Energy-Efficient Resource Allocation for 6G Backscatter-Enabled NOMA IoV Networks

Abstract

The integration of Ambient Backscatter Communication (AmBC) with Non-Orthogonal Multiple Access (NOMA) is expected to support connectivity of low-powered Internetof-Vehicle (IoV) in the upcoming Sixth-Generation (6G) transportation system. This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated. A Dinkelbach approach is first adopted to transform the optimization problem and then the transformed problem is decoupled into two subproblems for optimal transmit power at RSUs and efficient reflection power at BackTags, respectively. To solve the problems efficiently, dual theory and Karush-Kuhn-Tucker conditions are exploited, where the Lagrangian dual variables are iteratively calculated using the subgradient method. To check the performance of the proposed framework, a benchmark optimization without backscattering is also provided. Numerical results demonstrate the superiority of the proposed AmBC-enabled NOMA IoV framework over the benchmark conventional IoV framework.

Joint Design of Improved Spectrum and Energy Efficiency With Backscatter NOMA for IoT

“…In such system, an artificial noise scheme is employed to allow the radio frequency (RF) source acting as a jammer that makes interference signals to eavesdropper and the legitimate receivers. Another scenario exploring the Backscatter Tags (BackTags) in low-powered Internet-of-Vehicles (IoVs) was studied by reflecting the superimposed signals from multiple Road-Side Units (RSUs) to transfer signals towards nearby IoVs [23]. The authors formulated optimization of the total energy efficiency of the NOMA IoV network to achieve the minimum data rate of all IoVs.…”

“…The authors formulated optimization of the total energy efficiency of the NOMA IoV network to achieve the minimum data rate of all IoVs. A joint problem of total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags was examined under realistic scenario of imperfect SIC decoding [23]. The authors in [24] further studied NOMA to enable vehicle-to-everything (V2X) networks and enhance the achievable rates through cooperation.…”

An Interference-Managed Hybrid Clustering Algorithm to Improve System Throughput

“…Subsequently, in [19,20], the author implemented NOMA-enabled back-scattered V2X on a vehicular network to achieve improved system data rates, enhanced energy efficiency with NOMA, and supported connectivity of low-powered Internet-of-Vehicle (IoV) for a 6G transportation system. The results were verified through simulations to demonstrate an efficient scheme that outperformed the results gained with a conventional sub-optimal NOMA protocol.…”

Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

“…In V2X IoV communications, ETAC learning approaches enable to development of an intelligent resource management framework to intelligently make the decision. The process of ETAC is based on the Markov decision process (MDP) RL acts as a reward function and AC learning to describe the intelligent resource management issues [118]. Both AC learning and MDP work together to enable suitable solutions to optimize the policies for IoV networks resource management.…”