ICASSP '98 Main Page

General Information

Conference Schedule

Technical Program

    Overview
    50th Annivary Events
    Plenary Sessions
    Special Sessions
    Tutorials
    Technical Sessions

	By Date
		May 12, Tue
		May 13, Wed
		May 14, Thur
		May 15, Fri

By Category
	AE	ANNIV
	COMM	DSP
	IMDSP	MMSP
	NNSP	PLEN
	SP	SPEC
	SSAP	UA
	VLSI

By Author
	A	B	C	D	E
	F	G	H	I	J
	K	L	M	N	O
	P	Q	R	S	T
	U	V	W	X	Y
	Z

Invited Speakers

Registration

Exhibits

Social Events

Coming to Seattle

Satellite Events

Call for Papers/
Author's Kit

Future Conferences

Help

Abstract - COMM4

COMM4.1	Low-Complexity Digital Encoding Strategies for Wireless Sensor Networks H. Papadopoulos, G. Wornell, A. Oppenheim (MIT, USA) Low-complexity schemes for digital encoding of a noise-corrupted signal and associated signal estimators are presented. This problem arises in wireless distributed sensor networks where an environmental signal of interest is to be estimated at a central site from low-bandwidth digitized information received from collections of remote sensors. We show that the use of a properly designed and often easily implemented additive control input before signal quantization can significantly enhance overall system performance. In particular, efficient estimators can be constructed and used with optimized pseudo-noise, deterministic, and feedback-based control inputs, resulting in a hierarchy of practical systems with very attractive performance-complexity characteristics.
COMM4.2	Applications of Blind Equalization in Wireless ATM Network J. Bao, L. Tong (University of Connecticut, USA) We investigated the feasibility of applying blind equalization to wireless ATM networks. Making use of the information exploited from the wireless ATM cell structure and Meduim Access Control(MAC), blind channel estimation together with a Non-linear Data Directed Estimator achieve good equalization performance withour transmitting extra preamble. Simulation results are presented for ATM CBR and ABR traffic.
COMM4.3	Demodulation of CPM Signals Using Piecewise Polynomial-Phase Modeling S. Barbarossa, A. Scaglione (University of Rome, La Sapienza, Italy) In this work we propose a novel approach for demodulating Continuous Phase Modulation (CPM) signals based on the modeling of the instantaneous phase as a piecewise polynomial-phase function. The crucial step in the demodulation process is then the estimation of the polynomial coefficients, which is carried out using the so called product high order ambiguity function (PHAF). The proposed approach is suboptimal with respect to the optimal maximum likelihood sequence estimation (MLSE) method, but is much simpler to implement and offers important advantages such as independence of initial phase, tolerance to Doppler shift and time-offset, blind channel identification. We show theoretical results concerning the minimum distance together with some simulation results.
COMM4.4	Digital Modulation Classification Using Power Moment Matrices A. Hero, H. Hadinejad-Mahram (University of Michigan, USA) With the rising number of modulation types used in multi-user and multi-service digital communication systems, the need to find efficient methods for their discrimination in the presence of noise has become increasingly important. Here, we present a new approach based on a recently developed pattern recognition method previously applied to word spotting problems in binary images. In this approach, a large number of spatial moments are arranged in a symmetric positive definite matrix for which eigendecomposition and noise subspace processing methods can be applied. The resultant denoised moment matrix has entries which are used in place of the raw moments for improved pattern classification. In this paper, we generalize the moment matrix technique to grey scale images and apply the technique to discrimination between M-ary PSK and QAM constellations in signal space. Invariance to unknown phase angle and signal amplitude is achieved by representing the in-phase and quadrature components of the signal in the complex plane, and computing joint moments of normalized magnitude and phase components.
COMM4.5	A Simple and Robust Modulation Classification Method via Counting X. Huo, D. Donoho (Stanford University, USA) Automatic modulation classification (or recognition) is an intrinsically interesting problem with a variety of regulatory and military applications. We developed a method which is simple, fast, efficient and robust. The feature being used is the counts of signals falling into different parts of the signal plane. Compared with the likelihood method and the High Order Correlation method, it is much easier to be implemented, and the execution is much faster. When the channel model is correct, our method is efficient, in the sense that it will achieve the "optimal" classification rate. When unknown contamination is present, our method can automatically overcome to certain degree. At SNR being 10 and 15 dB, examples of classifying two modulation types--QAM4 and PSK6--are given. Simulations demonstrate its ability to deal with unknown noises.
COMM4.6	Co-channel FM Voice Separation via Cross Coupled Phase Locked Loops E. Satorius (Jet Propulsion Laboratory/Cal Tech, USA) This paper presents the results of simulation experiments that successfully demonstrate FM co-channel voice separation via cross-coupled phase locked loops (CCPLL). Unlike previous CCPLL studies which are typically restricted to the situation where the FM modulation waveforms are steady state sinusoidal, triangular, etc., we have empirically determined CCPLL loop paramaters that provide for stable separation of co-channel FM voice signals with comparable bandwidth (100% spectral overlap) and comparable mod indices. The resulting CCPLL parameters differ somewhat from existing CCPLL design rules; however, the differences can yield a significant improvement in CCPLL performance.
COMM4.7	A Joint Viterbi Algorithm to Separate Cochannel FM Signals J. Hamkins (Jet Propulsion Lab, USA) This paper presents a method for separating cochannel FM signals. We show that the Viterbi algorithm, traditionally limited to estimation of digital sequences, can jointly track analog FM signals by separately quantizing the derivatives of their instantaneous frequencies. We employ per-survivor processing in the trellis to estimate unknown channel effects. The approach works well when the signal to interference ratio (SIR) is less than or equal to zero, in contrast to conventional interference suppression algorithms that degrade as SIR approaches zero and fail catastrophically when SIR < 0. Comparisons of mean squared error (MSE) between the estimates and the true signals are given for varying SIR, SNR, Doppler offsets, and frequency deviations. The same approach can also be used for any other continuous phase modulation scheme, such as continuous-phase frequency-shift keying (CPFSK).
COMM4.8	COPERM: Transform-Domain Energy Compaction by Optimal Permutation N. Sidiropoulos (University of Virginia, USA); M. Pattichis, A. Bovik, J. Havlicek (University of Texas, Austin, USA) COPERM is a novel paradigm for energy compaction and signal compression, whose foundation is a simple but powerful idea: any signal can be transformed to resemble a more desirable signal from a class of ``target'' signals, by means of a suitable permutation of its samples. The approach is well-suited for transform domain energy compaction prior to transform-domain compression of persistent broadband signals. The associated optimal permutation precoders are surprisingly simple, and the permutation precoding overhead can be made modest - resulting in improved overall rate-distortion performance.

< Previous Abstract - COMM3

COMM5 - Next Abstract >