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

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

Preparation and characterization of aluminum composite closed-cell foams

Thesis (Master)--Izmir Institute of Technology, Materials Science and Engineering, Izmir, 2001Includes bibliographical references (leaves: 43-47)Text in English; Abstract: Turkish and Englishix, 47 leavesAn experimental study has been conducted to investigate the feasibility of the production of SiC-particulate (SiCp) reinforced Al (Aluminum) closed-cell foams using the foaming from powder compacts process and to determine the effect of SiCp addition on the foaming behavior of Al compacts and the mechanical properties of Al foams.The foaming behavior of SiCp/Al composite powder compacts and the compression mechanical behavior of SiCp/Al composite foams were determined and compared with those of pure Al compacts and Al foams prepared by the same processing parameters.Composite and Al powder compacts were prepared by hot uniaxial compaction inside a steel die at 425 oC for 1/2 hour under a constant die pressure of 220 MPa.Compacts of 99 % dense with a small amount of blowing agent of TiH2 (0.5 wt%) were heated above the melting temperature of Al inside a pre-heated furnace. During heating, as the TiH2 decomposed and released hydrogen, the compact expanded uniaxially. Foamed/partially foamed samples were taken from the furnace at the specified furnace holding times and their heights were measured in order to calculate linear expansion.Initial foaming experiments with Al compacts at 750 and 850 oC have shown that foaming at the former temperature was slower and more controllable, although linear expansion was similar at both temperatures. From these experiments, it was also found that rapid cooling of the liquid metal was necessary in order to maintain the liquid foam structure in the solid state.Foaming experiments of SiCp/Al and Al compacts at 750 oC have shown that SiCp addition a) increased linear expansion of the powder compacts and b) reduced the extent of liquid metal drainage. SiCp addition also increased the plateau stress and energy absorption capability of the Al foams. These results have shown the potential of composite foams for tailoring energy absorption of Al foams for varying levels of impact stresses.Foaming experiments have also been conducted on aluminum oxideparticulate/Al and SiC-whisker/Al composites compacts prepared using the same compaction parameters and foamed at the same temperature, 750 oC

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

Valorization of Moroccan olive stones by using it in particleboard panels

The main objective of this work was to find new applications to valorize olive stones (endocarp and seed). In order to improve knowledge on olive stones, the phenolic compounds concentration of three varieties of Moroccan olive trees: Moroccan Picholine, Menara and Haouzian were studied. Olive stones of three varieties were characterized by Fourier Transform Mid Infrared Spectroscopy (FT-MIR). Total phenolic compounds are quantified after solid-liquid extraction by an assay of Folin-Ciocalteu. Moroccan Picholine stones (11.32 mg GAE/g DM) have a higher content of total phenolic compounds than Haouzia stones (4.55 mg GAE/g DM) and Menara stones (3.56 mg GAE/g DM). Thermogravimetric analysis indicates that up to 195 degrees C; there is no degradation of the stones. The biocide performance on agar-agar was tested with decay fungi. Biodegradation studies show that the most interesting results are obtained with Moroccan Picholine stones. The presence of Moroccan Picholine in a particleboard panels improves the total resistance of the particleboard panels against both Coriolus versicolor and Coniophora puteana rot fungi

Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO

Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output. In previous works, model training is mostly done via centralized learning (CL), where the whole training dataset is collected from the users at the base station (BS). This approach introduces huge communication overhead for data collection. In this paper, to address this challenge, we propose a federated learning (FL) framework for channel estimation. We design a convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS. We develop FL-based channel estimation schemes for both conventional and RIS (intelligent reflecting surface) assisted massive MIMO (multiple-input multiple-output) systems, where a single CNN is trained for two different datasets for both scenarios. We evaluate the performance for noisy and quantized model transmission and show that the proposed approach provides approximately 16 times lower overhead than CL, while maintaining satisfactory performance close to CL. Furthermore, the proposed architecture exhibits lower estimation error than the state-of-the-art ML-based schemes.Comment: Accepted paper in IEEE Transactions on Wireless Communication

Cognitive Learning-Aided Multi-Antenna Communications

Cognitive communications have emerged as a promising solution to enhance, adapt, and invent new tools and capabilities that transcend conventional wireless networks. Deep learning (DL) is critical in enabling essential features of cognitive systems because of its fast prediction performance, adaptive behavior, and model-free structure. These features are especially significant for multi-antenna wireless communications systems, which generate and handle massive data. Multiple antennas may provide multiplexing, diversity, or antenna gains that, respectively, improve the capacity, bit error rate, or the signal-to-interference-plus-noise ratio. In practice, multi-antenna cognitive communications encounter challenges in terms of data complexity and diversity, hardware complexity, and wireless channel dynamics. The DL-based solutions tackle these problems at the various stages of communications processing such as channel estimation, hybrid beamforming, user localization, and sparse array design. There are research opportunities to address significant design challenges arising from insufficient data coverage, learning model complexity, and data transmission overheads. This article provides synopses of various DL-based methods to impart cognitive behavior to multi-antenna wireless communications.Comment: 7pages5figures1table. 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

Thermodynamic Analysis of the Integrated System that Produces Energy by Gradual Expansion from the Waste Heat of the Solid Waste Facility

The rapid increase in consumer societies leads to a rise in waste facilities. Especially when considering the amount of power used in waste plants and the corresponding waste heat generated, an approach to recover waste heat from these facilities has been proposed. Initially, the waste heat from the solid waste facility was assessed using the Rankine cycle. Subsequently, an Organic Rankine Cycle (ORC) system was integrated into the lower cycle of the steam Rankine cycle. The integrated system was completed by harnessing waste heat from the Rankine steam cycle in the carbon dioxide cycle. These power generation systems are designed with two turbines, each with gradual expansion. Using sub-cycles, 1 kg/s of air at 873.2 K was obtained by evaluating the waste heat. In terms of energy efficiency, it can be observed that the R744 gradual expansion cycle exhibits the highest energy and exergy efficiency. Cooling with water in heat exchangers reduces exhaust efficiency. The highest mass flow requirement is found in the ORC system when the R123 fluid is used. The energy efficiency for the entire system was calculated as 22,4%, and the exergy efficiency for the entire system was calculated as 60.7%. When Exergo Environment Analysis was made, exergy stability factor was found to be %60.7, exergetic sustainability index was found to be 2.66. There is also 370K waste heat available, which is recommended for use in drying units. These calculations were performed using the Engineering Equation Solver (EES) program

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