2 research outputs found
Optimal Channel Sensing Strategy for Cognitive Radio Networks with Heavy-Tailed Idle Times
In Cognitive Radio Network (CRN), the secondary user (SU) opportunistically
access the wireless channels whenever they are free from the licensed / Primary
User (PU). Even after occupying the channel, the SU has to sense the channel
intermittently to detect reappearance of PU, so that it can stop its
transmission and avoid interference to PU. Frequent channel sensing results in
the degradation of SU's throughput whereas sparse sensing increases the
interference experienced by the PU. Thus, optimal sensing interval policy plays
a vital role in CRN. In the literature, optimal channel sensing strategy has
been analysed for the case when the ON-OFF time distributions of PU are
exponential. However, the analysis of recent spectrum measurement traces
reveals that PU exhibits heavy-tailed idle times which can be approximated well
with Hyper-exponential distribution (HED). In our work, we deduce the structure
of optimal sensing interval policy for channels with HED OFF times through
Markov Decision Process (MDP). We then use dynamic programming framework to
derive sub-optimal sensing interval policies. A new Multishot sensing interval
policy is proposed and it is compared with existing policies for its
performance in terms of number of channel sensing and interference to PU.Comment: 20 pages (single column), 7 figures, Submitted to IEEE Transactions
on Cognitive Communications and Networking for possible publicatio
A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access
Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio
(CR) was introduced as a technique to attain high spectral efficiency. As the
number of secondary users (SUs) connecting to the cognitive radio network is on
the rise, there is an imminent need for centralized algorithms that provide
high throughput and energy efficiency of the SUs while ensuring minimum
interference to the licensed users. In this work, we propose a multi-stage
algorithm that - 1) effectively assigns the available channel to the SUs, 2)
employs a non-parametric learning framework to estimate the primary traffic
distribution to minimize sensing, and 3) proposes an adaptive framework to
ensure that the collision to the primary user is below the specified threshold.
We provide comprehensive empirical validation of the method with other
approaches.Comment: 9 pages, 7 figures, 1 tabl