Abstract
To improve their ability to find spectrum opportunities, intelligent secondary radios (SR) can learn from their past observations and predict possible spectrum opportunities. However, because of the diverse behavior of primary users (PU) in different spectrum bands, spectrum holes exhibit diverse characteristics, which in turn affect the performance of a learning algorithm. This paper studies the effect of the PU's activity on channel predictability. In particular, we introduce a Markov process-based learning algorithm, and we investigate the dependency of its spectrum decisions on the duty cycle (DC) and on the complexity of each channel activity, for both synthetic and real data. Our findings show that the probability of finding a free channel among a group of considered channels strongly depends on the DC and the complexity of the channel activity. Moreover, it is possible to reduce the number of observed channels without compromising the probability of finding a free channel, by only considering the more informative channels.
| Original language | English |
|---|---|
| Title of host publication | 2013 IEEE International Conference on Communications (ICC) |
| Pages | 2829-2834 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 1 Jun 2013 |
Keywords
- learning (artificial intelligence)
- Markov processes
- neural nets
- radio spectrum management
- telecommunication computing
- wireless channels
- opportunistic spectrum access
- DC
- duty cycle
- Markov process-based learning algorithm
- PU
- primary user
- SR
- intelligent secondary radio
- spectrum opportunity diversity
- Complexity theory
- Prediction algorithms
- Hidden Markov models
- Accuracy
- Entropy
- Computational modeling