15,244 research outputs found
Delay Sensitive Communications over Cognitive Radio Networks
Supporting the quality of service of unlicensed users in cognitive radio
networks is very challenging, mainly due to dynamic resource availability
because of the licensed users' activities. In this paper, we study the optimal
admission control and channel allocation decisions in cognitive overlay
networks in order to support delay sensitive communications of unlicensed
users. We formulate it as a Markov decision process problem, and solve it by
transforming the original formulation into a stochastic shortest path problem.
We then propose a simple heuristic control policy, which includes a
threshold-based admission control scheme and and a largest-delay-first channel
allocation scheme, and prove the optimality of the largest-delay-first channel
allocation scheme. We further propose an improved policy using the rollout
algorithm. By comparing the performance of both proposed policies with the
upper-bound of the maximum revenue, we show that our policies achieve
close-to-optimal performance with low complexities.Comment: 11 pages, 8 figure
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Delay sensitive and power-aware SMDP-based connection admission control mechanism in cognitive radio sensor networks
© 2017 Elsevier B.V. Due to the opportunistically resource usage of users in cognitive radio sensor networks (CRSNs), the availability of network resources is highly variable. Therefore, admission control is an essential mechanism to manage the traffic of cognitive radio users in order to satisfy the quality of service (QoS) requirements of applications. In this study, a connection admission control (CAC) mechanism is introduced to satisfy the requirements of delay sensitivity and power consumption awareness. This proposed mechanism is modeled through a semi Markov decision process (SMDP) and a linear programming problem is derived with the aim of obtaining the optimal policy to control the traffic of CRSNs and achieving maximum reward. The number of required channels at each network state is estimated through a graph coloring approach. An end to end delay constraint is defined for the optimization problem which is inspired from Kleinrock independence approximation. Furthermore, a power-aware weighting method is proposed for this mechanism. We conduct different simulation-based scenarios to investigate the performance of the proposed mechanism. The experimental results demonstrate the efficiency of this SMDP-based mechanism in comparison to the last CAC mechanism in CRSNs
Improved Spectrum Mobility using Virtual Reservation in Collaborative Cognitive Radio Networks
Cognitive radio technology would enable a set of secondary users (SU) to
opportunistically use the spectrum licensed to a primary user (PU). On the
appearance of this PU on a specific frequency band, any SU occupying this band
should free it for PUs. Typically, SUs may collaborate to reduce the impact of
cognitive users on the primary network and to improve the performance of the
SUs. In this paper, we propose and analyze the performance of virtual
reservation in collaborative cognitive networks. Virtual reservation is a novel
link maintenance strategy that aims to maximize the throughput of the cognitive
network through full spectrum utilization. Our performance evaluation shows
significant improvements not only in the SUs blocking and forced termination
probabilities but also in the throughput of cognitive users.Comment: 7 pages, 10 figures, IEEE ISCC 201
Cooperation and Underlay Mode Selection in Cognitive Radio Network
In this research, we proposes a new method for cooperation and underlay mode
selection in cognitive radio networks. We characterize the maximum achievable
throughput of our proposed method of hybrid spectrum sharing. Hybrid spectrum
sharing is assumed where the Secondary User (SU) can access the Primary User
(PU) channel in two modes, underlay mode or cooperative mode with admission
control. In addition to access the channel in the overlay mode, secondary user
is allowed to occupy the channel currently occupied by the primary user but
with small transmission power. Adding the underlay access modes attains more
opportunities to the secondary user to transmit data. It is proposed that the
secondary user can only exploits the underlay access when the channel of the
primary user direct link is good or predicted to be in non-outage state.
Therefore, the secondary user could switch between underlay spectrum sharing
and cooperation with the primary user. Hybrid access is regulated through
monitoring the state of the primary link. By observing the simulation results,
the proposed model attains noticeable improvement in the system performance in
terms of maximum secondary user throughput than the conventional cooperation
and non-cooperation schemes
Stochastic models for cognitive radio networks
During the last decade we have seen an explosive development of wireless technologies. Consequently the demand for electromagnetic spectrum has been growing dramatically resulting in the spectrum scarcity problem. In spite of this, spectrum utilization measurements have shown that licensed bands are vastly underutilized while unlicensed bands are too crowded. In this context, Cognitive Radio emerges as an auspicious paradigm in order to solve those problems. Even more, this concept is envisaged as one of the main components of future wireless technologies, such as the fifth-generation of mobile networks. In this regard, this thesis is founded on cognitive radio networks. We start considering a paid spectrum sharing approach where secondary users (SUs) pay to primary ones for the spectrum utilization. In particular, the first part of the thesis bears on the design and analysis of an optimal SU admission control policy, i.e. that maximizes the long-run profit of the primary service provider. We model the optimal revenue problem as a Markov Decision Process and we use dynamic programming (and other techniques such as sample-path analysis) to characterize properties of the optimal admission control policy. We introduce different changes to one of the best known dynamic programming algorithms incorporating the knowledge of the characterization. In particular, those proposals accelerate the rate of convergence of the algorithm when is applied in the considered context. We complement the analysis of the paid spectrum sharing approach using fluid approximations. That is to say, we obtain a description of the asymptotic behavior of the Markov process as the solution of an ordinary differential equation system. By means of the fluid approximation of the problem, we propose a methodology to estimate the optimal admission control boundary of the maximization profit problem mentioned before. In addition, we use the deterministic model in order to propose some tools and criteria that can be used to improve the mean spectrum utilization with the commitment of providing to secondary users certain quality of service levels. In wireless networks, a cognitive user can take advantage of either the time, the frequency, or the space. In the first part of the thesis we have been concentrated on timefrequency holes, in the second part we address the complete problem incorporating the space variable. In particular, we first introduce a probabilistic model based on a stochastic geometry approach. We focus our study in two of the main performance metrics: medium access probability and coverage probability. Finally, in the last part of the thesis we propose a novel methodology based on configuration models for random graphs. With our proposal, we show that it is possible to calculate an analytic approximation of the medium access probability (both for PUs and, most importantly, SUs) in an arbitrary large heterogeneous random network. This performance metric gives an idea of the possibilities offered by cognitive radio to improve the spectrum utilization. The introduced robust method, as well as all the results of the thesis, are evaluated by several simulations for different network topologies, including real scenarios of primary network deployments. Keywords: Markov decision process, fluid limit, stochastic geometry, random graphs,dynamic spectrum assignment, cognitive radi
Queueing Game For Spectrum Access in Cognitive Radio Networks
In this paper, we investigate the problem of spectrum access decision-making
for the Secondary Users (SUs) in the cognitive radio networks. When the Primary
Users (PUs) are absent on certain frequency bandwidth, SUs can formulate a
queue and wait for the Base Station (BS) to serve. The queue of the SUs will be
dismissed if the PU is emerging in the system. Leveraging the queueing game
approaches, the decision-making process of the SUs that whether to queue or not
is studied. Both individual equilibrium and social optimization strategies are
derived analytically. Moreover, the optimal pricing strategy of the service
provider is investigated as well. Our proposed algorithms and corresponding
analysis are validated through simulation studies
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