2,316 research outputs found
Joint Energy and Spectrum Cooperation for Cellular Communication Systems
Powered by renewable energy sources, cellular communication systems usually
have different wireless traffic loads and available resources over time. To
match their traffics, it is beneficial for two neighboring systems to cooperate
in resource sharing when one is excessive in one resource (e.g., spectrum),
while the other is sufficient in another (e.g., energy). In this paper, we
propose a joint energy and spectrum cooperation scheme between different
cellular systems to reduce their operational costs. When the two systems are
fully cooperative in nature (e.g., belonging to the same entity), we formulate
the cooperation problem as a convex optimization problem to minimize their
weighted sum cost and obtain the optimal solution in closed form. We also study
another partially cooperative scenario where the two systems have their own
interests. We show that the two systems seek for partial cooperation as long as
they find inter-system complementarity between the energy and spectrum
resources. Under the partial cooperation conditions, we propose a distributed
algorithm for the two systems to gradually and simultaneously reduce their
costs from the non-cooperative benchmark to the Pareto optimum. This
distributed algorithm also has proportional fair cost reduction by reducing
each system's cost proportionally over iterations. Finally, we provide
numerical results to validate the convergence of the distributed algorithm to
the Pareto optimality and compare the centralized and distributed cost
reduction approaches for fully and partially cooperative scenarios.Comment: This is the longer version of a paper to appear in IEEE Transactions
on Communication
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
Non-Cash Auction for Spectrum Trading in Cognitive Radio Networks: A Contract Theoretical Model with Joint Adverse Selection and Moral Hazard
In cognitive radio networks (CRNs), spectrum trading is an efficient way for
secondary users (SUs) to achieve dynamic spectrum access and to bring economic
benefits for the primary users (PUs). Existing methods requires full payment
from SU, which blocked many potential "buyers", and thus limited the PU's
expected income. To better improve PUs' revenue from spectrum trading in a CRN,
we introduce a financing contract, which is similar to a sealed non-cash
auction that allows SU to do a financing. Unlike previous mechanism designs in
CRN, the financing contract allows the SU to only pay part of the total amount
when the contract is signed, known as the down payment. Then, after the
spectrum is released and utilized, the SU pays the rest of payment, known as
the installment payment, from the revenue generated by utilizing the spectrum.
The way the financing contract carries out and the sealed non-cash auction
works similarly. Thus, contract theory is employed here as the mathematical
framework to solve the non-cash auction problem and form mutually beneficial
relationships between PUs and SUs. As the PU may not have the full
acknowledgement of the SU's financial status, nor the SU's capability in making
revenue, the problems of adverse selection and moral hazard arise in the two
scenarios, respectively. Therefore, a joint adverse selection and moral hazard
model is considered here. In particular, we present three situations when
either or both adverse selection and moral hazard are present during the
trading. Furthermore, both discrete and continuous models are provided in this
paper. Through extensive simulations, we show that the adverse selection and
moral hazard cases serve as the upper and lower bounds of the general case
where both problems are present
Relay Selection for OFDM Wireless Systems under Asymmetric Information: A Contract-Theory Based Approach
User cooperation although improves performance of wireless systems, it
requires incentives for the potential cooperating nodes to spend their energy
acting as relays. Moreover, these potential relays are better informed than the
source about their transmission costs, which depend on the exact channel
conditions on their relay-destination links. This results in asymmetry of
available information between the source and the relays. In this paper, we use
contract theory to tackle the problem of relay selection under asymmetric
information in OFDM-based cooperative wireless system that employs
decode-and-forward (DF) relaying. We first design incentive compatible
offers/contracts, consisting of a menu of payments and desired
signal-to-noise-ratios (SNR)s at the destination and then the source broadcasts
this menu to nearby mobile nodes. The nearby mobile nodes who are willing to
relay notify back the source with the contracts they are willing to accept in
each subcarrier. We show that when the source is under a budget constraint, the
problem of relay selection in each subcarrier in order to maximize the capacity
is a nonlinear non-separable knapsack problem. We propose a heuristic relay
selection scheme to solve this problem. We compare the performance of our
overall mechanism and the heuristic solution with a simple relay selection
scheme and selected numerical results showed that our solution performs better
and is close to optimal. The overall mechanism introduced in this paper is
simple to implement, requires limited interaction with potential relays and
hence requires minimal signalling overhead.Comment: 30 Pages, 8 figures, 3 tables, journa
Analysis of Cognitive Radio Scenes Based on Non-cooperative Game Theoretical Modelling
A noncooperative game theoretical approach for analysing opportunistic
spectrum access (OSA) in cognitive radio (CR) environments is proposed. New
concepts from game theory are applied to spectrum access analysis in order to
extract rules of behaviour for an emerging environment. In order to assess OSA
scenarios of CRs, two oligopoly game models are reformulated in terms of
resource access: Cournot and Stackelberg games. Five CR scenes are analysed:
simultaneous access of unlicensed users (commons regime) with symmetric and
asymmetric costs, with and without bandwidth constraints and sequential access
(licensed against unlicensed). Several equilibrium concepts are studied as game
solutions: Nash, Pareto and the joint NashPareto equilibrium. The latter
captures a game situation where players are non-homogeneous users, exhibiting
different types of rationality, Nash and Pareto. This enables a more realistic
modelling of interactions on a CR scene. An evolutionary game equilibrium
detection method is used. The Nash equilibrium indicates the maximum number of
channels a CR may access without decreasing its payoff. The Pareto equilibrium
describes a larger range of payoffs, capturing unbalanced as well as equitable
solutions. The analysis of the Stackelberg modelling shows that payoffs are
maximised for all users if the incumbents are Nash oriented and the new
entrants are Pareto driven.Comment: 8 double-column pages, 10 figures. arXiv admin note: text overlap
with arXiv:1209.538
Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network
Future wireless networks will progressively displace service provisioning
towards the edge to accommodate increasing growth in traffic. This paradigm
shift calls for smart policies to efficiently share network resources and
ensure service delivery. In this paper, we consider a cognitive dynamic network
architecture (CDNA) where primary users (PUs) are rewarded for sharing their
connectivities and acting as access points for secondary users (SUs). CDNA
creates opportunities for capacity increase by network-wide harvesting of
unused data plans and spectrum from different operators. Different policies for
data and spectrum trading are presented based on centralized, hybrid and
distributed schemes involving primary operator (PO), secondary operator (SO)
and their respective end users. In these schemes, PO and SO progressively
delegate trading to their end users and adopt more flexible cooperation
agreements to reduce computational time and track available resources
dynamically. A novel matching-with-pricing algorithm is presented to enable
self-organized SU-PU associations, channel allocation and pricing for data and
spectrum with low computational complexity. Since connectivity is provided by
the actual users, the success of the underlying collaborative market relies on
the trustworthiness of the connections. A behavioral-based access control
mechanism is developed to incentivize/penalize honest/dishonest behavior and
create a trusted collaborative network. Numerical results show that the
computational time of the hybrid scheme is one order of magnitude faster than
the benchmark centralized scheme and that the matching algorithm reconfigures
the network up to three orders of magnitude faster than in the centralized
scheme.Comment: 15 pages, 12 figures. A version of this paper has been published in
IEEE/ACM Transactions on Networking, 201
Applications of Game Theory in Vehicular Networks: A Survey
In the Internet of Things (IoT) era, vehicles and other intelligent
components in an intelligent transportation system (ITS) are connected, forming
Vehicular Networks (VNs) that provide efficient and secure traffic and
ubiquitous access to various applications. However, as the number of nodes in
ITS increases, it is challenging to satisfy a varied and large number of
service requests with different Quality of Service and security requirements in
highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for
limited network resources to achieve either an individual or a group's
objectives. Game Theory (GT), a theoretical framework designed for strategic
interactions among rational decision-makers sharing scarce resources, can be
used to model and analyze individual or group behaviors of communicating
entities in VNs. This paper primarily surveys the recent developments of GT in
solving various challenges of VNs. This survey starts with an introduction to
the background of VNs. A review of GT models studied in the VNs is then
introduced, including its basic concepts, classifications, and applicable
vehicular issues. After discussing the requirements of VNs and the motivation
of using GT, a comprehensive literature review on GT applications in dealing
with the challenges of current VNs is provided. Furthermore, recent
contributions of GT to VNs integrating with diverse emerging 5G technologies
are surveyed. Finally, the lessons learned are given, and several key research
challenges and possible solutions for applying GT in VNs are outlined.Comment: It has been submitted to "IEEE communication surveys and
tutorials".This is the revised versio
Game Theoretic Approaches in Vehicular Networks: A Survey
In the era of the Internet of Things (IoT), vehicles and other intelligent
components in Intelligent Transportation System (ITS) are connected, forming
the Vehicular Networks (VNs) that provide efficient and secure traffic,
ubiquitous access to information, and various applications. However, as the
number of connected nodes keeps increasing, it is challenging to satisfy
various and large amounts of service requests with different Quality of Service
(QoS ) and security requirements in the highly dynamic VNs. Intelligent nodes
in VNs can compete or cooperate for limited network resources so that either an
individual or group objectives can be achieved. Game theory, a theoretical
framework designed for strategic interactions among rational decision-makers
who faced with scarce resources, can be used to model and analyze individual or
group behaviors of communication entities in VNs. This paper primarily surveys
the recent advantages of GT used in solving various challenges in VNs. As VNs
and GT have been extensively investigate34d, this survey starts with a brief
introduction of the basic concept and classification of GT used in VNs. Then, a
comprehensive review of applications of GT in VNs is presented, which primarily
covers the aspects of QoS and security. Moreover, with the development of
fifth-generation (5G) wireless communication, recent contributions of GT to
diverse emerging technologies of 5G integrated into VNs are surveyed in this
paper. Finally, several key research challenges and possible solutions for
applying GT in VNs are outlined
DR9.3 Final report of the JRRM and ASM activities
Deliverable del projecte europeu NEWCOM++This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.Postprint (published version
Recommended from our members
Spectrum utilization using game theory
This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.Spectrum utilization is the most recent communications issue which takes great deal of attention from communication researchers where most of the efforts have been dedicated for spectral efficient utilization. Spectrum sharing is one of the solutions considered in the problem of lack of available frequency for new communication services which are unlicensed. In this work we propose an optimal method for spectrum utilization to increase spectral efficiency. It considers the problem of spectrum holes found in Primary User's (PU) band and detected using one of the spectral sensing methods. The solution is formulated with the help of Game theory approach in such a way that the primary user who has unoccupied frequency can share it with a group of secondary users (SU) in a competitive way. One of the SUs will be a secondary primary user (SPU), share available frequency from PU then offer his sharing to serve other SUs in different rate of sharing. Each user in the group of secondary users has a chance to be secondary primary user depending on reputation of each SU. Enhancing reputation is the only way for any SU to assure a share in the spectrum where it considered the factor of increasing or decreasing rate of sharing as well as factor of being SPU or an ordinary SU. A theoretical non-cooperative game model is introduced in a comparison with a proposed non-dynamic technique which depends on number of subscribers who occupy frequency in each time period. Multi-users compete on sharing the frequency from one of the users who offers sharing at a time when he has low number of subscribers that occupy his band. It is found that non-dynamic sharing results in inefficient spectrum utilization which is one of the reasons of spectrum scarcity where this resource is allocated in fixed way. Spectrum sharing using game theory solves this problem by its ability to make users compete to gain highest rate of spectrum allocation according to the real requirement of each user at each time interval. The problem of urgent case is also discussed when the primary user comes back to using his band which is the specific band of sharing with the secondary users group. SPU makes it easy to unload the required band from multi-users because PU does not need to request his band from each SU in the group
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