The demand for faster data transfer rates rises along with the number of mobile devices, such as smartphones and IoT gadgets, which makes the radio spectrum more crowded. The forthcoming 5G wireless communication technology seeks to significantly enhance data speeds and spectrum efficiency by dynamically adjusting to fluctuating channel conditions. This research presents a new approach in the form of a hierarchical machine learning system for automation of modulation classification and adaptive parameter selection that optimizes spectral efficiency for different communication channels. A hierarchical approach is adopted in place of traditional methods that classify modulation schemes as separate entities. This method first predicts the modulation type (e.g., PSK, FSK, CPM), and subsequently determines the optimal parameters (M, h, L) corresponding to the identified channel conditions. During experimentation, seven modulation schemes were tested (2-PSK, 4-PSK, 8-PSK, 2-FSK, 4-FSK, 8-FSK, and CPM) for different modulation orders (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_12277_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="111" /> </InlineMediaObject> <EquationSource Format="TEX">\(M~=~[2,~4,~8]\)</EquationSource> </InlineEquation>) and spectral efficiencies <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_12277_Article_IEq2.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="209" /> </InlineMediaObject> <EquationSource Format="TEX">\((h~=~[1/2,~1/4,~1/8,~1/16])\)</EquationSource> </InlineEquation> as well as for overlap factors <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_12277_Article_IEq3.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="116" /> </InlineMediaObject> <EquationSource Format="TEX">\((L~=~[1,~2,~3])\)</EquationSource> </InlineEquation>. A detailed MATLAB simulation was built and signals were transmitted over different channels (AWGN and SUI Stanford University Interin) for evaluation over different frequency ranges. Performance of our proposed hierarchical framework was examined based on the Bit Error Rate (BER) and achievable data rate in different signal-to-noise ratio (SNR) situations. The accuracy achieved by our proposed hierarchical classifier was 98.57%, proving effectiveness in adaptive modulation selection. These achievements suggest plainly how cognitive radio systems and next generation wireless networks can benefit by the real-time spectrum adaptation and improvement in data reliability in transmission.

Cognitive link adaptation via modulation scheme classification in narrowband networks under AWGN and SUI channel conditions

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2025年7月29日

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Fatima Ismail,
Sajid Gul Khawaja,
Asad Mansoor Khan,
Umer Hameed Shah,
Muhammad Usman Akram,
Arslan Shaukat

摘要

The demand for faster data transfer rates rises along with the number of mobile devices, such as smartphones and IoT gadgets, which makes the radio spectrum more crowded. The forthcoming 5G wireless communication technology seeks to significantly enhance data speeds and spectrum efficiency by dynamically adjusting to fluctuating channel conditions. This research presents a new approach in the form of a hierarchical machine learning system for automation of modulation classification and adaptive parameter selection that optimizes spectral efficiency for different communication channels. A hierarchical approach is adopted in place of traditional methods that classify modulation schemes as separate entities. This method first predicts the modulation type (e.g., PSK, FSK, CPM), and subsequently determines the optimal parameters (M, h, L) corresponding to the identified channel conditions. During experimentation, seven modulation schemes were tested (2-PSK, 4-PSK, 8-PSK, 2-FSK, 4-FSK, 8-FSK, and CPM) for different modulation orders ( \(M~=~[2,~4,~8]\) ) and spectral efficiencies \((h~=~[1/2,~1/4,~1/8,~1/16])\) as well as for overlap factors \((L~=~[1,~2,~3])\) . A detailed MATLAB simulation was built and signals were transmitted over different channels (AWGN and SUI Stanford University Interin) for evaluation over different frequency ranges. Performance of our proposed hierarchical framework was examined based on the Bit Error Rate (BER) and achievable data rate in different signal-to-noise ratio (SNR) situations. The accuracy achieved by our proposed hierarchical classifier was 98.57%, proving effectiveness in adaptive modulation selection. These achievements suggest plainly how cognitive radio systems and next generation wireless networks can benefit by the real-time spectrum adaptation and improvement in data reliability in transmission.