Channel tracking for multiple input, single output systems using EM algorithm

Aldana, C.; Cioffi, J.M.

doi:10.1109/icc.2001.937007

Cited by 15 publications

(20 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper (see also [17]), we treat the unknown symbols as the unobserved (or missing) data and propose an EM algorithm for semi-blind maximum likelihood (ML) estimation of both the channel and spatial noise covariance in a single-input multi-output (SIMO) smart antenna scenario. This is unlike previous work in [7]- [9] and [13]- [15], where EM algorithms were applied to channel estimation in white noise.…”

Section: Introductionmentioning

confidence: 88%

“…Expectation-maximization (EM) and related algorithms (see [1]- [3]) have been applied to carrier phase recovery [4], demodulation for unknown carrier phase [5], timing estimation [6], and channel estimation [7]- [9] in single-input single-output (SISO) communication systems, and, more recently, to symbol detection [10]- [12] and channel estimation [13]- [15] in smart antenna systems. In this paper (see also [17]), we treat the unknown symbols as the unobserved (or missing) data and propose an EM algorithm for semi-blind maximum likelihood (ML) estimation of both the channel and spatial noise covariance in a single-input multi-output (SIMO) smart antenna scenario.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Semi-Blind SIMO Flat-Fading Channel Estimation in Unknown Spatially Correlated Noise Using the EM Algorithm

Dogandzic

Wang

2004

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

We present a maximum likelihood (ML) method for semi-blind estimation of single-input multi-output (SIMO) flat-fading channels in spatially correlated noise having unknown covariance. An expectationmaximization (EM) algorithm is utilized to compute the ML estimates of the channel and spatial noise covariance. We derive the Crame´r-Rao bound (CRB) matrix for the unknown parameters and present a symbol detector that utilizes the EM channel estimates. Numerical simulations demonstrate the performance of the proposed method. AbstractWe present a maximum likelihood (ML) method for semi-blind estimation of single-input multi-output (SIMO) flat-fading channels in spatially correlated noise having unknown covariance. An expectationmaximization (EM) algorithm is utilized to compute the ML estimates of the channel and spatial noise covariance. We derive the Cramér-Rao bound (CRB) matrix for the unknown parameters and present a symbol detector that utilizes the EM channel estimates. Numerical simulations demonstrate the performance of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Semi-Blind SIMO Flat-Fading Channel Estimation in Unknown Spatially Correlated Noise Using the EM Algorithm

Dogandzic

Wang

2004

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm can also be used to estimate multipath channels in unknown colored noise. This is unlike previous work in [55,19,4], where EM algorithms were applied to SISO and multi-input single-output (MISO) channel estimation in white noise.…”

Section: Introductionmentioning

confidence: 87%

“…We further assume a power constraint on the codebook C: 4) so that ρ in (5.1) is the average transmit power, regardless of the value of n T .…”

Section: System Modelmentioning

confidence: 99%

“…Expectation-maximization (EM) and related algorithms (see [22,73,66]) have been applied to carrier phase recovery [76], demodulation for unknown carrier phase [83], timing estimation [34], and channel estimation [55]- [19] in single-input single-output (SISO) communication systems, and, more recently, to symbol detection [65]- [20] and channel estimation [4] in smart antenna systems. In this chapter, we present an EM algorithm for semi-blind ML estimation of both the channel and spatial noise covariance matrices in a single-input multi-output (SIMO) smart antenna scenario.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MIMO communication systems: receiver design and diversity-multiplexing tradeoff analysis

View full text Add to dashboard Cite

An Efficient Adaptive Channel Estimation Algorithm for MIMO OFDM Systems—Study of Doppler Spread Tolerance

Karkhanechi

Levy

2008

J Sign Process Syst Sign Image Video Technol

View full text Add to dashboard Cite

In this paper we develop an adaptive MIMO channel estimation algorithm for space-time block coded OFDM systems. The presented algorithm is based on Expectation Maximization (EM) technique by decomposing the superimposed received signals into their signal components, and estimating the channel parameters of each signal component separately. We also study and compare our proposed EM-based algorithm with a previously introduced recursive-least-squares based algorithm for MIMO OFDM systems. At each iteration the EM algorithm decomposes the problem of multi-channel estimation into channel estimation for each transmit-receive link. In this paper we also study the Doppler spread tolerance of our proposed algorithm in a fast fading environment, and investigate how it affects the system BER performance.

show abstract

Channel tracking for multiple input, single output systems using EM algorithm

Cited by 15 publications

References 16 publications

Semi-Blind SIMO Flat-Fading Channel Estimation in Unknown Spatially Correlated Noise Using the EM Algorithm

Semi-Blind SIMO Flat-Fading Channel Estimation in Unknown Spatially Correlated Noise Using the EM Algorithm

MIMO communication systems: receiver design and diversity-multiplexing tradeoff analysis

An Efficient Adaptive Channel Estimation Algorithm for MIMO OFDM Systems—Study of Doppler Spread Tolerance

Contact Info

Product

Resources

About