2,765 research outputs found
Fast-Convergent Learning-aided Control in Energy Harvesting Networks
In this paper, we present a novel learning-aided energy management scheme
() for multihop energy harvesting networks. Different from prior
works on this problem, our algorithm explicitly incorporates information
learning into system control via a step called \emph{perturbed dual learning}.
does not require any statistical information of the system
dynamics for implementation, and efficiently resolves the challenging energy
outage problem. We show that achieves the near-optimal
utility-delay tradeoff with an
energy buffers (). More interestingly,
possesses a \emph{convergence time} of , which is much faster than the time of
pure queue-based techniques or the time of approaches
that rely purely on learning the system statistics. This fast convergence
property makes more adaptive and efficient in resource
allocation in dynamic environments. The design and analysis of
demonstrate how system control algorithms can be augmented by learning and what
the benefits are. The methodology and algorithm can also be applied to similar
problems, e.g., processing networks, where nodes require nonzero amount of
contents to support their actions
The Power of Online Learning in Stochastic Network Optimization
In this paper, we investigate the power of online learning in stochastic
network optimization with unknown system statistics {\it a priori}. We are
interested in understanding how information and learning can be efficiently
incorporated into system control techniques, and what are the fundamental
benefits of doing so. We propose two \emph{Online Learning-Aided Control}
techniques, and , that explicitly utilize the
past system information in current system control via a learning procedure
called \emph{dual learning}. We prove strong performance guarantees of the
proposed algorithms: and achieve the
near-optimal utility-delay tradeoff
and possesses an convergence time.
and are probably the first algorithms that
simultaneously possess explicit near-optimal delay guarantee and sub-linear
convergence time. Simulation results also confirm the superior performance of
the proposed algorithms in practice. To the best of our knowledge, our attempt
is the first to explicitly incorporate online learning into stochastic network
optimization and to demonstrate its power in both theory and practice
The Power of Online Learning in Stochastic Network Optimization
In this paper, we investigate the power of online learning in stochastic
network optimization with unknown system statistics {\it a priori}. We are
interested in understanding how information and learning can be efficiently
incorporated into system control techniques, and what are the fundamental
benefits of doing so. We propose two \emph{Online Learning-Aided Control}
techniques, and , that explicitly utilize the
past system information in current system control via a learning procedure
called \emph{dual learning}. We prove strong performance guarantees of the
proposed algorithms: and achieve the
near-optimal utility-delay tradeoff
and possesses an convergence time.
and are probably the first algorithms that
simultaneously possess explicit near-optimal delay guarantee and sub-linear
convergence time. Simulation results also confirm the superior performance of
the proposed algorithms in practice. To the best of our knowledge, our attempt
is the first to explicitly incorporate online learning into stochastic network
optimization and to demonstrate its power in both theory and practice
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Unified and Distributed QoS-Driven Cell Association Algorithms in Heterogeneous Networks
This paper addresses the cell association problem in the downlink of a
multi-tier heterogeneous network (HetNet), where base stations (BSs) have
finite number of resource blocks (RBs) available to distribute among their
associated users. Two problems are defined and treated in this paper: sum
utility of long term rate maximization with long term rate quality of service
(QoS) constraints, and global outage probability minimization with outage QoS
constraints. The first problem is well-suited for low mobility environments,
while the second problem provides a framework to deal with environments with
fast fading. The defined optimization problems in this paper are solved in two
phases: cell association phase followed by the optional RB distribution phase.
We show that the cell association phase of both problems have the same
structure. Based on this similarity, we propose a unified distributed algorithm
with low levels of message passing to for the cell association phase. This
distributed algorithm is derived by relaxing the association constraints and
using Lagrange dual decomposition method. In the RB distribution phase, the
remaining RBs after the cell association phase are distributed among the users.
Simulation results show the superiority of our distributed cell association
scheme compared to schemes that are based on maximum signal to interference
plus noise ratio (SINR)
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