Yongju Cho

On Channel State Inference and Prediction Using Observable Variables in 802.11b Network

Performance of cross-layer protocols that recommend the relay of corrupted packets to higher layers can be improved significantly by accurately inferring/predicting the bit error rate (BER) in the packets. In practice, higher layers observe the bits only after some hard decision. Hence physical layer link-quality indications, such as the signal strength of each individual bit, are not observable at higher layers. Therefore, it is essential to identify practically observable variables, which can be used for reasonably robust channel state inference/prediction (CSI/CSP). Here, inference specifically refers to estimating the BER in an already received packet, while prediction refers to anticipating the BER in a future packet. In this paper, we note that, in practical 802.11b devices, it is possible to acquire a Signal to Silence Ratio (SSR) indication and measure the background traffic intensity (p ) on a per packet basis. This paper, thus presents a measurement-based study that analyzes the utility of SSR and p as side-information for CSI/CSP. In this work, we exploit the method of types to measure the robustness of the observable side-information. Our analysis and simulations based on an extensive set of actual 802.11b traces exhibit the practical utility of the considered observable variables.

A Multi-Tier Model for BER Prediction over Wireless Residual Channels

Bit-error rate (BER) modeling and prediction over residual wireless channels, which represent errors not corrected by the physical layer, has emerged as an active research area. Recently, it has been shown that signal to noise ratio (SNR) is a useful side-information that could be employed for BER prediction. In this paper, we propose a novel and accurate three-tier model that leverages a received packets SNR and checksum side-information to predict BER in future packets over a wireless residual channel. We first observe that direct inference of BER from SNR results in optimistic estimates because of the relatively large amounts of error-free data (in comparison with corrupted data) received on viable wireless networks. Consequently, we propose a model that separates packet-and bit-error prediction. At the first tier, we employ a high-order packet-level Markov model which predicts whether or not a packet is in error. The second tier model is invoked only when a corrupted packet is predicted. The second tier consists of conditional probabilities that predict future SNR values based on the current packets SNR. Once the SNR is predicted, a third-tier provides the BER estimate for that SNR using a binary-symmetric channel model. We use 802.11b traces collected over an operational 802.11b LAN to compare the performance of the proposed predictor with state-of-the-art predictors. We show that at all three 802.11b data rates (2, 5.5 and 11 Mbps) the proposed model has higher BER prediction accuracy than the optimum Yule-Walker and finite-state Markov chain predictors.

A rate-distortion empirical model for rate adaptive wireless scalable video

Resent studies (L. Larzon, et al., June 1999)-(S. A. Khayam, et al., Aug, 2003) have indicated that a significant improvement in wireless video throughput can be achieved by cross layer design with side-information (CLDS) protocols. In (Y. Cho, et al.), for real-time video streaming over wireless LANs the optimal rate prediction architecture under CLDS ( ORPACLDS ) was developed. In (Y. Cho, et al.), we showed that an accurate source and channel coding rate prediction can be achieved by utilizing the video quality models and the distribution of channel prediction error process under CLDS. To optimally utilize ORPACLDS , the video quality function for rates exceeding the channel capacity (i.e., video rate-distortion function) should be taken into consideration. To this end, in this paper we propose an empirical model for video rate-distortion. To deduce the generic video rate- distortion model, actual scalable video coding (SVC) and low density parity check (LDPC) code were used. The outstanding performance of ORPACLDS in accordance with the video rate-distortion model is exhibited by using a comprehensive set of IEEE 802.11b wireless traces.

On Rate-adaptive LDPC-based Cross-layer SVC over Bursty Wireless Channels

Recent studies have indicated that a significant improvement in wireless video throughput can be achieved by Cross Layer Design with Side-information (CLDS) protocols. In this paper, we derive the operational rate of a CLDS protocol operating over a realistic wireless channel. Then, a Rate-Distortion (R-D) empirical model for above-capacity Scalable Video Coding (SVC) is deduced to estimate the loss of video quality incurred under inaccurate rate estimation scenarios. Finally, we develop a novel Unequal Error Protection (UEP) scheme which leverages the characteristics of LDPC codes to reduce the distortion of video quality in case of typically-observed burst wireless errors. The efficacy of the proposed rate adaptation architecture over conventional protocols is demonstrated by realistic video simulations using actual IEEE 802.11b wireless traces.

Automatic flaw detection for tube support plate signals

An automatic flaw detection algorithm for signals in tube support plate (TSP) region was created using the Affine for preprocessing, Wavelet transformation for feature extraction, and regression for evaluation. Tube support plate data were extracted from an entire tube data set. The Affine transformation is used to suppress TSP signals since they are not of interest. However, it does not suppress the TSP signal perfectly but leaves residuals. The residual signal is then transformed using Wavelets for feature extraction. The Wavelet transformation with the residual data provides time and frequency information that are the important factors in determining patterns of flaw and noise signals. The possible defective signals extracted from the Wavelet transformation are evaluated using phase angle information. A line is fit by least square to the lissajous pattern of the residuals. If the phase of the line lies in the approximate from 40 to 140 degrees, the candidate flaw is claimed to be a real flaw.