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Register a new userReconfigurable Intelligent Surfaces in Challenging Environments: Underwater, Underground, Industrial and Disaster
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Reconfigurable intelligent surfaces (RISs) have been introduced to improve the signal propagation characteristics by focusing the signal power in the preferred direction, thus making the communication environment smart. The typical use cases and applications for the smart environment include beyond 5G communication networks, smart cities, etc. The main advantage of employing RISs in such networks is a more efficient exploitation of spatial degrees of freedom. This advantage manifests in better interference mitigation as well as increased spectral and energy efficiency due to passive beam steering. Challenging environments comprise a range of scenarios, which share the fact that it is extremely difficult to establish a communication link using conventional technology due to many impairments typically associated with the propagation medium and increased signal scattering. Although the challenges for the design of communication networks, and specifically the Internet of Things (IoT), in such environments are known, there is no common enabler or solution for all these applications. Interestingly, the use of RISs in such scenarios can become such an enabler and a game changer technology. Surprisingly, the benefits of RIS for wireless networking in underwater and underground medium as well as in industrial and disaster environments have not been addressed yet. In this paper, we aim at filling this gap by discussing potential use cases, deployment strategies and design aspects for RIS devices in underwater IoT, underground IoT as well as Industry 4.0 and emergency networks. In addition, novel research challenges to be addressed in this context are described.
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A reconfigurable intelligent surface (RIS) is a metamaterial that can be integrated into walls and influence the propagation of electromagnetic waves. This, typically passive radio frequency (RF) technology is emerging for indoor and outdoor use with the potential of making wireless communications more reliable in increasingly challenging radio environments. This paper goes one step further and introduces mobile RIS, specifically, RIS carried by unmanned aerial vehicles (UAVs) to support cellular communications networks and services of the future. We elaborate on several use cases, challenges, and future research opportunities for designing and optimizing wireless systems at low cost and with low energy footprint.
Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), or large intelligent surfaces (LISs), have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) of communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multi-antenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for applications to next-generation networks.
With both the standardization and commercialization completed in an unforeseen pace for the 5th generation (5G) wireless network, researchers, engineers and executives from the academia and the industry have turned their sights on candidate technologies to support the next generation wireless networks. Reconfigurable intelligent surfaces (RIS), sometimes referred to as intelligent reflecting surfaces (IRS), have been identified to be potential components of the future wireless networks because they can reconfigure the propagation environment for wireless signals with low-cost passive devices. In doing so, the coverage of a cell can be expected to increase significantly as well as the overall throughput of the network. RIS has not only become an attractive research area but also triggered a couple of projects to develop appropriate solutions to enable the set-up of hardware demonstrations and prototypes. In parallel, technical discussions and activities towards standardization already took off in some regions. Promoting RIS to be integrated into future commercial networks and become a commercial success requires significant standardization work taken place both at regional level standards developing organizations (SDO) and international SDOs such as the 3rd Generation Partnership Project (3GPP). While many research papers study how RIS can be used and optimized, few effort is devoted to analyzing the challenges to commercialize RIS and how RIS can be standardized. This paper intends to shed some light on RIS from an industrial viewpoint and provide a clear roadmap to make RIS industrially feasible.
Reconfigurable intelligent surfaces (RISs) are planar structures with attached electronic circuitry that enable a partially programmable communication environment. RIS operation can be regarded as nearly passive since it acts by simply reflecting the impinging traveling waves towards desired directions, thus requiring energy only for the reconfiguration of its reflective elements (REs). This paper tackles the problem of wirelessly powering RIS circuitry via control signaling. Simultaneous wireless information and power transfer (SWIPT) is considered by taking into account two basic principles: that signal quality of the control signals is sufficient for information detection, and that there is enough harvested energy for the reconfiguration. Some of the most common SWIPT receivers (time sharing, power splitting, dynamic power splitting, and antenna selection) are studied and the corresponding proposed optimization problems implementing the aforementioned principles are formulated and solved in closed form. Numerical results show the effectiveness of the proposed methods in the presence of received power fluctuations.
Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL), which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the active/inactive binary status of the RIS elements. Simulation results validate the performance improvement offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach.
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