Location-Based Visible Region Recognition in Extra-Large Massive MIMO Systems

Liu, Daohua; Wang, Jue; Li, Ye; Han, Ye; Ding, Rui; Zhang, Jun; Jin, Shi; Quek, Tony Q. S.

doi:10.1109/tvt.2023.3242615

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The visible antennas in VR can be much fewer than M in the whole array, which helps reduce the complexity of precoding significantly by grouping users properly [12]. Since we assume that the BS has no prior knowledge on users' VR but acquires users' locations, we can group users according to their locations, due to the fact that adjacent users share the same scattering clusters and thus leads to the same VR at the BS [21,22]. Furthermore, for different non-overlapping VRs, there is no interference between different groups [14].…”

Section: System Modelmentioning

confidence: 99%

“…and the SINR γ FDD Â,k of FDD systems has the same structure as (21) in TDD systems. Similarly, to design the detection threshold ζ 0 and uplink pilot length τ , we give the deterministic approximation for the downlink SINR in FDD systems as follow.…”

Section: B Downlink Data Transmission and Average Eementioning

confidence: 99%

“…Moreover, a U-shaped dedicated multilayer perceptron (MLP) network was proposed in [20] to reconstruct the channel on VR with limited pilot overhead. By utilizing the dependence among users, clusters, and VR at the BS, the authors proposed a novel VR recognition method in [21], which achieves satisfactory recognition performance with limited dataset.…”

Section: Introductionmentioning

confidence: 99%

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Average Spectral Efficiency for TDD-based non-Stationary XL-MIMO with VR Estimation

Zhang

Han

et al. 2022

2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)

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Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multiuser XL-MIMO scenario, where only partial antennas can receive users' signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic average energy efficiency (EE) with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average EE is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average EE and demonstrate the effectiveness of our proposed algorithm.

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