Recent years have seen big improvements in signal systems, thanks to fast progress in communication technologies, artificial intelligence, and hardware capabilities. Signal systems are very important for modern technology. They are the basis for many different uses, including telecommunications, transportation, industrial automation, and medical diagnostics. Signal systems are all about making, sending, processing, and understanding signals, whether they are analogue or digital, so that information may be shared easily. As the need for quicker, more reliable, and smarter systems grows, both the theoretical and practical sides of signal processing have made great strides. This article talks about new things that have happened in several areas of signal systems, such as digital signal processing (DSP), adaptive filtering, wireless communication protocols, and smart transportation systems. The focus is on how to combine machine learning methods for real-time signal analysis, make spectral efficiency better, and come up with new modulation and coding schemes. The study also talks about new technologies like 5G and 6G signal systems, software-defined radios (SDRs), and how quantum computing can be used in signal processing. These advancements not only improve the performance and dependability of contemporary signal systems but also create new opportunities for multidisciplinary research and industrial applications.

Adaptive Systems, Machine Learning, Communication Technologies, Software-Defined Systems, and Signal Processing

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1. Iakovidis DK, et al. Measurement Science and Technology. 2022;33(1):012002.
2. So S. Refined “Chalk-and-Talk” of Lecture Content: Teaching Signals and Systems at the Griffith School of Engineering. In: Australasian Association for Engineering Education Annual Conference; 2012; Melbourne, Australia.
3. Nehl AH. Investigation of techniques for high speed CMOS arbitrary waveform generation [dissertation]. Portland State University; 1990.
4. Castro García JA, Olmo Fernández A, Pérez García P, Yúfera García A. Microcontroller-based sinusoidal voltage generation for electrical bio- impedance spectroscopy applications. 2016.
5. Appiah K, Andrew H. Digital signal processing: The influence of convergence on education, society, and design flow. In: 34th Annual Frontiers in Education Conference; 2004; Savannah, GA.
6. Gupta N, Dutta A. High performance CMOS wide-band RF front-end with subthreshold out-of-band sensing. 2018.
7. Daniyan L. A new optimised radio telescope receiver architecture [thesis]. 2017.
8. Rogozhkin EV, Aksyonov DP, Yeremin BN. Digital signal processing information capabilities with known carrier frequency. 2003.
9. Sadler BM, Hoyos S. Towards a standard mixed-signal parallel processing architecture for miniature and microrobotics. 2014.
10. King M. Systems engineering framework for railway control and safety systems [master’s thesis]. University of Birmingham; 2018.
11. Pham Q, Nguyen NT, Huynh-The T, Le LB, et al. Intelligent radio signal processing: A survey. 2020.
12. Samek W, Stanczak S, Wiegand T. The convergence of machine learning and communications. 2017.
13. Lian M, Häger C, Pfister HD. What can machine learning teach us about communications? 2019.
14. Riera JJ, Moreno Garcia-Loygorri de Haro L, Rodriguez C. A survey on future railway radio communications services: Challenges and opportunities. 2020.
15. Ryzhov AS. Upgrade of spaced systems for data transmission via analogue channels. Radio Industry (Russia). 2017;27(3):44–49..
16. Idachaba FE, Ike DU, Orovwode H. Future trends in fibre optics communication. 2014.
17. Nam H. Joint diversity combining technique and adaptive modulation in wireless communications. In: IEEE International Conference on Communications; 2004.
18. Ludicke D, Lehner A. Train communication networks and prospects. IEEE Communications Magazine. 2019;57(9):39–43.
19. Shozo M, Akinori I, Mitsuru E, Ken’iti K. A Japanese text dictation system based on phoneme recognition and a dependency grammar. 2010.
20. Arizabaleta M. Optimisation criteria for joint communication and positioning networks [master’s thesis]. Tampere University of Technology; 2017.
21. An S. Multi-gigabaud solutions for millimeter-wave communication [licentiate thesis]. Chalmers University of Technology; 2018.
22. Sleem OM, Ibrahim MS, Malhotra A, Beluri M, et al. Unsupervised learning for pilot-free transmission in 3GPP MIMO systems. 2023.
23. Kim DM, Pratas NK, Popovski P. Achieving low-latency two-way communication by downlink and uplink decoupled access. 2018.
24. Sattiraju R, Siemons J, Soliman M, Alshrafi W, et al. Design of a highly reliable wireless module for ultra-low-latency short range applications. In: IEEE 13th International Workshop on Factory Communication Systems (WFCS); 2017.
25. Barragán Guerrero D, Au M, Gagnon G, Gagnon F, et al. Early detection for optimal-latency communications in multi-hop links. 2019.

Recent years have seen big improvements in signal systems, thanks to fast progress in communication technologies, artificial intelligence, and hardware capabilities. Signal systems are very important for modern technology. They are the basis for many different uses, including telecommunications, transportation, industrial automation, and medical diagnostics. Signal systems are all about making, sending, processing, and understanding signals, whether they are analogue or digital, so that information may be shared easily. As the need for quicker, more reliable, and smarter systems grows, both the theoretical and practical sides of signal processing have made great strides. This article talks about new things that have happened in several areas of signal systems, such as digital signal processing (DSP), adaptive filtering, wireless communication protocols, and smart transportation systems. The focus is on how to combine machine learning methods for real-time signal analysis, make spectral efficiency better, and come up with new modulation and coding schemes. The study also talks about new technologies like 5G and 6G signal systems, software-defined radios (SDRs), and how quantum computing can be used in signal processing. These advancements not only improve the performance and dependability of contemporary signal systems but also create new opportunities for multidisciplinary research and industrial applications.

Adaptive Systems, Machine Learning, Communication Technologies, Software-Defined Systems, and Signal Processing

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Iakovidis DK, et al. Measurement Science and Technology. 2022;33(1):012002.
2. So S. Refined “Chalk-and-Talk” of Lecture Content: Teaching Signals and Systems at the Griffith School of Engineering. In: Australasian Association for Engineering Education Annual Conference; 2012; Melbourne, Australia.
3. Nehl AH. Investigation of techniques for high speed CMOS arbitrary waveform generation [dissertation]. Portland State University; 1990.
4. Castro García JA, Olmo Fernández A, Pérez García P, Yúfera García A. Microcontroller-based sinusoidal voltage generation for electrical bio- impedance spectroscopy applications. 2016.
5. Appiah K, Andrew H. Digital signal processing: The influence of convergence on education, society, and design flow. In: 34th Annual Frontiers in Education Conference; 2004; Savannah, GA.
6. Gupta N, Dutta A. High performance CMOS wide-band RF front-end with subthreshold out-of-band sensing. 2018.
7. Daniyan L. A new optimised radio telescope receiver architecture [thesis]. 2017.
8. Rogozhkin EV, Aksyonov DP, Yeremin BN. Digital signal processing information capabilities with known carrier frequency. 2003.
9. Sadler BM, Hoyos S. Towards a standard mixed-signal parallel processing architecture for miniature and microrobotics. 2014.
10. King M. Systems engineering framework for railway control and safety systems [master’s thesis]. University of Birmingham; 2018.
11. Pham Q, Nguyen NT, Huynh-The T, Le LB, et al. Intelligent radio signal processing: A survey. 2020.
12. Samek W, Stanczak S, Wiegand T. The convergence of machine learning and communications. 2017.
13. Lian M, Häger C, Pfister HD. What can machine learning teach us about communications? 2019.
14. Riera JJ, Moreno Garcia-Loygorri de Haro L, Rodriguez C. A survey on future railway radio communications services: Challenges and opportunities. 2020.
15. Ryzhov AS. Upgrade of spaced systems for data transmission via analogue channels. Radio Industry (Russia). 2017;27(3):44–49..
16. Idachaba FE, Ike DU, Orovwode H. Future trends in fibre optics communication. 2014.
17. Nam H. Joint diversity combining technique and adaptive modulation in wireless communications. In: IEEE International Conference on Communications; 2004.
18. Ludicke D, Lehner A. Train communication networks and prospects. IEEE Communications Magazine. 2019;57(9):39–43.
19. Shozo M, Akinori I, Mitsuru E, Ken’iti K. A Japanese text dictation system based on phoneme recognition and a dependency grammar. 2010.
20. Arizabaleta M. Optimisation criteria for joint communication and positioning networks [master’s thesis]. Tampere University of Technology; 2017.
21. An S. Multi-gigabaud solutions for millimeter-wave communication [licentiate thesis]. Chalmers University of Technology; 2018.
22. Sleem OM, Ibrahim MS, Malhotra A, Beluri M, et al. Unsupervised learning for pilot-free transmission in 3GPP MIMO systems. 2023.
23. Kim DM, Pratas NK, Popovski P. Achieving low-latency two-way communication by downlink and uplink decoupled access. 2018.
24. Sattiraju R, Siemons J, Soliman M, Alshrafi W, et al. Design of a highly reliable wireless module for ultra-low-latency short range applications. In: IEEE 13th International Workshop on Factory Communication Systems (WFCS); 2017.
25. Barragán Guerrero D, Au M, Gagnon G, Gagnon F, et al. Early detection for optimal-latency communications in multi-hop links. 2019.