You can also find my publications on Google Scholar.

My research centers on AI-native wireless communications, with a long-standing research foundation in channel intelligence for advanced MIMO and current interests in efficient, adaptive and trustworthy wireless AI, as well as wireless foundation models and environment intelligence.

Surveys, Perspectives, and Tutorials

[1] J. Guo, Y. Cui, S. Jin, and J. Zhang, “Large AI Models for Wireless Physical Layer,” IEEE Communications Magazine, vol. 64, no. 5, pp. 148–155, May 2026.

This article reviews large AI models for wireless physical-layer communications and organizes existing approaches into two paradigms: leveraging pre-trained large models and developing wireless-native large models.

[2] J. Guo, C.-K. Wen, S. Jin, and X. Li, “AI for CSI Feedback Enhancement in 5G-Advanced,” IEEE Wireless Communications, vol. 31, no. 3, pp. 169–176, Jun. 2024.

This article reviews AI-based CSI feedback enhancement from a 3GPP standardization perspective, including evaluation methodology, deployment challenges, and protocol evolution.

ESI Highly Cited Paper

[3] 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, Dec. 2022.

A comprehensive overview of deep learning-based CSI feedback, covering architectures, quantization, multi-rate feedback, practical deployment, and future research directions.

Invited Paper; ESI Highly Cited Paper

[4] Q. Xue, J. Guo, B. Zhou, Y. Xu, Z. Li, and S. Ma, “AI/ML for Beam Management in 5G-Advanced: A Standardization Perspective,” IEEE Vehicular Technology Magazine, vol. 19, no. 4, pp. 64–72, Dec. 2024.

This article reviews AI/ML-enabled beam management with emphasis on 5G-Advanced standardization, evaluation, and deployment.

[5] J. Guo, C.-K. Wen, and S. Jin, “AI-Native Air Interface,” in Fundamentals of 6G Communications and Networking, Springer, pp. 143–163, 2024.

This book chapter introduces AI-native air-interface design and discusses end-to-end communications, single-module enhancement, and practical challenges for integrating AI into future wireless systems.

Research Direction 1: Channel Intelligence for Advanced MIMO

My work in this direction investigates how advanced MIMO systems can efficiently acquire, represent, predict, and exploit channel knowledge under limited pilot, feedback, signaling, and computational resources.

CSI Representation and Feedback

[1] J. Guo, 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, Apr. 2020.

This work develops a flexible CSI compression framework supporting multiple feedback rates and practical quantization.

ESI Highly Cited Paper

[2] J. Guo, C.-K. Wen, and S. Jin, “CAnet: Uplink-Aided Downlink Channel Acquisition in FDD Massive MIMO Using Deep Learning,” IEEE Transactions on Communications, vol. 70, no. 1, pp. 199–214, Jan. 2022.

This work exploits uplink channel information to assist downlink channel acquisition in FDD massive MIMO.

[3] J. Guo, C.-K. Wen, and S. Jin, “Eliminating CSI Feedback Overhead via Deep Learning-Based Data Hiding,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 8, pp. 2267–2281, Aug. 2022.

This work explores a fundamentally different feedback mechanism in which CSI information is embedded into transmitted data rather than occupying dedicated feedback resources.

Task-Oriented Channel Acquisition

[4] J. Guo, C.-K. Wen, and S. Jin, “Deep Learning-Based CSI Feedback for Beamforming in Single- and Multi-Cell Massive MIMO Systems,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 7, pp. 1872–1884, Jul. 2021.

This work shifts CSI feedback optimization from minimizing reconstruction error toward directly maximizing downstream beamforming performance.

[5] J. Guo, T. Chen, S. Jin, G. Y. Li, X. Wang, and X. Hou, “Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems,” Digital Communications and Networks, vol. 10, no. 1, pp. 83–93, 2024.

This work jointly designs channel estimation, compression, and reconstruction instead of optimizing individual channel-acquisition modules separately.

ESI Highly Cited Paper

RIS and Cell-Free Massive MIMO

[6] J. Guo, W. Chen, C.-K. Wen, and S. Jin, “Deep Learning-Based Two-Timescale CSI Feedback for Beamforming Design in RIS-Assisted Communications,” IEEE Transactions on Vehicular Technology, vol. 72, no. 4, pp. 5452–5457, Apr. 2023.

This work develops a two-timescale CSI feedback framework for jointly supporting beamforming and RIS phase-shift design.

[7] J. Guo, X. Yang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep Learning-Based CSI Feedback for RIS-Assisted Multi-User Systems,” IEEE Transactions on Communications, vol. 73, no. 7, pp. 4974–4989, 2025.

This work exploits inter-user correlation in RIS-user channels to reduce redundant feedback and improve multi-user CSI acquisition efficiency.

[8] J. Guo, C.-K. Wen, X. Li, and S. Jin, “Deep Learning-Based Position-Domain Channel Extrapolation for Cell-Free Massive MIMO,” IEEE Transactions on Wireless Communications, vol. 25, pp. 1996–2011, 2026.

This work introduces position-domain channel extrapolation, using the user position as a bridge for transferring channel knowledge across geographically distributed links in cell-free massive MIMO.

Research Direction 2: Efficient, Adaptive and Trustworthy Wireless AI

This direction focuses on making wireless AI models lightweight, adaptive, reliable, and practically deployable under constraints on computation, data, latency, energy, and changing environments.

[1] J. Guo, J. 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, Aug. 2020.

This work investigates neural-network compression and acceleration for resource-constrained wireless AI deployment.

[2] J. Guo, X. Li, M. Chen, et al., “AI Enabled Wireless Communications with Real Channel Measurements: Channel Feedback,” Journal of Communications and Information Networks, vol. 5, no. 3, pp. 310–317, 2020.

This work evaluates AI-based CSI feedback using real wireless channel measurements and studies the gap between simulation-based development and practical deployment.

Cover Paper

[3] J. Guo, Y. Zuo, C.-K. Wen, and S. Jin, “User-Centric Online Gossip Training for Autoencoder-Based CSI Feedback,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 3, pp. 559–572, Apr. 2022.

This work develops distributed online adaptation that exploits spatial correlation among users without requiring centralized retraining.

[4] J. Guo, S. Ma, C.-K. Wen, and S. Jin, “Performance Monitoring-Enabled Reliable AI-Based CSI Feedback,” IEEE Transactions on Wireless Communications, vol. 24, no. 1, pp. 197–212, 2025.

This work introduces online performance monitoring for AI-based CSI feedback, enabling unreliable model outputs to be identified during deployment.

Selected Collaborative Work

[5] Y. Cui, J. Guo, Z. Cao, et al., “Lightweight Neural Network with Knowledge Distillation for CSI Feedback,” IEEE Transactions on Communications, vol. 72, no. 8, pp. 4917–4929, 2024.

[6] X. Li, J. Guo, C.-K. Wen, X. Geng, and S. Jin, “Auto-CsiNet: Scenario-Customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback,” IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 14759–14775, 2024.

[7] X. Li, J. Guo, C.-K. Wen, X. Geng, and S. Jin, “Facilitating AI-Based CSI Feedback Deployment in Massive MIMO Systems with Learngene,” IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11325–11340, 2024.

[8] J. Zhang, J. Guo, X. Li, C.-K. Wen, X. Geng, and S. Jin, “Efficient Deployment of Deep MIMO Detection Using Learngene,” IEEE Transactions on Wireless Communications, vol. 25, pp. 4405–4418, 2026.

Research Direction 3: Wireless Foundation Models and Environment Intelligence

My recent work explores how wireless AI can move beyond task-specific and scenario-specific models by learning and exploiting reusable channel, spatial, semantic, and environment knowledge.

[1] J. Guo, C.-K. Wen, M. Chen, and S. Jin, “Environment Knowledge-Aided Massive MIMO Feedback Codebook Enhancement Using Artificial Intelligence,” IEEE Transactions on Communications, vol. 70, no. 7, pp. 4527–4542, Jul. 2022.

This work explicitly introduces propagation-environment knowledge into CSI feedback while retaining compatibility with conventional codebook-based feedback procedures.

[2] J. Guo, Y. Lv, C.-K. Wen, X. Li, and S. Jin, “Learning-Based Integrated CSI Feedback and Localization in Massive MIMO,” IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 14988–15001, 2024.

This work jointly exploits channel and location information, demonstrating how shared wireless representations can benefit multiple channel-related tasks.

[3] J. Guo, Y. Cui, and S. Jin, “Semantic-Aware Digital Twin for AI-Based CSI Acquisition,” IEEE Communications Standards Magazine, vol. 9, no. 4, pp. 50–57, 2025.

This work introduces semantic-aware wireless digital twins as an interface between physical environments and AI-based CSI acquisition, with the goal of reducing data-collection and deployment costs.

[4] J. Guo, Y. Cui, C.-K. Wen, and S. Jin, “Prompt-Enabled Large AI Models for CSI Feedback,” IEEE Journal on Selected Areas in Communications, vol. 44, pp. 2654–2668, 2026.

This work investigates why AI-based CSI feedback generalizes across environments and develops a large CSI model that incorporates environment-specific channel-distribution knowledge through prompts.

Selected Collaborative Work

[5] T. Zheng, J. Guo, L. Dai, S. Jin, and J. Zhang, “MUSE-FM: Multi-Task Environment-Aware Foundation Model for Wireless Communications,” IEEE Transactions on Wireless Communications, vol. 25, pp. 19791–19806, 2026.

MUSE-FM develops a unified foundation-model architecture for heterogeneous wireless tasks and explicitly incorporates environmental context to improve cross-scenario adaptation.

[6] Y. Cui, J. Guo, X. Li, C.-K. Wen, and S. Jin, “Large and Small Model Collaboration for Air Interface,” IEEE Transactions on Wireless Communications, accepted, 2026.

This work develops a large-small model collaboration paradigm in which a large model provides reusable wireless knowledge while lightweight models support efficient environment-specific adaptation.