Deep Learning based Radio Resource Management in Cellular Vehicular Communication

Abstract The C-V2X sidelink communication was introduced by the 3rd generation partnership project (3GPP) in Long Term Evolution (LTE) to pave the way for future intelligent transport so-lutions. The vision of C-V2X sidelink communication is to support a diversified range of use cases, e.g., advanced driving and collision avoidance, with stringent quality of service (QoS) requirements. The QoS requirements vary from ultra-reliable low latency to high data rates de-pending on the supported application. Radio resource management (RRM) is vital to achieve the required QoS requirements in C-V2X sidelink communication due to the limited spec-trum availability. RRM becomes challenging in C-V2X sidelink communication due to the high mobility and dynamic traffic pattern of the Vehicle User Equipment (V-UEs) on top of the limited spectrum availability. Therefore, this proposes an intelligent RRM approach taking into account the dynamic behavior of the C-V2X ecosystem and meeting the required QoS requirements in terms of latency and reliability. Two problems under C-V2X RRM are discussed in the thesis. First, the radio resource allocation mechanism for V-UEs in unicast C-V2X sidelink communication is investigated. The proposed solution is a decentralized QoS-based DRL radio resource allocation approach. As the proposed approach is decentralized, it is assumed that each V-UE maintaining one unicast link at a time is acting as an agent. Hence, the intelligence is assumed to be embedded at the V-UE side. The Mode 2 in-coverage scenario consists of a Vehicle to Network (V2N) and Vehicle to Vehicle (V2V) links. The QoS parameter incorporated in the proposed scheme is the independent QoS parameter, i.e., the priority associated with each C-V2X message. The priority reflects the allowed latency budget within which a V-UE has to transmit a packet to meet the latency requirements of the sup-ported C-V2X application. The goal of the V-UE agent is to meet the latency constraints of V2V links associated with the respective priority while maximizing the throughput of all V2N links. A performance evaluation of the proposed approach is accomplished based on system-level simulations for both urban and highway scenarios. The results show that incorporating the QoS parameter (i.e., priority) in the DRL-based resource allocation is crucial for the V-UE agent to meet the latency requirements pertaining to different C-V2X applications. The proposed QoS-based DRL radio resource allocation approach is further analyzed for the scenario where V-UE can support multiple unicast links simultaneously. Performance compar-ison to evaluate the impact of multiple services is conducted through a single-agent reinforce-ment learning (SARL) and a multi-agent reinforcement learning (MARL) approach. Addition-ally, the QoS parameter considered here is the latency and the relative distance between the V-UEs with the established unicast link, which is mapped to the priority to reflect the packet delay budget (PDB). This scenario also assumes V2N and V2V links in unicast communication in an urban setting. The goal of the V-UE agent here also is to meet the latency constraints of V2V links associated with the respective priority while maximizing the throughput of all V2N links. System-level simulation-based results indicate that the MARL achieves a higher V2N throughput for single and multiple services support than SARL. However, in meeting the latency constraints, SARL performs better for multiple service support per V-UE. Also, it can be concluded that overall, in the case of multiple service support per V-UE, the probability of meeting the latency constraint by both SARL and MARL is reduced. The second problem investigated in the thesis is a DRL-based congestion control approach for V-UEs experiencing high channel load and hence performance degradation in achieving the required QoS requirements. The DRL-based congestion control approach is formulated for a unified and location-based segregated resource pool. The scenario in consideration consists of V-UEs in dynamic groupcast communication and mode 2 in coverage. The intelligence is as-sumed at the base station; therefore, the formulated approach is a centralized DRL congestion control. A performance evaluation of the algorithm is conducted for periodic and aperiodic traffic models in a realistic mobility scenario generated in the Simulation of Urban Mobil-ity (SUMO) platform. The simulation results show that the proposed DRL-based congestion control approach achieves the packet reception ratio (PRR) per the packet’s associated QoS irrespective of resource pool configuration. However, achieved PRR with a DRL-based conges-tion control approach is better for periodic traffic than aperiodic traffic. The DRL agent can maintain the average measured Channel Busy Ratio (CBR) below 0.65 irrespective of resource pool configuration

Evaluation of NR-Sidelink for Cooperative Industrial AGVs

Industry 4.0 has brought to attention the need for a connected, flexible, and autonomous production environment. The New Radio (NR)-sidelink, which was introduced by the third-generation partnership project (3GPP) in Release 16, can be particularly helpful for factories that need to facilitate cooperative and close-range communication. Automated Guided Vehicles (AGVs) are important for material handling and carriage within these environments, and using NR-sidelink communication can further enhance their performance. An efficient resource allocation mechanism is required to ensure reliable communication and avoid interference between AGVs and other wireless systems in the factory using NR-sidelink. This work evaluates the 3GPP standardized resource allocation algorithm for NR-sidelink for a use case of cooperative carrying AGVs. We suggest further improvements that are tailored to the quality of service (QoS) requirements of an indoor factory communication scenario with cooperative AGVs.The use of NR-sidelink communication has the potential to help meet the QoS requirements for different Industry 4.0 use cases. This work can be a foundation for further improvements in NR-sidelink in 3GPP Release 18 and beyond

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

White Paper on Machine Learning in 6G Wireless Communication Networks : 6G Research Visions, No. 7, 2020

This white paper discusses various topics, advances, and projections regarding machine learning (ML) in wireless communications. Sixth generation (6G) wireless communications networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research have enabled a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is made possible by the availability of advanced ML models, large datasets, and high computational power. In addition, the ever-increasing demand for connectivity will require even more extensive innovation in 6G wireless networks. Consequently, ML tools will play a major role in solving the new problems in the wireless domain. In this paper, we offer a vision of how ML will impact wireless communications systems. We first provide an overview of the ML methods that have the highest potential to be used in wireless networks. We then discuss the problems that can be solved by using ML in various layers of the network such as the physical, medium-access, and application layers. Zero-touch optimization of wireless networks using ML is another interesting aspect discussed in this paper. Finally, at the end of each section, a set of important future research questions is presented