Ivan Bradaric

Blind MIMO FIR channel identification based on second-order spectra correlations

We consider a problem of identifying a multiple-input multiple-output (MIMO) finite impulse response (FIR) system excited by colored inputs with known statistics. We propose a new, nonlinear optimization-based method that involves the power spectra and cross-spectra of the system output. The proposed algorithm is tested for the case of cyclostationary inputs (CDMA scenario) and stationary inputs (SDMA scenario). Simulation results indicate that the proposed scheme works well, even for large order systems, and is robust to noise and channel length mismatch.

Performance of training-based OFDM systems in the presence of time varying frequency-selective channels

We consider a single user OFDM system that experiences time-varying and frequency selective channels. We study a training based scenario, where the channel is estimated based on pilots that proceed the transmission of the information bearing blocks, and then used to equalize the subsequent data blocks. In such a scheme, due to the time-varying channel, the BER increases as the index of the data block increases. Considering commonly used models for time-varying channels, we derive an analytical expression for the bit error rate as a function of the block index. The proposed analysis is important in optimizing the system performance, as it can be exploited at the transmitter to determine how often pilots need to be transmitted, or what kind of modulation should be used on each carrier in order to maintain a certain error level.

Analysis of physical layer performance of IEEE 802.11A in an ad-hoc network environment

The IEEE 802.11a standard using orthogonal frequency division multiplexing (OFDM) can provide data rates up to 54 Mbps, which makes it a good candidate for high-speed communications in wireless local area networks (WLANs). Although the standard is meant for indoor applications, we tested its performance in a city environment. Our measurements indicated that the users experience a significantly lower throughput than that promised by the standard, with the performance becoming worse as the speed of the users increased. To better understand the reasons for this behavior, we measured outdoor channels, corresponding to stationary and mobile users. Analysis of experimental data showed that the channel could vary even within the duration of one data frame. Thus, the training-based scheme used in 802.11a, which estimates the channel once during the initial part of the frame and then uses it to equalize the entire frame, results in imperfect channel equalization. This results in high bit-error rate (BER), causing packets to be discarded and increasing the probability of outage. The paper presents experimental results demonstrating the 802.11a weaknesses when operating in an outdoors mobile environment, and provides some recommendations for improving its performance.

On blind identifiability of FIR-MIMO systems with cyclostationary inputs using second order statistics

We consider a general n × n MIMO system excited by unobservable inputs that are spatially independent, cyclostationary with unknown statistics. We provide a set of conditions under which the system is uniquely identifiable based on second-order frequency-domain correlations of the system output. Such a MIMO problem appears in many applications, such as multi-user communications and separation of competing speakers.

Subspace design of low-rank estimators for higher-order statistics

Abstract Higher-order statistics (HOS) are well known for their robustness to additive Gaussian noise and ability to preserve phase. HOS estimates, on the other hand, have been criticized for high complexity and the need for long data in order to maintain small variance. Since rank reduction offers a general principle for reduction of estimator variance and complexity, we consider the problem of designing low-rank estimators for HOS. We propose three methods for choosing the transformation matrix that reduces the mean-square error (MSE) associated with the low-rank HOS estimates. We also demonstrate the advantages of using low-rank third-order moment estimates for blind system estimation. Results indicate that the full rank MSE corresponding to some data length N can be attained by a low-rank estimator corresponding to a length significantly smaller than N.

Blind equalization of multiuser CDMA channels: a frequency-domain approach

We address the problem of blind equalization of mixing channels, resulting from frequency selective fading and multipath in a multiuser CDMA system. We present a novel frequency-domain approach that employs second order spectral statistics of the observations and the users signatures. Unlike other methods, which are based on time-domain analysis, no particular assumptions are made about the support of the mixing channels, except that they have finite length. Since signatures correlations, rather that signatures themselves, are used, the proposed estimation is independent of users delays. Although performance does depend on the users-signatures, signatures orthogonality is not required.

Low rank approach in system identification using higher-order statistics

We consider the problem of designing low-rank estimators of higher-order statistics (HOS). In general, low rank estimators have smaller variance than the corresponding full rank estimators at the expense of increased bias. We propose a method for choosing the rank that minimizes the mean squared error associated with the low-rank HOS estimates, and derive analytical expressions for the mean squared error. We present simulation results of system reconstruction based on best rank low-rank HOS estimates of the system output, that indicate significant reduction in variance, when compared to the corresponding full-rank result. We also demonstrate that the full-rank mean-square error corresponding to some data length N can be attained by a low-rank estimator corresponding to a length significantly smaller than N.

A second-order statistics-based optimization approach for blind MIMO system identification

We consider the problem of identifying a multiple-input multiple-output (MIMO) finite impulse response system excited by colored inputs with known statistics. Among other applications this problem appears in the context of CDMA communications systems with spatial and temporal diversity. We propose a novel approach that optimizes a criterion involving spectra and cross-spectra of the system output. Simulation results indicate that the proposed scheme works well, even for large order systems, and is robust to noise and channel length mismatch.