4,502 research outputs found
Cross-layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks
This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc
wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints.
By dual decomposition, the resource allocation problem
naturally decomposes into three subproblems: congestion control,
routing and scheduling that interact through congestion price.
The global convergence property of this algorithm is proved. We
next extend the dual algorithm to handle networks with timevarying
channels and adaptive multi-rate devices. The stability
of the resulting system is established, and its performance is
characterized with respect to an ideal reference system which
has the best feasible rate region at link layer.
We then generalize the aforementioned results to a general
model of queueing network served by a set of interdependent
parallel servers with time-varying service capabilities, which
models many design problems in communication networks. We
show that for a general convex optimization problem where a
subset of variables lie in a polytope and the rest in a convex set,
the dual-based algorithm remains stable and optimal when the
constraint set is modulated by an irreducible finite-state Markov
chain. This paper thus presents a step toward a systematic way
to carry out cross-layer design in the framework of “layering as
optimization decomposition” for time-varying channel models
Energy Efficient User Association and Power Allocation in Millimeter Wave Based Ultra Dense Networks with Energy Harvesting Base Stations
Millimeter wave (mmWave) communication technologies have recently emerged as
an attractive solution to meet the exponentially increasing demand on mobile
data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave
technology are expected to increase both energy efficiency and spectral
efficiency. In this paper, user association and power allocation in mmWave
based UDNs is considered with attention to load balance constraints, energy
harvesting by base stations, user quality of service requirements, energy
efficiency, and cross-tier interference limits. The joint user association and
power optimization problem is modeled as a mixed-integer programming problem,
which is then transformed into a convex optimization problem by relaxing the
user association indicator and solved by Lagrangian dual decomposition. An
iterative gradient user association and power allocation algorithm is proposed
and shown to converge rapidly to an optimal point. The complexity of the
proposed algorithm is analyzed and the effectiveness of the proposed scheme
compared with existing methods is verified by simulations.Comment: to appear, IEEE Journal on Selected Areas in Communications, 201
Joint routing and resource allocation for wireless backhauling of small cell networks
The future communication networks are destined to support an increasingly large amount of data traffic, and for that reason, efficient mechanisms to manage them are necessary. Based on a backhaul network, and starting from specific scenarios, we develop methods to jointly optimize the routing parameters and resources of this network. We relate this optimization with the Software Defined Networks and network virtualization concepts, which allow us to have an overall vision of the network, and lead us to study its decomposition. To do this, we use convex optimization techniques, which have very efficient resolution mechanisms, and help us to obtain tools for interpreting the obtained results and perform analysis on the network parameters. The achieved results show a great improvement in relation to the non-optimized case in terms of carried traffic, which is an assessment we make in the final economic analysis.Las redes de comunicaciones del futuro están destinadas a soportar una cantidad de tráfico de datos cada vez más elevada, y por eso son necesarios mecanismos eficientes para gestionarlas. Basándonos en una red de backhaul y partiendo de escenarios concretos, desarrollamos métodos para optimizar conjuntamente los parámetros de enrutamiento y los recursos de esta red. Esta optimización la ligamos con los conceptos de Software Defined Networksk y de network virtualization, que nos permiten tener una visión general de la red, y nos conducen a estudiar su descomposición. Esto lo hacemos usando técnicas de optimización convexa, que tiene mecanismos de resolución muy eficientes, y nos ayuda a obtener herramientas para interpretar los resultados obtenidos y hacer análisis de los parámetros de la red. Los resultados conseguidos muestran una gran mejora con relación al caso no optimizado en términos de tráfico transportado, valoración que recogemos en un análisis económico final.Les xarxes de comunicacions del futur estan destinades a suportar una quantitat de trànsit de dades cada cop més elevada, i per això són necessaris mecanismes eficients per a gestionar-les. Basant-nos en una xarxa de backhaul i partint d?escenaris concrets, desenvolupem mètodes per a optimitzar conjuntament els paràmetres d?encaminament i els recursos d?aquesta xarxa. Aquesta optimització la lliguem amb els conceptes de Software Defined Network i de network virtualization, que ens permeten tenir una visió general de la xarxa, i ens condueixen a estudiar-ne la seva descomposició. Tot això ho fem utilitzant tècniques d?optimització convexa, que té mecanismes de resolució molt eficients, i ens ajuda a obtenir eines per a interpretar els resultats obtinguts i fer anàlisis dels punts forts i febles de la xarxa. Els resultats aconseguits mostren una gran millora respecte el cas no optimitzat en termes de trànsit transportat, valoració que recollim en una anàlisi econòmica final
Separable Convex Optimization with Nested Lower and Upper Constraints
We study a convex resource allocation problem in which lower and upper bounds
are imposed on partial sums of allocations. This model is linked to a large
range of applications, including production planning, speed optimization,
stratified sampling, support vector machines, portfolio management, and
telecommunications. We propose an efficient gradient-free divide-and-conquer
algorithm, which uses monotonicity arguments to generate valid bounds from the
recursive calls, and eliminate linking constraints based on the information
from sub-problems. This algorithm does not need strict convexity or
differentiability. It produces an -approximate solution for the
continuous problem in time
and an integer solution in time, where is
the number of decision variables, is the number of constraints, and is
the resource bound. A complexity of is also achieved
for the linear and quadratic cases. These are the best complexities known to
date for this important problem class. Our experimental analyses confirm the
good performance of the method, which produces optimal solutions for problems
with up to 1,000,000 variables in a few seconds. Promising applications to the
support vector ordinal regression problem are also investigated
Simultaneous Routing and Power Allocation using Location Information
To guarantee optimal performance of wireless networks,
simultaneous optimization of routing and resource allocation
is needed. Optimal routing of data depends on the link
capacities which, in turn, are determined by the allocation of communication resources to the links. Simultaneous routing and resource allocation (SRRA) problems have been studied under the assumption that (global) channel state information (CSI) is collected at a central node. This is a drawback as SRRA depends on channels between all pairs of nodes in the network, thus leading to poor scalability of the CSI-based approach. In this paper, we first investigate to what extent it is possible to rely solely on location information (i.e., position of nodes) when solving the SRRA problem. We also propose a distributed heuristic based on
which nodes can locally adjust their rate based on the local CSI. Our numerical results show that the proposed heuristic achieves near-optimal flow in the network under different shadowing conditions
Stability and Distributed Power Control in MANETs with Outages and Retransmissions
In the current work the effects of hop-by-hop packet loss and retransmissions
via ARQ protocols are investigated within a Mobile Ad-hoc NET-work (MANET).
Errors occur due to outages and a success probability function is related to
each link, which can be controlled by power and rate allocation. We first
derive the expression for the network's capacity region, where the success
function plays a critical role. Properties of the latter as well as the related
maximum goodput function are presented and proved. A Network Utility
Maximization problem (NUM) with stability constraints is further formulated
which decomposes into (a) the input rate control problem and (b) the scheduling
problem. Under certain assumptions problem (b) is relaxed to a weighted sum
maximization problem with number of summants equal to the number of nodes. This
further allows the formulation of a non-cooperative game where each node
decides independently over its transmitting power through a chosen link. Use of
supermodular game theory suggests a price based algorithm that converges to a
power allocation satisfying the necessary optimality conditions of (b).
Implementation issues are considered so that minimum information exchange
between interfering nodes is required. Simulations illustrate that the
suggested algorithm brings near optimal results.Comment: 25 pages, 6 figures, 1 table, submitted to the IEEE Trans. on
Communication
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