Daniel O'Neill

Power Control By Geometric Programming

In wireless cellular or ad hoc networks where Quality of Service (QoS) is interference-limited, a variety of power control problems can be formulated as nonlinear optimization with a system-wide objective, e.g., maximizing the total system throughput or the worst user throughput, subject to QoS constraints from individual users, e.g., on data rate, delay, and outage probability. We show that in the high Signal-to- interference Ratios (SIR) regime, these nonlinear and apparently difficult, nonconvex optimization problems can be transformed into convex optimization problems in the form of geometric programming; hence they can be very efficiently solved for global optimality even with a large number of users. In the medium to low SIR regime, some of these constrained nonlinear optimization of power control cannot be turned into tractable convex formulations, but a heuristic can be used to compute in most cases the optimal solution by solving a series of geometric programs through the approach of successive convex approximation. While efficient and robust algorithms have been extensively studied for centralized solutions of geometric programs, distributed algorithms have not been explored before. We present a systematic method of distributed algorithms for power control that is geometric-programming-based. These techniques for power control, together with their implications to admission control and pricing in wireless networks, are illustrated through several numerical examples.

QoS and fairness constrained convex optimization of resource allocation for wireless cellular and ad hoc networks

For wireless cellular and ad hoc networks with QoS constraints, we propose a suite of problem formulations that allocate network resources to optimize SIR, maximize throughput and minimize delay. The distinguishing characteristics of these resource allocation formulations is that, by using convex optimization, they accommodate a variety of realistic QoS and fairness constraints. Their globally optimal solutions can be computed efficiently through polynomial time interior point methods, even though they use nonlinear objectives and constraints. Through power control in wireless cellular networks, we optimize SIR and delay for a particular QoS class, subject to QoS constraints for all other QoS classes. For wireless ad hoc networks with multihop transmissions and Rayleigh fading, we optimize various objectives, such as the overall system throughput, subject to constraints on power, probability of outage, and data rates. These formulations can also be used for admission control and relative pricing. Both proportional and minmax fairness can be implemented under the convex optimization framework, where fairness parameters can be jointly optimized with QoS criteria. Simple heuristics are also shown and tested using the convex optimization tools.

Residential Demand Response Using Reinforcement Learning

We present a novel energy management system for residential demand response. The algorithm, named CAES, reduces residential energy costs and smooths energy usage. CAES is an online learning application that implicitly estimates the impact of future energy prices and of consumer decisions on long term costs and schedules residential device usage. CAES models both energy prices and residential device usage as Markov, but does not assume knowledge of the structure or transition probabilities of these Markov chains. CAES learns continuously and adapts to individual consumer preferences and pricing modifications over time. In numerical simulations CAES reduced average end-user financial costs from

Optimal Demand Response Using Device-Based Reinforcement Learning

16\%

Resource allocation for QoS provisioning in wireless ad hoc networks

to

Dynamic Control and Optimization of Distributed Energy Resources in a Microgrid

40\%

Optimizing Adaptive Modulation in Wireless Networks via Utility Maximization

with respect to a price-unaware energy allocation.

Wireless NUM: Rate and Reliability Tradeoffs in Random Environment

Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated energy management system (EMS) is a necessary prerequisite to DR in these areas. In this paper, we propose a novel EMS formulation for DR problems in these sectors. Specifically, we formulate a fully automated EMSs rescheduling problem as a reinforcement learning (RL) problem, and argue that this RL problem can be approximately solved by decomposing it over device clusters. Compared with existing formulations, our new formulation does not require explicitly modeling the users dissatisfaction on job rescheduling, enables the EMS to self-initiate jobs, allows the user to initiate more flexible requests, and has a computational complexity linear in the number of device clusters. We also demonstrate the simulation results of applying Q-learning, one of the most popular and classical RL algorithms, to a representative example.

Adaptive management of network resources

For wireless ad hoc networks with multihop, transmissions and Rayleigh fading, this paper maximizes the overall system throughput subject to QoS constraints on power, probability of outage, and data rates. Formulations are also given which minimize delay and optimize network resources in a wireless ad hoc network, where each link is shared by multiple streams of traffic from different QoS classes, and each traffic traverses many links. Although these optimal resource allocation problems are non-linear, they can be posed as geometric programs, which are transformed into convex optimizations, and can be solved globally and efficiently through interior-point methods.

Robust and QoS constrained optimization of power control in wireless cellular networks

As we transition toward a power grid that is increasingly based on renewable resources like solar and wind, the intelligent control of distributed energy resources (DERs) including photovoltaic (PV) arrays, controllable loads, energy storage, and plug-in electric vehicles (EVs) will be critical to realizing a power grid that can handle both the variability and unpredictability of renewable energy sources as well as increasing system complexity. Realizing such a decentralized and dynamic infrastructure will require the ability to solve large scale problems in real-time with hundreds of thousands of DERs simultaneously online. Because of the scale of the optimization problem, we use an iterative distributed algorithm previously developed in our group to operate each DER independently and autonomously within this environment. The algorithm is deployed within a framework that allows the microgrid to dynamically adapt to changes in the operating environment. Specifically, we consider a commercial site equipped with on-site PV generation, partially curtailable load, EV charge stations and a battery electric storage unit. The site operates as a small microgrid that can participate in the wholesale market on the power grid. We report results for simulations using real-data that demonstrate the ability of the optimization framework to respond dynamically in real-time to external conditions while maintaining the functional requirements of all DERs.