Nan, Y, Sun, X and Zhang, L (2015) Near-optimal pilot allocation in sparse channel estimation for massive MIMO OFDM systems. In: Proceedings IEEE International Symposium on Wireless Communication Systems (ISWCS 2015). Twelfth International Symposium on Wireless Communication Systems, 25-28 Aug 2015, Brussels, Belgium. IEEE ISBN 9781467365406
Abstract
Inspired by the success in sparse signal recovery, compressive sensing has already been applied for the pilot-based channel estimation in massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. However, little attention has been paid to the pilot design in the massive MIMO system. To obtain the near-optimal pilot placement, two efficient schemes based on the block coherence (BC) of the measurement matrix are introduced. The first scheme searches the pilot pattern with the minimum BC value through the simultaneous perturbation stochastic approximation (SPSA) method. The second scheme combines the BC with probability model and then utilizes the cross-entropy optimization (CEO) method to solve the pilot allocation problem. Simulation results show that both of the methods outperform the equispaced search method, exhausted search method and random search method in terms of mean square error (MSE) of the channel estimate. Moreover, it is demonstrated that SPSA converges much faster than the other methods thus are more efficient, while CEO could provide more accurate channel estimation performance.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works |
Keywords: | Massive MIMO; optimal pilot allocation; compressive sensing |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Oct 2015 13:31 |
Last Modified: | 20 Jan 2018 17:34 |
Published Version: | http://dx.doi.org/10.1109/ISWCS.2015.7454372 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ISWCS.2015.7454372 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:88974 |