Spectrum Occupancy Models Research Articles

A Spectrum Occupancy Model for Primary Users in Cognitive Radio Systems

A Spectrum Occupancy Model for Primary Users in Cognitive Radio Systems

Estimation of Distribution of Primary Channel Periods Based on Imperfect Spectrum Sensing in Cognitive Radio

Spectrum occupancy modeling plays an important role in improving the performance of Cognitive Radio (CR) systems, because most of the methods to improve Dynamic Spectrum Access (DSA) performance need to make assumptions about the channel periods’ distribution. However, Secondary Users (SUs) cannot directly observe the accurate channel periods’ distribution due to the sensing errors caused by noise, which will lead to inaccurate analysis of transmission efficiency or throughput. In this paper, we analyze the influence of different types of sensing errors on the observed channel periods, and establish the relationship between the Probability Mass Function (PMF) of idle periods under Imperfect Spectrum Sensing (ISS) and the results under Perfect Spectrum Sensing (PSS). In addition, we derive a closed-form expression for estimating the PMF of channel periods from the sensing results under ISS. Simulation results show that the proposed estimation method is more accurate than the existing methods, and is not affected by the sensing period, channel mean period and sensing error probability.

Spectrum Occupancy Model Based on Empirical Data for FM Radio Broadcasting in Suburban Environments.

It is well-known that the analog FM radio channels in suburban areas are underutilized. Before reallocating the unused channels for other applications, a regulator must analyze the spectrum occupancy. Many researchers proposed the spectrum occupancy models to find vacant spectrum. However, the existing models do not analyze each channel individually. This paper proposes an approach consisting (i) a spectrum measurement strategy, (ii) an appropriate decision threshold, and (iii) criteria for channel classification, to find the unused channels. The measurement strategy monitors each channel’s activity by capturing the power levels of the passband and the guardband separately. The decision threshold is selected depending on the monitored channel’s activity. The criteria classifies the channels based on the passband’s and guardband’s duty cycles. The results show that the proposed channel classification can identify 42 unused channels. If the power levels of wholebands (existing model) were analyzed instead of passband’s and guardband’s duty cycles, only 24 unoccupied channels were found. Furthermore, we propose the interference criteria, based on relative duty cycles across channels, to classify the abnormally used channels into interference sources and interference sinks, which have 16 and 15 channels, respectively. This information helps the dynamic spectrum sharing avoid or mitigate the interferences.

Spectrum Occupancy and Interference Model Based on Network Experimentation in Hospital

Emerging healthcare radio technologies are designed to operate in the 2.4GHz industrial, scientific and medical (ISM) band. Since both standardized (Bluetooth and Wi-Fi) and non-standardized (proprietary) devices use the same frequency band, the aggregate interference may significantly affect the performance of medical wireless systems. This paper characterizes the spatiotemporal spectrum occupancy and proposes models for the aggregate interference in hospital environments. In particular, time–frequency and cluster-based statistical models for the aggregate interference are developed based on network experimentation. The proposed models enable the design of wireless networks for e-health applications and medical services.

A Lightweight Spectrum Occupancy and Service Time Model for Centralized Cognitive Radio Networks

In cognitive radio networks (CRNs), secondary users (SUs) exploit the unused or sparsely used spectrum by primary users (PUs) without causing any harmful interference. Consequently, spectrum occupancy modeling appears as an essential task for CRN operations. In this paper, spectrum occupancy has been modeled using a queueing theory based approach in order to analyze the performance of CRNs in terms of network capacity, number of cognitive radio users waiting for service, and average waiting time. A compact model is presented for a CRN, where the queue adopted has variable service capacity and can be considered as a multi-service queue with server failure where each channel acts as a server. When a channel is occupied by a PU, it is regarded as a server failure for SUs. Using the probability generating function, the closed-form expressions of various performance parameters for different arrival distributions are derived. Numerical results for the remaining services for SUs, the expected number of SUs, and average waiting time as the CRN capacity, average service demands and average arrival rate vary, are illustrated.

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Spectrum occupancy models are very useful in cognitive radio designs. They can be used to increase spectrum sensing accuracy for more reliable operation, to remove spectrum sensing for higher resource usage efficiency, or to select channels for better opportunistic access, among other applications. In this survey, various spectrum occupancy models from measurement campaigns taken around the world are investigated. These models extract different statistical properties of the spectrum occupancy from the measured data. In addition to these models, spectrum occupancy prediction is also discussed, where autoregressive and/or moving-average models are used to predict the channel status at future time instants. After comparing these different methods and models, several challenges are also summarized based on this survey.

A Survey on Spectrum Occupancy Measurement for Cognitive Radio

In order to support the ever growing demand of wireless communication systems and wireless local area network based 802.11 devices, the regulators are developing techniques to overcome the permanent scarcity of radio resources. Spectrum sensing in cognitive radio (CR) is considered as a key technology which estimates the availability of the licensed spectrum band for additional usages. The underutilized licensed band can be exploited among the secondary users provided that they would not cause any interference to the primary user. So, evaluation of spectrum occupancy is the main approach towards the successful deployment of CR. This paper studies the various spectrum occupancy models used in diverse locations by research campaigns worldwide. The detail analyses of the empirical results in different scenarios of measurement have been compared. The purpose of this survey is to evaluate up to what percentage the whole spectrum band is occupied by different services.

Empirical Time-Dimension Model of Spectrum Use Based on a Discrete-Time Markov Chain With Deterministic and Stochastic Duty Cycle Models

The spectrum occupancy models widely used to date in dynamic spectrum access/cognitive radio (DSA/CR) research frequently rely on assumptions and oversimplifications that have not been validated with empirical measurement data. In this context, this paper presents an empirical time-dimension model of spectrum use that is appropriate for DSA/CR studies. Concretely, a two-state discrete-time Markov chain with novel deterministic and stochastic duty cycle models is proposed as an adequate mean to accurately describe spectrum occupancy in the time domain. The validity and accuracy of the proposed modeling approach is evaluated and corroborated with extensive empirical data from a multiband spectrum measurement campaign. The obtained results demonstrate that the proposed approach is able to accurately capture and reproduce the relevant statistical properties of spectrum use observed in real-world channels of various radio technologies. The importance of accurately modeling spectrum use in the design and evaluation of novel DSA/CR techniques is highlighted with a practical case study.

A framework for statistical wireless spectrum occupancy modeling

In this paper, we propose a novel spectrum occupancy model designed to generate accurate temporal and frequency behavior of various wireless transmissions. Our proposed work builds upon existing concepts in open literature in order to develop a more accurate time-varying spectrum occupancy model. This model can be employed by wireless researchers for evaluating new wireless communication and networking algorithms and techniques designed to perform dynamic spectrum access (DSA). Using statistical characteristics extracted from actual radio frequency measurements, first- and second-order parameters are employed in a statistical spectrum occupancy model based on a combination of several different probability density functions (PDFs) defining various features of a specific spectrum band with several concurrent transmissions. To assess the accuracy of the model, the output characteristics of the proposed spectrum occupancy model are compared with realtime radio frequency measurements in the television and paging bands.