Cepstral Analysis for Classifying Car Collisions in LOS/NLOS Vehicle-to-Vehicle Networks

Kihei, Billy; Copeland, John A.; Chang, Yusun

doi:10.1109/glocom.2017.8253973

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…In the simulation, we first measure the channels separately for each pair of communicating antennas H k n = (H k n (f 1 ), .., H k n (f Ns )) T where n ∈ [1, ..., 256], k ∈ [1, ..., 4], N s = 1200, and H k n (f i ) is defined in Eqn. (19). Then we crop the measured channels for the two transmitting antennas (H 1 n , H 2 n ) by providing each antenna with 600 subcarriers.…”

Section: Results On Full Downlink Channel Reconstructionmentioning

confidence: 99%

“…In mobility scenarios, the modeling of the downlink channel becomes much more complicated due to the Doppler effect since an arbitrary movement in 3D space causes different velocities on each antenna. Hence, in a multipath propagation environment, the signals sent from different antennas can experience different Doppler shifts depending on the propagation path and receiving antenna [19]:…”

Section: A Channel Modelingmentioning

confidence: 99%

“…The CE given in Eqn. (19) can be represented in a convenient form for orientation reconstruction as follows:…”

Section: B Parameter Estimation For Transmitting Antennasmentioning

confidence: 99%

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Full Downlink Channel Reconstruction Using Incomplete Uplink Channel Measurements in Massive MIMO Networks

Fedorov

Zhang

Сидоренко

et al. 2019

2019 IFIP Networking Conference (IFIP Networking)

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While more and more antennas are integrated into a single mobile user equipment to increase communication quality and throughput, the number of antennas used for transmission is commonly restricted due to the concerns on hardware complexity and energy consumption, making it impossible to achieve the maximum channel capacity. This paper investigates the problem of reconstructing the full downlink channel from incomplete uplink channel measurements in Massive MIMO systems. We present ARDI, a scheme that builds a bridge between radio channel and physical signal propagation environment to link spatial information about the non-transmitting antennas with their radio channels. By inferring locations and orientations of the non-transmitting antennas from an incomplete set of uplink channels, ARDI can reconstruct the downlink channels for non-transmitting antennas. We derive closed-form solution to reconstruct antenna orientation in both single-path and multi-path propagation environments. The performance of ARDI is evaluated using simulations with realistic human movement. The results demonstrate that ARDI is capable of accurately reconstructing full downlink channels when the signal-to-noise ratio is higher than 15dB, thereby expanding the channel capacity of Massive MIMO networks.

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Section: Results On Full Downlink Channel Reconstructionmentioning

confidence: 99%

Section: A Channel Modelingmentioning

confidence: 99%