Abstract
This paper proposes a autocorrelation-based method to classify traffic
patterns of primary channels in cognitive radio systems to allow a more
accurate prediction of the future idle times. The classification algorithm
uses binary information collected by spectrum sensing. It searches periodicity
from the sensed binary pattern using a discrete autocorrelation function.
Errors that are caused by noise and possible false sensing reports are
filtered away from the autocorrelation function. We tested the method with
Pareto, Weibull, and exponentially distributed stochastic traffic, and with
deterministic traffic. The proposed method finds the type of traffic with a
high probability when the channels of interest include both stochastic and
deterministic traffic. Stochastic traffic is always classified right and
regarding the deterministic traffic the probability of correct classification
is over 95 % when the probability of missed detection or probability of false
alarms is below 10 %.
| Original language | English |
|---|---|
| Title of host publication | 2011 IEEE Vehicular Technology Conference (VTC Fall) |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-4244-8327-3, 978-1-4244-8326-6 |
| DOIs | |
| Publication status | Published - 2011 |
| MoE publication type | A4 Article in a conference publication |
| Event | 74th IEEE Vehicular Technology Conference, VTC Fall 2011 - San Francisco, United States Duration: 5 Sept 2011 → 8 Sept 2011 Conference number: 74 |
Conference
| Conference | 74th IEEE Vehicular Technology Conference, VTC Fall 2011 |
|---|---|
| Abbreviated title | VTC Fall 2011 |
| Country/Territory | United States |
| City | San Francisco |
| Period | 5/09/11 → 8/09/11 |
Keywords
- learning
- predictive channel selection
- dynamic spectrum access
- sensors
- traffic control
- filtering algorithms
- primary channels
- Pareto distribution