Ideal Channel Research Articles

Quantum Advantage of Binary Discrete Modulations for Space Channels

We assume information is discrete-modulated over coherent states of the single-mode electromagnetic field and transmitted over space quantum channels. In this work, we provide a comparison of quantum detection with classical detection for communication using on-off keying (OOK) and binary phase shift keying (BPSK) for illustrative system parameters for LEO, MEO and GEO satellites. To do so, we assume an attenuation-only (pure-loss) channel and compute the Shannon capacities for the following detection methods: classical (shot-noise limited) single-photon and coherent detectors as well as optimal quantum hypothesis-testing (Helstrom) detection. As in classical system engineering design, we also obtain theoretical references for comparison, namely, Shannon capacities and Holevo bounds for the ideal Gaussian optical channel. Our results show the ranges of transmitted power and orbital heights for which either classical or quantum detection is optimal, thus providing useful insights into design guidelines and operations of future space communication systems. We also obtain the remarkable result that the BPSK Holevo bound achieves the ultimate quantum capacity of the considered channel.

Research on the Corporate Bond Risk Factors

The global bond market is greater than the global equity market meanwhile it grows gradually in recent years. Issuing corporate bonds is an ideal channel for enterprises to raise funds in the course of COVID-19 and also provides resilience in the market. Moody's, Standard & Poor's and Fitch are well-known global credit rating agencies and suggest investors purchasing investment-grade bonds for reasonable risks and returns. But local credit rating agencies have limited capacities to appraise local bonds. In the COVID-19 crisis, widen yield spreads represent likelihood of default which can be a measure of credit risk. Besides, government interventions (i.e., Quantitative Easing Program) can effectively eliminate credit risks and Confucian culture is a factor in assessing credit risks of corporate bonds. As for liquidity risk, Chinese bond market is less liquid than the US bond market and financial bonds are the most liquid in the Chinese market. The liquidity risk is caused by inaccurate information and market risk tolerance whereas market risk tolerance integrates credit and liquidity, the main measurement of liquidity risk is transaction cost which means that higher transaction cost can impede liquidity in the bond market. Finally, market risk comprises of COVID-19 pandemic, market design and biodiversity risk. The epidemic tightens the financial condition of developing countries and depreciates the currency of the bond market which raises term structure of interest rate up. A well-designed financial market system can help stabilize fluctuations during financial crisis. Besides, biodiversity risk is relevant to the operation apartment of companies.

Joint Sensing, Communication, and Localization of a Silent Abnormality Using Molecular Diffusion

In this article, we propose a molecular communication system to localize an abnormality in a diffusion-based medium. We consider a general setup to perform joint sensing, communication, and localization. This setup consists of three types of devices, each for a different task: mobile sensors for navigation and molecule releasing (for communication), fusion centers (FCs) for sampling, amplifying, and forwarding the signal, and a gateway (GW) for making decision or exchanging the information with an external device. The sensors move randomly in the environment to reach the abnormality. We consider both collaborative and noncollaborative sensors that simultaneously release their molecules to the FCs when the number of activated sensors or the moving time reaches a certain threshold, respectively. The FCs amplify the received signal and forward it to the GW for making a decision using either an ideal or a noisy communication channel. A practical application of the proposed model is drug delivery in a tissue of the human body, to guide the nanomachine-bound drug to the exact location and so to eliminate the adverse effects of the drug on normal cells. Further applications are health-care, treatments of localized disease (e.g., tumors and inflammations), immune system triggering, and nanosurgery. The decision rules and probabilities of error are obtained for two considered sensor types in both ideal and noisy communication channels.

Wiener predistorter for nonlinear satellite downlinks

SummaryThe nonlinear power amplifier and the analogue output channel filter with nonconstant group delay add nonlinear and linear distortions to the satellite transmitted signal, respectively. Recently, there has been growing interest in using Wiener predistorters, consisting of separate nonlinear and linear components, to compensate for these distortions in the satellite downlinks. The Wiener predistorter has been shown to effectively deal with signal distortions and has lower complexity compared to other state‐of‐the‐art methods. In this paper, we argue that fully compensating the nonconstant group‐delay distortion degrades the overall performance of the Wiener predistortion systems. This is primarily due to the increased peak‐to‐average power ratio of the signal at the output of the linear predistorter. We show that the overall performance of the Wiener predistorter can be improved by undercompensating the group‐delay distortion. We propose two optimizations to address the PAPR growth problem and show using simulations that our approach leads to significant improvement in predistortion performance. Using our Wiener predistorter, the total degradation gap to the ideal limiter channel can be reduced to merely 0.34 dB for 64‐APSK modulated signals.

Structured channel covariance estimation from limited samples for large antenna arrays

In massive multiuser multiple antenna systems, the knowledge of the users’ channel covariance matrix is crucial for minimum mean square error channel estimation in the uplink and it plays an important role in several multiuser beamforming schemes in the downlink. Due to the large number of base station antennas, accurate covariance estimation is challenging especially in the case where the number of samples is limited and thus comparable to the channel vector dimension. As a result, the standard sample covariance estimator may yield a too large estimation error which in turn may yield significant system performance degradation with respect to the ideal channel covariance knowledge case. To address such problem, we propose a method based on a parametric representation of the channel angular scattering function. The proposed parametric representation includes a discrete specular component which is addressed using the well-known MUltiple SIgnal Classification (MUSIC) method, and a diffuse scattering component, which is modeled as the superposition of suitable dictionary functions. To obtain the representation parameters, we propose two methods, where the first solves a nonnegative least-squares problem and the second maximizes the likelihood function using expectation–maximization. Our simulation results show that the proposed methods outperform the state of the art with respect to various estimation quality metrics and different sample sizes.

An Efficient Approximate Expectation Propagation Detector With Block-Diagonal Neumann-Series

Expectation propagation (EP) achieves near-optimal performance for large-scale multiple-input multiple-output (L-MIMO) detection, however, at the expense of unaffordable matrix inversions. To tackle the issue, several low-complexity EP detectors have been proposed. However, they all fail to exploit the properties of channel matrices, thus resulting in unsatisfactory performance in non-ideal scenarios. To this end, in this paper, a block-diagonal Neumann-series-based expectation propagation approximation (BD-NS-EPA) algorithm is proposed, which is applicable for both ideal uncorrelated channels and the correlated channels with multiple-antenna user equipment system. First, a block-diagonal-based Neumann iteration is employed, which skillfully exerts the main information of the channels while reducing computational cost. An adjustable sorting message updating scheme then is introduced to reduce the update of redundant nodes during iterations. Numerical results show that, for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$128\times 32$ </tex-math></inline-formula> MIMO with the non-ideal channel, the proposed algorithm exhibits 0.3 dB away from the original EP when bit error-rate (BER) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=10^{-3}$ </tex-math></inline-formula> , at the cost of mere 3% normalized complexity. The implementation results on SMIC 65-nm CMOS technology suggest that the proposed detector can achieve 1.252 Gbps/W and 0.275 Mbps/kGE hardware efficiency, further demonstrating that the proposed detectors can achieve a good trade-off between error-rate performance and hardware efficiency.

Aligned Carbon Nanotube Arrays with Germanium Protective Layers for Improving the Performance of Radio Frequency Transistors

Aligned carbon nanotube arrays with high semiconducting purity and high density have been regarded as an ideal channel material for constructing high-frequency field effect transistors and high-speed integrated circuits. However, contaminants (mainly resist residues) that leave the surface of carbon nanotubes during the fabrication process seriously deteriorate the interface contact quality, which will trigger large contact resistance and thus inhibit the ultimate performance of the carbon nanotube-based transistors. Here, we demonstrate a germanium protective layer technique that can keep a clean surface of carbon nanotube films from the contaminants during device fabrication and thus boost the quality of ohmic contact between carbon nanotubes (CNTs) and metal. The transfer length method (TLM) is used to evaluate the effect of the germanium protective layer technique on the ohmic contact resistance. The contact resistance from metal–CNTs extracted using the TLM was reduced by 93%, from 1.66 to 0.123 KΩ μm before and after the germanium process treatment. Owing to the great improvement of contact between metal and CNTs by introducing the germanium protective layer technique, the fabricated aligned CNT array-based radio-frequency field-effect transistors with a gate length of 110 nm on the quartz substrate present excellent DC performance, with an on-state current of 1.28 mA/μm and maximum transconductance of up to 1.05 mS/μm at a bias of −1.2 V. Meanwhile, the as-measured current gain cut-off frequency (fT,EXT) and power gain frequency (fMAX,EXT) increase from 12 to 110 GHz and 13 to 105 GHz, respectively. This approach provides a simple and reproducible means of improving the interface quality and reducing contact resistance in CNT-based devices to enhance performance.

A Multi-Dimensional Covert Transaction Recognition Scheme for Blockchain

Covert communication was widely studied in recent years in terms of keeping the communication of entities on the Internet secret from the point of view of information security. Due to the anonymity of accounts and the publicness of the ledger, blockchain is a natural and ideal channel for helping users establish covert communication channels. Senders can embed secret messages into certain fields in transactions, and receivers can extract those messages from the transactions without attracting the attention of other users. However, to the best of our knowledge, most existing works have aimed at designing blockchain-based covert communication schemes. Few studies concentrated on the recognition of transactions used for covert communication. In this paper, we first analyze convolutional neural network (CNN)-based and attention-based covert transaction recognition schemes, and we explore the deep relationship between the appropriate extraction of features and the embedded fields of covert transactions. We further propose a multi-dimensional covert transaction recognition (M-CTR) scheme. It can simultaneously support both one-dimensional and two-dimensional feature extraction to recognize covert transactions. The experimental results show that the precision and recall of the M-CTR in recognizing covert transactions outperformed those of existing covert communication schemes.

Sparse Multiuser Receiver Design in Large Scale Array System

This paper focuses on the problem of utilizing millimeter wave (mmWave) and Terahertz (THz) massive hybrid arrays to serve multiple users simultaneously. The mmWave and THz massive arrays are characterized by wide bandwidth and high gain, leading to extensive application prospects. Moreover, a hybrid structure array can combine multiple antenna signals through the phase shifter network. Compared with the full digital array, it is a cost-effective technique that can be functional with fewer radio frequency (RF) chains. However, owing to the adoption of a discrete Fourier transform (DFT) structure, most traditional massive hybrid arrays, which allocate one chain to each user, are restricted to scenarios where the number of RF chains is more than that of users. Otherwise, even users with ideal channel conditions and short distances are inherently difficult to assign an independent chain. Thus, it will limit the scale of users that the base station (BS) can support. Inspired by the above analysis, this paper develops a method to provide service for more users with limited RF chains. Firstly, an analog matrix designing method based on the minimax criterion, which enables arrays access to multiple users, is proposed to guarantee each user a good array gain. Secondly, we establish a receiver designing scheme by the GAMP algorithm to receive signals from multiple users at the same time. Additionally, good bit error rate (BER) performance can be obtained under the condition that the observation matrix is not of full rank. Finally, numerical simulations demonstrate the effectiveness of our proposed method.

High‐Performance Transparent Silicon Nanowire Thin Film Transistors Integrated on Glass Substrates via a Room Temperature Solution Passivation

AbstractCatalytic synthesized ultrathin silicon nanowires (SiNWs) are ideal 1D channel materials to fabricate high‐performance transparent and low‐cost thin film transistors (TFTs) that are widely needed for flexible electronics and displays. In this work, a scalable integration of orderly array of SiNW array, with a uniform diameter of only 52 ± 4 nm, grown directly upon glass/wafer substrates, via a guided in‐plane solid–liquid–solid (IPSLS) process, and passivated by a new solution oxidizing/etching cycling technique is demonstrated. This has enabled an all‐low‐temperature (&lt;350 °C) fabrication of high‐performance SiNW‐TFTs, achieving Ion/Ioff current ratio and subthreshold swing (SS) of &gt;106 and 120 mV dec−1 respectively, with excellent negative and positive bias stabilities. Importantly, the SiNW‐TFTs fabricated on glasses with ITO/or metal electrodes demonstrate a high transparency of 90% or 73% respectively, making them ideal candidates for building the next generation of high aperture displays, transparent electronics, and augmented reality applications.

Multi-Layer QoE Learning System Implemented by Fiberhost

Modern telecommunications networks, despite their ever increasing capacity, mostly attributed to optical fiber technologies, still fail to provide ideal channels for transmitting information. Disruptions in ensuring data throughput or the continuous flow of data required by applications remain as major unresolved problems. Most network mechanisms, protocols and applications feature adaptations that allow them to change the parameters of the transmission channel and try to minimize the negative impact of the network on the perceived quality, for example by temporarily changing the modulation scheme, or coding scheme, or by re-transmitting lost packets, or buffering to compensate for the interruptions in transmission. To respond appropriately, network operators are interested in knowing how well these adaptations are performing in order to assess the ultimate quality of their networks from the user perspective, i.e., Quality of Experience (QoE). Due to the huge amount of data associated with the collection of various parameters of the telecommunications network, machine learning methods are often needed to discover the relationships between various parameters and to identify the root cause of the observed network quality. In this paper, we present a Multi-layer QoE learning system implemented by Fiberhost for QoE analysis with a multi-layer approach based on machine learning tools.

Single-shot wide-field imaging in reflection by using a single multimode fiber

A single multimode fiber (MMF) provides almost an ideal optical channel to constitute a hair-thin endoscope for minimally invasive biomedical imaging at depths in tissue, especially if the imaging operation can be performed with one single shot in reflection mode, which, however, remains challenging to date. In this work, we present single-shot wide-field reflectance imaging by using a single MMF as the illumination unit and imaging probe simultaneously. To achieve single-shot image capture, a reflection matrix of the fiber was built by a learning-assisted approach for the universal inverse conversion from the output amplitudes to the input amplitudes. The performance was tested by imaging more than 30 000 natural scenes projected by a digital micromirror device, and an averaged Pearson correlation coefficient over 0.84 with respect to the ground truth was achieved in the experiment. Furthermore, the ability to image dynamic scenes at a high frame rate of up to 180 frames per second was demonstrated together with real-time observation of a freely moving microneedle located at the distal end of the MMF. The proposed reflection-mode single-fiber imaging scheme paves the way for practical video-rate microendoscopy at depths in tissue in a minimally invasive manner.

Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP

Because the operation of space networks is carefully planned, it is possible to predict future contact opportunities from link budget analysis using the anticipated positions of the nodes over time. In the standard approach to space delay-tolerant networking (DTN), such knowledge is used by contact graph routing (CGR) to decide the paths for data bundles. However, the computation assumes nearly ideal channel conditions, disregarding the impact of the convergence layer retransmissions (e.g., as implemented by the Licklider transmission protocol (LTP)). In this paper, the effect of the bundle forwarding time estimation (i.e., the link service time) to routing optimality is analyzed, and an accurate expression for lossy channels is discussed. The analysis is performed first from a general and protocol-agnostic perspective, assuming knowledge of the statistical properties and general features of the contact opportunities. Then, a practical case is studied using the standard space DTN protocol, evaluating the performance improvement of CGR under the proposed forwarding time estimation. The results of this study provide insight into the optimal routing problem for a space DTN and a suggested improvement to the current routing standard.

NiCoCuP foam with a 3D hierarchical porous structure as all-pH efficient and stable HER electrocatalyst

Though various active electrocatalysts for hydrogen evolution reaction (HER) have been reported, it remains a challenge to develop efficient non-noble HER catalysts with pH-independent catalytic activity and high stability. In this work, novel porous NiCoCuP was prepared by combining the dynamic hydrogen bubble templated deposition with a low-temperature phosphorization process. The three-dimensional hierarchical porous structure provided the NiCoCuP with both self-supporting ability and ideal channels for fast mass transportation. As a consequence, the NiCoCuP foam exhibited excellent HER catalytic activity and outstanding durability over the entire pH range.

Compressive Beam and Channel Tracking With Reconfigurable Hybrid Beamforming in mmWave MIMO OFDM Systems

An efficient beam and channel acquisition and tracking scheme is developed under a reconfigurable hybrid beamforming (R-HBF) architecture for wideband millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Under such an R-HBF, a hierarchical multi-resolution beam scanning and space-frequency compressive sensing method is proposed to achieve fast beam and channel acquisitions in mmWave MIMO orthogonal frequency division multiplexing (OFDM) systems. With the initial frequency-domain beam space channel estimates, Kalman-based algorithms are further developed to track the transmit and receive beamforming directions and the channel coefficients of the sparse propagation paths in mmWave mobile channels. Simulations show that the proposed method can greatly reduce the beam training time, and that the channel capacity evaluated with the estimated channels is only 2 dB inferior to the one with ideal channels. Moreover, the beam selection error rates are less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> in typical urban mmWave mobile channels and traveling speeds even if the transmit power is only 10 dBm.

Development of direct Z-schemes 2D/2D Bi2O2CO3/ SrTiO3 photocatalyst with interfacial interaction for photocatalytic CO2 reduction

Herein, a two-dimensional/two-dimensional (2D/2D) Bi2O2CO3/SrTiO3 (BOC/STO) direct Z-schemes heterojunction photocatalysts with perfect lattice matching are successfully prepared for efficient photocatalytic CO2 reduction. Results show that as-prepared BOC/STO composites follow the orientation relationship of (020) BOC// (200) STO and both two planes have a lattice spacing of 0.26 nm, resulting in the formation of perfect lattice matching between BOC and STO, which implying BOC/STO heterostructure has an ideal channel for charge transport. In addition, the DFT calculation confirms the direct Z-schemes heterojunction is formed at the BOC/STO interface, which can facilitate efficient migration and separation of photogenerated carriers. Combining the advantage of direct Z-schemes heterojunction and good lattice matching, the optimized BOC/STO catalyst performs the photocatalytic reduction of CO2 to CO and CH4 which are ∼ 2.61 times and ∼ 20.47 times for STO, respectively. The selectivity of 20 %-BOC/STO for CH4 increases to 79 %. The calculations confirm the higher adsorption energy of CHOO– and CO and the lower reaction energy of CHO– on BOC/STO than pure BOC or STO, resulting in the higher selectivity performance of BOC/STO for CO2 photocatalytic reduction.

FIR-SIMO channel order determination by invariant-signal-subspace matching

We present a novel and computationally simple solution to the problem of determining the order of finite impulse response (FIR) single-input multiple-output (SIMO) channel. The solution is applicable to ideal and realistic wireless channels, with both white and colored noise. It is based on a non-asymptotic goodness-of-fit metric, referred to as invariant-signal-subspace matching (ISSM), aimed at matching pairs of invariant-signal-subspaces, created by exploiting the shift-invariance property of the block-Toeplitz data matrix. A pair of projection matrices corresponding to these subspaces is created for every potential FIR-SIMO order, and the order is then determined as that for which this metric is minimized. Simulation results are included, demonstrating the performance of the proposed solution for both ideal and realistic channels, with both white and colored noise.

DNN-Based Fractional Doppler Channel Estimation for OTFS Modulation

In this paper, we proposed a deep neural network (DNN) based fractional Doppler channel estimation scheme for orthogonal time frequency space (OTFS) modulation in the air-to-ground communication scenario with high-dynamic Doppler. Based on the zero-padded OTFS structure, the traditional pilot pattern with guard symbols is adopted. The received pilots in the OTFS domain are used as the inputs of the network to estimate the channel parameters which are used in the MRC algorithm to demodulate the signal. In our proposed method, it can achieve the similar performance with 14 dB boost of pilot energy comparing with the ideal channel estimation case, while the conventional method requires 30 dB higher. Both the accuracy and generalization ability of the DNN network are validated.

Three-dimensional unified electrode design using CuO embedded MnO2 Nano-dandelions@Ni(OH)2 nanoflakes as electrode material for high-performance supercapacitors

In this study, we report successive chemical bath deposition (CBD) method to prepare three-dimensional (3D) hierarchical porous Ni(OH)2 nanoflakes on CuO-doped MnO2 nano-dandelions named as MnO2/CuO@Ni(OH)2 nanorod-nanoflake clusters. The as-prepared hierarchical MnO2/CuO@Ni(OH)2 nanorod-nanoflake clusters were composed of radially grown nanorods with a length of about 1.0 μm and surrounded by the interconnected nanoflakes clusters with a diameter of about 17 nm. Electrochemical evaluations revealed that the as-prepared electrode delivers a high specific capacitance of 3800 mF cm−2 at 1.0 mA cm−2 and long cycle life. The superior electrochemical performances were attributed to ideal ion transfer channels derived from radially grown 1D nanorods, which in combination with battery-type behavior of Ni(OH)2 nanoflakes, afford synergistic and multipurpose effects. Especially, the existence of CuO inner layer in the hierarchical MnO2/CuO@Ni(OH)2 nanorod-nanoflake clusters can fill the Schottky barrier at the interface between MnO2 and Ni(OH)2. This work suggest a new design of metal oxide/hydroxides and indicates that the hierarchical MnO2/CuO@Ni(OH)2 nanorod-nanoflake clusters can be considered as potential electrode material in high-rate supercapacitors.

Centralized Monitored Spectrum Management using Multi-resource Parallel Sensing in Cognitive Radio Networks

Spectrum sensing has a very important role in the operation of cognitive radio network. A proper sensing and utilization improve the performance of cognitive radio network (CRN). Different methodologies were developed in past to improve the sensing and utilization performance in CRN. Parallel sensing is used as an optimal approach in spectrum sensing with reduced delay of operation but restraint with multiple requests for spectrum sensing. A random generation of sensing requests results in large time delay and interference in the network. To minimize the interference effect and requesting overhead, this paper presents a new approach of spectrum sensing by centralized monitoring of spectrum engagement of Primary User (PU). The proposed approach operates on a repository monitoring of channel engagement to control the request granting for spectrum sensing in PUs. In addition, the sharing of spectrum usage is controlled by monitoring the spectral efficiency and interference in the network. Simulation results obtained illustrates an enhancement in network throughput, spectrum sensing and spectrum utilization performance where an average network throughput is observed to improve by 80% for varying number of primary channels and varying load conditions. The spectrum sensing is improved by 70% in average for varying primary channels and load conditions and Ideal channel utilization for the proposed approach is observed to improve by 90%.