Low-latency Low-complexity Subspace Methods for mmWave MIMO-OFDM Channel Estimation

Cerutti, Mattia; Nicoli, Monica; Spagnolini, Umberto

doi:10.1109/icc40277.2020.9149353

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…LR are algebraic-based methods that leverage on the sparsity of the MIMO channel, as opposite to CS. Originally proposed in [30]- [33] for lower-spectrum systems, where the channel is not sufficiently sparse to boost the LR application to practical systems, the LR has recently been resumed for mmW/sub-THz systems, for Fully Digital (FD) systems only [28], [34]. In particular, the work in [28] demonstrates that LR methods attain similar performances to CS with lower sensitivity to hardware impairments.…”

Section: Related Workmentioning

confidence: 99%

“…( 30)-( 31) highlight how the analog precoder/combiner pair affects the eigenvalues of the Tx and Rx channel correlation matrices. The last term in (30), for instance, represents the overall matching between the steering vectors of the AoAs with the combiner W RF : for a fixed precoder F RF , the Tx side experiences different channel gains (sum of eigenvalues) for different combiners. The same applies for the Rx side, with an optimum precoder/combiner pair for the best pointing between Tx and Rx.…”

Section: B Disjoint Space Low Rank Estimationmentioning

confidence: 99%

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Channel Estimation for 6G V2X Hybrid Systems Using Multi-Vehicular Learning

Mizmizi

Tagliaferri

Badini³

et al. 2021

IEEE Access

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Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves/sub-THz is a fundamental, despite challenging, prerequisite for an efficient design of hybrid MIMO precoding/combining. Most works propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels and consequently cannot apply to highly dynamic scenarios such as Vehicle-to-Everything (V2X). To address the latter ones, we leverage recurrent vehicle passages to design a novel Multi Vehicular (MV) hybrid MIMO channel estimation suited for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) systems. Our approach derives the analog precoder/combiner through a MV beam alignment procedure. For the digital precoder/combiner, we adapt the Low-Rank (LR) channel estimation method to learn the position-dependent eigenmodes of the received digital signal (after beamforming), which is used to estimate the compressed channel in the communication phase. Extensive numerical simulations, obtained with ray-tracing channel data and realistic vehicle trajectories, demonstrate the benefits of our solution in terms of both achievable spectral efficiency and mean square error compared to the unconstrained maximum likelihood estimate of the compressed digital channel, making it suitable for both 5G and future 6G systems. Most notably, in some scenarios, we obtain the performance of the optimal fully digital systems.

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Section: Related Workmentioning

confidence: 99%

Section: B Disjoint Space Low Rank Estimationmentioning

confidence: 99%

Channel Estimation for 6G V2X Hybrid Systems Using Multi-Vehicular Learning

Mizmizi

Tagliaferri

Badini³

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…LR are algebraic-based methods that leverage on the sparsity of the MIMO channel, as opposite to CS. Originally proposed in [30]- [33] for low-frequency systems, where the channel is not sufficiently sparse to boost the LR application to practical systems, the LR has recently been studied for mmW/sub-THz systems, for Fully Digital (FD) systems only [28], [34]. In particular, the work in [28] demonstrates that LR methods attain similar performances to CS with lower sensitivity to hardware impairments.…”

Section: Related Workmentioning

confidence: 99%

Channel Estimation for 6G V2X HybridSystems using Multi-Vehicular Learning

Mizmizi¹,

Tagliaferri²,

Badini³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves (mmW)/sub-THz is a fundamental, despite challenging, prerequisite for an efficient design of hybrid MIMO precoding/combining. Most works propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels and consequently cannot apply to highly dynamic scenarios such as Vehicle-to-Everything (V2X). To address the latter ones, we leverage recurrent vehicle passages to design a novel Multi Vehicular (MV) hybrid MIMO channel estimation suited for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) systems. Our approach derives the analog precoder/combiner through a MV beam alignment procedure. For the digital precoder/combiner, we adapt the Low-Rank (LR) channel estimation method to learn the position-dependent eigenmodes of the received digital signal (after beamforming), which is used to estimate the compressed channel in the communication phase. Extensive numerical simulations, obtained with ray-tracing channel data and realistic vehicle trajectories, demonstrate the benefits of our solution in terms of both achievable Spectral Efficiency (SE) and Mean Square Error (MSE) compared to the Unconstrained Maximum Likelihood (U-ML) estimate of the compressed digital channel, making it suitable for both 5G and future 6G systems. Most notably, in some scenarios, we obtain the performance of the optimal Fully Digital (FD) systems.

show abstract