Scholarly Communications Project

Robust GMSK Demodulation Using Demodulator Diversity and BER Estimation


Jeffery D. Laster

PhD Dissertation submitted to the Faculty of the Virginia Tech in partial fulfillment of the requirements for the degree of



Electrical Engineering


Jeffrey H. Reed, Chair
Warren L. Stutzman
Theodore S. Rappaport
A. A. Beex
Keying Ye

January 28, 1997
Blacksburg, Virginia


This research investigates robust demodulation of Gaussian Minimum Shift Keying (GMSK) signals, using demodulator diversity and real-time bit-error-rate (BER) estimation. GMSK is particularly important because of its use in prominent wireless standards around the world (GSM, DECT, CDPD, DCS1800, and PCS1900). The dissertation begins with a literature review of GMSK demodulation techniques (coherent and noncoherent) and includes an overview of single-channel interference rejection techniques in digital wireless communications. Various forms of GMSK demodulation are simulated, including the limiter discriminator and differential demodulator (i.e., twenty-five variations in all). Ten represent new structures and variations. The demodulator performances are evaluated in realistic wireless environments, such as additive white Gaussian noise, co-channel interference, and multipath environments modeled by COST207 and SMRCIM. Certain demodulators are superior to others for particular channel impairments, so that no demodulator is necessarily the best in every channel impairment. This research formally introduces the concept of demodulator diversity, a new idea which consists of a bank of demodulators which simultaneously demodulate the same signal and take advantage of the redundancy in the similar signals. The dissertation also proposes practical real-time BER estimation techniques which have tremendous ramifications for communications. Using Parzen's estimator for probability density functions (pdfs) and Gram-Charlier series approximation for pdfs, BER can be estimated using short observation intervals (10 to 500 training symbols) and, in some cases, without any training sequence. We also introduce new variations of Gram-Charlier estimation using robust estimators. BER (in place of MSE) can now drive adaptive signal processing. Using a cost function and gradient for Parzen's estimator (derived in this paper), BER estimation is applied to demodulator diversity with substantial gains of 1-10 dB in carrier-to-interference ratio over individual receivers in realistic channels (with adaptive selection and weighting). With such gains, a BER-based demodulator diversity scheme can allow the employment of a frequency reuse factor of N=4, instead of N=7, with no degradation in performance. A lower reuse factor means more channels are available in a cell, thus increasing overall capacity. The resulting techniques are simple and easily implemented at the mobile. BER estimation techniques can also be used in BER-based equalization and dynamic allocation of resources.

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