The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO
Open accessAbstract
This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér-Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.
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Cites
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Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays
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Scaling up MIMO: Opportunities and challenges with very large arrays
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An Overview of Massive MIMO: Benefits and Challenges
Lu, L., Li, G. Y., Swindlehurst, A. L., Ashikhmin, A., & Zhang, R. (2014) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2014.2317671 · Cited by 2,838
Fundamentals of Statistical Signal Processing
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Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need?
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A Coordinated Approach to Channel Estimation in Large-Scale Multiple-Antenna Systems
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Cited by
Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey
Busari, S. A., Huq, K. M. S., Mumtaz, S., Dai, L., & Rodriguez, J. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2787460 · Cited by 730
Self-citationA Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model
Xie, H., Gao, F., Zhang, S., & Jin, S. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2594706 · Cited by 454
Ke, M., Gao, Z., Wu, Y., Gao, X., & Schober, R. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2967175 · Cited by 417
Self-citationCompressed Sensing for Wireless Communications: Useful Tips and Tricks
Choi, J. W., Shim, B., Ding, Y., Rao, B., & Kim, D. I. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2664421 · Cited by 351
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems
Zhou, Z., Fang, J., Yang, L., Li, H., Chen, Z., & Blum, R. S. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2699338 · Cited by 309
Metadata
- Authors
- Gao, Z., Dai, L., Wang, Z., & Chen, S.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2015-12
- Volume
- 63
- Issue
- 23
- Pages
- 6169-6183
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W1938636034
- Cited by
- 497
- References
- 21
- FWCI
- 21.65
- Type
- journal article
- Language
- en
- Open access
- green
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- Gao, Z., Dai, L., Wang, Z., & Chen, S. (2015). Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2015.2463260
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