3,305 research outputs found
Cognitive radio-enabled Internet of Vehicles (IoVs): a cooperative spectrum sensing and allocation for vehicular communication
Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75MHz spectrum in the 5.9GHz band to support vehicular communication which many studies have found insufficient. In this paper, we studied the application of Cognitive Radio (CR) technology to IoVs in order to increase the spectrum resource opportunities available for vehicular communication, especially when the officially allocated 75MHz spectrum in 5.9GHz band is not enough due to high demands as a result of increasing number of connected vehicles as already foreseen in the near era of IoTs. We proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum bands on the highways. We also developed a novel co-operative three-state spectrum sensing and allocation model which makes CR vehicular secondary units (SUs) aware of additional spectrum resources opportunities on their current and future positions and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing and provide vehicles with additional spectrum opportunities without harmful interference against the Primary Users (PUs) activities
Design and Experimental Evaluation of a Database-Assisted V2V Communications System Over TV White Space
Automakers are increasingly employing wireless communications technologies into vehicles, which are expected to be one of the primary tools to improve traffic flow and traffic safety. Anticipating a significant increase in the accompanying spectrum and capacity requirements, in this paper, we speculate about using dynamic spectrum access in general, and TV white space in particular for vehicular communications. To this end, we describe the concept, design, general architecture and operation principles of a vehicle-to-vehicle communications system over TV white space. This system makes dual use of a geolocation database and spectrum sensing to understand spectrum vacancies. In this architecture, whenever a database query result is available, that information is prioritized over sensing results and when the database access is disrupted, vehicles rely on the spectrum sensing results. After describing the general concepts, we numerically analyze and evaluate the benefits of using proxy vehicles for geolocation database access. Finally, we present the middleware-centric implementation and field test results of a multi-hop vehicle-to-vehicle communications system over the licensed TV-band. We present results regarding multi-hop throughput, delay, jitter, channel switching and database access latencies. This study complements our previous work which described spectrum sensing based vehicle-to-vehicle communications design and testing
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
Vandermonde-subspace Frequency Division Multiplexing for Two-Tiered Cognitive Radio Networks
Vandermonde-subspace frequency division multiplexing (VFDM) is an overlay
spectrum sharing technique for cognitive radio. VFDM makes use of a precoder
based on a Vandermonde structure to transmit information over a secondary
system, while keeping an orthogonal frequency division multiplexing
(OFDM)-based primary system interference-free. To do so, VFDM exploits
frequency selectivity and the use of cyclic prefixes by the primary system.
Herein, a global view of VFDM is presented, including also practical aspects
such as linear receivers and the impact of channel estimation. We show that
VFDM provides a spectral efficiency increase of up to 1 bps/Hz over cognitive
radio systems based on unused band detection. We also present some key design
parameters for its future implementation and a feasible channel estimation
protocol. Finally we show that, even when some of the theoretical assumptions
are relaxed, VFDM provides non-negligible rates while protecting the primary
system.Comment: 9 pages, accepted for publication in IEEE Transactions on
Communication
A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment
The application of multiple-input multiple-output (MIMO) techniques to
non-orthogonal multiple access (NOMA) systems is important to enhance the
performance gains of NOMA. In this paper, a novel MIMO-NOMA framework for
downlink and uplink transmission is proposed by applying the concept of signal
alignment. By using stochastic geometry, closed-form analytical results are
developed to facilitate the performance evaluation of the proposed framework
for randomly deployed users and interferers. The impact of different power
allocation strategies, such as fixed power allocation and cognitive radio
inspired power allocation, on the performance of MIMO-NOMA is also
investigated. Computer simulation results are provided to demonstrate the
performance of the proposed framework and the accuracy of the developed
analytical results
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