2,827 research outputs found
Throughput Analysis of Primary and Secondary Networks in a Shared IEEE 802.11 System
In this paper, we analyze the coexistence of a primary and a secondary
(cognitive) network when both networks use the IEEE 802.11 based distributed
coordination function for medium access control. Specifically, we consider the
problem of channel capture by a secondary network that uses spectrum sensing to
determine the availability of the channel, and its impact on the primary
throughput. We integrate the notion of transmission slots in Bianchi's Markov
model with the physical time slots, to derive the transmission probability of
the secondary network as a function of its scan duration. This is used to
obtain analytical expressions for the throughput achievable by the primary and
secondary networks. Our analysis considers both saturated and unsaturated
networks. By performing a numerical search, the secondary network parameters
are selected to maximize its throughput for a given level of protection of the
primary network throughput. The theoretical expressions are validated using
extensive simulations carried out in the Network Simulator 2. Our results
provide critical insights into the performance and robustness of different
schemes for medium access by the secondary network. In particular, we find that
the channel captures by the secondary network does not significantly impact the
primary throughput, and that simply increasing the secondary contention window
size is only marginally inferior to silent-period based methods in terms of its
throughput performance.Comment: To appear in IEEE Transactions on Wireless Communication
Deep Learning Meets Cognitive Radio: Predicting Future Steps
Learning the channel occupancy patterns to reuse
the underutilised spectrum frequencies without interfering with
the incumbent is a promising approach to overcome the spectrum
limitations. In this work we proposed a Deep Learning (DL)
approach to learn the channel occupancy model and predict its
availability in the next time slots. Our results show that the
proposed DL approach outperforms existing works by 5%. We
also show that our proposed DL approach predicts the availability
of channels accurately for more than one time slot
Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Errors
We address the design of opportunistic spectrum access (OSA) strategies that
allow secondary users to independently search for and exploit instantaneous
spectrum availability. Integrated in the joint design are three basic
components: a spectrum sensor that identifies spectrum opportunities, a sensing
strategy that determines which channels in the spectrum to sense, and an access
strategy that decides whether to access based on imperfect sensing outcomes.
We formulate the joint PHY-MAC design of OSA as a constrained partially
observable Markov decision process (POMDP). Constrained POMDPs generally
require randomized policies to achieve optimality, which are often intractable.
By exploiting the rich structure of the underlying problem, we establish a
separation principle for the joint design of OSA. This separation principle
reveals the optimality of myopic policies for the design of the spectrum sensor
and the access strategy, leading to closed-form optimal solutions. Furthermore,
decoupling the design of the sensing strategy from that of the spectrum sensor
and the access strategy, the separation principle reduces the constrained POMDP
to an unconstrained one, which admits deterministic optimal policies. Numerical
examples are provided to study the design tradeoffs, the interaction between
the spectrum sensor and the sensing and access strategies, and the robustness
of the ensuing design to model mismatch.Comment: 43 pages, 10 figures, submitted to IEEE Transactions on Information
Theory in Feb. 200
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