20 research outputs found
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
In this paper, a sequential probing method for interference constraint
learning is proposed to allow a centralized Cognitive Radio Network (CRN)
accessing the frequency band of a Primary User (PU) in an underlay cognitive
scenario with a designed PU protection specification. The main idea is that the
CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire
the binary ACK/NACK packet. This feedback indicates whether the probing-induced
interference is harmful or not and can be used to learn the PU interference
constraint. The cognitive part of this sequential probing process is the
selection of the power levels of the Secondary Users (SUs) which aims to learn
the PU interference constraint with a minimum number of probing attempts while
setting a limit on the number of harmful probing-induced interference events or
equivalently of NACK packet observations over a time window. This constrained
design problem is studied within the Active Learning (AL) framework and an
optimal solution is derived and implemented with a sophisticated, accurate and
fast Bayesian Learning method, the Expectation Propagation (EP). The
performance of this solution is also demonstrated through numerical simulations
and compared with modified versions of AL techniques we developed in earlier
work.Comment: 14 pages, 6 figures, submitted to IEEE JSTSP Special Issue on Machine
Learning for Cognition in Radio Communications and Rada
Active Learning in Cognitive Radio Networks
In this thesis, numerous Machine Learning (ML) applications for Cognitive Radios Networks
(CRNs) are developed and presented which facilitate the e cient spectral coexistence
of a legacy system, the Primary Users (PUs), and a CRN, the Secondary Users
(SUs). One way to better exploit the capacity of the legacy system frequency band
is to consider a coexistence scenario using underlay Cognitive Radio (CR) techniques,
where SUs may transmit in the frequency band of the PU system as long as the induced
to the PU interference is under a certain limit and thus does not harmfully a ect the
legacy system operability
A Bayesian Poisson-Gaussian Process Model for Popularity Learning in Edge-Caching Networks
Edge-caching is recognized as an efficient technique for future cellular
networks to improve network capacity and user-perceived quality of experience.
To enhance the performance of caching systems, designing an accurate content
request prediction algorithm plays an important role. In this paper, we develop
a flexible model, a Poisson regressor based on a Gaussian process, for the
content request distribution.
The first important advantage of the proposed model is that it encourages the
already existing or seen contents with similar features to be correlated in the
feature space and therefore it acts as a regularizer for the estimation.
Second, it allows to predict the popularities of newly-added or unseen contents
whose statistical data is not available in advance. In order to learn the model
parameters, which yield the Poisson arrival rates or alternatively the content
\textit{popularities}, we invoke the Bayesian approach which is robust against
over-fitting.
However, the resulting posterior distribution is analytically intractable to
compute. To tackle this, we apply a Markov Chain Monte Carlo (MCMC) method to
approximate this distribution which is also asymptotically exact. Nevertheless,
the MCMC is computationally demanding especially when the number of contents is
large. Thus, we employ the Variational Bayes (VB) method as an alternative low
complexity solution. More specifically, the VB method addresses the
approximation of the posterior distribution through an optimization problem.
Subsequently, we present a fast block-coordinate descent algorithm to solve
this optimization problem. Finally, extensive simulation results both on
synthetic and real-world datasets are provided to show the accuracy of our
prediction algorithm and the cache hit ratio (CHR) gain compared to existing
methods from the literature
A Feature-Based Bayesian Method for Content Popularity Prediction in Edge-Caching Networks
Edge-caching is recognized as an efficient technique for future wireless
cellular networks to improve network capacity and user-perceived quality of
experience. Due to the random content requests and the limited cache memory,
designing an efficient caching policy is a challenge. To enhance the
performance of caching systems, an accurate content request prediction
algorithm is essential. Here, we introduce a flexible model, a Poisson
regressor based on a Gaussian process, for the content request distribution in
stationary environments. Our proposed model can incorporate the content
features as side information for prediction enhancement. In order to learn the
model parameters, which yield the Poisson rates or alternatively content
popularities, we invoke the Bayesian approach which is very robust against
over-fitting.
However, the posterior distribution in the Bayes formula is analytically
intractable to compute. To tackle this issue, we apply a Monte Carlo Markov
Chain (MCMC) method to approximate the posterior distribution. Two types of
predictive distributions are formulated for the requests of existing contents
and for the requests of a newly-added content. Finally, simulation results are
provided to confirm the accuracy of the developed content popularity learning
approach.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0306
Interference Constraint Active Learning with Uncertain Feedback for Cognitive Radio Networks
In this paper, an intelligent probing method for
interference constraint learning is proposed to allow a centralized
cognitive radio network (CRN) to access the frequency band of
a primary user (PU) in an underlay cognitive communication
scenario. The main idea is that the CRN probes the PU and
subsequently eavesdrops the reverse PU link to acquire the
binary ACK/NACK packet. This feedback is implicit channel
state information of the PU link, indicating whether the probinginduced
interference is harmful or not. The intelligence of this
sequential probing process lies in the selection of the power levels
of the secondary users, which aims to minimize the number of
probing attempts, a clearly active learning (AL) procedure, and
expectantly the overall PU QoS degradation. The enhancement
introduced in this paper is that we incorporate the probability
of each feedback being correct into this intelligent probing
mechanism by using a multivariate Bayesian AL method. This
technique is inspired by the probabilistic bisection algorithm and
the deterministic cutting plane methods (CPMs). The optimality
of this multivariate Bayesian AL method is proven and its
effectiveness is demonstrated through numerical simulations.
Computationally cheap CPM adaptations are also presented,
which outperform existing AL methods
Centralized Power Control in Cognitive Radio Networks Using Modulation and Coding Classification Feedback
In this paper, a centralized Power Control (PC)
scheme and an interference channel learning method are jointly
tackled to allow a Cognitive Radio Network (CRN) access to
the frequency band of a Primary User (PU) operating based
on an Adaptive Coding and Modulation (ACM) protocol. The
learning process enabler is a cooperative Modulation and Coding
Classification (MCC) technique which estimates the Modulation
and Coding scheme (MCS) of the PU. Due to the lack of
cooperation between the PU and the CRN, the CRN exploits
this multilevel MCC sensing feedback as implicit channel state
information (CSI) of the PU link in order to constantly monitor
the impact of the aggregated interference it causes. In this paper,
an algorithm is developed for maximizing the CRN throughput
(the PC optimization objective) and simultaneously learning how
to mitigate PU interference (the optimization problem constraint)
by using only the MCC information. Ideal approaches for this
problem setting with high convergence rate are the cutting
plane methods (CPM). Here, we focus on the analytic center
cutting plane method (ACCPM) and the center of gravity cutting
plane method (CGCPM) whose effectiveness in the proposed
simultaneous PC and interference channel learning algorithm is
demonstrated through numerical simulations