The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Resource Allocation for D2D-Enabled Vehicular Communications
Abstract
The widely deployed cellular network, assisted with device-to-device (D2D) communications, can provide a promising solution to support efficient and reliable vehicular communications. Fast channel variations caused by high mobility in a vehicular environment need to be properly accounted for when designing resource allocation schemes for the D2D-enabled vehicular networks. In this paper, we perform spectrum sharing and power allocation based only on slowly varying large-scale fading information of wireless channels. Pursuant to differing requirements for different types of links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultrareliability for vehicle-to-vehicle (V2V) links, we attempt to maximize the ergodic capacity of the V2I connections while ensuring reliability guarantee for each V2V link. Sum ergodic capacity of all V2I links is first taken as the optimization objective to maximize the overall V2I link throughput. Minimum ergodic capacity maximization is then considered to provide a more uniform capacity performance across all V2I links. Novel algorithms that yield optimal resource allocation and are robust to channel variations are proposed. Their desirable performance is confirmed by computer simulation.
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Cites
Fundamentals of Wireless Communication
Tse, D., & Viswanath, P. (2005) · doi:10.1017/cbo9780511807213 · Cited by 13,162
Table of Integrals, Series, and Products
Gradshteyn, I. S., Ryzhik, I. M., & Hays, D. F. (1976) · Journal of Lubrication Technology · doi:10.1115/1.3452897 · Cited by 4,742
Fields, J. (2001) · Cited by 4,044
Dedicated Short-Range Communications (DSRC) Standards in the United States
Kenney, J. B. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2132790 · Cited by 2,366
1 A Survey on Device-to-Device Communication in Cellular NetworksPreprint
Asadi, A., Member, S., Wang, Q., Member, S., & Mancuso, V. (2016) · Cited by 2,007
Device-to-device communication as an underlay to LTE-advanced networks
Doppler, K., Rinne, M., Wijting, C., Ribeiro, C., & Hugl, K. (2009) · IEEE Communications Magazine · doi:10.1109/mcom.2009.5350367 · Cited by 1,903
A Survey on Device-to-Device Communication in Cellular Networks
Asadi, A., Wang, Q., & Mancuso, V. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2319555 · Cited by 1,824
Karagiannis, G., Altintas, O., Ekici, E., Heijenk, G., Jarupan, B., Lin, K., & Weil, T. (2011) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2011.061411.00019 · Cited by 1,529
Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks
Yu, C.-H., Doppler, K., Ribeiro, C. B., & Tirkkonen, O. (2011) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2011.060811.102120 · Cited by 1,049
Device-to-Device Communications Underlaying Cellular Networks
Feng, D., Lu, L., Yuan-Wu, Y., Li, G. Y., Feng, G., & Li, S. (2013) · IEEE Transactions on Communications · doi:10.1109/tcomm.2013.071013.120787 · Cited by 924
Self-citation
Research works printed but not linked: 7
- xu, 2012, Performance enhanced transmission in device-to-device communications: Beamforming or interference cancellation?, Proc IEEE Globecom, 4296
- 10.1109/LCOMM.2011.011011.102026 · doi:10.1109/lcomm.2011.011011.102026
- 2016, WF on SLS Evaluation Assumptions for eV2X
- 10.1109/JSAC.2015.2435374 · doi:10.1109/jsac.2015.2435374
- 2013, Scenarios Requirements and KPIs for 5G Mobile and Wireless System
- 2007, WINNER II Channel Models
- 2016, 3rd Generation Partnership Project Technical Specification Group Radio Access Network Study LTE-Based V2X Services (Release 14)
Cited by
Deep Reinforcement Learning Based Resource Allocation for V2V Communications
Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2897134 · Cited by 870
Self-citationSpectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Liang, L., Ye, H., & Li, G. Y. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933962 · Cited by 559
Self-citationDeep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks
Liang, L., Ye, H., Yu, G., & Li, G. Y. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2957798 · Cited by 282
Self-citationVehicular Communications: A Physical Layer Perspective
Liang, L., Peng, H., Li, G. Y., & Shen, X. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2750903 · Cited by 267
Self-citationDeep Reinforcement Learning for Resource Allocation in V2V Communications
Ye, H., & Li, G. Y. (2018) · 2018 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2018.8422586 · Cited by 222
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Metadata
- Authors
- Liang, L., Li, G. Y., & Xu, W.
- Venue
- IEEE Transactions on Communications
- Published
- 2017-07
- Volume
- 65
- Issue
- 7
- Pages
- 3186-3197
- ISSN
- 0090-6778 (ISSN-L)
- OpenAlex
- W2607844539
- Cited by
- 409
- References
- 23
- FWCI
- 18.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Funders
- research gift from Intel Corporation
- National Science Foundation (1405116, 1443894)
- NSFC (61471114)
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