SmartCom Virtual Workshop †
電子情報通信学会 スマート無線研究会では、国際ワークショップ（SmartCom Virtual Workshop）を企画しております。
- Title: Discreteness-aware signal detection via compressed sensing technique
- Lecturer: Professor Kazunori Hayashi, Kyoto University Japan
- Date: March 15, 2023
- Start Time: 3:00 pm, JST
- Registration Site
(Deadline of Registration, March 13, 2023)
Summary of Tutorial
Unknown vector reconstruction from its linear measurements is one of typical signal processing problems in wireless communications systems including channel equalization, channel estimation, MIMO (multi-input multi-output) signal detection, and IoT (internet-of-things) signal detection. In conventional approaches, the number of observations in linear measurements is generally greater than or equal to that of the elements of unknown vector. For example, in MIMO signal detection problem, the number of receiving antennas should be greater than or equal to that of transmit streams. However, it is preferable if we can cope with the cases of underdetermined linear measurements from a viewpoint of spectral efficiency, which is of great importance in wireless communications systems. In this lecture, we call such signal processing as overloaded signal detection, and explain fundamental ideas to recover an unknown discrete and/or sparse vector from underdetermined linear measurements via compressed sensing technique assuming the application of the typical IoT signal detection problem.
- Title: Emerging Technology Trends for Smart Radio in Japan and Singapore
- Date: 24 May 2022
- Time: 10:30 (JST) - 13:00 (JST)
- (1) Opening Remarks
- Prof. Kei Sakaguchi (Tokyo Tech, Japan)
- Dr. Sumei Sun (I2R, Singapore)
- (2) Technical Session
- Prof. Yuen Chau (Singapore University of Technology and Design, Singapore), AI-Assisted Reconfigurable Intelligent Surfaces (RIS) Wireless Networks
In this talk, we will present some recent results on the reconfigurable intelligent surfaces (RIS) wireless network empowered by AI, in particular a deep-learning-based hybrid beamforming for RIS-empowered multi-hop teraherz communications, intelligent spectrum learning with RIS, and AI-assisted MAC. Such AI-based solution is particular of important when the network involves multiple users, as the signals impinging upon an RIS can be contaminated by interfering signals which are usually dynamic and unknown. To address this issue, ‘learning’ the properties of the surrounding spectral environment is a promising solution. Motivated by the convergence of artificial intelligence and spectrum sensing, we termed here as spectrum learning, where the RIS controller becomes capable of intelligently ‘think-and-decide’ whether to reflect or not the incident signals.
- Mr. Masashi Iwabuchi (NTT, Japan), RIS-assisted Wireless Channel Control for Intelligent Radio-wave Design
We have proposed a concept of intelligent radio-wave design that has potential to enhance various performances on wireless communication and non-communication functions by dynamically controlling wireless channels. In order to achieve extremely high performances, millimeter-wave (mmWave) and higher-frequency bands are expected to be utilized in future wireless access, such as 5G evolution and 6G. For pursuing extremely high data rate and high capacity wireless communication with improved reliability, it is ideal to communicate in a shorter distance with a Line of Sight environment and increase the number of communication paths to provide more redundancy. In particular, for mmWave and higher-frequency bands, improving wireless channel is important to take advantage of the wider bandwidth because these bands greatly depends on the actual wireless channels.
Satisfying such conditions will require a network topology distributed in the space domain. Therefore, making new communication paths is very important for these bands. In order to generate wireless paths and provide better communication quality in mmWave communications, we have studied a channel control method utilizing relay solutions such as smart repeater, reconfigurable intelligent surface (RIS) and IAB, so far. In this talk, we will introduce the technical concept and our activities related to RIS.
- Dr. Zeng Yonghong (I2R, Singapore), Joint Radar and Communication for Future Wireless Systems
Radar sensing and wireless communication are crucial in many applications like autonomous driving, intelligent transportation, virtual/augmented reality (VR/AR), industrial automation, robot coordination and tracking, disaster relief, etc. Both radar and wireless communications use RF signals and have a lot of similarities in hardware and software. However, currently dedicated spectrum, hardware, and software are used separately for them. This separation has caused huge spectrum, hardware and energy waste. With the increasing usage of software defined radio and digital signal processing, the hardware and RF front-end for radar and communication tends to be similar. Thus, in recent years there is a trend to integrate radar sensing and wireless communication as one of the key technologies in future wireless systems.
There are different approaches for joint radar and communication, which have different advantages and problems. In 6G, higher frequency like the THz band will be used and massive antenna arrays will be employed. These not only increase the communication throughput and reliability, but also create opportunities for very high accuracy environment sensing, where millimetre level localization and high resolution imaging can be achieved. However, it is still unclear how to design the joint sensing and communication system and maximize the overall benefit. There are still many issues and difficulties to be addressed.
- Prof. Naoki Ishikawa (Yokohama National University, Japan), Fundamentals of Quantum Speedup for Wireless Communications
In designing wireless communication systems, the trade-off between performance and complexity is a source of concern for engineers and researchers, who pursue a balance between both from a practical perspective. From a long-term perspective, quantum computation is a promising approach that may strike the fundamental trade-off thanks to quantum superposition and entanglement. In particular, the Grover search is a famous quantum algorithm; it can find an element from a list of unsorted N elements with the query complexity of the square root of N, known as quadratic or quantum speedup. This surprising theoretical capability has inspired those who dream of achieving the ultimate performance with reduced complexity.
This tutorial provides the fundamentals of quantum computation. Quantum circuits and wireless communications are mathematically similar in certain aspects. Focusing on this similarity, we study the basics of quantum computation and quantum circuits so that wireless researchers can easily understand. Then, quantum algorithms such as the Grover search, BBHT, DH, and Grover adaptive search are introduced to explain how they provide quadratic speedup to solve a binary optimization problem (including QUBO and HUBO). Finally, state-of-the-art studies in wireless communications that exploit the Grover-type algorithms are briefly reviewed.
- (3) Closing Session
- Title: Radio Digital Twin: Spatial Radio Resource Recognition and Control
- Date: 26 January 2022
- Time: 15:00 - 17:30 (JST)
- 1. Professor Dr. Katsuya Suto（University of Electro-Communications, Tokyo, Japan）
- Title: How We Recognize Radio Environment with Deep Learning
- Keywords: Deep learning, 6G, Digital Twin, Radio environment prediction
Radio environment estimation plays a key role in decisionmaking in 6G systems, i.e., resource management for cell-free wireless networks, spatial spectrum sharing, intelligent reflecting surface (IRS). However, it remains an open challenge. Deep learning (especially image driven deep learning) has been developing as a promising solution to express the complex radio propagation features in the urban area using feature extraction from 3D maps of cities. The approach learns the correlation between the building features and propagation features to recognize the reflection and diffraction by the buildings. By use of rapid advancement of GPU, it achieves high estimation accuracy with low computation time.
The main objective of this tutorial is to provide a fundamental background of deep learning and then show how to address practical challenges in radio environment estimation. In particular, we first give a tutorial of deep learning used in wireless network fields to provide comprehensive knowledge to the audiences. We then give the current research trend together with implementation details to have a better understanding. After that, we introduce our proposed method, the first approach of using image-to-image translation techniques for radio environment estimation. Finally, we provide how to use the information on radio environment for IRS systems.
- 2. Professor Dr. Yuichi Kawamoto (Tohoku University, Japan）
- Title: How we construct spatial radio environment with intelligent reflecting surface
- Keywords: Intelligent Reflecting Surface (IRS), 6G, Radio propagation, Mobility
Intelligent Reflecting Surface (IRS)-aided wireless communication system has attracted much attention. An IRS consists of a planar surface comprising artificial structures, which are called meta-material, with a large number of passive reflecting elements. By using IRS, it will become possible to flexibly control radio propagation. In this tutorial, we will talk about the researches on how to use IRS to construct spatial radio environment efficiently. Firstly, IRS–user association strategy considering user mobility for IRS-aided multibeam transmission systems will be introduced. We then give some ideas to control IRS while keeping signaling overhead low. Finally, we talk the expected role and the direction of the development of the IRS-aided system in 6G era.