Topic
Communication channel
About: Communication channel is a research topic. Over the lifetime, 137411 publications have been published within this topic receiving 1715077 citations. The topic is also known as: communication channel & communications channel.
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Papers
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TL;DR: In this article, a technique to design the channel frequency allocation in order to minimize the crosstalk due to FWM is presented, which is obtained at the expense of some expansion of the system bandwidth.
Abstract: Crosstalk due to four-wave mixing (FWM) is the dominant nonlinear effect in long-haul multichannel optical communication systems employing dispersion-shifted fiber. A technique to design the channel frequency allocation in order to minimize the crosstalk due to FWM is presented. It is shown that suitable unequal channel separations can be found for which no four-wave mixing product term is superimposed on any of the transmitted channels. This is obtained at the expense of some expansion of the system bandwidth. Simulations are presented to show the effectiveness of this technique in a 10-channel, 10-Gb/s per channel, system. >
263 citations
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TL;DR: An adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level and the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance.
Abstract: This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize joint active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity present in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.
262 citations
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TL;DR: It is proved that, in the limit of increasing block length N, the capacity of the discrete-time Gaussian channel (DTGC) with ISI using a per block average-energy input constraint (N-block DTGC) is indeed also the capacity when using the per symbol average- energy constraint.
Abstract: The discrete-time Gaussian channel with intersymbol interference (ISI) where the inputs are subject to a per symbol average-energy constraint is considered. The capacity of this channel is derived by means of a hypothetical channel model called the N-circular Gaussian channel (NCGC), whose capacity is readily derived using the theory of the discrete Fourier transform. The results obtained for the NCGC are used further to prove that, in the limit of increasing block length N, the capacity of the discrete-time Gaussian channel (DTGC) with ISI using a per block average-energy input constraint (N-block DTGC) is indeed also the capacity when using the per symbol average-energy constraint. >
262 citations
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16 Aug 1998TL;DR: In this work, necessary and sufficient conditions for optimality are introduced along with algorithms for solving the discrete bit allocation problems for a general class of channels and can be used to construct efficient loading algorithms in practice.
Abstract: In this work the problem of discrete bit allocation for multicarrier modulation systems is considered. Necessary and sufficient conditions for optimality are introduced along with algorithms for solving the discrete bit allocation problems for a general class of channels. These results can be used to construct efficient loading algorithms in practice.
262 citations
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TL;DR: This work proposes a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point, and investigates the achievable performance of the game in terms of system throughput and fairness.
Abstract: We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.
262 citations