Channel Knowledge Research Articles

Recent Advances in the Physiology of Ion Channels in Plants.

Our knowledge of plant ion channels was significantly enhanced by the first application of the patch-clamp technique to isolated guard cell protoplasts over 35 years ago. Since then, research has demonstrated the importance of ion channels in the control of gas exchange in guard cells, their role in nutrient uptake in roots, and the participation of calcium-permeable cation channels in the regulation of cell signaling affected by the intracellular concentrations of this second messenger. In recent years, through the employment of reverse genetics, mutant proteins, and heterologous expression systems, research on ion channels has identified mechanisms that modify their activity through protein-protein interactions or that result in activation and/or deactivation of ion channels through posttranslational modifications. Additional and confirmatory information on ion channel functioning has been derived from the crystallization and molecular modeling of plant proteins that, together with functional analyses, have helped to increase our knowledge of the functioning of these important membrane proteins that may eventually help to improve crop yield. Here, an update on the advances obtained in plant ion channel function during the last few years is presented.

Artificial Noise-Aided Secure Relay Communication With Unknown Channel Knowledge of Eavesdropper

In this article, a new relay-aided secure communication system is investigated, where a transmitter sends signals to a destination via an amplify-and-forward (AF) relay in the presence of an eavesdropper. We consider a general system configuration, where the source, relay, destination, and eavesdropper are all equipped with multiple antennas. In the practical scenarios of unknown eavesdropper's channel state information (CSI) and uncertainty of the eavesdropper's location, we aim to maximize the expected value of the system secrecy rate over the presumed distribution of the eavesdropper's channels, by exploiting the artificial noise (AN) transmitted by the source and relay nodes. The system design issue is formulated as a nonconvex stochastic optimization problem with a source transmission power constraint and a nonconvex relay transmission power constraint. A novel computational method is proposed to solve this challenging problem. The new method is developed based on an exact penalty function method together with a parallel stochastic decomposition algorithm. Numerical simulations are performed to study the effectiveness of the proposed scheme at various locations of the eavesdropper. Simulation results show that for most cases, secure communication can be achieved without the CSI knowledge of eavesdropper's channels, and the achievable secrecy rate follows the trend of a benchmark system where the eavesdropper's full CSI is available. In particular, the achievable system secrecy rate increases with the number of antennas at the legitimate users. Moreover, the optimal power allocated for the transmission of the AN increases with the system signal-to-noise ratio. The proposed computational method achieves a higher system secrecy rate than a conventional penalty function based approach.

Estimation of Sparse Channel Using Bayesian Gaussian Mixture and CS-Aided Techniques for Pilot Contaminated Massive MIMO System

Massive MIMO is established by future wireless communication systems to facilitate real-time user applications with having a higher of data transfer rates and spectral efficacy through perfect channel knowledge. Many pilot reuse schemes are working to improve channel performance regarding pilot overhead and channel knowledge. However, the pilot contamination becomes a major challenging issue in massive MIMO applications due to erroneous channel state information between neighboring cells. To tackle this issue, analyzing to the estimation of channel parameters in the preferred paths and interference from neighboring cells in the underdetermined system is essential. For estimating the channel behavior over Sparse Bayesian and Compressed sensing Aided methods are proposed by surviving the Pilot contamination effects by changing the angular to beam domain for sparse channel approximation. Simulation results have demonstrated the effectiveness of Bayesian and compressed sensing by compared with the standard estimators in connection with estimation accuracy, especially in pilot contamination.

Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning

Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise massive numbers of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full channel knowledge. Estimating these channels at the LIS, however, is a key challenging problem. With the massive number of LIS elements, channel estimation or reflection beam training will be associated with (i) huge training overhead if all the LIS elements are passive (not connected to a baseband) or with (ii) prohibitive hardware complexity and power consumption if all the elements are connected to the baseband through a fully-digital or hybrid analog/digital architecture. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. In the second approach, we develop a deep-learning based solution where the LIS learns how to interact with the incident signal given the channels at the active elements, which represent the state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead and with only a few active elements, making them promising for future LIS systems.

Online Deep Neural Networks for MmWave Massive MIMO Channel Estimation With Arbitrary Array Geometry

In this paper, we propose an online training framework for mmWave Massive MIMO channel estimation (CE) with limited pilots, where the training is based on real-time received pilot samples from the base station without requiring knowledge of the true channel. To realize this, we propose four axioms for a legitimate online loss function, based on which we develop a model-free online training algorithm with convergence analysis. Simulation shows that the proposed online deep neural network (DNN) achieves comparable CE accuracy to model-based compressive sensing (CS) algorithms, while enjoying much faster computation. In addition, the proposed method is robust to various model mismatches and can adapt to the change of the underlying propagation environment.

Artificially Time-Varying Differential MIMO for Achieving Practical Physical Layer Security

In this paper, we propose a differential multiple-input multiple-output (MIMO) scheme based on the novel concept of chaos-based time-varying unitary matrices to demonstrate—for the first time in the literature—the ability of differential encoding in achieving practical physical layer security even without the need for using channel estimation. In the proposed scheme, an erroneous secret key, which is extracted from the wireless nature, is used to initialize a chaos sequence that is responsible for generating artificially time-varying unitary matrices capable of obfuscating the transmitted data symbols from illegitimate eavesdroppers. Contrary to conventional studies, the key agreement ratio in this study is assumed to be imperfect, which is often true and very realistic in high-mobility scenarios. Following this, we conceive a new calibration algorithm for reconciling the chaotic sequence generated at the legitimate parties, thus making this calibration algorithm a unique, novel solution to the key sharing problem of conventional chaos-based communication techniques, which has been overlooked over the past few decades. It is found out that differential encoding obviates additional complexity and insecurity in dealing with channel estimation, whereas an eavesdropper must tackle the complicated differentially encoded patterns, which have an exponentially increasing complexity order. In addition, the obtained simulation results demonstrate that the proposed scheme can outperform conventional chaos-based MIMO schemes that assume perfect channel knowledge.

A Deep Learning Based Modeling of Reconfigurable Intelligent Surface Assisted Wireless Communications for Phase Shift Configuration

Reconfigurable Intelligent Surface (RIS) has emerged as a promising technology in wireless networks to achieve high spectrum and energy efficiency. RIS typically comprises a large number of low-cost nearly passive elements that can smartly interact with the impinging electromagnetic waves for performance enhancement. However, optimally configuring massive number of RIS elements remains a challenge. In this article, we present a machine learning (ML) based modeling approach that learns the interactions between the phase shifts of the RIS elements and receiver (Rx) location attributes and uses them to predict the achievable rate directly without using channel state information (CSI) or received pilots. Once learned, our model can be used to predict optimal RIS configuration for any new receiver locations in the same wireless network. We leverage deep learning (DL) techniques to build our model and study its performance and robustness. Simulation results demonstrate that the proposed DL model can recommend near-optimal RIS configurations for test receiver locations which achieved close to an upper bound performance that assumes perfect channel knowledge. Our DL model was trained using less than 2% of the total receiver locations. This promising result represents great potential in developing a practical solution for the optimal phase shifts of RIS elements without requesting CSI from the wireless network infrastructure.

A review on secrecy performance of cooperative relay network with diversity

In the past few years, there has been a rapid increase in demand on transmitting data over the open wireless medium. Severe issues of information security and privacy thus arise, as the wireless transmissions may be intercepted by any unintended receiver in the network. There is a need to design the transmission system in an efficacious way, such that the available space and time can be used adequately. This becomes a challenging job as there are random variations in the channel quality due to deleterious effects of the multi-path fading. Hence, to improve the transmission reliability, coverage probability and throughput, relay aided communication draw in great attention from both academia and industry. Relays provide the advantage of cooperative diversity, where the directly transmitted signal and the relayed signal can be combined at the receiver using various diversity combining schemes to mitigate the fading effect. In this paper, we review the secrecy performance of dual-hop cooperative relay system with diversity reception and relay selection. The significance and secrecy effect of channel knowledge at the transmitter is also studied. We have corroborated that CSI knowledge at the transmitter can improve secrecy.

Potential of aquaporins and connexins in dogs and their relation to the reproductive tract

The complexity of processes in the female reproductive system of mammals is extremely sophisticated. The overall relationship between the processes during the oestrus cycle in animals is quite well understood, but the molecular background of these processes still requires an in-depth analysis. Bitches are distinguished by a specific course of sexual cycle during which the oocyte matures after ovulation in the oviduct. Other species of mammals are characterized by maturation of the oocyte within the ovary. Acquisition of developmental competence by cumulus – oocyte complexes seems to be a process with a complex molecular background, and the key to understanding it may be the analysis of intercellular channels. Aquaporins and connexins are structural proteins that are built into the cell membrane. Their location is widespread in many body tissues. Recent years have shown that they exhibit significant expression in different parts of the mammalian reproductive system, although the number of studies on dogs is still negligible. This review paper presents the current state of knowledge of water channels and gap junction connections in different animal species, with particular focus on dogs, and also explores the role of aquaporins and connexins in the acquisition of reproductive competences.

Advanced Noncoherent Detection in Massive MIMO Systems via Digital Beamspace Preprocessing

Noncoherent detection in massive multiple-input/multiple-output (MIMO) uplink systems provides a low-complexity alternative to its coherent counterpart. Requiring no actual channel knowledge but the per-user induced power at the base station, comparable performance as channel-estimation-based detection can be achieved when the users are located in the near-field of the base station. However, noncoherent detection fails in scenarios where users are in the far-field due to an insufficient capability to separate the users in terms of their spatially induced power. For this purpose, a dielectric lens or an analog beamforming structure can be employed, which are capable to focus the power of the incident waves to a smaller subset of the antennas at the base station. These so-called analog beamspace techniques have been demonstrated to enable again the noncoherent detection scheme. Analogous to a spatial Fourier transform, beamspace techniques can be also realized in the digital domain offering more flexibility. Its applicability to noncoherent detection is studied in this paper. It is shown numerically that by means of digital beamspace preprocessing, considerable performance gains can be achieved. Applied in dominant line-of-sight channels, a large number of users can be accommodated and the residual performance gap to coherent detection with perfect channel knowledge is minimal.

Performance of Two-Hop Links With an Energy Buffer-Aided IoT Source and a Data Buffer-Aided Relay

In this article, we analyze the performance of a two-hop communication link between an energy-buffer-equipped IoT-type self-sustaining source and a destination, aided by a data-buffer-equipped relay. The source harvests radio-frequency energy from the ambience and stores it in an energy buffer. Using a discrete-time continuous-state Markov chain to model the energy buffer and a discrete-state Markov chain to model the data buffer, we analyze outage and throughput performance of the link. In typical applications, feedback of channel state information and buffer state information is not feasible, so simple link selection schemes are evolved. In the first only buffer-status (OBS) scheme, link selection is based on the energy-buffer status alone. A modified OBS scheme is then suggested, and its performance is analyzed. The third scheme uses both energy-buffer status and first-hop channel knowledge for link selection. Expressions are derived for throughput and limiting distributions of stored energy with all the three schemes. We show how the continuous-state energy buffer and the discrete-state data buffer can both be balanced while maximizing the throughput by suitable choice of the source transmit power, relay transmit power, and target information rates at the source and the relay. We also analyze statistical properties of the energy-buffer size with these schemes. The derived analytical expressions are validated by the Monte Carlo simulations.

DACASN: Dynamic ant routing‐based channel accessing cognitive sensor network in Internet of Things

SummaryThe Internet of Things (IoT) is a structure with sensors, base station, gateway, and network servers. Users can access the information contained in it and use it as they need. These sensors are embedded in several data collection locations in a uniform or random manner to collect data. The users in many parts of the world obtain such data that fit their requirements. For example, sensors are used in remote areas to monitor various areas such as forests, wildlife sanctuaries, fields, industrial needs, human condition, and changes in the ocean. The changes like these that take place in the open spaces require the selection of the best routing method and seamless delay reduction in data collection helps to immediately reach those involved in it. In the proposed protocol, dynamic ant routing‐based channel accessing cognitive sensor network (DACASN) in the IoT, cognitive sensors are used. It is made with the knowledge of choosing a channel for communication as well as selecting the parameters for path selection and setting the most accurate path with the channel knowledge and communicating with the internet server. The sensors set the paths for this route selection using the centrifugal method and the anticolonial method. As a result, it can be seen in the results and discussion section that the quality of the routing information is high because it is delivered to the destination in the shortest possible time.

Energy Efficient Resource Allocation Optimization in Fog Radio Access Networks With Outdated Channel Knowledge

Fog Radio Access Networks (F-RAN) are gaining worldwide interests for enabling mobile edge computing for Beyond 5G. However, to realize the future real-time and delay-sensitive applications, F-RAN tailored radio resource allocation and interference management become necessary. This work investigates user association and beamforming issues for providing energy efficient F-RANs. We formulate the energy efficiency maximization problem, where the F-RAN specific constraint to guarantee local edge processing is explicitly considered. To solve this intricate problem, we design an algorithm based on the Augmented Lagrangian (AL) method. Then, to alleviate the computational complexity, a heuristic low-complexity strategy is developed, where the tasks are split in two parts: one solving for user association and Fog Access Points (F-AP) activation in a centralized manner at the cloud, based on global but outdated user Channel State Information (CSI) to account for fronthaul delays, and the second solving for beamforming in a distributed manner at each active F-AP based on perfect but local CSIs. Simulation results show that the proposed heuristic method achieves an appreciable performance level as compared to the AL-based method, while largely outperforming the energy efficiency of the baseline F-RAN scheme and limiting the sum-rate degradation compared to the optimized sum-rate maximization algorithm. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Part of this article has been presented at IEEE CCNC 2019 [1].

Navigating the New Era of Influencer Marketing: How to be Successful on Instagram, TikTok, &amp; Co.

Influencer marketing represents a $10 billion industry in 2020 and is becoming of increasing relevance for many firms, especially those operating in a business-to-consumer environment. Few firms in the fashion, beauty, travel, food, or beverage industries are running marketing campaigns these days that do not include, at least to some share, a collaboration with popular users on platforms such as Instagram and TikTok. However, many marketing managers still have a less than adequate understanding of those platforms compared with their knowledge of more traditional media channels and often find it hard to make the right decision in this fast-moving environment. To provide some guidance in this respect, this article aims to give an introduction to the most critical platforms for influencer marketing. It then presents advice to firms who want to engage in influencer marketing as well as specific questions on identifying the right influencers to collaborate with.

Pseudonoise Waveform Design for Spectrum Sharing Systems

With an increasing demand for high-resolution radar and wideband communication systems, the frequency spectrum is becoming a limited resource. Many such systems must coexist in a given frequency band. In order to minimize interference among these systems, a radar waveform design is used. Without a priori knowledge of the transmission channel and communication waveform, one viable strategy is to use spectral nulls for the frequencies used by communication system. This article describes an improved method for generating pseudonoise waveforms with limited range and Doppler sidelobes. The algorithm permits adjustable power spectral density of the generated waveform. An additional parameter, “significance factor,” is a tradeoff between power spectral density shape and sidelobe suppression depth. The method also allows the generation of a set of mutually orthogonal waveforms for multitransmitter scenarios. Effectiveness of the algorithm is confirmed by both numerical simulation and field trials.

Distributed Parameter Detection in Massive MIMO Wireless Sensor Networks Relying on Imperfect CSI

Distributed parameter detection is conceived for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs), where multiple sensors collaborate to detect the presence/ absence of a spatially correlated parameter. Neyman-Pearson (NP) and generalized likelihood ratio test (GLRT)-based detectors are developed at the fusion center (FC) for known and unknown parameter detection scenarios, respectively. More explicitly, the GLRT detector also has to estimate the unknown parameter value. Closed-form expressions are derived for the probabilities of detection (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> ) and false alarm (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FA</sub> ) in order to characterize the performance of the proposed schemes. Furthermore, the optimal sensor transmit gains are determined for maximising the detection performance attained. An asymptotic performance analysis is carried out for determining the gain scaling laws for the massive MIMO WSN considered, when the number of antennas tends to infinity. The proposed framework is also extended to the realistic imperfect channel knowledge scenario at the FC, followed by the development of the associated fusion rules and analytical results to characterize the performance. Our simulation results closely tally the theoretical findings.

해외여행 온라인 구매채널 선택에서 구매채널 지식, 여행 경험, 성별 차이의 영향 : 항공권과 숙박 구매 서비스의 비교

The demand for travel services have increased, as more focus of consumption have shifted from goods to experiences. Naturally, consumer’s online dependency for travel information search and travel service purchase have escalated. We address the influence of consumer characteristics, including travel knowledge(purchase channel knowledge, travel experience), individual’s leisure consumption level, and gender, on online travel reservation channel choices: flight/hotel company’s own website, general shopping app/site, online travel agency(OTA), etc. Also, we compare these effects between hotel reservation channel choices and flight ticket reservation channel choices. Travel characteristics, such as travel length, number of companions, and purchase price level, are included as control variables. Findings show that consumers with greater purchase channel knowledge are more likely to use general shopping apps/sites to purchase flight ticket and OTAs to make hotel reservations. Consumers with greater travel experiences are more inclined to directly purchase from the suppliers of flight and hotel services, while demonstrating lower tendency to use search portal sites(Naver, Daum). Male consumers are more likely to make purchases from the suppliers’ direct online channels, while female consumers show relatively higher use of general shopping apps/sites purchasing flight tickets and OTAs booking hotels online.

해외여행 온라인 구매채널 선택에서 구매채널 지식, 여행 경험, 성별 차이의 영향 : 항공권과 숙박 구매 서비스의 비교

The demand for travel services have increased, as more focus of consumption have shifted from goods to experiences. Naturally, consumer’s online dependency for travel information search and travel service purchase have escalated. We address the influence of consumer characteristics, including travel knowledge(purchase channel knowledge, travel experience), individual’s leisure consumption level, and gender, on online travel reservation channel choices: flight/hotel company’s own website, general shopping app/site, online travel agency(OTA), etc. Also, we compare these effects between hotel reservation channel choices and flight ticket reservation channel choices. Travel characteristics, such as travel length, number of companions, and purchase price level, are included as control variables. Findings show that consumers with greater purchase channel knowledge are more likely to use general shopping apps/sites to purchase flight ticket and OTAs to make hotel reservations. Consumers with greater travel experiences are more inclined to directly purchase from the suppliers of flight and hotel services, while demonstrating lower tendency to use search portal sites(Naver, Daum). Male consumers are more likely to make purchases from the suppliers’ direct online channels, while female consumers show relatively higher use of general shopping apps/sites purchasing flight tickets and OTAs booking hotels online.

Connexin therapeutics: blocking connexin hemichannel pores is distinct from blocking pannexin channels or gap junctions.

Compounds that block the function of connexin and pannexin protein channels have been suggested to be valuable therapeutics for a range of diseases. Some of these compounds are now in clinical trials, but for many of them, the literature is inconclusive about the molecular effect on the tissue, despite evidence of functional recovery. Blocking the different channel types has distinct physiological and pathological implications and this review describes current knowledge of connexin and pannexin protein channels, their function as channels and possible mechanisms of the channel block effect for the latest therapeutic compounds. We summarize the evidence implicating pannexins and connexins in disease, considering their homeostatic versus pathological roles, their contribution to excesive ATP release linked to disease onset and progression.

Energy-Efficient Time-Domain Equilibrium Scheduling and Optimization Scheme for Energy Harvesting-Powered D2D Communication

Energy Harvesting- (EH-) powered Device-to-Device (D2D) Communication underlaying Cellular Network (EH-DCCN) has been deemed as one of the basic building blocks of Internet of Things due to its green energy efficiency and adjacent communication. But available energy will be one of the biggest obstacles when implementing EH-DCCN due to the immaturity of EH technology and the volatility of environmental energy resources. To improve energy utilization, this study investigates an efficient scheduling and power allocation scheme about transmission load equilibrium in the time domain. Accordingly, a short-term Sum Energy Efficiency (stSEE) maximization problem for EH-powered D2D communication is modelled, while ensuring a fundamental transmission rate requirement of cellular users. Consequently, the optimization problem is a nonconvex mixed integer nonlinear programming problem. Thus, we propose a two-layer convex approximation iteration algorithm which can obtain a feasible quasioptimal solution for the stSEE problem. Simultaneously, a two-step heuristic algorithm in a slot-by-slot fashion is also developed to acquire a suboptimal solution without requiring statistical knowledge of channel and energy arrival processes. Simulated analysis indicates that the short-term scheduling strategy can obtain better performances in terms of energy efficiency and transmission rate than conventional real-time scheduling scheme. Besides, the maximum scheduled number of EH-D2D pairs underlaying one cellular user under different EH efficiency is analysed, which can give us a theoretical reference about the deployment of future EH-DCCN.