15,928 research outputs found
Cellular-Base-Station Assisted Device-to-Device Communications in TV White Space
This paper presents a systematic approach to exploit TV white space (TVWS)
for device-to-device (D2D) communications with the aid of the existing cellular
infrastructure. The goal is to build a location-specific TVWS database, which
provides a look-up table service for any D2D link to determine its maximum
permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To
achieve this goal, the idea of mobile crowd sensing is firstly introduced to
collect active spectrum measurements from massive personal mobile devices.
Considering the incompleteness of crowd measurements, we formulate the problem
of unknown measurements recovery as a matrix completion problem and apply a
powerful fixed point continuation algorithm to reconstruct the unknown elements
from the known elements. By joint exploitation of the big spectrum data in its
vicinity, each cellular base station further implements a nonlinear support
vector machine algorithm to perform irregular coverage boundary detection of a
licensed DTV transmitter. With the knowledge of the detected coverage boundary,
an opportunistic spatial reuse algorithm is developed for each D2D link to
determine its MPEP. Simulation results show that the proposed approach can
successfully enable D2D communications in TVWS while satisfying the
interference constraint from the licensed DTV services. In addition, to our
best knowledge, this is the first try to explore and exploit TVWS inside the
DTV protection region resulted from the shadowing effect. Potential application
scenarios include communications between internet of vehicles in the
underground parking, D2D communications in hotspots such as subway, game
stadiums, and airports, etc.Comment: Accepted by IEEE Journal on Selected Areas in Communications, to
appear, 201
General Spectrum Sensing in Cognitive Radio Networks
The successful operation of cognitive radio (CR) between CR transmitter and
CR receiver (CR link) relies on reliable spectrum sensing. To network CRs
requires spectrum sensing at CR transmitter and further information regarding
the spectrum availability at CR receiver. Redefining the spectrum sensing along
with statistical inference suitable for cognitive radio networks (CRN), we
mathematically derive conditions to allow CR transmitter forwarding packets to
CR receiver under guaranteed outage probability, and prove that the correlation
of localized spectrum availability between a cooperative node and CR receiver
determines effectiveness of the cooperative scheme. Applying our novel
mathematical model to potential hidden terminals in CRN, we illustrate that the
allowable transmission region of a CR, defined as neighborhood, is no longer
circular shape even in a pure path loss channel model. This results in
asymmetric CR links to make bidirectional links generally inappropriate in CRN,
though this challenge can be alleviated by cooperative sensing. Therefore,
spectrum sensing capability determines CRN topology. For multiple cooperative
nodes, to fully utilize spectrum availability, the selection methodology of
cooperative nodes is developed due to limited overhead of information exchange.
Defining reliability as information of spectrum availability at CR receiver
provided by a cooperative node and by applying neighborhood area, we can
compare sensing capability of cooperative nodes from both link and network
perspectives. In addition, due to lack of centralized coordination in dynamic
CRN, CRs can only acquire local and partial information within limited sensing
duration, robust spectrum sensing is therefore proposed. Limits of cooperative
schemes and their impacts on network operation are also derived.Comment: 26 pages, 7 figures, 47 references, submitted to IEEE Trans. on
Information Theor
Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning
In this paper, we develop a distributed mechanism for spectrum sharing among
a network of unmanned aerial vehicles (UAV) and licensed terrestrial networks.
This method can provide a practical solution for situations where the UAV
network may need external spectrum when dealing with congested spectrum or need
to change its operating frequency due to security threats. Here we study a
scenario where the UAV network performs a remote sensing mission. In this
model, the UAVs are categorized into two clusters of relaying and sensing UAVs.
The relay UAVs provide a relaying service for a licensed network to obtain
spectrum access for the rest of UAVs that perform the sensing task. We develop
a distributed mechanism in which the UAVs locally decide whether they need to
participate in relaying or sensing considering the fact that communications
among UAVs may not be feasible or reliable. The UAVs learn the optimal task
allocation using a distributed reinforcement learning algorithm. Convergence of
the algorithm is discussed and simulation results are presented for different
scenarios to verify the convergence.Comment: 16 Pages, 8 Figure
Spectrum Sensing with USRP-E110
Spectrum sensing is one of the key topics towards the implementation of
future wireless services like SuperWiFi. This new wireless proposal aims at
using the freed spectrum resulting from the analog-to-digital transition of TV
channels for wireless data transmission (UHF TV White Spaces). The benefits
range from better building penetration to longer distances when compared to the
set of IEEE 802.11 standards. Nevertheless, the effective use of the available
spectrum is subject to strict regulation that prohibits unlicensed users to
interfere with incumbents (like wireless microphones). Cognitive Radios (CR)
and dynamic spectrum allocation are suggested to cope with this problem. These
techniques consist on frequency sweeps of the TV-UHF band to detect White
Spaces that could be used for SuperWiFi transmissions. In this paper we develop
and implement algorithms from GNURadio in the Ettus USRP-E110 to build a
standalone White Spaces detector that can be consulted from a centralized
location via IP networks.Comment: 5th International Workshop on Multiple Access Communications,
11/2012, Dublin, Ireland, (2012
Effect of Location Accuracy and Shadowing on the Probability of Non-Interfering Concurrent Transmissions in Cognitive Ad Hoc Networks
Cognitive radio ad hoc systems can coexist with a primary network in a scanning-free region, which can be dimensioned by location awareness. This coexistence of networks improves system throughput and increases the efficiency of radio spectrum utilization. However, the location accuracy of real positioning systems affects the right dimensioning of the concurrent transmission region. Moreover, an ad hoc connection may not be able to coexist with the primary link due to the shadowing effect. In this paper we investigate the impact of location accuracy on the concurrent transmission probability and analyze the reliability of concurrent transmissions when shadowing is taken into account. A new analytical model is proposed, which allows to estimate the resulting secure region when the localization uncertainty range is known. Computer simulations show the dependency between the location accuracy and the performance of the proposed topology, as well as the reliability of the resulting secure region
Predictive and Recommendatory Spectrum Decision for Cognitive Radio
Cognitive radio technology enables improving the utilization efficiency of
the precious and scarce radio spectrum. How to maximize the overall spectrum
efficiency while minimizing the conflicts with primary users is vital to
cognitive radio. The key is to make the right decisions of accessing the
spectrum. Spectrum prediction can be employed to predict the future states of a
spectrum band using previous states of the spectrum band, whereas spectrum
recommendation recommends secondary users a subset of available spectrum bands
based on secondary user's previous experiences of accessing the available
spectrum bands. In this paper, a framework for spectrum decision based on
spectrum prediction and spectrum recommendation is proposed. As a benchmark, a
method based on extreme learning machine (ELM) for single-user spectrum
prediction and a method based on Q-learning for multiple-user spectrum
prediction are proposed. At the stage of spectrum decision, two methods based
on Q-learning andMarkov decision process (MDP), respectively, are also proposed
to enhance the overall performance of spectrum decision. Experimental results
show that the performance of the spectrum decision framework is much better
Automatic Detection and Query of Wireless Spectrum Events from Streaming Data
Several alternatives for more efficient spectrum management have been
proposed over the last decade, resulting in new techniques for automatic
wideband spectrum sensing. However, while spectrum sensing technology is
important, understanding, using and taking actions on this data for better
spectrum and network resource management is at least equally important. In this
paper, we propose a system that is able to automatically detect wireless
spectrum events from streaming spectrum sensing data, and enables the
consumption of the events as they are produced, as a statistical report or on a
per-query basis. The proposed system is referred to as spectrum streamer and is
wireless technology agnostic, scalable, able to deliver actionable information
to humans and machines and also enables application development by custom
querying of the detected events.Comment: 11 pages, 8 figures, 2 tables, 5 listings, Submitted to an IEEE
journa
Building accurate radio environment maps from multi-fidelity spectrum sensing data
In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated
Analog to Digital Cognitive Radio: Sampling, Detection and Hardware
The proliferation of wireless communications has recently created a
bottleneck in terms of spectrum availability. Motivated by the observation that
the root of the spectrum scarcity is not a lack of resources but an inefficient
managing that can be solved, dynamic opportunistic exploitation of spectral
bands has been considered, under the name of Cognitive Radio (CR). This
technology allows secondary users to access currently idle spectral bands by
detecting and tracking the spectrum occupancy. The CR application revisits this
traditional task with specific and severe requirements in terms of spectrum
sensing and detection performance, real-time processing, robustness to noise
and more. Unfortunately, conventional methods do not satisfy these demands for
typical signals, that often have very high Nyquist rates.
Recently, several sampling methods have been proposed that exploit signals' a
priori known structure to sample them below the Nyquist rate. Here, we review
some of these techniques and tie them to the task of spectrum sensing in the
context of CR. We then show how issues related to spectrum sensing can be
tackled in the sub-Nyquist regime. First, to cope with low signal to noise
ratios, we propose to recover second-order statistics from the low rate
samples, rather than the signal itself. In particular, we consider
cyclostationary based detection, and investigate CR networks that perform
collaborative spectrum sensing to overcome channel effects. To enhance the
efficiency of the available spectral bands detection, we present joint spectrum
sensing and direction of arrival estimation methods. Throughout this work, we
highlight the relation between theoretical algorithms and their practical
implementation. We show hardware simulations performed on a prototype we built,
demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of
CR.Comment: Submitted to IEEE Signal Processing Magazin
Techniques for Cooperative Cognitive Radio Networks
The frequency spectrum is an essential resource for wireless communication.
Special sections of the spectrum are used for military purposes, governments
sell some frequency bands to broadcasting and mobile communications companies
for commercial use, others such as ISM (Industrial, Science and Medical) bands
are available for the public free of charge. As the spectrum becomes
overcrowded, there seem to be two possible solutions: pushing the frequency
limits higher to frequencies of 60 GHz and above, or reaggregating the densely
used licensed frequency bands. The new Cognitive Radio (CR) approach comes with
the feasible solution to spectrum scarcity. Secondary utilization of a licensed
spectrum band can enhance the spectrum usage and introduce a reliable solution
to its dearth. In such a cognitive radio network, secondary users can access
the spectrum under the constraint that a minimum quality of service is
guaranteed for the licensed primary users. In this thesis, we focus on spectrum
sharing techniques in cognitive radio network where there is a number of
secondary users sharing unoccupied spectrum holes. More specifically, we
introduce two collaborative cognitive radio networks in which the secondary
user cooperate with the primary user to deliver the data of the primary user.Comment: Master's thesi
- …