The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information
Open accessAbstract
Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose using spatial information extracted at sub-6 GHz to help establish the mmWave link. Assuming a fully digital architecture at sub-6 GHz; and an analog architecture at mmWave, we outline a strategy to extract spatial information from sub-6 GHz and its use in mmWave compressed beam-selection. Specifically, we formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active subcarriers at mmWave. To simulate multi-band frequency dependent channels, we review the prior work on frequency dependent channel behavior and outline a multi-frequency channel model. The simulation results for achievable rate show that out-of-band aided beam-selection can considerably reduce the training overhead of in-band only beam-selection.
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Cites
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
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Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!
Rappaport, T. S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., Wong, G. N., Schulz, J. K., Samimi, M., & Gutierrez, F. (2013) · IEEE Access · doi:10.1109/access.2013.2260813 · Cited by 7,351
Cubic convolution interpolation for digital image processing
Keys, R. (1981) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1981.1163711 · Cited by 3,255
Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
Alkhateeb, A., El Ayach, O., Leus, G., & Heath, R. W. (2014) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2014.2334278 · Cited by 2,562
Self-citationAn introduction to millimeter-wave mobile broadband systems
Pi, Z., & Khan, F. (2011) · IEEE Communications Magazine · doi:10.1109/mcom.2011.5783993 · Cited by 2,538
Coverage and Rate Analysis for Millimeter-Wave Cellular Networks
Bai, T., & Heath, R. W. (2015) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2014.2364267 · Cited by 1,425
Self-citationDeconstructing multiantenna fading channels
Sayeed, A. M. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.803324 · Cited by 1,120
Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks
Hur, S., Kim, T., Love, D. J., Krogmeier, J. V., Thomas, T. A., & Ghosh, A. (2013) · IEEE Transactions on Communications · doi:10.1109/tcomm.2013.090513.120848 · Cited by 1,062
Compressed sensing with coherent and redundant dictionaries
Candès, E. J., Eldar, Y. C., Needell, D., & Randall, P. (2011) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2010.10.002 · Cited by 898
Cited by
Toward 6G Networks: Use Cases and Technologies
Giordani, M., Polese, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900411 · Cited by 2,241
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels
Alrabeiah, M., & Alkhateeb, A. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3003670 · Cited by 346
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
Zeng, Y., Chen, J., Xu, J., Wu, D., Xu, X., Jin, S., Gao, X., Gesbert, D., Cui, S., & Zhang, R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3364508 · Cited by 303
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
“A result on beam training exploiting out-of-band information [13] formulates the beam selection as a weighted sparse recovery problem.”
BackgroundSelf-citationBeam Alignment in mmWave V2X Communications: A Survey
Tan, J., Luan, T. H., Guan, W., Wang, Y., Peng, H., Zhang, Y., Zhao, D., & Lu, N. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3383093 · Cited by 75
Metadata
- Authors
- Ali, A., González-Prelcic, N., & Heath, R. W.
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2018-02
- Volume
- 17
- Issue
- 2
- Pages
- 1038-1052
- ISSN
- 1536-1276 (ISSN-L)
- 1558-2248
- OpenAlex
- W2593150731
- Cited by
- 285
- References
- 38
- FWCI
- 9.06
- Type
- journal article
- Language
- en
- Open access
- green
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