Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems : A Deep Learning Approach

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.Peer reviewe

A Unified Approach for Beam-Split Mitigation in Terahertz Wideband Hybrid Beamforming

The sixth generation networks envision the deployment of terahertz (THz) band as one of the key enabling property thanks to its abundant bandwidth. However, the ultra-wide bandwidth in THz causes beam-split phenomenon due to the use of a single analog beamformer (AB). Specifically, beam-split makes different subcarriers to observe distinct directions since the same AB is adopted for all subcarriers. Previous works mostly employ additional hardware components, e.g., time-delayer networks to mitigate beam-split by realizing virtual subcarrier-dependent ABs. This paper introduces an efficient and unified approach, called beam-split-aware (BSA) hybrid beamforming. In particular, instead of virtually generating subcarrier-dependent ABs, a single AB is used and the effect of beam-split is computed and passed into the digital beamformers, which are subcarrier-dependent while maximizing spectral efficiency. Hence, the proposed BSA approach effectively mitigates the impact of beam-split and it can be applied to any hybrid beamforming architecture. Manifold optimization and orthogonal matching pursuit techniques are considered for the evaluation of the proposed approach in multi-user scenario. Numerical simulations show that significant performance improvement can be achieved as compared to the conventional techniques.Comment: This work has been submitted to the IEEE for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Near-field Hybrid Beamforming for Terahertz-band Integrated Sensing and Communications

Terahertz (THz) band communications and integrated sensing and communications (ISAC) are two main facets of the sixth generation wireless networks. In order to compensate the severe attenuation, the THz wireless systems employ large arrays, wherein the near-field beam-squint severely degrades the beamforming accuracy. Contrary to prior works that examine only either narrowband ISAC beamforming or far-field models, we introduce an alternating optimization technique for hybrid beamforming design in near-field THz-ISAC scenario. We also propose an efficient approach to compensate near-field beam-squint via baseband beamformers. Via numerical simulations, we show that the proposed approach achieves satisfactory spectral efficiency performance while accurately estimating the near-field beamformers and mitigating the beam-squint without additional hardware components.Comment: Accepted Paper in 2023 IEEE Global Communications Conference (GLOBECOM), Kuala Lumpur, Malaysia, 202

Federated Learning in Vehicular Networks

Machine learning (ML) has already been adopted in vehicular networks for such applications as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, the training of the ML model brings significant overhead for the data transmission between the parameter server and the edge devices in the vehicles. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing this transmission overhead while also achieving privacy through the transmission of only the model updates of the learnable parameters rather than the whole dataset. In this article, we investigate the usage of FL over ML in vehicular network applications to develop intelligent transportation systems. We provide a comprehensive analysis on the feasibility of FL for the ML based vehicular applications. Then, we identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead/delay, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks.Comment: 4 figures 7 pages. This work has been submitted to the IEEE for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Spherical Wavefront Near-Field DoA Estimation in THz Automotive Radar

Automotive radar at terahertz (THz) band has the potential to provide compact design. The availability of wide bandwidth at THz-band leads to high range resolution. Further, very narrow beamwidth arising from large arrays yields high angular resolution up to milli-degree level direction-of-arrival (DoA) estimation. At THz frequencies and extremely large arrays, the signal wavefront is spherical in the near-field that renders traditional far-field DoA estimation techniques unusable. In this work, we examine near-field DoA estimation for THz automotive radar. We propose an algorithm using multiple signal classification (MUSIC) to estimate target DoAs and ranges while also taking beam-squint in near-field into account. Using an array transformation approach, we compensate for near-field beam-squint in noise subspace computations to construct the beam-squint-free MUSIC spectra. Numerical experiments show the effectiveness of the proposed method to accurately estimate the target parameters

Antenna Selection With Beam Squint Compensation for Integrated Sensing and Communications

Next-generation wireless networks strive for higher communication rates, ultra-low latency, seamless connectivity, and high-resolution sensing capabilities. To meet these demands, terahertz (THz)-band signal processing is envisioned as a key technology offering wide bandwidth and sub-millimeter wavelength. Furthermore, THz integrated sensing and communications (ISAC) paradigm has emerged jointly access spectrum and reduced hardware costs through a unified platform. To address the challenges in THz propagation, THz-ISAC systems employ extremely large antenna arrays to improve the beamforming gain for communications with high data rates and sensing with high resolution. However, the cost and power consumption of implementing fully digital beamformers are prohibitive. While hybrid analog/digital beamforming can be a potential solution, the use of subcarrier-independent analog beamformers leads to the beam-squint phenomenon where different subcarriers observe distinct directions because of adopting the same analog beamformer across all subcarriers. In this paper, we develop a sparse array architecture for THz-ISAC with hybrid beamforming to provide a cost-effective solution. We analyze the antenna selection problem under beam-squint influence and introduce a manifold optimization approach for hybrid beamforming design. To reduce computational and memory costs, we propose novel algorithms leveraging grouped subarrays, quantized performance metrics, and sequential optimization. These approaches yield a significant reduction in the number of possible subarray configurations, which enables us to devise a neural network with classification model to accurately perform antenna selection.Comment: 14pages10figures, submitted to IEE

NBA-OMP: Near-field Beam-Split-Aware Orthogonal Matching Pursuit for Wideband THz Channel Estimation

The sixth-generation networks envision the terahertz (THz) band as one of the key enabling technologies because of its ultrawide bandwidth. To combat the severe attenuation, the THz wireless systems employ large arrays, wherein the near-field beam-split (NB) severely degrades the accuracy of channel acquisition. Contrary to prior works that examine only either narrowband beamforming or far-field models, we estimate the wideband THz channel via an NB-aware orthogonal matching pursuit (NBA-OMP) approach. We design an NBA dictionary of near-field steering vectors by exploiting the corresponding angular and range deviation. Our OMP algorithm accounts for this deviation thereby ipso facto mitigating the effect of NB. Numerical experiments demonstrate the effectiveness of the proposed channel estimation technique for wideband THz systems.Comment: Accepted Paper in 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP