Joint channel estimation algorithm based on structured compressed sensing for FDD multi-user massive MIMO

Zhang, Ruoyu; Zhao, Honglin; Shi, Jia; Shan, Chengzhao

doi:10.1109/icsp.2016.7878018

Cited by 4 publications

(1 citation statement)

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“…For a mmWave MIMO channel, assumption of channel sparsity is extensively adopted and applied into different topics, for example, see recent works [3][4][5][6]. Based on this assumption, in [7], [8] and [9], a series of matching tracking algorithms are proposed by adopting compressed sensing (CS) to estimate channels and reduce pilot overhead. However, the above methods need to estimate the specific parameters of the channel, including the angle-of-arrival (AOA), the angle-of-departure (AOD) and delay.…”

Section: Introductionmentioning

confidence: 99%

Adaptive training‐feedback scheme for FDD in massive MIMO systems

et al. 2022

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Accurate acquisition of channel state information (CSI) is crucial but difficult in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. To improve the estimation accuracy and to minimize the training consumption, an adaptive training-feedback scheme based on spatial reciprocity in FDD is proposed. The main idea of this scheme is to construct a reference frame that matches the channel structure. Reference vectors with the strongest correlation with the uplink channel are selected to design the pilots. The pilots can adapt to channel changes in this proposed scheme, which lead to substantial reduction of the training and feedback overheads. The simulation results show that under full feedback, the throughput of proposed adaptive training-feedback scheme can approach to the optimal performance, and under finite-bit feedback, the channel utilization is also significantly high with less training.

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