Professor Geoffrey Ye Li

Prof. Geoffrey Ye Li currently focuses on the following three areas: machine learning for signal processing and communications, Internet of Things, and new radios. He once worked in blind signal processing, OFDM and MIMO for wireless communications, cross-layer optimization for SE and EE wireless networks, cognitive radio and device-to-device communications.

Deep learning (DL) has great potentials to break the bottleneck of communication systems. Dr Li and his group have investigated DL in physical communications, wireless resource allocation, and DL-enabled semantic communications.

DL can improve the performance of each individual (traditional) module in a communication system or optimise the whole transmitter or receiver. Therefore, DL in physical layer communications can be categorised into with and without block processing structures, as shown in the figure below. For DL based communication systems with the block structure, they have studied joint channel estimation and signal detection based on a data-driven fully connected deep neural network (FC-DNN) and model-driven DL. For those without the block structure, Dr Li and his group are developing DL based end-to-end transmitter and receiver to maximise the date transmission rate of communication systems. 

Dr Li and his group have also investigated DL for wireless resource allocation. Traditionally, resource allocation in wireless networks is formulated into an optimisation problem. DL can help reduce the complexity in solving the optimisation problem or improve the solution accuracy. Deep reinforcement learning can be directly used in resource allocation to address high mobility and stringent latency requirements in vehicular communications. 

Recently, Dr. Li and his group start to investigate DL-enabled semantic communications, which can significantly improved the efficiency of communication systems beyond Shannon paradigm.

Representative Publications

Overview Articles

1. Z.-J. Qin, H. Ye, G. Y. Li, and B.-H. Juang, “Deep learning in physical layer communications,” IEEE Wireless Communications, vol. 26, no. 2, pp. 93-99, April 2019. (Web of Science highly cited paper and hot paper)
2. H.-T. He, S. Jin, C.-K. Wen, F.-F. Gao, G. Y. Li, and Z.-B. Xu, “Model-driven deep learning for physical layer communications,” IEEE Wireless Communications, vol. 26, no. 5, pp. 77 - 83, October 2019.
3. Liang, H. Ye, G.-D. Yu, and G. Y. Li, “Deep learning based wireless resource allocation with application in vehicular networks,” the Proceedings of the IEEE, vol. 108, no. 2, pp. 341 - 356, February 2020.
4. J.-J. Gao, J.-H. Wang, C.-K. Wen, S. Jin, and G. Y. Li, “Compression and acceleration of neural networks for communications,” IEEE Wireless Communications, vol. 27, no. 4, pp. 110-117, August 2020. 
5. Z.-J. Qin, G. Y. Li, and H. Ye “Federated learning and wireless communications,” IEEE Wireless Communications, vol. 28, no. 5, pp. 134 – 140, October 2021. 
6. Tong and G. Y. Li “Nine critical issues in AI and wireless communications to ensure successful 6G,” IEEE Wireless Communications, vol. 29, no. 4, pp. 140 – 145, August 2022. 
7. M.-Y. Lee, G.-D. Yu, H.-Y. Dai, and G. Y. Li, “Graph neural networks meet wireless communications: motivation, applications, and future directions,” IEEE Wireless Communications, vol. 29, no. 5, pp. 12 – 19, October 2022. 
8. J.-J. Guo, C.-K. Wen, S. Jin, and G. Y. Li, “Overview of deep learning-based CSI feedback in massive MIMO systems,” IEEE Transactions on Communications, vol. 70, no. 12, pp. 8017-8045, December 2022. 
9. P.-W. Jiang, C.-K. Wen, J. Shi, and G. Y. Li, “Wireless semantic transmission via revising modules in conventional communications,” IEEE Wireless Communications, vol. 30, no. 3, pp 28-34, June 2023. 
10. Z.-J. Qin, X.-M. Tao, J.-H. Lu, W. Tong, and G. Y. Li, “Semantic communications: Principles and challenges,”  https://arxiv.org/abs/2201.01389.

Technical Papers in ML for Physical Layer Communications

1. H. Ye, G. Y. Li, and B.-H. F. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114 – 117, February 2018. (once among top 100 documents downloaded of all papers in 300 journals in IEEE Xplore, once the most popular article of all papers in the journal, and in best readings at http://www.comsoc.org/best-readings, source codes)
2. H.-T. He, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmwave massive MIMO systems,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 852 – 855, October 2018. (Web of Science highly cited paper, in best readings at http://www.comsoc.org/best-readings)
3. P.-H. Dong, H. Zhang, G. Y. Li, N. Naderializadeh, and I. S. Gaspar, “Deep CNN based channel estimation for mmwave massive MIMO Systems,” IEEE Journal on Selected Topics in Signal Processing, vol. 13, no. 5, pp. 989 - 1000, September 2019.
4. S. Gao, P.-H. Dong, Z.-W. Pan, G. Y. Li, “Deep learning based channel estimation for massive MIMO with mixed resolution ADCs,” IEEE Communications Letters, vol. 23, no. 11, pp. 1989 – 1993, November 2019.
5. H.-T. He, C.-K. Wen, S. Jin, and G. Y. Li, “Model-driven deep learning for joint MIMO channel estimation and signal detection,” IEEE Transactions on Signal Processing, vol. 68, pp. 1702-1715, March 2020.
6. J.-J. Gao, C.-K. Wen, S. Jin, and G. Y. Li, “Convolutional neural network based multiple-rate compressive sensing for massive MIMO CSI feedback: design, simulation, and analysis,” ,” IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2827-2840, April 2020.
7. H. Ye, L. Liang, G. Y. Li, and B.-H. F. Juang, “Deep learning based end-to-end wireless communication systems with GAN as unknown channel,” IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3133-3143, May 2020. (source codes)
8. Q. Hu, F.-F. Gao, H. Zhang, Shi Jin, and G. Y. Li, “Deep learning for channel estimation: interpretation, performance, and comparison”, IEEE Transactions on Wireless Communications, vol. 20, no. 4, pp. 2398-2412, April 2021.
9. H.-Q. Xie, Z.-J. Qin, G. Y. Li, and B.-H. F. Juang “Deep learning enabled semantic communication systems,” IEEE Transactions on Signal Processing, vol. 69, pp. 2663-2675, 2021. (Web of Science highly cited paper, in best readings at https://www.comsoc.org/publications/best-readings)
10. H. Ye, L. Liang, G. Y. Li, and B.-H. F. Juang, “Deep learning based end-to-end wireless communication systems without pilots,” IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 3, pp. 702 – 714, September 2021.
11. M.-H. Chen, J.-J. Gao, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based implicit CSI feedback in massive MIMO,” IEEE Transactions on Communications, vol. 70, no. 2, pp. 935 – 950, February 2022.
12. J.-B. Gao, C.-J. Zhong, G. Y. Li, and Z.-Y. Zhang, “Deep learning-based channel estimation for massive MIMO with hybrid transceiver,” IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5162 – 5174, July 2022.
13. P.-W. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep source-channel coding for sentence semantic transmission with HARQ,” IEEE Transactions on Communications, vol. 70, no. 8, pp. 5225 – 5240, August 2022.
14. P.-W. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Wireless semantic communications for video conferencing,” IEEE Journal on Selected Areas in Communications. vol. 41, no. 1, pp. 230-244, January 2023.
15. O.-Y. Wang, J.-B. Gao, and G. Y. Li, “Learning to adapt to current environment from past experience: Few-shot online learning in wireless communications,” IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 2, pp. 373-385, April 2023.
16. J.-B. Gao, C.-J. Zhong, G. Y. Li, J. B. Soriaga, and A. Behboodi, “Deep learning-based channel estimation for wideband hybrid mmwave massive MIMO,” IEEE Transactions on Communications, vol. 71, no. 6, pp. 3679-3693, June 2023.
17. J.-C. Shi, W. Zhong, X.-Q. Gao, and G. Y. Li, “Robust WMMSE precoder with deep learning design for massive MIMO,” IEEE Transactions on Communications, vol. 71, no. 7, pp. 3963-3976, July 2023.
18. B.-W. Zhang, H. Sifaou, and G. Y. Li, “CSI-fingerprinting indoor localization via attention-augmented residual convolutional neural network,” IEEE Transactions on Wireless Communications, early access.
19. Z.-Z. Wang, Z.-J. Qin, X.-M. Tao, C.-K. Pan, G.-Y. Liu, and G. Y. Li, “Deep learning enabled semantic communications with speech recognition and synthesis,” IEEE Transactions on Wireless Communications, early access.
20. B.-W. Zhang, Z.-J. Qin, and G. Y. Li, “Semantic communications with variable-length coding for extended reality,” IEEE Journal on Selected Topics in Signal Processing, early access.

Technical Papers in ML for Resource Allocation

1. H. Ye, G. Y. Li, B.-H. F. Juang, “Deep reinforcement learning based resource allocation for V2V communications,” IEEE Transactions on Vehicular Technology, vol. 68, no. 3, March 2019. (Web of Science highly cited paper, source codes)
2. L. Liang, H. Ye, and G. Y. Li, “Spectrum sharing in vehicular networks based on multi-agent reinforcement learning,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2282-2292, October 2019. (source codes)
3. M.-Y. Lee, G.-D. Yu, and G. Y. Li, “Learning to branch: accelerating resource allocation in wireless networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 958 - 970, January 2020.
4. L. Wang, H. Ye, L. Liang, and G. Y. Li, “Learn to compress CSI and allocate resources in vehicular networks,” IEEE Transactions on Communications, vol. 68, no. 6, pp. 3640 – 3653, June 2020. (source codes)
5. R. Liu, M.-Y. Lee, G.-D. Yu, and G. Y. Li, “User association for millimeter-wave networks: a machine learning approach,” IEEE Transactions on Communications, vol. 68, no. 7, pp. 4162 – 4174, July 2020.
6. J.-C. Shi, W.-N. Wang, X.-P. Yi, J.-H. Wang, X.-Q. Gao, Q. Liu, and G. Y. Li, “Learning to compute ergodic rate for multi-cell scheduling massive MIMO”, IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 785-797, February 2021.
7. M.-Y. Lee, G.-D. Yu, and G. Y. Li, “Graph embedding based wireless link scheduling with few training samples”, IEEE Transactions on Wireless Communications, vol. 20, no. 4, pp. 2282-2294, April 2021.
8. L. Yan, Z.-J. Qin, Y.-Z. Li, R. Zhang, and G. Y. Li, “Resource allocation for semantic-aware networks,” IEEE Wireless Communications Letters, vol. 11, no. 7, pp. 1394 – 1398, July 2022.
9. K.-D. Xu, H. Nguyen, and G. Y. Li, “Distributed-training-and-execution multi-agent reinforcement learning for power control in HetNet,” IEEE Transactions on Communications, early access.
10. C.-T. Guo, Z.-C. Li, L. Liang, and G. Y. Li, “Reinforcement learning based dynamic power control for reliable wireless transmission,” to appear in IEEE Internet of Things Journal.

Technical Papers in ML for Federated Learning and Communications

1. Ye, L. Liang, and G. Y. Li, “Decentralized learning with unreliable communications,” IEEE Journal on Selected Topics in Signal Processing, vol. 16, no. 3, pp. 487 – 500, April 2022.
2. S.-L. Zhou and G. Y. Li, “FedGiA: an efficient hybrid algorithm for federated learning,” IEEE Transactions on Signal Processing, vol. 71, pp. 1941-1508, 2023.
3. S-L. Zhou and G. Y. Li, “Federated learning via inexact ADMM,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 9699-9780, August 2023.

Vehicular communication is an enabling technique for intelligent transportation. Dr Li and his group have addressed resource allocation in vehicular communications. Different from traditional resource allocation, strong dynamics caused by high mobility in the vehicular environments poses a serious obstacle to the acquisition of high-quality channel state information (CSI). To deal with the issue, they have investigated the delay impacts of periodic CSI feedback and developed efficient graph-based centralised resource management schemes to meet the diverse quality-of-service (QoS) requirements in vehicular networks. To further reduce signalling overhead, they have taken advantage of recent advances in reinforcement learning (RL) and developed an effective distributed resource allocation scheme. They have showed that the demanding latency and reliability requirements of vehicular communications, which are hard to address using traditional methods, can be explicitly accounted for in the proposed deep RL framework.

Unmanned aerial vehicle (UAV) communications have great potentials in military and civilian application due to many benefits, such as on-demand deployment, high mobility, and low cost. Recently, Dr Li and his group have also investigated some critical issues in UAV communications, including security, access, and application in edge computing.

Representative Publications

Overview Articles

1. L. Liang, H.-X. Peng, G. Y. Li, and X. M. Shen, “Vehicular communications: a physical layer perspective,” IEEE Transactions on Vehicular Technology, vol. 66, no. 12, pp. 10647-10659, December 2017.
2. H.-X. Peng, L. Liang, X.-M. Shen, and G. Y. Li, “Vehicular communications: a network layer perspective,” IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1064-1078, February 2019. (Web of Science highly cited paper)
3. H. Ye, L. Liang, G. Y. Li, L. Lu, J.-B. Kim, and M. Wu, “Machine learning for vehicular networks: Recent advances and application examples,” IEEE Vehicular Technology Magazine. vol. 13, no. 2, pp. 94 – 101, June 2018. (Web of Science highly cited paper)
4. L. Liang, H. Ye, and G. Y. Li, “Towards intelligent vehicular networks: a machine learning framework,” IEEE Internet of Things Journal, vol. 6, no. 1, pp. 124-135, February 2019.  (Web of Science highly cited paper)
5. Y.-W. Liu, Z.-J. Qin, Y.-L. Cai, Y. Gao, G. Y. Li, and A. Nallanathan, “UAV communications based on non-orthogonal multiple access,” IEEE Wireless Communications, vol. 26, no. 1, pp. 52-57, February 2019. (in best readings at http://www.comsoc.org/best-readings)
6. A. Frϕytlog, T. Foss, O. Bakker, G. Jevne, M. A. Haglund, F. Y. Li, J. Oller, and G. Y. Li, “Ultra-low power wake-up radio for 5G IoT,” IEEE Communications Magazine, vol. 27, no. 3, pp. 111 -117, March 2019.
7. Z.-J. Qin, F. Y. Li, G. Y. Li, J. A. McCann, and Q. Ni, “Low-power wide-area networks for sustainable IoT,” IEEE Wireless Communications, vol. 26, no. 3, pp. 140-145, June 2019.
8. L. Liang, H. Ye, G.-D. Yu, and G. Y. Li, “Deep learning based wireless resource allocation with application in vehicular networks,” the Proceedings of the IEEE, vol. 108, no. 2, pp. 341 - 356, February 2020.  (Web of Science highly cited paper)

V2X Communications

1. L. Liang, G. Y. Li, and W. Xu, “Resource allocation for D2D-enabled vehicular communications,” IEEE Transactions on Communications, vol. 65, no. 7, pp. 3186-3197, July 2017. (Web of Science highly cited paper, in best readings at http://www.comsoc.org/best-readings, source codes)
2. L. Liang, G. Y. Li, and W. Xu, “Corrections to “Resource allocation for D2D-enabled vehicle communications”,” IEEE Transactions on Communications, vol. 65, no. 9, pp. 4096 – 4098, September 2017.
3. L. Liang J.-B. Kim, S. C. Jha, K. Sivanesan, and G. Y. Li, “Spectrum and power allocation for vehicular communications with CSI latency,” IEEE Wireless Communications Letters, vol. 6, no. 4, pp. 458-461, August 2017.
4. H. Ye, G. Y. Li, B.-H. F. Juang, “Deep reinforcement learning based resource allocation for V2V communications,” IEEE Transactions on Vehicular Technology, vol. 68, no. 3, March 2019. (Web of Science highly cited paper, source codes)
5. L. Liang, S.-J. Xie, G. Y. Li, Z. Ding, and X.-X. Yu, “Graph-based resource sharing for vehicular communication,” IEEE Transactions on Wireless Communications, vol. 17, no. 7, pp. 4579 – 4592, July 2018.
6. C.-T. Guo, L. Liang, and G. Y. Li, “Resource allocation for low-latency vehicular communications: an effective capacity perspective,” IEEE Journal on Selected Areas in Communications, , vol. 37, no. 4, pp. 905-917, April 2019.
7. L. Liang, H. Ye, and G. Y. Li, “Spectrum sharing in vehicular networks based on multi-agent reinforcement learning,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2282-2292, October 2019. (source codes)
8. C.-T. Guo, L. Liang, and G. Y. Li, “Resource allocation for vehicular communications with low latency and high reliability,” IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 3887 - 3902, August 2019.
9. L. Wang, H. Ye, L. Liang, and G. Y. Li, “Learn to compress CSI and allocate resources in vehicular networks,” IEEE Transactions on Communications, vol. 68, no. 6, pp. 3640 – 3653, June 2020.
10. L. Liang, H. Ye, G.-D. Yu, and G. Y. Li, “Deep learning based wireless resource allocation with application in vehicular networks,” the Proceedings of the IEEE, vol. 108, no. 2, pp. 341 - 356, February 2020.  (Web of Science highly cited paper)

UAV Communications

1. Y.-L. Cai, F.-Y. Cui, Q.-J. Shi, M.-J. Zhao, and G. Y. Li, “Dual-UAV enabled secure communications: joint trajectory design and user scheduling,” IEEE Journal on Selected Areas in Communications, vol. 36, no. 9, pp. 1972 – 1985, September 2018.
2. Q.-Y. Hu, Y.-L. Cai, G.-D. Yu, Z.-J. Qin, M.-J. Zhao, and G. Y. Li, “Joint computation offloading and trajectory design for UAV-enabled mobile edge computing systems,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1897-1892, April 2019.
3. F.-Y Cui, Y.-L. Cai, Z.-J. Qin, Q.-J. Shi, M.-J. Zhao, and G. Y. Li, “Multiple access for mobile-UAV enabled networks: joint trajectory design and resource allocation,” IEEE Transactions on Communications, vol. 67, no. 7, pp. 4980 - 4994, July 2019.
4. Q.-Y. Hu, Y.-L. Cai, A. Liu, G.-D. Yu, and G. Y. Li, “Low-complexity joint resource allocation and trajectory design for UAV-aided relay networks with segmented ray-tracing channel model,” IEEE Transactions on Wireless Communications, vol. 19, no. 9, pp. 6179 -6195, September 2020.
5. Y.-W. Liu, Z.-J. Qin, Y.-L. Cai, Y. Gao, G. Y. Li, and A. Nallanathan, “UAV communications based on non-orthogonal multiple access,” IEEE Wireless Communications, vol. 26, no. 1, pp. 52-57, February 2019. (in best readings at http://www.comsoc.org/best-readings, Web of Science highly cited paper)

Millimeter-wave (mmWave) and Terahertz (THz) communications have been envisioned as a key technology for future wireless systems. Dr Li and his group have investigated design guides on signal transmission and processing for mmWave and THz communications. For the higher-mmWave and THz bands, i.e., over 60 GHz, they have proposed the array-of-subarrays architecture and demonstrated its benefits in system power consumption, spectral efficiency, and energy efficiency. They have also studied subarray-based coordinated beamforming training with multi-resolution time-delay codebooks for the mmWave and THz systems. For mmWave communications, they have analysed fundamental performance limits and investigated hybrid transceiver design and optimisation.

Representative Publications

1. C. Lin and G. Y. Li, “Distance-aware multi-carrier indoor TeraHertz communications with antenna array selection” Proc. IEEE 2014 International Symposium on Personal, Indoor and Mobile Radio Communications, Washington, DC, September 2014. (won the best paper award)
2. C. Lin and G. Y. Li, “Indoor Terahertz communications: How many antenna arrays are needed?” IEEE Transactions on Wireless Communications, vol. 14, no. 6, pp. 3097-3107, June 2015.
3. C. Lin and G. Y. Li, “Adaptive beamforming and resource allocation for distance-aware indoor Terahertz communications,” IEEE Transactions on Communications, vol. 63, no. 8, pp. 2985-2995, August 2015.
4. C. Lin and G. Y. Li, “Energy-efficient design of antenna arrays for indoor mmWave and sub-Thz communications,” IEEE Transactions on Wireless Communications, vol. 15, no. 7, pp. 4660-4672, July 2016.
5. C. Lin and G. Y. Li, “Terahertz communications: array-of-subarray solution,” IEEE Communications Magazine, vol. 54, no. 12, pp. 124-131, December 2016.
6. L. You, X.-Q. Gao, G. Y. Li, X.-G. Xia, and N. Ma, “BDMA for millimeter-wave/Terahertz massive MIMO transmission with per-beam synchronization,” IEEE Journal of Selected Areas in Communications, vol. 35, no. 7, pp. 1550-1563, July 2017.
7. C. Lin, G. Y. Li, and L. Wang, “Subarray-based coordinated beamforming training for mmWave and sub-THz communications,” IEEE Journal of Selected Areas in Communications, vol. 35, no. 9, pp. 2155 – 2126, September 2017.
8. X.-F. Zhai, Y.-L. Cai, Q.-J. Shi, M.-J. Zhao, G. Y. Li, and B. Champagne, “Joint transceiver design with antenna selection for large-scale MU-MIMO millimeter-wave systems,” IEEE Journal of Selected Areas in Communications, vol. 35, no. 9, pp. 2085 – 2096, September 2017.
9. Y.-L. Cai, C.-Z. Zhao, Q.-J. Shi, G. Y. Li, and B. Champagne, “Joint beamforming and jamming for mmWave information surveillance systems,” IEEE Journal on Selected Areas in Communications, vol. 36, no. 7, pp. 1410 – 1425, July 2018.
10. B.-L. Wang, F.-F. Gao, S. Jin, H. Lin, and G. Y. Li, “Spatial- and frequency-wideband effects in massive MIMO,” IEEE Transactions on Signal Processing, vol. 66, no. 13, pp. 3393 – 3406, July 2018. (Web of Science highly cited paper)
11. H.-T. He, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmwave massive MIMO systems,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 852 – 855, October 2018.
12. B.-L. Wang, F.-F. Gao, S. Jin, G. Y. Li, S. Sun, and T. S. Rappaport, “Spatial-wideband effect in massive MIMO with application to mmWave systems,” IEEE Communications Magazine, vol. 56, no. 12, pp. 134 – 141, December 2018.
13. B.-L. Wang, F.-F. Gao, G. Y. Li, S. Jin, and H. Lin, “Wideband channel estimation for mmwave massive MIMO systems with beam-squint effect,” Proc. IEEE Global Communications Conference, Abu Dhabi, UAE, December 2018. (won the best paper award)
14. J.-D. Xu, W. Xu, H. Zhang, G. Y. Li, and X.-H. You, “Performance analysis of multi-cell millimeter wave MIMO network with low-precision ADCs,” IEEE Transactions on Communications, vol. 67, no. 1, pp. 301-317, January 2019.
15. M. A. ElMossallamy, M. Pan, R. Jäntti, K. G. Seddik, G. Y. Li, Z. Han, “Noncoherent backscatter communications over ambient OFDM signals,” IEEE Transactions on Communications, vol. 67, no. 5, pp. 3597-3611, May 2019.
16. C.-W. Xing, X. Zhao, W. Xu, X.-D. Dong, and G. Y. Li, “A framework on hybrid MIMO transceiver design based on matrix-monotonic optimization,” IEEE Transactions on Signal Processing, vol. 67, no. 13, pp. 3531-3546, July 2019.
17. S. Gao, P.-H. Dong, Z.-W. Pan, G. Y. Li, “Deep learning based channel estimation for massive MIMO with mixed resolution ADCs,” IEEE Communications Letters, vol. 23, no. 11, pp. 1989 – 1993, November 2019.
18. P.-H. Dong, H. Zhang, G. Y. Li, N. Naderializadeh, and I. S. Gaspar, “Deep CNN based channel estimation for mmwave massive MIMO Systems,” IEEE Journal on Selected Topics in Signal Processing, vol. 13, no. 5, pp. 989 - 1000, September 2019.
19. B.-L. Wang, X. Li, F.-F. Gao, and G. Y. Li, “Power leakage elimination for wideband mmwave massive MIMO: An energy focusing window approach,” IEEE Transactions on Signal Processing, vol. 67, no. 21, pp. 5479 - 5494, November 2019.
20. B.-L. Wang, M.-N. Jian, F.-F. Gao, G. Y. Li, S. Jin, and H. Lin, “Beam squint and channel estimation for millimeter-wave massive MIMO-OFDM systems,” IEEE Transactions on Signal Processing, vol. 67, no. 23, pp. 5893 – 5908, December 2019.
21. K.-J. Chen, C.-H. Qi, and G. Y. Li, “Two-step codeword design for mmwave massive MIMO systems with quantized phase shifters,” IEEE Transactions in Signal Processing, vol. 68, pp. 170 - 180, January 2020.
22. W.-Y. Ma, C.-H. Qi, and G. Y. Li, “High-resolution channel estimation for frequency-selective mmWave massive MIMO systems,” IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3517-3529, May 2020.
23. C.-H. Qi, P.-H. Dong, W.-Y. Ma, H. Zhang, Z.-C. Zhang, and G. Y. Li, “Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches,” Science China - Information Science, vol. 64, no. 8, August 2021.
24. C.-H. Qi, Q. Liu, X.-H. Yu, and G. Y. Li, “Hybrid precoding for mixture use of phase shifters and switches in mmWave massive MIMO,” IEEE Transactions on Communications, vol. 70, no. 6, pp. 4121 – 4133, June 2022.
25. J.-B. Gao, C.-J. Zhong, G. Y. Li, J. B. Soriaga, and A. Behboodi, “Deep learning-based channel es-timation for wideband hybrid mmwave massive MIMO,” IEEE Transactions on Communications, vol. 71, no. 6, pp. 3679-3693, June 2023.

The spirit of cooperation can also be extended from multiple users to multiple networks. Cognitive radio can be configured to dynamically use the best radio channels to increase the spectrum utilization efficiency while avoiding user interference and congestion, which is enabled by the cooperation of the licensed and the unlicensed networks to achieve high spectral efficiency, thus becoming a critical technology to mitigate the spectrum scarcity in wireless networks. Dr. Geoffrey Ye Li is one of the earliest researchers in the field and has developed cooperative techniques to detect vacant licensed spectrum bands accurately and quickly and to assign spectrum bands and transmission power to different users to optimize performance while minimizing interference to the licensed users. His outstanding work in the area won the 2014 IEEE VTS Jack Neubauer Memorial Award.

Today, any communication between mobile devices must involve a base station, which can be inefficient when the mobile users are close to each other.  Future cellular systems will support device-to-device (D2D) communications, the smallest cognitive radio network, which can double the spectral efficiency for closely spaced devices. He found an optimal approach to identify when two mobile users should use D2D communications and to allocate frequency and transmission power, revealed potential hop gain, proximity gain, and reuse gain incurred by D2D communications, and investigated how to use them to optimize the performance of both cellular and D2D users. Dr. Li’s influential work solved major challenges in D2D communications.  Remarkably, Dr. Li’s work in D2D communications was highlighted in the IEEE ComSoc Technology News (http://www.comsoc.org/ctn/) in December 2014.

Representative Publications

1. G. Ganesan and Y. (G.) Li, “Cooperative spectrum sensing in cognitive radio: Part I: two user networks,” IEEE Transactions on Wireless Communications, vol. 6, pp. 2204-2213, June 2007. (with over 1,000 citations and in best readings at http://www.comsoc.org/best-readings)
2. G. Ganesan and Y. (G.) Li, “Cooperative spectrum sensing in cognitive radio: Part II: multiuser networks,” IEEE Transactions on Wireless Communications, vol. 6, pp. 2214-2222, June 2007. (with over 1,000 citations and in best readings at http://www.comsoc.org/best-readings)
3. G. Ganesan, Y. (G.) Li, B. Bing, and S.-Q. Li, “Spatial-temporal sensing in cognitive radio networks,” IEEE Journal on Selected Areas in Communications, vol. 26, pp. 5 – 12, January 2008.
4. J. Ma, G.-D. Zhao, and Y. (G.) Li, “Soft combination and detection for cooperative spectrum sensing in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 11, pp. 4502 – 4507, November 2008. (with over 1,000 citations and Web of Science highly cited paper)
5. J. Ma, G. Y. Li, and B.-H. Juang, “Signal processing in cognitive radio,” The Proceedings of IEEE, vol. 97, no. 5, pp. 805 – 823, May 2009. (Web of Science highly cited paper)
6. L.-Y. Li, X.-W. Zhou, H.-B. Xu, and G. Y. Li, D.-D. Wang, and A. C. K. Soong, “Simplified relay selection and power allocation in cooperative cognitive radio system,” IEEE Transactions on Wireless Communications, vol. 10, no. 1, pp. 33 – 36, January 2011. (Web of Science highly cited paper)
7. Y.-C. Liang, K.-C. Chen, G. Y. Li, and P. Mähönen, “Cognitive radio networking and communications: an overview,” IEEE Transactions on Vehicular Technology, vol. 60, no. 7, pp. 3386 – 3407, September 2011. (Web of Science highly cited paper, among top 100 documents downloaded of all papers in 300 journals in IEEE Xplore, and won 2014 IEEE VTS Jack Neubauer Memorial Award)
8. D.-Q. Feng, L. Lu, Y. Yuan-Wu, G. Y. Li, G. Feng, and S.-Q. Li, “Device-to-device communications in underlying cellular networks,” IEEE Transactions on Communications, vol. 61, no. 8, pp. 3541 – 3551, August 2013. (Web of Science highly cited paper, highlighted in IEEE ComSoc Technology News in December 2014 at http://www.comsoc.org/ctn/archive, in best readings at http:// www.comsoc.org/best-readings, and among top 100 documents downloaded of all papers in 300 journals in IEEE Xplore)
9. D.-Q. Feng, L. Lu, Y. Yuan-Wu, G. Y. Li, S.-Q. Li, and G. Feng, “Device-to-device communications in cellular networks,” IEEE Communications Magazine, vol. 52, no. 4, pp. 49 – 55, April 2014. (Web of Science highly cited paper, in best readings at http://www.comsoc.org/best-readings, and among top 100 documents downloaded of all papers in 300 journals in IEEE Xplore)
10. G.-D. Yu, L.-K. Xu, D.-Q. Feng, R. Yin, G. Y. Li, and Y.-K. Jiang, “Joint mode selection and resource allocation for device-to-device communications,” IEEE Transactions on Communications, vol. 62, no. 11, pp. 3814-3824, November 2014. (Web of Science highly cited paper and in best readings at http://www.comsoc.org/best-readings)
11. R. Yin, G.-D. Yu, H.-Z. Zhang, and G. Y. Li, “Pricing-based interference coordination for D2D communications in cellular networks,” IEEE Transactions on Wireless Communications, vol. 14, no. 3, pp. 1519-1532, March 2015. (in best readings at http://www.comsoc.org/best-readings)
12. L. Lu, D.-W. He, G. Y. Li, and X.-X. Yu, “Graph-based robust resource allocation for cognitive radio networks,” IEEE Transactions on Signal Processing, vol. 63, no. 14, pp. 3825-3836, July 2015.
13. D.-Q. Feng, G.-D. Yu, C. Xiong, Y. Yuan-Wu, G. Y. Li, G. Feng, and S.-Q. Li, “Mode switching for energy-efficient device-to-device communications in cellular networks,” IEEE Transactions on Wireless Communications, vol. 14, no. 12, pp. 6693-7002, December 2015.
14. L. Liang, G. Y. Li, and W. Xu, “Resource allocation for D2D-enabled vehicular communications,” IEEE Transactions on Communications, vol. 65, no. 7, pp. 3186-3197, July 2017. (in best readings at http://www.comsoc.org/best-readings)
15. Z.-J. Qin, J.-C. Fan, Y.-W. Liu, Y. Gao, and G. Y. Li, “Sparse representation for wireless communications, a compressive sensing approach” IEEE Signal Processing Magazine, vol. 35, no. 3, pp. 40 – 58, May 2018.
16. Z.-J. Qin, X.-W. Zhou, L. Zhang, Y. Gao, Y.-C. Liang, and G. Y. Li, “20 years of evolution from cognitive to intelligent communications,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 6-20, March 2020.
17. Y.-C. Liang, Q.-Q. Zhang, E. G. Larsson, and G. Y. Li, “Symbiotic radio: Cognitive backscattering communications for future wireless networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 4, no. 6, pp. 1242-1255, December 2020.

At the end of the last century, wireless networks were designed based on the seven-layer model as in the wired networks, which ignored the new challenges and opportunities in wireless communications. Dr. Geoffrey Li pioneered the use of resource cooperation to break through barriers and exploit new opportunities.  Dr. Li established a theoretical framework and developed practical algorithms for joint media access control (MAC) and physical layer optimisation according to each user’s channel condition at different locations and times so that a high quality-of-service (QoS) can be provided with limited wireless resources. In particular, Dr. Li innovatively investigated packet scheduling algorithms for streaming of delay-sensitive multimedia contents in OFDMA-based cellular data networks. He not only developed a novel multi-channel packet scheduling approach, max-delay-utility (MDU) packet scheduling, based on utility functions in terms of queuing delay, but also analysed its stability based on the concept of maximum stability region (MSR) to reveal the relationship between the scheduling algorithms and queuing stability. With the utility-maximisation mechanism, MDU scheduling enables the network to achieve the right balance between capacity enlargement and QoS guarantee according to the channel condition and the level of network congestion. The novel stability analysis can be applied for stationary systems and a much wider range of scheduling algorithms compared to the existing related work. It will facilitate the design of cross-layer scheduling algorithms and QoS provisioning solutions for advanced wireless broadband networks. As a result, his paper based on the above results won the 2010 IEEE ComSoc. Stephen O. Rice Prize Paper Award in the field of communications theory.

The exponentially growing data traffic and the requirement for ubiquitous access have triggered a dramatic expansion of network infrastructure and a fast escalation of energy demands. With the help of multi-user cooperation and cross-layer optimisation, Dr. Li pioneered spectral- and energy-efficiency trade-off in wireless networks and developed a unified framework for green communications with four fundamental trade-offs through multi-user cooperation. The practical methodology and design principles based on his seminal framework have been adopted to deliver valuable solutions for both existing systems and next generation systems. His landmark research in this area was recognised by the 2017 IEEE ComSoc Award for Advances in Communication for opening a new line of work in the previous 15 years, the IEEE ComSoc TCGCC Distinguished Technical Achievement Recognition Award in 2017 and a Best Paper Award in 2019.

Representative Publications: 

1. Z. Jiang, Y. Ge, and Y. (G.) Li, “Max-utility wireless resource management for best effort traffic,” IEEE Transactions on Wireless Communications, vol. 4, no. 1, pp. 100-111, January 2005.
2. G.-C. Song and Y. (G.) Li, “Cross-layer optimization for OFDM wireless networks – Part I: theoretical framework,” IEEE Transactions on Wireless Communications, vol. 4, no. 2, pp. 614 – 624, March 2005.
3. G.-C. Song and Y. (G.) Li, “Cross-layer optimization for OFDM wireless networks – Part II: algorithm development,” IEEE Transactions on Wireless Communications, vol. 4, no. 2, pp. 625 – 634, March 2005.
4. G.-C. Song and Y. (G.) Li, “Utility-based resource allocation and scheduling in OFDM-based wireless networks,” IEEE Communications Magazine, vol. 43, no. 12, pp. 127 - 135, December 2005.
5. G. Song, Y. (G.) Li, and L. J. Cimini, Jr., “Joint channel- and queue-aware scheduling for multiuser diversity in wireless multicarrier networks,” IEEE Transactions on Communications, vol. 57, no. 7, pp. 2109 – 2121, July 2009. (won 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award)
6. G.-W. Miao, N. Himayat, and G. Y. Li, “Energy-efficient link adaptation in frequency-selective channels,” IEEE Transactions on Communications, vol. 58, no. 4 pp. 545 – 554, February 2010. (Web of Science highly cited paper)
7. G.-W. Miao, N. Himayat, G. Y. Li, S. Talwar, “Distributed interference-aware energy-efficient power optimization,” IEEE Transactions on Wireless Communications, vol. 10, no. 4, pp. 1323 – 1333, April 2011. (Web of Science highly cited paper)
8. Y. Chen, S.-Q. Zhang, S.-G. Xu, and G. Y. Li, “Fundamental tradeoffs on green wireless networks,” IEEE Communications Magazine, vol. 49, no. 6, pp. 30 – 37, June 2011. (Web of Science highly cited paper, with over 1,000 citations, in best readings at http://www.comsoc.org/best-readings, among top 100 documents downloaded of all papers in 300 journals in IEEE Xplore, and won 2017 IEEE ComSoc Award for Advances in Communication)
9. C. Xiong, G. Y. Li, S.-Q. Zhang, Y. Chen, and S.-G. Xu, “Energy- and spectral-efficient tradeoff in downlink OFDMA networks,” IEEE Transactions on Wireless Communications, vol. 10, no. 11, pp. 3874 – 3886, November 2011. (Web of Science highly cited paper)
10. G. Y. Li, Z.-K. Xu, C. Xiong, C.-Y. Yang, S.-Q. Zhang, Y. Chen, and S.-G. Xu, “Energy-efficient wireless communications: tutorial, survey, and open issues,” IEEE Wireless Communications, vol. 18, no. 6, pp. 28 – 35, December 2011. (Web of Science highly cited paper, in best readings at http://www.comsoc.org/best-readings, and among top 100 documents downloaded of all papers in 300 journals in IEEE Xplore)
11. G.-W. Miao, N. Himayat, G. Y. Li, and S. Talwar, “Low-complexity energy-efficient scheduling for uplink OFDMA,” IEEE Transactions on Communications, vol. 60, no. 1, pp. 112 – 120, January 2012. (Web of Science highly cited paper)
12. C. Xiong, G. Y. Li, S.-Q. Zhang, Y. Chen, and S.-G. Xu, “Energy-efficient resource allocation in OFDMA networks,” IEEE Transactions on Communications, vol. 60, no. 12, pp. 3767 – 3778, December 2012. (Web of Science highly cited paper)
13. D.-Q. Feng, C.-Z. Jiang, G.-B. Lim, L. J. Cimini, Jr., G. Feng, and G. Y. Li, “A survey of energy efficient wireless communications,” IEEE Communications Surveys and Tutorials, vol. 15, no. 1, pp. 167-178, the 1^st Quarter 2013. (Web of Science highly cited paper, in best readings at http://www.comsoc.org/best-readings, and among top 100 documents downloaded of all papers in 300 journals in IEEE Xplore)
14. Q.-Q. Wu, G. Y. Li, W. Chen, D. W. K. Ng, and R. Schober, “An overview of sustainable green 5G networks,” IEEE Wireless Communications, vol. 24, no. 4, pp. 72 – 80, August 2017. (Web of Science highly cited paper)
15. S.-Q. Zhang, S.-G. Xu, and G. Y. Li, “Fundamental green tradeoffs: progresses, challenges and impacts on 5G networks,” IEEE Communications Surveys and Tutorials, vol. 19, no. 1, pp. 33-56, First Quarter, 2017. (Web of Science highly cited paper)
16. S.-Q. Zhang, S.-G. Xu, G. Y. Li, and E. Ayanoglu, “First 20 years of green radios,” IEEE Transactions on Green Communications and Networks, vol. 4, no. 1, pp.1-15, March 2020.

The success of both Internet access and wireless communications has stimulated the growing interest in high-speed wireless systems, with the ultimate goal of approaching the theoretical channel capability. Dr. Li, with Dr. Cimini, Jr., had used orthogonal frequency division multiplexing (OFDM) to break through this barrier in the middle of 1990’s. His robust channel estimator for OFDM systems has been used in a fourth-generation cellular architecture design and prototype development at AT&T Labs. He also developed a general framework to quantify the bounds for inter-carrier interference (ICI) to facilitate the design of OFDM for high mobility wireless systems. The methodology has been used as the optimum norm by other researchers and recommended by the IEEE 802.16 standard. Due to his landmark contribution in the area of OFDM for wireless communications, he received the 2013 IEEE VTS James Evans Avant Garde Award and 2024 IEEE Eric E. Sumner Award.

In the area of multiple-input multiple-output (MIMO) wireless systems, he was the first to investigate MIMO-OFDM and made influential achievements in interference suppression, pilot design, and channel estimation. Due to its substantial impact, his paper in massive MIMO won the 2017 IEEE SPS Donald G. Fink Overview Paper Award. Recently, he has revealed spatial- and frequency-wideband effects in mmWave massive MIMO, which have been ignored by many researchers and significantly degrade system performance under certain situations. Dr. Li and his group provided effective approaches to address the issues.

Representative Publications

1. Y. (G.) Li, L. J. Cimini, Jr., and N. R. Sollenberger, “Robust channel estimation for OFDM systems with rapid dispersive fading channels,” IEEE Transactions on Communications, vol. 46, pp. 902-915, July 1998. (with over 1,000 citations) (US Patent No.: 6,327,314)
2. Y. (G.) Li and N. R. Sollenberger, “Adaptive antenna arrays for OFDM systems with co-channel interference,” IEEE Transactions on Communications, vol. 47, pp. 217-229, February 1999. (US Patent No.: 5,973,642)
3. Y. (G.) Li, N. Seshadri, and S. Ariyavisitakul, “Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels,” IEEE Journal on Selected Areas in Communications, vol. 17, pp. 461-471, March 1999. (with over 1,000 citations) (US Patent No.: 7,012,966/7,127,001/7,443,919/ 7,756,212)
4. Y. (G.) Li, J. Chuang, and N. R. Sollenberger, “Transmitter diversity for OFDM systems and its impact on high-rate data wireless networks,” IEEE Journal on Selected Areas in Communications, vol. 17, pp. 1233-1243, July 1999. (US Patent No.: 7,103,115/7,305,051)
5. J. Chuang, L. J. Cimini, Jr., Y. (G.) Li, B. McNair, N. Sollenberger, H. Zhao, L. Lin, and M. Suzuki, “High-speed wireless data access based on combining EDGE with wideband OFDM,” IEEE Communications Magazine, vol. 37, pp. 92-98, November 1999. (US Patent No.: 6,477,210/7,099,413/7,460,620/7,940,852)
6. Y. (G.) Li, “Pilot-symbol-aided channel estimation for OFDM in wireless systems,’’ IEEE Transactions on Vehicular Technology, vol. 49, pp. 1207-1215, July 2000. (US Patent No.: 6,654,429/7,292,651)
7. Y. (G.) Li and L. J. Cimini, Jr., “Bounds on the interchannel interference of OFDM in time-varying channels,’’ IEEE Transactions on Communications, vol. 49, pp. 401-404, March 2001.
8. R. S. Blum, Y. (G.) Li, J. H. Winters, and Q. Yan, “Improved space-time coding for MIMO-OFDM wireless communications,” IEEE Transactions on Communications, vol. 49, pp. 1873-1878, November 2001.
9. Y. (G.) Li, “Simplified channel estimation for OFDM systems with multiple transmit antennas,’’ IEEE Transactions on Wireless Communications, vol. 1, pp. 67-75, January 2002. (US Patent No.: 7,583,761/8,724,553/8,724,725. 9,654,309)
10. Y. (G.) Li, J. H. Winters, and N. R. Sollenberger, “MIMO-OFDM for wireless communications: signal detection with enhanced channel estimation,” IEEE Transactions on Communications, vol. 50, pp. 1471-1477, September 2002. (once among top 100 documents downloaded of all papers in 300 journals in IEEE Xplore) (US Patent No.: 7,068,628/7,643,404/8,121,022/9,426,009)
11. G. L. Stuber, J. Barry, S. McLaughlin, Y. (G.) Li, M. A. Ingram, and T. Pratt, “Broadband MIMO-OFDM wireless communications,” The Proceedings of IEEE, vol. 92, pp.271-294, February 2004. (with over 1,000 citations)
12. T. Hwang, C.-Y. Yang, G. Wu, S.-Q. Li, and G. Y. Li, “OFDM and its wireless applications: a survey,” IEEE Transactions on Vehicular Technology, vol. 58, no. 4, pp. 1673 – 1694, May 2009. (Web of Science highly cited paper)
13. L. Lu, G. Y. Li, L. A. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: benefits and challenges,” IEEE Journal on Selected Topics in Signal Processing, vol. 8, no. 5, pp. 742 – 758, October 2014. (over with 1,000 citations, in best readings at http://www.comsoc.org/best-readings, once among top 100 documents downloaded of all papers in 300 journals and the most popular article of all papers in the jorunal in IEEE Xplore, and won 2017 IEEE SPS Donald G. Fink Overview Paper Award )
14. Y.-L. Cai, Z.-J. Qin, F.-Y. Cui, G. Y. Li, and J. A. McCann, “Modulation and multiple access for 5G networks,” IEEE Communications Surveys and Tutorials, vol. 20, no. 1, pp. 629-646, First Quarter, 2018. (Web of Science highly cited paper)
15. Ye, G. Y. Li, and B.-H. F. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114 – 117, February 2018. (Web of Science highly cited paper, in best readings at http://www.comsoc.org/best-readings, once among top 100 documents downloaded of all papers in 300 journals in IEEE Xplore, once the most popular article of all papers in the journal)
16. B.-L. Wang, F.-F. Gao, S. Jin, H. Lin, and G. Y. Li, “Spatial- and frequency-wideband effects in massive MIMO,” IEEE Transactions on Signal Processing, vol. 66, no. 13, pp. 3393 – 3406, July 2018. (Web of Science highly cited paper)
17. H.-T. He, C.-K. Wen, S. Jin, and G. Y. Li, “Model-driven deep learning for MIMO detection,” IEEE Transactions on Signal Processing, vol. 68, pp. 1702-1715, March 2020.
18. B.-L. Wang, M.-N. Jian, F.-F. Gao, G. Y. Li, and H. Lin, “Beam squint and channel estimation for millimeter-wave massive MIMO-OFDM systems,” IEEE Transactions on Signal Processing, vol. 67, no. 23, pp. 5893 – 5908, December 2019. 
19. J.-J. Gao, C.-K. Wen, S. Jin, and G. Y. Li, “Convolutional neural network based multiple-rate compressive sensing for massive MIMO CSI feedback: design, simulation, and analysis,” IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2827-2840, April 2020. (Web of Science highly cited paper)
20. F.-F. Gao, B.-L. Wang, C.-W. Xing, J.-P. An, and G. Y. Li, “Wideband beamforming for hybrid massive MIMO terahertz communications,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 6, pp. 1725-1740, June 2021.
21. P.-W. Jiang, S. Jin, C.-K. Wen, and G. Y. Li, “Dual CNN based channel estimation for MIMO-OFDM systems,” IEEE Transactions on Communications, vol. 69, no. 9, pp. 5859 – 5872, September 2021.
22. P.-W. Jiang, T.-Q. Wang, B. Han, X.-X. Gao, J. Zhang, C.-K. Wen, S. Jin, and G. Y. Li, “AI-aided online adaptive OFDM receiver: Design and experimental results,” IEEE Transactions on Wireless Communications, vol. 20, no. 11, pp. 7756 – 7768, November 2021.
23. J.-B. Gao, M. Hu, C.-J. Zhong, G. Y. Li, and Z.-Y. Zhang, “An attention-aided deep learning framework for massive MIMO channel estimation,” IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1823 – 1835, March 2022.
24. J.-J. Guo, C.-K. Wen, S. Jin, and G. Y. Li, “Overview of deep learning-based CSI feedback in massive MIMO systems,” IEEE Transactions on Communications, vol. 70, no. 12, pp. 8017-8045, December 2022.
25. Hu, F.-F. Gao, H. Zhang, G. Y. Li, and Z.-B. Xu, “Understanding deep MIMO detection,” IEEE Transactions on Wireless Communications, early access.

In early 1990’s, Dr. Li, with his PhD adviser, Dr. Zhi Ding, investigated blind system identification and equalisation. Among his many outstanding contributions in blind signal processing, Dr. Li pioneered an adaptive blind algorithm for fractionally-spaced equalisation (FSE). He revealed global convergence of FSE using the constant modulus algorithm (CMA) under a length-and-zero condition, where the zero condition relates to the excessive bandwidth of the channel and the length condition indicates the length of the FSE. The FSE-CMA is also with low insensitivity to timing phase error. Moreover, the CMA-FSE can be applied to spatial and spectral channel diversities. He also extended blind algorithms to space-time processing. He defined cost- and length-dependent local minima and static and dynamic convergence, which categorise and reveal the properties of the ill convergence of different blind algorithms. His book, with Prof. Zhi Ding, has been the first one that systematically describes research results in the area of blind equalisation and identification.

Selected Publications

1. Y. Li and Z. Ding, “ARMA system identification based on second order cyclostationarity,” IEEE Transactions on Signal Processing, vol. 42, pp. 3483-3494, December 1994
2. Y. Li and Z. Ding, “Convergence analysis of finite length blind adaptive equalizers,” IEEE Transactions on Signal Processing, vol. 43, pp. 2120-2129, September 1995.
3. Y. Li and Z. Ding, “Global convergence of fractionally spaced Godard adaptive equalizers,” IEEE Transactions on Signal Processing, vol. 44, pp. 818-826, April 1996.
4. Y. Li, K. J. R. Liu, and Z. Ding, “Length and cost dependent local minima of blind channel equalizers,” IEEE Transactions on Signal Processing, vol. 44, pp. 2726-2735, November 1996.
5. Y. Li and K. J. R. Liu, “Static and dynamic convergence of adaptive blind equalizers,” IEEE Transactions on Signal Processing, vol. 44, pp. 2736-2745, November 1996.
6. Y. (G.) Li and K. J. R. Liu, “Adaptive blind multi-channel equalization for multiple signal separation,” IEEE Transactions on Information Theory, vol. 44, pp. 2864-2876, November 1998.
7. Z. Ding and Y. (G.) Li, Blind Equalization and Identification, Marcel Dekker, Inc., New York, December 2000. (418 pp.)