Spread spectrum (SS) communications offers a promising solution to an overcrowded frequency spectrum amid growing demand for mobile and personal communications services. The proposed overlay of spread spectrum signals on existing narrowband users implies strong interference for the SS system. This paper discusses how system performance can be improved by preprocessing to suppress narrowband interference. Linear prediction filters have been proposed since the 1980s for suppression of narrowband interference. In 1991 Vijayan and Poor proposed nonlinear methods of suppressing the narrowband signal with significant increase in the SNR improvement. We derive an enhancement to this nonlinear prediction and achieve further improvement by applying the technique to interpolating filter structures. Finally, we extend results to the case of multiple spread spectrum users and demonstrate how nonlinear filtering can dramatically outperform linear filtering. I. Introduction IEEE Transactions on Com...

We consider the problem of estimating and suppressing many unknown independent and time-varying interferers in a spread-spectrum communication system. The interferers are assumed to be present in a wide frequency range. In order to detect, estimate and track the interference, we use a bank of hidden Markov model lters operating in the frequency domain. The hidden Markov model lters' outputs are then used to suppress the existing interference. The computational complexity of our scheme is only linear in the number of interferers. The simulation studies show that our proposed novel schemes adapt quickly in tracking the time-varying nature of the interference. 1 Introduction In this paper, we consider the problem of detecting, tracking and suppressing interference in a spread-spectrum communication system. The interference is assumed to consist of many unknown, independent and time-varying narrowband interferers. There are several advantages with spreading the spectrum of the signal th...

Conventional detection and demodulation rules are not ideally suited for use with discrete Fourier transform (DFT) based narrow-band interference suppression, if the DFT incorporates time-weighting to localize the interference spectrally. Detection rules which are adapted to the time-weighting are introduced here and shown to offer significant improvements in performance, but their implementation is complex. It is found that if a spectrally-contained orthogonal transform (SCOT) is employed in place of the windowed DFT however, the conventional detection rules have a level of performance comparable to that obtained with DFT based suppression and the adapted rules. In addition to yielding good interference localization, SCOTs benefit from the fact that they provide a KarhunenLoeve like transform for each component of the observation. The primary focus of the paper is the application of interference suppression for the detection of the presence of covert signals; however, it is also demon...

Time-variant multi-path propagation Doppler spread is caused by moving objects in the mobile radio channel. Changes in the phases and amplitudes of the arriving waves lead to time-variant multi-path propagation. Even small movements on the order of the wavelength may result in a totally different wave superposition. The varying signal strength due to time-variant multi-path propagation is referred to as fast fading. Shadowing is caused by obstruction of the transmitted waves by, for example, hills, buildings, walls, and trees, which results in more or less strong attenuation of the signal strength. Compared to fast fading, longer distances have to be covered to change the shadowing constellation significantly. The varying signal strength due to shadowing is called slow fading and can be described by a log-normal distribution [41]. Path loss indicates how the mean signal power decays with distance between the transmitter and receiver. In free space, the mean signal power decreases with the square of the distance between the base station (BS) and terminal station (TS). In a mobile radio channel, where often no line of sight (NLOS) exists, signal power decreases with a power higher than two and is typically in the order of three to five. Variations of the received power due to shadowing and path loss can be efficiently counteracted by power control. In the following, the mobile radio channel is described with respect to its fast fading characteristic. 1.1.2 Channel Modeling The mobile radio channel can be characterized by the time-variant channel impulse response h(τ, t) or by the time-variant channel transfer function H (f, t), which is the Fourier transform of h(τ, t). The channel impulse response represents the response of the channel at time t due to an impulse applied at time t − τ. The mobile radio channel is assumed to be a wide-sense stationary random process, i.e. the channel has a fading statistic that remains constant over short periods of time or small spatial distances. In environments with multi-path propagation, the channel impulse response is composed ofRadio Channel Characteristics 17 a large number of scattered impulses received over Np different paths: h(τ, t) = Np−1 p=0 ape j(2πfD,pt+ϕp) δ(τ − τp), (1.1) where δ(τ − τp) =