Performance analysis of instantaneous frequency-based interference excision techniques in spread spectrum communications

Wang, Chenshu; Amin, Moeness G.

doi:10.1109/78.651179

Cited by 45 publications

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Interference Suppression in Spread‐Spectrum Communication Systems

Amin

Zhang

2003

Wiley Encyclopedia of Telecommunications

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This chapter discusses interference excision techniques in spread spectrum communications systems. Both stationary and nonstationary interferers are considered. Sinusoidal, autoregressive, digital communication, and polynomial phase interference signals are effectively suppressed by different excision methods; each is suitable for one or more types of interferers. Time‐, frequency‐, and time‐frequency domain methods for analysis and estimation of the interference parameters are summarized. Domains other than time and frequency, such as the Gabor‐domain, the Wavelet‐domain, and quadratic time‐frequency signal representations, are appropriate for nontraditional smart jamming in which the interference parameters are highly dependent on time. Excision methods can be linear, bilinear, or nonlinear with performance dependent on the interference power relative to the desired signal and noise. In some cases, the slight performance enhancement offered by bilinear and nonlinear methods may not properly justify the increase in the algorithm complexity. The BER and receiver SINR expressions and curves are presented in this chapter for some of the key interference excision techniques.

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Interference Suppression in Spread‐Spectrum Communication Systems

Amin

Zhang

2003

Wiley Encyclopedia of Telecommunications

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Robust transform domain signal processing for GNSS

Borio

Closas

2019

NAVIGATION

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Robust signal processing techniques are effective mitigation approaches that can enable Global Navigation Satellite System (GNSS) receiver operations even in the presence of strong interference. We extend robust GNSS signal processing to the case where interference admits a sparse representation in a transform domain (TD). More specifically, an orthonormal transformation is used to project the received GNSS samples into an appropriate TD. Robust signal processing strategies are then applied. The robust transform domain (RTD) cross‐ambiguity function (CAF) is obtained by applying a zero‐memory nonlinearity (ZMNL) to the TD samples, which, in turn, are used for the evaluation of the CAF. Different nonlinearities are analyzed and different implementations for the evaluation of the RTD CAF are proposed. The RTD approach is characterized from a theoretical point of view, with the usage of Monte Carlo simulations and through the processing of GNSS signals generated with a hardware simulator.

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