The fading channel in mmWave communications has a very high chance to be frequency selective owing the large transmission bandwidth. To overcome selectivity we propose novel method, namely RF beamforming with time delay compensation. Simultaneously, baseband precoder at transmitter side and combiner receiver are employed maximize capacity of system. For effectively flattening by on both sides, it is necessary separate individual rays. Motivated achieving best case beam separation,...

Millimeter wave (mmWave) band is characterized by very high path loss. To overcome this, spatial multiplexing and beamforming can be employed in mmWave due to the small wavelength, large antenna arrays used provide required gain. However, unlike traditional microwave systems it not feasible afford one RF chain per antenna. this practical limitation, hybrid utilized complexity of transceiver reduced implementing domain processing using only analog phase shifters. In paper, we propose a two...

Due to the very large transmission bandwidth of millimeter wave (mmWave) communications, fading channel is expected be frequency selective. Recent work in this field has mainly focused on cyclic-prefixed OFDM as a method combat selectivity. But paper, we shift focus novel time delay compensation obtain flat over wide since it proves more effective reducing computational complexity and achieving high power efficiency at receiver side. We employ time-shifted beams compensate spread achieve...

In a practical large scale antenna system (LSAS) over the millimeter Wave (mmWave) bands, implementation of hybrid beamforming structure plays an important role as it provides high precoding gains to overcome path loss and achieve sufficient link margins. While employing such arrays, becomes challenging estimate mmWave channel using conventional algorithms. this paper, we develop two novel multi-resolution estimation algorithms that exploit dimensional discrete Fourier transform technique...

Large scale antenna systems (LSAS) in the mmWave bands provide a solution to problems of spectrum shortage experienced conventional microwave bands. In practical large deployment, implementation hybrid beamforming structures is very important as it provides high array gains overcome path loss and achieve sufficient link margins. However, while implementing this we encounter challenges such complexity, cost energy consumption. paper power consumption model for system presented analysed. From...

This work asks: with abundant, unlabeled real faces, how to learn a robust and transferable facial representation that boosts various face security tasks respect generalization performance? We make the first attempt propose self-supervised pretraining framework fundamental representations of images, FSFM, leverages synergy between masked image modeling (MIM) instance discrimination (ID). explore masking strategies for MIM present simple yet powerful CRFR-P masking, which explicitly forces...

Currently, Techniques for detecting objects using deep learning methods are widely used. However, the FPN-based multi-scale feature fusion technology may not fully capture network's representational capacity. In addition, in use of attention mechanism, there's a lack an mechanism that can effectively long-distance dependency relationships sequences and adjust importance different channels. To address these issues, novel algorithm is presented, which incorporates combination YOLOv5 BiFPN...

Channel estimation is an important issue for systems employing large antenna arrays with beamforming targeting 60 GHz short range wireless communications. New methods have to be derived overcome the growing complexity of tradition channel algorithms. E.g. a Hierarchical Beamforming Training inspired by sparsity mm-wave promising approach lower this complexity. This paper presents two efficient VLSI implementation similar training algorithm. A following survey indicates benefits and costs...

In this paper the millimeter wave (mmWave) single carrier transmission systems operating on wideband channels with frequency selectivity are considered. We propose two selective hybrid precoding solutions, namely time delay compensation (TDC) based domain equalization (TDE) and phase shift (PSC) (FDE). First, we implement a sectorized beamforming in RF to compensate large path-loss at mmWave range reduce intra cluster spread. Here, precoder is assumed be flat during one block due...

When an iterative receiver has more than one iteration loop, scheduling the decoding process is important issue. To find optimal scheduling, extrinsic information transfer (EXIT) function was main tool in literature. However, it only very efficient when codeword significantly long. In this paper, under consideration of a packet-based transmission scenario, we propose modeling search problem as foraging ant colony. And then, colony optimization (ACO) algorithm, labeled max-min system (MMAS),...

Abstract Focus of the article is on achieving maximum data rates under a processing energy constraint. For given amount per information bit, overall power consumption increases with rate. When targeting beyond 100 Gb/s, system’s soon exceeds which can be dissipated without forced cooling. To achieve rate this constraint, bit must minimized. Therefore, in article, suitable efficient transmission schemes together architectures and their implementations are investigated true cross-layer...

Manipulating facial images results in negative influences on the social association, with deep generative models. Although many detection methods have been proposed, they either designed sophisticated neural networks that lack enough interpretability, or found defects specific to one manipulation method. To address this issue, we propose a new approach explore of fake after wavelet transform and call it GFS (Grayscale Frequency Statistics). First, utilize Haar transformation decompose image...

As the remarkable development of facial manipulation technologies is accompanied by severe security concerns, face forgery detection has become a recent research hotspot. Most existing methods train binary classifier under global supervision to judge real or fake. However, advanced manipulations only perform small-scale tampering, posing challenges comprehensively capture subtle and local artifacts, especially in high compression settings cross-dataset scenarios. To address such limitations,...