no code implementations • 14 Nov 2023 • Zijian Chen, Ming-Min Zhao, Min Li, Fan Xu, Qingqing Wu, Min-Jian Zhao
Based on the estimation results from the first phase, we formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem.
no code implementations • 13 Nov 2023 • Zixing Tang, Yuanbin Chen, Ying Wang, Tianqi Mao, Qingqing Wu, Marco Di Renzo, Lajos Hanzo
To tackle this challenge, we exploit the sparsity inherent in the cascaded channel.
no code implementations • 1 Nov 2023 • Kaitao Meng, Qingqing Wu, Christos Masouros, Wen Chen, Deshi Li
Integrated sensing and communication (ISAC) has attracted growing interests for sixth-generation (6G) and beyond wireless networks.
no code implementations • 3 Oct 2023 • Chuang Zhang, Geng Sun, Qingqing Wu, Jiahui Li, Shuang Liang, Dusit Niyato, Victor C. M. Leung
Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network.
no code implementations • 18 Aug 2023 • Yapeng Zhao, Qingqing Wu, Guangji Chen, Wen Chen, Ruiqi Liu, Ming-Min Zhao, Yuan Wu, Shaodan Ma
Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT.
no code implementations • 17 Aug 2023 • Geng Sun, Long He, Zemin Sun, Qingqing Wu, Shuang Liang, Jiahui Li, Dusit Niyato, Victor C. M. Leung
Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost.
no code implementations • 3 Aug 2023 • Jiahui Li, Geng Sun, Lingjie Duan, Qingqing Wu
The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs.
no code implementations • 13 Jul 2023 • Ziheng Zhang, Wen Chen, Qingqing Wu, Zhendong Li, Xusheng Zhu, Jinhong Yuan
Drawing inspiration from the advantages of intelligent reflecting surfaces (IRS) in wireless networks, this paper presents a novel design for intelligent omni surface (IOS) enabled integrated sensing and communications (ISAC).
no code implementations • 4 May 2023 • Ziwei Liu, Wen Chen, Zhendong Li, Jinhong Yuan, Qingqing Wu, Kunlun Wang
In this paper, we investigated the downlink transmission problem of a cognitive radio network (CRN) equipped with a novel transmissive reconfigurable intelligent surface (TRIS) transmitter.
no code implementations • 10 Dec 2022 • Zhendong Li, Wen Chen, Ziheng Zhang, Qingqing Wu, Huanqing Cao, Jun Li
Since the coupling of optimization variables, the whole optimization problem is non-convex and cannot be solved directly.
no code implementations • 5 Sep 2022 • Yanan Ma, Ming Li, Yang Liu, Qingqing Wu, Qian Liu
Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices.
no code implementations • 1 Sep 2022 • Wenhao Cai, Ming Li, Yang Liu, Qingqing Wu, Qian Liu
Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment.
no code implementations • 13 Apr 2022 • Wenhao Cai, Rang Liu, Ming Li, Yang Liu, Qingqing Wu, Qian Liu
Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/hardware-efficient manner.
no code implementations • 26 Mar 2022 • Jie Zhang, Jun Li, Yijin Zhang, Qingqing Wu, Xiongwei Wu, Feng Shu, Shi Jin, Wen Chen
Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
no code implementations • 14 Jan 2022 • Zhi Ji, Wendong Yang, Xinrong Guan, Xiao Zhao, Guoxin Li, Qingqing Wu
In this letter, we investigate an unmanned aerial vehicle (UAV) communication system, where an intelligent reflecting surface (IRS) is deployed to assist in the transmission from a ground node (GN) to the UAV in the presence of a jammer.
no code implementations • 16 Dec 2021 • Rang Liu, Ming Li, Yang Liu, Qingqing Wu, Qian Liu
Reconfigurable intelligent surface (RIS) is a promising technology for 6G networks owing to its superior ability to enhance the capacity and coverage of wireless communications by smartly creating a favorable propagation environment.
no code implementations • 11 Sep 2021 • Asim Ihsan, Wen Chen, Wali Ullah Khan, Qingqing Wu, Kunlun Wang
In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal reflection coefficient for roadside sensors under their quality of service (QoS) and a circuit power constraint.
no code implementations • 23 Aug 2021 • Yifan Liu, Bin Duo, Qingqing Wu, Xiaojun Yuan, Jun Li, Yonghui Li
This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange.
no code implementations • 5 Jun 2021 • Yifan Liu, Bin Duo, Qingqing Wu, Xiaojun Yuan, Yonghui Li
This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS).
no code implementations • 17 Feb 2021 • Qingqing Wu, Xiaobo Zhou, Robert Schober
Although adopting different IRS phase shifts for DL WPT and UL WIT, i. e., dynamic IRS beamforming, is in principle possible but incurs additional signaling overhead and computational complexity, it is an open problem whether it is actually beneficial.
Information Theory Hardware Architecture Emerging Technologies Networking and Internet Architecture Information Theory
no code implementations • 23 Dec 2020 • Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, Massimo Tornatore, Stefano Secci
Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated.
no code implementations • 22 Nov 2020 • Yang Liu, Jun Zhao, Ming Li, Qingqing Wu
In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS).
no code implementations • 19 Oct 2020 • Qingqing Wu, Jie Xu, Yong Zeng, Derrick Wing Kwan Ng, Naofal Al-Dhahir, Robert Schober, A. Lee Swindlehurst
On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference.
no code implementations • 17 Aug 2020 • Nandana Rajatheva, Italo Atzeni, Simon Bicais, Emil Bjornson, Andre Bourdoux, Stefano Buzzi, Carmen D'Andrea, Jean-Baptiste Dore, Serhat Erkucuk, Manuel Fuentes, Ke Guan, Yuzhou Hu, Xiaojing Huang, Jari Hulkkonen, Josep Miquel Jornet, Marcos Katz, Behrooz Makki, Rickard Nilsson, Erdal Panayirci, Khaled Rabie, Nuwanthika Rajapaksha, MohammadJavad Salehi, Hadi Sarieddeen, Shahriar Shahabuddin, Tommy Svensson, Oskari Tervo, Antti Tolli, Qingqing Wu, Wen Xu
Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G.
no code implementations • 29 Jul 2020 • Rang Liu, Ming Li, Qian Liu, A. Lee Swindlehurst, Qingqing Wu
Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion.
no code implementations • 26 Jul 2020 • Hongyu Li, Wenhao Cai, Yang Liu, Ming Li, Qian Liu, Qingqing Wu
Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.
no code implementations • 6 Jul 2020 • Qingqing Wu, Shuowen Zhang, Beixiong Zheng, Changsheng You, Rui Zhang
Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal prorogation in wireless networks.
no code implementations • 3 Jul 2020 • Yang Liu, Jun Zhao, Ming Li, Qingqing Wu
In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS).
no code implementations • 21 Apr 2020 • Samira Jaber, Wen Chen, Kunlun Wang, Qingqing Wu
Moreover, the proposed method is compared with orthogonal frequency devision multiple access (OFDMA) and code devision multiple access (CDMA) in terms of spectral efficiency (SE) and energy efficiency (EE) respectively.
no code implementations • 27 Feb 2020 • Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Liang Xiao, Qingqing Wu
As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments.
no code implementations • 14 Oct 2019 • Qingqing Wu, Rui Zhang
Moreover, by exploiting the short-range coverage of IRSs, we further propose a low-complexity algorithm by optimizing the phase shifts of all IRSs in parallel.