140 E. O. NUALLAIN
To this end a highly accurate and robust propagation
predictor capable of operating in a dynamic environment
is needed.
2. Propagation Prediction
Propagation prediction over terrain has received scant
attention in the academic literature in recent years. This
contrasts sharply with the attention it received in the
eighties and early nineties with the widespread deploy-
ment of 2G cellular systems; the industry generally set-
tling for sub-optimal models such as that of Hata, Wal-
fisch-Igekami and simple Ray-Tracing combined with
basic diffraction models such as Knife-Edge Diffraction
with which to pl an t hei r net w orks.
The reasons for this were twofold: Firstly, highly ac-
curate models are unnecessary for 2G and 3G networks.
Except in circumstances where terrain topology would
make it obvious to an experienced engineer where not to
place transmitters, large-scale fading would be present
regardless of transmitter location and so there is little
need for its precise prediction. The impact of these
large-scale fades on service quality is borne by the user,
which for voice and SMS is largely tolerable if overall
coverage is good.
Secondly, more sophisticated models, such as those
based on IE methods, were at this time underdeveloped
and computationally too expensive.
Furthermore there is no need for dynamic propagation
prediction for these networks. Propagation prediction is,
in the main, a once-off process concerned with base sta-
tions only and is performed at the time of network roll-
out.
It is for these reasons that IE methods have largely
remained the subject of scattering prediction from small
and medium-sized objects despite offering inherent ad-
vantages over other methods in propagation prediction
over terrain.
These advantages are: Given that the IE formulation is
the exact solution to the coverage problem there is no
need to reduce the problem to a number of canonical
ones. All electromagnetic phenomena (reflection, dif-
fraction etc.) are encapsulated in a single formulation. In
this sense IE methods can be said to be “automatic”
—that is, they can b e directly applied to an arbitrary ter-
rain database without human intervention and without
pre- processing and so are suitable for addressing dy-
namic propagat i on prediction pr o bl ems.
It is generally envisaged that CR networks will largely
be land-based and operate over distances of less than a
few kilometres. It is well known that th e large-scale fad-
ing signal can vary in the order of tens of decibels over
much shorter distances at frequencies of a few hundred
megahertz and upwards. To set reliable power limitations
when granting permission to transmit it is thus necessary
to use a propagation model which predicts large-scale
fading which is an inherent feature of IE methods.
Because IE methods are based on the exact numerical
formulation of the propagation problem, more reliable
numerical error margins can be established.
The main drawback of IE methods has been their
computational cost. This has been overcome by theoreti-
cal developments and by advances in computer technol-
ogy to the point where coverage results within approx.
8dB of measurement results for circa. 10Km two-di-
mensional rural profiles have been obtained on a stan-
dard desktop at sub-second speeds [2].
The formulation of the problem to be solved as an IE
is intuitively more acceptable than a differential equation
formulation. This eases the route to understanding and
physically inspired development.
For these reasons it is proposed that IE-based algo-
rithms form the basis for the real-time mapping of the
radio environment for CR in order to provide the degree
of reliability necessary in decision making regarding
transmissions and, as we shall see presently, in address-
ing some fundamental Physical-Layer security issues.
3. Security
Physical Layer problems such as malfunctioning nodes
and intentional jamming can be addressed in the follow-
ing manner. Central to this discussion is the fact that the
PU signal is unique to each topology and extremely dif-
ficult to replicate ph ysically outside of the vicinity of the
primary transmitter. It is in effect a digital signature.
By comparison of collected RSS data from CR nodes
and sensors with the predicted PU signal strength identi-
fication of malfunctioning nodes and/or security threats is
enabled.
To illustrate this concept let us consider the scenario
of an intentional ja mming attack. From the outset the PU
signal strength map as a function of location has been
established using a propagation predictor initially de-
rived from its location, a digital terrain database and the
maximum radiated power which are obtained from the
licensing authority or ‘learned’ by the network. This map
will have been refined with readings from sensors and
CR devices. This is our “pre-established” PU signal
strength map. During operation it is then observed that a
significant number of nodes are reporting the presence of
a PU signal. We may then check if the reported PU sig-
nal strength values versus location deviate from the
pre-established signal strength versus location values of
the PU. If so then we have grounds for suspecting that an
intentional jamming attack or a primary receiver jam-
Copyright © 2011 SciRes. IJCNS
