Channel Fluctuations Research Articles

Limited Feedback-Based User Clustering for Non-Orthogonal Multiple Access in mmWave Systems

Non-orthogonal multiple access (NOMA) and mmWave are considered to be promising techniques for 5G and beyond cellular network communication systems. While the mmWave band provides a very wide under-utilized bandwidth, NOMA enhances the spectral efficiency compared to orthogonal multiple access (OMA). The combination of these two techniques could be considered as the key solution to the high required data rates in next-generation communication systems. The implementation of NOMA is studied and verified under an ideal condition with perfect knowledge of the channel state information (CSI) at the base station (BS). However, under the practical conditions, the fluctuation of the wireless channel makes perfect CSI unachievable at the BS. Hence, we proposed to use the angle of departure (AoD) as feedback information from UEs to the BS. We assume that, mobile users (UEs) perfectly estimate the channel by detecting the pilot signals. Then, UEs quantize the AoD and feed back to the BS. Finally, the BS uses the AoD to perform user clustering, power allocation and beamforming. To reduce the feedback overhead further, we proposed a user clustering algorithm which uses one-bit feedback to determine the change of the AoD. Numerical results demonstrate that the proposed NOMA system outperforms the conventional orthogonal multiple-access (OMA) system with the same amount of feedback information.

Reconfigurable Intelligent Surfaces: Principles and Opportunities

Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), or large intelligent surfaces (LISs), have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) of communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multi-antenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for applications to next-generation networks.

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Given the ever-increasing interest in the Internet of Underwater Things (IoUT), various studies are ongoing to solve some of the practical problems affecting the development of underwater wireless communication. The main problems are related to the use of acoustic waves in a water medium, in which extremely high propagation loss and drastic channel fluctuation are common. On the basis of hands-on experience and measurements made in real underwater environments, the conventional Adaptive Modulation and Coding (AMC), which uses the high correlation between SNR (Signal to Noise Ratio) and BER (Bit Error Rate), might not be affordable in underwater environments because the normal correlation between SNR and BER almost disappears altogether. This work therefore collectively takes into account multiple quality factors of communication at the same time by creating, analysing and validating the machine learning model to predict the most adequate communication parameters to solve the problem. The dataset of underwater wireless communication used in the learning models was obtained from measurements made in a real underwater environment near the Gulf of Incheon, South Korea, using a practical testbed designed and implemented by the authors. The estimated network throughput based on the communications parameters predicted using the machine learning models was enhanced by up to 25% compared with the conventional handcraft method.

Noise induces continuous and noncontinuous transitions in neuronal interspike intervals range

Noise appears in the brain due to various sources, such as ionic channel fluctuations and synaptic events. They affect the activities of the brain and influence neuron action potentials. Stochastic differential equations have been used to model firing patterns of neurons subject to noise. In this work, we consider perturbing noise in the adaptive exponential integrate-and-fire (AEIF) neuron. The AEIF is a two- dimensional model that describes different neuronal firing patterns by varying its parameters. Noise is added in the equation related to the membrane potential. We show that a noise current can induce continuous and noncontinuous transitions in neuronal interspike intervals. Moreover, we show that the noncontinuous transition occurs mainly for parameters close to the border between tonic spiking and burst activities of the neuron without noise

Peak-to-Average Power Ratio Reduction for High-Mobility Massive MIMO With Angle-Domain Doppler Suppression

Doppler suppression techniques with angle-domain Doppler compensation target reducing the time fluctuations of time-varying channel for high-mobility massive MIMO communications. However, the existing Doppler suppression solutions suffer from high peak-to-average power ratio (PAPR). In this letter, we propose a PAPR reduction scheme for Doppler suppression, where an optimization problem is formulated to minimize the Doppler spread under a constraint of tolerable PAPR level. We further develop a suboptimal solution which decomposes the original joint optimization problem into two steps. First, an iterative Fourier technique is employed and the idea of basis expansion model (BEM) is proposed to reduce the computational complexity. Then, the antenna weighting vector is optimized which takes into consideration both Doppler spread and PAPR. Simulation results are provided to demonstrate that the proposed scheme can substantially outperform the existing schemes.

User Pairing, Link Selection, and Power Allocation for Cooperative NOMA Hybrid VLC/RF Systems

Despite the promising high-data rate features of visible light communications (VLC), they still suffer from unbalanced services due to blockages and channel fluctuation among users. This paper introduces and evaluates a new transmission scheme which adopts cooperative non-orthogonal multiple access (Co-NOMA) in hybrid VLC/radio-frequency (RF) systems, so as to improve both system sum-rate and fairness. Consider a network consisting of one VLC access point (AP) and multiple strong and weak users, where each weak user is paired with a strong user. Each weak user can be served either directly by the VLC AP, or via the strong user which converts light information received through the VLC link, and forwards the information to the weak user via the RF link. The paper then maximizes a network-wide weighted sum-rate, so as to jointly determine the strong-weak user-pairs, the serving link of each weak user (i.e., either direct VLC or hybrid VLC/RF), and the power of each user message, subject to user connectivity and transmit power constraints. The paper tackles such a mixed-integer non-convex optimization problem using an iterative approach. Simulations show that the proposed scheme significantly improves the VLC network performance (i.e., sum-rate and fairness) as compared to the conventional NOMA scheme.

Artificial Intelligence Aided Next-Generation Networks Relying on UAVs

In this article, we propose artificial intelligence (AI) enabled unmanned aerial vehicle (UAV) aided wireless networks (UAWN) for overcoming the challenges imposed by the random fluctuation of wireless channels, blocking and user mobility effects. In UAWN, multiple UAVs are employed as aerial base stations, which are capable of promptly adapting to the randomly fluctuating environment by collecting information about the users' position and tele-traffic demands, learning from the environment and acting upon the satisfaction level feedback received from the users. Moreover, AI enables the interaction among a swarm of UAVs for cooperative optimization of the system. As a benefit of the AI framework, several challenges of conventional UAWN may be circumvented, leading to enhanced network performance, improved reliability and agile adaptivity. As a further benefit, dynamic trajectory design and resource allocation are demonstrated. Finally, potential research challenges and opportunities are discussed.

Influence of Unsteady Flow Induced by a Large-Scale Hydropower Station on the Water Level Fluctuation of Multi-Approach Channels: A Case Study of the Three Gorges Project, China

Unsteady flow induced by hydropower stations exerts a significant impact on the water level in multi-approach channels, which directly threatens the safe passage of ships. In this study, a one-dimensional and a two-dimensional hydrodynamic model are adopted to simulate the water level fluctuations at the entrance of multi-approach channels and the lower lock head of a ship lift with consideration of initial water surface elevation, base flow, flow amplitude, regulation time, and locations of hydropower stations, unfavorable conditions are successfully identified; and the fluctuations at the approach channel entrance and the lower lock head of a ship lift under single-peak and double-peak regulating mode are analyzed considering the flow regulating of the Gezhouba Hydropower Station (GHS), thus, the water level oscillation process in the multi-approach channels is presented. Results show that the largest wave amplitude in the multi-approach channels manifests under unfavorable conditions including lower initial water surface elevation, smaller base flow, larger flow variation, and shorter regulation time; and water level fluctuation in the multi-approach channel is primarily induced by flow amplitude and net flow between the Three Gorges Hydropower Station (TGHS) and the GHS, with consideration of the counter-regulation process of the GHS. This research contributes to providing a reference for a similar large-scale cascade hydropower station regarding regulation and control of navigation conditions.

Formation and uniformity of bubbles in highly viscous fluids in symmetric parallel microchannels

This work focuses on the formation and uniformity of bubbles in highly viscous fluids in symmetric parallel microchannels by using a high-speed camera. Nitrogen and high viscous glycerol-water solution containing 0.3% SDS are used as the gas and liquid phases respectively. The interface evolution during bubble information is reported. The effects of liquid flow rates, liquid viscosities, gas pressures, and microchannel configurations on bubble size distribution are investigated based on the gas-liquid two-phase flow resistance model. It is found that the difference in the downstream resistance, the pressure fluctuation of the upstream channels, and the feedback effect of bubble behaviors in the cavity affect the uniformity of bubble size. In the configuration that the widths of the microchannels engineered according to Murray's law, the uniformity is better, under low gas pressures, high viscosities and flow rates of liquids. Finally, the prediction models of bubble size in both geometries are proposed.

Energy-Spectral Efficiency Optimization in Vehicular Communications: Joint Clustering and Pricing-Based Robust Power Control Approach

Smart, and green cities impose stringent requirements on spectral efficiency (SE), and energy efficiency (EE) of vehicular networks. For the current vehicular ad-hoc networks (VANETs), vehicle's mobility leads to rapid topology changes, and high channel uncertainty. However, clustering schemes for establishing stable clusters, and robust power control (RPC) combating with channel fluctuation are investigated independently. In this paper, joint clustering, and RPC schemes are proposed to optimize the SE, and EE of the involved VANETs. Via the same fixed-length slot, the synchronized interference constraints of cluster heads (CHs) are formed, and offer conditions for RPC. Due to the random channel fluctuations, all CHs’ synchronized interference constraints are formulated as probability constraints. Besides, a pricing-based utility which avoids the separate optimization between SE, and EE is introduced, and the price's impact on the tradeoff between them is involved. Since the probability constraints are intractable, and the unified utility is nonconvex, the Bernstein approximation, and successive convex approximation (SCA) are used to transform the problem into a tractable convex one. Through dual decomposition, two RPC algorithms are proposed to determine the optimal solutions for the fixed price $C$ , and the optimal price $C^*$ , respectively. Numerical simulations are used to evaluate the algorithmic performances in high-dynamic system, and the results show that the proposed algorithms are effective. The validity of the clustering method, and the proposed RPC scheme is further verified by comparisons.

A Statistical Analysis of Global Ionospheric E‐Layer Scintillation During 2007–2014

AbstractThis paper presents the global three‐dimensional morphology and seasonal variation of the scintillation index at E‐layer altitudes varying from 70 to 140 km. An attempt is conducted to analyze the scintillation distribution in different latitude bins and to explore solar and geomagnetic activity‐dependent characteristic of E‐layer scintillations. The scintillation index is derived from the signal‐to‐noise ratio intensity fluctuations of L1 channels in GPS radio occultation (RO) using the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) during 2007–2014. The results show that the summer scintillation index has the greatest values, followed by the winter, autumn, and spring months. The summer scintillation is in close connection with the solar radiation intensity and sunshine duration, but it exhibits seasonal asymmetry whereby the summer scintillation in the northern hemisphere is more intense than in the southern hemisphere. Meanwhile, the scintillation in the autumn hemisphere is slightly more intense than in the spring hemisphere in approximately midlatitude regions during most daytimes. The conspicuous scintillation activity in the middle and low latitudes starts post‐sunrise and often persists until post‐midnight. Some observed results show that the greatest scintillation occurrence rate of the latitude profile occurs at approximately sunset and at an altitude range of 100–120 km in the middle‐low latitudes. Additionally, the enhanced solar activity possibly can result in the slight decrease of scintillation occurrence, and geomagnetic activity has slight effect on E‐layer scintillation in most cases.

Adaptive Body Area Networks Using Kinematics and Biosignals

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for everyday human activities was developed to assess the efficacy of the proposed techniques. Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively. The channel emulator and simulation code is made publicly available at https://github.com/a-moin/wban-pathloss.

A Deep Reinforcement Learning-Based Transcoder Selection Framework for Blockchain-Enabled Wireless D2D Transcoding

The boom of video streaming industry has resulted in the increasing demands for transcoding services from heterogeneous users. Recent advances of blockchain technology allow some startups to realize decentralized collaborative transcoding through device-to-device (D2D) networks, where a group of transcoders are selected to perform transcoding cooperatively. For the blockchain-enabled D2D transcoding systems, it's imperative to jointly design transcoder selection, task scheduling and resource allocation schemes in order to provide efficient and trustworthy transcoding services. In this paper, viewing the involved multi-dimensional complex factors and channel fluctuation, we propose a novel deep reinforcement learning (DRL) based transcoder selection framework for blockchain enabled D2D transcoding systems where both the platform dynamics and channel statistics are captured. To reduce the action space size, we adopt a two-stage decision approach to first select the transcoders through a normal DRL based framework and then obtain the optimal task scheduling, power control, and resource allocation scheme by solving a stochastic optimization problem with the constrained stochastic successive convex approximation (CSSCA) approach. Simulation results show that our proposed framework can achieve high transcoding revenue while meeting the quality of service (QoS) requirements, and it can well handle dynamic cases.

Identifying the risk-Taking channel of monetary transmission and the connection to economic activity

Abstract I use loan-level data from the syndicated loan market in the U.S. to investigate how monetary policy affects banks’ sensitivity to risk. Using loan-level data and banks’ sensitivity to risk enables me to identify the risk-taking channel and disentangle it from other monetary channels. I show that banks change their behavior toward risk following changes in monetary policy where loose monetary policy reduces banks’ sensitivity to risk. I then provide evidence for the significant contribution of risk-taking shocks and changes in banks’ risk-taking behavior to economic outcomes and business cycle fluctuations. The paper’s primary contribution is in providing new loan-level evidence for the existence of the risk-taking channel in the U.S., as well as a possible link between the risk-taking channel and business cycle fluctuations.

An Online Buffer-Aware Resource Allocation Algorithm for Multiuser Mobile Video Streaming

Mobile video traffic has experienced exponential growth in recent years due to increasing prevalence of mobile devices and continuous advancement of wireless communications. However, network fluctuations often occur when transmitting video data, which greatly impact the quality of experience (QoE) of mobile users. Buffering technique has been widely adopted to mitigate the effect of network fluctuations on QoE for mobile users by prefetching certain video data to mobile devices in advance. However, mobile users often abort video watching before the videos are fully watched and such early departure behavior can result in considerable wastage of buffered video data. In this paper, we study buffer-aware resource allocation for multiuser mobile video streaming system by jointly considering video users early departure behavior and wireless channel fluctuation property. We formulate a stochastic optimization problem in which the objective is to reduce both the amount of wasted buffered data and the video re-buffering time. The problem is then transformed into a series of deterministic per-slot problems by using Lyapunov optimization theory. We propose an online buffer-aware resource allocation algorithm to solve the per-slot optimization problems. After proving the convexity of the per-slot optimization problem, we further design a fast-convergent iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) to perform each of the per-slot resource allocations. Simulation results show that our proposed algorithm has very low running time, near optimal performance, and is able to achieve a good trade-off between QoE improvement and data wastage reduction.

Architecture and experimental evaluation of context-aware adaptation in vehicular networks

In vehicular networks, the propagation environment changes rapidly for mobile nodes. To achieve high throughput, wireless devices need to be highly adaptive to these environmental changes by altering their transmission parameters across different layers of the network stack. Sensors in mobile and vehicular nodes can be used to form an understanding of the surrounding context. Such contextual awareness is particularly important in vehicular networks as the frequent context switching and increased channel fluctuations can cause existing adaptation protocols to fail to converge to the optimal transmission parameters. In this paper, we leverage information about the environmental context to enable improved rate adaptation performance in vehicular networks. In particular, we propose a classification-based link-level adaptation framework, which can effectively learn the relationship between context information (such as velocity, SNR, and channel type) and the throughput of various transmission modes. We then quantify the throughput improvement using the proposed scheme and show that our proposed framework can significantly enhance the performance of rate adaptation. With experiments on emulated and in-field channels, we observe that the throughput increases by up to 245% over protocols which use SNR alone to make rate decisions. Based on an analysis of attribute importance, we identify channel type as a key parameter that affects classification performance. Since channel type often cannot be directly obtained, we propose a multi-dimensional channel inference method for use when knowledge about the channel type is not available. We demonstrate that the proposed channel inference achieves an accuracy of up to 94% in previously encountered channels and can quickly signal that a channel has not yet been encountered. The robustness of the proposed methods are demonstrated using experimental data from two different hardware platforms and three different carrier frequency bands. Lastly, we evaluate the most predominant Linux-based rate selection algorithm (Minstrel), study the relative rate selection accuracy of our approach, and analyze the key role that the retry mechanism in Minstrel plays on its performance.

1/f critical current noise in short ballistic graphene Josephson junctions

Short ballistic graphene Josephson junctions sustain superconducting current with a non-sinusoidal current-phase relation up to a critical current threshold. The current-phase relation, arising from proximitized superconductivity, is gate-voltage tunable and exhibits peculiar skewness observed in high quality graphene superconductors heterostructures with clean interfaces. These properties make graphene Josephson junctions promising sensitive quantum probes of microscopic fluctuations underlying transport in two-dimensions. We show that the power spectrum of the critical current fluctuations has a characteristic 1/f dependence on frequency, f, probing two points and higher correlations of carrier density fluctuations of the graphene channel induced by carrier traps in the nearby substrate. Tunability with the Fermi level, close to and far from the charge neutrality point, and temperature dependence of the noise amplitude are clear fingerprints of the underlying material-inherent processes. Our results suggest a roadmap for the analysis of decoherence sources in the implementation of coherent devices by hybrid nanostructures.

Continuous-Variable Quantum Key Distribution Over Air Quantum Channel With Phase Shift

Continuous-variable quantum key distribution (CV-QKD) over air quantum channel enables to provide unconditional information security, which is the one of most promising techniques for information wireless transfer. Since the air channel is of fluctuation in nature due to the presence of air turbulence, the random variations have to be taken into account when the security of CV-QKD is analyzed. We study the secret key rate of CV-QKD over the air quantum channel in this paper. The fluctuation of air channel is characterized by beam wandering and phase distortion under weak air turbulence conditions. The impact of fluctuation on the CV quantum signals is reflected by transmittance perturbation and phase shift. Our numerical results show that the secret key rate may keep relatively high level as the air quantum link increases at the outset. However, when the air link distance further increases beyond certain range, the secret key rate decreases strikingly since the both transmittance perturbation and the phase shift simultaneously deteriorate the performance of CV-QKD. Nevertheless, operating the CV-QKD under weak air turbulence conditions is available.

NexGen D-TCP: Next Generation Dynamic TCP Congestion Control Algorithm

With the advancement of wireless access networks and mmWave New Radio (NR), new applications emerged, which requires a high data rate. The random packet loss due to mobility and channel conditions in a wireless network is not negligible, which degrades the significant performance of the Transmission Control Protocol (TCP). The TCP has been extensively deployed for congestion control in the communication network during the last two decades. Different variants are proposed to improve the performance of TCP in various scenarios, specifically in lossy and high bandwidth-delay product (high-BDP) networks. Implementing a new TCP congestion control algorithm whose performance is applicable over a broad range of network conditions is still a challenge. In this article, we introduce and analyze a Dynamic TCP (D-TCP) congestion control algorithm over mmWave NR and LTE-A networks. The proposed D-TCP algorithm copes up with the mmWave channel fluctuations by estimating the available channel bandwidth. The estimated bandwidth is used to derive the congestion control factor N. The congestion window is increased/decreased adaptively based on the calculated congestion control factor. We evaluated the performance of D-TCP in terms of congestion window growth, goodput, fairness and compared it with legacy and existing TCP algorithms. We performed simulations of mmWave NR during LOS NLOS transitions and showed that D-TCP curtails the impact of under-utilization during mobility. The simulation results and live air experiment points out that D-TCP achieves 32.9% gain in goodput as compared to TCP-Reno and attains 118.9% gain in throughput as compared to TCP-Cubic.

Operating experience feedback from incidental fluctuations of the power measurement channel at ETRR-2

Abstract This paper provides analysis of the operating experience feedback from two events of incidental fluctuations of the power measurement channels at Egypt Second Research Reactor (ETRR-2). In the first event, the fluctuation was due to improper functioning of the secondary cooling system, while in the second one the fluctuation was associated with a leakage of a gadolinium nitrate solution into chambers surrounding the reactor core (which are part of a diverse second shutdown system of the reactor). The paper describes these events, including observation of the reactor operation performance and operators’ actions, and provides the root causes analysis of these events along with the corrective actions. Lessons learned from these events, which are generically applicable to other research reactors, are also identified and presented. The analysis showed the vital role of continuing training of reactor operators, consideration of human factors in all activities during the reactor lifetime, and quality checks and field verifications during operation and post-maintenance works to the safety and availability of research reactors. The analysis also showed that underperformance of non-safety components could have an effect on those important to safety and could negatively impact the availability and the operational performance of the reactor.