Document Text (Pages 101-110) Back to Document

Adaptive secure data transmission method for OSI level 1

by Lallo, Pauli, PhD


Page 101

5.9.5. Sensitivity of the Soft DFT Detection in Granular Noisy
Channel (ADM-channel)

Simulation settings in Figure 5.23 are:
- Granular noise is generated in the ADM-channel and it is present in the detected signal.
- AWGN noise is added to the signal in the granular channel before D/A conversion in the

ADM decoder.
- Analog waveform and the symbol value detection method is soft detection using a 26-point

DFT.

Fig. 5.23 Performance of DFT soft detection in AWGN noise ADM-channel

The evaluated granular channel was found to be not very suitable for analog data transmission.
Figure 5.23 presents the amplitude variations of a three-tone component in respect to granularity
and AWGN noise level. The granularity is the main source of variations. The signal amplitude
values vary form normal to about -20 %. The increasing AWGN noise increases the amplitude
variations slowly to about -40%.

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5.9.6. Sensitivity of the Soft DFT Detection in Multi-Path Noisy
Channel

General simulation settings in Figure 5.24 are as before and:
- Granular noise is not present.
- AWGN noise is set on the noise levels S/N= 28…77 dB dB.
- Single-tone transmission (ASCII, QAM) is used as the signal.
- The reference received normal value used in the simulation is without granular or additive

noise (at noise floor S/N>>80 dB) and without multi-path propagation.
- Multipath signals (I) are generated with additive signal components.

Fig. 5.24 Performance of DFT soft detection in multi-path noise channel

Figure 5.24 presents the received signal amplitude versus the S/I in the multi-path signal case.
The multi-path components are made according to random phase and an exponential amplitude
distribution. In the figure the mean power of all interfering multi-path components are calculated
and the resulting S/I is used as a variable. In general, the first multi-path component is the
main component of interfering signal power I of the S/I ratio. The reference signal is on the
normal amplitude level, amplitude=1.

The multi-path error performance of the soft detection system is seen as the deviation of the
amplitude compared to the normal amplitude value (ampl = 1). In the simulated results the
multi-path signal varies with the received interference signal level. Error increases to 25% with
the decreasing S/I levels 15…7 dB. The deviation in the variation is higher at lower S/N levels
as expected. High S/N-values (76…77 dB) represent pure multi-path effects.

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5.9.7. Orthogonal Signal Space

Simulation settings in Figure 5.25 are:
- Granular noise is not present.
- AWGN noise is on the noise floor level S/N>>80 dB.
- The single-tone, two-tone and three-tone signals are generated as before in an IDFTprocess,
A=0.5 and random phase.
- Detection is made using 26-point DFT optimized to each frequency used in the multi-carrier

signal.

Fig. 5.25 Performance of DFT soft detection method

Figure 5.25 presents the performance of DFT in multi-carrier signal detection. Five different
signal spaces are evaluated one-tone, two-tone and three-tone spaces. Each signal is detected
with its normal amplitude value. No distortion components are found. The system is orthogonal
and the detection method is matched to the signal frequency.

Errors in the soft detection are due to noise channel, granular noise generated in voice coding
and parameter errors (frequency) in transmission. These situations have been discussed earlier
and presented in Figures 5.19-5.24.

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Chapter VI

6. Summary

A new adaptive method for data transmission over different radio or telecommunication channels
has been developed. The results are based on the modeling and simulation system, described
in chapters 4-5, and the use of DFT (discrete Fourier transmission) in the soft generation
and detection of waveforms. It is a band-limited variant of a generally known OFDM technology.
The basic element is the adaptive modem, which has a standard electronic interface to supported
communication systems and software algorithms for selecting its own functionality (synchronization,
waveform, modulation etc). The basic theory used was made by Shannon, Fourier
and Chang.

The complex waveforms generated in the simulations and in a prototype modem are the practical
solutions for the capacity limit of Shannon’s theory. The selection of adaptive waveforms
have been simulated and tested in the field in 2000, which ended the investigation of the method
by proving its functionality in practice.

The main result and benefit of this work is a theory and an early version (prototype modems) of
the adaptive band-limited multi-carrier data communication system for alert communications,
telemedicine purposes and other security communication needs on the physical level. The including
of a warning multi-tone into a broadcasted waveform is proof of the effectiveness of a
steganographic method used in the signal space.

Data communication is improved with the presented adaptive waveforms and the secure adaptive
communication (modulation) method compared to known band-limited methods as
- We have the physical OSI level security in local networks.
- We have secure band-limited end-to-end voice channels.
- We can optimize the bandwidth versus bit rate at the selected quality level (S/N).
- We can manage bit error rates in communication with adaptive selection of waveforms

(modulation method).
These results are simulated and tested in the field proving that the goals of the thesis have been
achieved in general with the presented adaptive data communication method.

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List of Original Papers

This thesis is based on the following publications of the author.

1. [Lal97a] Lallo, P., Analysis and Measurements of Information Transmission over an
Adaptive Delta Modulated Voice Channel, IEEE Military Communications Conference
November 2-5, 1997, in Proceedings of MILCOM 97, Monterey, CA 1997, pp.
1057-1061.

2. [Lal97b] Lallo, P., Investigation of Data Transmission over an Adaptive Delta
Modulated Voice Channel by Simulations using a Spreadsheet Program, IEEE Military
Communications Conference November 2-5, 1997, in Proceedings of MILCOM
97, Monterey, CA 1997, pp. 554-559.

3. [Lal99] Lallo, P., Signal Classification by Discrete Fourier Transform, in Proceedings
of Milcom 1999, Atlantic City, NJ, IEEE ComSoc Digital Library, USA, 1999,
pp. 197-201.
http://dl.comsoc.org/cocoon/comsoc

4. [Lal00] Lallo, P., Adaptive Modem, EuroComm2000, München, Germany.

5. [Lal01] Lallo, P., Adaptive Software Modem Technology, Proceedings of Milcom
2001, McLean, VA, IEEE ComSoc Digital Library, USA, 2001, pp. 175-179.
http://dl.comsoc.org/cocoon/comsoc

6. [Lal02] Lallo, P., Basic Theory of Adaptive Data Transmission,in Proceedings of
MILCOM 2002, Anaheim, CA, IEEE ComSoc Digital Library, USA, 2002, pp.
1054-1056.
http://dl.comsoc.org/cocoon/comsoc

7. [Lal04a] Lallo, P., Modeling and Simulations of Biomedical Data Networks,
ESM2004 SCS Europe, Magdeburg, Germany, 2004.

8. [Lal04b] Lallo, P., Robust Simulation of Waveforms, Proceedings of Milcom 2004
CD, Monterey, CA, USA, 2004.

9. [Lal75] Lallo, P., Analysis of a Special Telecommunication Network and Associated
Factors, Licentiate Thesis, Helsinki University of Technology, 1975, 88 pages.

10. [Lal87] Lallo, P., The Evaluation of the Availability of the Telecommunication
Networks, Report3/87, ISBN 951-754-158-9, ISSN 0781-7622, Helsinki University
of Technology 19.5.1987, 75 pages.

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