Reconfigurable Intelligent Surfaces for Future Wireless Networks: A Channel Modeling Perspective

Reconfigurable Intelligent Surface (RIS) in the Sub-6 GHz Band: Design, Implementation, and Real-World Demonstration

“…A substantial amount of research has been conducted regarding the theoretical modelling of the RIS which of them, some recently published are [12]- [16], but when it comes to an actual implementation, there are very few test-bed systems that have been realized so far to evaluate the realistic functionality of a RIS. In [17], a couple of reflecting surface prototypes are introduced; one for 2.3 GHz and the other for 28.5 GHz.…”

6G Cognitive Information Theory: A Mailbox Perspective

“…The real-time analysis and mining of data are pushed in the direction of depth by edge intelligence and cloud intelligence. Therefore, research on data analysis and fusion can be summarized in four aspects: (1) what to fuse (data type and fusion type), (2) where to fuse (fusion location), (3) when to fuse (fusion target), and (4) how to fuse (fusion technologies). As shown in Figure 7, we review existing data-fusion technologies according to the above four aspects: (1) data type (structured data, unstructured data, and semistructured data), (2) fusion target (improving data quality, multi-source data integration, in-depth information mining, and enhancing decision making and evaluation), (3) fusion technologies (the statistical method and the deep-learning method in machine learning), (4) fusion location (terminal fusion and analysis, edge cloud fusion and analysis, cloud fusion and analysis), (5) fusion type (data in data out, data in feature out, feature in feature out, feature in decision out, and decision in decision out).…”

“…As air flight equipment, safe flight is the primary premise and basic guarantee for UAVs to provide many airborne services including computing and offloading. Among them, there are three open problems that need to be solved: (1) In order to improve the accuracy of obstacle recognition, many researchers use a variety of sensor devices to seek higher perception efficiency, and propose a variety of pattern recognition algorithms for target segmentation and location, (2) Considering the uncertainty of flight time, path length, and flight energy consumption, path planning in multi obstacle environment is a typical NP hard problem, (3) Achieving dynamic path planning and obstacle avoidance in the envi-ronment of coexistence of dynamic and static obstacles has high performance requirements for the time precision and distance precision of path planning for UAV in flight.…”

“…In the UAV-assisted MEC offloading scenario, the characteristics of IoT devices, UAV, and MEC networks need to be considered comprehensively. (1) In the research of cooperative computing offloading between the UAV and MEC base station, the design and implementation of network hardware and software architecture of UAV-assisted new MEC computing offloading algorithm is a practical problem of both engineering and academic research. (2) In order to meet the demand of computing the offloading delay, the process of joint optimization of dynamically changing network resources and states (the mobility characteristics of IoT devices and UAVs, trajectory changes, offloading service times, task computation and data volume, computing and communication resources of different computing nodes, etc.)…”

“…This can be attributed to the explosive popularity of wireless mobile terminals and further results in an extensive integration of various sensors in mobile devices, thus providing the powerful sensory and computing ability of devices. Under such conditions, the transmission of data, including images and videos, requires wide frequency bandwidth [1]. The related studies have proven that data transmitted in this way contains a large amount of repeated and useless information, such as videos with limited motion activity captured by a traffic camera at a certain moment.…”