Distributed Compressed Sensing Aided Sparse Channel Estimation in FDD Massive MIMO System
نویسندگان
چکیده
منابع مشابه
Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing
Massive multiple-input multiple-output (massive-MIMO) is foreseen as a potential technology for future 5G cellular communication networks due to its substantial benefits in terms of increased spectral and energy efficiency. These advantages of massive-MIMO are a consequence of equipping the base station (BS) with quite a large number of antenna elements, thus resulting in an aggressive spatial ...
متن کاملChannel Estimation for Fdd Massive Mimo Using Bayesian Estimator
Massive MIMO systems that for a cellular network, the channel from user equipment to a base station is composed of few grouped paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. In the FDD mode, each BS sends a downlink training matrix to its served UEs which estimates the desired channel based on the downlink measurements and feeds ...
متن کاملFDD Massive MIMO Channel Estimation with Arbitrary 2D-Array Geometry
This paper addresses the problem of downlink channel estimation in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. The existing methods usually exploit hidden sparsity under a discrete Fourier transform (DFT) basis to estimate the cdownlink channel. However, there are at least two shortcomings of these DFT-based methods: 1) they are applicable to unifor...
متن کاملHierarchical Sparse Channel Estimation for Massive MIMO
The problem of wideband massive MIMO channel estimation is considered. Targeting for low complexity algorithms as well as small training overhead, a compressive sensing (CS) approach is pursued. Unfortunately, due to the Kroneckertype sensing (measurement) matrix corresponding to this setup, application of standard CS algorithms and analysis methodology does not apply. By recognizing that the c...
متن کاملDictionary Learning Based Sparse Channel Representation and Estimation for FDD Massive MIMO Systems
Downlink beamforming in FDD Massive MIMO systems is challenging due to the large training and feedback overhead, which is proportional to the number of antennas deployed at the base station, incurred by traditional downlink channel estimation techniques. Leveraging the compressive sensing framework, compressed channel estimation algorithm has been applied to obtain accurate channel estimation w...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2018
ISSN: 2169-3536
DOI: 10.1109/access.2018.2818281