Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

Cazzella, Lorenzo; Tagliaferri, Dario; Mizmizi, Marouan; Badini, Damiano; Mazzucco, Christian; Matteucci, Matteo; Spagnolini, Umberto

doi:10.1109/tap.2022.3169950

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…The authors of [32] proposed a DL-based MIMO radar-assisted channel estimation method that divides channel estimation into two parts-angle of arrival (AOA) or angle of departure (AOD) estimation, and a gain estimation phase-and achieves efficient channel estimation performance with less training overhead. The authors of [33] developed a DL-based low-rank channel estimation approach that does not require knowledge of the vehicle's location information and achieved a rather good mean square error performance. The authors of [34] presented a DL-based judgment-oriented channel estimation method that requires only a priori knowledge of the Doppler rate without the exact Doppler rate calculation and outperforms previous judgment-oriented channel estimation algorithms that need Doppler rate estimation.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic-Aided Fast-Layered Alternating Iterative Tensor Channel Estimation for V2X Millimeter-Wave Massive MIMO Systems

Zhou

et al. 2022

Electronics

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Millimeter-wave massive multiple-input multiple-output (MIMO) vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of millimeter-wave V2X communications is, however, not exempt from challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an ultrasonic-aided tensor channel estimation for V2X millimeter-wave massive MIMO systems to improve the safety and traffic efficiency of cooperative automated driving. At the receiver, the dimension of multidimensional complex V2X information is reduced by the subspace tensor decomposition model. In order to quickly track beam angle changes caused by vehicle position changes, the ultrasonic-aided direction of arrival (DOA) tracking method is adopted to provide information about the surrounding environment. Based on the ultra-high resolution quantization grid and adaptive iterative update of the dictionary matrix, the DOA of the ultrasonic signal can be tracked. The angle update information of the millimeter-wave signal can be obtained by converting the estimated angle information. Using the cost function with global characteristics and ultrasonic-aided DOA tracking, a fast-layered alternating iterative tensor algorithm is proposed for joint iterative channel estimation. Simulation results show that the proposed solution outperforms some advanced alternative methods.

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Section: Introductionmentioning

confidence: 99%

Ultrasonic-Aided Fast-Layered Alternating Iterative Tensor Channel Estimation for V2X Millimeter-Wave Massive MIMO Systems

Zhou

et al. 2022

Electronics

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“…Solutions for blockage detection should exploit the whole power-delay-angle profile of the channel impulse response (CIR) as this embeds a wide range of geographical data and propagation characteristics [53]. In 5G industrial use-cases, e.g., automated driving, historical CIR data are largely available in roadside units that receive continuous information from geolocalized vehicles [54], [55], [56], [57]. ML algorithms This work is licensed under a Creative Commons Attribution 4.0 License.…”

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confidence: 99%

On the Latent Space of mmWave MIMO Channels for NLOS Identification in 5G-Advanced Systems

Tedeschini

Nicoli

Win

2023

IEEE J. Select. Areas Commun.

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In mission-critical verticals such as automated driving, 5G-advanced networks must provide centimeter-level dynamic positioning along with ultra-reliable low-latency communication services. Massive Multiple-Input Multiple-Output (mMIMO) and millimeter waves (mmWave) are the key enablers, allowing high accuracy angle and delay estimation. Still, extracting such information from highly-dimensional Channel Impulse Responses (CIRs) results in a complex task, due to channel sparsity and intermittent blockage. In this paper we focus on non-lineof-sight (NLOS) identification from CIR data, proposing a Deep Autoencoding Kernel Density Model (DAKDM) to characterize the statistics of the channel latent features. We formulate the problem as a semi-supervised anomaly detection task in which only LOS samples, i.e., normal data, are adopted for training. DAKDM is a single-stage training model that takes as input the full CIR thanks to an AutoEncoder (AE) structure. The proposed method is able to learn the latent distribution by means of a Kernel Density Estimator (KDE) in combination with a deep learning likelihood network. We validate the proposed solution in a 5G Urban micro (UMi) vehicular scenario. Results show that the proposed model can significantly outperform conventional algorithms and obtain similar performances to variational Bayes algorithms at one tenth of the inference time.

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MIMO-Based Physical Layer Evaluation on IEEE 802.11bd

Guo,

Aloi,

et al. 2023

IEEE Access

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To enable vehicle-to-everything (V2X) communications, both the dedicated short-range communications (DSRC) and the cellular V2X (C-V2X) involved in the radio access technologies (RATs) are experiencing extensive development to support advanced vehicular applications and scenarios. Compared with the C-V2X whose effective reliability and scalability are still to be completely verified, the DSRC maintains its superiority due to the extensive safety-related field trials performed worldwide. Furthermore, IEEE 802.11bd, the next-generation V2X (NGV) standard for the vehicular ad hoc networks (VANETs) in the DSRC, is expected to greatly improve the performance compared to IEEE 802.11p with new physical layer (PHY) and medium access control (MAC) technologies. Hence, aimed to obtain the complete PHY evaluation on IEEE 802.11bd in terms of the packet error rate (PER), the packet reception ratio (PRR), the effective data rate, and the packet inter-arrival time (IAT), the various antenna configurations, vehicle-to-vehicle (V2V) scenarios, packet sizes, and modulation and coding schemes (MCSs) are investigated and compared in a full PHY simulation. The results indicate that the multiple-input multiple-output (MIMO) configuration is the most advantageous technique in decreasing the PER, increasing the PRR and the transmission coverage, elevating the output effective data rate, and reducing the output packet IAT in contrast with the single-input multiple-output (SIMO), multiple-input single-output (MISO), and single-input single-output (SISO) configurations at the farther distance. The urban non-lineof-sight (NLOS) scenario experiences the slightly better quality of communication and is more robust than the highway NLOS scenario. Both the packet size and the MCS need to be selected properly to satisfy the high-reliability, high-throughput, or low-latency requirements.INDEX TERMS IEEE 802.11bd, V2X, MIMO, physical layer evaluation.

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Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

Cited by 6 publications

References 48 publications

Ultrasonic-Aided Fast-Layered Alternating Iterative Tensor Channel Estimation for V2X Millimeter-Wave Massive MIMO Systems

Ultrasonic-Aided Fast-Layered Alternating Iterative Tensor Channel Estimation for V2X Millimeter-Wave Massive MIMO Systems

On the Latent Space of mmWave MIMO Channels for NLOS Identification in 5G-Advanced Systems

MIMO-Based Physical Layer Evaluation on IEEE 802.11bd

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