Asset Details
Do you wish to reserve the book?
Structured compressive sensing based superimposed pilot design in downlink large-scale MIMO systems
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Structured compressive sensing based superimposed pilot design in downlink large-scale MIMO systems
Do you wish to request the book?
Structured compressive sensing based superimposed pilot design in downlink large-scale MIMO systems
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Structured compressive sensing based superimposed pilot design in downlink large-scale MIMO systems
2014
Overview
Large-scale multiple-input multiple-output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large-scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, a spectrum-efficient superimposed pilot design for downlink large-scale MIMO scenarios is proposed, where frequency-domain pilots of different transmit antennas occupy completely the same subcarriers in the frequency domain. Meanwhile, spatial–temporal common sparsity of large-scale MIMO channels motivates us to exploit the emerging theory of structured compressive sensing (CS) for reliable MIMO channel estimation, which is realised by the proposed structured subspace pursuit (SSP) algorithm to simultaneously recover multiple channels with low pilot overhead. Simulation results demonstrate that the proposed scheme performs well and can approach the performance bound.
Publisher
The Institution of Engineering and Technology,Institution of Engineering and Technology,John Wiley & Sons, Inc
Subject
/ Antennas
/ Channels
/ Detection, estimation, filtering, equalization, prediction
/ downlink large‐scale MIMO systems
/ Exact sciences and technology
/ Information, signal and communications theory
/ Pilots
/ Signal and communications theory
/ spatial–temporal common sparsity
/ spectrum‐efficient superimposed pilot design
/ SSP
/ structured compressive sensing
/ structured subspace pursuit algorithm
