Channel Uncertainty Research Articles

Analysis of Localization Performance in mm-Wave 5G Network Under Channel Uncertainties

This letter investigates localization exploiting mm-waves and massive multiple antennas configuration. The particularity of this work is to consider channel estimation errors to evaluate their impact on localization accuracy. The analysis and simulations show that mm-waves provide better performance at high SNR values while cm-waves perform better at low SNR values. Also, the results highlight the resilience of the multistage localization processing to channel estimation errors.

Complete Power Reallocation for MU-MISO Under Per-Antenna Power Constraint

This paper proposes a beamforming method under a per-antenna power constraint (PAPC). Although many beamformer designs with the PAPC need to solve complex optimization problems, the proposed complete power reallocation (CPR) method can generate beamformers with excellent performance only with linear operations. CPR is designed to have a simple structure, making it highly flexible and practical. In this paper, three CPR variations considering algorithm convergence speed, sum-rate maximization, and robustness to the channel uncertainty are developed. Simulation results verify that CPR and its variations satisfy their design criteria, and, hence, CPR can be readily utilized for various purposes.

Geometric formulations on wireless sensors based spatial protocol for intrusion detection using factor graphs

Restriction on intrusions is one of the foremost aspects of security to conserve the resources in a wireless network. The perimeter of the shadowing zone where in sensing and connectivity are simultaneously changing had to be estimated to prevent intruders. So the proposed work estimates a relationship between the data transmissions and computational time to find the trust factor of channels using factor graphs. Subsequently, estimating the Value of Information (VoI), contention time, and topological significance based on sensing power to alleviate blind spots had been observed to identify intrusions. Factor graph-based routing scenarios capable of handling channel uncertainties in time-triggered wireless sensor communication are indicated with edges and their factors. The process of local interactions with edges provides inference of intrusions to determine global solutions to preserve the network and its data transfer.

MaReSPS for energy efficient spectral precoding technique in large scale MIMO-OFDM

Spectral pre-coding is a capable method to restrain Out-Of-Band Emission (OOBE) and act in accordance with leaking parameters over neighboring frequency channels while masking unnecessary emissions. Nevertheless, spectral pre-coding might deform the real data vector that is articulated as the Error Vector Magnitude (EVM), which shows a harmful effect on the performance of Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM)-oriented schemes. In this research, a new Mapper Reducer for spectral pre-coded signal (MaReSPS) for energy-constrained signal receiver is proposed for energy efficient spectral precoding in the MIMO-OFDM system. This model involves Mapper Reducer (MR) framework for detecting the received signal, which renders an error rate, and graceful degradation is observed in the throughput under channel uncertainty. The proposed scheme alleviates the resultant Transmit EVM (TxEVM) observed at the receiver by capitalizing on the massive MIMO system, and as a result the throughput is improved. The comparison is done with respect to Block Error Rate (BLER), throughput, and Power Spectral Density (PSD) for proving the betterment of the proposed precoding model for MIMO-OFDM. In particular, the normalized throughput for conventional No OOBE Reduction (OOBER), Mask Compliant Spectral Pre-coder (MSP), Notching Spectral Pre-coder + Zero Forcing (NSP + ZF), P1/P2: CVX and P1/P2: Top- Alternating Direction Method of Multipliers (ADMM) models, as well as proposed MaReSPS model, is lower at a Signal to Noise Ratio (SNR) from 0 dB to 15 dB. With an increase in SNR, the normalized throughput increases and when SNR =40 dB, the normalized throughput values reach their peak values. However, compared to existing models, the proposed MaReSPS model showed high normalized throughput.

A Traffic-Aware Fair MAC Protocol for Layered Data Collection Oriented Underwater Acoustic Sensor Networks

Underwater acoustic channels are characterized by long propagation delay, limited available bandwidth and high energy costs. These unique characteristics bring challenges to design media access control (MAC) protocol for underwater acoustic sensor networks (UASNs). Especially in data-collection-oriented UASNs, data generated at underwater nodes are transmitted hop-by-hop to the sink node. The traffic loads undertaken by nodes of different depths are different. However, most existing MAC protocols do not consider the traffic load imbalance in data-collection-oriented UASNs, resulting in unfairness in how the nodes transmit their own generated data. In this paper, we propose a novel traffic-aware fair MAC protocol for layered data-collection-oriented UASNs, called TF-MAC. TF-MAC accesses a medium by assigning time slots of different lengths to each layer via different traffic loads to achieve traffic fairness of nodes. To improve throughput and avoid collision in the network, an overlapping time slot division mechanism for different layers and multi-channel allocation scheme within each single layer is employed. Considering the time-varying traffic loads of the nodes, an adaptive packet length algorithm is proposed by taking advantage of the spatial-temporal uncertainty of underwater channels. A sea experiment was conducted to prove the spatial-temporal uncertainty of UASNs, which provides a feasibility basis for the proposed algorithm. Simulation results show that TF-MAC can greatly improve the network performance in terms of throughput, delay, energy consumption, and fairness in the layered data-collection-oriented UASNs.

Measurement of the branching fraction and search for CP violation in D0→KS0KS0π+π− decays at Belle

We measure the branching fraction for the Cabibbo-suppressed decay D0→KS0KS0π+π− and search for CP violation via a measurement of the CP asymmetry ACP as well as the T-odd triple-product asymmetry aCPT. We use 922 fb−1 of data recorded by the Belle experiment, which ran at the KEKB asymmetric-energy e+e− collider. The branching fraction is measured relative to the Cabibbo-favored normalization channel D0→KS0π+π−; the result is B(D0→KS0KS0π+π−)=[4.79±0.08(stat)±0.10(syst)±0.31(norm)]×10−4, where the first uncertainty is statistical, the second is systematic, and the third is from uncertainty in the normalization channel. We also measure ACP=[−2.51±1.44(stat)−0.10+0.11(syst)]%, and aCPT=[−1.95±1.42(stat)−0.12+0.14(syst)]%. These results show no evidence of CP violation.Received 16 July 2022Accepted 30 January 2023DOI:https://doi.org/10.1103/PhysRevD.107.052001Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBranching fractionElectroweak interactionPhysical SystemsCharmed mesonsMesonsPropertiesCP violationParticles & Fields

Resource Allocation for an IRS-Assisted Dual-Functional Radar and Communication System: Energy Efficiency Maximization

Dual-functional radar and communication (DFRC) techniques are promising in the next generation wireless communication network. An intelligent reflecting surface (IRS) can improve the energy efficiency (EE) of the DFRC system, which is of crucial importance in practical applications. However, the EE achieved by using the sum-rate can be limited by the channel conditions and the radar performance requirement. Thus, in this paper, the EE maximization problem is studied in an IRS-assisted DFRC system. The EE is maximized by jointly optimizing the transmit beamforming and the IRS phase shift matrix under both the perfect channel state information (CSI) and the imperfect CSI cases. To tackle the challenging nonconvex fractional optimization problem, an alternating optimization algorithm is proposed. The high-order challenging radar performance constraint is tackled by using properties of the convex function and Hermitian matrix involved in the analysis. In the imperfect CSI case, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{S}}$ </tex-math></inline-formula> -Procedure is applied to tackle the semi-infinite inequality arising from the channel uncertainty. Simulation results demonstrate that our proposed design enjoys fast convergence and achieves a higher EE compared to the benchmark schemes, even under imperfect CSI case. Furthermore, simulation results demonstrate that the EE of the DFRC system can be significantly improved by applying IRS.

Robust Transmission Design for RIS-Aided Wireless Communication With Both Imperfect CSI and Transceiver Hardware Impairments

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

SNR-Outage-Based Robust Artificial Noise-Aided Beamforming for Correlated MISO Wiretap Channels Under Gaussian Channel Uncertainties

This article studies robust artificial noise-aided beamforming for correlated multiple-input-single-output eavesdropping channels under Gaussian channel uncertainties. In the case of high reception correlation, under a secrecy outage probability constraint, the achievable secrecy rate is very small and does not satisfy the requirements of some high-rate applications. In view of this, optimization designs on the premise of quality-of-service (QoS) assurance are considered. By exploiting the correlation and employing a novel QoS criterion of the signal-to-noise ratio (SNR) outage probability, two optimization problems, namely minimization of the target SNR at eavesdroppers and maximization of the loose secrecy rate, are formulated. Despite less rigorous secrecy, a much lower target SNR at eavesdroppers and a higher loose secrecy rate are achieved. Compared with the conventional SNR-QoS, SNR-outage-QoS is a more flexible criterion that can be applied to Gaussian channel uncertainties, although challenging probabilistic constraints are introduced into the optimization problems. To make the optimization problems feasible, two approaches, namely, the Bernstein-type inequality approach and ellipsoid bounding approach, are applied to approximate the probabilistic constraints. Simulation results show that the robust transmit designs contribute to a secrecy performance gain from the perspective of SNR-outage-QoS.

User Scheduling Design for Covert Communication in Cooperative Cognitive Radio System

In this article, we investigate the covert communication for a cooperative cognitive radio (CCR) system, where the secondary transmitters (STs) help the primary transmitter (PT) to transmit information while covertly transmitting their own information to the secondary receiver (SR) against the warden of PT. In particular, the different ST scheduling schemes are proposed for covert communications, namely, covert rate maximization (CRM), best channel gain (BCG), and fairness oriented (FO). The closed-form expression is derived for the expected detection error probability at the warden PT. Based on that, we, respectively, analyze the covert rate and covert outage probability (COP) of the proposed ST scheduling schemes. The simulation results show that, under the both effects of channel uncertainty and noise uncertainty, the CRM scheme achieves the best covert rate performance, and the BCG and FO schemes ensure the quality of service (QoS) and the STs’ fairness as the cost of covert performance degraded.

Sum-rate maximization for cognitive relay NOMA Systems with channel uncertainty

In this paper, we study the sum-rate maximization problem for relay cognitive network based on decoding and forwarding (DF), where the primary network is implemented with the orthogonal frequency division multiple access (OFDMA) technology and the secondary users are in the underlying accessing mode. In the system, multiple primary users are included and one of them plays a role of relay. The relay forwards information to secondary users using the non-orthogonal multiple access (NOMA) technology. We attempt to improve the transmission rate of both primary and secondary users. The block power control strategy is developed by addressing interference of primary users caused by the channel sharing with secondary users. To tackle the tricky issue caused by the channel uncertainty, the worst-case method is adopted when solving the proposed sum-rate maximization problem. Considering the nonlinear objective function and constraints, the successive convex approximation (SCA) method is introduced to determine a suboptimal solution. Also, the Lagrangian dual algorithm is utilized to obtain the closed-form solutions of the transmission power and subchannel allocation. Numerical results verify that the proposed scheme significantly improves the sum-rate under the uncertainty channel gains compared to benchmarks.

Dual extended state observer–based prescribed performance control of output-constrained hydraulic-driven barrel pitching servo system

This research focuses on the position-tracking control of the output-constrained hydraulic-driven barrel pitching servo system with partial unknown states, strong nonlinearities, and huge model uncertainties. Based on the established nonlinear dynamic model of the hydraulic-driven barrel pitching servo system, a dual extended state observer–based prescribed performance dynamic surface control approach is proposed. The dual extended state observer is developed to simultaneously estimate the angular velocity, mismatched lumped uncertainty in the velocity dynamic channel, and matched lumped uncertainty in the pressure dynamic channel. A novel barrier Lyapunov function–inspired error transformation function contributes to the integration of the prescribed performance control into the backstepping controller and circumvents the complex logarithmic calculation caused by the traditional error transformation function. The prescribed performance control scheme restrains the tracking error within the constraint boundaries in the entire moving process. Dual uncertainty compensation is added to the prescribed performance control scheme to further improve the control performance of the hydraulic-driven barrel pitching servo system. The dynamic surface control technique is applied in the last step of the controller design to prevent the ‘differential explosion’ in the traditional backstepping controller. The theoretical stability and convergence of the proposed controller are proved in the sense of Lyapunov, and the practical effectiveness of the dual extended state observer–based prescribed performance dynamic surface control is verified via extensive comparative experiments.

Calibration and Validation of a Transfer Radiometer Applied to a Radiometric Benchmark Transfer Chain

A transfer radiometer (TR) applied to an on-orbit radiometric benchmark transfer chain has been developed, which can achieve the high-precision transformation of power and radiance responsivity and transmit the radiance responsivity traced to the cryogenic radiometer to remote sensors, such as an imaging spectrometer, so that the on-orbit remote sensors can achieve the high accuracy calibration of 10−3 magnitude. Radiance comparison experiments between the TR and the radiance standard of the National Institute of Metrology (NIM) were carried out to demonstrate the absolute accuracy of the TR radiance measurement. At 780.0 nm and 851.9 nm, the relative measurement uncertainties of the TR filter-free channel were 0.24% (k = 1). Additionally, the radiance measurement results of the TR were consistent with those of the NIM radiance meter, and the radiance measurement results’ relative differences between the TR and the NIM radiance meter were approximately 0.04% at 780.0 nm and 851.9 nm. The relative measurement uncertainties of TR 780.4 nm and 851.8 nm filter channels were 0.89% (k = 1) and 0.84% (k = 1), respectively. Additionally, the radiance measurement results of the TR 780.4 nm and 851.8 nm filter channels were consistent with the radiances of the integrating sphere source calibrated by the NIM at 780.4 nm and 851.8 nm; the relative differences between the radiances measured by the two TR filter channels and the radiances of the integrating sphere source itself were better than 0.56%. This proved that the TR could measure the monochromatic source radiance with a measurement uncertainty of 0.24% and measure the broadband source radiance with a measurement uncertainty better than 0.89%. The TR can be applied to the radiometric benchmark transfer chain to improve the measurement precision of on-orbit remote-sensing instruments.

Average symbol error rate analysis of reconfigurable intelligent surfaces-assisted free-space optical link over log-normal turbulence channels

&lt;span&gt;Optical wireless communication (OWC) has attracted significant interest recently in academia and industry. Free-space optical (FSO) communication systems are where free space acts as a communication channel between transceivers that are line of sight (LOS) for the successful transmission of optical signals. The FSO transmissions through the atmosphere, nevertheless, bring significant challenges, besides the uncertainty of atmospheric channels, especially the signal fading due to the atmospheric turbulence, attenuation and pointing errors caused by the random beam misalignments between transceivers, signal obstruction due to buildings or trees can pre-vent the transmitted message to reach the destination. This study theoretically investigates the average symbol error rate (ASER) of reconfigurable intelligent surfaces (RIS) assisted FSO link over log-normal turbulence channels. The RIS effect is examined by considering the influence of link distance, transmitted optical power, and quadrature amplitude modulation (QAM) scheme on the ASER.&lt;/span&gt;

Indirect adaptive neural tracking control of USVs under injection and deception attacks

This paper investigates the tracking control issue of unmanned surface vessels (USVs) under internal/external uncertainties as well as injection and deception attacks, and proposes a novel adaptive neural network tracking control solution. In this work, the backstepping design framework is used to solve the control design issue. To resolve the time-varying gain caused by the injection and deception attacks, indirect adaptive neural and single-parameter learning approaches are employed. In the kinematic channel, internal and external uncertainties as well as the additional terms caused by the injection and deception attacks are regarded as heterogeneous uncertainties. And then, the heterogeneous uncertain term is transformed into a linearly parameterized form with only one parameter with the aid of the neural network technique and single-parameter learning approach. Furthermore, an adaptive law is constructed to update the unknown parameter, and an indirect adaptive neural learning approach is developed. Finally, a novel indirect adaptive neural tracking control solution is proposed to implement the suppression of heterogeneous uncertainties including internal and external uncertainties as well as attacks. The theoretical analysis indicates that all signals in the closed-loop tracking control system of USVs are bounded under the presented scheme. Simulation results demonstrate the effectiveness of the proposed scheme.

Either-or communication-based identification of FIR systems with binary-valued observations and channel uncertainty

Event-triggered mechanism can effectively save communication resources, however, when it encounters channel uncertainty, the remote receiver cannot distinguish between “the sender did not send data” and “the sender sent data but the data lost” when it does not receive data, which causes that it is difficult to make full use of the information provided by the event-triggered mechanism. This paper addresses the identification of FIR (Finite Impulse Response) systems with binary-valued observations and either-or communication mechanism when the packet loss probability is known and unknown respectively. When the packet loss probability is known, it is used for compensation in the parameter estimation. An online identification algorithm is proposed, its strong convergence is proved, and its asymptotic normality is given. Furthermore, how does the packet loss probability affect the algorithm performance is discussed. When the packet loss probability is unknown, an identification algorithm is proposed to jointly estimate it and unknown system parameters by redesigning the either-or communication mechanism. The strong convergence of the algorithm is shown. The tradeoff between the communication rate and the convergence performance of the identification algorithm is modelled as a constrained optimization problem, and its solution is obtained. The rationality of theoretical results is verified by numerical simulation.

MetNet: A Novel Low-Complexity Neural Network-Aided Detection for Faster-Than-Nyquist (FTN) Signaling in ISI Channels

This paper studies the application of neural networks to Viterbi detection of FTN signals in an intersymbol interference (ISI) channel. The main contribution of this paper is to propose a receiver structure for detecting FTN signals in unknown static ISI channel. In particular, we propose a novel low-complexity neural network structure for calculating the branch metrics, and we explore its suitability for FTN signalling with channel uncertainty. We compare the proposed network, which we call the Metric Net (MetNet), to a benchmark neural network-based technique for metric calculation, the ViterbiNet, which was originally designed for ISI channels. The simulation results confirm that the MetNet outperforms the ViterbiNet, with two orders of magnitude lower complexity, and is much more resilient to channel uncertainty than the traditional Viterbi detector, which uses Euclidean distance for metric calculations. We further show that the MetNet exhibits robustness to being trained at mismatched SNR values and FTN pulse acceleration factors, meaning that the number of trained models required can be significantly reduced. Additionally, the results show that the proposed MetNet remains a favorable alternative at much higher levels of channel uncertainties. The results also reflect that we can generalize the MetNet to work with different channel models defined by different decaying factors. Finally, we show that we succeed in achieving a bandwidth efficiency gain of 33% due to FTN by using the MetNet in the presence of channel uncertainty.

Hybrid Precoding and Combining Based on Rate Balancing for Wideband mmWave Multiuser MIMO Systems

Considering wideband millimeter wave (mmWave) multiuser multiple-input multiple-output (MIMO) systems, we propose a new hybrid precoding and combining method for rate balancing among users. The orthogonal frequency division multiple access (OFDMA) scheme is employed for multiuser transmission, and a greedy-based algorithm is developed for subband allocation under the max-min user rate criterion. A new algorithm is derived that optimizes the radio frequency (RF) precoder and combiners under the constant-modulus constraints as well as the baseband precoders and combiners under the rate balancing power allocation criterion. The proposed algorithm iteratively updates the RF precoder and combiners using the conjugate gradient method, and baseband precoders and combiners through the singular value decomposition and numerical gradient-based power allocation among users and subcarriers. The complexity of the proposed method is compared to existing wideband hybrid processing schemes through analysis and numerical runtime measurement. The convergence of the proposed algorithm is verified via numerical simulations in mmWave MIMO-OFDMA systems. It is also shown through numerical simulations that the proposed wideband hybrid processing method outperforms the existing wideband hybrid techniques in terms of the minimum user rate, regardless of the signal-to-noise ratio, the number of antennas, and the number of users. Moreover, simulation results present that the proposed scheme is more advantageous than the conventional hybrid processing methods under channel uncertainty.

Joint Transmissive and Reflective RIS-Aided Secure MIMO Systems Design Under Spatially-Correlated Angular Uncertainty and Coupled PSEs

This paper investigates a joint transmissive and reflective reconfigurable intelligent surfaces (RIS) -aided secure multiple-input multiple-output (MIMO) system, where both a RIS-assisted transmitter and a RIS-based reflector are deployed to defend against the simultaneous jamming attack and wiretapping threat. Our design focuses on maximizing the sum rate under the unknown jammer’s beamforming, joint RISs’ coupled phase shift errors (PSEs), and spatially-correlated angular channel uncertainties. Besides, we take into account the various quality-of-service (QoS) requirement constraints for guaranteeing the secure performance. Since the problem is non-convex and mathematically intractable, a new optimization framework is established to facilitate the solution development to the formulated problem. Specifically, armed with the Akaike information criterion, a novel diagonalization method is first proposed to estimate the unknown jamming covariance matrix. Then, a series of fractional-eliminated rate expressions is derived that facilitates the application of the proposed Double Deterministic Transformation (DDT) to tackle the coupled stochastic PSEs. Besides, regardless of the spatial correlation matrix, a general discretization method is proposed to convert the e spatially-correlatd angular uncertainties into a worst-case robust one. Subsequently, building upon the above transformations which transform the original problem into tractable one, a two-layer iterative Lagrange multiplier algorithm capitalizing a low-complexity dual method is proposed to obtain the globally optimal solution of the digital precoder, where the multiple QoS constraints are handled without iteration. Meanwhile, we develop a novel polyblock-based multiple penalty method to obtain the globally optimal solutions to RISs’ phase shifts which can simultaneously satisfy the multiple QoS constraints. Moreover, to address the narrow feasibility region induced by the multiple QoS constraints, a heuristic initial optimization method is proposed, which strengthens the existing result. Finally, theoretical analysis and numerical results demonstrate the optimality and the excellent performance of our proposed optimization framework.

О выборе архитектуры нейрорегулятора

In practice, the choice of the type of neural network is carried out empirically based on an experience of an investigator and many training attempts. At the same time, the excessive complexity of the neural network leads to an increase in its training time, and in some cases, to the impossibility of learning at all. Thus, the justification of the choice of an artificial neural network structure and/or its preliminary calculation based on other models is an urgent task. An equally important task is the choice of an initial weighting coefficients of an neural network, the choice of which determines the speed of convergence of search algorithms. This paper demonstrates several approaches to solving the problem of choosing an architecture and initializing a weighting coefficients of a neural network. One of them is carried out on the basis of a previously calculated function using Petri nets. This approach is demonstrated for solving various tasks, which include the implementation of functions using previously defined neural network models of the simplest logical operations "and", "or", etc. An approach is given that allows optimizing an architecture of a neural network that solves the problem of approximating functions of one and several variables. The principle of determining an architecture and initial weight coefficients is also used in the tasks of training neural networks with reinforcement. A separate section is devoted to the formation of a methodology for determining an architecture and initialization of a weighting coefficients of a neural network of the controller based on information about the controller obtained by a modal method using a polynomial matrix decomposition of a system. The problem of synthesis of a neural network controller for an object model containing nonlinearities and nonparametric uncertainties in the control channel is solved.