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Register a new userIntelligent Reflecting Surfaces at Terahertz Bands: Channel Modeling and Analysis
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Added by
Konstantinos Dovelos
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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An intelligent reflecting surface (IRS) at terahertz (THz) bands is expected to have a massive number of reflecting elements to compensate for the severe propagation losses. However, as the IRS size grows, the conventional far-field assumption starts becoming invalid and the spherical wavefront of the radiated waves should be taken into account. In this work, we consider a spherical wave channel model and pursue a comprehensive study of IRS-aided multiple-input multiple-output (MIMO) in terms of power gain and energy efficiency (EE). Specifically, we first analyze the power gain under beamfocusing and beamforming, and show that the latter is suboptimal even for multiple meters away from the IRS. To this end, we derive an approximate, yet accurate, closed-form expression for the loss in the power gain under beamforming. Building on the derived model, we next show that an IRS can significantly improve the EE of MIMO when it operates in the radiating near-field and performs beamfocusing. Numerical results corroborate our analysis and provide novel insights into the design and performance of IRS-assisted THz communication.
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Intelligent reflecting surface (IRS)-assisted wireless communication is widely deemed a key technology for 6G systems. The main challenge in deploying an IRS-aided terahertz (THz) link, though, is the severe propagation losses at high frequency bands. Hence, a THz IRS is expected to consist of a massive number of reflecting elements to compensate for those losses. However, as the IRS size grows, the conventional far-field assumption starts becoming invalid and the spherical wavefront of the radiated waves must be taken into account. In this work, we focus on the near-field and analytically determine the IRS response in the Fresnel zone by leveraging electromagnetic theory. Specifically, we derive a novel expression for the path loss and beampattern of a holographic IRS, which is then used to model its discrete counterpart. Our analysis sheds light on the modeling aspects and beamfocusing capabilities of THz IRSs.
In this paper, unmanned aerial vehicles (UAVs) and intelligent reflective surface (IRS) are utilized to support terahertz (THz) communications. To this end, the joint optimization of UAVs trajectory, the phase shift of IRS, the allocation of THz sub-bands, and the power control is investigated to maximize the minimum average achievable rate of all the users. An iteration algorithm based on successive Convex Approximation with the Rate constraint penalty (CAR) is developed to obtain UAVs trajectory, and the IRS phase shift is formulated as a closed-form expression with introduced pricing factors. Simulation results show that the proposed scheme significantly enhances the rate performance of the whole system.
With the rapid development of advanced electromagnetic manipulation technologies, researchers and engineers are starting to study smart surfaces that can achieve enhanced coverages, high reconfigurability, and are easy to deploy. Among these efforts, simultaneously transmitting and reflecting intelligent omni-surface (STAR-IOS) is one of the most promising categories. Although pioneering works have demonstrated the benefits of STAR-IOSs in terms of its wireless communication performance gain, several important issues remain unclear including practical hardware implementations and physics-compliant models for STAR-IOSs. In this paper, we answer these pressing questions of STAR-IOSs by discussing four practical hardware implementations of STAR-IOSs, as well as three hardware modelling methods and five channel modelling methods. These discussions not only categorize existing smart surface technologies but also serve as a physicscompliant pipeline for further investigating the STAR-IOSs.
In this letter, simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are studied. Compared with the conventional reflecting-only RISs, the coverage of STAR-RISs is extended to 360 degrees via simultaneous transmission and reflection. A general hardware model for STAR-RISs is presented. Then, channel models are proposed for the near-field and the far-field scenarios, base on which the diversity gain of the STAR-RISs is analyzed and compared with that of the conventional RISs. Numerical simulations are provided to verify analytical results and to demonstrate that full diversity order can be achieved on both sides of the STAR-RIS.
Reconfigurable intelligent surfaces (RISs) are an emerging technology for future wireless communication. The vast majority of recent research on RIS has focused on system level optimizations. However, developing straightforward and tractable electromagnetic models that are suitable for RIS aided communication modeling remains an open issue. In this paper, we address this issue and derive communication models by using rigorous scattering parameter network analysis. We also propose new RIS architectures based on group and fully connected reconfigurable impedance networks that can adjust not only the phases but also the magnitudes of the impinging waves, which are more general and more efficient than conventional single connected reconfigurable impedance network that only adjusts the phases of the impinging waves. In addition, the scaling law of the received signal power of an RIS aided system with reconfigurable impedance networks is also derived. Compared with the single connected reconfigurable impedance network, our group and fully connected reconfigurable impedance network can increase the received signal power by up to 62%, or maintain the same received signal power with a number of RIS elements reduced by up to 21%. We also investigate the proposed architecture in deployments with distance-dependent pathloss and Rician fading channel, and show that the proposed group and fully connected reconfigurable impedance networks outperform the single connected case by up to 34% and 48%, respectively.
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