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Rui Zhang - One of the best experts on this subject based on the ideXlab platform.
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intelligent Reflecting Surface aided multicasting with random passive beamforming
IEEE Wireless Communications Letters, 2020Co-Authors: Qin Tao, Shuowen Zhang, Caijun Zhong, Rui ZhangAbstract:In this letter, we consider a multicast system where a single-antenna transmitter sends a common message to multiple single-antenna users, aided by an intelligent Reflecting Surface (IRS) equipped with N passive Reflecting elements. Prior works on IRS have mostly assumed the availability of channel state information (CSI) for designing its passive beamforming. However, the acquisition of CSI requires substantial training overhead that increases with N. In contrast, we propose in this letter a novel random passive beamforming scheme, where the IRS performs independent random reflection for Q≥1 times in each channel coherence interval without the need of CSI acquisition. For the proposed scheme, we first derive a closed-form approximation of the outage probability, based on which the optimal Q with best outage performance can be efficiently obtained. Then, for the purpose of comparison, we derive a lower bound of the outage probability with traditional CSI-based passive beamforming. Numerical results show that a small Q is preferred in the high-outage regime (or with high rate target) and the optimal Q becomes larger as the outage probability decreases (or as the rate target decreases). Moreover, the proposed scheme significantly outperforms the CSI-based passive beamforming scheme with training overhead taken into consideration when N and/or the number of users are large, thus offering a promising CSI-free alternative to existing CSI-based schemes.
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intelligent Reflecting Surface aided multicasting with random passive beamforming
arXiv: Signal Processing, 2020Co-Authors: Qin Tao, Shuowen Zhang, Caijun Zhong, Rui ZhangAbstract:In this letter, we consider a multicast system where a single-antenna transmitter sends a common message to multiple single-antenna users, aided by an intelligent Reflecting Surface (IRS) equipped with $N$ passive Reflecting elements. Prior works on IRS have mostly assumed the availability of channel state information (CSI) for designing its passive beamforming. However, the acquisition of CSI requires substantial training overhead that increases with $N$. In contrast, we propose in this letter a novel \emph{random passive beamforming} scheme, where the IRS performs independent random reflection for $Q\geq 1$ times in each channel coherence interval without the need of CSI acquisition. For the proposed scheme, we first derive a closed-form approximation of the outage probability, based on which the optimal $Q$ with best outage performance can be efficiently obtained. Then, for the purpose of comparison, we derive a lower bound of the outage probability with traditional CSI-based passive beamforming. Numerical results show that a small $Q$ is preferred in the high-outage regime (or with high rate target) and the optimal $Q$ becomes larger as the outage probability decreases (or as the rate target decreases). Moreover, the proposed scheme significantly outperforms the CSI-based passive beamforming scheme with training overhead taken into consideration when $N$ and/or the number of users are large, thus offering a promising CSI-free alternative to existing CSI-based schemes.
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intelligent Reflecting Surface aided wireless communications a tutorial
arXiv: Information Theory, 2020Co-Authors: Shuowen Zhang, Beixiong Zheng, Changsheng You, Rui ZhangAbstract:Intelligent Reflecting Surface (IRS) is an enabling technology to engineer the radio signal prorogation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive Reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communication to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.
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capacity characterization for intelligent Reflecting Surface aided mimo communication
IEEE Journal on Selected Areas in Communications, 2020Co-Authors: Shuowen Zhang, Rui ZhangAbstract:Intelligent Reflecting Surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive Reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices are optimized for different subcarriers while only one common set of IRS reflection coefficients is designed to cater to all the subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.
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intelligent Reflecting Surface practical phase shift model and beamforming optimization
International Conference on Communications, 2020Co-Authors: Samith Abeywickrama, Rui Zhang, Chau YuenAbstract:Intelligent Reflecting Surface (IRS) that enables the control of the wireless propagation environment has been looked upon as a promising technology for boosting the spectrum and energy efficiency in future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming the full signal reflection by each of the elements regardless of its phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper a practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection coefficient. Applying this new model to an IRS-aided wireless system, we formulate a problem to maximize its achievable rate by jointly optimizing the transmit beamforming and the IRS reflect beamforming. The formulated problem is non-convex and difficult to be optimally solved in general, for which we propose a low-complexity suboptimal solution based on the alternating optimization (AO) technique. Simulation results unveil a substantial performance gain achieved by the joint beamforming optimization based on the proposed phase shift model as compared to the conventional ideal model.
Erik G Larsson - One of the best experts on this subject based on the ideXlab platform.
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weighted sum rate maximization for intelligent Reflecting Surface enhanced wireless networks
Global Communications Conference, 2019Co-Authors: Huayan Guo, Yingchang Liang, Jie Chen, Erik G LarssonAbstract:Intelligent Reflecting Surface (IRS) is a romising solution to build a programmable wireless environment for future communication systems, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. This work focuses on the downlink of an IRSaided multiuser multiple-input single-output (MISO) system. A practical IRS assumption is considered, in which the incident signal can only be shifted with discrete phase levels. Then, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. This non-convex problem is firstly decomposed via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. In addition, an efficient algorithm with closed-form solutions is proposed for the passive beamforming, which is applicable to both the discrete phase- shift IRS and the continuous phaseshift IRS. Simulation results have verified the effectiveness of the proposed algorithm as compared to different benchmark schemes.
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weighted sum rate optimization for intelligent Reflecting Surface enhanced wireless networks
arXiv: Signal Processing, 2019Co-Authors: Huayan Guo, Yingchang Liang, Jie Chen, Erik G LarssonAbstract:Intelligent Reflecting Surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems. In practice, an IRS consists of massive low-cost elements, which can steer the incident signal in fully customizable ways by passive beamforming. In this paper, we consider an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. In particular, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels. This non-convex problem is firstly decoupled via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. The active beamforming at BS is optimized based on the fractional programming method. Then, three efficient algorithms with closed-form expressions are proposed for the passive beamforming at IRS. Simulation results have verified the effectiveness of the proposed algorithms as compared to different benchmark schemes.
Yingchang Liang - One of the best experts on this subject based on the ideXlab platform.
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intelligent Reflecting Surface assisted non orthogonal multiple access
Wireless Communications and Networking Conference, 2020Co-Authors: Gang Yang, Yingchang LiangAbstract:Intelligent Reflecting Surface (IRS) is a new and disruptive technology to achieve spectrum-, energy, and cost-efficient wireless networks. In this paper, we consider an IRS-assisted non-orthogonal-multiple-access (NOMA) system in which a base station (BS) transmits superposed downlink signals to multiple users. A combined-channel-strength (CCS) based user ordering scheme is first proposed. In order to optimize the rate performance and ensure user fairness, we further maximize the minimum decoding signal-to-interference-plus-noise-ratio (i.e., equivalently the rate) of all users, by jointly optimizing the power allocation at the BS and the phase shifts at the IRS. However, the formulated problem is non-convex and difficult to be solved optimally. By leveraging the block coordinate descent and semidefinite relaxation techniques, an efficient algorithm is then proposed to obtain a suboptimal solution. Simulation results show that the IRS-assisted downlink NOMA system can enhance the rate performance significantly, compared to traditional NOMA without IRS and traditional orthogonal multiple access with/without IRS, and the rate degradation due to the IRS’s finite phase resolution is slight.
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intelligent Reflecting Surface assisted non orthogonal multiple access
arXiv: Information Theory, 2019Co-Authors: Gang Yang, Yingchang LiangAbstract:Intelligent Reflecting Surface (IRS) is a new and disruptive technology to achieve spectrum- and energy-efficient as well as cost-efficient wireless networks. This paper considers an IRS-assisted downlink non-orthogonal-multiple-access (NOMA) system. To optimize the rate performance and ensure user fairness, we maximize the minimum decoding signal-to-interference-plus-noise-ratio (i.e., equivalently the rate) of all users, by jointly optimizing the (active) transmit beamforming at the base station (BS) and the phase shifts (i.e., passive beamforming) at the IRS. A combined-channel-strength based user ordering scheme is first proposed to decouple the user-ordering design and the joint beamforming design. Efficient algorithms are further proposed to solve the formulated non-convex problem for the cases of a single-antenna BS and a multi-antenna BS, respectively, by leveraging the block coordinated decent and semidefinite relaxation (SDR) techniques. For the single-antenna BS case, the optimal solution for the power allocation at the BS and the asymptotically optimal solution for the phase shifts at the IRS are obtained in closed forms. For the multi-antenna BS case, it is shown that the rank of the SDR solution to the transmit beamforming design is upper bounded by two. Also, the convergence proof and the complexity analysis are given for the proposed algorithms. Simulation results show that the IRS-assisted downlink NOMA system can enhance the rate performance significantly, compared to traditional NOMA without IRS and traditional orthogonal multiple access with/without IRS. In addition, numerical results demonstrate that the rate degradation due to the IRS's finite phase resolution is slight, and good rate fairness among users can be always guaranteed.
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weighted sum rate maximization for intelligent Reflecting Surface enhanced wireless networks
Global Communications Conference, 2019Co-Authors: Huayan Guo, Yingchang Liang, Jie Chen, Erik G LarssonAbstract:Intelligent Reflecting Surface (IRS) is a romising solution to build a programmable wireless environment for future communication systems, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. This work focuses on the downlink of an IRSaided multiuser multiple-input single-output (MISO) system. A practical IRS assumption is considered, in which the incident signal can only be shifted with discrete phase levels. Then, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. This non-convex problem is firstly decomposed via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. In addition, an efficient algorithm with closed-form solutions is proposed for the passive beamforming, which is applicable to both the discrete phase- shift IRS and the continuous phaseshift IRS. Simulation results have verified the effectiveness of the proposed algorithm as compared to different benchmark schemes.
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weighted sum rate optimization for intelligent Reflecting Surface enhanced wireless networks
arXiv: Signal Processing, 2019Co-Authors: Huayan Guo, Yingchang Liang, Jie Chen, Erik G LarssonAbstract:Intelligent Reflecting Surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems. In practice, an IRS consists of massive low-cost elements, which can steer the incident signal in fully customizable ways by passive beamforming. In this paper, we consider an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. In particular, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels. This non-convex problem is firstly decoupled via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. The active beamforming at BS is optimized based on the fractional programming method. Then, three efficient algorithms with closed-form expressions are proposed for the passive beamforming at IRS. Simulation results have verified the effectiveness of the proposed algorithms as compared to different benchmark schemes.
Jun Zhao - One of the best experts on this subject based on the ideXlab platform.
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Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning
2020Co-Authors: Helin Yang, Jun Zhao, Dusit Niyato, Zehui Xiong, Massimo Tornatore, Stefano SecciAbstract:Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent Reflecting Surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the Surface Reflecting elements at the IRS. 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. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLFCPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and Reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the the IRS-assisted system rate and transmission protection level compared with existing solutions.
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secrecy rate maximization for intelligent Reflecting Surface aided swipt systems
arXiv: Signal Processing, 2020Co-Authors: Wei Sun, Qingyang Song, Lei Guo, Jun ZhaoAbstract:Simultaneous wireless information and power transfer (SWIPT) and intelligent Reflecting Surface (IRS) are two promising techniques for providing enhanced wireless communication capability and sustainable energy supply to energy-constrained wireless devices. Moreover, the combination of the IRS and the SWIPT can create the "one plus one greater than two" effect. However, due to the broadcast nature of wireless media, the IRS-aided SWIPT systems are vulnerable to eavesdropping. In this paper, we study the security issue of the IRS-aided SWIPT systems. The objective is to maximize the secrecy rate by jointly designing the transmit beamforming and artificial noise (AN) covariance matrix at a base station (BS) and reflective beamforming at an IRS, under transmit power constraint at the BS and energy harvesting (EH) constraints at multiple energy receivers. To tackle the formulated non-convex problem, we first employ an alternating optimization (AO) algorithm to decouple the coupling variables. Then, reflective beamforming, transmit beamforming and AN covariance matrix can be optimized by using a penalty-based algorithm and semidefinite relaxation (SDR) method, respectively. Simulation results demonstrate the effectiveness of the proposed scheme over baseline schemes.
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Intelligent Reflecting Surface Aided Network: Power Control for Physical-Layer Broadcasting
2020Co-Authors: Huimei Han, Jun Zhao, Dusit Niyato, Marco Renzo, Quoc-viet PhamAbstract:As a recently proposed idea for future wireless systems, intelligent Reflecting Surface (IRS) can assist communications between entities which do not have high-quality direct channels in between. Specifically, an IRS comprises many low-cost passive elements, each of which reflects the incident signal by incurring a phase change so that the reflected signals add coherently at the receiver. In this paper, for an IRS-aided wireless network, we study the problem of power control at the base station (BS) for physical-layer broadcasting under quality of service (QoS) constraints at mobile users, by jointly designing the transmit beamforming at the BS and the phase shifts of the IRS units. Furthermore, we derive a lower bound of the minimum transmit power at the BS to present the performance bound for optimization methods. Simulation results show that, the transmit power at the BS approaches the lower bound with the increase of the number of IRS units, and is much lower than that of the communication system without IRS.
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Intelligent Reflecting Surface-Aided Backscatter Communications
arXiv: Information Theory, 2020Co-Authors: Xiaolun Jia, Jun Zhao, Xiangyun Zhou, Dusit NiyatoAbstract:We introduce a novel system setup where a backscatter device operates in the presence of an intelligent Reflecting Surface (IRS). In particular, we study the bistatic backscatter communication (BackCom) system assisted by an IRS. The phase shifts at the IRS are optimized jointly with the transmit beamforming vector of the carrier emitter, to minimize the transmit power consumption at the carrier emitter, whilst guaranteeing a required BackCom performance. The unique channel characteristics arising from multiple reflections at the IRS render the optimization problem highly non-convex. Therefore, we jointly utilize the minorization-maximization (MM) algorithm and the semidefinite relaxation (SDR) technique to present an approximate solution for the optimal IRS phase shift design. We also extend our analytical results to the monostatic BackCom system. Numerical results indicate that the introduction of the IRS brings about considerable reductions in transmit power, even with moderate IRS sizes, which can be translated to range increases over the non-IRS-assisted BackCom system.
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deep reinforcement learning based intelligent Reflecting Surface for secure wireless communications
arXiv: Signal Processing, 2020Co-Authors: Helin Yang, Jun Zhao, Dusit Niyato, Zehui Xiong, Liang XiaoAbstract:In this paper, we study an intelligent Reflecting Surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its Surface Reflecting elements to guarantee secure communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's Reflecting beamforming is formulated given the different quality of service (QoS) requirements and time-varying channel condition. 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. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning-based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.
Huayan Guo - One of the best experts on this subject based on the ideXlab platform.
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weighted sum rate maximization for intelligent Reflecting Surface enhanced wireless networks
Global Communications Conference, 2019Co-Authors: Huayan Guo, Yingchang Liang, Jie Chen, Erik G LarssonAbstract:Intelligent Reflecting Surface (IRS) is a romising solution to build a programmable wireless environment for future communication systems, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. This work focuses on the downlink of an IRSaided multiuser multiple-input single-output (MISO) system. A practical IRS assumption is considered, in which the incident signal can only be shifted with discrete phase levels. Then, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. This non-convex problem is firstly decomposed via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. In addition, an efficient algorithm with closed-form solutions is proposed for the passive beamforming, which is applicable to both the discrete phase- shift IRS and the continuous phaseshift IRS. Simulation results have verified the effectiveness of the proposed algorithm as compared to different benchmark schemes.
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weighted sum rate optimization for intelligent Reflecting Surface enhanced wireless networks
arXiv: Signal Processing, 2019Co-Authors: Huayan Guo, Yingchang Liang, Jie Chen, Erik G LarssonAbstract:Intelligent Reflecting Surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems. In practice, an IRS consists of massive low-cost elements, which can steer the incident signal in fully customizable ways by passive beamforming. In this paper, we consider an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. In particular, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels. This non-convex problem is firstly decoupled via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. The active beamforming at BS is optimized based on the fractional programming method. Then, three efficient algorithms with closed-form expressions are proposed for the passive beamforming at IRS. Simulation results have verified the effectiveness of the proposed algorithms as compared to different benchmark schemes.
