Abstract
This work proposes a new limited feedback channel estimation framework. The proposed approach exploits a sparse representation of the double directional wireless channel model involving an over complete dictionary that accounts for the antenna directivity patterns at both base station (BS) and user equipment (UE). Under this sparse representation, a computationally efficient limited feedback algorithm that is based on single-bit compressive sensing is proposed to effectively estimate the downlink channel. The algorithm is lightweight in terms of computation, and suitable for real-time implementation in practical systems. More importantly, under our design, using a small number of feedback bits, very satisfactory channel estimation accuracy is achieved even when the number of BS antennas is very large, which makes the proposed scheme ideal for massive MIMO 5G cellular networks. Judiciously designed simulations reveal that the proposed algorithm outperforms a number of popular feedback schemes in terms of beam forming gain for subsequent downlink transmission, and reduces feedback overhead substantially when the BS has a large number of antennas.
Original language | English (US) |
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Title of host publication | 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9783901882906 |
DOIs | |
State | Published - Jun 27 2017 |
Event | 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2017 - Paris, France Duration: May 15 2017 → May 19 2017 |
Publication series
Name | 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2017 |
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Other
Other | 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2017 |
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Country/Territory | France |
City | Paris |
Period | 5/15/17 → 5/19/17 |
Bibliographical note
Publisher Copyright:© 2017 IFIP.