This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support the sixth-generation wireless physical platforms (6G). Due to their ability to adjust the behavior of interacting electromagnetic (EM) waves through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a lowcost, green, sustainable, and energy-efficient solution for 6G systems. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid radio frequency/visible light communication (RF-VLC) systems, health considerations, and localization. INDEX TERMS 6G technology, large intelligent surfaces (LIS), massive multiple-input multiple-output (mMIMO), millimetre waves (mmWave) communication, wireless communication.
This research highlights the development of a two-dimensional (2D) and three-dimensional (3D) preview software for additive manufacturing (AM). The presented software can produce a virtual representation of an actuator’s path movements by reading and parsing the orders of the desired geometric code (G-code) file. It then simulates the coded sections into separate 2D layers and colored 3D objects in a graphical model. This allows users to validate the shapes before the 3D printing process. G-code is an operation language which is based on command lines of code written in an alphanumeric format. Each line of these commands controls one machining operation; this instructs the machine’s motion to move in an arc, a circle, or a straight line to perform a specific shape after compiling all code lines. AM technology is widely used in most manufacturing fields (e.g., medical, chemical, and research laboratories) as a prototyping technology due to its ability to produce rapid prototyping models. 3D printing creates physical 3D models by extruding material layer by layer as 2D layers. At present, the most critical challenges in AM technology are drastically reducing prototyping materials’ consumption and time spent. To address these challenges, the proposed software allows for visualization of G-code files and predicting the overall layers’ shapes, allowing both structure prediction and subsequent printing error reduction.
This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support next generation wireless physical platforms (6G). Due to their ability to adjust the channels through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a low cost, green, sustainable, and energy-efficient 6G solution. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid RF/VLC systems, health considerations, and localization.
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