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Machine Learning-Based Beam Selection for V2X Communication
Igbafe Orikumhi,Sunwoo Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
In this paper, we develop a machine learning (ML) framework for vehicular networks that aid beam selection in the presence of the other road users. The frequency of beam selection is determined by the speed of the vehicle, the channel coherence time and blockages between the transmitter and receiver. The results shows that the selection overhead can be greatly reduced even in a high-speed communication scenario. Subsequently the system throughput can b by allocating more time to data transmission.
Situational Awareness Acquisition for 5G mmWave V2X Communications: A Brief Review
Igbafe Orikumhi,Sunwoo Kim 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
High speed wireless communications and sensing technologies are keys to enabling advanced intelligent transportation systems (ITSs). To support the high data rate requirement with high-speed communications, millimeter-wave (mmwave) which provides large spectrum resources has been of interest. This interest is especially due to the large amount of sensor data required to be shared between different nodes in the networks. Hence hence gaining situational awareness of the communication environment is crucial to fully exploit its potentials. In this paper, we review situational awareness acquisition methods and also present some of the future challenges to be addressed in mm-wave vehicle-to-everything networks.
Location-Aided User Selection and Sum-Rate Analysis for mmWave NOMA
Igbafe, Orikumhi,Leow, Chee Yen,Kim, Sunwoo 한국통신학회 2023 Journal of communications and networks Vol.25 No.1
In this paper, we propose a user selection schemebased on location-aided interference prediction to reduce thetraining overhead in a non-orthogonal multiple access (NOMA)system. First, we cluster the users based on their locationinformation, enabling the use of non-orthogonal pilot sequencewithin a cluster and orthogonal pilot sequence between differentclusters to reduce the uplink pilot training length. Secondly,we exploit the location information in the computation of thecovariance matrices, enabling the prediction of the interferencebetween users. The predicted interference is employed to selectthe set of users with minimum interference for uplink channelestimation and downlink NOMA data transmission. Finally,the achievable sum-rate of the massive multiple-input multiple-output millimeter wave NOMA system is analyzed. The analyticaland numerical results reveal that the location information canbe exploited for user selection to reduce the effect of pilotcontamination, enhancing the uplink channel estimation anddownlink achievable sum-rate.
DRL-based Multi-UAV trajectory optimization for ultra-dense small cells
Orikumhi Igbafe,Bae Jungsook,박현우,김선우 한국통신학회 2023 ICT Express Vol.9 No.6
In this paper, we propose a deep reinforcement learning (DRL) based unmanned aerial vehicles (UAV)-assisted trajectory optimization for ultra-dense small cell networks. We assume that each UAV is equipped with a sensing radio to obtain distance information to the UEs and other UAVs in the network which are used to update the UAV’s trajectory. The proposed DRL-based system selects the optimal joint control actions for the UAVs that maximizes the system sum-rate. The simulation results show that the proposed DRL-based UAV controller provides fast UAV placement in the network with a high system performance when compared with the benchmark schemes.
ISAC-enable mobility-aware multi-UAV placement for ultra-dense networks
Orikumhi Igbafe,이훈기,Bae Jungsook,김선우 한국통신학회 2024 ICT Express Vol.10 No.4
This paper proposes a method for characterizing user mobility levels in multiple unmanned aerial vehicles (UAV)-assisted networks. In a dynamic environment with varying degrees of mobile ground users, optimizing the placement of UAVs is important in improving network throughput. Moreover, for integrated sensing and communication (ISAC) enabled UAVs, the resource allocation for sensing and communication relies on the dynamic nature of the network environment. To keep track of the changes in the environment UAVs are required to continuously update their trajectory, hence, characterizing the users’ mobility level is an important tool to improve the UAV trajectory optimization. In this paper, a mobility-aware resource allocation for joint sensing and communication is proposed. Our results demonstrate that the proposed algorithm can improve the resource allocation between sensing and communication in an ISAC-enabled UAV-assisted network.
Sensor Models for V2X Situational Awareness Learning
Igbafe Orikumhi,Sunwoo Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
Generating real-time data for autonomous vehicle is important to evaluate the vehicular system performance. However, such data may require high cost and safety concerns. In a virtual proving ground, complex vehicular scenario can be created, and novel beam selection and beam tracking algorithms can be developed. The important aspect of the virtual proving ground is the ability to produce realistic sensor measurement inside the simulated environment. In this paper, we present methods to generate situation awareness information using GPS and IMU through simulations.