4,540 research outputs found
Aggregate Interference Modeling in Cognitive Radio Networks with Power and Contention Control
In this paper, we present an interference model for cognitive radio (CR)
networks employing power control, contention control or hybrid power/contention
control schemes. For the first case, a power control scheme is proposed to
govern the transmission power of a CR node. For the second one, a contention
control scheme at the media access control (MAC) layer, based on carrier sense
multiple access with collision avoidance (CSMA/CA), is proposed to coordinate
the operation of CR nodes with transmission requests. The probability density
functions of the interference received at a primary receiver from a CR network
are first derived numerically for these two cases. For the hybrid case, where
power and contention controls are jointly adopted by a CR node to govern its
transmission, the interference is analyzed and compared with that of the first
two schemes by simulations. Then, the interference distributions under the
first two control schemes are fitted by log-normal distributions with greatly
reduced complexity. Moreover, the effect of a hidden primary receiver on the
interference experienced at the receiver is investigated. It is demonstrated
that both power and contention controls are effective approaches to alleviate
the interference caused by CR networks. Some in-depth analysis of the impact of
key parameters on the interference of CR networks is given via numerical
studies as well.Comment: 24 pages, 8 figures, submitted to IEEE Trans. Communications in July
201
Design and Optimal Configuration of Full-Duplex MAC Protocol for Cognitive Radio Networks Considering Self-Interference
In this paper, we propose an adaptive Medium Access Control (MAC) protocol
for full-duplex (FD) cognitive radio networks in which FD secondary users (SUs)
perform channel contention followed by concurrent spectrum sensing and
transmission, and transmission only with maximum power in two different stages
(called the FD sensing and transmission stages, respectively) in each
contention and access cycle. The proposed FD cognitive MAC (FDC-MAC) protocol
does not require synchronization among SUs and it efficiently utilizes the
spectrum and mitigates the self-interference in the FD transceiver. We then
develop a mathematical model to analyze the throughput performance of the
FDC-MAC protocol where both half-duplex (HD) transmission (HDTx) and FD
transmission (FDTx) modes are considered in the transmission stage. Then, we
study the FDC-MAC configuration optimization through adaptively controlling the
spectrum sensing duration and transmit power level in the FD sensing stage
where we prove that there exists optimal sensing time and transmit power to
achieve the maximum throughput and we develop an algorithm to configure the
proposed FDC-MAC protocol. Extensive numerical results are presented to
illustrate the characteristic of the optimal FDC-MAC configuration and the
impacts of protocol parameters and the self-interference cancellation quality
on the throughput performance. Moreover, we demonstrate the significant
throughput gains of the FDC-MAC protocol with respect to existing half-duplex
MAC (HD MAC) and single-stage FD MAC protocols.Comment: To Appear, IEEE Access, 201
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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