Alzaq and Ustundag EURASIP Journal on Wireless Communications and
Networking (2017) 2017:120
DOI 10.1186/s13638-017-0902-7
RESEARCH Open Access
Very-low-SNR cognitive receiver based on
wavelet preprocessed signal patterns and
neural network
Husam Y. Alzaq
*
and B. Berk Ustundag
Abstract
A pattern-based cognitive communication system (PBCCS) that optimizes non-periodic RF waveforms for security
applications is proposed. PBCCS is a cross-layer approach that merges the channel encoding and modulation. The
transmitter encodes sequences of bits into continuous signal patterns by selecting the proper symbol glossaries. The
cognitive receiver preprocesses the received signal by extracting a limited set of wavelet features. The extracted
features are fed into an artificial neural network (ANN) to recover the digital data carried by the distorted symbol. The
PBCCS system offers a flexible management for robustness against a high noise level and increases the spectral
efficiency. In this study, the spectral efficiency and robustness of a PBCCS scheme for an additive white Gaussian noise
(AWGN) channel is investigated. The results show that at an SNR of -5 dB, a 3-bit glossary achieves a bit error rate
(BER) of 10
-5
. Also, the link spectral efficiency (LSE) of the proposed system is 2.61 bps/Hz.
Keywords: Cognitive radio (CR), Pattern-based cognitive communication system (PBCCS), Artificial neural network,
Wavelet decomposition, Digital signal processing (DSP), Very-low-SNR
1 Introduction
The efficiency of bandwidth utilization takes an impor-
tant role in spectrum management [1, 2]. Due to fixed
spectrum assignment policies and its inadequate to meet
an unexpected increase in the number of higher-data-
rate devices, the spectrum is inefficiently used. Cognitive
radio (CR) [3–6] was proposed as a promising solution to
alleviate the spectrum scarcity problem through dynamic
management of the available spectrum. The pioneer work
of Mitola et al. [3] led to an efficient utilization of the spec-
tral bandwidth by allowing the secondary user (SU), who
is not serviced, to detect and access the primary network
spectrum gaps. CR allows detection of the state of the
spectrum to adjust its own system parameters (transmis-
sion power, frequency band, throughput and modulation
scheme) in real time [7]. The result is that the utilization
of the spectral bandwidth is performed with the soft-
ware flexibility in an adaptive manner with respect to the
system parameters.
*Correspondence: alzaq@itu.edu.tr
Department of Computer Engineering, Faculty of Computer Engineering,
Istanbul Technical University, Maslak, Ayazaga, 34469 Istanbul, Turkey
However, efficient spectral bandwidth usage under the
influence of higher noise is not the major consideration of
CR. Claude Shannon [8] showed that the SNR is a lead-
ing factor that influences the link spectral efficiency (LSE),
η = C/B, (in bps/Hz). SNR also limits the channel capac-
ity. Therefore, the utilization of spectral bandwidth and
the robustness to high SNR level are the keys to maximize
the channel capacity.
Thus, a pattern-based cognitive communication system
(PBCCS) was introduced to optimize the overall spec-
tral efficiency with respect to SNR [9, 10]. It is inspired
by the recognition capability of humans to concentrate
on a single conversation irrespective of the surround-
ing loudness. If human ears hear sounds from different
sources, the brain chooses to pay attention to a particu-
lar voice amongst a whole range of sound streams in an
environment. Similar to human cognitive capabilities, the
communication system in PBCCS selectively recognizes
and recovers the communication signal(s) a into known
symbol(s), even within the same frequency range.
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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