369 research outputs found
Smart PIN: utility-based replication and delivery of multimedia content to mobile users in wireless networks
Next generation wireless networks rely on heterogeneous connectivity technologies to support various rich media services such as personal information storage, file sharing and multimedia streaming. Due to usersâ mobility and dynamic characteristics of wireless networks, data availability in collaborating devices is a critical issue. In this context Smart PIN was proposed as a personal information network which focuses on performance of delivery and cost efficiency. Smart PIN uses a novel data replication scheme based on individual and overall system utility to best balance the requirements for static data and multimedia content delivery with variable device availability due to user mobility. Simulations show improved results in comparison with other general purpose data replication schemes in terms of data availability
Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio
Multimedia content especially videos is expected to dominate data traffic in
next-generation mobile networks. Caching popular content at the network edge
has emerged to be a solution for low-latency content delivery. Compared with
the traditional wireless communication, content delivery has a key
characteristic that many signals coexisting in the air carry identical popular
content. They, however, can interfere with each other at a receiver if their
modulation-and-coding (MAC) schemes are adapted to individual channels
following the classic approach. To address this issue, we present a novel idea
of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures
that all signals carry identical content are encoded using an identical MAC
scheme, achieving spatial MAC alignment. Consequently, interference can be
harnessed as signals, to improve the reliability of wireless delivery. In the
remaining part of the paper, we focus on quantifying the gain CAMAC can bring
to a content-delivery network using a stochastic-geometry model. Specifically,
content helpers are distributed as a Poisson point process, each of which
transmits a file from a content database based on a given popularity
distribution. It is discovered that the successful content-delivery probability
is closely related to the distribution of the ratio of two independent shot
noise processes, named a shot-noise ratio. The distribution itself is an open
mathematical problem that we tackle in this work. Using stable-distribution
theory and tools from stochastic geometry, the distribution function is derived
in closed form. Extending the result in the context of content-delivery
networks with CAMAC yields the content-delivery probability in different closed
forms. In addition, the gain in the probability due to CAMAC is shown to grow
with the level of skewness in the content popularity distribution.Comment: 32 pages, to appear in IEEE Trans. on Wireless Communicatio
Mean-Field Games for Distributed Caching in Ultra-Dense Small Cell Networks
In this paper, the problem of distributed caching in dense wireless small
cell networks (SCNs) is studied using mean field games (MFGs). In the
considered SCN, small base stations (SBSs) are equipped with data storage units
and cooperate to serve users' requests either from files cached in the storage
or directly from the capacity-limited backhaul. The aim of the SBSs is to
define a caching policy that reduces the load on the capacity-limited backhaul
links. This cache control problem is formulated as a stochastic differential
game (SDG). In this game, each SBS takes into consideration the storage state
of the other SBSs to decide on the fraction of content it should cache. To
solve this problem, the formulated SDG is reduced to an MFG by considering an
ultra-dense network of SBSs in which the existence and uniqueness of the
mean-field equilibrium is shown to be guaranteed. Simulation results show that
this framework allows an efficient use of the available storage space at the
SBSs while properly tracking the files' popularity. The results also show that,
compared to a baseline model in which SBSs are not aware of the instantaneous
system state, the proposed framework increases the number of served files from
the SBSs by more than 69%.Comment: Accepted for publication at American Control Conference 201
A survey of online data-driven proactive 5G network optimisation using machine learning
In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capitaland operational expenditure. Proactive network optimisation is widely acknowledged as on e of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area
Reinforcement learning for proactive content caching in wireless networks
Proactive content caching (PC) at the edge of wireless networks, that is, at the base stations (BSs) and/or user equipments (UEs), is a promising strategy to successfully handle the ever-growing mobile data traffic and to improve the quality-of-service for content delivery over wireless networks. However, factors such as limitations in storage capacity, time-variations in wireless channel conditions as well as in content demand profile pose challenges that need to be addressed in order to realise the benefits of PC
at the wireless edge.
This thesis aims to develop PC solutions that address these challenges. We consider PC directly at UEs equipped with finite capacity cache memories. This consideration is done within the framework of a dynamic system, where mobile users randomly request contents from a non-stationary content library; new contents are added to the library over time and each content may remain in the library for a random lifetime
within which it may be requested. Contents are delivered through wireless channels with time-varying quality, and any time contents are transmitted, a transmission cost associated with the number of bits downloaded and the channel quality of the receiving user(s) at that time is incurred by the system. We formulate each considered problem as a Markov decision process with the objective of minimising the long
term expected average cost on the system. We then use reinforcement learning (RL) to solve this highly challenging problem with a prohibitively large state and action spaces. In particular, we employ policy approximation techniques for compact representation of complex policy structures, and policy gradient RL methods to train the system. In a single-user problem setting that we consider, we show the optimality of a
threshold-based PC scheme that is adaptive to system dynamics. We use this result to characterise and design a multicast-aware PC scheme, based on deep RL framework, when we consider a multi-user problem setting. We perform extensive numerical simulations of the schemes we propose. Our results show not only significant improvements against the state-of-the-art reactive content delivery approaches, but also near-optimality of the proposed RL solutions based on comparisons with some lower bounds.Open Acces
From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey
Context data is in demand more than ever with the rapid increase in the
development of many context-aware Internet of Things applications. Research in
context and context-awareness is being conducted to broaden its applicability
in light of many practical and technical challenges. One of the challenges is
improving performance when responding to large number of context queries.
Context Management Platforms that infer and deliver context to applications
measure this problem using Quality of Service (QoS) parameters. Although
caching is a proven way to improve QoS, transiency of context and features such
as variability, heterogeneity of context queries pose an additional real-time
cost management problem. This paper presents a critical survey of
state-of-the-art in adaptive data caching with the objective of developing a
body of knowledge in cost- and performance-efficient adaptive caching
strategies. We comprehensively survey a large number of research publications
and evaluate, compare, and contrast different techniques, policies, approaches,
and schemes in adaptive caching. Our critical analysis is motivated by the
focus on adaptively caching context as a core research problem. A formal
definition for adaptive context caching is then proposed, followed by
identified features and requirements of a well-designed, objective optimal
adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys
Journal at this time of publishing in arxiv.or
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
Self-Evolving Integrated Vertical Heterogeneous Networks
6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table
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