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

Wo, Tianbin; Liu, Chunhui; Hoeher, Peter Adam

doi:10.1109/icc.2008.122

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…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%

“…However, these iterative receivers require tentative channel estimates based on training symbols and are therefore unable to reduce the training overhead. More recently, soft channel estimation based on iterative message-passing over a general factor graph has been applied to MIMO operating in block-fading channels [15], and was shown to attain a substantial reduction in training overhead at modest complexity.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper graph-based soft channel estimation [15] is applied to MIMO-OFDM over time-varying, frequencyselective channels, by extending the underlying factor graph to two dimensions with only linear increase in complexity. Initial channel estimates at training symbol locations are dispersed through the factor graph by message exchange over time and frequency dimensions via so-called Δ-transfer nodes.…”

Section: Introductionmentioning

confidence: 99%

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

Knievel

Shi

Hoeher

et al. 2010

2010 IEEE International Conference on Communications

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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.

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“…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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Knievel

Shi

Hoeher

et al. 2010

2010 IEEE International Conference on Communications

Self Cite

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“…Iterative receivers propagating exclusively soft information can be designed using the framework provided by factor graphs [19]. Based on previous work presented in [20], [21], a graph-based receiver for Multiple Input Multiple Output (MIMO) OFDM is proposed in [22], [23]. In these works, a message passing algorithm is applied on a three dimensional graph representing the channel.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Scalable Iterative Algorithms for IEEE 802.11p Receivers

Goubet

Baudic

Gabry

et al. 2015

IEEE Trans. Veh. Technol.

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In this paper, we investigate receivers for Vehicular to Vehicular (V2V) and Vehicular to Infrastructure (V2I) communications. Vehicular channels are characterized by multiple paths and time variations, which introduces challenges in the design of receivers. We propose an algorithm for IEEE 802.11p compliant receivers, based on Orthogonal Frequency Division Multiplexing (OFDM). We employ iterative structures in the receiver as a way to estimate the channel despite variations within a frame. The channel estimator is based on factor graphs, which allow the design of soft iterative receivers while keeping an acceptable computational complexity. Throughout this work, we focus on designing a receiver offering a good complexity performance trade-off. Moreover, we propose a scalable algorithm in order to be able to tune the trade-off depending on the channel conditions. Our algorithm allows reliable communications while offering a considerable decrease in computational complexity. In particular, numerical results show the trade-off between complexity and performance measured in computational time and BER as well as FER achieved by various interpolation lengths used by the estimator which both outperform by decades the standard least square solution. Furthermore our adaptive algorithm shows a considerable improvement in terms of computational time and complexity against state of the art and classical receptors whilst showing acceptable BER and FER performance.

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“…In case they are unavailable, we can simply set them to be some proper nonzero values (e.g. 1 and 10, respectively) and use the asymptotic LDPC codes to eliminate the phase ambiguity[8]. The joint iterative algorithm can then re-estimate their exact values.…”

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confidence: 99%

Grant-Free Rateless Multiple Access: A Novel Massive Access Scheme for Internet of Things

et al. 2016

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Abstract-Rateless Multiple Access (RMA) is a novel nonorthogonal multiple access framework that is promising for massive access in Internet of Things (IoT) due to its high efficiency and low complexity. In the framework, after certain registration, each active user respectively transmits to the access point (AP) randomly based on an assigned random access control function (RACf) until receiving an acknowledgement (ACK). In this work, by exploiting the intrinsic access pattern of each user, we propose a grant-free RMA scheme, which no longer needs the registration process as in the original RMA, thus greatly reduces the signalling overhead and system latency. Furthermore, we propose a low-complexity joint iterative detection and decoding algorithm in which the channel estimation, active user detection, and information decoding are done simultaneously. Finally, we propose a method based on density evolution (DE) to evaluate the system performance.Index Terms-Internet of Things (IoT), massive access, nonorthogonal multiple access.

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

Cited by 12 publications

References 11 publications

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

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

Low-Complexity Scalable Iterative Algorithms for IEEE 802.11p Receivers

Grant-Free Rateless Multiple Access: A Novel Massive Access Scheme for Internet of Things

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