|
 |
 |
|
 Thanh Phung Truong |
| Manh Cuong Ho |
|
|
|
 |
|
 |
|
| Intelligent Reflecting Surface |
| Intelligent Reflecting Surface (IRS), also called Reconfigurable Intelligent Surface (RIS) or Large Intelligent Surface (LIS), which is constituted by multiple passive scattering elements in a 2-D surface, provides advantages in throughput improvement. In IRS, the features of reflecting elements are adjusted by a central software-defined controller to yield efficient signal reflection. As a result, this effect can enhance the received signal quality of the end devices. The use of the IRS is capable of bringing unprecedented performance enhancement for future wireless systems by reconfiguring the previously uncontrollable wireless channels in favor of network performance optimization, which can effectively enhance the received signal power, extend the network coverage, increase the link capacity, minimize the transmit power, suppress the interference, enable better security and QoS provisioning to multiple users, etc.
|
 |
|
|
|
| Intellectual Merits and Broader Impacts |
| (1) A Study on distributed multiple IRS relays |
| (2) A Study on FlyReflect |
| (3) Broader Impact |
|
|
|
|
| (1) A Study on distributed multiple IRS relays |
| In the relaying system, the direct communication links may be blocked by both thick walls in indoor scenarios and trees and large buildings in outdoor scenarios, particularly in high-frequency millimeter-wave communication systems. Here, the IRS can be easily installed at the desired location as needed. |
| Achievable Rate Analysis of Two-Hop Interference Channel With Coordinated IRS Relay |
| In this study, we identified the achievable rate region of a two-hop interference channel with distributed multiple IRS relays. To do so, we formulated a non-convex problem that characterizes the rate-profile and found its solution using successive convex approximation (SCA). We then proposed an alternating direction method of multipliers (ADMM) and alternating optimization (AO) based distributed and low-complex IRS control that maximizes the achievable sum-rate and proved its convergence and optimality. |
| The proposed controls can be efficiently applied to large-scale multi-pair multihop device-to-device and machine-type device communications in the interference-limited or low-powered dense networks of and 6G environments. |
|
|
 |
|
|
|
|
| (2) A Study on Flying IRS |
| Aerial access infrastructures have been considered a compulsory component of the sixth-generation (6G) networks, where airborne vehicles play the role of mobile access points to service ground users (GUs) from the sky. In this scenario, intelligent reflecting surface (IRS) is one of the promising technologies associated with airborne vehicles for coverage extensions and throughput improvements, named flying IRS (F-IRS). |
| FlyReflect: Joint Flying IRS Trajectory and Phase Shift Design Using Deep Reinforcement Learning |
| This study considers a multiuser multiple-input single-output (MISO) F-IRS system, where the F-IRS reflects downlink signals from ground base stations (BSs) to users located at underserved areas where direct communications are unavailable. To achieve the system sum-rate maximization, we proposed a deep reinforcement learning (DRL) algorithm named FlyReflect to jointly optimize the flying trajectory and IRS phase shift matrix. |
| The proposed approach is feasible for scaling up the environment, as the complexity is first-order linear with the number of IRS elements and the number of users. |
|
|
 |
|
|
|
|
| (3) Broader Imapct |
| The project is expected to pave the way for the development and research of intelligent reflecting surfaces. The results can demonstrate the effectiveness and necessity of employing the intelligent reflecting surface to enhance wireless system performance. |
|
|
|
|
|
|
|
|
|
|
|
|
 |
|
|
T. V. Nguyen, T. P. Truong, T. M. T. Nguyen, W. Noh and S. Cho,
"Achievable Rate Analysis of Two-Hop Interference Channel With Coordinated IRS Relay,"
in IEEE Transactions on Wireless Communications, , vol. 21, no. 9, pp. 7055-7071, Sept. 2022, doi: 10.1109/TWC.2022.3154372.
[PDF]
[IEEE Xplore]
|
|
T. P. Truong, V. D. Tuong, N. -N. Dao and S. Cho,
"FlyReflect: Joint Flying IRS Trajectory and Phase Shift Design Using Deep Reinforcement Learning,"
in IEEE Internet of Things Journal, , vol. 10, no. 5, pp. 4605-4620, March 1, 2023, doi: 10.1109/JIOT.2022.3218740.
[PDF]
[IEEE Xplore]
|
|
|
|
|
|
|
|
|
|
