Random Fading Research Articles

Self-adaptive integrated photonic receiver for turbulence compensation in free space optical links

In Free Space Optical (FSO) communication systems, atmospheric turbulence distorts the propagating beams, causing a random fading in the received power. This perturbation can be compensated using a multi-aperture receiver that samples the distorted wavefront on different points and adds the various signals coherently. In this work, we report on an adaptive optical receiver that compensates in real time for scintillation in FSO links. The optical front-end of the receiver is entirely integrated in a silicon photonic chip hosting a 2D Optical Antenna Array and a self-adaptive analog Programmable Optical Processor made of a mesh of tunable Mach–Zehnder interferometers. The photonic chip acts as an adaptive interface to couple turbulent FSO beams to single-mode guided optics, enabling energy and cost-effective operation, scalability to systems with a larger number of apertures, modulation-format and data-protocol transparency, and pluggability with commercial fiber optics transceivers. Experimental results demonstrate the effectiveness of the proposed receiver with optical signals at a data rate of 10 Gbit/s transmitted in indoor FSO links where different turbulent conditions, even stronger than those expected in outdoor links of hundreds of meters, are reproduced.

Minimizing Task Age upon Decision for Low-Latency MEC Networks Task Offloading with Action-Masked Deep Reinforcement Learning.

In this paper, we consider a low-latency Mobile Edge Computing (MEC) network where multiple User Equipment (UE) wirelessly reports to a decision-making edge server. At the same time, the transmissions are operated with Finite Blocklength (FBL) codes to achieve low-latency transmission. We introduce the task of Age upon Decision (AuD) aimed at the timeliness of tasks used for decision-making, which highlights the timeliness of the information at decision-making moments. For the case in which dynamic task generation and random fading channels are considered, we provide a task AuD minimization design by jointly selecting UE and allocating blocklength. In particular, to solve the task AuD minimization problem, we transform the optimization problem to a Markov Decision Process problem and propose an Error Probability-Controlled Action-Masked Proximal Policy Optimization (EMPPO) algorithm. Via simulation, we show that the proposed design achieves a lower AuD than baseline methods across various network conditions, especially in scenarios with significant channel Signal-to-Noise Ratio (SNR) differences and low average SNR, which shows the robustness of EMPPO and its potential for real-time applications.

Construction of an Intelligent Identification Model for Drugs in Near Infrared Spectroscopy and Research on Drog Classification based on Improved Deep Algorithm

Near-infrared spectroscopy has important applications in drug and food identification. Combining machine learning with near-infrared spectroscopy to achieve intelligent identification of drugs has become a research hotspot in recent years. To solve the problem of machine learning’s inefficiency in classifying small-scale data, a drug identification model based on near-infrared spectroscopy combined with a random fading depth belief network is proposed. Aiming at the problem that the training time of the machine learning algorithm is too long, the extreme learning machine is used to replace the back propagation algorithm to optimize the stack sparse auto-encoder network. Additionally, the stack sparse auto-encoder algorithm based on extreme learning machine algorithm is constructed. The study found that the precision of the Dropout Deep Belief Network model was 99.12%, which was higher than the other three models. Additionally, the area under the curve value of the Dropout Deep Belief Network model was 0.87, which was 0.04 higher than the binary whale optimization algorithm model, 0.26 higher than the factor decomposition machine and depth neural network model, and 0.05 higher than the random forest network model. The sparse auto-encoder algorithm based on the extreme learning machine algorithm achieved a precision of 99.72%. The study proposes two algorithm models that can effectively identify drugs using near-infrared spectroscopy. This has a positive impact on the medical industry and the safety of patients' lives and health.

Visible Light Communication-Enabled Simultaneous Position and Orientation Detection for Harnessing Multipath Interference and Random Fading

Visible Light Communication-Enabled Simultaneous Position and Orientation Detection for Harnessing Multipath Interference and Random Fading

Iterative learning control for differential inclusion systems with random fading channels by varying average technique.

The aim of this paper is to study iterative learning control for differential inclusion systems with random fading channels between the plant and the controller. In reality, the phenomenon of fading will inevitably occur in network transmission, which will greatly affect the tracking ability of output trajectory. This study discusses the impact of fading channel on tracking performance at the input and output sides, respectively. First, a set-valued mapping in a differential inclusion system with uncertainty is converted into a single-valued mapping by means of a Steiner-type selector. Then, to offset the effect of the fading channel and improve the tracking ability, a variable local average operator is constructed. The convergence of the learning control algorithm designed by the average operator is proved. The results show that the parameters in the varying local average operator can be adjusted to trade-off between the learning rate and the fading offset rate. Finally, the theoretical results are verified by numerical simulation of the switched reluctance motors.

LTE Device Radio Frequency Fingerprints Blind Extraction Based on Temporal-Frequency Domain PRACH Signals

A frequency-domain cepstrum correlation and temporal transient filtered radio frequency fingerprints (RFF) blind extraction method is proposed for long term evolution uplink device authentication in this work. With the merits of the constant-modulus nature of the Zadoff-Chu (ZC) sequence and the cyclic prefix (CP) structure of the physical random access channel (PRACH) frame, the device RFF integrated in both the frequency-domain ZC sequence and the transient ramp-on/ramp-off sequences are extracted. For the frequency-domain RFF extraction, the cepstrum of the ZC sequence is calculated to enlarge the gap between the constant RFF and the random fading effects, and the correlation between two cepstrum fragments is then calculated to mitigate the random fading components. For the temporal RFF extraction, the ramp-on and ramp-off features induced by the device-specific power amplifier are extracted by exploiting the CP structure of the PRACH frame, where the CP part and the duplicate part are adaptively filtered and the final adapted weight of the least mean square filter is used as RFF. No prior PRACH parameter knowledge is required in RFF extraction and both the frequency-domain and the temporal RFF are combined as the input of the convolutional neural network to enhance the classification accuracy. With four-months sampling of PRACH signals emitted from universal software radio peripheral devices, the experimental results show that the authentication accuracy of the proposed combined RFF can reach as high as 99.9% in the line-of-sight (LOS) scenario and 99.6% in the non-LOS scenario.

Batch-Based Learning Consensus of Multiagent Systems With Faded Neighborhood Information.

This article addresses the batch-based learning consensus for linear and nonlinear multiagent systems (MASs) with faded neighborhood information. The motivation comes from the observation that agents exchange information via wireless networks, which inevitably introduces random fading effect and channel additive noise to the transmitted signals. It is therefore of great significance to investigate how to ensure the precise consensus tracking to a given reference leader using heavily contaminated information. To this end, a novel distributed learning consensus scheme is proposed, which consists of a classic distributed control structure, a preliminary correction mechanism, and a separated design of learning gain and regulation matrix. The influence of biased and unbiased randomness is discussed in detail according to the convergence rate and consensus performance. The iterationwise asymptotic consensus tracking is strictly established for linear MAS first to demonstrate the inherent principles for the effectiveness of the proposed scheme. Then, the results are extended to nonlinear systems with nonidentical initialization condition and diverse gain design. The obtained results show that the distributed learning consensus scheme can achieve high-precision tracking performance for an MAS under unreliable communications. The theoretical results are verified by two illustrative simulations.

Adaptive Diversity Algorithm Based on Block STBC for Massive MIMO Link Misalignment in UWOC Systems

In the past few years, underwater wireless optical communication (UWOC) has become a promising wireless communication technology in the underwater environment. Aiming at the problem formulation of sub-channel correlation enhancement occurring due to the joint impact of underwater link misalignment and turbulence in the process of optical signal transmission in an underwater optical massive multiple-input multiple-output (MIMO) system, we propose an adaptive diversity approach depending on partition space time block code (STBC). STBC technology is used to reduce the random fading of optical signals caused by turbulence. At the same time, the channel correlation occurring due to channel misalignment is effectively alleviated by adaptive processing. The adaptive diversity algorithm based on segmented STBC effectively improves the reliability and decrease complexity of underwater optical Massive MIMO communication systems. It determines the particular link misalignment degree by the channel gain matrix obtained from the channel estimation and selects different combinations of detectors according to the degree of misalignment to obtain the maximum gain of the received signal combination. Compared with the chunking scheme, simulation and result shows that the adaptive diversity algorithm improves the tolerance of the system to the link misalignment error from 30 mm to 60 mm under the same condition number of channel gain matrix, and it can still demodulate the source signal directly without requiring detection algorithm in case of a large error in the link misalignment.

Channel Model and Signal-Detection Algorithm for the Combined Effects of Turbulence and Link Misalignment in Underwater Optical Massive MIMO Systems

In recent years, underwater wireless optical communication (UWOC) has become a potential wireless carrier candidate for signal transmission in water mediums such as oceans. Underwater signal transmission is impaired by several challenges such as turbulence, scattering, attenuation, and misalignment. In this paper, we propose an improved-order successive interference cancellation (I-OSIC) algorithm based on partition space–time block coding (STBC) technology to solve the sub-channel correlation enhancement problem, which is caused by the combined effects of turbulence and link misalignment in the underwater optical massive multiple-input multiple-output (massive MIMO) systems. The partition STBC technology can make the encoded symbols orthogonality of space and time resist random fading under turbulence environments and fully use the communication link of the massive MIMO system. Under link misalignment conditions, the receiver detector will receive multiple beams. The proposed I-OSIC algorithm based on partition STBC can precisely track the degree of link misalignment error and reorder receiver signals based on the minimum interference criterion. It can use the channel matrix to estimate the interference magnitude of the link misalignment, and then eliminate the interference successively by demodulating the least interfered signal first. When the link misalignment error is large, the I-OSIC algorithm can provide a signal-to-noise ratio (SNR) gain of about 3 dB and provides the same error performance compared with the successive interference cancellation algorithm based on the received signal power.

Nonuniform Sampling Control for Multibody High-Speed Train Systems With Quantization Mechanisms via Stochastic Faded Channels

This paper studies the dissipative control problem of the multibody high-speed train (HST) systems with nonuniform sampling mechanisms and logarithmic quantizers, in which the transmitted signals are subject to random fading phenomenon. The tracking error dynamic model of HST is firstly established and the logarithmic quantizers for both the input and output (I/O) signals are designed. The faded I/O signals are described by the Rice fading model, in which the mathematical expectation and variance are given in advance. Then, based on the Lyapunov-Krasovskii functional approach with the consideration of time-varying delay, sufficient conditions are derived to ensure the convergence of tracking error and that HST is strictly dissipative. Further, the design method of the gain matrix is obtained by employing the linear matrix inequalities (LMI) techniques and a compensation algorithm is designed to offset the adverse effect brought by the fading measurements. Finally, the effectiveness of the proposed controller is verified by a numerical example from Japan Shinkansen HST.

Learning Control for Networked Stochastic Systems With Random Fading Communication

The learning control strategy is studied for networked stochastic systems, where the output and input data are transmitted through multiple independent fading channels. The traditional P-type learning control scheme is revised according to the specific fading positions, where the constant learning gain is replaced by a variable one to suppress the effect of various uncertainties. Strong convergence of the proposed scheme is established under random fading phenomena and system noise. The input error is shown convergent to zero as the cycle number increases. Two numerical examples demonstrate the applications of the proposed scheme.

Iterative learning guaranteed cost control based on two‐dimensional Roesser systems subject to denial‐of‐service attacks and fading measurements

AbstractIn this article, the network‐based iterative learning guaranteed cost control problem for the linear systems subject to denial‐of‐service (DoS) attacks at input and output (I/O) sides is studied via faded channels. First, the DoS attacks are modeled by independent Bernoulli sequences, where the expectation and variance are known. The fading measurements in I/O channels are described as independent Gaussian distributions with known expectations and variances respectively. Then, the repetitive system and the proposed ILC scheme involving both the iteration and time axes are transformed into a random two‐dimensional (2D) Roesser model by using the 2D system theory. The mean‐square asymptotic stability is introduced and followed by the definition of guaranteed cost function. Next, sufficient conditions that can not only ensure the asymptotic stability but also the cost index are derived. By applying the linear matrix inequality technology, the gain matrices and the upper bound of the control cost are further obtained. After exploring the adverse effect brought by the random fading phenomenon, a compensation algorithm is then designed and the analysis is strictly deduced. Finally, an injection molding process example is given to confirm the validity of the design.

A survey of statistical models for urban noise and their physical interpretations

Many different models have been used to describe statistical distributions of sound in urban environments. Some models may be justified empirically, whereas others are linked to physical phenomena such as random fading, multipath, multi-source mixtures, and variations in source-receiver geometries. This presentation reviews many of the available statistical distributions, what they are intended to represent physically, and their appearance on linear and logarithmic (decibel) axes. To evaluate the suitability of the various distributions, comparisons are made to an experiment in which one-third octave band sound-level data were measured at 37 locations in the North End of Boston, Massachusetts. Based on the Kullback-Leibler divergence as calculated across all the locations and frequencies, the exponentially modified Gaussian (EMG) distribution provides the most consistently good agreement with data, because it captures the positively skewed sound levels present in most of the data. The compound gamma distribution, which is applicable to situations involving varying sound levels, also fits the data well and even outperforms the EMG for the small minority of cases exhibiting negative skew. The log-normal distribution often provides a suitable fit in cases where particular non-traffic noise sources dominate.

Radix-Partition-Based Over-the-Air Aggregation and Low-Complexity State Estimation for IoT Systems Over Wireless Fading Channels

We consider remote state estimation for an internet-of-things (IoT) system, where a number of distributed IoT sensors monitor a dynamic plant and deliver the state measurements to a remote state estimator over an unreliable wireless network. We propose novel radix-partition-based over-the-air aggregation to coordinate the transmissions of the IoT sensors, which strongly enhances the spectral efficiency of radio resources and enables a smaller state estimation mean square error (MSE) at the remote state estimator, taking into consideration the quantization effect and radio resource allocation for the IoT sensors. We consider a fixed-filtering design at the remote state estimator, which significantly reduces the signal processing overhead at the remote state estimator compared to using the conventional Kalman-filtering-based state estimation approach. We show that the proposed low-complexity state estimation scheme enables state estimation stability via the linear matrix inequality (LMI) technique for on-off fading channels and via Lyapunov stability analysis for random fading channels. Based on this, we further propose efficient algorithms for the offline design of the filtering gain, connection topology and radix representation for IoT systems. Numerical results show that the proposed scheme has superior scalability performance and can achieve a smaller state estimation MSE compared to various state-of-the-art baselines.

Urban noise distributions and the influence of geometric spreading on skewness.

Statistical distributions of urban noise levels are influenced by many complex phenomena, including spatial and temporal variations in the source level, multisource mixtures, propagation losses, and random fading from multipath reflections. This article provides a broad perspective on the varying impacts of these phenomena. Distributions incorporating random fading and averaging (e.g., gamma and noncentral Erlang) tend to be negatively skewed on logarithmic (decibel) axes but can be positively skewed if the fading process is strongly modulated by source power variations (e.g., compound gamma). In contrast, distributions incorporating randomly positioned sources and explicit geometric spreading [e.g., exponentially modified Gaussian (EMG)] tend to be positively skewed with exponential tails on logarithmic axes. To evaluate the suitability of the various distributions, one-third octave band sound-level data were measured at 37 locations in the North End of Boston, MA. Based on the Kullback-Leibler divergence as calculated across all of the locations and frequencies, the EMG provides the most consistently good agreement with the data, which were generally positively skewed. The compound gamma also fits the data well and even outperforms the EMG for the small minority of cases exhibiting negative skew. The lognormal provides a suitable fit in cases in which particular non-traffic noise sources dominate.

Physical-Layer Authentication in Wirelessly Powered Communication Networks

This paper addresses the problem of authenticating the transmitter device in wirelessly powered communications networks (WPCNs). We proposed a physical-layer authentication scheme for a WPCN. In comparison with upper-layer authentication schemes, the proposed scheme has low complexity, low power consumption, low overhead, and high security. For comprehensively analyzing the performance of the proposed scheme by considering the requirements of transmission delay, security, and reliability together, we put forward an analytical framework by considering the probabilities of transmission, security outage, connection outage, and joint security-connection outage. Based on the proposed analytical framework, we further introduced a new systematic metric by calculating the achievable throughput of all users with considering the performance of transmission-delay, security, and reliability together. We defined this new systematic metric as the overall transmission efficiency (OTE) of a WPCN, which can effectively quantize the average efficiency of message transmission in the WPCN with physical layer authentication. We analyzed the events represented by these probability factors over random fading channels and explicitly derive their closed-form expressions. For defending against jamming attacks, we further proposed another metric to estimate which user has the high probability of suffering from jamming attacks. The new metric represents that the adversary has the largest attacking gain with the minimum cost. We implemented our scheme and conducted extensive performance comparisons through simulations. Our experimental results show that the proposed scheme accurately detects an impersonating attack and drops its contribution to the sum of long-term throughput.

5 G communication network signal propagation models

The next generation 5G / IMT-2020 technology, like any new technology, brings its own specific features to all aspects related to the practice of its application. One of these particularly important aspects is electromagnetic compatibility. At the stage of preparation for the introduction of 5G radio networks, called NewRadio, it is necessary to take early measures to assess effectively the electromagnetic compatibility conditions for these networks based on a thorough analysis of the features of 5G technology. Correct and accurate assessments of these conditions means successful provision of the electromagnetic compatibility of radio equipment of new networks.
 The World Radio Communication Conference WRC-15 identified new radio frequency bands for 5G, including centimeter and millimeter wave bands. In general, this RF spectrum is located in three regions: below 1 GHz, 1 GHz to 6 GHz, and above 6 GHz (up to 100 GHz). From the EMC standpoint, the following can be distinguished as the main features of this spectrum: different nature of losses during signal propagation, in particular, a significant influence of additional factors (gases – oxygen, water vapor, etc.) on the level of losses previously unknown in cellular communication.
 The mathematical model of signal propagation of 5 G communication networks has been developed which takes into account: the attenuation of signals in free space; attenuation of signals caused by the influence of walls and floor slabs, loss of signal energy, when space is filled with various objects; attenuation of signals caused by loss of energy of radio waves, when propagating through rains; signal attenuation due to loss of radio wave energy due to fog; signal attenuation, when propagating through tree leaves, slow and fast random fading.

Averaging Techniques for Balancing Learning and Tracking Abilities Over Fading Channels

With the wide use of networks in repetitive systems, channels between a plant and a controller may experience random fading, which is a common problem in long-distance wireless communication. However, the control problem over fading channels is far from resolved. In this article, we investigate learning control over fading channels to gradually improve tracking performance. We observe that the effect of fading on input transmission greatly compromises tracking ability in practical implementations. We examine three average techniques: moving average, general average with all historical information, and forgetting-based average. The results reveal a tradeoff between learning ability and tracking ability for learning control algorithms, where learning ability refers to the convergence rate of a proposed learning algorithm, and tracking ability refers to the final tracking precision of the output to the desired reference. The convergence results for the three schemes with these averaging techniques are strictly proved. The results demonstrate that the forgetting-based average operator-based scheme can connect the other two schemes by tuning the forgetting factor. We also provide extensions of several general scenarios to expand the application range. Illustrative simulations verify the theoretical results.

On the effective capacity of IRS-assisted wireless communication

We consider futuristic, intelligent reflecting surfaces (IRS)-aided communication between a base station (BS) and a user equipment (UE) for two distinct scenarios: a single-input, single-output (SISO) system whereby the BS has a single antenna, and a multi-input, single-output (MISO) system whereby the BS has multiple antennas. For the considered IRS-assisted downlink, we compute the effective capacity (EC), which is a quantitative measure of the statistical quality-of-service (QoS) offered by a communication system experiencing random fading. For our analysis, we consider the two widely-known assumptions on channel state information (CSI)—i.e., perfect CSI and no CSI, at the BS. Thereafter, we first derive the distribution of the signal-to-noise ratio (SNR) for both SISO and MISO scenarios, and subsequently derive closed-form expressions for the EC under perfect CSI and no CSI cases, for both SISO and MISO scenarios. Furthermore, for the SISO and MISO systems with no CSI, it turns out that the EC could be maximized further by searching for an optimal transmission rate r∗, which is computed by exploiting the iterative gradient-descent method. We provide extensive simulation results which investigate the impact of the various system parameters, e.g., QoS exponent, power budget, number of transmit antennas at the BS, number of reflective elements at the IRS etc., on the EC of the system.

Sum Rate and Reliability Analysis for Power-Domain Nonorthogonal Multiple Access (PD-NOMA)

Nonorthogonal multiple access (NOMA) is seen as an important technology for tomorrow's Internet-of-Things (IoT) systems. In uplink power-domain NOMA (PD-NOMA), allocating the uplink transmit power of the IoT devices is important to maximize both the sum rate and the reliability of devices. However, it is challenging to optimize the uplink transmit power when the received signal power is affected by a random fading channel. Hence, in this article, the problem of uplink transmit power assignment is studied for a wireless network with PD-NOMA that serves uplink IoT services. This is posed as a problem of determining the target received signal power at the base station (BS) so that the reliability and upper bound of sum rate of the users are jointly maximized, where the received signal power at the BS is unknown to the devices due to Nakagami- m fading channel. To find an optimal allocation of the lower and higher target received power values for the devices using PD-NOMA, the reliability and upper bound of sum rate are derived in terms of target received power values and power difference threshold. For a special case of Nakagami- m fading channel, the theoretical analysis shows that the highest reliability and the highest upper bound of sum rate are achieved, when the target received power values are highest. For a general Nakagami- m fading channel, simulation results show that there is a tradeoff between reliability and sum-rate upper bound and, thus, allocation of lower and higher target received power values is necessary to satisfy the communication requirements of IoT devices. Moreover, for a special case of Nakagami- m fading channel, simulation results show that the derived optimal transmit power achieves the optimal sum-rate upper bound and reliability, and the target received power values of two devices must be highest for the maximum upper bound of sum rate and reliability. Furthermore, in simulation results, increasing the lower and higher target received power values increases both the upper bound of sum rate and reliability.