A Graph-Based Iterative Gaussian Detector for Frequency-Selective MIMO Channels

Wo, Tianbin; Fricke, J.C.; Hoeher, Peter Adam

doi:10.1109/itw2.2006.323701

Cited by 12 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the same way it is possible to reduce the LN T × LN T matrix inversion, which is necessary to compute μ μ μ (i) 6 , to the inversion of a (L − 1)N T × (L − 1)N T matrix.…”

Section: ) Messages For Continuous Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Equalization Algorithms for MIMO Communication Systems Based on Factor Graphs

Etzlinger

Haselmayr

Springer

2011

2011 IEEE International Conference on Communications (ICC)

View full text Add to dashboard Cite

In this paper, we consider a bit-interleaved coded spatial multiplexing MIMO communication system over a frequency-selective MIMO channel. We present a factor-graphbased derivation of two different equalization algorithms. To this end, we propose a cycle-free factor graph representation of the equalizer, to which we apply the sum-product algorithm (SPA). By using different message representations in the SPA, the resulting equalization algorithms correspond to the optimal MAP equalizer and the low-complexity LMMSE equalizer, respectively. Both algorithms are soft-input soft-output equalization algorithms and can be used in turbo processing. We demonstrate that after 3 iterations the BER performance of the LMMSE equalizer is similar to that of the MAP equalizer.

show abstract

“…In the same way it is possible to reduce the LN T × LN T matrix inversion, which is necessary to compute μ μ μ (i) 6 , to the inversion of a (L − 1)N T × (L − 1)N T matrix.…”

Section: ) Messages For Continuous Variablesmentioning

confidence: 99%

“…For example, several noniterative equalization algorithms were derived in [3] and [4] for a single-input single-output (SISO) system based on a cyclefree factor graph. For the MIMO system considered, various iterative detection algorithms that are based on cyclic factor graphs were proposed in [5] and [6].…”

Section: Introductionmentioning

confidence: 99%

Equalization Algorithms for MIMO Communication Systems Based on Factor Graphs

Etzlinger

Haselmayr

Springer

2011

2011 IEEE International Conference on Communications (ICC)

View full text Add to dashboard Cite

show abstract

“…In combination with the sum-product algorithm for message exchange, powerful channel estimators have been derived in [15], [18], [19].…”

Section: Graph-based Iterative Receivermentioning

confidence: 99%

2D Graph-Based Soft Channel Estimation for MIMO-OFDM

Knievel

Shi

Hoeher

et al. 2010

2010 IEEE International Conference on Communications

View full text Add to dashboard Cite

We address joint channel estimation and data detection based on factor graphs. The considered graph-based approach utilizes reliability information of channel estimates to facilitate soft-output data detection, and in-turn reliability information about the data symbols is taken into account for channel estimation. In this paper graph-based soft channel estimation and detection is extended to an OFDM based air interface, where the channel response varies in two dimensions; time and frequency. Initial channel estimates obtained by training symbols are conveyed by a two dimensional (2D) factor-graph in time and frequency with only a linear increase in complexity. The required training overhead for the proposed 2D graph-based soft channel estimation scheme may be substantially reduced by taking the redundancy introduced by the channel coding into account.

show abstract

“…Channel coding is done separately at each transmit antenna. A binary regular (3,5,200,80) LDPC code is applied. The parity check matrix has a uniform column weight of 3, and a uniform row weight of 5 which makes the code strongly asymmetric.…”

Section: A Simulation Setupmentioning

confidence: 99%

“…The authors would refer interested readers to[4],[5] for detailed explanation on this issue.This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2008 proceedings.…”

mentioning

confidence: 99%

Graph-Based Soft Channel and Data Estimation for MIMO Systems with Asymmetric LDPC Codes

Liu

Hoeher

2008

2008 IEEE International Conference on Communications

View full text Add to dashboard Cite

In this paper, we propose an iterative soft channel estimation and data detection algorithm based on a factor graph. Channel coefficients as well as data symbols are treated as variable nodes and are all estimated in a low-complexity element-wise manner. Applying asymmetric LDPC codes, this algorithm is able to deliver ambiguity-free outputs for MIMO systems with or without training symbols. Training symbols are inherently utilized as a type of a priori information. This algorithm thoroughly relaxes the troublesome constraints on training design in the sense that an arbitrary (even zero) number of training symbols can be placed at arbitrary positions within a data burst.

show abstract

A Graph-Based Iterative Gaussian Detector for Frequency-Selective MIMO Channels

Cited by 12 publications

References 7 publications

Equalization Algorithms for MIMO Communication Systems Based on Factor Graphs

Equalization Algorithms for MIMO Communication Systems Based on Factor Graphs

2D Graph-Based Soft Channel Estimation for MIMO-OFDM

Graph-Based Soft Channel and Data Estimation for MIMO Systems with Asymmetric LDPC Codes

Contact Info

Product

Resources

About