, the Clinton Administration is relying on market forces to drive demand toward self-sustaining development. There is little doubt that many firms will embrace the new telecommunications services for a variety of reasons including market differentiation, vertical market integration, and other organization-specific factors. However, there is little evidence at the firm level that adopting the use of increased-capacity telecommunications technologies is associated with improvements in firm profitability. This study seeks to identify the presence of impacts on firm income that can be associated with the adoption of T1 telecommunications services.
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Potter, Christopher G., Adam G. Panagos, Kurt Kosbar, and William Weeks. "OPTIMAL TRAINING PARAMETERS FOR CONTINUOUSLY VARYING MIMO CHANNELS." International Foundation for Telemetering, 2005. http://hdl.handle.net/10150/605025.

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ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada
To correctly demodulate a signal sent through a multiple-input multiple-output (MIMO) channel, a receiver may use training to learn the channel parameters. The choice of training parameters can significantly impact system performance. Training too often yields low throughput while training infrequently produces poor channel estimates and increased transmission errors. Previous work on optimal training parameters has focused on the block fading Rayleigh model. This work examines a more general case; finding the training parameters that maximize throughput for a continuously varying channel. Training parameters that maximize a lower bound on channel capacity are determined via simulation, and general guidelines are presented for selecting optimal training parameters.
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Guha, Saikat 1980. "Classical capacity of the free-space quantum-optical channel." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/87908.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
MIT Institute Archives copy has MIT Research Laboratory of Electronics t.p.
Also issued with MIT Research Laboratory of Electronics t.p. preceding thesis t.p.
Includes bibliographical references (leaves 114-116).
Exploring the limits to reliable communication rates over quantum channels has been the primary focus of many researchers over the past few decades. In the present work, the classical information carrying capacity of the free-space quantum optical channel has been studied thoroughly in both the far-field and near-field propagation regimes. Results have been obtained for the optimal capacity, in which information rate is maximized over both transmitter encodings and detection schemes at the receiver, for the entanglement-assisted capacity, and also for sub-optimal systems that employ specific transmitter and receiver structures. For the above cases, several new broadband results have been obtained for capacity in the presence of both diffraction limited loss and additive fluctuations emanating from a background blackbody radiation source at thermal equilibrium.
by Saikat Guha.
S.M.
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Bonello, Nicholas. "Near-capacity fixed-rate and rateless channel code constructions." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/68774/.

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Fixed-rate and rateless channel code constructions are designed for satisfying conflicting design tradeoffs, leading to codes that benefit from practical implementations, whilst offering a good bit error ratio (BER) and block error ratio (BLER) performance. More explicitly, two novel low-density parity-check code (LDPC) constructions are proposed; the first construction constitutes a family of quasi-cyclic protograph LDPC codes, which has a Vandermonde-like parity-check matrix (PCM). The second construction constitutes a specific class of protograph LDPC codes, which are termed as multilevel structured (MLS) LDPC codes. These codes possess a PCM construction that allows the coexistence of both pseudo-randomness as well as a structure requiring a reduced memory. More importantly, it is also demonstrated that these benefits accrue without any compromise in the attainable BER/BLER performance. We also present the novel concept of separating multiple users by means of user-specific channel codes, which is referred to as channel code division multiple access (CCDMA), and provide an example based on MLS LDPC codes. In particular, we circumvent the difficulty of having potentially high memory requirements, while ensuring that each user’s bits in the CCDMA system are equally protected. With regards to rateless channel coding, we propose a novel family of codes, which we refer to as reconfigurable rateless codes, that are capable of not only varying their code-rate but also to adaptively modify their encoding/decoding strategy according to the near-instantaneous channel conditions. We demonstrate that the proposed reconfigurable rateless codes are capable of shaping their own degree distribution according to the nearinstantaneous requirements imposed by the channel, but without any explicit channel knowledge at the transmitter. Additionally, a generalised transmit preprocessing aided closed-loop downlink multiple-input multiple-output (MIMO) system is presented, in which both the channel coding components as well as the linear transmit precoder exploit the knowledge of the channel state information (CSI). More explicitly, we embed a rateless code in a MIMO transmit preprocessing scheme, in order to attain near-capacity performance across a wide range of channel signal-to-ratios (SNRs), rather than only at a specific SNR. The performance of our scheme is further enhanced with the aid of a technique, referred to as pilot symbol assisted rateless (PSAR) coding, whereby a predetermined fraction of pilot bits is appropriately interspersed with the original information bits at the channel coding stage, instead of multiplexing pilots at the modulation stage, as in classic pilot symbol assisted modulation (PSAM). We subsequently demonstrate that the PSAR code-aided transmit preprocessing scheme succeeds in gleaning more information from the inserted pilots than the classic PSAM technique, because the pilot bits are not only useful for sounding the channel at the receiver but also beneficial for significantly reducing the computational complexity of the rateless channel decoder.
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Panagos, Adam G., and Kurt Kosbar. "A GRAPHICAL USER INTERFACE MIMO CHANNEL SIMULATOR." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/605799.

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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California
Multiple-input multiple-output (MIMO) communication systems are attracting attention because their channel capacity can exceed single-input single-output systems, with no increase in bandwidth. While MIMO systems offer substantial capacity improvements, it can be challenging to characterize and verify their channel models. This paper describes a software MIMO channel simulator with a graphical user interface that allows the user to easily investigate a number of MIMO channel characteristics for a channel recently proposed by the 3rd Generation Partnership Project (3GPP).
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Tau, Sieskul Bamrung [Verfasser]. "NLoS Localization and UWB Channel Capacity Analysis / Bamrung Tau Sieskul." Aachen : Shaker, 2010. http://d-nb.info/1080766995/34.

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Shukla, Rahul. "Effects of UE Speed on MIMO Channel Capacity in LTE." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc862877/.

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With the introduction of 4G LTE, multiple new technologies were introduced. MIMO is one of the important technologies introduced with fourth generation. The main MIMO modes used in LTE are open loop and closed loop spatial multiplexing modes. This thesis develops an algorithm to calculate the threshold values of UE speed and SNR that is required to implement a switching algorithm which can switch between different MIMO modes for a UE based on the speed and channel conditions (CSI). Specifically, this thesis provides the values of UE speed and SNR at which we can get better results by switching between open loop and closed loop MIMO modes and then be scheduled in sub-channels accordingly. Thus, the results can be used effectively to get better channel capacity with less ISI. The main objectives of this thesis are: to determine the type of MIMO mode suitable for a UE with certain speed, to determine the effects of SNR on selection of MIMO modes, and to design and implement a scheduling algorithm to enhance channel capacity.
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Books on the topic "Channel capacity"

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Board, British Railways, ed. Channel Tunnel train services: BR study report on long-term route and terminal capacity. [London]: British Railways Board, 1988.

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Lau, Allen N. L. Probabilistic characterization of capacity and adaptive power control for multi-cell DS-CDMA reverse link channel. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.

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Society, Railway Development, ed. Channel tunnel train services: RDS commentary on BR study report on long-term route and terminal capacity. [Leatherhead]: Railway Development Society, 1990.

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Koltun, G. F. Hydrologic considerations for estimation of storage-capacity requirements of impounding and side-channel reservoirs for water supply in Ohio. Columbus, Ohio: U.S. Dept. of the Interior, U.S. Geological Survey, 2001.

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Koltun, G. F. Hydrologic considerations for estimation of storage-capacity requirements of impounding and side-channel reservoirs for water supply in Ohio. Columbus, Ohio: U.S. Dept. of the Interior, U.S. Geological Survey, 2001.

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Rupf, Marcel. Coding for CDMA channels and capacity. Konstanz: Hartung-Gorre, 1994.

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B, Smith Joel, Klein Richard J. T, Huq Saleemul, and Potsdam-Institut für Klimafolgenforschung, eds. Climate change, adaptive capacity and development. London: Imperial College Press, 2003.

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E, Goertz Margaret, Floden Robert E, and Educational Resources Information Center (U.S.), eds. Building capacity for education reform. [New Brunswick, NJ: Consortium for Policy Research in Education, 1995.

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Teuatabo, Nakibae. National capacity self assessment project: Thematic area relating to capacity needs to implement the United Nations framework convention on climate change. Bikenibeu, Kiribati: [s.n., 2007.

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IUCN--The World Conservation Union. Bangladesh Country Office., ed. Policy reforms in response to climate change and capacity of local institutions: Bangladesh perspective. Dhaka: IUCN Bangladesh Country Office, 2008.

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Book chapters on the topic "Channel capacity"

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Palm, Günther. "Channel Capacity." In Novelty, Information and Surprise, 89–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29075-6_7.

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Yeung, Raymond W. "Channel Capacity." In A First Course in Information Theory, 149–86. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4419-8608-5_8.

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Gibson, Jerry. "Channel Capacity." In Information Theory and Rate Distortion Theory for Communications and Compression, 49–80. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01680-6_4.

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Palm, Günther. "Channel Capacity." In Information Science and Statistics, 105–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65875-8_8.

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Djordjevic, Ivan, William Ryan, and Bane Vasic. "Optical Channel Capacity." In Coding for Optical Channels, 353–98. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-5569-2_10.

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Battail, Gérard. "Channel Capacity and Channel Coding." In Information and Life, 93–131. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7040-9_5.

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Laurenti, Nicola. "Channel Coding and Capacity." In Principles of Communications Networks and Systems, 373–429. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119978589.ch6.

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Balado, Félix. "Genetic Channel Capacity Revisited." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 85–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32711-7_7.

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Belavkin, Roman V., Panos M. Pardalos, Jose C. Principe, and Ruslan L. Stratonovich. "Channel capacity. Important particular cases of channels." In Theory of Information and its Value, 249–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-22833-0_8.

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Gazi, Orhan. "Entropy for Continuous Random Variables Discrete Channel Capacity, Continuous Channel Capacity." In Information Theory for Electrical Engineers, 97–173. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8432-4_2.

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Conference papers on the topic "Channel capacity"

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Kol, Gillat, and Ran Raz. "Interactive channel capacity." In the 45th annual ACM symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2488608.2488699.

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Millen, Jonathan K. "Covert Channel Capacity." In 1987 IEEE Symposium on Security and Privacy. IEEE, 1987. http://dx.doi.org/10.1109/sp.1987.10013.

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Langberg, Michael, and Oron Sabag. "Competitive Channel-Capacity." In 2023 IEEE International Symposium on Information Theory (ISIT). IEEE, 2023. http://dx.doi.org/10.1109/isit54713.2023.10206801.

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Papazafeiropoulos, Anastasios, and Tharmalingam Ratnarajah. "Ergodic channel capacity for generalized fading channels." In 2014 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2014. http://dx.doi.org/10.1109/wcnc.2014.6951936.

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Islam, Mohammad Rakibul, Jinsang Kim, and Md Shamsul Arefin. "MIMOME channel secrecy capacity." In 2008 11th International Conference on Computer and Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccitechn.2008.4802990.

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Lebrun, G., M. Faulkner, M. Shafi, and P. J. Smith. "MIMO Ricean channel capacity." In 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577). IEEE, 2004. http://dx.doi.org/10.1109/icc.2004.1313068.

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Buyukcorak, Saliha, and Gunes Karabulut Kurt. "Mimo channel capacity measurements." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204679.

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Whiteson, Adam. "Streak-tube channel capacity." In San Diego - DL tentative, edited by Paul A. Jaanimagi. SPIE, 1992. http://dx.doi.org/10.1117/12.50529.

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Bartunik, Max, Matthias Streb, Harald Unterweger, Jakob Haller, and Jens Kirchner. "Increasing the Channel Capacity." In NANOCOM '21: The Eighth Annual ACM International Conference on Nanoscale Computing and Communication. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3477206.3477449.

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Haeupler, Bernhard. "Interactive Channel Capacity Revisited." In 2014 IEEE 55th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2014. http://dx.doi.org/10.1109/focs.2014.32.

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Reports on the topic "Channel capacity"

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Baker, C. R., and S. Ihara. Capacity of the Stationary Gaussian Channel. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada207254.

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Frey, Michael R. Capacity of the Independent Increment Noise Channel. Fort Belvoir, VA: Defense Technical Information Center, October 1990. http://dx.doi.org/10.21236/ada235545.

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Baker, C. R., and S. Ihara. Information Capacity of the Stationary Gaussian Channel. Fort Belvoir, VA: Defense Technical Information Center, July 1989. http://dx.doi.org/10.21236/ada215408.

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Bhandari, Vartika, and Nitin H. Vaidya. Connectivity and Capacity of Multi-Channel Wireless Networks with Channel Switching Constraints. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada486514.

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Moskowitz, Ira S., Patricia A. Lafferty, and Farid Ahmed. On LSB Spatial Domain Steganography and Channel Capacity. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada489843.

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Bhandari, Vartika, and Nitin H. Vaidya. Capacity of Multi-Channel Wireless Networks with Random Channel Assignment: A Tight Bound. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada495206.

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Friedland, Gerald, and Alfredo Metere. An Isomorphism between Lyapunov Exponents and Shannon's Channel Capacity. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1377767.

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Frey, Michael R. Capacity of the Poisson Channel with Random Noise Intensity. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada207226.

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Heifetz, Alexander, Jafar Saniie, Xin Huang, Dmitry Shribak, Eugene R. Koehl, Sasan Bakhtiari, and Richard B. Vilim. Evaluation of Acoustic Channel Capacity for Complex Piping Topology. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1571245.

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Bhandari, Vartika, and Nitin H. Vaidya. Capacity of Multi-Channel Wireless Networks with Random (c,f) Assignment. Fort Belvoir, VA: Defense Technical Information Center, June 2007. http://dx.doi.org/10.21236/ada486638.

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