8,799 research outputs found
Spectrum Sensing for Cognitive Radio Systems Through Primary User Activity Prediction
Traditional spectrum sensing techniques such as energy detection, for instance, can sense the spectrum only when the cognitive radio (CR) is is not in operation. This constraint is relaxed recently by some blind source separation techniques in which the CR can operate during spectrum sensing. The proposed method in this paper uses the fact that the primary spectrum usage is correlated across time and follows a predictable behavior. More precisely, we propose a new spectrum sensing method that can be trained over time to predict the primary user's activity and sense the spectrum even while the CR user is in operation. Performance achieved by the proposed method is compared to classical spectrum sensing methods. Simulation results provided in terms of receiver operating characteristic curves indicate that in addition to the interesting feature that the CR can transmit during spectrum sensing, the proposed method outperforms conventional spectrum sensing techniques
Compressive Sensing for Spread Spectrum Receivers
With the advent of ubiquitous computing there are two design parameters of
wireless communication devices that become very important power: efficiency and
production cost. Compressive sensing enables the receiver in such devices to
sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in
the two design parameters. This paper investigates the use of Compressive
Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We
show that when using spread spectrum codes in the signal domain, the CS
measurement matrix may be simplified. This measurement scheme, named
Compressive Spread Spectrum (CSS), allows for a simple, effective receiver
design. Furthermore, we numerically evaluate the proposed receiver in terms of
bit error rate under different signal to noise ratio conditions and compare it
with other receiver structures. These numerical experiments show that though
the bit error rate performance is degraded by the subsampling in the CS-enabled
receivers, this may be remedied by including quantization in the receiver
model. We also study the computational complexity of the proposed receiver
design under different sparsity and measurement ratios. Our work shows that it
is possible to subsample a CDMA signal using CSS and that in one example the
CSS receiver outperforms the classical receiver.Comment: 11 pages, 11 figures, 1 table, accepted for publication in IEEE
Transactions on Wireless Communication
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
Joint Spectrum Sensing and Resource Allocation for OFDM-based Transmission with a Cognitive Relay
In this paper, we investigate the joint spectrum sensing and resource
allocation problem to maximize throughput capacity of an OFDM-based cognitive
radio link with a cognitive relay. By applying a cognitive relay that uses
decode and forward (D&F), we achieve more reliable communications, generating
less interference (by needing less transmit power) and more diversity gain. In
order to account for imperfections in spectrum sensing, the proposed schemes
jointly modify energy detector thresholds and allocates transmit powers to all
cognitive radio (CR) subcarriers, while simultaneously assigning subcarrier
pairs for secondary users (SU) and the cognitive relay. This problem is cast as
a constrained optimization problem with constraints on (1) interference
introduced by the SU and the cognitive relay to the PUs; (2) miss-detection and
false alarm probabilities and (3) subcarrier pairing for transmission on the SU
transmitter and the cognitive relay and (4) minimum Quality of Service (QoS)
for each CR subcarrier. We propose one optimal and two sub-optimal schemes all
of which are compared to other schemes in the literature. Simulation results
show that the proposed schemes achieve significantly higher throughput than
other schemes in the literature for different relay situations.Comment: EAI Endorsed Transactions on Wireless Spectrum 14(1): e4 Published
13th Apr 201
An Online Approach to Dynamic Channel Access and Transmission Scheduling
Making judicious channel access and transmission scheduling decisions is
essential for improving performance as well as energy and spectral efficiency
in multichannel wireless systems. This problem has been a subject of extensive
study in the past decade, and the resulting dynamic and opportunistic channel
access schemes can bring potentially significant improvement over traditional
schemes. However, a common and severe limitation of these dynamic schemes is
that they almost always require some form of a priori knowledge of the channel
statistics. A natural remedy is a learning framework, which has also been
extensively studied in the same context, but a typical learning algorithm in
this literature seeks only the best static policy, with performance measured by
weak regret, rather than learning a good dynamic channel access policy. There
is thus a clear disconnect between what an optimal channel access policy can
achieve with known channel statistics that actively exploits temporal, spatial
and spectral diversity, and what a typical existing learning algorithm aims
for, which is the static use of a single channel devoid of diversity gain. In
this paper we bridge this gap by designing learning algorithms that track known
optimal or sub-optimal dynamic channel access and transmission scheduling
policies, thereby yielding performance measured by a form of strong regret, the
accumulated difference between the reward returned by an optimal solution when
a priori information is available and that by our online algorithm. We do so in
the context of two specific algorithms that appeared in [1] and [2],
respectively, the former for a multiuser single-channel setting and the latter
for a single-user multichannel setting. In both cases we show that our
algorithms achieve sub-linear regret uniform in time and outperforms the
standard weak-regret learning algorithms.Comment: 10 pages, to appear in MobiHoc 201
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