Wireless Spectrum Occupancy Prediction Based on Partial Periodic Pattern Mining

Abstract

Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users in an efficient way. When unlicensed users opportunistically utilize spectrum holes, prediction models that infer the availability of spectrum holes can help to improve the spectrum extraction rate and reduce the collision rate. In this paper, a spectrum occupancy prediction model based on Partial Periodic Pattern Mining (PPPM) is introduced. The mining aims at identifying frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). Based on the prediction, unlicensed users are able to utilize spectrum holes aggressively without introducing significant interference to licensed users. PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly due to noise, sensing errors, and irregular behaviors. Using real-world Wi-Fi and personal communication service (PCS) activities, we show a significant reduction on miss rate in channel state prediction. With the proposed prediction mechanism, the performance of Dynamic Spectrum Access (DSA) is substantially improved. Further, we extend the three-state PPPM to an N-state PPPM to predict the duration of high/low utilization in a channel. The frequent patterns of channel utilization duration are critical in optimizing channel switch strategies. The high prediction accuracy is validated with data collected in the paging bands.

Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

“…When the insertion of one entry is finished, the uo and ruo of the node are updated (Lines 14-15). If the TIvecs of G G and i j have the same tid, both TIvecs are shifted to the next entries (lines [16][17]. If the tid of G i is greater than the tid of G j , the TIvec of G j is moved to the next entry.…”

“…This method considers the share and the influence of the item in the transaction, so it extracts more meaningful patterns 16 . The examples of real‐world applications utilizing occupancy pattern mining are wireless spectrum 17 and spectrum 18 . Still, occupancy pattern mining cannot express the quantity or the weight of an item.…”

Sequential Pattern Mining Algorithms – Recent Trends

“…Some well know ones are STAGGER [13], WARP [14] etc. Huang et al [15] devised an algorithm for prediction of spectrum occupancy in wireless, which is based on frequent partial periodic pattern tree using prediction based on this algorithm enables an unlicensed user to avail of the licensed wireless spectrum bands that are not utilized. This ensures better channel utilization lowers rates of collision.…”

Joint network coding and machine learning for error-prone wireless broadcast

“…By mining the dataset, the algorithms create more efficient cognitive radio networks. In addition, [15] introduces frequency pattern mining to efficiently allocate wireless spectrum between licensed and unlicensed users. In [16], the authors present a review of using machine learning algorithms for wireless sensor networks to adapt the environment changes over time, from that, to increase the network efficiency and save the limited resources of the networks.…”