2,321 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Applications of Repeated Games in Wireless Networks: A Survey
A repeated game is an effective tool to model interactions and conflicts for
players aiming to achieve their objectives in a long-term basis. Contrary to
static noncooperative games that model an interaction among players in only one
period, in repeated games, interactions of players repeat for multiple periods;
and thus the players become aware of other players' past behaviors and their
future benefits, and will adapt their behavior accordingly. In wireless
networks, conflicts among wireless nodes can lead to selfish behaviors,
resulting in poor network performances and detrimental individual payoffs. In
this paper, we survey the applications of repeated games in different wireless
networks. The main goal is to demonstrate the use of repeated games to
encourage wireless nodes to cooperate, thereby improving network performances
and avoiding network disruption due to selfish behaviors. Furthermore, various
problems in wireless networks and variations of repeated game models together
with the corresponding solutions are discussed in this survey. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
Machine Learning Algorithms for Provisioning Cloud/Edge Applications
Mención Internacional en el título de doctorReinforcement Learning (RL), in which an agent is trained to make the most
favourable decisions in the long run, is an established technique in artificial intelligence. Its
popularity has increased in the recent past, largely due to the development of deep neural
networks spawning deep reinforcement learning algorithms such as Deep Q-Learning. The
latter have been used to solve previously insurmountable problems, such as playing the
famed game of “Go” that previous algorithms could not. Many such problems suffer the
curse of dimensionality, in which the sheer number of possible states is so overwhelming
that it is impractical to explore every possible option.
While these recent techniques have been successful, they may not be strictly necessary
or practical for some applications such as cloud provisioning. In these situations, the
action space is not as vast and workload data required to train such systems is not
as widely shared, as it is considered commercialy sensitive by the Application Service
Provider (ASP). Given that provisioning decisions evolve over time in sympathy to
incident workloads, they fit into the sequential decision process problem that legacy RL
was designed to solve. However because of the high correlation of time series data, states
are not independent of each other and the legacy Markov Decision Processes (MDPs)
have to be cleverly adapted to create robust provisioning algorithms.
As the first contribution of this thesis, we exploit the knowledge of both the application
and configuration to create an adaptive provisioning system leveraging stationary Markov
distributions. We then develop algorithms that, with neither application nor configuration
knowledge, solve the underlying Markov Decision Process (MDP) to create provisioning
systems. Our Q-Learning algorithms factor in the correlation between states and the
consequent transitions between them to create provisioning systems that do not only
adapt to workloads, but can also exploit similarities between them, thereby reducing
the retraining overhead. Our algorithms also exhibit convergence in fewer learning steps
given that we restructure the state and action spaces to avoid the curse of dimensionality
without the need for the function approximation approach taken by deep Q-Learning
systems.
A crucial use-case of future networks will be the support of low-latency applications
involving highly mobile users. With these in mind, the European Telecommunications Standards Institute (ETSI) has proposed the Multi-access Edge Computing (MEC)
architecture, in which computing capabilities can be located close to the network edge,
where the data is generated. Provisioning for such applications therefore entails migrating
them to the most suitable location on the network edge as the users move. In this thesis,
we also tackle this type of provisioning by considering vehicle platooning or Cooperative
Adaptive Cruise Control (CACC) on the edge. We show that our Q-Learning algorithm
can be adapted to minimize the number of migrations required to effectively run such
an application on MEC hosts, which may also be subject to traffic from other competing
applications.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Antonio Fernández Anta.- Secretario: Diego Perino.- Vocal: Ilenia Tinnirell
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