16,319 research outputs found
Peak to average power ratio based spatial spectrum sensing for cognitive radio systems
The recent convergence of wireless standards for incorporation of spatial dimension in wireless systems has made spatial spectrum sensing based on Peak to Average Power Ratio (PAPR) of the received signal, a promising approach. This added dimension is principally exploited for stream multiplexing, user multiplexing and spatial diversity. Considering such a wireless environment for primary users, we propose an algorithm for spectrum sensing by secondary users which are also equipped with multiple antennas. The proposed spatial spectrum sensing algorithm is based on the PAPR of the spatially received signals. Simulation results show the improved performance once the information regarding spatial diversity of the primary users is incorporated in the proposed algorithm. Moreover, through simulations a better performance is achieved by using different diversity schemes and different parameters like sensing time and scanning interval
Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks
Energy harvester based cognitive radio is a promising solution to address the
shortage of both spectrum and energy. Since the spectrum access and power
consumption patterns are interdependent, and the power value harvested from
certain environmental sources are spatially correlated, the new power dimension
could provide additional information to enhance the spectrum sensing accuracy.
In this paper, the Markovian behavior of the primary users is considered, based
on which we adopt a hidden input Markov model to specify the primary vs.
secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and
power (harvested) sensing scheme to improve the primary user detection
performance, which is also capable of estimating the primary transmit power
level. Theoretical and simulated results demonstrate the effectiveness of the
proposed scheme, in term of the performance gain achieved by considering the
new power dimension. To the best of our knowledge, this is the first work to
jointly consider the spectrum and power dimensions for the cognitive primary
user detection problem
Listen-and-Talk: Protocol Design and Analysis for Full-duplex Cognitive Radio Networks
In traditional cognitive radio networks, secondary users (SUs) typically
access the spectrum of primary users (PUs) by a two-stage "listen-before-talk"
(LBT) protocol, i.e., SUs sense the spectrum holes in the first stage before
transmitting in the second. However, there exist two major problems: 1)
transmission time reduction due to sensing, and 2) sensing accuracy impairment
due to data transmission. In this paper, we propose a "listen-and-talk" (LAT)
protocol with the help of full-duplex (FD) technique that allows SUs to
simultaneously sense and access the vacant spectrum. Spectrum utilization
performance is carefully analyzed, with the closed-form spectrum waste ratio
and collision ratio with the PU provided. Also, regarding the secondary
throughput, we report the existence of a tradeoff between the secondary
transmit power and throughput. Based on the power-throughput tradeoff, we
derive the analytical local optimal transmit power for SUs to achieve both high
throughput and satisfying sensing accuracy. Numerical results are given to
verify the proposed protocol and the theoretical results
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Optimal Selection of Spectrum Sensing Duration for an Energy Harvesting Cognitive Radio
In this paper, we consider a time-slotted cognitive radio (CR) setting with
buffered and energy harvesting primary and CR users. At the beginning of each
time slot, the CR user probabilistically chooses the spectrum sensing duration
from a predefined set. If the primary user (PU) is sensed to be inactive, the
CR user accesses the channel immediately. The CR user optimizes the sensing
duration probabilities in order to maximize its mean data service rate with
constraints on the stability of the primary and cognitive queues. The
optimization problem is split into two subproblems. The first is a
linear-fractional program, and the other is a linear program. Both subproblems
can be solved efficiently.Comment: Accepted in GLOBECOM 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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