activity
20102026
most citedDeep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding

449 citations · 619 across the 51 of their papers we have counts for

collaborators
Showing 2014Show all

7 papers · 1 filter

cs.IT2014★ 1 cited

Block Bayesian Sparse Learning Algorithms With Application to Estimating Channels in OFDM Systems

Guan Gui, Li Xu, Lin Shan

Cluster-sparse channels often exist in frequencyselective fading broadband communication systems. The main reason is received scattered waveform exhibits cluster structure which is…

cs.IT2014

Affine Combination of Two Adaptive Sparse Filters for Estimating Large Scale MIMO Channels

Guan Gui, Li Xu

Large scale multiple-input multiple-output (MIMO) system is considered one of promising technologies for realizing next-generation wireless communication system (5G) to increasing…

cs.IT2014★ 1 cited

Adaptive MIMO Channel Estimation using Sparse Variable Step-Size NLMS Algorithms

Guan Gui, Li Xu, Lin Shan +1

To estimate multiple-input multiple-output (MIMO) channels, invariable step-size normalized least mean square (ISSNLMS) algorithm was applied to adaptive channel estimation (ACE).…

cs.IT2014

Novel Realization of Adaptive Sparse Sensing with Sparse Least Mean Fourth Algorithm

Guan Gui, Li Xu, Xiao-mei Zhu +1

Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing (CS) in many applications such as Radar imaging and sparse channel estimation. Unlike…

cs.IT2014★ 4 cited

Extra Gain:Improved Sparse Channel Estimation Using Reweighted l_1-norm Penalized LMS/F Algorithm

Guan Gui, Li Xu, Fumiyuki Adachi

The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse channel. Comparing with tra…

cs.IT2014★ 12 cited

RZA-NLMF algorithm based adaptive sparse sensing for realizing compressive sensing problems

Guan Gui, Li Xu, Fumiyuki Adachi

Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as Radar imaging. Unlike the NSS, in this paper, we propose…