In previous chapters, we have studied the model 

$$y = A\beta + \epsilon, $$

where the mean Ey = Aβ depends linearly on the parameters β, the errors are normal (Gaussian), and the errors are additive. We have also seen (Chapter 7) that in some situations, a transformation of the problem may help to correct some departure from our standard model assumptions. For example, in §7.3 on variance-stabilising transformations, we transformed our data from y to some function g(y), to make the variance constant (at least approximately). We did not there address the effect on the error structure of so doing. Of course, $g(y) = g(A\beta + \epsilon )$ as above will not have an additive Gaussian error structure any more, even approximately, in general.

Generalised Linear Models

Most existing studies of spectrum usage have been performed by actively sensing the energy levels in specific RF bands including cellular bands. In this paper, we provide a unique, complementary analysis of cellular primary usage by analyzing a dataset collected inside a cellular network operator. One of the key aspects of our dataset is its scale - it consists of data collected over three weeks at hundreds of base stations. We dissect this data along different dimensions to characterize and model primary usage as well as understand its temporal and spatial variations. Our analysis reveals several results that are relevant if dynamic spectrum access (DSA) approaches are to be deployed for cellular frequency bands. For instance, we find that call durations show significant deviations from the often- used exponential distribution, which makes call-based modeling more complicated. We also show that a random walk process, which does not use call durations, can often be used for modeling the aggregate cell capacity. Furthermore, we highlight some applications of our results to improve secondary usage of licensed spectrum.

http://www.tkn.tu-berlin.de/fileadmin/fg112/Papers/dyspan08_measurement-study.pdf

Primary Users in Cellular Networks: A Large-Scale Measurement Study

2011 Special Issue: Reliable prediction intervals with regression neural networks

This paper presents the development of Neural Network models to predict the likelihood of interference experienced by Broadcast users in the HF spectrum (3-30 MHz). The models are based upon several years of measurements recorded at Linkoping (Sweden) across the HF band, covering a substantial part of a sunspot cycle. The dataset used for the model development is a result of a long-term project being undertaken jointly by the University of Manchester and by the Swedish Defence Research Establishment, to measure systematically and to analyze the occupancy of the entire HF spectrum. The measure of occupancy used is congestion, which is defined as the fraction of channels within a certain frequency allocation with signals exceeding a given threshold. The procedures for measuring and modeling congestion as a function of solar activity, time of day, day of year and incident field strength threshold are briefly presented. The accuracy of the predictions produced by the developed models demonstrate their ability to successfully capture the 24-hour, seasonal and long-term trend in the variability of congestion. These models can be used to advise operators on typical interference occupancy levels and assist the HF broadcast service in the planning of frequency usage and management by assessing the interference effect to short-wave broadcasting in an effort to alleviate spectral congestion in the HF broadcast bands.

24-Hour Neural Network Congestion Models for High-Frequency Broadcast Users

Generalised linear models (2nd edition), by P. McCullagh and J. A. Nelder. Pp 511. £30. 1989. ISBN 0-412-31760-5 (Chapman and Hall)

A long-term joint UK-Swedish-German project has been undertaken on the measurement and analysis of HF spectral occupancy over northern Europe. This has resulted in an extensive database of spectral occupancy measurements made at four sites over a complete sunspot cycle, and mathematical models for spectral occupancy have been developed. The procedures for measuring and modelling spectral occupancy are briefly presented, with final stable-day and stable-night models for northern Europe, when using calibrated monopole antennas. Accuracy of fit and examples of extrapolation in time and in space are presented.

Models of HF spectral occupancy over a sunspot cycle

This paper presents the application of neural networks for the prediction of the Total Electron Content (TEC) over Cyprus. This ionospheric characteristic constitutes an important parameter in trans-ionospheric links since it is used to derive the signal delay imposed by the ionosphere. The model is based on TEC measurements obtained over a period of 11 years.

A local Total Electron Content neural network model over Cyprus

Stable day and stable night European occupancy models are presented, with examples of their extrapolation and interpolation properties to predict occupancy both outside and inside the area covered by the measurement sites. An investigation into the possible long-term change of the utilisation of the HF spectrum is also presented and observations are made on the effects of the 1999 eclipse.

Aspects of HF spectral occupancy

In 1995, UMIST with the support of DRA (Malvern) initiated the measurement of the azimuthal distribution of HF spectral occupancy in southern England, using a sloping vee antenna array installed at Cobbett Hill Radio Station, near Farnborough, UK. A description of this programme of work is given, together with examples of preliminary mathematical models for the azimuthal distribution of occupancy.

Variation of HF spectral occupancy with azimuth in the UK

This paper deals with the research made during the COST 296 action in the WG2, WP 2.3 in the antennas and HF spectrum management fields, focusing the Mitigation of Ionospheric Effects on Radio Systems as the subject of this COST action.

/pdf/hf-spectrum-occupancy-and-antennas-vl9mwsghrl.pdf

L. Economou

Papers

Models of HF spectral occupancy over a sunspot cycle

A local Total Electron Content neural network model over Cyprus

Aspects of HF spectral occupancy

Variation of HF spectral occupancy with azimuth in the UK

HF spectrum occupancy and antennas.