@inproceedings{HegdeStahlLoboetal.2022, author = {Hegde, Anupama and Stahl, Ringo and Lobo, Silas and Festag, Andreas}, title = {Modeling Cellular Network Infrastructure in SUMO}, volume = {2}, booktitle = {SUMO Conference Proceedings}, publisher = {TIB Open Publishing}, address = {Hannover}, issn = {2750-4425}, doi = {https://doi.org/10.52825/scp.v2i.97}, pages = {99 -- 113}, year = {2022}, abstract = {Communication networks are becoming an increasingly important part of the mobility system. They allow traffic participants to be connected and to exchange information related to traffic and roads. The information exchange impacts the behavior of traffic participants, such as the selection of travel routes or their mobility dynamics. Considering infrastructure-based networks, the information exchange depends on the availability of the network infrastructure and the quality of the communication links. Specifically in urban areas, today's 4G and 5G networks deploy small cells of high capacity, which do not provide ubiquitous cellular coverage due to their small range, signal blocking, etc. Therefore, the accurate modeling of the network infrastructure and its integration in simulation scenarios in microscopic traffic simulation software is gaining relevance. Unlike traffic infrastructure, such as traffic lights, the simulation of a cellular network infrastructure is not natively supported in SUMO. Instead, the protocols, functions and entities of the communication system with the physical wireless transmission are modeled in a dedicated and specialized network simulator that is coupled with SUMO. The disadvantage of this approach is that the simulated SUMO entities, typically vehicles, are not aware which portions of the roads are covered by wireless cells and what quality the wireless communication links have. In this paper, we propose a method for modeling the cellular infrastructure in SUMO that introduces a cellular coverage layer to SUMO. This layer models cell sites in a regular hexagonal grid, where each site is served by a base station. Following commonly accepted guidelines for the evaluation of cellular communication system, the method facilitates standardized and realistic modeling of the cellular coverage, including cell sites, antenna characteristics, cell association and handover. In order to ease the applicability of the method, we describe the work flow to create cell sites. As a representative case, we have applied the method to InTAS, the SUMO Ingolstadt traffic scenario and applied real data for the cellular infrastructure. We validate the approach by simulating a Cellular V2X system with sidelink connectivity in an urban macro cell environment by coupling SUMO enhanced by the proposed connectivity sublayer with ARTERY-C, a network simulator for Cellular V2X. As a proof-of-concept, we present a signal-to-interference noise ratio (SINR) coverage map and further evaluate the impact of different types of interference. We also demonstrate the effect of advanced features of cellular networks such as inter-cell interference coordination (ICIC) and sidelink communication modes of Cellular V2X with dynamic switching between the in-coverage and out-of-coverage mode.}, language = {en} } @inproceedings{HegdeLoboFestag2022, author = {Hegde, Anupama and Lobo, Silas and Festag, Andreas}, title = {Cellular-V2X for Vulnerable Road User Protection in Cooperative ITS}, booktitle = {2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6975-3}, issn = {2160-4894}, doi = {https://doi.org/10.1109/WiMob55322.2022.9941707}, pages = {118 -- 123}, year = {2022}, language = {en} } @article{HegdeSongFestag2023, author = {Hegde, Anupama and Song, Rui and Festag, Andreas}, title = {Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach}, volume = {11}, journal = {IEEE Access}, publisher = {IEEE}, address = {Piscataway}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2023.3305267}, pages = {87225 -- 87244}, year = {2023}, abstract = {The efficiency of radio resource allocation and scheduling procedures in Cellular Vehicle-to-X (Cellular V2X) communication networks directly affects link quality in terms of latency and reliability. However, owing to the continuous movement of vehicles, it is impossible to have a centralized coordinating unit at all times to manage the allocation of radio resources. In the unmanaged mode of the fifth generation new radio (5G-NR) V2X, the sensing-based semi-persistent scheduling (SB-SPS) loses its effectiveness when V2X data messages become aperiodic with varying data sizes. This leads to misinformed resource allocation decisions among vehicles and frequent resource collisions. To improve resource selection, this study formulates the Cellular V2X communication network as a decentralized multi-agent networked markov decision process (MDP) where each vehicle agent executes an actor-critic-based radio resource scheduler. Developing further the actor-critic methodology for the radio resource allocation problem in Cellular V2X, two variants are derived: independent actor-critic (IAC) and shared experience actor-critic (SEAC). Results from simulation studies indicate that the actor-critic schedulers improve reliability, achieving a 15-20\% higher probability of reception under high vehicular density scenarios with aperiodic traffic patterns.}, language = {en} } @inproceedings{HegdeFestag2020, author = {Hegde, Anupama and Festag, Andreas}, title = {Mode Switching Performance in Cellular-V2X}, booktitle = {2020 IEEE Vehicular Networking Conference (VNC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-9221-5}, issn = {2157-9865}, doi = {https://doi.org/10.1109/VNC51378.2020.9318394}, year = {2020}, language = {en} } @inproceedings{HegdeFestag2020, author = {Hegde, Anupama and Festag, Andreas}, title = {Artery-C}, booktitle = {MSWiM '20 : Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems}, subtitle = {an OMNeT++ Based Discrete Event Simulation Framework for Cellular V2X}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-8117-8}, doi = {https://doi.org/10.1145/3416010.3423240}, pages = {47 -- 51}, year = {2020}, language = {en} } @inproceedings{SongHegdeSeneletal.2022, author = {Song, Rui and Hegde, Anupama and Senel, Numan and Knoll, Alois and Festag, Andreas}, title = {Edge-Aided Sensor Data Sharing in Vehicular Communication Networks}, booktitle = {2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) Proceedings}, publisher = {IEEE}, address = {Piscataway (NJ)}, isbn = {978-1-6654-8243-1}, issn = {2577-2465}, doi = {https://doi.org/10.1109/VTC2022-Spring54318.2022.9860849}, year = {2022}, language = {en} } @inproceedings{HegdeDeloozMariyakllaetal.2023, author = {Hegde, Anupama and Delooz, Quentin and Mariyaklla, Chethan L. and Festag, Andreas and Klingler, Florian}, title = {Radio Resource Allocation for Collective Perception in 5G-NR Vehicle-to-X Communication Systems}, booktitle = {2023 IEEE Wireless Communications and Networking Conference (WCNC): Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-9122-8}, issn = {1558-2612}, doi = {https://doi.org/10.1109/WCNC55385.2023.10118606}, year = {2023}, language = {en} } @article{HegdeFestag2019, author = {Hegde, Anupama and Festag, Andreas}, title = {Mode Switching Strategies in Cellular-V2X}, volume = {52}, journal = {IFAC-PapersOnLine}, number = {8}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2405-8963}, doi = {https://doi.org/10.1016/j.ifacol.2019.08.052}, pages = {81 -- 86}, year = {2019}, language = {en} } @phdthesis{Hegde2024, author = {Hegde, Anupama}, title = {Radio Resource Allocation in Cellular V2X: From Rule Based to Reinforcement Learning Based Approaches}, publisher = {Friedrich-Alexander-Universit{\"a}t Erlangen-N{\"u}rnberg}, address = {Erlangen}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-49215}, pages = {viii, 178}, school = {Friedrich-Alexander-Universit{\"a}t Erlangen-N{\"u}rnberg}, year = {2024}, abstract = {The wireless communication technology has gained significant attention in the transportation industry over the recent years. Cellular Vehicle-to-Everything (V2X) communication facilitates the information exchange among road users (such as vehicles, pedestrians etc.) and the infrastructure with an intention to improve the overall road safety, driving comfort, traffic efficiency and save energy. Advanced use-cases aim towards enhancing key functionalities of vehicle automation by means of sensor data sharing and cooperative maneuver \& trajectory planning. The introduction of the PC5 interface for sidelink (SL) communication within the mobile communication systems, supports direct exchange of messages between users, independent of the cellular network infrastructure. Two types of radio resource allocation modes are supported in Cellular V2X: managed mode and the unmanaged mode. In the managed mode, a User Equipment (UE) remains connected to the cellular network and the process of resource allocation is coordinated by the base station. In the unmanaged mode, a UE selects its radio resources from a pre-configured resource pool without any assistance from the base station. Originally both these modes were developed by considering that the vehicles exchange periodic messages which are safety-critical in nature. The existing rule based radio resource allocation algorithms in both the modes are unable to adapt their selection parameters in the events of aperiodic data traffic patterns resulting from the diverse generation rules of different V2X messaging protocols. We begin this PhD thesis by carrying out system level network simulations within the developed framework Artery-C, where we study the metrics and parameters that influence the performance of the rule-based radio resource allocation in the sidelink modes. In the first step, we derive the baseline conditions where each mode performs to its best efficiency. By varying the generation rules of the messaging protocols, we further analyze the behavior of the modes when V2X data traffic does not follow a specific pattern. Our studies have shown that both the modes suffer from frequent re-allocations because the messages are no longer periodic and the data sizes do not fit into the previously allocated radio resources. This results in poor utilization of the allocated resources. The unmanaged mode is particularly susceptible to radio resource collisions because the vehicles only have partial awareness about the resource selection decisions of other road traffic participants. As a second contribution, we examine the criteria for sidelink mode selection and the possibilities for a mode switching operation within the sidelink modes and also between the sidelink and the cellular (Uu) modes. We have formulated the strategies for mode switching and calculate the latency in each phase of the mode switch procedure. Although the managed mode has shown advantages with regard to allocation and management of radio resources, it is to be noted that a vehicle cannot remain connected to a base station at all instants of time. Also, switching between different modes is not seamless considering the associated latencies in each phase. This leads us towards the goal of improving the efficiency of the allocation \& scheduling of radio resources in the unmanaged mode. After a careful review of the enhancements that can be implemented within the rule based algorithm in the unmanaged mode, it was found that there needs to be a mechanism where vehicles can continuously share their resource selection decisions, adapt their selection parameters and even re-evaluate them (if needed) within a grant period. Therefore, we investigated the Reinforcement Learning (RL) based Artificial Intelligence (AI) approaches that facilitate independent learning, adapting and decision making among spatially distributed vehicular agents. We have developed a fully decentralized multi agent networked Markovian Decision Process (MDP) model of the Cellular V2X communication network where each agent executes an AI based radio resource scheduler. By extending the actor-critic methodology of the RL, we have derived two variants - Independent Actor Critic (IAC) and Shared Experience Actor Critic (SEAC). The results of our evaluations have indicated that both these schedulers have a potential to achieve better radio resource utilization with a reduced risk of radio resource collisions among the agents. Subsequently, it brings about 15 - 20\% improvement in the reliability of the communication link which we regard as a valuable contribution. To summarize, this PhD thesis investigates the performance of the rule based radio resource allocation algorithms in Cellular V2X and proposes the qualitative improvements that can be achieved by means of reinforcement learning.}, language = {en} }