Spectrum Occupancy Measurement and Analysis

This paper analyzes current spectrum utilization from all aspects based on related methods of spectrum measurement. The measurement results show that some spectrum resources are not used effectively due to current fixed spectrum allocation policy, and the spectrum occupancy varies dramatically in terms of time and space. These results provide basis for the development of next generation wireless communication technologies such as Cognitive Radio (CR).

Spectrum Occupancy Prediction for Realistic Traffic Scenarios: Time Series versus Learning-Based Models

Spectrum occupancy information is neces-sary in a cognitive radio network(CRN)as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access(DSA).However,in a CRN,it is difficult to ascertain a priori the pattern of the spectrum usage of the primary user due to its stochastic behavior.In this context,the spectrum occupancy predic-tion proves to be very useful in enhancing the quality of experience of the secondary user.This paper investigates the practical prowess of various time-series modeling approaches and the machine learning(ML)techniques for predicting spectrum occupancy,based on a spectrum measurement campaign conducted in Jaipur,Rajasthan,India.Moreover,the comparison analysis conducted between the above two approaches highlights the trade-off in terms of the respective performance depending upon the nature of the spectrum occupancy data.Nevertheless,prediction through ML-based recurrent neural network proves to perform reasonably well,thereby providing an accurate future spectrum occupancy information for DSA.

Quantitative spectrum occupancy evaluation in China: based on a large scale concurrent spectrum measurement

Due to the usable frequency becomes more and more crowed, dynamic spectrum access （DSA） is a new hope to solve this problem. However, DSA in China requires a quantitative analysis of the current spectrum utilization in frequency, temporal and spatial domains. In order to free the precious spectrum, spectrum regulation organizations must have a clear, detailed, up-to-date understanding of where, how and by whom spectrum is currently being used--such data is essential to sound policy decisions in the context of cognitive radio （CR）. In this paper, a concurrent spectrum occupancy measurement in south China was conducted to evaluate the practical spectrum occupancy with a digital wideband receiver covering from 20 MHz to 3 GHz. We also propose systemic spectrum measurement methodology, matrix format data storage, duty cycle （DC） evaluation metric and data mining process which can be a guideline for other researchers when they conduct the similar experiments. Quantitative analysis and characterization of the 4 different measurement locations are evaluated to promote the popularization of CR application in China. And a uniform Beta distribution channel occupancy model is also validated using real-scene measurement data. The experimental results demonstrate that there is a significant scope for license-exemption use of the released spectrum using CR technology.

Adaptive Data-Driven Wideband Compressive Spectrum Sensing for Cognitive Radio Networks

This paper presents a novel adaptive wide-band compressed spectrum sensing scheme for cognitive radio(CR)networks.Compared to the traditional CSS-based CR scenarios,the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure.On the contrary,a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements.Then,the spectrum occupancy is determined directly from the reconstructed support vector.To carry out this process,a data-driven methodology is utilized to obtain the mini-mum number of necessary samples required for support reconstruction,and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels.Following this phase,an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision.Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance.Furthermore,the remainder of the sensing time can be employed by secondary users for data transmission,thus leading to the enhancement of the total throughput of the CR network.