Matthias Wellens

Evaluation of Spectrum Occupancy in Indoor and Outdoor Scenario in the Context of Cognitive Radio

Dynamic spectrum access is an integral part of the Cognitive Radio paradigm. However, efficient spectrum sensing techniques are crucial on the way towards systems, which use idle spectrum bands opportunistically and increase the overall spectral efficiency. Current spectrum occupancy was evaluated in few measurement campaigns at different locations mostly located in the USA. In this paper we report about an extensive measurement campaign conducted in Aachen, Germany, comparing indoor-and outdoor measurement results. The highly sensitive measurement system enabled us to measure also man-made or ambient noise. Since an energy detector cannot differentiate such noise from other primary user signals we determine a very high spectrum occupancy in the outdoor scenario in the band from 20 MHz up to 3 GHz. Considerably less occupation was measured in the indoor scenario also because of less ambient noise. Our measurements confirm that the spectrum band 3-6 GHz is rarely occupied. We further provide a case study how the amplitude probability distribution can be used together with detailed regulatory information to infer additional information about the spectral usage. Such information is beneficial in order to optimize the spectrum sensing process and identify candidate bands for further investigation and possible secondary usage.

Full length article: Empirical time and frequency domain models of spectrum use

Dynamic spectrum access (DSA) has been proposed as a solution to the spectrum scarcity problem. However, the models for spectrum use, that are commonly used in DSA research, are either limited in scope or have not been validated against real-life measurement data. In this paper we introduce a flexible spectrum use model based on extensive measurement results that can be configured to represent various wireless systems. We show that spectrum use is clustered in the frequency domain and should be modelled in the time domain using geometric or lognormal distributions. In the latter case the probability of missed detection is significantly higher due to the heavy-tailed behaviour of the lognormal distribution. The listed model parameters enable accurate modelling of primary user spectrum use in time and frequency domain for future DSA studies. Additionally, they also provide a more empirical basis to develop regulatory or business models.

Performance Evaluation of IEEE 802.11-based WLANs in Vehicular Scenarios

Communication between cars can be exploited for various applications. Security enhancements as well as interactive games played by occupants of different cars are only two examples. The IEEE 802.11p standard will specify physical and MAC-layer for such scenarios. As one of the design goals is to partially reuse existing WLAN technology we investigate in this paper the performance of IEEE 802.11a, 802.11b and 802.11g devices in car-to-car (C2C) as well as car-to-roadside (C2R) scenarios. The determined performance is mostly independent of the car velocity up to the maximum measured speed of 180 km/h. The major impact factors found are the C2C distance, the availability of line-of-sight between sender and receiver and the rate adaptation algorithm.

Characterization and modelling of spectrum for dynamic spectrum access with spatial statistics and random fields

There is need to develop better models and characterization methods for spectrum usage and radio environments of cognitive radios. Currently different theoretical and simulation based approaches towards enabling dynamic spectrum access would greatly benefit from the possibility to generate synthetic data for testing purposes. Such Radio Environment Maps must statistically exhibit the characteristics of realistic environments. Previous and on-going spectrum measurement campaigns are generating a vast amount of such data. In this paper we provide a partial answer to the spectrum modelling problem by showing how one can characterize and model spectrum maps with spatial statistics and random fields. We present the basic mathematical premises for building models and also through examples outline how one can generate useful statistics from real measurement data.

Lessons learned from an extensive spectrum occupancy measurement campaign and a stochastic duty cycle model

Several measurement campaigns have shown that numerous spectrum bands are vacant although licenses have been issued by the regulatory agencies. Dynamic spectrum access (DSA) has been proposed in order to alleviate this problem and increase the spectral utilization. In this paper we present our spectrum measurement setup and discuss lessons learned during our measurement activities. We compare measurement results gathered at three locations and show differences in the background noise processes. Additionally, we introduce a new model for the duty cycle distribution that has multiple applications in the DSA research. We point out that fully loaded and completely vacant channels should be modelled explicitly and discuss the impact of duty cycle correlation in the frequency domain. Finally, we evaluate the efficiency of an adaptive spectrum sensing process as an example for applications of the introduced model.

Spatial statistics and models of spectrum use

In order to opportunistically exploit unused radio spectrum nodes of dynamic spectrum access (DSA) networks monitor the spectrum around them. Such cognitive radios can greatly benefit from a spatial characterization of spectrum use. However, there is need to find an efficient way to describe spatial use, something which has not been studied in details so far. In this paper, we introduce spatial statistics techniques as promising methods to describe spectrum use and enable optimization of DSA networks. We discuss two approaches to spatial modelling of spectrum, namely a deterministic approach based on a system model of the complete radio environment and an empirical approach that exploits passive measurements of the spectrum use. We elaborate on the impact of different network properties on the models and provide realistic parameter sets for generation of simulation scenarios. Additionally, we investigate cooperative sensing as a use case for spatial statistics based runtime optimization of the network configuration.

Performance of dynamic spectrum access based on spectrum occupancy statistics

At present multiband wireless devices are able to select their working frequency only to a limited extent because of the strict, current spectrum regulation. Dynamic spectrum access is a promising approach that might solve this inefficiency. The authors focus on spectrum sensing, one of the main tasks involved. First, three strategies are compared to efficiently sense the current spectrum based on the spectrum occupancy information statistics. In contrast to the simulation-based studies, the authors evaluate the performance of those strategies on real spectrum occupancy data gathered during an extensive measurement campaign. The authors show that the usage of historical information considerably improves the spectrum sensing process and also that the modelling of the periodic behaviour of the licensed signals leads to negligible performance enhancements because only very few periods shorter than several minutes can be found within 20 MHz–6 GHz. Secondly, the authors unveil the fundamental tradeoff between the required bandwidth for the transmission and the total bandwidth that has to be sensed in order to guarantee that the required bandwidth is available. All the results are provided in terms of outage probability that can be viewed as an approximation of the packet loss rate.

Exploiting Historical Spectrum Occupancy Information for Adaptive Spectrum Sensing

At present wireless devices are able to select their working frequency only to a limited extend although several measurements have shown that the current spectrum regulations are inefficient. Dynamic spectrum access is seen as a promising approach that might solve this inefficiency. Spectrum sensing is one of the main tasks involved. We compare in this paper four methods to efficiently sense the current spectrum based on the spectrum occupancy information statistics. The parameters of all methods are extracted from spectrum occupancy data gathered during an extensive measurement campaign. We show that the usage of historical information considerably improves the spectrum sensing process. We also show that the modelling of the periodic behaviour of the licensed signals leads to negligible performance enhancements because only very few periods shorter than several minutes can be found within 20 MHz-6 GHz.

Modelling Primary System Activity in Dynamic Spectrum Access Networks by Aggregated ON/OFF-Processes

Accurate modelling of primary system activity is key to reliable performance evaluation of dynamic spectrum access (DSA) technologies. Measurements on spectrum use show that in some cases the lengths of the vacant periods in a given frequency band can be correlated, a feature commonly used semi-Markov ON/OFF-models cannot reproduce. In this paper we study the stochastic properties of a simple primary system activity model constructed by aggregating the realisations of several semi-Markov ON/OFF-models. We show that despite its simplicity, our model does result in the desired correlations provided heavy-tailed distributions are used for the period length distributions of the individual processes. We also study in detail the statistical behaviour of our model as parameters such as number of processes being aggregated and distributions of the period lengths are changed. The results show that by suitable parameter selection wide range of behaviours can be modelled, making the model of interest in performance evaluation of algorithms and protocols for DSA networks.

Spatial Statistics of Spectrum Usage: From Measurements to Spectrum Models

Several measurement studies have found a large amount of underutilized radio spectrum. More flexible regulation employing dynamic spectrum access (DSA) has been proposed as solution to this problem. The analysis of several aspects of DSA systems, e.g., cooperative sensing, requires good spatial models of spectrum usage. However, only very focused models such as propagation or shadowing correlation models exist. In this paper we apply techniques developed by the spatial statistics community to the modelling of spectrum. In more detail, we use random fields and the semivariogram to describe the spatial correlation of spectrum usage.We extract parameters from extensive real-life measurements for multiple wireless technologies. These parameter sets enable other researchers to use the model for different tasks ranging from theoretical to simulation-based studies.