Presented at the 1997 IEEE Asilomar Conference on Signals, Systems, and Computers

Blind Estimation of FIR Channels in CDMA Systems with Aperiodic Spreading Sequences

Murat Torlak, Brian L. Evans, and Guanghan Xu

Department of Electrical and Computer Engineering, Engineering Science Building, The University of Texas at Austin, Austin, TX 78712-1084 USA
torlak@ece.utexas.edu - bevans@ece.utexas.edu - xu@ece.utexas.edu

Abstract

CDMA systems commonly use aperiodic spreading codes to distribute a signal spectrum uniformly over the channel bandwidth and differentiate neighboring cell sites. CDMA receivers often suffer from interference due to multipath fading. Blind signal estimation schemes cannot be used because they require periodic spreading sequences. RAKE receivers are often used, but they cannot fully exploit the rich structure of CDMA signals to minimize interference. This paper presents an iterative technique to estimate multipath parameters which can serve as a preprocessing step in a receiver to increase signal-to-interference ratio. We investigate the performance of the proposed method using computer simulations. Preliminary simulation results show an average of 10 dB gain on channel parameter estimation.

Paper - Presentation

Questions and Answers about the Paper

Q1. Channel model. Did you make a narrowband assumption?
A1. Yes. We used the general channel model reported by Hui and Zoltowski's paper on the Principal Components algorithm and used by Hui and Xu in their previous synchronous CDMA paper. For CDMA, the carrier is 1.8 GHz and the transmission bandwidth is on the order of 10 MHz, so a narrowband assumption is reasonable.

Q2. Channel model. Why did you write the array steering matrix as a function of the angle-of-arrival of the lth multipath signal?
A2. It is a convenient model which we have validated in our real-time wireless communications testbed.

Q3. Channel model. How do you know the channel lengths a priori?
A3. We can assume longer channel lengths. The extra taps will be close to zero. When the algorithm converges, you just throw those extra taps away.

Q4. Data model. Are the k_i terms, which represent the chip delay index for the ith user, known a priori?
A4. Yes. It is very common to assume that delays are estimated, e.g. in the Liu and Zoltowski paper. This assumption is okay.

Q5. Iterative Least Squares with Projection (ILSP). Is there any proof that ILSP is guaranteed to converge?
Q5. No, but the literature and experience give strong evidence that the algorithm always converges. A recent paper addressed this issue for TDMA systems, but did not give a proof. We are working on a proof.

Q6. Modulation. What type of modulation did you use in the simulations?
Q6. BPSK.

Last Updated 02/06/99.