TY - GEN
T1 - PCI Classification in 5G-NR
T2 - 21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024
AU - Hossain, Md Rabiul
AU - Krunz, Marwan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Accurate detection of the Physical Cell Identity (PCI) is critical for rapid synchronization and connection establishment in 5G New Radio (5G-NR) systems. This paper introduces a deep learning-based approach for PCI classification, aiming to mitigate the computational complexity associated with traditional methods that rely on decoding the Synchronization Signal Block (SSB). Our approach processes only time-domain baseband samples of the downlink signal, arranged in fixed-length windows. These windows are inputted into a pre-trained Convolutional Neural Network (CNN), which classifies the samples into one of several known PCI values (representing nearby cells) or into an ‘other’ category (representing all non-nearby cells, as well as windows that do not contain SSB samples). Because PCI-related information is contained only in the SSB symbols of a frame, it is possible for an input window to include no or a few SSB samples. Accordingly, in labeling the training set, we use a threshold Ttrain on the fraction of the samples within a window: if Ttrain or more of the samples belong to an SSB of cell with a target PCI value, the true label for that window is set to that PCI value; otherwise, it is set to ‘other.’ A separate threshold Ttest is used for labeling the test windows. We also study another labeling mechanism whereby only samples of the third OFDM symbol in an SSB (which contains the Secondary Synchronization Signal) is used to determine the label. Our analysis considers two commonly used SSB formats that correspond to 15 and 30 kHz subcarrier spacings, respectively. Extensive simulations are conducted which reveal that the proposed classifier can reliably (above 98%) identify the PCI value of a captured signal even under Signal-to-Noise Ratio (SNR) values as low as −10 dB. This performance comes with a significant reduction in computational complexity as it bypasses the need for traditional SSB decoding procedures used for PCI estimation in 5G networks.
AB - Accurate detection of the Physical Cell Identity (PCI) is critical for rapid synchronization and connection establishment in 5G New Radio (5G-NR) systems. This paper introduces a deep learning-based approach for PCI classification, aiming to mitigate the computational complexity associated with traditional methods that rely on decoding the Synchronization Signal Block (SSB). Our approach processes only time-domain baseband samples of the downlink signal, arranged in fixed-length windows. These windows are inputted into a pre-trained Convolutional Neural Network (CNN), which classifies the samples into one of several known PCI values (representing nearby cells) or into an ‘other’ category (representing all non-nearby cells, as well as windows that do not contain SSB samples). Because PCI-related information is contained only in the SSB symbols of a frame, it is possible for an input window to include no or a few SSB samples. Accordingly, in labeling the training set, we use a threshold Ttrain on the fraction of the samples within a window: if Ttrain or more of the samples belong to an SSB of cell with a target PCI value, the true label for that window is set to that PCI value; otherwise, it is set to ‘other.’ A separate threshold Ttest is used for labeling the test windows. We also study another labeling mechanism whereby only samples of the third OFDM symbol in an SSB (which contains the Secondary Synchronization Signal) is used to determine the label. Our analysis considers two commonly used SSB formats that correspond to 15 and 30 kHz subcarrier spacings, respectively. Extensive simulations are conducted which reveal that the proposed classifier can reliably (above 98%) identify the PCI value of a captured signal even under Signal-to-Noise Ratio (SNR) values as low as −10 dB. This performance comes with a significant reduction in computational complexity as it bypasses the need for traditional SSB decoding procedures used for PCI estimation in 5G networks.
KW - 5G-NR
KW - PSS
KW - Physical cell identity
KW - SSS
KW - deep learning
KW - synchronization signal block
UR - https://www.scopus.com/pages/publications/105002293109
UR - https://www.scopus.com/pages/publications/105002293109#tab=citedBy
U2 - 10.1109/SECON64284.2024.10934817
DO - 10.1109/SECON64284.2024.10934817
M3 - Conference contribution
AN - SCOPUS:105002293109
T3 - Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
BT - 2024 21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024
PB - IEEE Computer Society
Y2 - 2 December 2024 through 4 December 2024
ER -