Joint detection-estimation of directional channel parameters using the 2-D frequency domain SAGE algorithm with serial interference cancellation

Chong, Chia‐Chin; Laurenson, David; Tan, CM; McLaughlin, Stephen; Beach, M.A.; Nix, Andrew R

doi:10.1109/icc.2002.996987

Cited by 56 publications

(23 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two super-resolution algorithms, namely, the space-alternating generalized expectation maximization (SAGE) [11] and the unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) [12], examples of maximum-likelihood (ML) and subspace-based methods, respectively, were investigated. Here, the frequency domain SAGE (FD-SAGE) algorithm is used in conjunction with the serial interference cancellation technique [13] to jointly detect and estimate the required multipath channel parameters such as the number of MPCs, their complex amplitudes, TOAs, and AOAs. 1 It was demonstrated that the FD-SAGE outperforms the standard SAGE algorithm [11] in a multipath rich environment in terms of its accuracy and stability [13].…”

Section: A Channel Parameter Estimationmentioning

confidence: 99%

“…Here, the frequency domain SAGE (FD-SAGE) algorithm is used in conjunction with the serial interference cancellation technique [13] to jointly detect and estimate the required multipath channel parameters such as the number of MPCs, their complex amplitudes, TOAs, and AOAs. 1 It was demonstrated that the FD-SAGE outperforms the standard SAGE algorithm [11] in a multipath rich environment in terms of its accuracy and stability [13]. The ML-based method is chosen in favor of the subspace-based method because of the advantages it offers as detailed in [14].…”

Section: A Channel Parameter Estimationmentioning

confidence: 99%

See 1 more Smart Citation

A new statistical wideband spatio-temporal channel model for 5-GHz band WLAN systems

Chong

Tan

Laurenson

et al. 2003

IEEE J. Select. Areas Commun.

Self Cite

178

View full text Add to dashboard Cite

Generalized Expectation maximization (FD-SAGE) algorithm and clusters are identified in the spatio-temporal domain by a nonparametric density estimation procedure. The description of the clustering observed within the channel relies on two classes of parameters, namely, intercluster and intracluster parameters which characterize the cluster and MPC, respectively. All parameters are described by a set of empirical probability density functions (pdfs) derived from the measured data. The correlation properties are incorporated in two joint pdfs for cluster and MPC positions, respectively. The clustering effect also gives rise to two classes of channel power density spectra (PDS)-intercluster and intracluster PDS which are shown to exhibit exponential and Laplacian functions in the delay and angular domains, respectively. Finally, the model validity is confirmed by comparison with two existing models reported in the literature.

show abstract

Section: A Channel Parameter Estimationmentioning

confidence: 99%

Section: A Channel Parameter Estimationmentioning

confidence: 99%

A new statistical wideband spatio-temporal channel model for 5-GHz band WLAN systems

Chong

Tan

Laurenson

et al. 2003

IEEE J. Select. Areas Commun.

Self Cite

178

View full text Add to dashboard Cite

show abstract

“…Due to the limited resolution of the measurement system and finite point-source modeling errors in the FD-SAGE algorithm [9] when used in a distributed-source environment (i.e., in the presence of clusters of closely spaced multipaths), the estimated spatial-temporal parameters corresponding to a particular cluster of rays will correspond to the nominal parameter values within the cluster. Note that this observation is valid when the spatial-temporal extent of the cluster is smaller than the spatial-temporal Rayleigh resolution of the measurement system.…”

Section: B Identification Of Path "Birth" and "Death"mentioning

confidence: 99%

“…The super-resolution frequency domain space-alternating generalized expectation maximization (FD-SAGE) algorithm is used to detect and estimate the required multipath channel parameters such as the number of MPCs, their complex path gains, time of arrivals (TOAs), and angle of arrivals (AOAs) [9]. Since the total time to record 20 SIMO snapshots (or one FDB) is well within the coherence time of the channel, averaging can be carried out across a FDB (i.e., averaging a block of 20 SIMO snapshots) in order to enhance the signal-to-noise ratio before using the FD-SAGE algorithm to estimate the required parameters.…”

Section: A Multipath Channel Parameter Estimationmentioning

confidence: 99%

A novel wideband dynamic directional indoor channel model based on a Markov process

Chong

Tan

Laurenson

et al. 2005

IEEE Trans. Wireless Commun.

Self Cite

View full text Add to dashboard Cite

Abstract-A novel stochastic wideband dynamic spatiotemporal indoor channel model which incorporates both the spatial and temporal domain properties as well as the dynamic evolution of paths when the mobile moves is proposed based on the concept of a Markov process. The derived model is based on dynamic measurement data collected at a carrier frequency of 5.2 GHz in typical indoor environments. Multipath components are estimated using the super-resolution frequency domain spacealternating generalized expectation maximization algorithm prior to identification of path "birth" and "death" using a new data analysis method. Analysis shows that multiple births and deaths are possible at any instant of time. Furthermore, correlation exists between the number of births and deaths. Thus, an M -step 4-state Markov channel model (MCM) is proposed in order to account for these two effects. The spatio-temporal variations of paths within their lifespans are taken into consideration by the spatio-temporal vector which was found to be well modeled by a Gaussian probability density function while the power variation can be modeled by a simple low-pass filter. In addition, the methodology used to extract the MCM parameters from the measurement data is also presented. Due to the distinction in the birth-death statistics, the model is generalized through segmentation of the measurement runs and can be completely parameterized by several sets of Markov parameters associated with the type of environment and scenario under consideration. The implementation of the model is also detailed and, finally, the model is evaluated by comparing key statistics of the simulation results with the measurement results.

show abstract

“…Without the removal of the antenna pattern the measured channel response is inaccurate and provides a statistical concatenation of error in the subsequent channel model. Some other researchers have used image resolution tools such as CLEAN and SAGE to remove deteriorating effects from the measurement data [8], [9], [10], [11], [12], [13], [14]. Such techniques require a point spread function (PSF) of the channel data image [15].…”

Section: Introductionmentioning

confidence: 99%

On wireless channel modelling : extraction of channel from channel measurements

Gupta

Abhayapala

Pollock

2007

2007 International Symposium on Communications and Information Technologies

View full text Add to dashboard Cite

Abstract-Next generation systems are using directional antennas for indoor high data rate devices. This arises a need for measurement and modelling of directional channels. Indoor environments also introduce extreme multipath behaviour into the directional channel. Directional measurements raise the problem of complicated data sets with 2-D spatio-temporal information. In order to analyse these complex data sets, it is first imperative to make sure the data set actually represents the propagation channel and is not influenced by the measuring apparatus. In order to construct the propagation channel, we discuss a novel technique of eliminating such effects from the measured data set. The method involves solving sets of simultaneous equations in a three stage process. Firstly, the data is cleaned in the spatial domain, secondly in the in the time domain and finally the channel parameters are extracted. The technique is verified on real and simulation data sets.

show abstract

Joint detection-estimation of directional channel parameters using the 2-D frequency domain SAGE algorithm with serial interference cancellation

Cited by 56 publications

References 8 publications

A new statistical wideband spatio-temporal channel model for 5-GHz band WLAN systems

A new statistical wideband spatio-temporal channel model for 5-GHz band WLAN systems

A novel wideband dynamic directional indoor channel model based on a Markov process

On wireless channel modelling : extraction of channel from channel measurements

Contact Info

Product

Resources

About