Primary User Activity Modeling Using First-Difference Filter Clustering and Correlation in Cognitive Radio Networks

Abstract

Abstract-In many recent studies on cognitive radio (CR) networks, the primary user activity is assumed to follow the Poisson traffic model with exponentially distributed interarrivals. The Poisson modeling may lead to cases where primary user activities are modeled as smooth and burst-free traffic. As a result, this may cause the cognitive radio users to miss some available but unutilized spectrum, leading to lower throughput and high false-alarm probabilities. The main contribution of this paper is to propose a novel model to parametrize the primary user traffic in a more efficient and accurate way in order to overcome the drawbacks of the Poisson modeling. The proposed model makes this possible by arranging the first-difference filtered and correlated primary user data into clusters. In this paper, a new metric called the Primary User Activity Index, 8, is introduced, which accounts for the relation between the cluster filter output and correlation statistics. The performance of the proposed model is evaluated by means of traffic estimation accuracy, false-alarm probabilities while keeping the detection probability of primary users at a constant value. Simulation results show that the appropriate selection of the Primary User Activity Index, higher primary-user detection accuracy, reduced false-alarm probabilities, and higher throughput can be achieved by the proposed model. Index Terms-Clustering, cognitive radio (CR) networks, primary user activity modeling.

Multiuser Resource Allocation Optimization Using Bandwidth-Power Product in Cognitive Radio Networks

“…One of them is its awareness of PU activity in order to protect the PU from harmful interference [5]. In our proposed framework, this awareness is obtained in two dimension (i.e., time and space) through two measured parameters: the activity ω (k) of PU p at channel k, and the channel gain g P Up between CR-BS and PU.…”

“…These monitored PU activity samples collected by CR-BS are accumulated into clusters using first-difference filtering and their temporal correlation. Based on these statistics, CR-BS is able to keep track of highly dynamic changes in PU activity and calculate the PU activity index according to the method in [5]. Interested reader on the subject of PU activity modeling and estimation can refer to [5].…”

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

“…Autoregressive integrated moving-average (ARIMA) process generalises the ARMA model to ARIMA(p, d, q) and written as [24,60] An autoregressive with Gaussian distributed random variables was used to model spectrum occupancy in [99][100][101]. Similarly, moving-average [100] and ARIMA [66] were proposed for spectrum occupancy status modelling. Random walk model was proposed in [102] to model spectrum occupancy duty cycle.…”

A QoS-aware framework for available spectrum characterization and decision in Cognitive Radio networks

“…More specifically, the spectrum usage of PUs can show significant short-term variations, and the CR users must be aware of them. Therefore, available spectrum characterization schemes should consider both the time-varying RF environment and the spiky traffic characteristics of the PU activity [2].…”

“…However, the short-term fluctuations and spiky characteristics of the available spectrum should also be captured to achieve higher throughput [2]. Recent studies consider some basic CR user applications without clear distinction of their traffic types.…”