Session 11: WOS-2 & VAP2022-05-11T13:54:40+00:00

Session 11: WOS-2 + VAP

Thursday, 9 June 2022, 10:30-12:00

(room tbd)

Session Chair: TBD ( , )

Ultra-Reliable Low-Latency Communication for Aerial Vehicles via Multi-Connectivity

Fateme Salehi and Mustafa Ozger (KTH Royal Institute of Technology, Sweden); Naaser Neda (University of Birjand, Iran); Cicek Cavdar (KTH Royal Institute of Technology, Sweden)
Aerial vehicles (AVs) such as electrical vertical take-off and landing (eVTOL) make aerial passenger transportation a reality in urban environments. However, their communication connectivity is still under research to realize their safe and full-scale operation, which requires stringent end-to-end (E2E) reliability and delay. In this paper, we evaluate reliability and delay for the downlink communication of AVs, i.e., remote piloting, control/telemetry traffic of AVs. We investigate direct air-to-ground (DA2G) and air-to-air (A2A) communication technologies, along with high altitude platforms (HAPs) to explore the conditions of how multi-connectivity options satisfy the demanding E2E connectivity requirements under backhaul link bottleneck. Our considered use case is ultra-reliable low-latency communication (URLLC) under the finite blocklength (FBL) regime due to the nature of downlink control communication to AVs. In our numerical study, we find that providing requirements by single connectivity to AVs is very challenging due to the line-of-sight (LoS) interference and reduced gains of downtilt ground base station (BS) antenna. We also find that even with very efficient interference mitigation, existing cellular networks designed for terrestrial users are not capable of meeting the URLLC requirements calling for multi-connectivity solutions.

A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks

Eslam Eldeeb, Dian Echevarria Perez, Jean Michel S Sant’Ana, Mohammad Shehab, Nurul Huda Mahmood, Hirley Alves and Matti Latva-aho (University of Oulu, Finland)
Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. Age of Information (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This work considers a setup of deployed sensors in an IoT network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs’ trajectory, while minimizing the AoI of the received messages. This ensures that the received information at the base station is as fresh as possible. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower AoI than the random walk scheme. Our proposed algorithm reduces the average age by approximately 25% and requires down to 50% less energy when compared to the baseline scheme.

UAV/HAP-Assisted Vehicular Edge Computing in 6G: Where and What to Offload?

Alessandro Traspadini, Marco Giordani and Michele Zorzi (University of Padova, Italy)
In the context of 6th generation (6G) networks, vehicular edge computing (VEC) is emerging as a promising solution to let battery-powered ground vehicles with limited computing and storage resources offload processing tasks to more powerful devices. Given the dynamic vehicular environment, VEC systems need to be as flexible, intelligent, and adaptive as possible. To this aim, in this paper we study the opportunity to realize VEC via non-terrestrial networks (NTNs), where ground vehicles offload resource-hungry tasks to Unmanned Aerial Vehicles (UAVs), High Altitude Platforms (HAPs), or a combination of the two. We define an optimization problem in which tasks are modeled as a Poisson arrival process, and apply queuing theory to find the optimal offloading factor in the system. Numerical results show that aerial-assisted VEC is feasible even in dense networks, provided that high-capacity HAP/UAV platforms are available.

Optimizing Beam Selection and Resource Allocation in UAV-Aided Vehicular Networks

Silvia Mignardi (University of Bologna, Italy); Danila Ferretti, Riccardo Marini and Francesca Conserva (University of Bologna & WiLab/CNIT, Italy); Stefania Bartoletti (National Research Council of Italy (IEIIT-CNR), Italy); Roberto Verdone and Chiara Buratti (University of Bologna, Italy)
Future mobile radio networks require a degree of flexibility that technologies like Unmanned Aerial Vehicles (UAVs) carrying Base Stations (BSs) can provide. It is expected that the lower space above cities will be populated by many different types of UAVs, such as taxis and smaller drones used for logistics or patrolling, which can be equipped with BSs to serve users on the ground, while flying for their given mission. We investigate an urban scenario with terrestrial macro BSs (MBSs) deployed, where multiple UAVs are flying on a given path. Vehicles in the area are moving while relying on network services, and MBSs alone might not serve them adequately. UAVs operate as BSs, helping the MBSs. Vehicles are assumed to be satisfied if an appropriate quality of experience (QoE) is fulfilled, that is they are able to upload a given amount of data during a given time window, continuously. We assume BSs use beamforming and a limited number of beams can be activated at the same time on UAVs. This paper proposes an optimization algorithm allowing to select the best set of beams to be activated at each UAV and the best set of resource units per vehicle, in order to maximize the QoE. The algorithm jointly considers: i) resource management at both MBSs and UAVs; ii) traffic prioritization to attain the continuous service; iii) a limited backhaul capacity. Numerical results show the notable improvement of satisfied users when the flying BSs are present and report the impact of backhaul capacity.

[VAP]Sensing Based Contention Access for 6G Low Latency Networks

Bikramjit Singh and Sarthak Seth (Ericsson, Finland)
The 5th generation is aimed to provide Ultra-Reliable and Low Latency Communications. In view of that, 5G has standardized Grant-Free method in the form of Configured Grant for low latency data transmission. However, if network allocates multiple users on same Configured Grant resource collision may happen and the reliability may degrade. The 5G has no means to resolute collisions in a proactive manner and this leads to retransmissions, causing an increase in latency. In this paper, we aim to study a sensing design for users to transmit in a Grant-Free manner, so that they can resolute collision by themselves and save latency. We analyze the collision probability of proposed algorithms utilizing
sensing design and existing prior-art, and accordingly the results are simulated and compared with their analytical models.

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