Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization and Superimposed Training

Carrasco-Alvarez, Roberto; Parra-Michel, R.; Orozco-Lugo, A.G.; Tugnait, J.K.

doi:10.1109/tsp.2012.2195658

Cited by 17 publications

(25 citation statements)

References 17 publications

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“…This work is intended to highlight the use of the modified version of the Prolate basis expansion [16][17][18], in MLSE and MAP equalization processes [13,15,19,20]. The application of MPF in the time-delay domain conveys a more accurate channel representation, which leads to a reduction in the error floor.…”

Section: Methodsmentioning

confidence: 99%

Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion

2019

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Maximum likelihood sequence estimation (MLSE) and maximum a posteriori probability (MAP) equalizers are optimum receivers for dealing with intersymbol interference (ISI) in time-dispersive channels. However, their high complexity and latency limit their widespread implementation; therefore, research into reducing their complexity is an open topic. This paper proposes a novel modification to reduce the computational complexity of the aforementioned algorithms, which exploits the representation of the communication channels in a time-delay-domain basis expansion model (BEM). It is shown that an appropriate basis is a set of modified prolate functions, in which the transmitter and receiver filters are considered in the kernel construction. Simulation results show that a reduction in sums and multiplications on the order of 55% can be obtained, maintaining the same bit error rate performance as in the traditional implementation.

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Section: Methodsmentioning

confidence: 99%

Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion

2019

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“…Note that in [13], n is allowed to be −∞ < n < ∞, but in the context of channel modeling, a finite n will suffice. It has been shown in [14] that the DPSS is the best suboptimal basis for representing DSF wide-sense stationary uncorrelated scattering channels in the mean-squared sense, assuming the channel is τ − and ν−limited, implying that the delay-Doppler spreading function…”

Section: A Dpss Representation For Dsf Channelsmentioning

confidence: 99%

“…Given the channel is Doppler-limited, i.e., Doppler shift limited, a few DPS coefficients (the principal components or PCs) are sufficient to represent the channel, implying that DPS basis has good energy compaction property. DPSS has also been used to model frequency-selective and doubly-selective fading (DSF) channels [14]. Unfortunately, the PCs of the communicating and interfering channels are naturally aligned, which hinders performance.…”

Section: Introductionmentioning

confidence: 99%

Sparsity Enhanced Mismatch Model for Robust Intercell Interference Management in Heterogeneous Networks With Doubly-Selective Fading Channels

Chang

Fung

2015

IEEE Trans. Commun.

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Transmission over doubly-selective fading (DSF) interference channel often relies on the use of robust precoder due to a lack of accurate channel state information, with performance often depending on the conservativeness of the mismatch model. Previously proposed mismatch models either have been deemed too conservative (deterministic models) or are prone to error due to inaccuracy in the probability density function (pdf) and corresponding parameters (stochastic models). A deterministic mismatch model called Sparsity Enhanced Mismatch Model-Reverse discrete prolate spheroidal sequence, or SEMMR, is proposed herein in an attempt to alleviate this problem. Different from all previously deterministic models, the proposed model exploits the inherent sparse characteristics of DSF interference channels which lead to a two-stage robust transceiver design that outperforms precoding only strategy incorporating conventional norm ball mismatch model (NBMM). The inherent sparsity in the channel is brought forth by modeling the channel using a basis expansion model (BEM) where discrete prolate spheroidal sequence (DPSS) is used as a basis. Analytical and simulation results are provided to validate the performance gains of the SEMMR transceiver over the NBMM precoder.

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“…Given that the channel is Doppler-limited, i.e., Doppler-shift-limited, a few DPS coefficients (the principal components or PCs) are sufficient to represent the channel, implying that DPS basis has a good energy compaction property. DPSS has also been used to model frequency-and doubly selective fading (DSF) SISO channels (Carrasco-Alvarez et al, 2012). Unfortunately, the PCs of the communicating and interfering channels are naturally aligned, which hinders performance.…”

Section: Underlay Spectrum Sharing For Heterogeneous Network With Domentioning

confidence: 99%

“…T S represents the symbol interval with cyclic prefix (CP), i.e., T S ¼ T CP + T s , with T CP and T s denoting the duration of the CP and useful OFDM symbol, respectively. It has been shown in Carrasco-Alvarez et al (2012) that the DPSS is the best suboptimal basis for representing doubly selective fading wide-sense stationary uncorrelated scattering channels in the mean-squared sense, assuming the channel is tÀ and nÀ limited. G d can be vectorized as…”

Section: Sparsity-enhanced Mismatch Model-reverse Dpss (Semmr)mentioning

confidence: 99%

Robust Transmission for Heterogeneous Networks with Cognitive Small Cells

Fung¹

2015

Bio-Inspired Computation in Telecommunications

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Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization and Superimposed Training

Cited by 17 publications

References 17 publications

Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion

Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion

Sparsity Enhanced Mismatch Model for Robust Intercell Interference Management in Heterogeneous Networks With Doubly-Selective Fading Channels

Robust Transmission for Heterogeneous Networks with Cognitive Small Cells

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