111,987 research outputs found
Evolutionary Approaches to Minimizing Network Coding Resources
We wish to minimize the resources used for network coding while achieving the
desired throughput in a multicast scenario. We employ evolutionary approaches,
based on a genetic algorithm, that avoid the computational complexity that
makes the problem NP-hard. Our experiments show great improvements over the
sub-optimal solutions of prior methods. Our new algorithms improve over our
previously proposed algorithm in three ways. First, whereas the previous
algorithm can be applied only to acyclic networks, our new method works also
with networks with cycles. Second, we enrich the set of components used in the
genetic algorithm, which improves the performance. Third, we develop a novel
distributed framework. Combining distributed random network coding with our
distributed optimization yields a network coding protocol where the resources
used for coding are optimized in the setup phase by running our evolutionary
algorithm at each node of the network. We demonstrate the effectiveness of our
approach by carrying out simulations on a number of different sets of network
topologies.Comment: 9 pages, 6 figures, accepted to the 26th Annual IEEE Conference on
Computer Communications (INFOCOM 2007
Spatial Coordination Strategies in Future Ultra-Dense Wireless Networks
Ultra network densification is considered a major trend in the evolution of
cellular networks, due to its ability to bring the network closer to the user
side and reuse resources to the maximum extent. In this paper we explore
spatial resources coordination as a key empowering technology for next
generation (5G) ultra-dense networks. We propose an optimization framework for
flexibly associating system users with a densely deployed network of access
nodes, opting for the exploitation of densification and the control of overhead
signaling. Combined with spatial precoding processing strategies, we design
network resources management strategies reflecting various features, namely
local vs global channel state information knowledge exploitation, centralized
vs distributed implementation, and non-cooperative vs joint multi-node data
processing. We apply these strategies to future UDN setups, and explore the
impact of critical network parameters, that is, the densification levels of
users and access nodes as well as the power budget constraints, to users
performance. We demonstrate that spatial resources coordination is a key factor
for capitalizing on the gains of ultra dense network deployments.Comment: An extended version of a paper submitted to ISWCS'14, Special Session
on Empowering Technologies of 5G Wireless Communication
A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks
Cell Switch-Off (CSO) is recognized as a promising approach to reduce the
energy consumption in next-generation cellular networks. However, CSO poses
serious challenges not only from the resource allocation perspective but also
from the implementation point of view. Indeed, CSO represents a difficult
optimization problem due to its NP-complete nature. Moreover, there are a
number of important practical limitations in the implementation of CSO schemes,
such as the need for minimizing the real-time complexity and the number of
on-off/off-on transitions and CSO-induced handovers. This article introduces a
novel approach to CSO based on multiobjective optimization that makes use of
the statistical description of the service demand (known by operators). In
addition, downlink and uplink coverage criteria are included and a comparative
analysis between different models to characterize intercell interference is
also presented to shed light on their impact on CSO. The framework
distinguishes itself from other proposals in two ways: 1) The number of
on-off/off-on transitions as well as handovers are minimized, and 2) the
computationally-heavy part of the algorithm is executed offline, which makes
its implementation feasible. The results show that the proposed scheme achieves
substantial energy savings in small cell deployments where service demand is
not uniformly distributed, without compromising the Quality-of-Service (QoS) or
requiring heavy real-time processing
Maximizing Profit in Green Cellular Networks through Collaborative Games
In this paper, we deal with the problem of maximizing the profit of Network
Operators (NOs) of green cellular networks in situations where
Quality-of-Service (QoS) guarantees must be ensured to users, and Base Stations
(BSs) can be shared among different operators. We show that if NOs cooperate
among them, by mutually sharing their users and BSs, then each one of them can
improve its net profit. By using a game-theoretic framework, we study the
problem of forming stable coalitions among NOs. Furthermore, we propose a
mathematical optimization model to allocate users to a set of BSs, in order to
reduce costs and, at the same time, to meet user QoS for NOs inside the same
coalition. Based on this, we propose an algorithm, based on cooperative game
theory, that enables each operator to decide with whom to cooperate in order to
maximize its profit. This algorithms adopts a distributed approach in which
each NO autonomously makes its own decisions, and where the best solution
arises without the need to synchronize them or to resort to a trusted third
party. The effectiveness of the proposed algorithm is demonstrated through a
thorough experimental evaluation considering real-world traffic traces, and a
set of realistic scenarios. The results we obtain indicate that our algorithm
allows a population of NOs to significantly improve their profits thanks to the
combination of energy reduction and satisfaction of QoS requirements.Comment: Added publisher info and citation notic
- âŚ