Full-Duplex Communication – Simultaneous Transmit/Receive Capability

Authors

  • Prasanth Kosaraju Dataquest Corp. Author

DOI:

https://doi.org/10.63282/3117-5481/AIJCST-V6I2P102

Keywords:

Full-duplex wireless communication, Simultaneous transmit and receive, Self-interference cancellation, Spectral efficiency, Latency reduction, Analog and digital cancellation, Passive suppression techniques

Abstract

Full-duplex (FD) wireless communication defined as the ability of a transceiver to transmit and receive simultaneously on the same frequency band has emerged as a promising technique for doubling spectral efficiency and reducing latency in next-generation networks (Sabharwal et al., 2014). Despite its theoretical advantages, practical implementation remains constrained by the challenge of suppressing self-interference (SI), which can exceed the power of the desired signal by over 100 dB (Duarte & Sabharwal, 2010). Recent prototype demonstrations have achieved over 110 dB of SI cancellation through a combination of passive suppression, analog cancellation, and digital signal processing (Bharadia et al., 2013), yet wideband, mobile, and power-efficient FD systems still face limitations caused by nonlinearities and hardware impairments (Riihonen et al., 2020). Advances in adaptive filtering, machine learning-based cancellation, and MIMO-assisted spatial suppression have further improved FD performance, positioning the technology as a key enabler for 5G/6G small cells, intelligent IoT devices, and ultra-dense networks (Ahmed et al., 2024). This paper evaluates the architectural components, cancellation techniques, and emerging research directions of full-duplex communication, highlighting the remaining gaps and the potential for achieving robust real-world FD deployment

References

[1] Ahmed, E., Eltawil, A. M., & Sabharwal, A. (2013). Self-interference cancellation with nonlinear distortion suppression for full-duplex systems. IEEE Transactions on Wireless Communications, 12(7), 3257-3270.

[2] Anttila, L., Korpi, D., Syrjälä, V., & Valkama, M. (2014). Cancellation of power amplifier induced nonlinear self-interference in full-duplex transceivers. IEEE Communications Magazine, 55(10), 30-36.

[3] Bharadia, D., McMilin, E., & Katti, S. (2013). Full duplex radios. In Proceedings of the ACM SIGCOMM (pp. 375-386). ACM.

[4] Duarte, M., & Sabharwal, A. (2010). Full-duplex wireless communications using off-the-shelf radios: Feasibility and first results. In Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (pp. 1558-1562). IEEE.

[5] Duarte, M., Dick, C., & Sabharwal, A. (2012). Experiment-driven characterization of full-duplex wireless systems. IEEE Transactions on Wireless Communications, 11(12), 4296-4307.

[6] Everett, E., Sahai, A., & Sabharwal, A. (2014). Passive self-interference suppression for full-duplex infrastructure nodes. IEEE Transactions on Wireless Communications, 13(2), 680-694.

[7] Korpi, D., Anttila, L., Valkama, M., & Wichman, R. (2017). Nonlinear self-interference cancellation in MIMO full-duplex transceivers under crosstalk. EURASIP Journal on Wireless Communications and Networking, 2017, Article 80.

[8] Li, X., [and colleagues]. (2021). Full-duplex relay designs for throughput enhancement in wireless networks. IEEE Transactions on Wireless Communications, 20(4), 2456-2469.

[9] Sabharwal, A., Schniter, P., Guo, D., Bliss, D. W., Rangarajan, S., & Wichman, R. (2014). In-band full-duplex wireless: Challenges and opportunities. IEEE Journal on Selected Areas in Communications, 32(9), 1637-1652.

[10] Zhang, Y., [and colleagues]. (2022). Full-duplex MIMO and mmWave systems: Spatial nulling and beamforming for self-interference suppression. IEEE Communications Magazine, 60(3), 56-63.

[11] Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., & Bhumireddy, J. R. (2021). Enhancing IoT (Internet of Things) Security Through Intelligent Intrusion Detection Using ML Models. Available at SSRN 5609630.

[12] Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Big Text Data Analysis for Sentiment Classification in Product Reviews Using Advanced Large Language Models. International Journal of AI, BigData, Computational and Management Studies, 2(2), 55-65.

[13] Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2021). Smart Healthcare: Machine Learning-Based Classification of Epileptic Seizure Disease Using EEG Signal Analysis. International Journal of Emerging Research in Engineering and Technology, 2(3), 61-70.

[14] Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 2(3), 70-80.

[15] Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.

[16] Gupta, A. K., Buddula, D. V. K. R., Patchipulusu, H. H. S., Polu, A. R., Narra, B., & Vattikonda, N. (2021). An Analysis of Crime Prediction and Classification Using Data Mining Techniques.

[17] Gupta, K., Varun, G. A. D., Polu, S. D. E., & Sachs, G. Enhancing Marketing Analytics in Online Retailing through Machine Learning Classification Techniques.

[18] Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Tyagadurgam, M. S. V. (2022). Efficient Framework for Forecasting Auto Insurance Claims Utilizing Machine Learning Based Data-Driven Methodologies. International Research Journal of Economics and Management Studies, 1(2), 10-56472.

[19] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Enokkaren, S. J., & Attipalli, A. (2022). Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies. International Journal of AI, BigData, Computational and Management Studies, 3(4), 49-59.

[20] Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2022). Designing an Intelligent Cybersecurity Intrusion Identify Framework Using Advanced Machine Learning Models in Cloud Computing. Universal Library of Engineering Technology, (Issue).

[21] Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., & Bhumireddy, J. R. (2022). Leveraging Big Datasets for Machine Learning-Based Anomaly Detection in Cybersecurity Network Traffic. Available at SSRN 5538121.

[22] Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2022). Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models. Journal of Artificial Intelligence and Big Data, 2(1), 153-164.

[23] Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2022). Leveraging Artificial Intelligence Algorithms for Risk Prediction in Life Insurance Service Industry. Available at SSRN 5459694.

[24] Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., & Nandiraju, S. K. K. (2022). Efficient Machine Learning Approaches for Intrusion Identification of DDoS Attacks in Cloud Networks. Available at SSRN 5515262.

[25] Polu, A. R., Narra, B., Buddula, D. V. K. R., Patchipulusu, H. H. S., Vattikonda, N., & Gupta, A. K. BLOCKCHAIN TECHNOLOGY AS A TOOL FOR CYBERSECURITY: STRENGTHS. WEAKNESSES, AND POTENTIAL APPLICATIONS.

[26] Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2022). Advance of AI-Based Predictive Models for Diagnosis of Alzheimer’s Disease (AD) in Healthcare. Journal of Artificial Intelligence and Big Data, 2(1), 141–152.DOI: 10.31586/jaibd.2022.1340

[27] Gopalakrishnan Nair, T. R., & Krutthika, H. K. (2010). An Architectural Approach for Decoding and Distributing Functions in FPUs in a Functional Processor System. arXiv e-prints, arXiv-1001.

[28] Krutthika H. K. & A.R. Aswatha. (2021). Implementation and analysis of congestion prevention and fault tolerance in network on chip. Journal of Tianjin University Science and Technology, 54(11), 213–231. https://doi.org/10.5281/zenodo.5746712

[29] Singh, A. A., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Hybrid AI Models Combining Machine-Deep Learning for Botnet Identification. International Journal of Humanities and Information Technology, (Special 1), 30-45.

[30] Kothamaram, R. R., Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., & Maniar, V. (2021). A Survey of Adoption Challenges and Barriers in Implementing Digital Payroll Management Systems in Across Organizations. International Journal of Emerging Research in Engineering and Technology, 2(2), 64-72.

[31] Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., Maniar, V., & Kothamaram, R. R. (2021). Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environmen. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 83-91.

[32] Maniar, V., Tamilmani, V., Kothamaram, R. R., Rajendran, D., Namburi, V. D., & Singh, A. A. S. (2021). Review of Streaming ETL Pipelines for Data Warehousing: Tools, Techniques, and Best Practices. International Journal of AI, BigData, Computational and Management Studies, 2(3), 74-81.

[33] Singh, A. A. S., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Predictive Modeling for Classification of SMS Spam Using NLP and ML Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 60-69.

[34] Attipalli, A., Enokkaren, S. J., Bitkuri, V., Kendyala, R., Kurma, J., & Mamidala, J. V. (2021). A Review of AI and Machine Learning Solutions for Fault Detection and Self-Healing in Cloud Services. International Journal of AI, BigData, Computational and Management Studies, 2(3), 53-63.

[35] Enokkaren, S. J., Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., & Attipalli, A. (2021). Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly Detection. International Journal of Emerging Research in Engineering and Technology, 2(2), 43-54.

[36] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Attipalli, A., & Enokkaren, S. J. (2021). A Survey on Hybrid and Multi-Cloud Environments: Integration Strategies, Challenges, and Future Directions. International Journal of Computer Technology and Electronics Communication, 4(1), 3219-3229.

[37] Kendyala, R., Kurma, J., Mamidala, J. V., Attipalli, A., Enokkaren, S. J., & Bitkuri, V. (2021). A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future Directions. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 35-42.

[38] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, V., Enokkaren, S. J., & Attipalli, A. (2021). Systematic Review of Artificial Intelligence Techniques for Enhancing Financial Reporting and Regulatory Compliance. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 73-80.

[39] Enokkaren, S. J., Attipalli, A., Bitkuri, V., Kendyala, R., Kurma, J., & Mamidala, J. V. (2022). A Deep-Review based on Predictive Machine Learning Models in Cloud Frameworks for the Performance Management. Universal Library of Engineering Technology, (Issue).

[40] Namburi, V. D., Singh, A. A. S., Maniar, V., Tamilmani, V., Kothamaram, R. R., & Rajendran, D. (2023). Intelligent Network Traffic Identification Based on Advanced Machine Learning Approaches. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 118-128.

[41] Kurma, J., Mamidala, J. V., Attipalli, A., Enokkaren, S. J., Bitkuri, V., & Kendyala, R. (2022). A Review of Security, Compliance, and Governance Challenges in Cloud-Native Middleware and Enterprise Systems. International Journal of Research and Applied Innovations, 5(1), 6434-6443.

[42] Mamidala, J. V., Enokkaren, S. J., Attipalli, A., Bitkuri, V., Kendyala, R., & Kurma, J. (2022). Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning.

[43] Namburi, V. D., Rajendran, D., Singh, A. A., Maniar, V., Tamilmani, V., & Kothamaram, R. R. (2022). Machine Learning Algorithms for Enhancing Predictive Analytics in ERP-Enabled Online Retail Platform. International Journal of Advance Industrial Engineering, 10(04), 65-73.

[44] Rajendran, D., Singh, A. A. S., Maniar, V., Tamilmani, V., Kothamaram, R. R., & Namburi, V. D. (2022). Data-Driven Machine Learning-Based Prediction and Performance Analysis of Software Defects for Quality Assurance. Universal Library of Engineering Technology, (Issue).

[45] Namburi, V. D., Tamilmani, V., Singh, A. A. S., Maniar, V., Kothamaram, R. R., & Rajendran, D. (2022). Review of Machine Learning Models for Healthcare Business Intelligence and Decision Support. International Journal of AI, BigData, Computational and Management Studies, 3(3), 82-90.

[46] Rajendran, D., Maniar, V., Tamilmani, V., Namburi, V. D., Singh, A. A. S., & Kothamaram, R. R. (2023). CNN-LSTM Hybrid Architecture for Accurate Network Intrusion Detection for Cybersecurity. Journal Of Engineering And Computer Sciences, 2(11), 1-13.

[47] Singh, A. A. S. S., Mania, V., Kothamaram, R. R., Rajendran, D., Namburi, V. D. N., & Tamilmani, V. (2023). Exploration of Java-Based Big Data Frameworks: Architecture, Challenges, and Opportunities. Journal of Artificial Intelligence & Cloud Computing, 2(4), 1-8.

[48] Kothamaram, R. R., Rajendran, D., Namburi, V. D., Tamilmani, V., Singh, A. A., & Maniar, V. (2023). Exploring the Influence of ERP-Supported Business Intelligence on Customer Relationship Management Strategies. International Journal of Technology, Management and Humanities, 9(04), 179-191.

[49] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Enokkaren, S. J., & Attipalli, A. (2023). Forecasting Stock Price Movements With Deep Learning Models for time Series Data Analysis. Journal of Artificial Intelligence & Cloud Computing, 2(4), 1-9.

[50] Mamidala, J. V., Attipalli, A., Enokkaren, S. J., Bitkuri, V., Kendyala, R., & Kurma, J. (2023). A Survey of Blockchain-Enabled Supply Chain Processes in Small and Medium Enterprises for Transparency and Efficiency. International Journal of Humanities and Information Technology, 5(04), 84-95.

[51] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Enokkaren, S. J., & Attipalli, A. (2023). Efficient Resource Management and Scheduling in Cloud Computing: A Survey of Methods and Emerging Challenges. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 112-123.

[52] Mamidala, J. V., Attipalli, A., Enokkaren, S. J., Bitkuri, V., Kendyala, R., & Kurma, J. (2023). A Survey on Hybrid and Multi-Cloud Environments: Integration Strategies, Challenges, and Future Directions. International Journal of Humanities and Information Technology, 5(02), 53-65.

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Published

2024-03-06

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Articles

How to Cite

[1]
P. Kosaraju, “Full-Duplex Communication – Simultaneous Transmit/Receive Capability”, AIJCST, vol. 6, no. 2, pp. 11–21, Mar. 2024, doi: 10.63282/3117-5481/AIJCST-V6I2P102.

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