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

JTimeWarp: A software for Aligning Biological Signals using Warping Methods

It is a very common problem to align signals upon time-axis for analysis of datasets obtained from biological experiments. Since biological or chemical signals may be measured differently due to some factors such as temparature, pressure and others laboratory conditions, the signals may have different time scales. In this study, three commanly used signal alignment methods are implemented in a software named JTimeWarp.First, Dynamic Time Warping (DTW), which is the most popular method, is implemented. DTW method takes a look for an optimal warping path between two time series. DTW method has three basic steps: (1) generates cost matrix using a distance function; (2) computes accumulated cost matrix from the values contained in cost matrix; (3) finds warping path through the use of accumulated cost matrix. While building a warping path, DTW uses the elements of the accumulated cost matrix whose values are the smallest along the way [1]. Correlation Optimized Warping (COW) is another method derived from DTW to deliver better performance in finding an optimal alignment between two given time-dependent sequences under certain restrictions. COW applies piecewise linear stretching or compression of one signal, instead of pointwise warping like DTW. The dynamic programming optimization is used to determine the optimal positions of end points or nodes of the predetermined segments [1]. Parametric Time Warping (PTW) is unique with its approach to signal warping. PTW tries to fit a polynomial function defining the misalignment of signals. The polynomial functions generated by PTW include many terms in the parametric time warping. For these reasons, PTW approach is different amongst others warping methods [1]. In this study, a user friendly and interactive software called JTimeWarp is developed to align signals automatically. The software is implemented using java programming language and java swing library. User can load data and select a warping method for alignment. Since there is no perfect alignment methods, the software gives the users option of the manual correction. User can apply one of the warping methods and then correct the errors manually using interactive options. User also can apply all three methods at the same time and the select the best one for the signal alignment

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

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

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