Cited by 107Millimeter-Wave Propagation and ModelingVehicular Ad Hoc Networks (VANETs)Radar Systems and Signal Processing

The DOI matches its Crossref record; 14 of the 16 references it cites were found in a registry or an index.

Propagation Channels for mmWave Vehicular Communications: State-of-the-art and Future Research Directions

Open access

Jameel, Furqan, Wyne, Shurjeel, Nawaz, Syed Junaid, Chang, Zheng·2019·IEEE Wireless Communications

DOI 10.1109/mwc.2018.1800174

Open this paper in CoChat — free account Open PDF

Abstract

Vehicular communications essentially support automotive applications for safety and infotainment. For this reason, industry leaders envision an enhanced role for vehicular communications in the fifth generation of mobile communications technology. Over the years, the number of vehicle- mounted sensors has increased steadily, which potentially leads to more volume of critical data communications in a short time. Also, emerging applications such as remote/autonomous driving and infotainment such as high-definition movie streaming require data-rates on the order of multiple Gb/s. Such high data rates require a large system bandwidth, but very limited bandwidth is available in the sub-6 GHz cellular bands. This has sparked research interest in the millimeter wave (mmWave) band (10 GHz-300 GHz), where a large bandwidth is available to support the high data rate and low latency communications envisioned for emerging vehicular applications. However, leveraging mmWave communications requires a thorough understanding of the relevant vehicular propagation channels, which are significantly different from those investigated below 6 GHz. Despite their significance, very few investigations of mmWave vehicular channels are reported in the literature. This work highlights the key attributes of mmWave vehicular communication channels and surveys the recent literature on channel characterization efforts in order to provide a gap analysis and propose possible directions for future research.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1109/mwc.2018.1800174

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice
Shafi, M., Molisch, A. F., Smith, P. J., Haustein, T., Zhu, P., Silva, P. D., Tufvesson, F., Benjebbour, A., & Wunder, G. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2692307 · Cited by 2,312
Wideband Millimeter-Wave Propagation Measurements and Channel Models for Future Wireless Communication System Design
Rappaport, T. S., MacCartney, G. R., Samimi, M. K., & Sun, S. D. (2015) · IEEE Transactions on Communications · doi:10.1109/tcomm.2015.2434384 · Cited by 1,591
On mm-Wave Multipath Clustering and Channel Modeling
Gustafson, C., Haneda, K., Wyne, S., & Tufvesson, F. (2014) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2013.2295836 · Cited by 298
Self-citation
The Impact of Beamwidth on Temporal Channel Variation in Vehicular Channels and Its Implications
Va, V., Choi, J. J., & Heath, R. W. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2622164 · Cited by 259
Geometrical-Based Modeling for Millimeter-Wave MIMO Mobile-to-Mobile Channels
He, R., Ai, B., Stüber, G. L., Wang, G., & Zhong, Z. D. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2774808 · Cited by 224
Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment
Va, V., Choi, J. J., Shimizu, T., Bansal, G., & Heath, R. W. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2787627 · Cited by 220
Millimeter-Wave Communication with Out-of-Band Information
Gonzalez-Prelcic, N., Ali, A., Va, V., & Heath, R. W. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1700207 · Cited by 137
Modeling and Design of Millimeter-Wave Networks for Highway Vehicular Communication
Tassi, A., Egan, M., Piechocki, R. J., & Nix, A. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2734684 · Cited by 136
MmWave Vehicle-to-Infrastructure Communication: Analysis of Urban Microcellular Networks
Wang, Y., Venugopal, K., Molisch, A. F., & Heath, R. W. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2827259 · Cited by 108
The Impact of Interference From the Side Lanes on mmWave/THz Band V2V Communication Systems With Directional Antennas
Петров, В. И., Kokkoniemi, J., Moltchanov, D., Lehtomäki, J. J., Juntti, M. J., & Koucheryavy, Y. A. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2799564 · Cited by 76

Show all 16 references

Research works printed but not linked: 1

10.1109/TWC.2017.2703109 · DOI

Sorted by citations

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658
Self-citation
A Survey on 5G Millimeter Wave Communications for UAV-Assisted Wireless Networks
Zhang, L., Zhao, H. M., Hou, S., Zhao, Z. W., Xu, H., Wu, X., Wu, Q., & Zhang, R. (2019) · IEEE Access · doi:10.1109/access.2019.2929241 · Cited by 359
Beamforming Technologies for Ultra-Massive MIMO in Terahertz Communications
Ning, B., Tian, Z., Mei, W., Chen, Z., Han, C. Z., Li, S., Yuan, J., & Zhang, R. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3245669 · Cited by 286
Cognition in UAV-Aided 5G and Beyond Communications: A Survey
Ullah, Z., Al-Turjman, F., & Mostarda, L. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.2968311 · Cited by 226
Electronically Steerable Antennas for Future Heterogeneous Communication Networks: Review and Perspectives
Chaloun, T., Boccia, L., Arnieri, E., Fischer, M. D., Valenta, V., Fonseca, N. J. G., & Waldschmidt, C. (2022) · IEEE Journal of Microwaves · doi:10.1109/jmw.2022.3202626 · Cited by 133
A Survey of Collaborative Machine Learning Using 5G Vehicular Communications
Balkus, S. V., Wang, H., Cornet, B., Mahabal, C., Ngo, H. T., & Fang, H. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3149714 · Cited by 119
Non-Coherent and Backscatter Communications: Enabling Ultra-Massive Connectivity in 6G Wireless Networks
Nawaz, S. J., Sharma, S. K., Mansoor, B., Patwary, M., & Khan, N. M. (2021) · IEEE Access · doi:10.1109/access.2021.3061499 · Cited by 112
Self-citation
Efficient Power-Splitting and Resource Allocation for Cellular V2X Communications
Jameel, F., Khan, W. U., Kumar, N., & Jäntti, R. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3001682 · Cited by 70
Self-citation
Outage Analysis for Intelligent Reflecting Surface Assisted Vehicular Communication Networks
Wang, J., Zhang, W., Bao, X., Song, T., & Pan, C. (2020) · Globecom · doi:10.1109/globecom42002.2020.9322158 · Cited by 67
Deep Reinforcement Learning-Based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications
Aung, P. S., Nguyen, L. X., Tun, Y. K., Han, Z., & Hong, C. S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329893 · Cited by 62
Intelligent Reflecting Surface Aided Vehicular Communications
Dampahalage, D., Manosha, K. B. S., Rajatheva, N., & Latva‐aho, M. (2020) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps50303.2020.9367569 · Cited by 56
Sensing and Communication Integrated System for Autonomous Driving Vehicles
Zhang, Q., Sun, H., Wei, Z., & Feng, Z. (2020) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps50562.2020.9162963 · Cited by 55
Conformal Metasurfaces: A Novel Solution for Vehicular Communications
Mizmizi, M., Ayoubi, R. A., Tagliaferri, D., Dong, K., Gentili, G. G., & Spagnolini, U. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3213901 · Cited by 48
Efficient Mining Cluster Selection for Blockchain-Based Cellular V2X Communications
Jameel, F., Javed, M. A., Zeadally, S., & Jäntti, R. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3006176 · Cited by 48
Self-citation
Next-generation Wireless Solutions for the Smart Factory, Smart Vehicles, the Smart Grid and Smart CitiesPreprint
Ho, T. M., Tran, T. D., Nguyen, T. T., Kazmi, S. M. A., Le, L. B., Hong, C. S., & Hanzo, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10102 · Cited by 49
Socially Aware V2X Networks With RIS: Joint Resource Optimization
Gu, X., Duan, W., Zhang, G., Ji, Y., Wen, M., & Ho, P. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3158955 · Cited by 41
Green Spectrum Sharing Framework in B5G Era by Exploiting Crowdsensing
Wang, X., Umehira, M., Akimoto, M., Han, B., & Zhou, H. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3186282 · Cited by 41
Characterization for the Vehicle-to-Infrastructure Channel in Urban and Highway Scenarios at the Terahertz Band
Yi, H., Guan, K., He, D., Ai, B., Dou, J., & Kim, J. (2019) · IEEE Access · doi:10.1109/access.2019.2953890 · Cited by 40
Heterogeneous User-Centric Cluster Migration Improves the Connectivity-Handover Trade-Off in Vehicular Networks
Lin, Y., Zhang, Z. J., Huang, Y., Li, J., Shu, F., & Hanzo, L. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3041521 · Cited by 34
Low‐altitude UAV air‐ground propagation channel measurement and analysis in a suburban environment at 3.9 GHz
Cui, Z., Briso-Rodríguez, C., Guan, K., Matolak, D. W., Calvo‐Ramírez, C., Ai, B., & Zhong, Z. D. (2019) · IET Microwaves Antennas & Propagation · doi:10.1049/iet-map.2019.0067 · Cited by 31
A Millimeter Wave-Based Sensor Data Broadcasting Scheme for Vehicular Communications
Chen, X., Leng, S., Tang, Z., Xiong, K., & Qiao, G. (2019) · IEEE Access · doi:10.1109/access.2019.2932428 · Cited by 30
Weighted-Sum-Rate Maximization for an Reconfigurable Intelligent Surface Aided Vehicular Network
Dampahalage, D., Manosha, K. B. S., Rajatheva, N., & Latva‐aho, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3069253 · Cited by 30
Channel Estimation for 6G V2X Hybrid Systems Using Multi-Vehicular Learning
Mizmizi, M., Tagliaferri, D., Badini, D., Mazzucco, C., & Spagnolini, U. (2021) · IEEE Access · doi:10.1109/access.2021.3095121 · Cited by 30
Beamforming Optimization for IRS-Assisted mmWave V2I Communication Systems via Reinforcement Learning
Lee, Y., Lee, J.-H., & Ko, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3181152 · Cited by 29
Vehicular Blockage Modelling and Performance Analysis for mmWave V2V Communications
Dong, K., Mizmizi, M., Tagliaferri, D., & Spagnolini, U. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838711 · Cited by 26
Protocol Solutions for IEEE 802.11bd by Enhancing IEEE 802.11ad to Address Common Technical Challenges Associated With mmWave-Based V2X
Abd, R. I., & Kim, K. S. (2022) · IEEE Access · doi:10.1109/access.2022.3208235 · Cited by 24
Deep Learning-Based Path Loss Prediction for Fifth-Generation New Radio Vehicle Communications
Sung, S., Choi, W., Kim, H., & Jung, J.-I. (2023) · IEEE Access · doi:10.1109/access.2023.3297215 · Cited by 23
Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
Saraereh, O. A., Ali, A. A., Khan, I., & Rabie, K. M. (2020) · Electronics · doi:10.3390/electronics9020262 · Cited by 23
Cooperative Beamforming With Predictive Relay Selection for Urban mmWave Communications
Dimas, A., Kalogerias, D. S., & Petropulu, A. P. (2019) · IEEE Access · doi:10.1109/access.2019.2950274 · Cited by 22
Blockage Effects of Road Bridge on mmWave Channels for Intelligent Autonomous Vehicles
Yan, D., Guan, K., He, D., Kim, J., Chung, H., Tian, D., & Zhong, Z. D. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3271133 · Cited by 22
Feasibility Validation of a 5G-Enabled mmWave Vehicular Communication System on a Highway
Noh, G., Kim, J., Choi, S., Lee, N., Chung, H., & Kim, I. (2021) · IEEE Access · doi:10.1109/access.2021.3062907 · Cited by 22
A Comprehensive Survey on Emerging AI Technologies for 6G Communications: Research Direction, Trends, Challenges, and Opportunities
Das, B. R., Hasan, S. R., Sabuj, S. R., Hossain, M. A., & Ray, S. K. (2025) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2025.06.001 · Cited by 35
Context-and-Social-Aware Online Beam Selection for mmWave Vehicular Communications
Li, D., Wang, S., Zhao, H., & Wang, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3047676 · Cited by 21
Blockage Avoidance Based Sensor Data Dissemination in Multi-Hop MmWave Vehicular Networks
Lv, J., He, X., & Luo, T. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3097831 · Cited by 20
Optimizing Blockchain Networks with Artificial Intelligence: Towards Efficient and Reliable IoT Applications
Jameel, F., Javaid, U., Sikdar, B., Khan, I., Mastorakis, G. N., & Mavromoustakis, C. X. (2020) · Internet of things · doi:10.1007/978-3-030-44907-0_12 · Cited by 19
Self-citation
Deterministic 3D Ray-Launching Millimeter Wave Channel Characterization for Vehicular Communications in Urban Environments
Rodríguez-Corbo, F. A., Azpilicueta, L., Celaya-Echarri, M., López-Iturri, P., Picallo, I., Falcone, F., & Alejos, A. V. (2020) · Sensors · doi:10.3390/s20185284 · Cited by 19
State of the Art: A Review on Vehicular Communications, Impact of 5G, Fractal Antennas for Future Communication
Rahim, A., Malik, P. K., & Ponnapalli, V. A. S. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-3369-3_1 · Cited by 18
Millimeter Wave Reflection Pattern Codebook Design for RIS-Assisted V2V Communications
Song, J., Lee, J.-H., Noh, S., & Choi, J. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3166183 · Cited by 17
Propagation Models in Vehicular Communications
Rodríguez-Corbo, F. A., Azpilicueta, L., Celaya-Echarri, M., Alejos, A. V., & Falcone, F. (2021) · IEEE Access · doi:10.1109/access.2021.3049884 · Cited by 19
Machine Learning Techniques for Wireless-Powered Ambient Backscatter Communications: Enabling Intelligent IoT Networks in 6G Era
Jameel, F., Sharma, N., Khan, M. A., Khan, I., Alam, M. M., Mastorakis, G. N., & Mavromoustakis, C. X. (2020) · Internet of things · doi:10.1007/978-3-030-44907-0_8 · Cited by 16
Self-citation
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach
Huynh, N., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2020) · UTS ePRESS (University of Technology Sydney) · Cited by 16
A survey of intelligent reflecting surfaces: Performance analysis, extensions, potential challenges, and open research issues
Khan, A., Mohsan, S. A. H., Elfikky, A., Boghdady, A. I., Ahmad, S., & Innab, N. (2024) · Vehicular Communications · doi:10.1016/j.vehcom.2024.100859 · Cited by 17
Millimeter-Wave V2X Channel Measurements in Urban Environments
Molisch, A. F., Mecklenbräuker, C. F., Zemen, T., Prokeš, A., Hofer, M., Pasic, F., & Hammoud, H. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3521637 · Cited by 14
Conformal Intelligent Reflecting Surfaces for 6G V2V Communications
Tagliaferri, D., Mizmizi, M., Ayoubi, R. A., Gentili, G. G., & Spagnolini, U. (2022) · International Conference on 6G Networking (6GNet) · doi:10.1109/6gnet54646.2022.9830337 · Cited by 14
Vehicular MMW Communication: Channel Characterization Based on Ray Tracing Study
Aghaei, F., Uysal, M., de Lamare, R. C., & Landau, L. T. N. (2024) · International Symposium on Wireless Communication Systems · doi:10.1109/iswcs61526.2024.10639124 · Cited by 13
Vehicle Behavior-Cognition-Based Particle-Filter-Enabled mmWave Beam Tracking for Connected Automated Vehicles
Zhang, Q., Ji, K., Feng, Z., Han, Z., & Gao, H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3181247 · Cited by 13
Beam Alignment-Based mmWave Spectrum Sensing in Cognitive Vehicular Networks
Zhang, H., & Guo, C. (2019) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip45357.2019.8969532 · Cited by 12
Performance Analysis of a Reconfigurable-Intelligent-Surfaces-Assisted V2V Communication System
Li, K., Zhou, S., & Tan, G. (2023) · Electronics · doi:10.3390/electronics12112383 · Cited by 11
A geographical hierarchy greedy routing strategy for vehicular big data communications over millimeter wave
Du, S., Hou, J., Song, S., Song, Y., & Zhu, Y. (2020) · Physical Communication · doi:10.1016/j.phycom.2020.101065 · Cited by 11
EM Model-Based Device-Free Localization of Multiple Bodies
Rampa, V., Nicoli, M., Manno, C., & Savazzi, S. (2021) · Sensors · doi:10.3390/s21051728 · Cited by 11
Unmanned aerial vehicle-collaborative 5G: a cooperative technology for enhancement of 5G NR
Mahbub, M. (2021) · International Journal of Information Technology · doi:10.1007/s41870-021-00623-z · Cited by 10
Propagation Characteristics Comparisons between mmWave and Visible Light Bands in the Conference Scenario
Liu, B., Tang, P., Zhang, J., Yin, Y., Liu, G., & Xia, L. (2022) · Photonics · doi:10.3390/photonics9040228 · Cited by 10
Blockage-Aware Beamforming Design for Active IRS-Aided mmWave Communication Systems
Zhang, G., Shen, C., Liu, Y., Lin, Y., Ai, B., & Zhong, Z. D. (2022) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc51071.2022.9771698 · Cited by 10
UAV Assisted 5G Het-Net: A Highly Supportive Technology for 5G NR Network Enhancement
Mahbub, M. (2020) · EAI Endorsed Transactions on Internet of Things · doi:10.4108/eai.13-7-2018.166003 · Cited by 8
Hashing Beam Training for Integrated Ground-Air-Space Wireless Networks
Xu, Y., Huang, C., Wei, L., Yang, Z., Hammadi, A. A., Yang, J., Zhang, Z., Yuen, C., & Debbah, M. A. (2024) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2024.3459088 · Cited by 8
Camera-Sensing-Assisted Fast mmWave Beam Tracking for Connected Automated Vehicles
Zhang, Q., Zhang, X., & Yang, C. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176025 · Cited by 8
Efficient Joint Channel Equalization and Tracking for V2X Communications Using SC-FDE Schemes
Pedrosa, P., Castanheira, D., Silva, A., Dinis, R., & Gameiro, A. (2020) · IEEE Access · doi:10.1109/access.2020.2981717 · Cited by 8
The Potential Short- and Long-Term Disruptions and Transformative Impacts of 5G and Beyond Wireless Networks: Lessons Learnt From the Development of a 5G Testbed Environment
Patwary, M. N., Nawaz, S. J., Rahman, M. A., Sharma, S. K., Rashid, M., & Barnes, S. J. (2020) · IEEE Access · doi:10.1109/access.2020.2964673 · Cited by 76
Self-citation
Robust Beam Management in Position and Velocity Aware V2V Communications Using Distributed Antenna Subarrays
Kim, K.-W., Song, J., Lee, J.-H., Hyun, S.-H., & Kim, S.-C. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3190898 · Cited by 7
Characterization of Fading Statistics of mmWave (28 GHz and 38 GHz) Outdoor and Indoor Radio Propagation Channels
Gulfam, S. M., Nawaz, S. J., Baltzis, K. B., Ahmed, A., & Khan, N. M. (2019) · Technologies · doi:10.3390/technologies7010009 · Cited by 7
Self-citation
Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
del Valle, R. S., Kose, A., & Lee, H. (2025) · Sensors · doi:10.3390/s25133924 · Cited by 6
3D Modeling and Analysis of Cooperative Perception-Oriented Millimeter-Wave V2I Networks With Information Value-Based Relay
Wu, P., Li, X., Zheng, H. J., Wang, K., Qin, J., & Tang, M. A. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3261826 · Cited by 6
Training Sequence Based Doppler Shift Estimation for Vehicular Communication
Ma, Q., Qiu, G., Zhang, Q., Sun, H., Feng, Z., & Han, Z. (2020) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc45663.2020.9120798 · Cited by 6
Millimeter Wave Spatial Channel Characterization for Vehicular Communications
Rodríguez-Corbo, F. A., Azpilicueta, L., Celaya-Echarri, M., López-Iturri, P., Picallo, I., Falcone, F., & Alejos, A. V. (2019) · International Electronic Conference on Sensors and Applications (ECSA) · doi:10.3390/ecsa-6-06562 · Cited by 6
A Two-stage Prediction-based Beam Selection Algorithm in MmWave Massive MIMO Systems
Sheng, Y., Xu, J., & Tao, X. (2023) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc56721.2023.10293811 · Cited by 5
Conformal Metasurfaces for Recovering Dynamic Blockage in Vehicular Systems
Mizmizi, M., Tagliaferri, D., Khosronejad, M., Resteghini, L., Gentili, G. G., Draghi, L., & Spagnolini, U. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001090 · Cited by 5
Secrecy energy efficiency optimization for DF relaying IoT systems with passive eavesdropping terminal
Song, K., Wang, Q., Peng, L., Li, C., & Wu, X. (2020) · Physical Communication · doi:10.1016/j.phycom.2020.101254 · Cited by 5
Hashing Beam Training for Near-Field Communications
Xu, Y., Li, W., Huang, C., Zhu, C., Yang, Z., Yang, J., He, J., Zhang, Z., & Debbah, M. A. (2024) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops59551.2024.10615793 · Cited by 4
Beam Training Technique for Millimeter-Wave Cellular Systems Using Retrodirective Arrays
Kim, Y. J., Lee, H. J., Lee, H. L., & Cho, Y. S. (2020) · IEEE Access · doi:10.1109/access.2020.3021402 · Cited by 4
The state of the art in beyond 5G distributed massive multiple-input multiple-output communication system solutions
Meyer, E., Kruglov, D., Krivic, M., Tanveer, M., Argaez-Ramirez, R., Zhang, Y., Ojeda, A. B., Smirnova, K. D., Alekseev, K., Mugisho, M. S., et al. (2022) · Open Research Europe · doi:10.12688/openreseurope.14501.1 · Cited by 4
Contextual Bandits With Non-Stationary Correlated Rewards for User Association in mmWave Vehicular Networks
He, X., Huang, X., & Li, L. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3552717 · Cited by 3
Analysis of space time block codes and cosets for 5G and its applications
Asif, H. M. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.10.015 · Cited by 3
SVM‐based online learning for interference‐aware multi‐cell mmWave vehicular communications
Li, D., Zhu, J., Zhao, H., Wang, X., & Jiang, R. (2021) · IET Communications · doi:10.1049/cmu2.12138 · Cited by 3
Joint Vehicle Association and Power Allocation for Energy Efficient Connected Automated Vehicles
Zhang, Q., Yan, L., Feng, Z., Zhang, K., & Zhang, Y. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685711 · Cited by 3
Impact of IRS Random Scatterings on NLOS-V2X Channel Characteristics
Haider, S. I., Li, E., & Tan, S. (2026) · IEEE Antennas and Wireless Propagation Letters · doi:10.1109/lawp.2026.3655743 · Cited by 2
ACK-UCB: An Asynchronous Contextual Kernel-Based Bandit Approach for User Association in mmWave Vehicular Networks
He, X., Huang, X., & Tsukada, M. (2026) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2026.3658630 · Cited by 2
Research and verification of sensing information assisted millimeter wave beam tracking algorithm for automated vehicles
Zhang, Q., Yu, B., & Feng, Z. (2022) · ACM MobiCom Workshop on Integrated Sensing and Communications Systems (ISACom) · doi:10.1145/3556562.3558570 · Cited by 2
Stochastic Geometry Analysis for Distributed RISs-Assisted mmWave Communications
Xu, Y., Wei, L., Huang, C., Zhu, Y., Yang, Z., Yang, J. Q., He, J., Zhang, Z., & Debbah, M. A. (2024) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2024-spring62846.2024.10683675 · Cited by 2
Spherical RIS-Assisted mmWave MIMO Wireless Communications With Concentric UCAs
Jing, H., Cheng, W., & Zhang, W. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3560378 · Cited by 2
Pilot Sequence Design for mmWave Cellular Systems With Relay Stations in the Presence of Blockage
Kim, Y. J., Sultan, Q., & Cho, Y. S. (2020) · IEEE Access · doi:10.1109/access.2020.2990710 · Cited by 2
A Cost Efficient Edge Computing Scheme in Dual-band Cooperative Vehicular Network
Cheng, K., & Fang, X. (2023) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc55385.2023.10118669 · Cited by 2
Throughput Analysis for Dynamic Millimeter-Wave V2I Networks with 3D Beamforming
Zheng, H. J., Wu, P., & Cao, L. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838822 · Cited by 2
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach
Huynh, N. V., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3088305 · Cited by 16
Task Offloading and Resource Allocation based on Enhanced Artificial Hummingbird Algorithm in NOMA-Assisted Mobile Edge Computing
Yang, J., Chen, D., Cao, X., & Zeng, J. (2024) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct62411.2024.10946407 · Cited by 1
Human Exposure to RF-EMF From Active RIS in Outdoor Environments: A Vehicular Communication Case Study
Benini, M., Gallucci, S., Fiocchi, S., Parazzini, M., Ravazzani, P., & Tognola, G. (2025) · IEEE Open Journal of Antennas and Propagation · doi:10.1109/ojap.2025.3625767 · Cited by 1
Smart Autonomous Breaking System for Vehicles to Improve Road Safety in India
Malik, P. K., Rahim, A., & Le, D. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9512-5_58 · Cited by 1
Fast Beam Tracking based on 28 GHz mmWave Phased Array: Algorithm and Prototype Experiment
Sun, Z., Deng, X., Li, A., Zou, X., Pan, W., & Yan, L. (2024) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc62479.2024.10681764 · Cited by 1
Cross-Layer Data Driven Beam Selection for mmWave Vehicular Communications
Wang, S., Li, D., Zhao, H., & Wang, X. (2020) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp49889.2020.9299785 · Cited by 1
Base Station Multibeam Antenna for 5G Network Based on the Lunenburg Lens Structure
Denisov, D. V., Shabunin, S. N., Kusaykin, D. V., & Klevakin, M. A. (2021) · International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE) · doi:10.1109/apeie52976.2021.9647657 · Cited by 1
Vehicle-to-Vehicle Communications With Focus on the Usage of Millimeter Wave TechnologyPreprint
Falade, O. S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4581783 · Cited by 1
Measurement Based Path Loss Study for Indoor Device-to-Device Communications at 60 GHz
Yoo, S. K., Cotton, S. L., & Zhang, L. (2019) · Asia-Pacific Conference on Antennas and Propagation (APCAP) · doi:10.1109/apcap47827.2019.9472183
Characterizing the Energy Trade-Offs of End-to-End Vehicular Communications using an Hyperfractal Urban ModellingPreprint
Popescu, D. G., Jacquet, P., Mans, B., & Blaszczyszyn, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01241
Methodology for estimating reception efficiency of mutually correlated or in- phase signals at diversity transmission
Zvonarev, V. V., Pimenov, V. F., & Popov, A. S. (2021) · Issues of radio electronics · doi:10.21778/2218-5453-2020-12-38-43
Recent Advances of Propagation Channel Research for 6G Wireless Communication Systems
Huq, K. M. S., Rodrı́guez, J., Otung, I. E., & Stavrou, S. (2021) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9682152
Energy and delay trade-offs of end-to-end vehicular communications using a hyperfractal urban modelling
Błaszczyszyn, B., Jacquet, P., Mans, B., & Popescu, D. G. (2023) · Annals of Telecommunications · doi:10.1007/s12243-022-00939-2
Cyber Security Attacks Detecting Thread in the Virtual World of Corporate Sectors
Manasa, R., & Devi, A. J. (2023) · International Journal of Case Studies in Business IT and Education · doi:10.47992/ijcsbe.2581.6942.0261
Acquisition of state and DOS features based channel estimation for VTV mmWave-Massive MIMO: A Deep nested with Layered LSTM approach
Porselvi, R., & Murugan, M. (2023) · Applied Acoustics · doi:10.1016/j.apacoust.2023.109590
Integrated Sensing and Communication enabled Doppler Frequency Shift Estimation and Compensation
Jia, J., Wei, Z., Zhang, R., & Wang, L. (2023) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc57788.2023.10233416
Odometry-Aided mmWave Communications Using Overparameterized Beamforming Optimization
Lee, G. C. F., Kurniawan, E., & Zheng, Y. (2024) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2024-spring62846.2024.10683075
Theories and methods for near-field communication and sensing towards 6G
Wang, D., Hu, Y., Ran, J. I., Yuan, X., Li, W., Gong, T., Yang, Z., Xiaoming, C. H. E. N., Huang, C., & Debbah, M. A. (2025) · Scientia Sinica Informationis · doi:10.1360/ssi-2024-0347

Authors: Jameel, F., Wyne, S., Nawaz, S. J., & Chang, Z.
Affiliations: University of Jyväskylä (FI)
COMSATS University Islamabad (PK)
ORCID: Jameel, Furqan
Wyne, Shurjeel
Nawaz, Syed Junaid
Chang, Zheng
Venue: IEEE Wireless Communications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-02
Volume: 26
Issue: 1
Pages: 144-150
ISSN: 1536-1284 (ISSN-L)
1558-0687
DOI: 10.1109/mwc.2018.1800174
OpenAlex: W2902466218
Cited by: 107
References: 16
Citations by year: 2026: 5 · 2025: 8 · 2024: 9 · 2023: 14 · 2022: 19 · 2021: 15 · 2020: 24 · 2019: 13
FWCI: 4.96
Type: journal article
Language: en
Open access: green
urn.fi
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Wireless Communications (publishedVersion)
Jyväskylä University Digital Archive (University of Jyväskylä) (submittedVersion)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
Keywords: millimeter-wave communication, vehicular communication, propagation channel, channel characterization, channel modeling, 5G, autonomous driving, infotainment, high-speed data transmission, bandwidth

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Propagation Channels for mmWave Vehicular Communications: State-of-the-art and Future Research Directions

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks