Achievable Rate Optimization for MIMO Systems With Reconfigurable Intelligent Surfaces

Perović, Nemanja Stefan; Tran, Le-Nam; Renzo, Marco Di; Flanagan, Mark F.

doi:10.1109/twc.2021.3054121

Cited by 151 publications

(113 citation statements)

References 28 publications

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“…Recent researches on the capacity optimization of IRS-aided communication system mainly focus on jointly optimizing the active beamforming at the transmitter and the passive beamforming at the IRS [118,[125][126][127][128][129][130]. Specifically, a locally optimal solution for the achievable rate maximization problem can be obtained by iteratively optimizing one matrix variable (i.e.…”

Section: Capacity Optimizationmentioning

confidence: 99%

“…the active beamforming matrix or the phase shift matrix of the IRS) with the other matrix variable that is fixed [126]. The prior works in [118,[125][126][127][128] all consider that the IRS uses the continuous phase shift. However, the reflecting elements with continuous phase shifts are practically difficult to implement due to the complex hardware design, and definitely result in much higher system cost.…”

Section: Capacity Optimizationmentioning

confidence: 99%

“…Specifically, the ability of the IRS fulfills the concepts of beamforming and beam tracking. With respect to the beam control, although the beamforming technique for the IRS assisted communication system is extensively studied in [118,[125][126][127][128][129][130], the beam tracking technique, which requires the dynamic CSI acquisition between the IRS and the mobile station (MS), is still a major challenge for IRS assisted THz communications due to the passive reflecting elements at IRS side. As shown in Figure 9, a typical IRS-aided beam tracking scenario is presented, where a BS serves a MS via the IRS.…”

Section: Beam Controlmentioning

confidence: 99%

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Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

et al. 2021

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Terahertz (THz) communications have been widely envisioned as a promising enabler to provide adequate bandwidth and achieve ultra-high data rates for sixth generation (6G) wireless networks. In order to mitigate blockage vulnerability caused by serious propagation attenuation and poor diffraction of THz waves, an intelligent reflecting surface (IRS), which manipulates the propagation of incident electromagnetic waves in a programmable manner by adjusting the phase shifts of passive reflecting elements, is proposed to create smart radio environments, improve spectrum efficiency and enhance coverage capability. Firstly, some prospective application scenarios driven by the IRS empowered THz communications are introduced, including wireless mobile communications, secure communications, unmanned aerial vehicle (UAV) scenario, mobile edge computing (MEC) scenario and THz localization scenario. Then, we discuss the enabling technologies employed by the IRS empowered THz system, involving hardware design, channel estimation, capacity optimization, beam control, resource allocation and robustness design. Moreover, the arising challenges and open problems encountered in the future IRS empowered THz communications are also highlighted. Concretely, these emerging problems possibly originate from channel modeling, new material exploration, experimental IRS testbeds and intensive deployment. Ultimately, the combination of THz communications and IRS is capable of accelerating the

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Section: Capacity Optimizationmentioning

confidence: 99%

Section: Capacity Optimizationmentioning

confidence: 99%

Section: Beam Controlmentioning

confidence: 99%

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Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

et al. 2021

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“…Overall, the modeling, analysis, and optimization of largescale RIS-assisted wireless networks is a major open research issue, which requires the development of new tools for the link-level modeling of RIS, which needs to account for the scattering (e.g., reflections) introduced by RIS-coated objects, and which needs to consider appropriate association strategies between the users and the RIS so as to best use the available RISs [374], [375]. Finally, the development of tools that combine ML and SG for optimizing large-scale networks is another major research venue [38], [39].…”

Section: The Promising Approach Of Metasurfacesmentioning

confidence: 99%

New Trends in Stochastic Geometry for Wireless Networks: A Tutorial and Survey

et al. 2021

Self Cite

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Next generation wireless networks are expected to be highly heterogeneous, multi-layered, with embedded intelligence at both the core and edge of the network. In such a context, system-level performance evaluation will be very important to formulate relevant insights into tradeoffs that govern such a complex system. Over the past decade, stochastic geometry (SG) has emerged as a powerful analytical tool to evaluate system-level performance of wireless networks and capture their tendency towards heterogeneity. However, with the imminent onset of this crucial new decade, where global commercialization of fifthgeneration (5G) is expected to emerge and essential research questions related to beyond fifth-generation (B5G) are intended to be identified, we are wondering about the role that a powerful tool like SG should play. In this paper, we first aim to track and summarize the novel SG models and techniques developed during the last decade in the evaluation of wireless networks. Next, we will outline how SG has been used to capture the properties of emerging radio access networks (RANs) for 5G/B5G and quantify the benefits of key enabling technologies. Finally, we will discuss new horizons that will breathe new life into the use of SG in the foreseeable future. For instance, using SG to evaluate performance metrics in the visionary paradigm of molecular communications. Also, we will review how SG is envisioned to cooperate with machine learning seen as a crucial component in the race towards ubiquitous wireless intelligence. Another important insight is Grothendieck toposes considered as a powerful mathematical concept that can help to solve longstanding problems formulated in SG.

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“…, is the complexity-accuracy trade-off factor and is the error term that can be neglected at sufficiently higher value of . The final approximate closed-form expression for 1 is given in (29), shown on the top of the next page.…”

Section: B Csi Estimation and Central Control Unitmentioning

confidence: 99%

Non-Linear Energy Harvesting in RIS-assisted URLLC Networks for Industry Automation

Dhok¹,

Raut²,

Sharma³

et al. 2021

Preprint

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A reconfigurable intelligent surface (RIS)-assisted wireless communication system with non-linear energy harvesting (EH) and ultra-reliable low-latency constraints is considered for its possible applications in industrial automation. A distant data-center (DC) communicates with the multiple destination machines with the help of a full-duplex (FD) server machine (SM) and RIS. Assuming the deficiency of enough transmission power at the FD-SM, the SM is considered in the near vicinity of the destinations in the industry to forward the data received from the distant DC. The reception at SM is assisted by the RIS and a non-linear hybrid power-time splitting (PTS) based EH receiver architecture is adopted to extend the lifespan of SM, thus increasing network lifetime. The scheduling of multiple destinations is done by SM based on the considered selection criteria namely, random (RND) scheduling, absolute (ABS) channel-power-based (CPB) scheduling and normalized (NRM) CPB scheduling. The end-to-end performance of the considered FD RIS-assisted network is analyzed, and the expressions for the block error rate (BLER) for all scheduling schemes are derived. Moreover, the effects of number of RIS elements, packet size, channel uses on the system performance are analyzed for the considered ultra-reliable and low-latency communication (URLLC) network. The scheduling fairness of all the scheduling schemes is also analyzed to study the performance-fairness trade-off. The derived analytical results are verified through Monte-Carlo simulations.

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Achievable Rate Optimization for MIMO Systems With Reconfigurable Intelligent Surfaces

Cited by 151 publications

References 28 publications

Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

New Trends in Stochastic Geometry for Wireless Networks: A Tutorial and Survey

Non-Linear Energy Harvesting in RIS-assisted URLLC Networks for Industry Automation

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