Experimental Validation of Channel State Prediction Considering Delays in Practical Cognitive Radio

Abstract

Abstract-As a part of the effort toward building a cognitive radio network testbed, we have demonstrated real-time spectrum sensing. Spectrum sensing is the cornerstone of cognitive radio. However, current hardware platforms for cognitive radio introduce time delays that undermine the accuracy of spectrum sensing. The time delay, named response delay, incurred by hardware and software can be measured at two antennas colocated at a secondary user (SU), the receiving antenna and the transmitting antenna. In this paper, minimum response delays are experimentally quantified and reported based on two hardware platforms, the universal software radio peripheral 2 (USRP2) and the small form factor software defined radio development platform (SFF SDR DP). The response delay has negative impact on the accuracy of spectrum sensing.

Cooperative Prediction for Cognitive Radio Networks

“…These methods include: an approach based on MCs [13,24], the normalised least mean square (NLMS) approach [1,19], the first nearest neighbour approach (1-NN) [10] and a simple sliding occupancy window (OW) based method (proposed by the authors) [7]. These methods will all be compared to a uniformly distributed random prediction process.…”

“…A linear regression model for predicting video traffic was proposed in [27] and methods based on adaptive filter theory have been described in [1,19,18]. The concept of combining cooperation amongst SUs with prediction was briefly explored in [10], where it was suggested that a cooperative gain could be obtained if a large number of SUs with diverse channel characteristics could be employed.…”

“…In this article the authors explore the premise that if collaboration between SUs may improve the SS process, then collaboration between SUs should also allow for better accuracy in the prediction process, needed for proactive decision making [10]. Firstly, a practical approach to implementing the maximum normal fit (MNF) algorithm for SS, a statistical approach to calculating the noise floor [5], is investigated.…”

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

“…Given the estimated statistical model, spectrum occupancy prediction at future instances is achievable. Such models examine several statistical techniques with a major literature focus on Markov processes [10,45], Poisson processes [12,13], Bayesian models [9,14], neural networks [5,11,46], linear regression [15,16], space vector machine [47], pattern mining [48,49], and dictionary-based prediction [9]. In a sequential prediction framework, these techniques represent different parametrised predictor classes.…”

“…By predicting the channel status in advance, more processing time is available for spectrum sensing, decisions, and mobility [5]. SOP models address prediction either explicitly [9][10][11] or implicitly. Implicit approaches present SOP models as primary/secondary user activity models.…”