Foad Sohrabi

3.6k total citations · 2 hit papers
33 papers, 2.5k citations indexed

About

Foad Sohrabi is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Foad Sohrabi has authored 33 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Electrical and Electronic Engineering, 11 papers in Computer Networks and Communications and 4 papers in Artificial Intelligence. Recurrent topics in Foad Sohrabi's work include Advanced MIMO Systems Optimization (24 papers), Millimeter-Wave Propagation and Modeling (16 papers) and Indoor and Outdoor Localization Technologies (7 papers). Foad Sohrabi is often cited by papers focused on Advanced MIMO Systems Optimization (24 papers), Millimeter-Wave Propagation and Modeling (16 papers) and Indoor and Outdoor Localization Technologies (7 papers). Foad Sohrabi collaborates with scholars based in Canada, United States and China. Foad Sohrabi's co-authors include Wei Yu, Zhilin Chen, Ya‐Feng Liu, Tao Jiang, Faouzi Bellili, Wei Cui, Hei Victor Cheng, Timothy N. Davidson, Shahram Shahbazpanahi and Raviraj Adve and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and IEEE Journal on Selected Areas in Communications.

In The Last Decade

Foad Sohrabi

31 papers receiving 2.5k citations

Hit Papers

Hybrid Digital and Analog Beamforming Design for Large-Sc... 2016 2026 2019 2022 2016 2018 250 500 750 1000

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Foad Sohrabi Canada 18 2.3k 744 368 143 114 33 2.5k
Hosein Nikopour United States 14 2.6k 1.1× 444 0.6× 945 2.6× 175 1.2× 139 1.2× 42 2.7k
Jianhua Mo United States 18 2.3k 1.0× 511 0.7× 602 1.6× 116 0.8× 171 1.5× 36 2.5k
Chenhao Qi China 27 1.6k 0.7× 749 1.0× 347 0.9× 289 2.0× 60 0.5× 117 1.9k
Aditya K. Jagannatham India 22 1.6k 0.7× 327 0.4× 714 1.9× 93 0.7× 77 0.7× 195 1.8k
Namyoon Lee South Korea 28 2.6k 1.1× 622 0.8× 1.5k 4.0× 80 0.6× 212 1.9× 159 2.9k
Hadi Baligh Canada 12 2.0k 0.9× 364 0.5× 743 2.0× 104 0.7× 61 0.5× 16 2.0k
Maxime Guillaud France 17 1.4k 0.6× 383 0.5× 750 2.0× 53 0.4× 47 0.4× 68 1.5k
Ruoyu Zhang China 17 718 0.3× 394 0.5× 274 0.7× 55 0.4× 81 0.7× 80 1.1k
Alireza Bayesteh Canada 14 1.6k 0.7× 363 0.5× 656 1.8× 91 0.6× 62 0.5× 44 1.7k
Trinh Van Chien Vietnam 19 1.3k 0.5× 496 0.7× 485 1.3× 39 0.3× 62 0.5× 107 1.5k

Countries citing papers authored by Foad Sohrabi

Since Specialization
Citations

This map shows the geographic impact of Foad Sohrabi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Foad Sohrabi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Foad Sohrabi more than expected).

Fields of papers citing papers by Foad Sohrabi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Foad Sohrabi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Foad Sohrabi. The network helps show where Foad Sohrabi may publish in the future.

Co-authorship network of co-authors of Foad Sohrabi

This figure shows the co-authorship network connecting the top 25 collaborators of Foad Sohrabi. A scholar is included among the top collaborators of Foad Sohrabi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Foad Sohrabi. Foad Sohrabi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sohrabi, Foad, Carl Nuzman, Jinfeng Du, Hong Yang, & Harish Viswanathan. (2025). Energy-Efficient Flat Precoding for MIMO Systems. IEEE Transactions on Signal Processing. 73. 795–810.
2.
Sohrabi, Foad, et al.. (2024). End-to-End Deep Learning for TDD MIMO Systems in the 6G Upper Midbands. IEEE Transactions on Wireless Communications. 24(3). 2110–2125. 4 indexed citations
3.
Jiang, Tao, Foad Sohrabi, & Wei Yu. (2023). Active Sensing for Two-Sided Beam Alignment and Reflection Design Using Ping-Pong Pilots. IEEE Journal on Selected Areas in Information Theory. 4. 24–39. 7 indexed citations
4.
Sohrabi, Foad, et al.. (2022). Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems. IEEE Transactions on Wireless Communications. 21(12). 10839–10853. 27 indexed citations
5.
Sohrabi, Foad, Tao Jiang, & Wei Yu. (2022). Learning Progressive Distributed Compression Strategies From Local Channel State Information. IEEE Journal of Selected Topics in Signal Processing. 16(3). 573–584. 13 indexed citations
6.
Sohrabi, Foad, Tao Jiang, Wei Cui, & Wei Yu. (2022). Active Sensing for Communications by Learning. IEEE Journal on Selected Areas in Communications. 40(6). 1780–1794. 39 indexed citations
7.
Yu, Wei, Foad Sohrabi, & Tao Jiang. (2022). Role of Deep Learning in Wireless Communications. 2(2). 56–72. 39 indexed citations
8.
Jiang, Tao, Foad Sohrabi, & Wei Yu. (2022). Active Sensing for Two-Sided Beam Alignment Using Ping-Pong Pilots. 913–918. 4 indexed citations
9.
Sohrabi, Foad, et al.. (2021). Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO. IEEE Transactions on Wireless Communications. 20(7). 4044–4057. 120 indexed citations
10.
Sohrabi, Foad, Zhilin Chen, & Wei Yu. (2021). Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment. IEEE Journal on Selected Areas in Communications. 39(8). 2347–2360. 37 indexed citations
11.
Chen, Zhilin, Foad Sohrabi, Ya‐Feng Liu, & Wei Yu. (2021). Phase Transition Analysis for Covariance-Based Massive Random Access With Massive MIMO. IEEE Transactions on Information Theory. 68(3). 1696–1715. 38 indexed citations
12.
Chen, Zhilin, Foad Sohrabi, & Wei Yu. (2021). Sparse Activity Detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading. IEEE Transactions on Signal Processing. 69. 3768–3781. 36 indexed citations
13.
Shahbazpanahi, Shahram, et al.. (2021). Hybrid Analog and Digital Beamforming Design for Channel Estimation in Correlated Massive MIMO Systems. arXiv (Cornell University). 14 indexed citations
14.
Sohrabi, Foad, et al.. (2020). MMSE-Based Channel Estimation for Hybrid Beamforming Massive MIMO with Correlated Channels. 5015–5019. 5 indexed citations
15.
Bellili, Faouzi, Foad Sohrabi, & Wei Yu. (2019). Generalized Approximate Message Passing for Massive MIMO mmWave Channel Estimation With Laplacian Prior. IEEE Transactions on Communications. 67(5). 3205–3219. 57 indexed citations
16.
Chen, Zhilin, Foad Sohrabi, Ya‐Feng Liu, & Wei Yu. (2019). Covariance Based Joint Activity and Data Detection for Massive Random Access with Massive MIMO. 1–6. 86 indexed citations
17.
Sohrabi, Foad, Ya‐Feng Liu, & Wei Yu. (2018). One-Bit Precoding and Constellation Range Design for Massive MIMO With QAM Signaling. IEEE Journal of Selected Topics in Signal Processing. 12(3). 557–570. 70 indexed citations
18.
Sohrabi, Foad, et al.. (2017). Construction of the RSRP map using sparse MDT measurements by regression clustering. 1–6. 19 indexed citations
19.
Sohrabi, Foad & Wei Yu. (2016). Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays. IEEE Journal of Selected Topics in Signal Processing. 10(3). 501–513. 1038 indexed citations breakdown →
20.
Sohrabi, Foad & Wei Yu. (2016). Hybrid analog and digital beamforming for OFDM-based large-scale MIMO systems. 1–6. 20 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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