Channel Estimation Based on Divergence Minimization for OFDM Systems with Co-Channel Interference

Manchón, Carles Navarro; Fleury, Bernard Henri; Kirkelund, Gunvor Elisabeth; Mogensen, Preben; Deneire, Luc; Sørensen, Troels B.; Rom, Christian

doi:10.1109/icc.2009.5198818

Cited by 4 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The factor function for factor node f Y k is the likelihood function of observation y(k). Specifically we consider the likelihood function based on signal model (29) where the channel coefficients are expressed in terms of the time-domain taps as given by (26).…”

Section: Mf Message Derivationsmentioning

confidence: 99%

“…Subsequent works have applied the BP-MF framework to various channel estimation scenarios [16]- [23]. Combined BP-MF message passing has also been applied to multi-user code-division multiple access (CDMA) [12], multiple-input multipleoutput (MIMO) systems [13], [14], [24], [25], co-channel interference [26], and frequencydomain equalization [27]. 1 Joint channel estimation is included in the majority of these works as well [12]- [14], [24]- [26].…”

Section: Introductionmentioning

confidence: 99%

“…Combined BP-MF message passing has also been applied to multi-user code-division multiple access (CDMA) [12], multiple-input multipleoutput (MIMO) systems [13], [14], [24], [25], co-channel interference [26], and frequencydomain equalization [27]. 1 Joint channel estimation is included in the majority of these works as well [12]- [14], [24]- [26]. In scenarios involving detection of multiple signals, the standard application of the BP-MF framework is to include the equalization task within the MF subgraph.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic Receiver Architecture Combining BP, MF, and EP for Multi-Signal Detection

Jakubisin¹,

Buehrer²,

Silva³

2016

Preprint

View full text Add to dashboard Cite

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. In this paper, we show that the MF approximation is not particularly effective for multi-signal detection. We develop a new factor graph construction for application of the BP-MF framework to problems involving the detection of multiple signals. We then develop a low-complexity variant to the proposed construction in which Gaussian BP is applied to the equalization factors. In this case, the factor graph of the joint probability distribution is divided into three subgraphs: (i) a MF subgraph comprised of the observation factors and channel estimation, (ii) a Gaussian BP subgraph which is applied to multi-signal detection, and (iii) a discrete BP subgraph which is applied to demodulation and decoding. Expectation propagation is used to approximate discrete distributions with a Gaussian distribution and links the discrete BP and Gaussian BP subgraphs. The result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi-signal detection.

show abstract

Section: Mf Message Derivationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Receiver Architecture Combining BP, MF, and EP for Multi-Signal Detection

Jakubisin¹,

Buehrer²,

Silva³

2016

Preprint

View full text Add to dashboard Cite

show abstract

Interference Cancellation Based on Divergence Minimization for MIMO-OFDM Receivers

Manchón

Kirkelund

Fleury

et al. 2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

View full text Add to dashboard Cite

In this paper, we present a novel iterative receiver for MIMO-OFDM systems with synchronous interferers. The receiver is derived based on the Kullback-Leibler divergence minimization framework, and combines channel estimation, interference cancellation and residual noise estimation in an iterative manner. By using both the pilot and data symbols, the channel estimator improves the accuracy of the estimates in each iteration, which leads to a more effective interference cancellation and data detection process. A performance evaluation based on Monte-Carlo simulations shows that the proposed scheme can effectively mitigate the effect of interferers, and operates very close to the single-user performance even in severe interference scenarios.This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE "GLOBECOM" 2009 proceedings.

show abstract

BP, MF, and EP for Joint Channel Estimation and Detection of MIMO-OFDM Signals

Jakubisin

Buehrer

Silva

2016

2016 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

Channel Estimation Based on Divergence Minimization for OFDM Systems with Co-Channel Interference

Abstract: Channel estimation based on divergence minimization for OFDM systems with co-channel interference. In

Cited by 4 publications

References 12 publications

Probabilistic Receiver Architecture Combining BP, MF, and EP for Multi-Signal Detection

Probabilistic Receiver Architecture Combining BP, MF, and EP for Multi-Signal Detection

Interference Cancellation Based on Divergence Minimization for MIMO-OFDM Receivers

BP, MF, and EP for Joint Channel Estimation and Detection of MIMO-OFDM Signals

Contact Info

Product

Resources

About