Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks

Abstract

Autonomous driving and intelligent transportation demand ultra-low latency and high reliability communication in future vehicular networks. Proactive wireless communication can facilitate minimal latency by open-loop communication, which discards traditional feedback control mechanisms. However, appropriate radio resource allocation in such proactive mobile networks has not been fully studied due to lacking channel state information (CSI) and the alleviation of multiple access interference (MAI) in multiple virtual cells. This paper aims to ensure the reliability of downlink communication by a novel radio resource allocation scheme in proactive vehicular networks with ultra-low latency. We regard data transmission success rate as the reliability indicator and propose a joint radio resource allocation model based on the ''generalized closed-loop'', where anchor node (AN) uses the radio resource utilization information (RRUI) from the vehicle in the immediate past uplink as a guide to assist resource allocation. Subsequently, we study the radio resource allocation model solution on the vehicle side and the network side respectively. On the vehicle side, vehicles use the local or global data transmission experience to select the radio resource with the best quality as the RRUI. On the network side, according to the latest RRUI of vehicle and resource occupancy information, deep reinforcement learning is proposed to make appropriate radio resource allocation decisions. Simulations demonstrate the effectiveness of the intelligent joint radio resource allocation scheme under the cooperation between vehicles and AN. When the resource load rate reaches 40%, the joint radio resource allocation scheme can achieve a data transmission success rate of more than 98%.

RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach

“…1. Without RIS : This scenario depicts the PMN downlink transmission in its original state, without any interference suppression mechanisms [ 10 ]. In this case, RIS-related processes are eliminated and the interference capacity is provided by …”

“…For the specific data transmission and resource management method, refs. [ 6 , 10 , 16 ] provide uplink and downlink solutions, respectively. The core challenge in the uplink is to ensure transmission reliability when the network is in passive service without control interaction.…”

“…Ref. [ 10 ] recommends that the SM controls the network side during downlink transmission, and by introducing non-real-time information in the preceding uplink process, it facilitates the selection of resources used in the present downlink transmission. Although research on PMNs is still in the exploratory stage, the proposed scheme has its limitations as it only examines performance from a single strategy.…”

“…Consequently, a proactive mobile network (PMN) architecture is proposed [ 5 , 6 , 7 ]. The PMN architecture is considered to have significant theoretical value and holds the potential for deployment in future 6G networks [ 8 , 9 , 10 ].…”

Minimum Latency-Secure Key Transmission for Cloud-Based Internet of Vehicles Using Reinforcement Learning

“…While inside the car, automotive monitors like brake sensors, limited fuel, and tyres pressure sensors, among others, are fitted, and the exterior of the car has security mechanisms like CCTV and parking sensors. Reference [ 17 , 18 ] proposed using reinforcement learning to assist mobile devices in optimizing relay scheme, transmit power, and communication frequency to resist hostile interference. This scheme can reduce energy consumption and ensure communication reliability.…”