Design of ReconFIGUREurable Modulation System for Selective Cellular Interference with SDR
Abstrak
Conventional signal interference systems are often constrained by the rigidity of hardware architecture, which limits operational flexibility and increases cost inefficiency. This research aims to overcome these limitations through the de- velopment of an adaptive signal modulation system based on Software Defined Radio (SDR) that replaces the role of static analog components with reconFIGUREurable software conFIGUREurations. The method used involves the implementation of HackRF One SDR controlled through the GNU Radio framework to perform interference emissions on strategic cellular frequency bands. Performance evaluation was carried out by comparing the real time output power profile with the technical specifications of the device and conducting a comparative analysis with conventional Digital Cellular System (DCS) modules to assess bandwidth adequacy and radiation intensity. The experimental results show that although the SDR conFIGUREuration offers high spectral agility, there are deficiencies in transmission power and bandwidth for large scale cellular coverage, requiring the integration of external amplification. However, validation testing confirmed the system’s effectiveness in significantly reducing the Reference Signal Re- ceived Power (RSRP) value on the target device compared to the received power level from the Base Transceiver Station (BTS). Overall, this approach successfully improved the architectural efficiency and functional flexibility of the jammer modulation system in mitigating signals on the cellular frequency spectrum.
Keywords— software defined radio, hackrf one, gnu radio, signal interference, spectral agility.
Referensi
S. Xia and Q. Wu, “Mitigating the Electromagnetic Coupling Between the Tx and Rx Channels in Software-Defined Radio Through a Digital-Domain Cancellation Algorithm,” IEEE Transactions on Electromagnetic Compatibility, vol. 66, no. 5, pp. 1389–1398, Oct. 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10608440/
Z. U. A. Tariq, E. Baccour, A. Erbad, and M. Hamdi, “Reinforcement Learning for Resilient Aerial-IRS Assisted Wireless Communications Networks in the Presence of Multiple Jammers,” IEEE Open Journal of the Communications Society, vol. 5, pp. 15–37, 2024. [Online].
Available: https://ieeexplore.ieee.org/document/10325598/
A. Thavai, M. Subhedar, Y. Thakur, and M. Patil, “Design an RF Up-Down Convertor using Software Defined Radio and GNU Radio,” Journal of Mobile Multimedia, pp. 901–916, Oct. 2024. [Online]. Available: https://journals.riverpublishers.com/index. php/JMM/article/view/24999
M. Gummineni and T. R. Polipalli, “Implementation of ReconFIGUREurable Transceiver using GNU Radio and HackRF One,” Wireless Personal Communications, vol. 112, no. 2, pp. 889–905, May 2020. [Online].
Available: http://link.springer.com/10.1007/s11277-020-07080-0
Rustamaji, S. Kliwati, and W. Widada, “The angle of arrival estimation of frequency-hopping cooperative object based on software-defined radio,” Scientific Reports, vol. 14, no. 1, p. 7732, Apr. 2024. [Online].
Available: https://www.nature.com/articles/s41598-024-58488-8
M. Z. Khan, A. Taha, W. Taylor, M. A. Imran, and Q. H. Abbasi, “Non-Invasive Localization Using Software-Defined Radios,” IEEE Sensors Journal, vol. 22, no. 9, pp. 9018–9026, May 2022. [Online].
Available: https://ieeexplore.ieee.org/document/9738615/
D. Kafetzis, S. Vassilaras, G. Vardoulias, and I. Koutsopoulos, “Software-Defined Networking Meets Software-Defined Radio in Mobile ad hoc Networks: State of the Art and Future Directions,” IEEE Access, vol. 10, pp. 9989–10 014, 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9684421/
Z. Htay, Z. Ghassemlooy, M. M. Abadi, A. Burton, N. Mohan, and S. Zvanovec, “Performance Analysis and Software-Defined Implementation of Real-Time MIMO FSO With Adaptive Switching in GNU Radio Platform,” IEEE Access, vol. 9, pp. 92 168–92 177, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9466115/
J. Manco, I. Dayoub, A. Nafkha, M. Alibakhshikenari, and H. B. Thameur, “Spectrum Sensing Using Software Defined Radio for Cognitive Radio Networks: A Survey,” IEEE Access, vol. 10, pp. 131 887–131 908, 2022. [Online]. Available: https://ieeexplore.ieee.org/ document/9989359/
R. Ferreira, J. Gaspar, P. Sebastia˜o, and N. Souto, “A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities,” Sensors, vol. 22, no. 4, p. 1487, Feb. 2022. [Online].
Available: https://www.mdpi.com/1424-8220/22/4/1487
E. Faulkner, Z. Yun, S. Zhou, Z. J. Shi, S. Han, and G. B. Giannakis, “An Advanced GNU Radio Receiver of IEEE 802.15.4 OQPSK Physical Layer,” IEEE Internet of Things Journal, vol. 8, no. 11, pp. 9206–9218, Jun. 2021. [Online]. Available: https:
//ieeexplore.ieee.org/document/9348953/
A. Daud, M. B. Khan, A. B. Khattak, S. A. K. Tanoli, A. Mustafa,
M. Rehman, and O. L. A. Lo´pez, “Next-Generation Security: Detecting Suspicious Liquids Through Software Defined Radio Frequency Sensing and Machine Learning,” IEEE Sensors Journal, vol. 24, no. 5, pp. 7140–7152, Mar. 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10400410/
M. Marey and H. Mostafa, “Turbo Modulation Identification Algorithm for OFDM Software-Defined Radios,” IEEE Communications Letters, vol. 25, no. 5, pp. 1707–1711, May 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9335963/
D. M. Molla, H. Badis, L. George, and M. Berbineau, “Software Defined Radio Platforms for Wireless Technologies,” IEEE Access, vol. 10, pp. 26 203–26 229, 2022. [Online]. Available: https://ieeexplore. ieee.org/document/9721283/
Z. Dai, Y. He, V. Tran, N. Trigoni, and A. Markham, “DeepAoANet: Learning Angle of Arrival From Software Defined Radios With Deep Neural Networks,” IEEE Access, vol. 10, pp. 3164–3176, 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9667511/
