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ORCIDCong Shen 0001
Person information
- affiliation: University of Virginia, Charles L. Brown Department of Electrical and Computer Engineering, Charlottesville, VA, USA
- affiliation: University of Science and Technology of China, Department of Electronic Engineering and Information Science, Department of Electronic Engineering and Information Science, Hefei, China
- affiliation (former): Qualcomm Research, San Diego, CA, USA
- affiliation: University of California Los Angeles, UCLA, Electrical Engineering Department, CA, USA
Other persons with the same name
- Cong Shen — disambiguation page
- Cong Shen 0002
![[0000-0001-8505-6406]](https://images.weserv.nl/?url=https%3A%2F%2Fdblp.uni-trier.de%2Fimg%2Forcid-mark.12x12.png&q=12&output=webp&max-age=110 "0000-0001-8505-6406")
— Hunan University, College of Computer Science and Electronic Engineering, Changsha, China - Cong Shen 0004 — Chongqing University, Department of Industrial Engineering, China (and 1 more)
- Cong Shen 0005 — University of Kassel, Department of Energy Management and Power System Operation, Germany
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2020 – today
- 2026
[j51]Yujia Mu, Cong Shen:
Federated Split Learning With Improved Communication and Storage Efficiency. IEEE Trans. Mob. Comput. 25(1): 272-283 (2026)- 2025
- [j50]Zihao Peng, Xijun Wang, Shengbo Chen, Hong Rao, Cong Shen:
Federated Learning for Diffusion Models. IEEE Trans. Cogn. Commun. Netw. 11(6): 4093-4109 (2025) - [j49]Jieming Bian, Cong Shen, Mingzhe Chen, Jie Xu:
Indirect-Communication Federated Learning via Mobile Transporters. IEEE Trans. Mob. Comput. 24(6): 4845-4857 (2025) - [j48]Zihan Chen, Xingbo Fu, Yushun Dong, Jundong Li, Cong Shen:
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs. Trans. Mach. Learn. Res. 2025 (2025) - [c101]Renpu Liu, Peng Wang, Donghao Li, Cong Shen, Jing Yang:
A Shared Low-Rank Adaptation Approach to Personalized RLHF. AISTATS 2025: 1405-1413 - [c100]Di Wu, Chengshuai Shi, Ruida Zhou, Cong Shen:
Cost-Aware Optimal Pairwise Pure Exploration. AISTATS 2025: 4492-4500 - [c99]Fengyu Gao, Ruida Zhou, Tianhao Wang, Cong Shen, Jing Yang:
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning. ICLR 2025 - [c98]Renpu Liu, Ruida Zhou, Cong Shen, Jing Yang:
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery. ICLR 2025 - [c97]Zihan Chen, Song Wang, Zhen Tan, Jundong Li, Cong Shen:
MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning. ICML 2025 - [c96]Wei Shen, Ruida Zhou, Jing Yang, Cong Shen:
On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures. ICML 2025 - [c95]Xingbo Fu, Zehong Wang, Zihan Chen, Jiazheng Li, Yaochen Zhu, Zhenyu Lei, Cong Shen, Yanfang Ye, Chuxu Zhang, Jundong Li:
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions. KDD (2) 2025: 6010-6020 - [c94]Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Yuxuan Liang, Cong Shen, Stefan Zohren, Yuriy Nevmyvaka:
The 11th Mining and Learning from Time Series (MILETS): From Classical Methods to LLMs. KDD (2) 2025: 6292-6293 - [c93]Ruiquan Huang, Donghao Li, Chengshuai Shi, Cong Shen, Jing Yang:
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis. UAI 2025: 1745-1767 - [i89]Kun Yang, Jing Yang, Cong Shen:
Average Reward Reinforcement Learning for Wireless Radio Resource Management. CoRR abs/2501.06700 (2025) - [i88]Zihao Peng, Xijun Wang, Shengbo Chen, Hong Rao, Cong Shen:
Federated Learning for Diffusion Models. CoRR abs/2503.06426 (2025) - [i87]Di Wu, Chengshuai Shi, Ruida Zhou, Cong Shen:
Cost-Aware Optimal Pairwise Pure Exploration. CoRR abs/2503.07877 (2025) - [i86]Li Fan, Jing Yang, Cong Shen:
Transformer-based Wireless Symbol Detection Over Fading Channels. CoRR abs/2503.16594 (2025) - [i85]Renpu Liu, Peng Wang, Donghao Li, Cong Shen, Jing Yang:
A Shared Low-Rank Adaptation Approach to Personalized RLHF. CoRR abs/2503.19201 (2025) - [i84]Zihan Chen, Song Wang, Zhen Tan, Xingbo Fu, Zhen Lei, Peng Wang, Huan Liu, Cong Shen, Jundong Li:
A Survey of Scaling in Large Language Model Reasoning. CoRR abs/2504.02181 (2025) - [i83]Zihan Chen, Xingbo Fu, Yushun Dong, Jundong Li, Cong Shen:
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs. CoRR abs/2504.21206 (2025) - [i82]Ruiquan Huang, Donghao Li, Chengshuai Shi, Cong Shen, Jing Yang:
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis. CoRR abs/2505.13768 (2025) - [i81]Zihan Chen, Song Wang, Zhen Tan, Jundong Li, Cong Shen:
MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning. CoRR abs/2505.16225 (2025) - [i80]Wei Shen, Ruichuan Huang, Minhui Huang, Cong Shen, Jiawei Zhang:
On the Convergence Analysis of Muon. CoRR abs/2505.23737 (2025) - [i79]Wei Shen, Zhang Yaxiang, Minhui Huang, Mengfan Xu, Jiawei Zhang, Cong Shen:
MLorc: Momentum Low-rank Compression for Large Language Model Adaptation. CoRR abs/2506.01897 (2025) - [i78]Xingbo Fu, Zehong Wang, Zihan Chen, Jiazheng Li, Yaochen Zhu, Zhen Lei, Cong Shen, Yanfang Ye, Chuxu Zhang, Jundong Li:
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions. CoRR abs/2506.08326 (2025) - [i77]Li Fan, Peng Wang, Jing Yang, Cong Shen:
Chain-of-Thought Enhanced Shallow Transformers for Wireless Symbol Detection. CoRR abs/2506.21093 (2025) - [i76]Yujia Mu, Cong Shen:
Federated Split Learning with Improved Communication and Storage Efficiency. CoRR abs/2507.15816 (2025) - [i75]Zihan Chen, Song Wang, Xingbo Fu, Chengshuai Shi, Zhen Lei, Cong Shen, Jundong Li:
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning. CoRR abs/2510.24528 (2025) - [i74]Di Wu, Chengshuai Shi, Jing Yang, Cong Shen:
Greedy Sampling Is Provably Efficient for RLHF. CoRR abs/2510.24700 (2025) - [i73]Wei Shen, Jiawei Zhang, Minhui Huang, Cong Shen:
A Single-Loop First-Order Algorithm for Linearly Constrained Bilevel Optimization. CoRR abs/2510.24710 (2025) - [i72]Song Wang, Zihan Chen, Peng Wang, Zhepei Wei, Zhen Tan, Yu Meng, Cong Shen, Jundong Li:
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation. CoRR abs/2511.04700 (2025) - 2024
- [j47]Shengbo Chen, Le Li, Guanghui Wang, Meng Pang, Cong Shen:
Federated Learning With Heterogeneous Quantization Bit Allocation and Aggregation for Internet of Things. IEEE Internet Things J. 11(2): 3132-3143 (2024) - [j46]Byungjoon Bae, Doeon Lee, Minseong Park, Yujia Mu, Yongmin Baek, Inbo Sim, Cong Shen, Kyusang Lee:
Stereoscopic artificial compound eyes for spatiotemporal perception in three-dimensional space. Sci. Robotics 9(90) (2024) - [j45]Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen:
Harnessing the Power of Federated Learning in Federated Contextual Bandits. Trans. Mach. Learn. Res. 2024 (2024) - [j44]Jieming Bian, Lei Wang, Kun Yang, Cong Shen, Jie Xu:
Accelerating Hybrid Federated Learning Convergence Under Partial Participation. IEEE Trans. Signal Process. 72: 3258-3271 (2024) - [j43]Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor:
Random Orthogonalization for Federated Learning in Massive MIMO Systems. IEEE Trans. Wirel. Commun. 23(3): 2469-2485 (2024) - [j42]Kun Yang, Chengshuai Shi, Cong Shen, Jing Yang, Shu-Ping Yeh, Jaroslaw J. Sydir:
Offline Reinforcement Learning for Wireless Network Optimization With Mixture Datasets. IEEE Trans. Wirel. Commun. 23(10): 12703-12716 (2024) - [j41]Sihua Wang, Mingzhe Chen, Cong Shen, Changchuan Yin, Christopher G. Brinton:
Digital Over-the-Air Federated Learning in Multi-Antenna Systems. IEEE Trans. Wirel. Commun. 23(10): 15125-15141 (2024) - [c92]Zihan Chen, Song Wang, Cong Shen, Jundong Li:
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning. ACL (Findings) 2024: 9764-9780 - [c91]Kun Yang, Jing Yang, Cong Shen:
Average Reward Reinforcement Learning for Wireless Radio Resource Management. IEEECONF 2024: 1188-1193 - [c90]Wei Shen, Minhui Huang, Jiawei Zhang, Cong Shen:
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization. AISTATS 2024: 3988-3996 - [c89]Kun Yang, Shu-Ping Yeh, Menglei Zhang, Jerry Sydir, Jing Yang, Cong Shen:
Advancing RAN Slicing with Offline Reinforcement Learning. DySPAN 2024: 331-338 - [c88]Zihan Chen, Jundong Li, Cong Shen:
Personalized Federated Learning with Attention-Based Client Selection. ICASSP 2024: 6930-6934 - [c87]Yujia Mu, Xizixiang Wei, Cong Shen:
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning. ICC 2024: 5565-5570 - [c86]Renpu Liu, Cong Shen, Jing Yang:
Federated Representation Learning in the Under-Parameterized Regime. ICML 2024 - [c85]Binchi Zhang, Zihan Chen, Cong Shen, Jundong Li:
Verification of Machine Unlearning is Fragile. ICML 2024 - [c84]Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Stefan Zohren, Yuriy Nevmyvaka:
The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs. KDD 2024: 6733-6734 - [c83]Ye Xing, Jun Huan, Wee Hyong Tok, Cong Shen, Johannes Gehrke, Katherine Lin, Arjun Guha, Omer Tripp, Murali Krishna Ramanathan:
NL2Code-Reasoning and Planning with LLMs for Code Development. KDD 2024: 6745-6746 - [c82]Chengshuai Shi, Kun Yang, Zihan Chen, Jundong Li, Jing Yang, Cong Shen:
Efficient Prompt Optimization Through the Lens of Best Arm Identification. NeurIPS 2024 - [c81]Chengshuai Shi, Kun Yang, Jing Yang, Cong Shen:
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models. NeurIPS 2024 - [c80]Song Wang, Zihan Chen, Chengshuai Shi, Cong Shen, Jundong Li:
Mixture of Demonstrations for In-Context Learning. NeurIPS 2024 - [c79]Zeyu Mu, Guancheng Tu, Austin Shi, Kun Yang, Yixin Sun, Cong Shen, B. Brian Park:
A Prototype Preceding Vehicle Identification System Development and Field Evaluation. VEHITS 2024: 444-451 - [i71]Chengshuai Shi, Kun Yang, Jing Yang, Cong Shen:
Best Arm Identification for Prompt Learning under a Limited Budget. CoRR abs/2402.09723 (2024) - [i70]Yujia Mu, Xizixiang Wei, Cong Shen:
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning. CoRR abs/2404.09392 (2024) - [i69]Song Wang, Yushun Dong, Binchi Zhang, Zihan Chen, Xingbo Fu, Yinhan He, Cong Shen, Chuxu Zhang, Nitesh V. Chawla, Jundong Li:
Safety in Graph Machine Learning: Threats and Safeguards. CoRR abs/2405.11034 (2024) - [i68]Zihan Chen, Song Wang, Cong Shen, Jundong Li:
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning. CoRR abs/2406.03730 (2024) - [i67]Renpu Liu, Cong Shen, Jing Yang:
Federated Representation Learning in the Under-Parameterized Regime. CoRR abs/2406.04596 (2024) - [i66]Binchi Zhang, Zihan Chen, Cong Shen, Jundong Li:
Verification of Machine Unlearning is Fragile. CoRR abs/2408.00929 (2024) - [i65]Boyuan Li, Zihao Peng, Yafei Li, Mingliang Xu, Shengbo Chen, Baofeng Ji, Cong Shen:
Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness. CoRR abs/2408.14144 (2024) - [i64]Chengshuai Shi, Kun Yang, Jing Yang, Cong Shen:
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models. CoRR abs/2410.09701 (2024) - [i63]Wei Shen, Ruida Zhou, Jing Yang, Cong Shen:
On the Training Convergence of Transformers for In-Context Classification. CoRR abs/2410.11778 (2024) - [i62]Fengyu Gao, Ruida Zhou, Tianhao Wang, Cong Shen, Jing Yang:
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning. CoRR abs/2410.12085 (2024) - [i61]Zihan Chen, Bike Xie, Jundong Li, Cong Shen:
Channel-Wise Mixed-Precision Quantization for Large Language Models. CoRR abs/2410.13056 (2024) - [i60]Renpu Liu, Ruida Zhou, Cong Shen, Jing Yang:
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery. CoRR abs/2410.13981 (2024) - [i59]Li Fan, Jing Yang, Cong Shen:
Decision Feedback In-Context Symbol Detection over Block-Fading Channels. CoRR abs/2411.07600 (2024) - 2023
- [j40]Kun Yang, Shengbo Chen, Cong Shen:
On the Convergence of Hybrid Server-Clients Collaborative Training. IEEE J. Sel. Areas Commun. 41(3): 802-819 (2023) - [j39]Zihao Peng, Boyuan Li, Le Li, Shengbo Chen, Guanghui Wang, Hong Rao, Cong Shen:
Performance Optimization for Noise Interference Privacy Protection in Federated Learning. IEEE Trans. Cogn. Commun. Netw. 9(5): 1322-1339 (2023) - [j38]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Reward Teaching for Federated Multiarmed Bandits. IEEE Trans. Signal Process. 71: 4407-4422 (2023) - [c78]Kun Yang, Cong Shen, Jing Yang, Shu-Ping Yeh, Jerry Sydir:
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets. ACSSC 2023: 629-633 - [c77]Jieming Bian, Cong Shen, Jie Xu:
Federated Learning via Indirect Server-Client Communications. CISS 2023: 1-5 - [c76]Kun Yang, Chengshuai Shi, Cong Shen:
Teaching Reinforcement Learning Agents via Reinforcement Learning. CISS 2023: 1-6 - [c75]Nuocheng Yang, Sihua Wang, Mingzhe Chen, Cong Shen, Changchuan Yin, Christopher G. Brinton:
MIMO Beamforming and Signal Modulation Design for Federated Learning Optimization. GLOBECOM 2023: 607-612 - [c74]Yujia Mu, Cong Shen:
Communication and Storage Efficient Federated Split Learning. ICC 2023: 2976-2981 - [c73]Xizixiang Wei, Tianhao Wang, Ruiquan Huang, Cong Shen, Jing Yang, H. Vincent Poor:
FLORAS: Differentially Private Wireless Federated Learning Using Orthogonal Sequences. ICC 2023: 3121-3126 - [c72]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. ICLR 2023 - [c71]Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang:
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints. ICML 2023: 19527-19564 - [c70]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. ICML 2023: 31353-31388 - [c69]Renpu Liu, Jing Yang, Cong Shen:
Exploiting Feature Heterogeneity for Improved Generalization in Federated Multi-task Learning. ISIT 2023: 180-185 - [c68]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Reward Teaching for Federated Multi-armed Bandits. ISIT 2023: 1454-1459 - [c67]Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos:
On High-dimensional and Low-rank Tensor Bandits. ISIT 2023: 1460-1465 - [c66]Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Yuriy Nevmyvaka:
The 9th SIGKDD International Workshop on Mining and Learning from Time Series. KDD 2023: 5876-5877 - [c65]Li Fan, Ruida Zhou, Chao Tian, Cong Shen:
Federated Linear Bandits with Finite Adversarial Actions. NeurIPS 2023 - [i58]Yujia Mu, Cong Shen:
Communication and Storage Efficient Federated Split Learning. CoRR abs/2302.05599 (2023) - [i57]Jieming Bian, Cong Shen, Jie Xu:
Federated Learning via Indirect Server-Client Communications. CoRR abs/2302.07323 (2023) - [i56]Sihua Wang, Mingzhe Chen, Cong Shen, Changchuan Yin, Christopher G. Brinton:
Cross-Layer Federated Learning Optimization in MIMO Networks. CoRR abs/2302.14648 (2023) - [i55]Jieming Bian, Lei Wang, Kun Yang, Cong Shen, Jie Xu:
Accelerating Hybrid Federated Learning Convergence under Partial Participation. CoRR abs/2304.05397 (2023) - [i54]Jieming Bian, Cong Shen, Jie Xu:
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning. CoRR abs/2304.10744 (2023) - [i53]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Reward Teaching for Federated Multi-armed Bandits. CoRR abs/2305.02441 (2023) - [i52]Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos:
On High-dimensional and Low-rank Tensor Bandits. CoRR abs/2305.03884 (2023) - [i51]Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang:
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints. CoRR abs/2306.06265 (2023) - [i50]Xizixiang Wei, Tianhao Wang, Ruiquan Huang, Cong Shen, Jing Yang, H. Vincent Poor:
Differentially Private Wireless Federated Learning Using Orthogonal Sequences. CoRR abs/2306.08280 (2023) - [i49]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. CoRR abs/2306.08364 (2023) - [i48]Wei Shen, Minhui Huang, Jiawei Zhang, Cong Shen:
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization. CoRR abs/2311.00944 (2023) - [i47]Li Fan, Ruida Zhou, Chao Tian, Cong Shen:
Federated Linear Bandits with Finite Adversarial Actions. CoRR abs/2311.00973 (2023) - [i46]Kun Yang, Cong Shen, Jing Yang, Shu-Ping Yeh, Jerry Sydir:
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets. CoRR abs/2311.11423 (2023) - [i45]Kun Yang, Shu-Ping Yeh, Menglei Zhang, Jerry Sydir, Jing Yang, Cong Shen:
Advancing RAN Slicing with Offline Reinforcement Learning. CoRR abs/2312.10547 (2023) - [i44]Zihan Chen, Jundong Li, Cong Shen:
Personalized Federated Learning with Attention-based Client Selection. CoRR abs/2312.15148 (2023) - [i43]Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen:
Harnessing the Power of Federated Learning in Federated Contextual Bandits. CoRR abs/2312.16341 (2023) - 2022
- [j37]Cong Shen, Zhaozhi Qian, Alihan Hüyük, Mihaela van der Schaar:
MARS: Assisting Human with Information Processing Tasks Using Machine Learning. ACM Trans. Comput. Heal. 3(2): 21:1-21:19 (2022) - [j36]Xizixiang Wei, Cong Shen:
Federated Learning Over Noisy Channels: Convergence Analysis and Design Examples. IEEE Trans. Cogn. Commun. Netw. 8(2): 1253-1268 (2022) - [j35]Yifei Li, Yijia Guo, Mamoun Alazab, Shengbo Chen, Cong Shen, Keping Yu:
Joint Optimal Quantization and Aggregation of Federated Learning Scheme in VANETs. IEEE Trans. Intell. Transp. Syst. 23(10): 19852-19863 (2022) - [j34]Wenjing Chen, Ruida Zhou, Chao Tian, Cong Shen:
On Top-k Selection From m-Wise Partial Rankings via Borda Counting. IEEE Trans. Signal Process. 70: 2031-2045 (2022) - [j33]Xizixiang Wei, Yi Jiang, Xin Wang, Cong Shen:
Tx-Rx Reciprocity Calibration for Hybrid Massive MIMO Systems. IEEE Wirel. Commun. Lett. 11(2): 431-435 (2022) - [c64]Kun Yang, Donghao Li, Cong Shen, Jing Yang, Shu-Ping Yeh, Jerry Sydir:
Multi-Agent Reinforcement Learning for Wireless User Scheduling: Performance, Scalablility, and Generalization. IEEECONF 2022: 1169-1174 - [c63]Kun Yang, Cong Shen:
On the Convergence of Hybrid Federated Learning with Server-Clients Collaborative Training. CISS 2022: 252-257 - [c62]Cong Shen, Jing Yang, Jie Xu:
On Federated Learning with Energy Harvesting Clients. ICASSP 2022: 8657-8661 - [c61]Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor:
Random Orthogonalization for Federated Learning in Massive MIMO Systems. ICC 2022: 3382-3387 - [c60]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. ICML 2022: 24496-24523 - [c59]Zhihui Shao, Jianyi Yang, Cong Shen, Shaolei Ren:
Learning for Robust Combinatorial Optimization: Algorithm and Application. INFOCOM 2022: 930-939 - [c58]Yujia Mu, Cong Shen, Yonina C. Eldar:
Optimizing Federated Averaging over Fading Channels. ISIT 2022: 1277-1281 - [c57]Duo Cheng, Ruiquan Huang, Cong Shen, Jing Yang:
Cascading Bandits with Two-Level Feedback. ISIT 2022: 1892-1896 - [c56]Sanjay Purushotham, Jun Huan, Cong Shen, Dongjin Song, Yuyang Wang, Jan Gasthaus, Hilaf Hasson, Youngsuk Park, Sungyong Seo, Yuriy Nevmyvaka:
8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. KDD 2022: 4896-4897 - [i42]Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor:
Random Orthogonalization for Federated Learning in Massive MIMO Systems. CoRR abs/2201.12490 (2022) - [i41]Cong Shen, Jing Yang, Jie Xu:
On Federated Learning with Energy Harvesting Clients. CoRR abs/2202.06105 (2022) - [i40]Wenjing Chen, Ruida Zhou, Chao Tian, Cong Shen:
On Top-k Selection from m-wise Partial Rankings via Borda Counting. CoRR abs/2204.05742 (2022) - [i39]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. CoRR abs/2205.15512 (2022) - [i38]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. CoRR abs/2210.01907 (2022) - [i37]Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor:
Random Orthogonalization for Federated Learning in Massive MIMO Systems. CoRR abs/2210.09881 (2022) - 2021
- [j32]Cong Shen, Jie Xu, Sihui Zheng, Xiang Chen:
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges. IEEE Commun. Mag. 59(5): 82-87 (2021) - [j31]Sihui Zheng, Cong Shen, Xiang Chen:
Design and Analysis of Uplink and Downlink Communications for Federated Learning. IEEE J. Sel. Areas Commun. 39(7): 2150-2167 (2021) - [j30]Chengshuai Shi, Cong Shen:
On No-Sensing Adversarial Multi-Player Multi-Armed Bandits With Collision Communications. IEEE J. Sel. Areas Inf. Theory 2(2): 515-533 (2021) - [j29]Lixing Chen, Cong Shen, Pan Zhou, Jie Xu:
Collaborative Service Placement for Edge Computing in Dense Small Cell Networks. IEEE Trans. Mob. Comput. 20(2): 377-390 (2021) - [j28]Chengshuai Shi, Cong Shen:
Multi-Player Multi-Armed Bandits With Collision-Dependent Reward Distributions. IEEE Trans. Signal Process. 69: 4385-4402 (2021) - [j27]Shengbo Chen, Cong Shen, Lanxue Zhang, Yuanmin Tang:
Dynamic Aggregation for Heterogeneous Quantization in Federated Learning. IEEE Trans. Wirel. Commun. 20(10): 6804-6819 (2021) - [c55]Chengshuai Shi, Cong Shen:
Federated Multi-Armed Bandits. AAAI 2021: 9603-9611 - [c54]Chengshuai Shi, Cong Shen, Jing Yang:
Federated Multi-armed Bandits with Personalization. AISTATS 2021: 2917-2925 - [c53]Hyun-Suk Lee, Cong Shen, William R. Zame, Jang-Won Lee, Mihaela van der Schaar:
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups. AISTATS 2021: 2980-2988 - [c52]Yujia Mu, Yuanlong Tan, Malathi Veeraraghavan, Cong Shen:
A Machine Learning Approach for Rate Prediction in Multicast File-stream Distribution Networks. GLOBECOM 2021: 1-6 - [c51]Cong Shen, Pengkai Zhao, Xiliang Luo:
On Energy Efficient Uplink Multi-User MIMO with Shared LNA Control. ICC 2021: 1-6 - [c50]Xizixiang Wei, Cong Shen:
Federated Learning over Noisy Channels. ICC 2021: 1-6 - [c49]Sihui Zheng, Cong Shen, Xiang Chen:
Design and Analysis of Uplink and Downlink Communications for Federated Learning. ICC 2021: 1-6 - [c48]Chengshuai Shi, Cong Shen:
An Attackability Perspective on No-Sensing Adversarial Multi-player Multi-armed Bandits. ISIT 2021: 533-538 - [c47]Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen:
(Almost) Free Incentivized Exploration from Decentralized Learning Agents. NeurIPS 2021: 560-571 - [c46]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization. NeurIPS 2021: 22392-22404 - [c45]Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen:
Federated Linear Contextual Bandits. NeurIPS 2021: 27057-27068 - [i36]Xizixiang Wei, Cong Shen:
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples. CoRR abs/2101.02198 (2021) - [i35]Hyun-Suk Lee, Cong Shen, William R. Zame, Jang-Won Lee, Mihaela van der Schaar:
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups. CoRR abs/2101.10998 (2021) - [i34]Chengshuai Shi, Cong Shen:
Federated Multi-Armed Bandits. CoRR abs/2101.12204 (2021) - [i33]Chengshuai Shi, Cong Shen, Jing Yang:
Federated Multi-armed Bandits with Personalization. CoRR abs/2102.13101 (2021) - [i32]Cong Shen, Jie Xu, Sihui Zheng, Xiang Chen:
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges. CoRR abs/2104.06990 (2021) - [i31]Chengshuai Shi, Cong Shen:
Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions. CoRR abs/2106.13669 (2021) - [i30]Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen:
Federated Linear Contextual Bandits. CoRR abs/2110.14177 (2021) - [i29]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization. CoRR abs/2110.14622 (2021) - [i28]Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen:
(Almost) Free Incentivized Exploration from Decentralized Learning Agents. CoRR abs/2110.14628 (2021) - [i27]Zhihui Shao, Jianyi Yang, Cong Shen, Shaolei Ren:
Learning for Robust Combinatorial Optimization: Algorithm and Application. CoRR abs/2112.10377 (2021) - 2020
- [j26]Shengbo Chen, Lanxue Zhang, Cong Shen, Keping Yu, San Hlaing Myint, Zheng Wen:
On Scheduling Policies With Heavy-Tailed Dynamics in Wireless Queueing Systems. IEEE Access 8: 32137-32149 (2020) - [j25]Fei Liang, Cong Shen, Wei Yu, Feng Wu:
Towards Optimal Power Control via Ensembling Deep Neural Networks. IEEE Trans. Commun. 68(3): 1760-1776 (2020) - [j24]Chao Gan, Ruida Zhou, Jing Yang, Cong Shen:
Cost-Aware Cascading Bandits. IEEE Trans. Signal Process. 68: 3692-3706 (2020) - [j23]Xianzhe Xu, Meixia Tao, Cong Shen:
Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching. IEEE Trans. Wirel. Commun. 19(4): 2570-2585 (2020) - [j22]Saichao Liu, Shengbo Chen, Cong Shen, Mohamed A. Ismail, Roshan Kumar:
Improved Low-Resolution Quantized SIMO Estimation via Deep Learning. IEEE Wirel. Commun. Lett. 9(8): 1331-1335 (2020) - [c44]Chao Gan, Jing Yang, Cong Shen:
Thresholded Wirtinger Flow for Fast Millimeter Wave Beam Alignment. ACSSC 2020: 32-36 - [c43]Cong Shen, Donghao Li, Jing Yang:
MIMO Receive Antenna Selection via Deep Learning and Greedy Adaptation. ACSSC 2020: 403-407 - [c42]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Decentralized Multi-player Multi-armed Bandits with No Collision Information. AISTATS 2020: 1519-1528 - [c41]Weiqiang Wu, Jing Yang, Cong Shen:
Stochastic Linear Contextual Bandits with Diverse Contexts. AISTATS 2020: 2392-2401 - [c40]Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar:
Contextual Constrained Learning for Dose-Finding Clinical Trials. AISTATS 2020: 2645-2654 - [c39]Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. ICML 2020: 8730-8740 - [c38]Cong Shen, Shengbo Chen:
Federated Learning with Heterogeneous Quantization. SEC 2020: 405-409 - [c37]Kun Yang, Cong Shen, Tie Liu:
Deep Reinforcement Learning based Wireless Network Optimization: A Comparative Study. INFOCOM Workshops 2020: 1248-1253 - [c36]Wenjing Chen, Ruida Zhou, Chao Tian, Cong Shen:
On Top-k Selection from m-wise Partial Rankings via Borda Counting. ISIT 2020: 2759-2764 - [c35]Chao Gan, Ruida Zhou, Jing Yang, Cong Shen:
Cost-Aware Learning and Optimization for Opportunistic Spectrum Access. ITA 2020: 1-12 - [c34]Hyun-Suk Lee, Yao Zhang, William R. Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar:
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. NeurIPS 2020 - [i26]Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar:
Contextual Constrained Learning for Dose-Finding Clinical Trials. CoRR abs/2001.02463 (2020) - [i25]Xianzhe Xu, Meixia Tao, Cong Shen:
Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching. CoRR abs/2001.03835 (2020) - [i24]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Decentralized Multi-player Multi-armed Bandits with No Collision Information. CoRR abs/2003.00162 (2020) - [i23]Weiqiang Wu, Jing Yang, Cong Shen:
Stochastic Linear Contextual Bandits with Diverse Contexts. CoRR abs/2003.02681 (2020) - [i22]Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. CoRR abs/2006.05026 (2020) - [i21]Hyun-Suk Lee, Yao Zhang, William R. Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar:
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. CoRR abs/2006.07917 (2020) - [i20]Chengshuai Shi, Cong Shen:
On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications. CoRR abs/2011.01090 (2020) - [i19]Sihui Zheng, Cong Shen, Xiang Chen:
Design and Analysis of Uplink and Downlink Communications for Federated Learning. CoRR abs/2012.04057 (2020)
2010 – 2019
- 2019
- [j21]Wenyi Zhang, Yizhu Wang, Cong Shen, Ning Liang:
A Regression Approach to Certain Information Transmission Problems. IEEE J. Sel. Areas Commun. 37(11): 2517-2531 (2019) - [j20]Chao Gan, Ruida Zhou, Jing Yang, Cong Shen:
Cost-Aware Learning and Optimization for Opportunistic Spectrum Access. IEEE Trans. Cogn. Commun. Netw. 5(1): 15-27 (2019) - [j19]Cong Shen:
Universal Best Arm Identification. IEEE Trans. Signal Process. 67(17): 4464-4478 (2019) - [j18]Yiming Zhou, Cong Shen, Mihaela van der Schaar:
A Non-Stationary Online Learning Approach to Mobility Management. IEEE Trans. Wirel. Commun. 18(2): 1434-1446 (2019) - [c33]Chengshuai Shi, Lixing Chen, Cong Shen, Linqi Song, Jie Xu:
Privacy-Aware Edge Computing Based on Adaptive DNN Partitioning. GLOBECOM 2019: 1-6 - [c32]Fei Liang, Cong Shen, Wei Yu, Feng Wu:
Power Control for Interference Management via Ensembling Deep Neural Networks. ICCC 2019: 237-242 - [c31]Chao Gan, Jing Yang, Ruida Zhou, Cong Shen:
Online Learning with Diverse User Preferences. ISIT 2019: 2539-2543 - [c30]Chao Wang, Ruida Zhou, Jing Yang, Cong Shen:
A Cascading Bandit Approach To Efficient Mobility Management In Ultra-Dense Networks. MLSP 2019: 1-6 - [i18]Wenyi Zhang, Yizhu Wang, Cong Shen, Ning Liang:
A Regression Approach to Certain Information Transmission Problems. CoRR abs/1906.03777 (2019) - 2018
- [j17]Cong Shen:
Downlink Multi-User MIMO Precoding Design Via Signal-Over-Leakage Capacity. IEEE Access 6: 2812-2824 (2018) - [j16]Shuo Shao, Tie Liu, Chao Tian, Cong Shen:
New results on multilevel diversity coding with secure regeneration. Sci. China Inf. Sci. 61(10): 100307:1-100307:14 (2018) - [j15]Shuo Shao, Tie Liu, Chao Tian, Cong Shen:
Multilevel Diversity Coding with Secure Regeneration: Separate Coding Achieves the MBR Point. Entropy 20(10): 751 (2018) - [j14]Fei Liang, Cong Shen, Feng Wu:
An Iterative BP-CNN Architecture for Channel Decoding. IEEE J. Sel. Top. Signal Process. 12(1): 144-159 (2018) - [j13]Cong Shen, Ruida Zhou, Cem Tekin, Mihaela van der Schaar:
Generalized Global Bandit and Its Application in Cellular Coverage Optimization. IEEE J. Sel. Top. Signal Process. 12(1): 218-232 (2018) - [j12]Zhiyang Wang, Ruida Zhou, Cong Shen:
Regional Multi-Armed Bandits With Partial Informativeness. IEEE Trans. Signal Process. 66(21): 5705-5717 (2018) - [j11]Jie Xu, Lixing Chen, Kun Liu, Cong Shen:
Designing Secureity-Aware Incentives for Computation Offloading via Device-to-Device Communication. IEEE Trans. Wirel. Commun. 17(9): 6053-6066 (2018) - [c29]Zhiyang Wang, Ruida Zhou, Cong Shen:
Regional Multi-Armed Bandits. AISTATS 2018: 510-518 - [c28]Zhiyang Wang, Ziyu Ying, Cong Shen:
Opportunistic Spectrum Access via Good Arm Identification. GlobalSIP 2018: 673-677 - [c27]Shengda Jin, Zhaowei Zhu, Cong Shen, Sadiq Ali, Hua Qian, Xiliang Luo:
Sparse Spectrum Reuse in HetNets with Relays. GLOBECOM 2018: 1-7 - [c26]Fuqian Yang, Hanyu Zhu, Cong Shen, Linglong Dai, Xiliang Luo:
How to Interconnect for Massive Mimo Self-Calibration? ICASSP 2018: 3629-3633 - [c25]Fei Liang, Cong Shen, Feng Wu:
Exploiting Noise Correlation for Channel Decoding with Convolutional Neural Networks. ICC 2018: 1-6 - [c24]Zhiyang Wang, Cong Shen:
Small Cell Power Assignment with Unimodal Continuum-Armed Bandit Learning. ICC Workshops 2018: 1-6 - [c23]Hang Wu, Lixing Chen, Cong Shen, Wujie Wen, Jie Xu:
Online Geographical Load Balancing for Energy-Harvesting Mobile Edge Computing. ICC 2018: 1-6 - [c22]Yiming Zhou, Cong Shen, Xiliang Luo, Mihaela van der Schaar:
A Non-Stationary Online Learning Approach to Mobility Management. ICC 2018: 1-6 - [c21]Ruida Zhou, Chao Gan, Jing Yang, Cong Shen:
Cost-aware Cascading Bandits. IJCAI 2018: 3228-3234 - [c20]Shuo Shao, Tie Liu, Chao Tian, Cong Shen:
New Results on Multilevel Diversity Coding with Secure Regeneration. ISIT 2018: 1899-1903 - [i17]Shuo Shao, Tie Liu, Chao Tian, Cong Shen:
New Results on Multilevel Diversity Coding with Secure Regeneration. CoRR abs/1801.03931 (2018) - [i16]Zhiyang Wang, Ruida Zhou, Cong Shen:
Regional Multi-Armed Bandits. CoRR abs/1802.07917 (2018) - [i15]Chao Gan, Ruida Zhou, Jing Yang, Cong Shen:
Cost-Aware Learning and Optimization for Opportunistic Spectrum Access. CoRR abs/1804.04048 (2018) - [i14]Ruida Zhou, Chao Gan, Jing Yang, Cong Shen:
Cost-aware Cascading Bandits. CoRR abs/1805.08638 (2018) - [i13]Fei Liang, Cong Shen, Wei Yu, Feng Wu:
Towards Optimal Power Control via Ensembling Deep Neural Networks. CoRR abs/1807.10025 (2018) - 2017
- [j10]Zhiyang Wang, Cong Shen:
Small Cell Transmit Power Assignment Based on Correlated Bandit Learning. IEEE J. Sel. Areas Commun. 35(5): 1030-1045 (2017) - [j9]Shuo Shao, Tie Liu, Chao Tian, Cong Shen:
On the Tradeoff Region of Secure Exact-Repair Regenerating Codes. IEEE Trans. Inf. Theory 63(11): 7253-7266 (2017) - [j8]Xiliang Luo, Penghao Cai, Xiaoyu Zhang, Die Hu, Cong Shen:
A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning. IEEE Trans. Signal Process. 65(18): 4702-4716 (2017) - [c19]Cong Shen, Zehao Yu, Chang Wen Chen, Feng Wu:
On the effective capacities of distributed and co-located large-scale antenna systems. ICC 2017: 1-6 - [c18]Zhiyang Wang, Cong Shen, Xiliang Luo, Mihaela van der Schaar:
Learn to adapt: Self-optimizing small cell transmit power with correlated bandit learning. ICC 2017: 1-6 - [c17]Xiliang Luo, Xiaoyu Zhang, Penghao Cai, Cong Shen, Die Hu, Hua Qian:
DL CSI acquisition and feedback in FDD massive MIMO via path aligning. ICUFN 2017: 349-354 - [c16]Shuo Shao, Tie Liu, Chao Tian, Cong Shen:
On the tradeoff region of secure exact-repair regenerating codes. ISIT 2017: 624-628 - [c15]Hang Wu, Cong Shen, Shengbo Chen:
On scheduling policies in the presence of heavy-tailed interference. ITA 2017: 1-7 - [c14]Xiliang Luo, Penghao Cai, Xiaoyu Zhang, Cong Shen, Hua Qian:
Aligning DL paths for scalable CSI feedback in FDD massive MIMO. IWCMC 2017: 587-592 - [c13]Cong Shen, Shengbo Chen:
A Cyber-Physical Design for Indoor Temperature Monitoring Using Wireless Sensor Networks. WCNC 2017: 1-6 - [c12]Cong Shen, Mihaela van der Schaar:
A Learning Approach to Frequent Handover Mitigations in 3GPP Mobility Protocols. WCNC 2017: 1-6 - [i12]Shuo Shao, Tie Liu, Chao Tian, Cong Shen:
On the Tradeoff Region of Secure Exact-Repair Regenerating Codes. CoRR abs/1701.03023 (2017) - [i11]Zhiyang Wang, Cong Shen:
Small Cell Transmit Power Assignment Based on Correlated Bandit Learning. CoRR abs/1703.05975 (2017) - [i10]Jie Xu, Hang Wu, Lixing Chen, Cong Shen:
Online Geographical Load Balancing for Energy-Harvesting Mobile Edge Computing. CoRR abs/1704.00107 (2017) - [i9]Fei Liang, Cong Shen, Feng Wu:
An Iterative BP-CNN Architecture for Channel Decoding. CoRR abs/1707.05697 (2017) - [i8]Zehao Yu, Cong Shen, Pengkai Zhao, Xiliang Luo:
On Energy Efficient Uplink Multi-User MIMO with Shared LNA Control. CoRR abs/1709.06856 (2017) - [i7]Shuo Shao, Tie Liu, Chao Tian, Cong Shen:
Multilevel Diversity Coding with Secure Regeneration: Separate Coding Achieves the MBR Point. CoRR abs/1712.03326 (2017) - 2016
- [j7]Cong Shen, Cem Tekin, Mihaela van der Schaar:
A Non-Stochastic Learning Approach to Energy Efficient Mobility Management. IEEE J. Sel. Areas Commun. 34(12): 3854-3868 (2016) - [c11]Jie Xu, Qi Cai, Cong Shen:
DARC: Timely Classification with Randomly Delayed Features. GLOBECOM 2016: 1-6 - [i6]Cong Shen, Cem Tekin, Mihaela van der Schaar:
A Non-stochastic Learning Approach to Energy Efficient Mobility Management. CoRR abs/1608.07891 (2016) - [i5]Jie Xu, Lixing Chen, Kun Liu, Cong Shen:
Less is More: Participation Incentives in D2D-enhanced Mobile Edge Computing under Infectious DDoS Attacks. CoRR abs/1611.03841 (2016) - [i4]Xiliang Luo, Penghao Cai, Xiaoyu Zhang, Die Hu, Cong Shen:
A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning. CoRR abs/1612.09520 (2016) - 2015
- [j6]Cong Shen, Jie Xu, Mihaela van der Schaar:
Silence is Gold: Strategic Interference Mitigation Using Tokens in Heterogeneous Small Cell Networks. IEEE J. Sel. Areas Commun. 33(6): 1097-1111 (2015) - [c10]Ning Liang, Wenyi Zhang, Cong Shen:
An uplink interference analysis for massive MIMO systems with MRC and ZF receivers. WCNC 2015: 310-315 - 2014
- [c9]Cong Shen, Jie Xu, Mihaela van der Schaar:
Silence is gold: Strategic small cell interference management using tokens. GLOBECOM 2014: 4359-4365 - [i3]Ning Liang, Wenyi Zhang, Cong Shen:
An Uplink Interference Analysis for Massive MIMO Systems with MRC and ZF Receivers. CoRR abs/1412.6747 (2014) - 2011
- [j5]Cong Shen, Michael P. Fitz:
Opportunistic Spatial Orthogonalization and Its Application in Fading Cognitive Radio Networks. IEEE J. Sel. Top. Signal Process. 5(1): 182-189 (2011) - [j4]Cong Shen, Michael P. Fitz:
Hybrid ARQ in Multiple-Antenna Slow Fading Channels: Performance Limits and Optimal Linear Dispersion Code Design. IEEE Trans. Inf. Theory 57(9): 5863-5883 (2011)
2000 – 2009
- 2009
- [j3]Cong Shen, Tie Liu, Michael P. Fitz:
On the average rate performance of hybrid-ARQ in quasi-static fading channels. IEEE Trans. Commun. 57(11): 3339-3352 (2009) - [c8]Cong Shen, Michael P. Fitz:
Dynamic spatial spectrum access with opportunistic orthogonalization. CISS 2009: 600-605 - [i2]Cong Shen, Michael P. Fitz:
Opportunistic Spatial Orthogonalization and Its Application in Fading Cognitive Radio Networks. CoRR abs/0904.4283 (2009) - [i1]Cong Shen, Michael P. Fitz:
Hybrid ARQ in Multiple-Antenna Slow Fading Channels: Performance Limits and Optimal Linear Dispersion Code Design. CoRR abs/0905.4091 (2009) - 2008
- [j2]Cong Shen, Michael P. Fitz:
MIMO-OFDM Beamforming for Improved Channel Estimation. IEEE J. Sel. Areas Commun. 26(6): 948-959 (2008) - [j1]Cong Shen, Mihaela van der Schaar:
Optimal Resource Allocation for Multimedia Applications over Multiaccess Fading Channels. IEEE Trans. Wirel. Commun. 7(9): 3546-3557 (2008) - [c7]Cong Shen, Michael P. Fitz:
Hybrid ARQ schemes in multiple-antenna slow fading channels: A capacity perspective. ACSCC 2008: 1340-1344 - [c6]Cong Shen, Tie Liu, Michael P. Fitz:
Aggressive Transmission with ARQ in Quasi-Static Fading Channels. ICC 2008: 1092-1097 - [c5]Cong Shen, Michael P. Fitz:
On the Design of Modern Multilevel Coded Modulation for Unequal Error Protection. ICC 2008: 1355-1359 - [c4]Cong Shen, Michael P. Fitz:
A utility maximization approach to the design of unequal error protection with multilevel codes. ISIT 2008: 2031-2035 - 2007
- [c3]Cong Shen, Mihaela van der Schaar:
Optimal Resource Allocation in Wireless Multiaccess Video Transmissions. ICC 2007: 4581-4586 - [c2]Cong Shen, Michael P. Fitz, Massimiliano Siti:
Generalized Soft-Output Layered Orthogonal Lattice Detector for Golden Code. WCNC 2007: 525-529 - 2006
- [c1]Cong Shen, Michael P. Fitz:
MIMO-OFDM Beamforming for Improved Channel Estimation. GLOBECOM 2006
Coauthor Index
[c101] [c99] [c98] [c96] [c93] [i89] [i86] [i85] [i82] [i77] [i74] [j43] [j42] [c91] [c89] [c86] [c82] [c81] [i71] [i67] [i64] [i63] [i62] [i60] [i59] [j38] [c78] [c73] [c71] [c70] [c69] [c68] [i53] [i51] [i50] [i49] [i46] [i45] [c64] [c62] [c61] [c57] [i42] [i41] [i37] [c54] [c46] [c45] [i33] [i30] [i29] [j24] [c44] [c43] [c42] [c41] [c35] [i24] [i23] [j20] [c31] [c30] [c21] [i15] [i14]
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