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Communication Theory Lab
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The lab researches network technologies that will support the next-generation Internet.
AboutAbout the lab
Creating technologies that support the next-generation Internet
The Network Architecture Lab researches new network technologies to make the Internet faster, higher-performing, more energy-efficient, and more reliable.
Its research themes include AI-based network control, next-generation Internet architecture, congestion control, mobile networks, and the design and optimization of large-scale network systems.
Going beyond theory, the lab aims to create technologies that will support the information and communication infrastructure of the future, through simulation and implementation.
The lab belongs to the Research Institute of Electrical Communication, and in the Graduate School of Information Sciences it is listed under the Department of Fundamental Information Science as "Communication Theory."
Key terms
- Congestion control
- A mechanism by which the sender adjusts its own sending rate to avoid slowdowns caused by network congestion
- RTT
- The time from sending data until a response returns. It serves as a measure of communication delay
- In-network congestion control (NCC)
- A method that places nodes along the communication path, divides a single flow into multiple segments, and controls the sending rate for each segment
ResearchResearch themes
The lab aims to make the Internet, the foundation that supports modern information systems such as AI, cloud computing, and IoT, faster, higher-performing, more energy-efficient, and more reliable. It studies congestion control, network management, and system design and optimization from both theoretical and implementation perspectives.
*Illustrative imageInternet congestion control algorithms
When traffic becomes congested, speed drops and delay increases, so congestion control, which autonomously regulates the sending rate, is essential. Focusing mainly on BBR, the lab proposes methods that adaptively control the sending rate using information such as RTT and the state of competing traffic.
*Illustrative imageIn-network congestion control (NCC)
The lab proposes a new architecture in which NCC nodes are placed along the communication path, splitting a single flow into multiple subpaths and performing congestion control independently on each. This makes it possible to use control suited to the characteristics of each segment, such as satellite or mobile links.
*Illustrative imageEstimating network conditions with machine learning
Using machine learning, the lab estimates the number of competing flows and the congestion control algorithms they use from time-series data, such as RTT, obtained at the sending host. By switching its own algorithm based on these estimates, it has confirmed an improvement in unfairness.
*Illustrative imageInformation compression for large-scale network management
Commands and logs from network devices are treated as sequences of words and compressed using a tree-structured dictionary that has learned context. The lab proposes a scheme that synchronizes the dictionary between the sender and the receiver to reduce the amount of management traffic.
MembersFaculty
The members of the Network Architecture Lab consist of faculty, doctoral, master's, and undergraduate students, and administrative assistants.As of 2026
- ProfessorGo Hasegawa
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Google's First Production Server (1999) detail of networking - Computer History Museum ( by Carlo Nardone)” Carlo Nardone from Roma, Italy / CC BY-SA 2.0 (cropped)
- Internet congestion control algorithms: “ONE ACCESS voice and network router - front” hystiff / CC0 (cropped)
- In-network congestion control (NCC): “Cell tower antenna, Zagrebačka cesta” Mnalis / CC BY-SA 4.0 (cropped)
- Estimating network conditions with machine learning: “Visualizing Artificial Neural Networks (ANNs) with python library-ANN Visualizer” Serenechan3 / CC BY-SA 4.0 (cropped)
- Information compression for large-scale network management: “Wikimedia Foundation Servers-8055 08” Victorgrigas / CC BY-SA 3.0 (cropped)
- Related lab (再構成可能システム学): “TEI0001 Altera MAX 10 FPGA MAX1000 IoT Maker Board” Antti Lukats / CC0 (cropped)
- Related lab (ワイヤレスネットワーク学): “RouterBoard R52 miniPCI Wi-Fi card with U.FL-RSMA pigtail” Kozuch / CC BY-SA 4.0 (cropped)
- Related lab (ファームウェア科学): “Night sky radio towers” 作者不詳 / CC0 (cropped)
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