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\title{A Knowledge-Based Interference Rejection Scheme for Direct
Sequence Spread-Spectrum Systems
\thanks{Research Sponsored by USAF Rome Laboratory, Under Contract No. 
F30602-95-C-0204. }} 
\author{I. Demirkiran, V. N. S. Samarasooriya, P. K. Varshney, D. D. Weiner\\
Department of Electrical Engineering and Computer Science\\
Syracuse University, Syracuse, N.Y. 13244, USA. \\
    \\
Ramamurthy Mani, S. Hamid Nawab \\
Department of Electrical and Computer Engineering \\
Boston University, Boston, MA 02215.\\
  \\
Stephen Tyler \\
Rome Laboratory, RL/C3BB,
Griffiss AFB \\
525 Brooks Road, NY 13441-4505.}
\date{}
\vspace{-0.3in}
\maketitle
\thispagestyle{empty} 
 
\subsection*{\centering Abstract}

{\it 
%       abstract 
A knowledge-based interference rejection scheme for removal of Narrow Band
Interference (NBI) in Direct Sequence Spread-Spectrum Systems (DSSS) is
presented. The interference rejection system monitors the environment and 
identifies the types of
interference embedded in the DSSS signal.  An accurate estimate of the
characteristics of the NBI is obtained and is used to direct the subsequent
interference rejection schemes. System performance is illustrated by
means of simulation results.
 
Index Terms - Spread-spectrum systems, Interference rejection, Knowledge-based signal 
processing.   }

\baselineskip=3.0ex
 
\pagestyle{empty} 
 
\vspace{-4ex}
\section{Introduction}
\label{sec:intro}

In modern spread-spectrum communication systems, a large number of users
share the same channel giving rise to multiuser interference. Other common
interference sources include fading, multipath and intentional
jamming. Spread spectrum systems display an inherent interference immunity
by virtue of their design. However, we encounter many situations where the
interference immunity provided by the system processing gain is not
sufficient in meeting the desired performance goals. Thus, additional
interference rejection techniques are required to enhance receiver
performance. Development of effective interference rejection techniques is
an important design issue and is addressed in this paper.

We concentrate on rejection of Narrow Band Interference(NBI) in Direct
Sequence Spread-Spectrum (DSSS) systems.  Various schemes, including
adaptive notch filtering, estimation/prediction type filters, transform
domain processing schemes, decision feedback filters, and nonlinear
techniques have been presented in the literature for rejection of NBI
\cite{laster,milstein, gervar,goiser}.  The spread-spectrum signal is
represented in the frequency domain by a wide, flat spectrum, whereas
narrow-band interferers are represented in the spectrum by narrow
spikes. Therefore, narrow-band interferers can be removed by 
placing notch filters at the locations of the spikes in the received signal
spectrum \cite{tyler}. The problem of interference suppression then becomes
one of trying to accurately determine the frequency locations and time profile of each
individual narrow-band interferer in the signal.


The class of estimation/prediction type filters attempt to achieve interference
suppression by estimating the
narrow-band interferers and subtracting the estimated interferering components
from the received signal \cite{milstein,milstein2,milstein3}.  
The spread-spectrum signal and AWGN, being wide-band processes, display no
correlation between future and past sample values. Interference, being a
narrow-band process, displays correlation between past and future sample
values.  The interference can, therefore, be predicted from the past values
of the received signal samples and subtracted from the current input
signal. The wide-band spread-spectrum signal would then appear at the error
output of the filter. 
%This is the concept underlying the class of
%estimation prediction filters in removing NBI
%\cite{milstein,milstein2,milstein3}.
%-------------------------------------------------

The prediction type cancellation schemes suffer from the drawback that they
do not perform well in the presence of multiple narrow-band interferers and
in situations where the interference has significant amounts of frequency
modulation. Although these drawbacks may be partially overcome by
increasing the order of the filter employed, this imposes additional
problems in the form of filter transient times and receiver design
complexity. Alternative approaches \cite{patti} to narrow-band interference
rejection involving time-frequency analysis performed using filterbanks have been
proposed to handle scenarios with narrow-band interference with significant
frequency modulation rates. While these solutions are suitable for
suppressing a few interferers with slowly varying frequency modulations, 
they generally fail when applied to
signals with several interferers with rapidly varying modulations. 
The primary cause for failure is
the fixed tradeoff between time and frequency resolution at any given point
in the time-frequency plane.

A remedy for the drawbacks of fixed resolution time-frequency analysis
techniques is to adapt the analysis filters at each point in the
time-frequency plane in a data-dependent manner. One such adaptation
strategy developed by Parks and Jones \cite{atfr} involves choosing at each
time-frequency point a filter that maximizes a kurtosis-like local energy
concentration measure. This strategy suffers from the drawback that it is
computationally very expensive due to the exhaustive search over
${\mathcal{O}}(10^2)$ candidate filters performed at each time-frequency
point. It should be noted that for each candidate filter, a short-time
Fourier transform has to be computed over a time-frequency subregion of the
time-frequency point under consideration. Another major drawback in using
the concentration measure is that it does not utilize any knowledge about
the time-frequency behavior of spread-spectrum jammers that may possibly
exist in the environment.

We describe an alternative knowledge-based strategy for choosing
appropriate analysis filters at each time-frequency point. This strategy,
relies on an iterative procedure based upon the Integrated Processing and
Understanding of Signals (IPUS) paradigm \cite{ipus} for adapting the
filters at each time-frequency point. We begin in Section 2 with an overall
description of our interference rejection framework that encompasses this
knowledge-based filter adaptation strategy. In Section 3, we provide a
description of how this strategy achieves the isolation of individual
narrow-band interferers. Finally in Section 4, we describe the
filtering schemes utilized to suppress the isolated interferers.
Furthermore, we also present simulation results that demonstrate the
efficacy of our interference rejection framework and its superiority over
conventional interference rejection approaches.


\vspace{-5ex}
\section{Interference Rejection Framework}
\label{sec:frame}

The problem of interference cancellation may be formally described by
considering received signals of the form: 


\vspace{-4ex}
\begin{equation}
   x(t) = s(t) + \sum_{k=0}^{N} y_k(t) 
\end{equation}

\no where $s(t)$ is the
information-bearing spread spectrum signal and each $y_k(t)$ is a
narrow-band interferer of the form $y_k(t) = a_kcos\left[\omega_k(t) + \phi_k\right]$. 

%\vspace{-4ex}
%\begin{equation}
%   y_k(t) = cos\left[\omega_k(t) + \phi_k\right] 
%\end{equation}

Our objective is to cancel all the interferers $y_k(t)$ and recover
the original spread-spectrum signal $s(t)$. In our research we have have
assumed that interferers are restricted to be of the types listed below.
%as illustrated in
%Figure~\ref{fig:blokdiag}.  
%The interference present can be any
% combination of the three types of interference listed below.


\no $(1)$ Multiple tone interferers operating in continuous wave (CW) mode, 

\no $(2)$ Linear-FM ( or chirp) interfering signals, 

\no $(3)$ Multiple CW tones operating in ON-OFF and/or frequency-hopping modes.  


This assumption was made on the basis that scenarios limited to having
 combinations of these
interferer types offer ample scope for demonstrating the benefits of
our knowledge-based approach. While CW interferers are very common type of
interferers occurring in a spread-spectrum communication environment,
chirp and frequency hopped interferers also occur frequently 
and pose a significant challenge to interference rejection
schemes. The changing frequency content of these interferers
necessitates some form of adaptive filtering by the receiver.

%This assumption was made on the basis that the
%cancellation problem is fairly complex even in scenarios limited to have
%combinations of these interferer types and offers ample scope for demonstrating
%the benefits of our knowledge-based approach. CW tones are the most common
%type of interference occurring in a spread-spectrum communication environment.
%Chirp interferes pose a
%significant challenge to interference rejection schemes. Their instantaneous BW is
%narrow, but their effective bandwidth can cover the entire spread spectrum
%bandwidth.  Chirp interferes require some form of adaptive filtering by the
%receiver.

%{\bf ****Then we can have a para about our overall approach which
%introduces the former Figure 1. ****}
\begin{figure}
%\vspace{2in}
\centerline{\epsfig{figure=il2.ps,width=3.2in,height=1.4in}}
\caption{\sf Spread-spectrum system operating in the presence of 
interference.}
\label{fig:blockdiag}
\end{figure}

In Figure 1, we outline our overall knowledge-based framework for
interference rejection. At the receiver, the IPUS-based strategy for
adapting filters in a time-frequency analysis filter-bank is utilized in the
first stage for isolating individual narrow-band interferers. 
Information regarding the time-frequency locations of the isolated
interferers is then used within a subsequent interference cancellation stage. The
output of the cancellation stage is demodulated to recover the transmitted
data.


\vspace{-4ex}
\section{Knowledge-Based Interference Isolation}
\label{sec:ipus}
The isolation of individual interference signals from the contaminated spread
spectrum signal is carried out through time-frequency analysis performed
using a filterbank. Individual filters in the filterbank are adapted in a
data-dependent fashion to separately resolve individual interference
signals. This knowledge-based filter adaptation strategy for adapting the
analysis filters follows the IPUS blackboard paradigm. For processing any
particular signal interval, the IPUS-based strategy relies on an iterative
procedure guided by local and global constraints on the time-frequency
characteristics of the narrow-band interference signals. This procedure,
which is outlined in Figure~\ref{fig:knowipus}, begins by {\em predicting}
the time-frequency evolution of narrow-band interference signals from the
previous processing segment to the current one. The bandwidths of the
analysis filters in a uniform filterbank are then adjusted on the basis of
the predicted narrow-band interference.

An iteration in the adaptation procedure commences with the processing of
the signal data in the current analysis interval using the adjusted
filterbank.  Spectral peaks are picked from the output and fed to a Kalman
tracker \cite{kalman} that hypothesizes time-frequency trajectories
corresponding to partials. We refer to these trajectories as ``tracks.''
{\em Discrepancy detection} is carried out in order to search for
``distortions'' that cause mismatches between the predictions and the
tracks. Examples of such distortions include the absence of a track where
one has been predicted and mismatch in frequency modulation rates between a
prediction and a corresponding track. Following discrepancy detection, the
identified distortions are explained through a process of {\em discrepancy
diagnosis}. The diagnostic process is aided by knowledge regarding the
possible time-frequency interactions between narrow-band interference signals. 
We were able to determine these interactions by means of a thorough
analysis of all possible scenarios with the types of jammers considered in
our research. Time-frequency information extracted from the data, such as
the frequency modulation rate and the on-time of ON-OFF jammers, are
utilized within diagnosis to identify the relevant interactions within the
current signal context.  The identified interactions form the basis for
attributing causes such as low frequency resolution and low time resolution
to the distortions in the processed data.  A set of {\em reprocessing
plans} are then retrieved for removing each distortion by erasing its
causes.  Execution of the reprocessing plans result in an adaptation of the
analysis filters and also (if necessary) a refinement of predictions in the
current processing interval. This leads to the end of an iteration in our
knowledge-based strategy.  Reprocessing of the data followed by the
formation of fresh tracks marks the start of a new iteration. The iterative
procedure involving discrepancy detection, discrepancy diagnosis,
reprocessing planning, and reprocessing is repeated until all distortions
are either eliminated or an iteration limit is reached.  In the next
section, we illustrate the application of knowledge-based interference
isolation in a DSSS communication environment.

\begin{figure}
\centerline{\epsfig{figure=knowipus.ps,height=3.9in}}
\caption{\sf IPUS-based interference isolation.}
\label{fig:knowipus}
\end{figure}

\vspace{-5ex}
\section{DSSS Interference Cancellation System}
\label{sec:dsss}

%-----------------------------------

To demonstrate the efficacy of our knowledge-based approach, we have
simulated a  DSSS system that is as shown in Figure 1. A prototype
is built using Signal Processing WorkSystem (SPW). This system
utilizes binary PSK signaling with a carrier frequency of 4800 Hz and a
data rate of $100$ bps. A simple shift register generator (SSRG) of order
$10$ is used to generate a maximal length pseudo-noise spreading sequence
at $3200$ chips$/$sec. This results in a processing gain of $15$ dB and a
signaling bandwidth of $6400$ Hz.  Both the transmitter and receiver in our
prototype system utilize a sampling rate of $16$ kHz. In our simulations, we
ensure that the interfering signals contribute a combined interfering signal power
that is substantially greater than the power which can be handled by the system
processing gain alone.

Processing within our prototype system progresses in blocks of $4096$
samples. The IPUS-based interference isolation stage analyzes each such
block and provides estimates of the time-frequency locations and ON-OFF
times of the interfering signals present within the block. The interference
cancellation stage directs the cancellation circuit based on the number of
interfering signals identified and their corresponding ON-OFF times. For each
interfering signal, cancellation is achieved by placing an adaptive notch
filter at its estimated frequency location. In the case of interferers that are
found to be operating in an ON-OFF mode, the cancellation stage employs a
switching mechanism which ensures that the received signal is filtered only
in the intervals during which the tone is 'ON'. The bandwidth of the notch
was chosen to be 32 Hz on the basis of the fact that the accuracy of the
frequency estimates provided by IPUS-based interference isolation stage is
within 16 Hz of the true value.  In the case of a linear chirp
interference, such accuracy in the frequency estimates proves to be very
fruitful since the cancellation stage removes only a narrow segment of the
frequency spectrum around the instantaneous frequency of the chirp signal.
The notch filters are Chebyshev Type II IIR bandstop filter of order $6$.
These filters were chosen since they have a maximally flat 
attenuation in the passband and 
a stopband attenuation  that is guaranteed to remain below  
a user specified attenuation
level at the stopband edge. 
The attenuation at the stopband edge was 
selected so as to effectively reduce the interfering signal to a level that
can be handled by the system processing gain.
  The Chebyshev type II filter
makes the transition between the passband and the stopband more quickly
than the Bessel, Butterworth, and Chebyshev type I.

To illustrate the performance of our system, we provide the simulation
results for a scenario consisting of four linear chirp interfering signals
contaminating the transmitted spread-spectrum signal. Each linear chirp
contributes an interfering signal power of approximately $30$ dB over the
interfering signal power that can be handled by the system processing gain.
Upon analyzing the received interference corrupted data, the IPUS-based
interference isolation stage indicated the presence of four linear chirp
interferers and provided time-frequency tracks for each interfering chirp.
In Figure 3, we show time-frequency tracks corresponding
to one of the linear chirp signals. The frequency estimates are then used
to adaptively control the center frequency of bandstop filters. The
frequency location and time profile of each interferer are updated every 16 samples
in order to avoid having to adjust the bandstop filter parameters too
frequently.  The error introduced by updating the frequency estimates every
$16$ samples, is compensated by adjusting the stopband of the bandstop
filters.
In Figure \ref{fig:ber_comp}, we show the bit error rates obtained by our
cancellation scheme and the bit error rate resulting from employing 
Least Mean Squared (LMS) adaptive filter of order $20$ with an error-signal formed
to estimate and cancel the narrow-band interference \cite{ketchum}.
We see that by
utilizing our approach we achieve a bit error rate which is about one fifth
of the bit error rate achieved using the transversal filter.







%----------------------------------


%---------------------------


\begin{figure}[t]
\centerline{\epsfig{figure=track4.epsi,width=3.0in,height=2.2in}}
%\vspace{2.5in}
\label{fig:lfm4_est}
\caption{\sf Time-frequency tracks showing the actual instantaneous frequency of
a chirp and the estimated track generated by IPUS.}
\end{figure}

\begin{figure}[t]
%\vspace{2.5in}
\centerline{\epsfig{figure=BERcomp.epsi,width=3.0in,height=1.95in}}
\label{fig:ber_comp}
\caption{\sf Bit Error Rate plots for the IPUS-based notch filtering scheme and
 LMS filter based cancellation scheme.}
\end{figure}

\vspace{-6ex}
\section{Summary and Conclusions}
\label{sec:summary}

We have presented a knowledge-based interference rejection scheme for
spread-spectrum communication systems operating in the presence of multiple
narrow-band interferers. Effective cancellation of narrow-band interference
requires accurate estimates of the time-frequency locations of each
interferer embedded in the received spread-spectrum signal.  A
knowledge-based approach, based upon the Integrated Processing and
Understanding of Signals (IPUS) paradigm, is employed to isolate individual
narrow-band interfering signals. This approach relies on a novel strategy for
adapting the analysis filters within a filterbank in a manner that allows
the separation of individual interferers from a spread spectrum signal.
The analysis filters are adapted on the basis of knowledge regarding global
and local constraints on the time-frequency behaviors of the interference
present in the signal environment. Time-frequency tracks corresponding to
the isolated interferers are utilized in a subsequent cancellation stage to
adapt notch filters for removing the interference. Simulations conducted on
a prototype DSSS system have demonstrated the superiority of our
knowledge-based interference rejection scheme over conventional
interference rejection schemes in terms of both bit error rate performance
and reduction in receiver design complexity. 


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