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on Marine Navigation
and Safety of Sea Transportation
Number 2
183
1 INTRODUCTION
One of the main characteristics determining the ef-
fectiveness of a radio communication system is the
stability against disturbances [1,2]. It is character-
ized with the dependency of the fidelity of received
communications on the line energy parameters, al-
gorithms used to transmit information and statistical
characteristics of interferences [1,2]. With discrete
systems of connections, the error probability of dis-
tinguishing signals is used for fidelity assessment
[3,4].
The modern radio communication systems with
safety responsibility are required to guarantee that
the error-probability given will not exceed the pre-
liminary specified permissible value independently
of the variability activity of the channel. In essence
it means that the given quantity of the system func-
tioning is achieved thanks to the independency (par-
tial or complete) of the performance of the noise-
resistance from reasons causing the non-stationary
state of the channel of connection. In the theory of
automatic control this ability of the system to oppose
resist against the disturbing actions is known as in-
variance. In the systems of connection, the part of
disturbing effects is played by different disturbances
and noise-resistance is the feature of the system that
is invariant to them.
2 DEFINING THE CONDITIONS OF
INVARIANCE
For a radio channel, the typical situation is the one
where the performance of noise-resistance is deter-
mined by the presence of disturbances of several
classes (fluctuating, spectrum-concentrated, im-
pulse). The functional kind of the expression of the
error probability with receiving by elements depends
on the sets of signal parameters, the disturbances and
the interaction between them:
, (1)
where
and
express the mean statistical prop-
erties of the ratios between the energies of the i-th
signal variant and the j-th disturbance variant and
the white noise spectral density.
The part of parameters of interaction between the
signal and disturbances is played by set {
},
i=1÷n , j=1÷
, average statistical values of the co-
efficients of the reciprocal differences in the fre-
quency-and-time area of their structures.
As the degree of the interaction between the use-
ful signal and the disturbance on the frequency-and-
time plane is analogous to their mutual correlation
function, it is suitable to assume the average statisti-
cal value of the mutual difference coefficient in the
position of interaction between them. This value is
expressed in the kind of:
An Invariance of the Performance of Noise-
Resistance of Spread Spetrum Signals
G. Cherneva & E. Dimkina
University of Transport, Sofia, Bulgaria
ABSTRACT: The paper is a study on the invariance of the performance of noise-resistance with respect to the
quasi-determined spectrum-concentrated interferences with transmitting of spread spectrum signals with a
fixed algorithm of processing.