Joint channel estimation and data detection in space-time communications

Cozzo, C.; Hughes, B.L.

doi:10.1109/tcomm.2003.815062

Cited by 135 publications

(85 citation statements)

References 20 publications

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“…In contrast to our work, the authors in [7] and [16] take a data-centric approach, treating the transmitted signal as the desired parameter and the channel as the unobserved data. This algorithm further confines its pilots to the first ST block.…”

Section: ) Bench Markingmentioning

confidence: 99%

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Receiver design for MIMO OFDM transmission over time variant channels

Al-Naffouri

2007

2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications

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Abstract-This paper considers receiver design for space time block coded MIMO OFDM transmission over frequency and time selective channels. The receiver employs the expectation-maximization algorithm for joint channel and data recovery. It makes collective use of the data and channel constraints of the communication problem. The data constraints include pilots, the cyclic prefix, the finite alphabet constraint, and space-time block coding. The channel constraints include the finite delay spread and frequency and time correlation as well as spatial correlation. The channel estimation part of the receiver boils down to an EM-based forward-backward Kalman filter. To avoid the latency and storage associated with smoothing, we introduce a forward-only Kalman that performas channel (and data) recovery with no latency. Simulations show that the receiver outperforms other least-squares based iterative receivers.

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Section: ) Bench Markingmentioning

confidence: 99%

“…We compare our algorithm with an EM-based iterative MMSE receiver such as the one proposed in [7] and [16]. In contrast to our work, the authors in [7] and [16] take a data-centric approach, treating the transmitted signal as the desired parameter and the channel as the unobserved data.…”

Section: ) Bench Markingmentioning

confidence: 99%

Receiver design for MIMO OFDM transmission over time variant channels

Al-Naffouri

2007

2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications

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“…Furthermore, it is necessary to estimate the channel from the incoming data; numerous approaches have been proposed for this purpose. In Cozzo and Hughes (2003), expectation maximization-based channel estimation is considered; other approaches for channel estimation can be found in Pal (1992), Yang et al (2001), Van der Veen et al (1995) and Biguesh and Gershman (2006). The channel information lays the foundation for JADE, and thus, for the localization of an object such as a vulnerable road user.…”

Section: Introductionmentioning

confidence: 99%

Data preprocessing for a vehicle-based localization system used in road traffic applications

Patelczyk¹,

Löffler²,

Biebl³

2016

Adv. Radio Sci.

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Abstract. This paper presents a fixed-point implementation of the preprocessing using a field programmable gate array (FPGA), which is required for a multipath joint angle and delay estimation (JADE) used in road traffic applications. This paper lays the foundation for many model-based parameter estimation methods. Here, a simulation of a vehicle-based localization system application for protecting vulnerable road users, which were equipped with appropriate transponders, is considered. For such safety critical applications, the robustness and real-time capability of the localization is particularly important. Additionally, a motivation to use a fixedpoint implementation for the data preprocessing is a limited computing power of the head unit of a vehicle. This study aims to process the raw data provided by the localization system used in this paper.The data preprocessing applied includes a wideband calibration of the physical localization system, separation of relevant information from the received sampled signal, and preparation of the incoming data via further processing. Further, a channel matrix estimation was implemented to complete the data preprocessing, which contains information on channel parameters, e.g., the positions of the objects to be located.In the presented case of a vehicle-based localization system application we assume an urban environment, in which multipath propagation occurs. Since most methods for localization are based on uncorrelated signals, this fact must be addressed. Hence, a decorrelation of incoming data stream in terms of a further localization is required. This decorrelation was accomplished by considering several snapshots in different time slots. As a final aspect of the use of fixed-point arithmetic, quantization errors are considered. In addition, the resources and runtime of the presented implementation are discussed; these factors are strongly linked to a practical implementation.

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“…Blind methods not only impose high complexity and slow convergence but also suffer from unavoidable estimation and decision ambiguities [3]. To overcome this ambiguity problem, a few training symbols are usually employed, and this leads to many semi-blind methods [4,5,6,7,8,9]. In particular, the work [9] developed a semi-blind scheme of joint ML channel estimation and data detection in which the joint ML optimisation is decomposed into two levels.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimisation aided semi-blind joint maximum likelihood channel estimation and data detection for MIMO systems

Palally

Chen

Wang

et al. 2009

2009 IEEE/SP 15th Workshop on Statistical Signal Processing

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A novel scheme of semi-blind joint maximum likelihood (ML) channel estimation and data detection is proposed for multipleinput multiple-output (MIMO) systems by decomposing the joint ML optimisation over channel and data into an iterative two-level optimisation loop. Particle swarm optimisation (PSO) is invoked at the upper level to identify the unknown MIMO channel while an enhanced ML sphere detector is used at the lower level to detect the transmitted data. The scheme is semi-blind as a minimum pilot overhead is employed to aid the initialisation of the PSO based channel estimator.

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Joint channel estimation and data detection in space-time communications

Cited by 135 publications

References 20 publications

Receiver design for MIMO OFDM transmission over time variant channels

Receiver design for MIMO OFDM transmission over time variant channels

Data preprocessing for a vehicle-based localization system used in road traffic applications

Particle swarm optimisation aided semi-blind joint maximum likelihood channel estimation and data detection for MIMO systems

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