Abstract:
The demand for faster data transfer rates rises along with the number of mobile devices,
and the radio spectrum becomes more crowded. The forthcoming 5G wireless commu
nication technology seeks to significantly enhance data speeds and spectrum efficiency
by dynamically adjusting to fluctuating channel conditions. This research introduces
a hierarchical multimode, multiband, multi-scheme machine learning methodology for
the classification of several NBWF modulation schemes, including 2-PSK, 4-PSK, 8
PSK, 2-FSK, 4-FSK, 8-FSK, and CPM, with parameters such as modulation order (M
=[2, 4, 8]), spectral efficiency (h = [1/2, 1/4, 1/8, 1/16]), and overlap factor (L = [1, 2,
3]), across diverse channel conditions. In addition, a Multimode Multiband Wideband
Waveform Framework (WBWF) suitable for system communications is proposed. The
framework utilizes the prototype filter design method to suppress interference between
symbols. A self-optimizing hierarchial classifier is introduced for adaptive modulation
and parameter setting which guarantees the best channel mode selection. The study
also considers FBMC for WBWF scheme analyzing its respective parameters, including
modulation order (M = [2, 4, 8]), subcarrier count (N = [128, 256, 512]), code rate (C =
[1, 1/2, 2/3]), and overlap factor of Phydyas prototype filter (K = [2, 3, 4]) across sim
ilar channel conditions as utilized in NBWF. Using a thorough MATLAB simulation,
we modulated data with various parameters (M, h, and L) for NBWF, and (M, N, C,
and K) for WBWF, transmitted them through AWGN and Stanford University Interim
(SUI) channels, and used a variety of frequency ranges for demodulation. The bit error
rate (BER) and data rate were assessed over various signal-to-noise ratio (SNR) values.
Hierarchical classifiers were constructed to identify the modulation type and determine
the individual parameters affecting throughput under different SNR situations. The
suggested method achieved an accuracy of about 87.11% for NBWF and 78.47% for
WBWF, which shows that it could be useful for cognitive radio systems and flexible
NB/WB communication networks.
