Closed-form correlative coding (CFC/sub 2/) blind identification of MIMO channels: isometry fitting to second order statistics

Barroso, V.; Moura, J.M.F.

doi:10.1109/78.917810

Cited by 26 publications

(15 citation statements)

References 24 publications

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“…Furthermore, various joint approaches combining channel estimation with data symbol detection at the receiver were also proposed for CDMA [289], [291], SISO OFDM [292] and MIMO OFDM [184], [293] systems. However, in the context of BLAST or SDMA type multiuser MIMO OFDM systems, all channel estimation techniques found in the literature were developed under the assumption of either the underloaded [143], [168], [227], [238], [285], [288], [294] or the fully loaded [144], [184], [190], [209], [261], [293], [295] scenario mentioned above. Unsurprisingly, in rank-deficient MIMO OFDM systems the task of channel estimation becomes extremely challenging, since the associated significant degradation of the rank-deficient MUD's performance will inevitably result in a further degraded performance of the associated channel estimators, especially in decisiondirected type receivers, which are quite sensitive to error propagation [1].…”

Section: B Channel Estimationmentioning

confidence: 99%

“…In the literature, a number of blind channel estimation techniques have been proposed for MIMO OFDM systems [103], [227], [238], [261], [285], where an attempt is made to avoid the reduction of the effective throughout by dispensing with the transmission of known FD channelsounding pilots. However, most of these approaches suffer from either a slow convergence rate or a performance degradation, owing to the inherent limitations of blind search mechanisms.…”

Section: B Channel Estimationmentioning

confidence: 99%

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Multiuser MIMO-OFDM for Next-Generation Wireless Systems

2007

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Section: B Channel Estimationmentioning

confidence: 99%

Section: B Channel Estimationmentioning

confidence: 99%

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

2007

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“…Furthermore, various joint approaches combining channel estimation with data symbol detection at the receiver were also proposed for Code Division Multiple Access (CDMA) [12], [21], SISO OFDM [25]- [27] and MIMO OFDM [22], [28] systems. However, in the context of BLAST or SDMA type multi-user MIMO OFDM systems, all channel estimation techniques found in the literature were developed under the assumption that the number of users L is lower than [7]- [9], [17], [20] or equal to [10], [18], [22]-1536-1276/07$25.00 c 2007 IEEE [24], [28] the number of receiver antennas P . This assumption is critical for the following reasons.…”

mentioning

confidence: 99%

Iterative Joint Channel Estimation and Multi-User Detection for Multiple-Antenna Aided OFDM Systems

Jiang

Akhtman

Hanzo

2007

IEEE Trans. Wireless Commun.

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Abstract-Multiple-Input-Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to Single-Input-Single-Output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitterreceiver links to be estimated. In the context of the Bell LAyered Space-Time architecture (BLAST) or Space Division Multiple Access (SDMA) multi-user MIMO OFDM systems, none of the known channel estimation techniques allows the number of users to be higher than the number of receiver antennas, which is often referred to as a "rank-deficient" scenario, owing to the constraint imposed by the rank of the MIMO channel matrix. Against this background, in this paper we propose a new Genetic Algorithm (GA) assisted iterative Joint Channel Estimation and Multi-User Detection (GA-JCEMUD) approach for multi-user MIMO SDMA-OFDM systems, which provides an effective solution to the multi-user MIMO channel estimation problem in the above-mentioned rank-deficient scenario. Furthermore, the GAs invoked in the data detection literature can only provide a hard-decision output for the Forward Error Correction (FEC) or channel decoder, which inevitably limits the system's achievable performance. By contrast, our proposed GA is capable of providing "soft" outputs and hence it becomes capable of achieving an improved performance with the aid of FEC decoders. A range of simulation results are provided to demonstrate the superiority of the proposed scheme.Index Terms-Channel estimation, genetic algorithm, multiple-input-multiple-output, multi-user detection, orthogonal frequency division multiplexing, space division multiple access.

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“…The residual unknown rotation matrix can be solved under several identification strategies depending on the source characteristics, number of available data samples, etc. Some options include: iterative joint diagonalization of several cumulant matrices for non-Gaussian signals [7], iterative joint diagonalization of several covariance matrices for instantaneously mixed stationary sources with sufficiently diverse but unknown ¾nd order spectra [8], closed-form isometry fitting for convolutively mixed stationary sources with sufficiently diverse and known ¾nd order spectra [6], analytical signal separation for constantmodulus sources [2], iterative demodulation of finite-alphabet sources [3], globally convergent iterative separation of independent and identically distributed sources by kurtosis-based criteria [11].…”

Section: Introductionmentioning

confidence: 99%

“…In these wireless systems, unknown spacetime channels mix the co-channel user signals prior to base station reception. Blind signal separation techniques are needed at the receiver to reconstruct the source signals from the antenna array observations [2], [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation and Channel Identification: Exploiting 2nd-Order Statistics in Bayesian Frameworks

Lopes¹,

Xavier²,

Barroso³

2003

Journal of Communication and Information Systems

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Abstract-We study how ¾nd order statistics (SOS) can be exploited in two signal processing problems, blind separation of binary sources and trained-based multi-user channel identification, in a Bayesian context where a prior on the mixing channel matrix is available. It is well known that the SOS of the received data permit to resolve the unknown mixing matrix, up to an orthogonal factor. In a Bayesian framework, this residual orthogonal mixing matrix becomes a random object in its own right, with an associated distribution over the group of orthogonal matrices. This distribution is induced by the prior on the mixing matrix, and must be known for optimum statistical processing. We rely on a previous theoretical work to provide these answers, and discuss applications for this induced probability density function (pdf) over the orthogonal group, in the two aforementioned signal processing problems. Preliminary results, obtained through computer simulations, demonstrate the effectiveness of incorporating this induced distribution associated with the residual orthogonal matrix into the design of several estimators.

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Closed-form correlative coding (CFC/sub 2/) blind identification of MIMO channels: isometry fitting to second order statistics

Cited by 26 publications

References 24 publications

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

Iterative Joint Channel Estimation and Multi-User Detection for Multiple-Antenna Aided OFDM Systems

Blind Source Separation and Channel Identification: Exploiting 2nd-Order Statistics in Bayesian Frameworks

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