152 research outputs found
Robust-to-Noise Algorithms for Distributed Resource Allocation and Scheduling
Efficient resource allocation and scheduling algorithms are essential for
various distributed applications, ranging from wireless networks and cloud
computing platforms to autonomous multi-agent systems and swarm robotic
networks. However, real-world environments are often plagued by uncertainties
and noise, leading to sub-optimal performance and increased vulnerability of
traditional algorithms. This paper addresses the challenge of robust resource
allocation and scheduling in the presence of noise and disturbances. The
proposed study introduces a novel sign-based dynamics for developing
robust-to-noise algorithms distributed over a multi-agent network that can
adaptively handle external disturbances. Leveraging concepts from convex
optimization theory, control theory, and network science the framework
establishes a principled approach to design algorithms that can maintain key
properties such as resource-demand balance and constraint feasibility.
Meanwhile, notions of uniform-connectivity and versatile networking conditions
are also addressed.Comment: IEEE/RSI ICRoM202
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation
This paper proposes two nonlinear dynamics to solve constrained distributed
optimization problem for resource allocation over a multi-agent network. In
this setup, coupling constraint refers to resource-demand balance which is
preserved at all-times. The proposed solutions can address various model
nonlinearities, for example, due to quantization and/or saturation. Further, it
allows to reach faster convergence or to robustify the solution against
impulsive noise or uncertainties. We prove convergence over weakly connected
networks using convex analysis and Lyapunov theory. Our findings show that
convergence can be reached for general sign-preserving odd nonlinearity. We
further propose delay-tolerant mechanisms to handle general bounded
heterogeneous time-varying delays over the communication network of agents
while preserving all-time feasibility. This work finds application in CPU
scheduling and coverage control among others. This paper advances the
state-of-the-art by addressing (i) possible nonlinearity on the agents/links,
meanwhile handling (ii) resource-demand feasibility at all times, (iii)
uniform-connectivity instead of all-time connectivity, and (iv) possible
heterogeneous and time-varying delays. To our best knowledge, no existing work
addresses contributions (i)-(iv) altogether. Simulations and comparative
analysis are provided to corroborate our contributions
Inter-micro-operator interference protection in dynamic TDD system
Abstract. This thesis considers the problem of weighted sum-rate maximization (WSRM) for a system of micro-operators subject to inter-micro-operator interference constraints with dynamic time division duplexing. The WSRM problem is non-convex and non-deterministic polynomial hard. Furthermore, micro-operators require minimum coordination among themselves making the inter-micro-operator interference management very challenging. In this regard, we propose two decentralized precoder design algorithm based on over-the-air bi-directional signalling strategy. We first propose a precoder design algorithm by considering the equivalent weighted minimum mean-squared error minimization reformulation of the WSRM problem. Later we propose precoder design algorithm by considering the weighted sum mean-squared error reformulation. In both approaches, to reduce the huge signalling requirements in centralized design, we use alternating direction method of multipliers technique, wherein each downlink-operator base station and uplink-operator user determines only the relevant set of transmit precoders by exchanging minimal information among the coordinating base stations and user equipments. To minimize the coordination between the uplink-opeator users, we propose interference budget allocation scheme based on reference signal measurements from downlink-operator users. Numerical simulations are provided to compare the performance of proposed algorithms with and without the inter-micro-operator interference constraints
Communication-Efficient Algorithms For Distributed Optimization
This thesis is concerned with the design of distributed algorithms for
solving optimization problems. We consider networks where each node has
exclusive access to a cost function, and design algorithms that make all nodes
cooperate to find the minimum of the sum of all the cost functions. Several
problems in signal processing, control, and machine learning can be posed as
such optimization problems. Given that communication is often the most
energy-consuming operation in networks, it is important to design
communication-efficient algorithms. The main contributions of this thesis are a
classification scheme for distributed optimization and a set of corresponding
communication-efficient algorithms.
The class of optimization problems we consider is quite general, since each
function may depend on arbitrary components of the optimization variable, and
not necessarily on all of them. In doing so, we go beyond the common assumption
in distributed optimization and create additional structure that can be used to
reduce the number of communications. This structure is captured by our
classification scheme, which identifies easier instances of the problem, for
example the standard distributed optimization problem, where all functions
depend on all the components of the variable.
In our algorithms, no central node coordinates the network, all the
communications occur between neighboring nodes, and the data associated with
each node is processed locally. We show several applications including average
consensus, support vector machines, network flows, and several distributed
scenarios for compressed sensing. We also propose a new framework for
distributed model predictive control. Through extensive numerical experiments,
we show that our algorithms outperform prior distributed algorithms in terms of
communication-efficiency, even some that were specifically designed for a
particular application.Comment: Thesis defended on October 10, 2013. Dual PhD degree from Carnegie
Mellon University, PA, and Instituto Superior T\'ecnico, Lisbon, Portuga
- …