34 research outputs found
Distributed Caching in Small Cell Networks
The dense deployment of small cells in indoor and outdoor areas contributes mainly in increasing the capacity of cellular networks. On the other hand, the high number of deployed base stations coupled with the increasing growth of data traffic have prompted the apparition of base stations fi tted with storage capacity to avoid network saturation. The storage devices are used as caching units to overcome the limited backhaul capacity in small cells networks (SCNs). Extending the concept of storage to SCNs, gives rise to many new challenges related to the specific characteristics of these networks such as the heterogeneity of the base stations. Formulating the caching problem while taking into account all these specific characteristics with the aim to satisfy the users expectations result in combinatorial optimization problems. However, classical optimization tools do not ensure the optimality of the provided solutions or often the proposed algorithms have an exponential complexity. While most of the existing works are based on the classical optimization tools, in this thesis, we explore another approach to provide a practical solution for the caching problem. In particular, we focus on matching theory which is a game theoretic approach that provides mathematical tools to formulate, analyze and understand scenarios between sets of players. We model the caching problem as a one-to-one matching game between a set of files and a set of base stations and then, we propose an iterative extension of the deferred acceptance algorithm that needs a stable and optimal matching between the two sets. The experimental results show that the proposed algorithm reduces the backhaul load by 10-15 % compared to a random caching algorithm
Breaking the Economic Barrier of Caching in Cellular Networks: Incentives and Contracts
In this paper, a novel approach for providing incentives for caching in small
cell networks (SCNs) is proposed based on the economics framework of contract
theory. In this model, a mobile network operator (MNO) designs contracts that
will be offered to a number of content providers (CPs) to motivate them to
cache their content at the MNO's small base stations (SBSs). A practical model
in which information about the traffic generated by the CPs' users is not known
to the MNO is considered. Under such asymmetric information, the incentive
contract between the MNO and each CP is properly designed so as to determine
the amount of allocated storage to the CP and the charged price by the MNO. The
contracts are derived by the MNO in a way to maximize the global benefit of the
CPs and prevent them from using their private information to manipulate the
outcome of the caching process. For this interdependent contract model, the
closed-form expressions of the price and the allocated storage space to each CP
are derived. This proposed mechanism is shown to satisfy the sufficient and
necessary conditions for the feasibility of a contract. Moreover, it is shown
that the proposed pricing model is budget balanced, enabling the MNO to cover
all the caching expenses via the prices charged to the CPs. Simulation results
show that none of the CPs will have an incentive to choose a contract designed
for CPs with different traffic loads.Comment: Accepted for publication at Globecom 201
Many-to-Many Matching Games for Proactive Social-Caching in Wireless Small Cell Networks
In this paper, we address the caching problem in small cell networks from a
game theoretic point of view. In particular, we formulate the caching problem
as a many-to-many matching game between small base stations and service
providers' servers. The servers store a set of videos and aim to cache these
videos at the small base stations in order to reduce the experienced delay by
the end-users. On the other hand, small base stations cache the videos
according to their local popularity, so as to reduce the load on the backhaul
links. We propose a new matching algorithm for the many-to-many problem and
prove that it reaches a pairwise stable outcome. Simulation results show that
the number of satisfied requests by the small base stations in the proposed
caching algorithm can reach up to three times the satisfaction of a random
caching policy. Moreover, the expected download time of all the videos can be
reduced significantly
A Multi-Game Framework for Harmonized LTE-U and WiFi Coexistence over Unlicensed Bands
The introduction of LTE over unlicensed bands (LTE-U) will enable LTE base
stations (BSs) to boost their capacity and offload their traffic by exploiting
the underused unlicensed bands. However, to reap the benefits of LTE-U, it is
necessary to address various new challenges associated with LTE-U and WiFi
coexistence. In particular, new resource management techniques must be
developed to optimize the usage of the network resources while handling the
interdependence between WiFi and LTE users and ensuring that WiFi users are not
jeopardized. To this end, in this paper, a new game theoretic tool, dubbed as
\emph{multi-game} framework is proposed as a promising approach for modeling
resource allocation problems in LTE-U. In such a framework, multiple,
co-existing and coupled games across heterogeneous channels can be formulated
to capture the specific characteristics of LTE-U. Such games can be of
different properties and types but their outcomes are largely interdependent.
After introducing the basics of the multi-game framework, two classes of
algorithms are outlined to achieve the new solution concepts of multi-games.
Simulation results are then conducted to show how such a multi-game can
effectively capture the specific properties of LTE-U and make of it a
"friendly" neighbor to WiFi.Comment: Accepted for publication at IEEE Wireless Communications Magazine,
Special Issue on LTE in Unlicensed Spectru
Fast Design Space Exploration of Nonlinear Systems: Part II
Nonlinear system design is often a multi-objective optimization problem
involving search for a design that satisfies a number of predefined
constraints. The design space is typically very large since it includes all
possible system architectures with different combinations of components
composing each architecture. In this article, we address nonlinear system
design space exploration through a two-step approach encapsulated in a
framework called Fast Design Space Exploration of Nonlinear Systems (ASSENT).
In the first step, we use a genetic algorithm to search for system
architectures that allow discrete choices for component values or else only
component values for a fixed architecture. This step yields a coarse design
since the system may or may not meet the target specifications. In the second
step, we use an inverse design to search over a continuous space and fine-tune
the component values with the goal of improving the value of the objective
function. We use a neural network to model the system response. The neural
network is converted into a mixed-integer linear program for active learning to
sample component values efficiently. We illustrate the efficacy of ASSENT on
problems ranging from nonlinear system design to design of electrical circuits.
Experimental results show that ASSENT achieves the same or better value of the
objective function compared to various other optimization techniques for
nonlinear system design by up to 54%. We improve sample efficiency by 6-10x
compared to reinforcement learning based synthesis of electrical circuits.Comment: 14 pages, 24 figures. arXiv admin note: substantial text overlap with
arXiv:2009.1021
The 5G Cellular Backhaul Management Dilemma: To Cache or to Serve
With the introduction of caching capabilities into small cell networks
(SCNs), new backaul management mechanisms need to be developed to prevent the
predicted files that are downloaded by the at the small base stations (SBSs) to
be cached from jeopardizing the urgent requests that need to be served via the
backhaul. Moreover, these mechanisms must account for the heterogeneity of the
backhaul that will be encompassing both wireless backhaul links at various
frequency bands and a wired backhaul component. In this paper, the
heterogeneous backhaul management problem is formulated as a minority game in
which each SBS has to define the number of predicted files to download, without
affecting the required transmission rate of the current requests. For the
formulated game, it is shown that a unique fair proper mixed Nash equilibrium
(PMNE) exists. Self-organizing reinforcement learning algorithm is proposed and
proved to converge to a unique Boltzmann-Gibbs equilibrium which approximates
the desired PMNE. Simulation results show that the performance of the proposed
approach can be close to that of the ideal optimal algorithm while it
outperforms a centralized greedy approach in terms of the amount of data that
is cached without jeopardizing the quality-of-service of current requests.Comment: Accepted for publication at Transactions on Wireless Communication