English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper addresses the problem of uplink and downlink channel estimation inFDD Massive MIMO systems. By utilizing sparse recovery and compressive sensingalgorithms, we are able to improve the accuracy of the uplink/downlink channelestimation and reduce the number of uplink/downlink pilot symbols. Suchsuccessful channel estimation builds upon the assumption that the channel canbe sparsely represented under some basis/dictionary. Previous works model thechannel using some predefined basis/dictionary, while in this work, we presenta dictionary learning based channel model such that a dictionary is learnedfrom comprehensively collected channel measurements. The learned dictionaryadapts specifically to the cell characteristics and promotes a more efficientand robust channel representation, which in turn improves the performance ofthe channel estimation. Furthermore, we extend the dictionary learning basedchannel model into a joint uplink/downlink learning framework by observing thereciprocity of the AOA/AOD between the uplink/downlink transmission, andpropose a joint channel estimation algorithm that combines the uplink anddownlink received training signals to obtain a more accurate channel estimate.In other words, the downlink training overhead, which is a bottleneck in FDDMassive MIMO system, can be reduced by utilizing the information from simpleruplink training.

Dictionary Learning Based Sparse Channel Representation and Estimation for FDD Massive MIMO Systems