Imperfect Estimation Research Articles

Efficient Distributed Threshold-Based Offloading for Large-Scale Mobile Cloud Computing

Mobile cloud computing enables compute-limited mobile devices to perform real-time intensive computations such as speech recognition or object detection by leveraging powerful cloud servers. An important problem in large-scale mobile cloud computing is computational offloading, where each mobile device decides when and how much computation should be uploaded to cloud servers by considering the local processing delay and the cost of using cloud servers. In this paper, we develop a distributed threshold-based offloading algorithm where it uploads an incoming computing task to cloud servers if the number of tasks queued at the device reaches the threshold and processes it locally otherwise. The threshold is updated iteratively based on the computational load and the cost of using cloud servers. We formulate the problem as a symmetric game, and characterize the sufficient and necessary conditions for the existence and uniqueness of the Nash Equilibrium (NE) assuming exponential service times. Then, we show the convergence of our proposed distributed algorithm to the NE when the NE exists. Further, we characterize the performance gap between cost under our proposed distributed algorithm and the minimum cost in terms of Price of Anarchy (PoA) when the cost of using cloud servers is high. Finally, we perform extensive simulations to validate our theoretical findings, demonstrate the efficiency of our proposed distributed algorithm under various scenarios such as hyperexponential service times, imperfect server utilization estimation, and asynchronous threshold updates, and reveal the superior performance of threshold-based policies over their probabilistic counterpart.

Acoustic float tracking with the Kalman smoother

Acoustically tracked subsurface floats provide insights into ocean complexity and were first deployed over 60 years ago. A standard tracking method uses a Least-Squares algorithm to estimate float trajectories based on acoustic ranging from moored sound sources. However, infrequent or imperfect data challenge such estimates. Acoustic tracking is currently the only feasible strategy for recovering float positions in the sea ice region, a focus of this study. Acoustic records recovered from under-ice floats frequently lack continuous sound source coverage. This is because environmental factors such as surface sound channels and sea ice attenuate acoustic signals, while operational considerations make polar sound sources difficult to deploy. Here we present a Kalman Smoother approach that, by including some estimates of float behavior, extends tracking to situations with more challenging data sets. The Kalman Smoother constructs dynamically constrained, error-minimized float tracks using all possible position data. The Kalman Smoother is applied to previously-tracked floats from the southeast Pacific (DIMES experiment), and the results are compared with existing trajectories constructed using the Least-Squares algorithm. The Kalman Smoother is also used to reconstruct the trajectories of a set of previously untracked, acoustically-enabled Argo floats in the Weddell Sea.

Design and performance evaluation of successive interference cancellation based Slotted Aloha MAC protocol

As a result of densification, the performance of the wireless networks has become highly interference-limited and energy inefficient. To overcome this problem, interference mitigation techniques such as Successive Interference Cancellation (SIC) can be used to decode multiple packets simultaneously at the receiver. In this context, we analyze a SIC-based Slotted Aloha (SIC-SA) Medium Access Control (MAC) protocol for wireless networks. We derive expressions for packets decoding probability and optimal transmission probability of the nodes of the SIC-SA MAC protocol. Our derivation is based on the order statistics of Independent and Identical/non-Identical exponentially distributed received-signal-powers from the nodes under the Rayleigh channel condition. Throughput, delay, and energy efficiency of the SIC-SA MAC protocol have been derived and validated against simulation. The effect of path loss exponent, SINR threshold, and the number of nodes on the performance of SIC-SA have been studied. The performance of SIC-SA in a network of nodes distributed randomly according to the Poisson Point Process has been analyzed. Extension of our analysis to Power Domain Non-Orthogonal Multiple Access (NOMA) has been demonstrated. We also analyzed the impact of imperfect estimation of channel state information and imperfect SIC at the receiver. Results show an improvement in performance metrics of SIC-SA over the traditional Slotted Aloha.

Performance Analysis of Cascaded Reconfigurable Intelligent Surface Networks

Reconfigurable intelligent surfaces (RIS) have been presented as a solution to realize the concept of smart radio environments, wherein uninterrupted coverage and extremely high quality of service can be ensured. In this letter, assuming that multiple RIS are deployed in the propagation environment, the performance of a cascaded RIS network affected by imperfect phase estimation is evaluated. Specifically, we derive closed-form expressions for the outage probability, the ergodic capacity and the average symbol error probability that can be utilized to evaluate the coverage of the proposed network, as well as the average capacity and the data transmission accuracy. Finally, we validate the derived expressions through simulations and show that by choosing the number of the participating RIS correctly, a cascaded RIS network can outperform a single RIS-aided system and extend the network’s coverage efficiently.

Acoustic Float Tracking with the Kalman Smoother

Abstract Acoustically-tracked subsurface floats provide insights into ocean complexity and were first deployed over 60 years ago. A standard tracking method uses a Least-Squares algorithm to estimate float trajectories based on acoustic ranging from moored sound sources. However, infrequent or imperfect data challenge such estimates, and Least-Squares algorithms are vulnerable to non-Gaussian errors. Acoustic tracking is currently the only feasible strategy for recovering float positions in the sea ice region, a focus of this study. Acoustic records recovered from under-ice floats frequently lack continuous sound source coverage. This is because environmental factors such as surface sound channels and rough sea ice attenuate acoustic signals, while operational considerations make polar sound sources expensive and difficult to deploy. Here we present a Kalman Smoother approach that, by including some estimates of float behavior, extends tracking to situations with more challenging data sets. The Kalman Smoother constructs dynamically constrained, error-minimized float tracks and variance ellipses using all possible position data. This algorithm outperforms the Least-Squares approach and a Kalman Filter in numerical experiments. The Kalman Smoother is applied to previously-tracked floats from the southeast Pacific (DIMES experiment), and the results are compared with existing trajectories constructed using the Least- Squares algorithm. The Kalman Smoother is also used to reconstruct the trajectories of a set of previously untracked, acoustically-enabled Argo floats in the Weddell Sea.

Safe motion planning with environment uncertainty

We present an approach for safe motion planning under robot state and environment (obstacle and landmark location) uncertainties. To this end, we first develop an approach that accounts for the landmark uncertainties during robot localization. Existing planning approaches assume that the landmark locations are well known or are known with little uncertainty. However, this might not be true in practice. Noisy sensors and imperfect motions compound to the errors originating from the estimate of environment features. Moreover, possible occlusions and dynamic objects in the environment render imperfect landmark estimation. Consequently, not considering this uncertainty can wrongly localize the robot, leading to inefficient plans. Our approach thus incorporates the landmark uncertainty within the Bayes filter estimation framework. We also analyze the effect of considering this uncertainty and delineate the conditions under which it can be ignored. Second, we extend the state-of-the-art by computing an exact expression for the collision probability under Gaussian distributed robot motion, perception and obstacle location uncertainties. We formulate the collision probability process as a quadratic form in random variables. Under Gaussian distribution assumptions, an exact expression for collision probability is thus obtained which is computable in real-time. In contrast, existing approaches approximate the collision probability using upper-bounds that can lead to overly conservative estimate and thereby suboptimal plans. We demonstrate and evaluate our approach using a theoretical example and simulations. We also present a comparison of our approach to different state-of-the-art methods.

Incorporation of Human Factors to a Data-Driven Car-Following Model

In recent years, there have been intensive efforts to consider human factors (HFs) in the modeling of human driver behavior. In particular, “engineering” car-following models widely used in traffic simulation have been extended to include HFs. This extension is needed to generate critical situations, which are often attributable to human error. However, incorporation of reaction processes requires advanced models that take driver predictions and delayed responses into account. In this paper, a methodology for integrating HFs into driver behavior modeling is developed based on a long short-term memory architecture. The proposed methodology employed a three-layer psychological concept: perception, information processing, and action. The perception layer modeled (imperfect) estimation of visually received stimuli. Information processing included short-term memory and the projection of perceived stimuli into the near future. The executed action, based on the sensed as well as anticipated dynamic driving state, was delayed by the perception–reaction time. To represent individual differences among driver types, the available training dataset was classified in four clusters according to observable driver characteristics. The methodology was demonstrated with data recorded at an urban signalized intersection. Model performance was compared with that of two established engineering models, the intelligent driver model and the (extended) full velocity difference model. The results indicated that the human driver model developed here showed superior performance in replicating real-world trajectories compared with existing models and was able to represent the varying driving strategies of different groups.

Device-Free Sensing in OFDM Cellular Network

This paper considers device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular network to enable integrated sensing and communication (ISAC). A novel two-phase sensing framework is proposed to localize the passive targets that cannot transmit/receive reference signals to/from the base stations (BSs), where the ranges of the targets are estimated based on their reflected OFDM signals to the BSs in Phase I, and the location of each target is estimated based on its ranges to different BSs in Phase II. Specifically, in Phase I, we design a model-free range estimation approach by leveraging the OFDM channel estimation technique for determining the delay values of all the two-way BS-target-BS paths, which does not rely on any BS-target channel model. In Phase II, we reveal that ghost targets may be falsely detected in some cases as all the targets reflect the same signals to the BSs, which thus do not know how to match each estimated range with the right target. Interestingly, we show that the above data association issue is not a fundamental limitation for device-free sensing: under the ideal case of perfect range estimation in Phase I, the probability for ghost targets to exist is proved to be negligible when the targets are randomly located. Moreover, under the practical case of imperfect range estimation in Phase I, we propose an efficient algorithm for joint data association and target localization in Phase II. Numerical results show that our proposed two-phase framework can achieve very high accuracy in the localization of passive targets, which increases with the system bandwidth.

Efficient jamming attack against MIMO transceiver

This work addresses the issue of point-to-point intelligent jamming against MIMO architecture-based systems. The receiver uses an optimal Maximum Likelihood (ML) detector to extract the data stream from the received signal. Analyzing the receiver performance in the presence of a jamming signal, optimal attack strategies based on the pairwise error probability parameter as an objective function are designed, enabling the achievement of maximum degradation of the service quality. To perform the worst-case jamming of a legitimate transceiver, we assume that the jammer can eavesdrop on the Channel State Information (CSI). Depending on the jamming power budget constraint and the potential of jamming systems to estimate the CSI, different scenarios are envisaged, including imperfect CSI estimation cases. By minimizing the trace of Positive Semi-Definite (PSD) matrix products, the optimal jamming approaches that ensure maximum degradation of the legitimate link are designed. The disruption efficiency of the proposed approaches is comparable to the employment of the brute-force attack, but with a higher power level that is proportional to the number of jamming antennas in the case of partial knowledge of CSI, and inversely to the statistical expectation of the square root of the Wishart matrix's eigenvalues in the case of complete knowledge of CSI. To justify the relevance of the suggested policy, extensive simulations of the effect of these interventions are presented.

Continuous Hidden Markov Model Based Spectrum Sensing with Estimated SNR for Cognitive UAV Networks.

In this paper, to enhance the spectrum utilization in cognitive unmanned aerial vehicle networks (CUAVNs), we propose a cooperative spectrum sensing scheme based on a continuous hidden Markov model (CHMM) with a novel signal-to-noise ratio (SNR) estimation method. First, to exploit the Markov property in the spectrum state, we model the spectrum states and the corresponding fusion values as a hidden Markov model. A spectrum prediction is obtained by combining the parameters of CHMM and a preliminary sensing result (obtained from a clustered heterogeneous two-stage-fusion scheme), and this prediction can further guide the sensing detection procedure. Then, we analyze the detection performance of the proposed scheme by deriving its closed-formed expressions. Furthermore, considering imperfect SNR estimation in practical applications, we design a novel SNR estimation scheme which is inspired by the reconstruction of the signal on graphs to enhance the proposed CHMM-based sensing scheme with practical SNR estimation. Simulation results demonstrate the proposed CHMM-based cooperative spectrum sensing scheme outperforms the ones without CHMM, and the CHMM-based sensing scheme with the proposed SNR estimator can outperform the existing algorithm considerably.

Joint trajectory design and power allocation for unmanned aerial vehicles aided secure transmission in the presence of no‐fly zone

Unmanned aerial vehicles (UAVs) are widely considered as key enablers for future wireless networks due to their advantages, such as high mobility and flexible deployment. In this paper, the UAV assisted secure communication system is investigated, where the UAV is deployed as the mobile jammer to prevent the eavesdropper from overhearing the confidential message. With the objective of maximizing the secrecy rate, a joint optimization problem involving the trajectory and transmit power of UAV, as well as the transmit power of source node is formulated. Moreover, the effects of Non-Fly Zone (NFZ) and imperfect estimation on the location of eavesdropper are also taken into consideration. As the original problem is hardly trackable, the worst case secrecy rate (WCSR) assumption is first employed to bypass the uncertainty brought by the estimation error. Then a block coordinate descent (BCD) based algorithm is proposed to decompose the problem into three sub-ones, where the trajectory of UAV, the transmit power of UAV and the transmit power of source can be obtained in an iterative manner. Simulation results reveal that the proposed algorithm can improve the secrecy performance significantly. In addition, the robustness of the proposed algorithm under the estimation error can also be verified.

Grid integrated solar energy transfer system with a two‐layer complex coefficient filter‐based control

In this paper, a novel control strategy for grid-integrated solar energy transfer system (SETS) is proposed which enhances the grid power quality along with active power injection. Some of the conventional grid synchronization controls face problems due to imperfect estimation of fundamental active load current during both grid-side and load-side disturbances. The proposed two-layer multiple-complex coefficient filter control improves the performance of SETS by accurately estimating the fundamental active load current at adverse operating conditions. One layer extracts the fundamental positive sequence of the sensed three-phase grid voltages and derives the unit in-phase voltage templates for grid currents reference generation to achieve unity power factor operation. The second layer evaluates the average active fundamental component of the load currents, and finally derives the reference amplitude of three-phase grid currents. The two-stage SETS consists of a quadratic boost DC-DC converter to extract the maximum solar array power forming an interface between PV array and inverter. It also provides high voltage gain and decreases the length of the PV array string while maintaining a low DC-link voltage ripple, extending the service life of the SETS. The effect of a nonlinear unbalanced load is nullified and therefore the grid current's waveform quality is maintained in compliance with the IEEE-519 standard during the dynamic operating conditions. The SETS provides sinusoidal, balanced, and steady grid currents at unity power factor withstanding adverse operating conditions such as distorted grid voltages, varying irradiance and varying unbalanced nonlinear load currents. Due to its flexibility to operate in such adverse conditions, the proposed control based SETS is suitable for grid-connected applications. Simulations are carried out in MATLAB/Simulink and results validate the performance of the SETS. The hardware prototype of the proposed SETS is developed and rigorously tested under various operating conditions to validate the proposed claims.

Generalized Mutual Information Analysis for BICM-8QAM With Residual Phase Noise

Generalized mutual information (GMI) has become a key metric for bit-interleaved coded modulation (BICM) system design and performance analysis. As residual phase noise (RPN) normally exists after imperfect phase estimation, the mostly used mismatched Gaussian receiver is suboptimal for GMI analysis in phase noise. This letter thus analyzes the GMI of BICM systems using 8-ary quadrature-amplitude-modulations (QAM) in the presence of both RPN and additive white Gaussian noise (AWGN). We will use the maximum-likelihood receiver derived in our earlier work to calculate the GMI of Star-8QAM, Circular-8QAM, Rect-8QAM and 8PSK. The explicit symbol and bit log-likelihood ratios are specifically expressed in amplitude-phase form with RPN considered. Numerical results are given in details to show the GMI comparison in phase noise and the GMI loss compared to the pure AWGN case. It is shown that Star-8QAM is much more tolerant to large RPN. Moreover, the ratio parameters of Rect-8QAM and Star-8QAM are optimized to maximize the GMI as the RPN variance increases. We also optimize the bit mapping for non-Gray 8QAM.

Explaining classification performance and bias via network structure and sampling technique

Social networks are very important carriers of information. For instance, the political leaning of our friends can serve as a proxy to identify our own political preferences. This explanatory power is leveraged in many scenarios ranging from business decision-making to scientific research to infer missing attributes using machine learning. However, factors affecting the performance and the direction of bias of these algorithms are not well understood. To this end, we systematically study how structural properties of the network and the training sample influence the results of collective classification. Our main findings show that (i) mean classification performance can empirically and analytically be predicted by structural properties such as homophily, class balance, edge density and sample size, (ii) small training samples are enough for heterophilic networks to achieve high and unbiased classification performance, even with imperfect model estimates, (iii) homophilic networks are more prone to bias issues and low performance when group size differences increase, (iv) when sampling budgets are small, partial crawls achieve the most accurate model estimates, and degree sampling achieves the highest overall performance. Our findings help practitioners to better understand and evaluate their results when sampling budgets are small or when no ground-truth is available.

Imperfect Estimation of Lepeophtheirus salmonis Abundance and Its Impact on Salmon Lice Treatment on Atlantic Salmon Farms

Accurate monitoring of sea lice levels on salmon farms is critical to the efficient management of louse infestation, as decisions around whether and when to apply treatment depend on an estimation of abundance. However, as with all sampling, the estimated abundance of salmon lice through sampling salmon cannot perfectly represent the abundance on a given farm. While suggestions to improve the accuracy of lice abundance estimates have previously been made, the significance of the accuracy of such estimation has been poorly understood. Understanding the extent of error or bias in sample estimates can facilitate an assessment as to how influential this “imperfect” information will likely be on management decisions, and support methods to mitigate negative outcomes associated with such imperfect estimates. Here, we built a model of a hypothetical Atlantic salmon farm using ordinary differential equations and simulated salmon lice (Lepeophtheirus salmonis) abundance over an entire production cycle, during which salmon were periodically sampled using Monte Carlo approaches that adopted a variety of sample sizes, treatment thresholds, and sampling intervals. The model could thus track two instances of salmon lice abundance: true abundance (based on the underlying model) and monitored abundance (based on the values that could be estimated under different simulated sampling protocols). Treatments, which depend on monitored abundance, could be characterized as early, timely, or late, as a result of over-estimation, appropriate estimation, and under-estimation, respectively. To achieve timely treatment, it is important to delay treatments until true abundance equals some treatment threshold and to execute treatment as soon as this threshold is reached. Adopting larger sample sizes increased the frequency of timely treatments, largely by reducing the incidence of early treatments due to less variance in the monitored abundance. Changes in sampling interval and treatment threshold also influenced the accuracy of abundance estimates and thus the frequency of timely treatments. This study has implications for the manner in which fish should be sampled on salmon farms to ensure accurate salmon lice abundance estimates and consequently the effective application treatment.

Security Games with Limited Surveillance

Randomized first-mover strategies of Stackelberg games are used in several deployed applications to allocate limited resources for the protection of critical infrastructure. Stackelberg games model the fact that a strategic attacker can surveil and exploit the defender's strategy, and randomization guards against the worst effects by making the defender less predictable. In accordance with the standard game-theoretic model of Stackelberg games, past work has typically assumed that the attacker has perfect knowledge of the defender's randomized strategy and will react correspondingly. In light of the fact that surveillance is costly, risky, and delays an attack, this assumption is clearly simplistic: attackers will usually act on partial knowledge of the defender's strategies. The attacker's imperfect estimate could present opportunities and possibly also threats to a strategic defender.In this paper, we therefore begin a systematic study of security games with limited surveillance. We propose a natural model wherein an attacker forms or updates a belief based on observed actions, and chooses an optimal response. We investigate the model both theoretically and experimentally. In particular, we give mathematical programs to compute optimal attacker and defender strategies for a fixed observation duration, and show how to use them to estimate the attacker's observation durations. Our experimental results show that the defender can achieve significant improvement in expected utility by taking the attacker's limited surveillance into account, validating the motivation of our work.

Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions

Generative adversarial networks (GANs) have recently garnered significant attention for their use in speech enhancement tasks, in which they generally process and reconstruct speech waveforms directly. Existing GANs for speech enhancement rely solely on the convolution operation, which may not accurately characterize the local information of speech signals—particularly high-frequency components. Sinc convolution has been proposed in order to allow the GAN to learn more meaningful filters in the input layer, and has achieved remarkable success in several speech signal processing tasks. Nevertheless, Sinc convolution for speech enhancement is still an under-explored research direction. This paper proposes Sinc–SEGAN, a novel generative adversarial architecture for speech enhancement, which usefully merges two powerful paradigms: Sinc convolution and the speech enhancement GAN (SEGAN). There are two highlights of the proposed system. First, it works in an end-to-end manner, overcoming the distortion caused by imperfect phase estimation. Second, the system derives a customized filter bank, tuned for the desired application compactly and efficiently. We empirically study the influence of different configurations of Sinc convolution, including the placement of the Sinc convolution layer, length of input signals, number of Sinc filters, and kernel size of Sinc convolution. Moreover, we employ a set of data augmentation techniques in the time domain, which further improve the system performance and its generalization abilities. Compared to competitive baseline systems, Sinc–SEGAN overtakes all of them with drastically reduced system parameters, demonstrating its effectiveness for practical usage, e.g., hearing aid design and cochlear implants. Additionally, data augmentation methods further boost Sinc–SEGAN performance across classic objective evaluation criteria for speech enhancement.

Secrecy enhancing for SSK‐based communications in the presence of imperfect CSI estimation

In this letter, a new security enhancement scheme for space shift keying (SSK) systems under imperfect channel state information (CSI) is proposed. In this scheme, the legitimate receiver is always accompanied by an eavesdropper, and the secrecy enhancing of the SSK system is realized by adding the designed artificial noise (AN). Since the AN is designed by the CSI of the legitimate channel, it can be canceled at the legitimate receiver and remained at the eavesdropper. In addition, the impacts of the CSI estimation error on the secrecy rate (SR) and bit error ratio (BER) of the SSK system have also been taken into account. The simulation results show that compared with the traditional SSK under imperfect CSI environment, the proposed scheme improves the SR and BER. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Winners and losers over 35 years of dragonfly and damselfly distributional change in Germany

AbstractAimRecent studies suggest insect declines in parts of Europe; however, the generality of these trends across different taxa and regions remains unclear. Standardized data are not available to assess large‐scale, long‐term changes for most insect groups but opportunistic citizen science data are widespread for some. Here, we took advantage of citizen science data to investigate distributional changes of Odonata.LocationGermany.MethodsWe compiled over 1 million occurrence records from different regional databases. We used occupancy‐detection models to account for imperfect detection and estimate annual distributions for each species during 1980–2016 within 5 × 5 km quadrants. We also compiled data on species attributes that were hypothesized to affect species’ sensitivity to different drivers and related them to the changes in species’ distributions. We further developed a novel approach to cluster groups of species with similar patterns of distributional change to represent multispecies indicators.ResultsMore species increased (45%) than decreased (29%) or remained stable (26%) in their distribution (i.e. number of occupied quadrants). Species showing increases were generally warm‐adapted species and/or running water species, while species showing decreases were cold‐adapted species using standing water habitats such as bogs. Time series clustering defined five main patterns of change—each associated with a specific combination of species attributes, and confirming the key roles of species’ temperature and habitat preferences. Overall, our analysis predicted that mean quadrant‐level species richness has increased over most of the time period.Main conclusionsTrends in Odonata provide mixed news—improved water quality, coupled with positive impacts of climate change, could explain the positive trends of many species. At the same time, declining species point to conservation challenges associated with habitat loss and degradation. Our study demonstrates the great value of citizen science and the work of natural history societies for assessing large‐scale distributional change.

Global inequality remotely sensed

Economic inequality is notoriously difficult to quantify as reliable data on household incomes are missing for most of the world. Here, we show that a proxy for inequality based on remotely sensed nighttime light data may help fill this gap. Individual households cannot be remotely sensed. However, as households tend to segregate into richer and poorer neighborhoods, the correlation between light emission and economic thriving shown in earlier studies suggests that spatial variance of remotely sensed light per person might carry a signal of economic inequality. To test this hypothesis, we quantified Gini coefficients of the spatial variation in average nighttime light emitted per person. We found a significant relationship between the resulting light-based inequality indicator and existing estimates of net income inequality. This correlation between light-based Gini coefficients and traditional estimates exists not only across countries, but also on a smaller spatial scale comparing the 50 states within the United States. The remotely sensed character makes it possible to produce high-resolution global maps of estimated inequality. The inequality proxy is entirely independent from traditional estimates as it is based on observed light emission rather than self-reported household incomes. Both are imperfect estimates of true inequality. However, their independent nature implies that the light-based proxy could be used to constrain uncertainty in traditional estimates. More importantly, the light-based Gini maps may provide an estimate of inequality where previously no data were available at all.