To become a global telecommunications facility, one of the critical challenges for 5G services is to provide reliable communication in a high-mobility environment, such as vehicle-to-vehicle (V2V) communications with relative moving speeds of up to 220 km/h, MCA with 900 km/h, and low-earth-orbit satellites (LEOS) with even higher speeds [1]. Further, the research on underwater acoustic (UWA) communication is rapidly developing due to its crucial role in various applications, such as underwater exploration, marine biology, and oceanography. Underwater channel conditions pose challenges to communication, including temperature, salinity, pressure, noise, and currents, making it complex and unpredictable [2].

Wireless communications in such high-mobility and underwater environment environments suffer from severe Doppler spread effect caused by the relative motion between transceivers. It introduces severe, dynamic and rapid channel variations via fast fading. Doubly dispersive channels characterise high mobility, where multipath delays cause time dispersion and Doppler shifts cause frequency dispersion [3]. As a result, transmitted signals suffer from severe inter-carrier interference (ICI) due to the Doppler spread and inter-symbol interference (ISI) due to delays. Conventional orthogonal frequency division multiplexing (OFDM) modulation, which has been widely used in 4G/5G cellular mobile systems, may break because it is very vulnerable to ICI, and high Doppler spreads can destroy the orthogonality in OFDM waveforms.

Robust to these issues, a newer orthogonal time frequency space (OTFS) modulation technique holds promise to enable next-generation wireless communications in such high-mobility environments [3]. Required for the implementation of an OTFS receiver is an efficient channel estimation algorithm that can be deployed with similar energy efficiency to the channel estimation algorithms required in current OFDM receivers. The objective of the project is to design an energy-efficient receiver algorithm using a machine learning framework, to efficiently estimate the fast time-varying fading channels in OTFS modulated signals [4]. The energy efficiency of the channel estimation algorithm would permit deployment in a wide range of low-power wireless communication devices, such as mobile and Internet-of-Things devices.

In addition, the use of underwater acoustic communication channels presents a unique opportunity to test an effective channel estimation algorithm [5]. The presence of multipath propagation, severe Doppler shifting, and spreading in underwater acoustic propagation make it comparable to the time dispersion and Doppler shifts experienced in high-mobility wireless communications [6]. Moreover, the energy efficiency demands of the channel estimation algorithm are particularly relevant since underwater transmission typically involves portable battery-powered devices.


Electrical Engineering and Telecommunications

Research Area

Electrical engineering | Underwater acoustic systems | Wireless communications | Signal processing | Computer communications networks | Optimisation | Machine learning

This project proposes developing new channel estimation and modulation designs to substantially improve the data rate, reliability, and robustness of high-mobility communications for future mobile services, including those for underwater acoustic communication systems. The specific objective of this project is to design optimal energy-efficient receiver algorithms using a machine learning framework [7] to efficiently estimate the fast time-varying fading channels and robustly detect the data symbols to ensure reliable communications in a high-mobility and underwater acoustic communication system environment.

The interested student needs to be good in MATLAB programming and have basic knowledge of wireless communications, signal processing and Machine Learning. We will provide lectures to teach the student about the advanced concepts that will be used as a part of his/her training for this project. Also, the student will be part of our research team, which includes undergraduate/postgraduate thesis, MPhil, and PhD students. The student will have access to all the lab resources, sitting place, software and research facilities like our other research scholars to facilitate their work in a timely and productive fashion.

1) One short paper in tier-1 IEEE communication conference with the undergraduate student. A poster presentation and a short video highlighting the key results will also be provided.

2) One potential full transaction-type journal paper based on the extension of the conference paper to be led by me within two months after the end of this project.

3) Training of an undergraduate student and motivating to join higher degree research MPhil/PhD programs.

Head of Telecommunications Jinhong Yuan
Head of Telecommunications
Senior Research Associate  Deepak Mishra
Senior Research Associate

[1] D. Soldani, M. Shore, J. Mitchell, M. Gregory, "The 4G to 5G network architecture evolution in Australia," Australian Journal of Telecoms and Digital Economy, Vol. 6, N. 4, Dec. 2018.

[2] M. Lanzagorta, “Underwater communications,” Synthesis lectures oncommunications, vol. 5, no. 2, pp. 1–129, 2012

[3] Z. Wei et al., "Orthogonal Time-Frequency Space Modulation: A Promising Next-Generation Waveform," in IEEE Wireless Communications, vol. 28, no. 4, pp. 136-144, August 2021

[4] Y. K. Enku et al., "Two-Dimensional Convolutional Neural Network-Based Signal Detection for OTFS Systems," in IEEE Wireless Communications Letters, vol. 10, no. 11, pp. 2514-2518, Nov. 2021

[5] R. A. McCarthy and A. Sengupta , "Underwater channel estimation exploiting multipath feature morphology", The Journal of the Acoustical Society of America, vol. 149, pp 983-996, 2021

[6] N. Ansari, A. S. Gupta and A. Gupta, "Underwater acoustic channel estimation via CS with prior information," OCEANS 2017 - Aberdeen, 2017, pp. 1-5, doi: 10.1109/OCEANSE.2017.8084965.

[7] Y. Fu, S. Wang, C. Wang, X. Hong, and S. McLaughlin, "Artificial intelligence to manage network traffic of 5G wireless networks," IEEE/ACM Trans. Netw., vol. 32, no. 6, pp. 58–64, Nov./Dec. 2018.