6,554 research outputs found
Energy-Efficient Resource Allocation in Wireless Networks: An Overview of Game-Theoretic Approaches
An overview of game-theoretic approaches to energy-efficient resource
allocation in wireless networks is presented. Focusing on multiple-access
networks, it is demonstrated that game theory can be used as an effective tool
to study resource allocation in wireless networks with quality-of-service (QoS)
constraints. A family of non-cooperative (distributed) games is presented in
which each user seeks to choose a strategy that maximizes its own utility while
satisfying its QoS requirements. The utility function considered here measures
the number of reliable bits that are transmitted per joule of energy consumed
and, hence, is particulary suitable for energy-constrained networks. The
actions available to each user in trying to maximize its own utility are at
least the choice of the transmit power and, depending on the situation, the
user may also be able to choose its transmission rate, modulation, packet size,
multiuser receiver, multi-antenna processing algorithm, or carrier allocation
strategy. The best-response strategy and Nash equilibrium for each game is
presented. Using this game-theoretic framework, the effects of power control,
rate control, modulation, temporal and spatial signal processing, carrier
allocation strategy and delay QoS constraints on energy efficiency and network
capacity are quantified.Comment: To appear in the IEEE Signal Processing Magazine: Special Issue on
Resource-Constrained Signal Processing, Communications and Networking, May
200
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
Energy-Efficient Resource Allocation in Wireless Networks with Quality-of-Service Constraints
A game-theoretic model is proposed to study the cross-layer problem of joint
power and rate control with quality of service (QoS) constraints in
multiple-access networks. In the proposed game, each user seeks to choose its
transmit power and rate in a distributed manner in order to maximize its own
utility while satisfying its QoS requirements. The user's QoS constraints are
specified in terms of the average source rate and an upper bound on the average
delay where the delay includes both transmission and queuing delays. The
utility function considered here measures energy efficiency and is particularly
suitable for wireless networks with energy constraints. The Nash equilibrium
solution for the proposed non-cooperative game is derived and a closed-form
expression for the utility achieved at equilibrium is obtained. It is shown
that the QoS requirements of a user translate into a "size" for the user which
is an indication of the amount of network resources consumed by the user. Using
this competitive multiuser framework, the tradeoffs among throughput, delay,
network capacity and energy efficiency are studied. In addition, analytical
expressions are given for users' delay profiles and the delay performance of
the users at Nash equilibrium is quantified.Comment: Accpeted for publication in the IEEE Transactions on Communication
Fog Computing: A Taxonomy, Survey and Future Directions
In recent years, the number of Internet of Things (IoT) devices/sensors has
increased to a great extent. To support the computational demand of real-time
latency-sensitive applications of largely geo-distributed IoT devices/sensors,
a new computing paradigm named "Fog computing" has been introduced. Generally,
Fog computing resides closer to the IoT devices/sensors and extends the
Cloud-based computing, storage and networking facilities. In this chapter, we
comprehensively analyse the challenges in Fogs acting as an intermediate layer
between IoT devices/ sensors and Cloud datacentres and review the current
developments in this field. We present a taxonomy of Fog computing according to
the identified challenges and its key features.We also map the existing works
to the taxonomy in order to identify current research gaps in the area of Fog
computing. Moreover, based on the observations, we propose future directions
for research
Congestion control in multi-serviced heterogeneous wireless networks using dynamic pricing
Includes bibliographical references.Service providers, (or operators) employ pricing schemes to help provide desired QoS to subscribers and to maintain profitability among competitors. An economically efficient pricing scheme, which will seamlessly integrate users’ preferences as well as service providers’ preferences, is therefore needed. Else, pricing schemes can be viewed as promoting social unfairness in the dynamically priced network. However, earlier investigations have shown that the existing dynamic pricing schemes do not consider the users’ willingness to pay (WTP) before the price of services is determined. WTP is the amount a user is willing to pay based on the worth attached to the service requested. There are different WTP levels for different subscribers due to the differences in the value attached to the services requested and demographics. This research has addressed congestion control in the heterogeneous wireless network (HWN) by developing a dynamic pricing scheme that efficiently incentivises users to utilize radio resources. The proposed Collaborative Dynamic Pricing Scheme (CDPS), which identifies the users and operators’ preference in determining the price of services, uses an intelligent approach for controlling congestion and enhancing both the users’ and operators’ utility. Thus, the CDPS addresses the congestion problem by firstly obtaining the users WTP from users’ historical response to price changes and incorporating the WTP factor to evaluate the service price. Secondly, it uses a reinforcement learning technique to illustrate how a price policy can be obtained for the enhancement of both users and operators’ utility, as total utility reward obtained increases towards a defined ‘goal state’
Energy-Efficient Power Control in Impulse Radio UWB Wireless Networks
In this paper, a game-theoretic model for studying power control for wireless
data networks in frequency-selective multipath environments is analyzed. The
uplink of an impulse-radio ultrawideband system is considered. The effects of
self-interference and multiple-access interference on the performance of
generic Rake receivers are investigated for synchronous systems. Focusing on
energy efficiency, a noncooperative game is proposed in which users in the
network are allowed to choose their transmit powers to maximize their own
utilities, and the Nash equilibrium for the proposed game is derived. It is
shown that, due to the frequency selective multipath, the noncooperative
solution is achieved at different signal-to-interference-plus-noise ratios,
depending on the channel realization and the type of Rake receiver employed. A
large-system analysis is performed to derive explicit expressions for the
achieved utilities. The Pareto-optimal (cooperative) solution is also discussed
and compared with the noncooperative approach.Comment: Submitted to the IEEE Journal on Selected Topics in Signal Processing
- Special issue on Performance Limits of Ultra-Wideband System
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