Hwang-Soo Lee

Adaptive acquisition of PN sequences for DSSS communications

Since the received signal levels in mobile communications are unknown and the location is varying, acquisition schemes for pseudonoise (PN) sequences with fixed thresholds cannot provide satisfactory performance. This fixed-threshold scheme may cause too many false alarms or result in a low detection probability for a selected threshold value. We present an adaptive acquisition scheme for PN sequences which estimate the background power level, multiply it with a threshold coefficient to keep the false alarms constant, and use it as a threshold.

A decision-feedback equalizer with pattern-dependent feedback for magnetic recording channels

A new nonlinear equalizer for high-density magnetic recording channels is presented. It has a structure of the decision-feedback equalizer (DFE) with a nonlinear model at the feedback section and a dynamic threshold detector. The feedback nonlinear model is a sequence of look-up tables (LUTs) indexed by time, and each table is addressed by a transition pattern formed by one future and /spl nu/ past transitions. We call this new nonlinear equalizer the pattern-dependent DFE (PDFE). The feedback nonlinear model cancels the trailing nonlinear intersymbol interference (ISI), and then the data decision is made by considering the precursor nonlinear ISI caused by one future symbol. We propose a tap optimization criterion SNR/sub d/ for the PDFE which in effect tries to maximize the output signal to noise ratio, and derive a closed-form solution for the tap values. We compare the detection performance of PDFE with that of the DFE and the RAM-DFE on experimental channels. The RAM-DFE is a DFE with one large LUT at its feedback section. The results show that the PDFE yields a significant performance improvement over the DFE and the RAM-DFE. Also the PDFE derived in this paper achieves a superior performance compared with the PDFE derived by the minimum mean-square-error criterion.

Generalized OS CFAR detector with noncoherent integration

Analysis of constant false alarm rate (CFAR) detectors with noncoherent integration has been limited to the cell-averaging (CA) CFAR detector, the maximum mean level detector (MX-MLD) and the order statistics (OS) CFAR detector. Detection performance of the CA CFAR detector that employs noncoherent integration has been studied by several authors even though the false alarm rate of the CA CFAR detector is sensitive to changes in the background clutter-plus-noise level under nonhomogeneous situations. Shor and Levanon analyzed the detection performance of the OS CFAR detector with noncoherent integration in homogeneous situation, but their formula requires numerical integration. In this paper, we extend the detection analysis to the generalized order statistics (GOS) CFAR detector that employs M-pulse noncoherent integration for general chi-square fluctuating targets in nonhomogeneous environments, which covers various OS and CA CFAR detectors. We obtain unified formulas of the false alarm and the detection probabilities for the GOS CFAR detector in closed form. By properly choosing the coefficients of the GOS CFAR detector, one can realize various kinds of CFAR processors, such as the CA CFAR detector, the OS CFAR detector, the censored mean level detector (CMLD) and the trimmed mean (TM) CFAR detector.

Performance of modified order statistics CFAR detectors with noncoherent integration

Abstract The order statistics cell averaging (OSCA) constant false alarm rate (CFAR) detector and the order statistics greatest of (OSGO) CFAR detector have the good properties of the order statistics (OS) CFAR detector while the processing time of each detector is less than half the processing time of the OS CFAR detector in ordering magnitude of the cells in a reference window. In this paper, we analyze the OSCA and the OSGO CFAR detectors with M -pulse noncoherent integration for chi-square fluctuating targets. We obtain the explicit formulas of false alarm rates and detection probabilities for the OSCA and the OSGO CFAR detectors.

Detection analysis of a generalized order statistics CFAR detector for a correlated Rayleigh target

Abstract We analyze the performance of the generalized order statistics constant false alarm rate detector for a correlated Rayleigh target model. Detection probability is computed by contour integration. It is shown that the resulting detection performance depends on the degree of correlation. The more the correlation, the poorer the detection probability becomes.

A case study of policy-based QoS management in 3G networks

The proposed core network for the third-generation (3G) mobile telecommunication has the all IP architecture that IP protocol is used for transporting all user data and signaling traffic within the network. While this architecture can bring many benefits (e.g. providing various IP-based multi-media services), it is also noted that the QoS problems still exist since it provides the multi-media services using ordinary IP packets and routing. Especially, for the operator, it is necessary to manage the network resources and capabilities according to their business requirements. As a solution for this problem, the policy-based network management (PBNM) would be suggested by 3GPP. In this paper, we obtain the comparative performance results of specific cases of the network with the policy-based QoS management and with the existing QoS mechanism through ns-2 simulation. Simulation results show that the proposed PBNM architecture can guarantee the required quality of service to meet the business requirements better.

An efficient coding of LSP parameters using multiple type frame segmentation

The transmission of spectral information consumes a large portion of total bit rate in medium-to-low bit rate speech coding. The conventional coding methods of LSP parameters generate redundant spectral information or spectral distortion by the fixed update rate of LSP parameters independent of the order of coefficients and phonetic context. We propose a multiple type frame segmentation (MTFS) method which allows various types of two-dimensional segmentation of speech frames to save the transmission rate of the LSP parameters without increasing the spectral distortion. The intra-frame spectral distortion (IFSD) is defined to measure the spectral distortion of the reconstructed spectrum. The proposed method generates a less distorted spectrum with fewer bits compared with the conventional single type frame segmentation (STFS) method.

Spoken digit recognition using URAN (universally reconstructable artificial neural-network) VLSI chip

Explores the possibility of the URAN (universally reconstructable artificial neural-network) VLSI chip for speech recognition. URAN, a newly developed analog-digital hybrid neural chip, is discussed in respects to its input, output, and weight accuracy and their relations to its performance on speaker independent digit recognition. Multi-layer perceptron (MLP) nets including a large frame input layer are used to recognize a digit syllable at a forward retrieval. The simulation results using the full and limited floating precision computations for the input, output, and weight variables of the network give the comparable classification performance. An MLP with piecewise linear hidden and output units is also trained successfully using low accuracy computation.<<ETX>>