Task-Aware Network Coding Over Butterfly Network

Network coding allows distributed information sources such as sensors to efficiently compress and transmit data to distributed receivers across a bandwidth-limited network. Classical network coding is largely task-agnostic -- the coding schemes mainly aim to faithfully reconstruct data at the receivers, regardless of what ultimate task the received data is used for. In this paper, we analyze a new task-driven network coding problem, where distributed receivers pass transmitted data through machine learning (ML) tasks, which provides an opportunity to improve efficiency by transmitting salient task-relevant data representations. Specifically, we formulate a task-aware network coding problem over a butterfly network in real-coordinate space, where lossy analog compression through principal component analysis (PCA) can be applied. A lower bound for the total loss function for the formulated problem is given, and necessary and sufficient conditions for achieving this lower bound are also provided. We introduce ML algorithms to solve the problem in the general case, and our evaluation demonstrates the effectiveness of task-aware network coding

Adversarial Examples for Model-Based Control: A Sensitivity Analysis

We propose a method to attack controllers that rely on external timeseries forecasts as task parameters. An adversary can manipulate the costs, states, and actions of the controllers by forging the timeseries, in this case perturbing the real timeseries. Since the controllers often encode safety requirements or energy limits in their costs and constraints, we refer to such manipulation as an adversarial attack. We show that different attacks on model-based controllers can increase control costs, activate constraints, or even make the control optimization problem infeasible. We use the linear quadratic regulator and convex model predictive controllers as examples of how adversarial attacks succeed and demonstrate the impact of adversarial attacks on a battery storage control task for power grid operators. As a result, our method increases control cost by $8500\%$ and energy constraints by $13\%$ on real electricity demand timeseries.Comment: Submission to the 58th Annual Allerton Conference on Communication, Control, and Computin

Poisoning Attacks Against Data-Driven Predictive Control

Data-driven predictive control (DPC) is a feedback control method for systems with unknown dynamics. It repeatedly optimizes a system's future trajectories based on past input-output data. We develop a numerical method that computes poisoning attacks which inject additive perturbations to the output data to change the trajectories optimized by DPC. This method is based on implicitly differentiating the solution map of the trajectory optimization in DPC. We demonstrate that the resulting attacks can cause an output tracking error one order of magnitude higher than random perturbations in numerical experiments

Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks

Cellular networks are becoming increasingly heterogeneous with higher base station (BS) densities and ever more frequency bands, making BS selection and band assignment key decisions in terms of rate and coverage. In this paper, we decompose the mobility-aware user association task into (i) forecasting of user rate and then (ii) convex utility maximization for user association accounting for the effects of BS load and handover overheads. Using a linear combination of normalized mean-squared error and normalized discounted cumulative gain as a novel loss function, a recurrent deep neural network is trained to reliably forecast the mobile users' future rates. Based on the forecast, the controller optimizes the association decisions to maximize the service rate-based network utility using our computationally efficient (speed up of 100x versus generic convex solver) algorithm based on the Frank-Wolfe method. Using an industry-grade network simulator developed by Meta, we show that the proposed model predictive control (MPC) approach improves the 5th percentile service rate by 3.5x compared to the traditional signal strength-based association, reduces the median number of handovers by 7x compared to a handover agnostic strategy, and achieves service rates close to a genie-aided scheme. Furthermore, our model-based approach is significantly more sample-efficient (needs 100x less training data) compared to model-free reinforcement learning (RL), and generalizes well across different user drop scenarios

Robust Forecasting for Robotic Control: A Game-Theoretic Approach

Modern robots require accurate forecasts to make optimal decisions in the real world. For example, self-driving cars need an accurate forecast of other agents' future actions to plan safe trajectories. Current methods rely heavily on historical time series to accurately predict the future. However, relying entirely on the observed history is problematic since it could be corrupted by noise, have outliers, or not completely represent all possible outcomes. To solve this problem, we propose a novel framework for generating robust forecasts for robotic control. In order to model real-world factors affecting future forecasts, we introduce the notion of an adversary, which perturbs observed historical time series to increase a robot's ultimate control cost. Specifically, we model this interaction as a zero-sum two-player game between a robot's forecaster and this hypothetical adversary. We show that our proposed game may be solved to a local Nash equilibrium using gradient-based optimization techniques. Furthermore, we show that a forecaster trained with our method performs 30.14% better on out-of-distribution real-world lane change data than baselines

A Control Theoretic Approach to Infrastructure-Centric Blockchain Tokenomics

There are a multitude of Blockchain-based physical infrastructure systems, operating on a crypto-currency enabled token economy, where infrastructure suppliers are rewarded with tokens for enabling, validating, managing and/or securing the system. However, today's token economies are largely designed without infrastructure systems in mind, and often operate with a fixed token supply (e.g., Bitcoin). This paper argues that token economies for infrastructure networks should be structured differently - they should continually incentivize new suppliers to join the network to provide services and support to the ecosystem. As such, the associated token rewards should gracefully scale with the size of the decentralized system, but should be carefully balanced with consumer demand to manage inflation and be designed to ultimately reach an equilibrium. To achieve such an equilibrium, the decentralized token economy should be adaptable and controllable so that it maximizes the total utility of all users, such as achieving stable (overall non-inflationary) token economies. Our main contribution is to model infrastructure token economies as dynamical systems - the circulating token supply, price, and consumer demand change as a function of the payment to nodes and costs to consumers for infrastructure services. Crucially, this dynamical systems view enables us to leverage tools from mathematical control theory to optimize the overall decentralized network's performance. Moreover, our model extends easily to a Stackelberg game between the controller and the nodes, which we use for robust, strategic pricing. In short, we develop predictive, optimization-based controllers that outperform traditional algorithmic stablecoin heuristics by up to $2.4 \times$ in simulations based on real demand data from existing decentralized wireless networks