IEEE North Jersey Advanced Communications Symposium, 2014

Robust Multipath Channel Identification

with Partial Filter Information

Kuang Caiï¹¡, Hongbin Liï¹¡, Joseph Mitola III â€

ï¹¡Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA

â€ Mitola's STATISfaction, 4985 Atlantic View, St. Augustine, FL 32080, USA

Introduction

- CramÃ©râ€“Rao bound (CRB)
- To benchmark performances of proposed estimators
- The filter response becomes a random parameter with existence of the perturbation
- Bayesian CRB
- Write all data blocks in one equation
- Compute Fisher information matrix (FIM)
- Constraint is applied in blind estimation to eliminate ambiguity
- Compute constrained CRB based on FIM

Expansion for slowly time-varying channels - Channel response does not have significant change over one data block duration
- Compute an initial sample covariance matrix and update it when a new output data
block is available

- Applied estimation algorithms based on the time-varying sample covariance matrix

- The second-order statistics of the channel output contain sufficient
information for blind channel estimation due to the cyclostationarity

of the channel output induced by a fractionally spaced sampling

- A subspace-based multipath channel identification based on exploiting
knowledge of the transmit/receive filter was presented and popular

- Simplicity and good performance
- Requires full knowledge of the filter

- Transmit/receive filter response is often partially known due to perturbations
- I/Q imbalance at the transmit/receive side
- Physical distortions due to environmental factors (i.e., temperature, humidity)

- To improve the estimation performance, two algorithms are proposed

: Forgetting factor,

Numerical Results

System Model

- System model
- Composite channel
- Discrete time model
- Filter perturbation model

: Input information sequence

:Channel noise (AWGN)

: Transmit/receive filter

: Multipath channel

: th data block

: Block Toeplitz channel matrix

Fig.1

Fig.2

: Nominal filter response

: Unknown perturbation error modeled as a random

variable with zero mean and variance

Channel Identification

: Eigenvectors span on the signal subspace

: Eigenvectors span on the noise subspace

: Sample covariance matrix

Fig. 3

Fig.4

: th Eigenvector belongs to the noise subspace

: Block Toeplitz matrix formed by

: Convolutional matrix formed by filter response

Fig.5

Conclusions

: Convolutional matrix formed by channel response

- We examine two cases, in which the transmit/receive filter is affected by
unknown perturbation, and propose the corresponding estimators

- With the statistical knowledge of the perturbation, the robust estimation
achieved better performance than the conventional estimation

- Robust estimation algorithm also works for the slowly time-varying channels

Future Work

- Explore the application of robust channel identification in OFDM systems