IET Communications
Research Article
Non-cooperative burst detection and
synchronisation in downlink TDMA-based
wireless communication networks
ISSN 1751-8628
Received on 15th June 2018
Revised 21st November 2018
Accepted on 16th January 2019
E-First on 18th March 2019
doi: 10.1049/iet-com.2018.5536
www.ietdl.org
Maryam Zebarjadi
1
, Mehdi Teimouri
1
1
Information Theory and Coding Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Iran
E-mail: mehditeimouri@ut.ac.ir
Abstract: Blind signal detection is very important in various applications such as cognitive radio, spectrum surveillance, and
eavesdropping. This gets trickier when one needs to deal with burst signals (as opposed to continuously transmitted signals) in
non-cooperative environments. Here, existing methods for signal detection are studied and developed to use for burst detection.
For the special case of downlink time division multiple access (TDMA) burst transmission, performances of these detection
methods are improved by proposing a blind synchronisation algorithm which is applied to the output result of detection
algorithms. The results of Monte–Carlo simulations demonstrate the performance of the proposed detection and synchronisation
algorithms.
1 Introduction
Blind signal identification is an important research topic in modern
telecommunication technology. Nowadays, non-cooperative signal
processing is one of the essential necessities in the design of
intelligent receivers [1]. Blind signal detection, blind modulation
recognition, and blind channel code reconstruction are three
fundamental steps in non-cooperative receivers [2]. Signal
detection is the first step in such receivers. All the subsequent steps
rely on the results of signal detection phase, so an intelligent
receiver must be equipped with an accurate signal detector.
Various signal detection methods have been proposed and
utilised in different applications. In spectrum sensing technology,
various sensing methods are proposed [3]. Energy Detector (ED) is
the simplest and the most popular algorithm. In this method, the
receiver measures the received signal energy and compares it with
a threshold that depends on the noise level [4–12]. ED method does
not need to know any information about the signal and the fading
channel. However, this method needs to estimate the noise level.
As a result, a small error in the estimation of noise level affects the
detection result. Methods based on covariance matrix and
eigenvalues are proposed for resolving this issue. Signal detection
based on the eigenvalues of covariance matrix is investigated by
various papers [3, 13–16]. In [17], maximum eigenvalue detector
(MED) is proposed. The authors of [18] have suggested the ratio of
the maximum eigenvalue to the minimum eigenvalue detector.
Cyclostationary features are also proposed for signal detection [19,
20]. However, when the noise is stationary, ED methods
outperforms cyclostationary-based methods [21]. Matched filtering
detector (MFD) is also considered for signal detection in [22, 23].
MFD has higher accuracy in low signal-to-noise ratio (SNR)
conditions, and it is more robust to noise-level uncertainty.
However, MFD needs specific knowledge about the transmitted
waveform patterns which is the drawback of the method in blind
situations. For comprehensive comparisons between various signal
detection methods, the reader is referred to [21, 24].
In all the above-mentioned methods, signal-detection process is
modelled as a binary hypothesis testing. The null hypothesis is the
situation in which only noise is present and the alternative
hypothesis is the situation in which signal is received in a noisy
environment. This hypothesis testing is suited for environments in
which continuous signal is transmitted. Signal detection gets more
complicated when one needs to deal with burst signals (as opposed
to continuously transmitted signals). In this situation, signal
detection is not accomplished only by checking the presence or
absence of the signal. In this case, it is also necessary to determine
the exact position of bursts.
In communications based on burst signals, different users
transmit their burst information by using a radio channel and each
burst conveys a data packet to a particular receiver. In order to
prevent the interference between adjacent bursts, a guard time is
usually considered between them. In a non-cooperative
environment, the position of the bursts and the guard times must be
exactly determined by the receiver without any prior knowledge
about signal. So, we can define blind detection of burst signals as
determination of the beginning and ending time of all bursts. Since
the received signal may contain bursts with different lengths, an
efficient detector should have the ability to detect bursts of
different lengths. It also should detect very short guard times.
Transient signal detection is a concept which is very relevant to
burst detection. Transient signal detection arises in applications
such as telemetry and spread spectrum communications. Transient
signals are generally considered as short duration signals compared
to the observation time. The aim of transient signal detector is to
decide whether the observation consists of noise alone or the signal
is embedded in noise [25–27]. Various detection schemes have
been proposed for this purpose. Selecting an appropriate detection
method depends on the amount of knowledge that is available to
the detector. Such knowledge includes the arrival time of transient
signal, the duration of the transient signal, and the waveform shape
of the signal. For instance, matched filtering is the optimal method
if the transient signal is known. When the transient signal is
partially known, an often used procedure is the likelihood ratio test
(LRT). When the signal to be detected is unknown, energy-based
detectors become the only choice [25]. In [26], the performance of
transient detection based on higher order moments is investigated
and a detector based on the third-order absolute moment is
proposed. Order statistics (OS)-based signal processing is also
applied to transient detection, and it is shown that the duration of
an unknown transient signal can be estimated by use of OS
technique [27]. In [28], a method based on permutation entropy is
introduced for recognition of starting time of a transient signal.
GSM signal is used for evaluating the performance of this
detection technique where each time slot is considered as a
transient signal. Since there is no information available about the
frame structure and time slots in a non-cooperative environment,
the above-mentioned methods for transient signal detection are not
applicable. Moreover, existence of multiple bursts with different
lengths is not considered in transient signal-detection formulation.
IET Commun., 2019, Vol. 13 Iss. 7, pp. 863-872
© The Institution of Engineering and Technology 2019
863
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on September 02,2020 at 05:32:37 UTC from IEEE Xplore. Restrictions apply.
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