Channel Modeling and Analysis of Reconfigurable Intelligent Surfaces Assisted Vehicular Networks

Abstract

The new concept named reconfigurable intelligent surfaces (RIS) is becoming an appealing enabler due to its uniqueness with having low hardware complexity and low power consumption advantages simultaneously. In this paper, an RISaided vehicular Adhoc network (VANET) is considered, where the beacon vehicle is enabled with a passive RIS, the communication links between the beacon vehicle and client vehicle caused due to the multipath fading effects, are modeled with Fox's H-function distribution. This paper first models the inter-vehicle links for the given system setup and then investigates the outage probability and effective rate as performance metrics. More specifically, the unsupervised expectation-maximization (EM) algorithm is consequently used to characterize the distribution of the received signal-to-noise ratio (SNR) at the client vehicle, which is modeled as the mixture of Gaussian (MoG) distribution. The accuracy of our approach is further validated with the Kolmogorov-Smirnov (KS) goodness of fit test. The MoG-based approach successfully tackles the RIS-enabled inter-vehicle communication with an easy, accurate, and tractable solution compared to the widely used central limit theorem (CLT) method. It leads to the closedform outage probability and effective rate expressions.

Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN

“…RIS, being an appealing approach, has become a hot topic for researchers from both wireless communication and signal processing communities [13]. The machine learning-enabled RIS-assisted wireless communication systems have been under exploration in terms of channel modelling [4], [14], channel estimation, EE [15], etc. More specifically, the authors in [4] and [14] applied the unsupervised machine learning tool, namely, the expectation-maximization (EM) algorithm to model the RIS-assisted wireless communication links.…”

“…The machine learning-enabled RIS-assisted wireless communication systems have been under exploration in terms of channel modelling [4], [14], channel estimation, EE [15], etc. More specifically, the authors in [4] and [14] applied the unsupervised machine learning tool, namely, the expectation-maximization (EM) algorithm to model the RIS-assisted wireless communication links. It is also demonstrated that the machine learning methods are able to provide better performance than central limit theorem-based approaches [4] [14].…”

“…More recently, reconfigurable intelligent surfaces (RISs) are proposed and considered as key enablers for future wireless communications [3]. RIS is essentially an electronically operated metasurface controlled by programmable software, which is physically equivalent to digitally controllable scatterers and software defined surface [4]. A large number of small, lowcost, and passive artificial "meta-atoms" integrated into the RIS can smartly change the reflection direction towards any desired users by tuning a series of phase shifters.…”

RIS-Assisted Vehicular Network with Direct Transmission over Double-Generalized Gamma Fading Channels

“…The authors [17] developed an iterative algorithm to optimize the RIS phase shifts for the data rate maximization of a RISassisted mmWave vehicular communication network. In [18], the authors studied the RIS-enabled vehicle-to-vehicle (V2V) communications using the Fox's H-function distribution. In [19], the placement of multiple RIS is optimized for vehicleto-everything (V2X) communications.…”

RIS-Assisted Multihop FSO/RF Hybrid System for Vehicular Communications over Generalized Fading

“…The authors in [9] applied series expansion and central limit theorem (CLT) to approximate the outage probability performance of the RIS-assisted vehicular communication assuming Rayleigh and Rician fading channels. In [11], the authors provided an approximate analysis on the outage probability and effective rate for RIS-assisted V2V communications over Fox's-H function by approximating the distribution of the signal-to-noise ratio (SNR) of the resultant channel as a mixture of Gaussian distributions. The authors in [12] evaluated the performance of RIS-enabled vehicular system considering joint impact of Fisher-Snedecor (F) fading channel and the vehicle mobility modeled as a random way point.…”