6,593 research outputs found
Fully distributed optimal channel assignment for open spectrum access
In this paper we address the problem of fully distributed assignment of users
to sub-bands such that the sum-rate of the system is maximized. We introduce a
modified auction algorithm that can be applied in a fully distributed way using
an opportunistic CSMA assignment scheme and is optimal. We analyze
the expected time complexity of the algorithm and suggest a variant to the
algorithm that has lower expected complexity. We then show that in the case of
i.i.d Rayleigh channels a simple greedy scheme is asymptotically optimal as
\SNR increases or as the number of users is increased to infinity. We
conclude by providing simulated results of the suggested algorithms
Collaboration and Coordination in Secondary Networks for Opportunistic Spectrum Access
In this paper, we address the general case of a coordinated secondary network
willing to exploit communication opportunities left vacant by a licensed
primary network. Since secondary users (SU) usually have no prior knowledge on
the environment, they need to learn the availability of each channel through
sensing techniques, which however can be prone to detection errors. We argue
that cooperation among secondary users can enable efficient learning and
coordination mechanisms in order to maximize the spectrum exploitation by SUs,
while minimizing the impact on the primary network. To this goal, we provide
three novel contributions in this paper. First, we formulate the spectrum
selection in secondary networks as an instance of the Multi-Armed Bandit (MAB)
problem, and we extend the analysis to the collaboration learning case, in
which each SU learns the spectrum occupation, and shares this information with
other SUs. We show that collaboration among SUs can mitigate the impact of
sensing errors on system performance, and improve the convergence of the
learning process to the optimal solution. Second, we integrate the learning
algorithms with two collaboration techniques based on modified versions of the
Hungarian algorithm and of the Round Robin algorithm that allows reducing the
interference among SUs. Third, we derive fundamental limits to the performance
of cooperative learning algorithms based on Upper Confidence Bound (UCB)
policies in a symmetric scenario where all SU have the same perception of the
quality of the resources. Extensive simulation results confirm the
effectiveness of our joint learning-collaboration algorithm in protecting the
operations of Primary Users (PUs), while maximizing the performance of SUs.Comment: 28 pages. Paper submitted to a journa
QoS Provisioning for Multimedia Transmission in Cognitive Radio Networks
In cognitive radio (CR) networks, the perceived reduction of application
layer quality of service (QoS), such as multimedia distortion, by secondary
users may impede the success of CR technologies. Most previous work in CR
networks ignores application layer QoS. In this paper we take an integrated
design approach to jointly optimize multimedia intra refreshing rate, an
application layer parameter, together with access strategy, and spectrum
sensing for multimedia transmission in a CR system with time varying wireless
channels. Primary network usage and channel gain are modeled as a finite state
Markov process. With channel sensing and channel state information errors, the
system state cannot be directly observed. We formulate the QoS optimization
problem as a partially observable Markov decision process (POMDP). A low
complexity dynamic programming framework is presented to obtain the optimal
policy. Simulation results show the effectiveness of the proposed scheme
Exploiting Sparse Dynamics For Bandwidth Reduction In Cooperative Sensing Systems
Recently, there has been a significant interest in developing cooperative
sensing systems for certain types of wireless applications. In such systems, a
group of sensing nodes periodically collect measurements about the signals
being observed in the given geographical region and transmit these measurements
to a central node, which in turn processes this information to recover the
signals. For example, in cognitive radio networks, the signals of interest are
those generated by the primary transmitters and the sensing nodes are the
secondary users. In such networks, it is critically important to be able to
reliably determine the presence or absence of primary transmitters in order to
avoid causing interference. The standard approach to transmit these
measurements from sensor the nodes to the fusion center has been to use
orthogonal channels. Such an approach quickly places a burden on the
control-channel-capacity of the network that would scale linearly in the number
of cooperating sensing nodes. In this paper, we show that as long as one
condition is satisfied: the dynamics of the observed signals are sparse, i.e.,
the observed signals do not change their values very rapidly in relation to the
time-scale at which the measurements are collected, we can significantly reduce
the control bandwidth of the system while achieving the full (linear) bandwidth
performance.Comment: Submitted to IEEE Trans. on Signal Processin
Amazon in the White Space: Social Recommendation Aided Distributed Spectrum Access
Distributed spectrum access (DSA) is challenging since an individual
secondary user often has limited sensing capabilities only. One key insight is
that channel recommendation among secondary users can help to take advantage of
the inherent correlation structure of spectrum availability in both time and
space, and enable users to obtain more informed spectrum opportunities. With
this insight, we advocate to leverage the wisdom of crowds, and devise social
recommendation aided DSA mechanisms to orient secondary users to make more
intelligent spectrum access decisions, for both strong and weak network
information cases. We start with the strong network information case where
secondary users have the statistical information. To mitigate the difficulty
due to the curse of dimensionality in the stochastic game approach, we take the
one-step Nash approach and cast the social recommendation aided DSA decision
making problem at each time slot as a strategic game. We show that it is a
potential game, and then devise an algorithm to achieve the Nash equilibrium by
exploiting its finite improvement property. For the weak information case where
secondary users do not have the statistical information, we develop a
distributed reinforcement learning mechanism for social recommendation aided
DSA based on the local observations of secondary users only. Appealing to the
maximum-norm contraction mapping, we also derive the conditions under which the
distributed mechanism converges and characterize the equilibrium therein.
Numerical results reveal that the proposed social recommendation aided DSA
mechanisms can achieve superior performance using real social data traces and
its performance loss in the weak network information case is insignificant,
compared with the strong network information case.Comment: Xu Chen, Xiaowen Gong, Lei Yang, and Junshan Zhang, "Amazon in the
White Space: Social Recommendation Aided Distributed Spectrum Access,"
IEEE/ACM Transactions Networking, 201
Distributed Cooperative Spectrum Sensing in Mobile Ad Hoc Networks with Cognitive Radios
In cognitive radio mobile ad hoc networks (CR-MANETs), secondary users can
cooperatively sense the spectrum to detect the presence of primary users. In
this chapter, we propose a fully distributed and scalable cooperative spectrum
sensing scheme based on recent advances in consensus algorithms. In the
proposed scheme, the secondary users can maintain coordination based on only
local information exchange without a centralized common receiver. We use the
consensus of secondary users to make the final decision. The proposed scheme is
essentially based on recent advances in consensus algorithms that have taken
inspiration from complex natural phenomena including flocking of birds,
schooling of fish, swarming of ants and honeybees. Unlike the existing
cooperative spectrum sensing schemes, there is no need for a centralized
receiver in the proposed schemes, which make them suitable in distributed
CR-MANETs. Simulation results show that the proposed consensus schemes can have
significant lower missing detection probabilities and false alarm probabilities
in CR-MANETs. It is also demonstrated that the proposed scheme not only has
proven sensitivity in detecting the primary user's presence, but also has
robustness in choosing a desirable decision threshold
Learning Power Spectrum Maps from Quantized Power Measurements
Power spectral density (PSD) maps providing the distribution of RF power
across space and frequency are constructed using power measurements collected
by a network of low-cost sensors. By introducing linear compression and
quantization to a small number of bits, sensor measurements can be communicated
to the fusion center with minimal bandwidth requirements. Strengths of data-
and model-driven approaches are combined to develop estimators capable of
incorporating multiple forms of spectral and propagation prior information
while fitting the rapid variations of shadow fading across space. To this end,
novel nonparametric and semiparametric formulations are investigated. It is
shown that PSD maps can be obtained using support vector machine-type solvers.
In addition to batch approaches, an online algorithm attuned to real-time
operation is developed. Numerical tests assess the performance of the novel
algorithms.Comment: Submitted Jun. 201
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
Spectrum Monitoring Using Energy Ratio Algorithm For OFDM-Based Cognitive Radio Networks
This paper presents a spectrum monitoring algorithm for Orthogonal Frequency
Division Multiplexing (OFDM) based cognitive radios by which the primary user
reappearance can be detected during the secondary user transmission. The
proposed technique reduces the frequency with which spectrum sensing must be
performed and greatly decreases the elapsed time between the start of a primary
transmission and its detection by the secondary network. This is done by
sensing the change in signal strength over a number of reserved OFDM
sub-carriers so that the reappearance of the primary user is quickly detected.
Moreover, the OFDM impairments such as power leakage, Narrow Band Interference
(NBI), and Inter-Carrier Interference (ICI) are investigated and their impact
on the proposed technique is studied. Both analysis and simulation show that
the \emph{energy ratio} algorithm can effectively and accurately detect the
appearance of the primary user. Furthermore, our method achieves high immunity
to frequency-selective fading channels for both single and multiple receive
antenna systems, with a complexity that is approximately twice that of a
conventional energy detector
Sub-Nyquist Radar: Principles and Prototypes
In the past few years, new approaches to radar signal processing have been
introduced which allow the radar to perform signal detection and parameter
estimation from much fewer measurements than that required by Nyquist sampling.
These systems - referred to as sub-Nyquist radars - model the received signal
as having finite rate of innovation and employ the Xampling framework to obtain
low-rate samples of the signal. Sub-Nyquist radars exploit the fact that the
target scene is sparse facilitating the use of compressed sensing (CS) methods
in signal recovery. In this chapter, we review several pulse-Doppler radar
systems based on these principles. Contrary to other CS-based designs, our
formulations directly address the reduced-rate analog sampling in space and
time, avoid a prohibitive dictionary size, and are robust to noise and clutter.
We begin by introducing temporal sub-Nyquist processing for estimating the
target locations using less bandwidth than conventional systems. This paves the
way to cognitive radars which share their transmit spectrum with other
communication services, thereby providing a robust solution for coexistence in
spectrally crowded environments. Next, without impairing Doppler resolution, we
reduce the dwell time by transmitting interleaved radar pulses in a scarce
manner within a coherent processing interval or "slow time". Then, we consider
multiple-input-multiple-output array radars and demonstrate spatial sub-Nyquist
processing which allows the use of few antenna elements without degradation in
angular resolution. Finally, we demonstrate application of sub-Nyquist and
cognitive radars to imaging systems such as synthetic aperture radar. For each
setting, we present a state-of-the-art hardware prototype designed to
demonstrate the real-time feasibility of sub-Nyquist radars.Comment: 51 pages, 26 figures, 2 tables, Book chapter. arXiv admin note: text
overlap with arXiv:1611.0644
- …