It is well known that multiple-input-multiple-output (MIMO) systems promise to provide significant increases in system capacity for future wireless communication systems. However, realization of the highest potential ...
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ISBN:
(纸本)0780385217
It is well known that multiple-input-multiple-output (MIMO) systems promise to provide significant increases in system capacity for future wireless communication systems. However, realization of the highest potential capacity of a MIMO system will require a high signal to noise ratio and even more practical, any interference from other access point or other system has to be considered. The unpredicted interference was normally treated as an additional noise (colored-noise) in a MIMO system and this will significantly reduce the expected system capacity. In this paper, architecture of MIMO iterative arrayprocessing with LMMSE Turbo equalization is proposed to eliminate co-channel interference (CCI), multiple antenna interference (MAI) and inter symbol interference (ISI) in order to maintain high capacity of MIMO system. This architecture performs iterative operations between MIMO beamforming, soft interference cancellation, channel estimator and the turbo equalization. Also, a theoretical study of MIMO capacity with beamforming under CCI is presented.
The objective of the beamforming with the exploitation of a sensor array is to enhance the signals of the sources from desired directions, suppress the noises and the interfering signals from other directions, and/or ...
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The objective of the beamforming with the exploitation of a sensor array is to enhance the signals of the sources from desired directions, suppress the noises and the interfering signals from other directions, and/or simultaneously provide the localization of the associated sources. In this paper, we present a higher order cumulant-based beamforming algorithm, namely, the super-exponential blind adaptive beamforming algorithm, which is extended from the super-exponential algorithm (SEA) and the inverse filter criteria (IFC). While both SEA and IFC assume noise-free conditions, this requirement is no longer needed, and all the noise components are taken into account in the proposed algorithm. Two special conditions are derived under which the proposed blind beamforming algorithm achieves the performance of the corresponding optimal nonblind beamformer in the sense of minimum mean square error (MMSE). Simulation results show that the proposed algorithm is effective and robust to diverse initial weight vectors;its performance with the use of the fourth-order cumulants is close to that of the nonblind optimal MMSE beamformer.
This paper proposes an adaptive multiantenna transceiver for narrowband reception. Blind channel tracking algorithms are developed to track the eigen directions of the channel directly instead of the channel itself. T...
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This paper proposes an adaptive multiantenna transceiver for narrowband reception. Blind channel tracking algorithms are developed to track the eigen directions of the channel directly instead of the channel itself. Two algorithms are proposed to track the column space of the channel at the receiver, based on the received data. One of the algorithms is free of any division operation, which is more favorable in practice. For the row space of the channel, two approaches are proposed as well. The first approach requires periodic feedback of the demodulated signal from the receiver back to the transmitter where it can make use of its knowledge on the prior transmitted symbols to estimate the row space. In the second approach, the estimation is done at the receiver based on the detected symbols, and the estimated row space is sending back to the transmitter. adaptive resource allocation is also incorporated into the design.
In mobile communication systems the space-time (S-T) channel matrix at the base station (BS) can be parameterized by a reduced set of unknowns with negligible distortion. In this correspondence, it is proposed to esti...
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In mobile communication systems the space-time (S-T) channel matrix at the base station (BS) can be parameterized by a reduced set of unknowns with negligible distortion. In this correspondence, it is proposed to estimate the S-T channel under the constraint that the channel matrix is low-rank (the rank of the matrix accounts for the degrees of space and/or time diversity in the S-T channel). Low-rank properties of the S-T channel for a GSM/DCS system (rank is less than or equal to2), prompted us to adapt the reduced-rank method for linear regression to estimate a rank-one channel. The proposed estimates coincide (or converge) with the maximum-likelihood (ML) approach when the training sequence is uncorrelated (or when the transmitted symbols are independent, i.e., in stochastic optimization). The beamforming weights and temporal channel are estimated jointly to reduce both cochannel interference (CCI) and intersymbol interference (ISI). The proposed receiver is easily adapted to the front-end architecture of the existing BS with minor modifications. The adaptive version of the algorithm is based on direct matrix inversion, guaranteeing convergence within a GSM burst and tracking time-varying channel and/or asynchronous interference.
This paper describes a class of partially adaptivearrays with adaptiveprocessing applied to the outputs of steered subarrays, The problem is to detect a signal or estimate its direction of arrival in the presence of...
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This paper describes a class of partially adaptivearrays with adaptiveprocessing applied to the outputs of steered subarrays, The problem is to detect a signal or estimate its direction of arrival in the presence of jammers, The advantage of applying adaptiveprocessing to subarrays is that it requires much less CPU time than the corresponding fully adaptiveprocessing. The subarrayprocessing equations for the two kinds of problem are described. In this paper, we compare partially adaptiveprocessing performance with fully adaptiveprocessing performance in the case of the following antenna and signal sources: - array with regularly spaced sensors;- between 20 and 100 elements;- between 3 and 20 subarrays;- a single jammer;- desired signal from the antenna zenith. We suggest a method for determining the optimal subarray configurations in this case. An example is given to show that the performance of an antenna with five subarrays is comparable to that of a fully adaptive thirty-element array for eliminating a single jammer with a target at the zenith. (C) Elsevier, Paris.
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