Iterative channel estimation and decoding of turbo coded SFBC-OFDM systems

We consider the design of turbo receiver structures for space-frequency block coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in the presence of unknown frequency and time selective fading channels. The Turbo receiver structures for SFBC-OFDM systems under consideration consists of an iterative MAP Expectation/Maximization (EM) channel estimation algorithm, soft MMSE-SFBC decoder and a soft MAP outer-channel-code decoder. MAP-EM employs iterative channel estimation and it improves receiver performance by re-estimating the channel after each decoder iteration. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The optimal truncation property of the KL expansion can reduce computational load on the iterative estimation approach. The performance of the proposed approaches are studied in terms of mean square error and bit-error rate. Through computer simulations, the effect of a pilot spacing on the channel estimator performance and sensitivity of turbo receiver structures on channel estimation error are studied. Simulation results illustrate that receivers with turbo coding are very sensitive to channel estimation errors compared to receivers with convolutional codes. Moreover, superiority of the turbo coded SFBC-OFDM systems over the turbo coded STBC-OFDM systems is observed especially for high Doppler frequencies

Cram´ er-Rao Bounds for Direction of Arrival and Range Estimation of Near-Field Sources

Abstract -In this paper the performance of the conditional maximum likelihood location estimator for the near-field sources is studied based on the derivation of Cramér-Rao bounds. The Cramér-Rao bound results are further analyzed for the one source case to provide insight into the dependence of estimation accuracy on signal to noise ratio and the number of antenna elements. Some insights into the achievable performance of the conditional maximum likelihood algorithm is obtained by numerical evaluation of the Cramér-Rao bounds for different test cases

Support vector regression for surveillance purposes

This paper addresses the problem of applying powerful statistical pattern classification algorithm based on kernel functions to target tracking on surveillance systems. Rather than directly adapting a recognizer, we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers to use dynamic model together as feature vectors and makes the byperplane and the support vectors follow the changes in these features. The performance of the tracker is demonstrated in a sensor network scenario with a constant velocity moving target on a plane for surveillance purpose

Blind data detection in the presence of PLL phase noise by sequential Monte Carlo method

In this paper, based on a sequential Monte Carlo method, a computationally efficient algorithm is presented for blind data detection in the presence of residual phase noise generated at the output the phase tracking loop employed in a digital receiver. The basic idea is to treat the transmitted symbols as "missing data" and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise and the incoming data are obtained through these samples, sequentially drawn, together with their importance weights. The proposed receiver structure is seen to be ideally suited for high-speed parallel implementation using VLSI technology

Blind-phase noise estimation in OFDM systems by sequential Monte Carlo method

One of the main drawbacks of orthogonal frequency division multiplexing (OFDM) systems is the phase noise (PN) caused by the oscillator instabilities. Unfortunately, due to the PN, the most valuable feature namely orthogonality between the carriers, is destroyed resulting in a significant degradation in the performance of OFDM systems. In this paper, based on a sequential Monte Carlo method (particle filtering), a computationally efficient algorithm is presented for estimating the residual phase noise, blindly, generated at the output of the phase tracking loop employed in OFDM systems. The basic idea is to treat the transmitted symbols as 'missing data' and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise is obtained through these samples, sequentially drawn, together with their importance weights. The proposed receiver structure is seen to be ideally suited for highspeed parallel implementation using VLSI technology. The performance of the proposed approaches are studied in terms of average mean square error. Through experimental results, the effects of an initialisation on the tracking performance are also explored. Copyright (c) 2006 AEIT