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

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

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

The ability and utility of diffusion-weighted magnetic resonance imaging with different 'b' values in the differentiation of benign from malignant lung lesions

Purpose: To evaluate the ability and the utility of diffusion-weighted magnetic resonance imaging (MRI) with different 'b' values to visualise benign and malignant lung lesions, and to determine which 'b' value (b = 300, 500, or 1000 s/mm2) was most useful in differentiating benign from malignant lung lesions. Material and methods: A total of 100 patients (28 women, 72 men; mean age = 57.19 ± 13.44 years; age range = 20-83 years). Diffusion-weighted imaging (DWI) was obtained with 'b' values of 300, 500, and 1000 s/mm². The signal intensity of lesions on DWI images was analysed, and the apparent diffusion coefficient (ADC) values of the lesions were calculated. MRI was performed in all patients after having presented at our department for thoracic computed tomography for various reasons. Results: A statistically significant difference in DWI signal scores was detected between benign and malignant lesions for all 'b' factors (p < 0.0001 for each). The sensitivity and specificity were 95% and 64%, respectively, when a score of 3 for b = 300 s/mm²; 90% and 69%, respectively, when a score of 3 for b = 500 s/mm²; and 84% and 74%, respectively, when a score of 3 for b = 1000 s/mm². ADC values showed significant differences between benign and malignant lesions for all 'b' factors (p < 0.0001 for each). Conclusions: Using 'b' values of 300, 500, and 1000 s/mm², DWI signal intensity scores and ADC values are effective methods for the differential diagnosis of malignant and benign pulmonary lesions

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