Narrow-band interference rejection in spread spectrum using an eigen analysis based approach

A new adaptive technique is suggested for rejecting narrow-band interferences in spread spectrum communications. When data is coded using a pseudo-noise code, the received signal consists of a wide-band signal with almost white spectral properties, thermal noise, and correlated narrow-band interferences. A new approach is proposed which exploits the statistical properties of the received signal via eigenanalysis of the received data. While the energy of the wide-band signal is distributed over all the eigenvalues of the signal autocorrelation matrix, the energy of the interference is concentrated in a few large eigenvalues. Hence, the eigenvectors corresponding to the large eigenvalues are termed the interference subspace. The proposed method derives a. weight vector residing in the subspace spanned by the rest of the eigenvectors termed the noise subspace. Consequently, it is orthogonal to the interference subspace. The eigenanalysis based interference cancellation is sub-optimal in a known signal environment, but is superior to the Wiener-Hopf filter when the signal statistics are estimated from a limited amount of data. A fast and effective adaptive algorithm is derived using the power method

Filtering techniques for mitigating microwave oven interference on 802.11b wireless local area networks

Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 165-169).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.With the increasing popularity and assimilation of wireless devices into the everyday lives of people, the issue of their feasibility for coexisting with other radio frequency (RF) devices arises. Particularly strong interferers for the IEEE 802.11b standard are microwave ovens, since both operate at 2.4 GHz. The interference mitigation techniques all exploit the differences between the interference and the signal, since the former is sinusoidal in nature while the latter can be viewed as noise. The first mitigation filter operates in the frequency domain and filters the received signal's Fast Fourier Transform (FFT) sequence by detecting and removing peak sinusoidal components over the flat 3- dB bandwidth of the signal. The second is a Least Mean Square (LMS) Adaptive filter that produces an estimate of the interference through a recursive approximation method and subtracts it out from the received signal. The third and last is the Adaptive Notch Filter (ANF) which implements a lattice structure and has a time-varying notch frequency parameter that converges to and tracks the frequency of the interference in the received signal. The three filters are shown to produce improvements in the bit error rate (BER) and frame error rate (FER) performance of the receiver under various relative strengths of the signal with respect to the interference.by Lorenzo M. Lorilla.M.Eng.and S.B