Performance analysis of incremental LMS over flat fading channels

We study the effect of fading in the communication channels between sensor nodes on the performance of the incremental least mean square (ILMS) algorithm, and derive steady state performance metrics, including the mean-square deviation (MSD), excess mean-square error (EMSE) and mean-square error (MSE). We obtain conditions for mean convergence of the ILMS algorithm, and show that in the presence of fading channels, the ILMS algorithm is asymptotically biased. Furthermore, the dynamic range for mean stability depends only on the mean channel gain, and under simplifying technical assumptions, we show that the MSD, EMSE and MSE are non-decreasing functions of the channel gain variances, with mean-square convergence to the steady states possible only if the channel gain variances are limited. We derive sufficient conditions to ensure mean-square convergence, and verify our results through simulations

Analysis of incremental augmented affine projection algorithm for distributed estimation of complex-valued signals

This paper considers the problem of distributed estimation in an incremental network when the measurements taken by the node follow a widely linear model. The proposed algorithm which we refer to it as incremental augmented affine projection algorithm (incAAPA) utilizes the full second order statistical information in the complex domain. Moreover, it exploits spatio-temporal diversity to improve the estimation performance. We derive steady-state performance metric of the incAAPA in terms of the mean-square deviation (MSD). We further derive sufficient conditions to ensure mean-square convergence. Our analysis illustrate that the proposed algorithm is able to process both second order circular (proper) and noncircular (improper) signals. The validity of the theoretical results and the good performance of the proposed algorithm are demonstrated by several computer simulations

A Bayesian real-time electric vehicle charging strategy for mitigating renewable energy fluctuations

A novel pricing and scheduling mechanism is proposed here for Plug-in electric vehicles (PEVs) charging/discharging to track and synchronize with a renewable power generation pattern. Moreover, the proposed mechanism can be used in the demand-side management and ancillary service applications, respectively for the peak shaving and frequency regulation responding. We design a fully distributed stochastic optimization mechanism using Bayesian pure strategic repeated game by which the PEVs optimally schedule their demands. We also use a mixed Bayesian-diffusion Kalman filtering strategy for the customers to collaboratively estimate and track the stochastic price and regulation signals for the upcoming scheduling window. In the proposed paper all the characteristics of the PEVs, as well as the uncertainty about their deriving patterns are considered. As our framework converges to an equilibrium even with incomplete information, is agent-based, and the agents share the information only with their optional neighbors, it is scale-free, robust, and secure

Tracking analysis of minimum kernel risk-sensitive loss algorithm under general non-Gaussian noise

In this paper the steady-state tracking performance of minimum kernel risk-sensitive loss (MKRSL) in a non-stationary environment is analyzed. In order to model a non-stationary environment, a first-order random-walk model is used to describe the variations of optimum weight vector over time. Moreover, the measurement noise is considered to have non-Gaussian distribution. The energy conservation relation is utilized to extract an approximate closed-form expression for the steady-state excess mean square error (EMSE). Our analysis shows that unlike for the stationary case, the EMSE curve is not an increasing function of step-size parameter. Hence, the optimum step-size which minimizes the EMSE is derived. We also discuss that our approach can be used to extract steady-state EMSE for a general class of adaptive filters. The simulation results with different noise distributions support the theoretical derivations

Variants of partial update augmented CLMS algorithm and their performance analysis

Naturally complex-valued information or those presented in complex domain are effectively processed by an augmented complex least-mean-square (ACLMS) algorithm. In some applications, the ACLMS algorithm may be too computationally and memory-intensive to implement. In this paper, a new algorithm, termed partial-update ACLMS (PU-ACLMS) algorithm is proposed, where only a fraction of the coefficient set is selected to update at each iteration. Doing so, two types of partial update schemes are presented referred to as the sequential and stochastic partial-updates, to reduce computational load and power consumption in the corresponding adaptive filter. The computational cost for full-update PU-ACLMS and its partial update implementations are discussed. Next, the steady-state mean and mean-square performance of PU-ACLMS for noncircular complex signals are analyzed and closed-form expressions of the steady-state excess mean-square error (EMSE) and mean-square deviation (MSD) are given. Then, employing the weighted energy-conservation relation, the EMSE and MSD learning curves are derived. The simulation results are verified and compared with those of theoretical predictions through numerical examples

A robust scalable demand-side management based on diffusion-ADMM strategy for smart grid

Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed to encourage consumers to modify their level and pattern of electricity usage. This is performed following methods such as financial incentives and behavioral change through education. While the objective of the DSM is to achieve a balance between energy production and demand, effective and efficient implementation of the program rests within effective use of emerging Internet of things (IoT) concept for online interactions. Here, a novel DSM framework based on diffusion and alternating direction method of multipliers (ADMM) strategies, repeated under a model predictive control (MPC) protocol, is proposed. On the demand side, the customers autonomously and by cooperation with their immediate neighbors estimate the baseline price in real time. Based on the estimated price signal, the customers schedule their energy consumption using the ADMM cost-sharing strategy to minimize their incommodity level. On the supply side, the utility company determines the price parameters based on the customers real-time behavior to make a profit and prevent the infrastructure overload. The proposed mechanism is capable of tracking drifts in the optimal solution resulting from the changes in supply/demand sides. Moreover, it considers all classes of appliances by formulating the DSM problem as a mixed-integer programming (MIP) problem. Numerical examples are provided to show the effectiveness of the proposed framework

Partial diffusion Kalman filtering for distributed state estimation in multiagent networks

Many problems in multiagent networks can be solved through distributed learning (state estimation) of linear dynamical systems. In this paper, we develop a partial-diffusion Kalman filtering (PDKF) algorithm, as a fully distributed solution for state estimation in the multiagent networks with limited communication resources. In the PDKF algorithm, every agent (node) is allowed to share only a subset of its intermediate estimate vectors with its neighbors at each iteration, reducing the amount of internode communications. We analyze the stability of the PDKF algorithm and show that the algorithm is stable and convergent in both mean and mean-square senses. We also derive a closed-form expression for the steady-state mean-square deviation criterion. Furthermore, we show theoretically and by numerical examples that the PDKF algorithm provides a trade-off between the estimation performance and the communication cost that is extremely profitable

A robust adaptive estimation algorithm for Hamiltonian multi-agent networks

In this letter a robust incremental adaptation algorithm is presented to solve distributed estimation for a Hamiltonian network, where the measurements at each node may be corrupted by heavy-tailed impulsive noise. In the proposed algorithm, each node employs an error-nonlinearity into the update equation to mitigate the detrimental effects of impulsive noise. Moreover, the algorithm estimates both the optimal error non-linearity and the unknown parameter together, which in turn, obviates the requirement of prior knowledge about the statistical characteristics of measurement noise. In addition to algorithm development, its steady-state performance as well as convergence analysis have been provided. Simulation results validate the correctness of the analysis and reveal the superiority of the proposed algorithm over some existing algorithms

A distributed game-theoretic demand response with multi-class appliance control in smart grid

We propose an event-triggered game-theoretic strategy for managing the power grids demand side, capable of responding to changes in consumer preferences or the price parameters coming from the wholesale market. The relationship between the retailer and the residential consumers is modeled as one-leader, N-follower Stackelberg game. We provide a detailed characterization of the household appliances to reflect the reality and improve the efficiency of the demand response (DR). Moreover, to consider all the appliances’ essentials, the consumer's objective function is formulated as a mixed integer non-linear program (MINLP), which, unlike conventional procedures, is solved via an integrated method. The proposed method consists of a day-ahead stage, in which the DR problem is solved for the next scheduling horizon, and a real-time stage which runs repeatedly to tackle the change in the parameters and adapt to the new condition. For any change in the grid, the consumers use the estimated optimal parameters (given by the original objective function) and develop another Stackelberg game based solution to maximize the satisfaction level. Given the appliances of multi-class nature, the proposed method is shown to be very tractable for ancillary services and reducing the mismatch between the renewable power generation and the load demand

Agent-based decentralized optimal charging strategy for plug-in electric vehicles

This paper presents a game theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantages of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real time dynamic pricing model, and supports the vehicle-to-grid (V2G) and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short term uncertainties