Electrical and Electronic Engineering - Research Publications
Now showing items 1-12 of 635
Binary Power Optimality for Two Link Full-Duplex Network
In this paper, we analyse the optimality of binary power allocation in a network that includes full-duplex communication links. Considering a network with four communicating nodes, two of them operating in half-duplex mode and the other two in full-duplex mode, we prove that binary power allocation is optimum for the full-duplex nodes when maximizing the sum rate. We also prove that, for half-duplex nodes binary power allocation is not optimum in general. However, for the two special cases, 1) the low signal-to-noise-plus-interference (SINR) regime and, 2) the approximation by the arithmetic mean-geometric mean inequality, binary power allocation is optimum for the approximated sum rate even for the half-duplex nodes. We further analyse a third special case using a symmetric network for which the optimum power allocation is binary, under a sufficient condition. Numerical examples are included to illustrate the accuracy of the results.
On the Exact Outage Probability of 2×2 MIMO-MRC in Correlated Rician Fading
This paper addresses a classical problem in random matrix theory-finding the distribution of the maximum eigen-value of the correlated Wishart unitary ensemble. In particular, we derive a new exact expression for the cumulative distribution function (c.d. f.) of the maximum eigen-value of a 2 × 2 correlated non-central Wishart matrix with rank-l mean. By using this new result, we derive the exact outage probability of 2 × 2 multiple-input multiple-output maximum-ratio-combining (MIMO-MRC) in Rician fading with transmit correlation and a strong line-of-sight (LoS) component (rank-l channel mean). We also show that the outage performance is affected by the relative alignment of the eigen-spaces of the mean and correlation matrices. In general, when the LoS path aligns with the least eigenvector of the correlation matrix, in the high transmit signal-to-noise ratio (SNR) regime, the outage gradually improves with the increasing correlation. Moreover, we show that as K (Rician factor) grows large, the outage event can be approximately characterized by the c.d.f. of a certain Gaussian random variable.
Two-Way Communications via Reconfigurable Intelligent Surface
The novel reconfigurable intelligent surface (RIS) is an emerging technology which facilitates high spectrum and energy efficiencies in Beyond 5G and 6G wireless communication applications. Against this backdrop, this paper investigates two-way communications via reconfigurable intelligent surfaces (RISs) where two users communicate through a common RIS. We assume that uplink and downlink communication channels between two users and the RIS can be reciprocal. We first obtain the optimal phase adjustment at the RIS. We then derive the exact outage probability and the average throughput in closed-forms for single-element RIS. To evaluate multiple-element RIS, we first introduce a gamma approximation to model a product of Rayleigh random variables, and then derive approximations for the outage probability and the average throughput. For large average signal-to-interference-plus-noise ratio (SINR) \rho, asymptotic analXsis also shows that the outage decreases at the rate (\log(\rho)/\rho) where L is the number of elements, whereas the throughput increases with the rate \log(\rho).
A Game-Theoretic Approach for Non-Cooperative Load Balancing Among Competing Cloudlets
(Institute of Electrical and Electronics Engineers (IEEE), 2020-02-26)
To deliver high performance and reliability to the mobile users in accessing mobile cloud services, the major interest is currently given to the integration of centralized cloud computing and distributed edge computing infrastructures. In such a heterogeneous network ecosystem, multiple cloudlets from different service providers coexist. However, to meet the stringent latency requirements of computation-intensive and mission-critical applications, overloaded cloudlets can offload some of the incoming job requests to their relatively under-loaded neighboring cloudlets. In this paper, we propose a novel economic and non-cooperative game-theoretic model for load balancing among competitive cloudlets. This model aims to maximize the utilities of all the competing cloudlets while meeting the end-to-end latency of the users. We characterize the problem as a generalized Nash equilibrium problem and investigate the existence and uniqueness of a pure-strategy Nash equilibrium. We design a variational inequality based algorithm to compute the pure-strategy Nash equilibrium. We show that all the competing cloudlets are able to maximize their utilities by employing our proposed Nash equilibrium computation offload strategy in both under- and overloaded conditions. We also show through numerical evaluations that our load balancing model outperforms some of the existing game-theoretic load balancing frameworks, especially in a highly overloaded condition.
Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data
Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
Is backscatter link stronger than direct link in reconfigurable intelligent surface-assisted system?
This letter considers integrating a backscatter link with a reconfigurable intelligent surface to enhance backscatter communication while assisting the direct communication. We derive the probability that the backscatter channel dominates in the composite channel. This probability is a useful performance measure to determine the number of reflectors. Since the exact probability lacks a closed-form solution, we develop two approximations by modeling the gain of the backscatter link with a Gaussian or Gamma distribution. We found that these approximations match well with the exact value. Importantly, with a well-designed number of reflectors, the channel gain of the backscatter link may be always stronger than that of the direct one.
The dynamics of mixed layer deepening during open-ocean convection
(American Meteorological Society, 2020-06)
Open-ocean convection is a common phenomenon that regulates mixed layer depth and ocean ventilation in the high-latitude oceans. However, many climate model simulations overestimate mixed layer depth during open-ocean convection, resulting in excessive formation of dense water in some regions. The physical processes controlling transient mixed layer depth during open-ocean convection are examined using two different numerical models: a high-resolution, turbulence-resolving nonhydrostatic model and a large-scale hydrostatic ocean model. An isolated destabilizing buoyancy flux is imposed at the surface of both models and a quasi-equilibrium flow is allowed to develop. Mixed layer depth in the turbulence-resolving and large-scale models closely aligns with existing scaling theories. However, the large-scale model has an anomalously deep mixed layer prior to quasi-equilibrium. This transient mixed layer depth bias is a consequence of the lack of resolved turbulent convection in the model, which delays the onset of baroclinic instability. These findings suggest that in order to reduce mixed layer biases in ocean simulations, parameterizations of the connection between baroclinic instability and convection need to be addressed.
Extremum Seeking Control with Sporadic Packet Transmission for Networked Control Systems
Extremum Seeking Control (ESC) is a data-driven optimization technique that can steer a dynamic plant towards an extremum of an unknown but measurable, input to steady-state map. In the context of Networked Control Systems (NCS) a new implementation method for ESC inspired by the well known Luus-Jaakola algorithm is proposed. The main motivation is to minimize the communication burden associated with the search phase of ESC. In the proposed method the controller only requires a notification of a change registered at the sensor, rather than the full information available at the sensor. This event based approach leads to sporadic packet transmission. In addition the proposed method is able to directly account for constraints whilst seeking for the desired extremum. The constraints may be of the inequality or equality type. The algorithm's behavior is illustrated on a networked water pump control system.
Fast Calibration of a Robust Model Predictive Controller for Diesel Engine Airpath
A significant challenge in the development of control systems for diesel airpath applications is to tune the controller parameters to achieve satisfactory output performance, especially while adhering to input and safety constraints in the presence of unknown system disturbances. Model-based control techniques, such as model predictive control (MPC), have been successfully applied to multivariable and highly nonlinear systems, such as diesel engines, while considering operational constraints. However, efficient calibration of typical implementations of MPC is hindered by the high number of tuning parameters and their nonintuitive correlation with the output response. In this paper, the number of effective tuning parameters is reduced through suitable structural modifications to the controller formulation and an appropriate redesign of the MPC cost function to aid rapid calibration. Furthermore, a constraint tighteninglike approach is augmented to the control architecture to provide robustness guarantees in the face of uncertainties. A switched linear time-invariant MPC strategy with recursive feasibility guarantees during controller switching is proposed to handle transient operation of the engine. The robust controller is first implemented on a high-fidelity simulation environment, with a comprehensive investigation of its calibration to achieve desired transient response under step changes in the fuelling rate. An experimental study then validates and highlights the performance of the proposed controller architecture for the selected tunings of the calibration parameters for fuelling steps and over drive cycles.
Supervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells
(MDPI AG, 2020-06-26)
The emergence of new technologies to incorporate and analyze data with high-performance computing has expanded our capability to accurately predict any incident. Supervised Machine learning (ML) can be utilized for a fast and consistent prediction, and to obtain the underlying pattern of the data better. We develop a prediction strategy, for the first time, using supervised ML to observe the possible impact of weak radiofrequency electromagnetic field (RF-EMF) on human and animal cells without performing in-vitro laboratory experiments. We extracted laboratory experimental data from 300 peer-reviewed scientific publications (1990–2015) describing 1127 experimental case studies of human and animal cells response to RF-EMF. We used domain knowledge, Principal Component Analysis (PCA), and the Chi-squared feature selection techniques to select six optimal features for computation and cost-efficiency. We then develop grouping or clustering strategies to allocate these selected features into five different laboratory experiment scenarios. The dataset has been tested with ten different classifiers, and the outputs are estimated using the k-fold cross-validation method. The assessment of a classifier’s prediction performance is critical for assessing its suitability. Hence, a detailed comparison of the percentage of the model accuracy (PCC), Root Mean Squared Error (RMSE), precision, sensitivity (recall), 1 − specificity, Area under the ROC Curve (AUC), and precision-recall (PRC Area) for each classification method were observed. Our findings suggest that the Random Forest algorithm exceeds in all groups in terms of all performance measures and shows AUC = 0.903 where k-fold = 60. A robust correlation was observed in the specific absorption rate (SAR) with frequency and cumulative effect or exposure time with SAR×time (impact of accumulated SAR within the exposure time) of RF-EMF. In contrast, the relationship between frequency and exposure time was not significant. In future, with more experimental data, the sample size can be increased, leading to more accurate work.
Resource allocation in dynamic DF relay for swipt network with circuit power consumption
This paper considers simultaneous wireless information and power transfer (SWIPT) over a dual-hop dynamic decode-and-forward (DF) relay network with the power-splitting (PS) energy harvesting protocol at the relay. The circuit power consumption (CPC), which includes power requirements for both decoding and encoding circuits, is considered at the relay. For a rate- dependent linear CPC model, we formulate an optimization problem to decide the optimal throughput, PS ratio, relay transmit power and time ratio for the source to relay transmission. Although the resultant optimization problem is nonconvex, we derive an efficient optimization algorithm, requiring significantly less floating point operations than an interior point method. Finally, we present numerical results which lead to some interesting insights for system design.
Limited-feedback distributed relay selection for random spatial wireless networks
This paper considers a location-based optimal relay selection scheme for a relay-assisted wireless network where available decode-and- forward relays are distributed as a homogeneous Poisson point process. To solve an optimum relay selection problem, a central entity or the source requires information pertaining to all relay locations. Since the task of feeding this information back is impractical, we investigate a threshold-based limited feedback distributed relay selection policy. We show that the total number of relays feeding back is a Poisson distributed random variable. For a given threshold-based limited feedback distributed relay selection policy, we obtain analytical expressions for the average rate and the outage probability over the fading and no-fading communication scenarios. The derived analytical expressions are verified and the performance achieved by the proposed relay selection policy is illustrated through extensive simulations. It is observed that the limited feedback distributed relay selection policy can achieve almost the same performance with the optimum relay selection policy by only utilizing location information from a few number of relays.