Tao Yang

16 papers B 6Misc 1Journal 6Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A UE-Assisted Hybrid Transmission Scheme for Asynchronous Cell-Free Massive MIMO Systems With Imperfect RF Chains. IEEE Internet Things J. Liyuan Qin, Rui Zhang, Yongzhao Li, Yuhan Ruan, Tao Li, Hao Liu, Tao Yang
2025	J	`jnl`	L-SLNR-Based Distributed Clustering Towards Cell-Free Massive MIMO Systems With Local ZF. IEEE Wirel. Commun. Lett. Jiali Ren, Hangyu Zhang, Rui Zhang, Hao Liu, Tao Yang, Yuhan Ruan, Tao Li, Yongzhao Li
2024	—	`conf`	A Hybrid Transmission Scheme for Cell-Free Massive MIMO Systems with Phase Offset. VTC Spring Liyuan Qin, Rui Zhang, Yongzhao Li, Yuhan Ruan, Tao Li, Dong Yang, Hao Liu, Tao Yang
2024	B	`conf`	Low-Complexity Block-Krylov-Beamspace Beamforming for Extreme Massive MIMO. PIMRC Nuan Song, Tao Yang
2023	J	`jnl`	Angular Reciprocity-Based CCM Reconstruction for Downlink FDD Multi-Antenna Channels With Multiple Scattering Clusters. IEEE Wirel. Commun. Lett. Hangyu Zhang, Rui Zhang, Yongzhao Li, Yuhan Ruan, Nuan Song, Tao Yang
2023	J	`jnl`	Energy Efficient Hybrid Precoding for Adaptive Partially-Connected mmWave Massive MIMO: A Decomposition-Based Approach. IEEE Trans. Veh. Technol. Rui Zhang, Hangyu Zhang, Yongzhao Li, Yuhan Ruan, Octavia A. Dobre, Nuan Song, Tao Yang, Yiqing Lu
2023	—	`conf`	iDeepRx Enabled 100 Gb/s DFT-s-OFDM Data Transmission Over 220 GHz Testbed. ICC Wenliang Qi, Chenhui Ye, Dani Johannes Korpi, Chaohua Gong, Yingni Jin, Tao Yang
2021	B	`conf`	Machine Learning Enhanced CSI Acquisition and Training Strategy for FDD Massive MIMO. WCNC Nuan Song, Tao Yang
2020	B	`conf`	Deep Learning based Low-Rank Channel Recovery for Hybrid Beamforming in Millimeter-Wave Massive MIMO. WCNC Nuan Song, Chenhui Ye, Xiaofeng Hu, Tao Yang
2019	B	`conf`	Efficient Hybrid Beamforming for Relay Assisted Millimeter-Wave Multi-User Massive MIMO. WCNC Nuan Song, Tao Yang, Huan Sun
2018	J	`jnl`	Low-Complexity Hybrid Precoding With Dynamic Beam Assignment in mmWave OFDM Systems. IEEE Trans. Veh. Technol. Yazhou Zhu, Qingchuan Zhang, Tao Yang
2018	—	`conf`	Non-linear precoding based hybrid space-ground beamforming for multi-beam satellite systems. WSA Nuan Song, Tao Yang, Martin Haardt
2017	B	`conf`	Channel Alignment for Hybrid Beamforming in Millimeter Wave Multi-User Massive MIMO. GLOBECOM Nuan Song, Huan Sun, Qingchuan Zhang, Tao Yang
2017	Misc	`conf`	Efficient hybrid space-ground precoding techniques for multi-beam satellite systems. ICASSP Nuan Song, Tao Yang, Martin Haardt
2017	B	`conf`	Low Complexity Hybrid Beamforming for Uplink Multiuser mmWave MIMO Systems. WCNC Yazhou Zhu, Tao Yang
2013	J	`jnl`	An Enhanced DFT-Based Channel Estimator for LTE-A Uplink. IEEE Trans. Veh. Technol. Qingchuan Zhang, Xudong Zhu, Tao Yang, Jin Liu

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.