No-Regret Matching Game Algorithm for NOMA Based UAV-Assisted NB-IoT Systems

Abstract

NB-IoT is the standardized technology for machine type communication (MTC) in Long Term Evolution (LTE). NB-IoT can achieve IoT requirements nevertheless, it suffers a low rate and capacity. On the other hand, Unmanned aerial vehicles (UAV) and Non-Orthogonal Multiple Access (NOMA) are promising technology used to enhance the throughput, capacity, and coverage of wireless communication networks. In this paper, we propose a heterogeneous network scenario where a UAV small Base Station (UBS) is used to assist the LTE Macro Base Station (MBS) with the help of the Non-Orthogonal Multiple Access technique to solve the NB-IoT throughput and capacity issues. Matching game based no-regret learning algorithm is proposed to optimize the NB-IoT device association and using NOMA pairing at each base station to provide the maximum system total rate and capacity. Simulation results show that our proposed scheme increases the total rate of the system by 60% and the system capacity by at least 80%, compared to NOMA without UAV and the total rate and capacity of the system by 200% and 85% respectively, with OMA scheme.

Optimized QoS Routing in Software-Defined In-Vehicle Networks

“…With the substantial increase in the functional requirements of smart vehicles, the number and complexity of software have rapidly increased. Different applications have different delay and quality of service (QoS) constraints on data transmission, which puts forward higher requirements for invehicle communication [6,8,14,31,34]. To meet a series of complex deterministic communication requirements, such as high bandwidth, low delay, low jitter and zero congestion in IVNs, IVNs need to provide fast and effective data transmission and limited delay for real-time operation.…”