179 research outputs found
Learning for Matching Game in Cooperative D2D Communication with Incomplete Information
This paper considers a cooperative device-to-device (D2D) communication
system, where the D2D transmitters (DTs) act as relays to assist cellular users
(CUs) in exchange for the opportunities to use licensed spectrum. Based on the
interaction of each D2D pair and each CU, we formulate the pairing problem
between multiple cues and multiple D2D pairs as a one-to-one matching game.
Unlike most existing works, we consider a realistic scenario with incomplete
channel information. Thus, each CU lacks enough information to establish its
preference over D2D pairs. Therefore, traditional matching algorithms are not
suitable for our scenario. To this end, we convert the matching game to an
equivalent non-cooperative game, and then propose a novel learning algorithm,
which converges to a stable matching.Comment: Accepted by IEEE TVT as correspondenc
Matching Based Two-Timescale Resource Allocation for Cooperative D2D Communication
We consider a cooperative device-to-device (D2D) communication system, where
the D2D transmitters (DTs) act as relays to assist cellular users (CUs) in
exchange for the opportunities to use the licensed spectrum. To reduce the
overhead, we propose a novel two-timescale resource allocation scheme, in which
the pairing between CUs and D2D pairs is decided at a long timescale and time
allocation factor for CU and D2D pair is determined at a short timescale.
Specifically, to characterize the long-term payoff of each potential CU-D2D
pair, we investigate the optimal cooperation policy to decide the time
allocation factor based on the instantaneous channel state information (CSI).
We prove that the optimal policy is a threshold policy. Since CUs and D2D pairs
are self-interested, they are paired only when they agree to cooperate
mutually. Therefore, to study the behaviors of CUs and D2D pairs, we formulate
the pairing problem as a matching game, based on the long-term payoff of each
possible pairing. Furthermore, unlike most previous matching model in D2D
networks, we allow transfer between CUs and D2D pairs to improve the
performance. Besides, we propose an algorithm, which converges to an
epsilon-stable matching.Comment: Accepted by WCSP 201
Relay Assisted Device-to-Device Communication: Approaches and Issues
Enabling technologies for 5G and future wireless communication have attracted
the interest of industry and research communities. One of such technologies is
Device-to-Device (D2D) communication which exploits user proximity to offer
spectral efficiency, energy efficiency and increased throughput. Data
offloading, public safety communication, context aware communication and
content sharing are some of the use cases for D2D communication. D2D
communication can be direct or through a relay depending on the nature of the
channel in between the D2D devices. Apart from the problem of interference, a
key challenge of relay aided D2D communication is appropriately assigning
relays to a D2D pair while maintaining the QoS requirement of the cellular
users. In this article, relay assisted D2D communication is reviewed and
research issues are highlighted. We also propose matching theory with
incomplete information for relay allocation considering uncertainties which the
mobility of the relay introduces to the set up
Hybrid Centralized-Distributed Resource Allocation for Device-to-Device Communication Underlaying Cellular Networks
The basic idea of device-to-device (D2D) communication is that pairs of
suitably selected wireless devices reuse the cellular spectrum to establish
direct communication links, provided that the adverse effects of D2D
communication on cellular users is minimized and cellular users are given a
higher priority in using limited wireless resources. Despite its great
potential in terms of coverage and capacity performance, implementing this new
concept poses some challenges, in particular with respect to radio resource
management. The main challenges arise from a strong need for distributed D2D
solutions that operate in the absence of precise channel and network knowledge.
In order to address this challenge, this paper studies a resource allocation
problem in a single-cell wireless network with multiple D2D users sharing the
available radio frequency channels with cellular users. We consider a realistic
scenario where the base station (BS) is provided with strictly limited channel
knowledge while D2D and cellular users have no information. We prove a
lower-bound for the cellular aggregate utility in the downlink with fixed BS
power, which allows for decoupling the channel allocation and D2D power control
problems. An efficient graph-theoretical approach is proposed to perform the
channel allocation, which offers flexibility with respect to allocation
criterion (aggregate utility maximization, fairness, quality of service
guarantee). We model the power control problem as a multi-agent learning game.
We show that the game is an exact potential game with noisy rewards, defined on
a discrete strategy set, and characterize the set of Nash equilibria.
Q-learning better-reply dynamics is then used to achieve equilibrium.Comment: 35 page
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
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
As a promising paradigm to reduce both capital and operating expenditures,
the cloud radio access network (C-RAN) has been shown to provide high spectral
efficiency and energy efficiency. Motivated by its significant theoretical
performance gains and potential advantages, C-RANs have been advocated by both
the industry and research community. This paper comprehensively surveys the
recent advances of C-RANs, including system architectures, key techniques, and
open issues. The system architectures with different functional splits and the
corresponding characteristics are comprehensively summarized and discussed. The
state-of-the-art key techniques in C-RANs are classified as: the fronthaul
compression, large-scale collaborative processing, and channel estimation in
the physical layer; and the radio resource allocation and optimization in the
upper layer. Additionally, given the extensiveness of the research area, open
issues and challenges are presented to spur future investigations, in which the
involvement of edge cache, big data mining, social-aware device-to-device,
cognitive radio, software defined network, and physical layer security for
C-RANs are discussed, and the progress of testbed development and trial test
are introduced as well.Comment: 27 pages, 11 figure
Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey
This paper presents a comprehensive literature review on applications of
economic and pricing theory for resource management in the evolving fifth
generation (5G) wireless networks. The 5G wireless networks are envisioned to
overcome existing limitations of cellular networks in terms of data rate,
capacity, latency, energy efficiency, spectrum efficiency, coverage,
reliability, and cost per information transfer. To achieve the goals, the 5G
systems will adopt emerging technologies such as massive Multiple-Input
Multiple-Output (MIMO), mmWave communications, and dense Heterogeneous Networks
(HetNets). However, 5G involves multiple entities and stakeholders that may
have different objectives, e.g., high data rate, low latency, utility
maximization, and revenue/profit maximization. This poses a number of
challenges to resource management designs of 5G. While the traditional
solutions may neither efficient nor applicable, economic and pricing models
have been recently developed and adopted as useful tools to achieve the
objectives. In this paper, we review economic and pricing approaches proposed
to address resource management issues in the 5G wireless networks including
user association, spectrum allocation, and interference and power management.
Furthermore, we present applications of economic and pricing models for
wireless caching and mobile data offloading. Finally, we highlight important
challenges, open issues and future research directions of applying economic and
pricing models to the 5G wireless networks
Amazon in the White Space: Social Recommendation Aided Distributed Spectrum Access
Distributed spectrum access (DSA) is challenging since an individual
secondary user often has limited sensing capabilities only. One key insight is
that channel recommendation among secondary users can help to take advantage of
the inherent correlation structure of spectrum availability in both time and
space, and enable users to obtain more informed spectrum opportunities. With
this insight, we advocate to leverage the wisdom of crowds, and devise social
recommendation aided DSA mechanisms to orient secondary users to make more
intelligent spectrum access decisions, for both strong and weak network
information cases. We start with the strong network information case where
secondary users have the statistical information. To mitigate the difficulty
due to the curse of dimensionality in the stochastic game approach, we take the
one-step Nash approach and cast the social recommendation aided DSA decision
making problem at each time slot as a strategic game. We show that it is a
potential game, and then devise an algorithm to achieve the Nash equilibrium by
exploiting its finite improvement property. For the weak information case where
secondary users do not have the statistical information, we develop a
distributed reinforcement learning mechanism for social recommendation aided
DSA based on the local observations of secondary users only. Appealing to the
maximum-norm contraction mapping, we also derive the conditions under which the
distributed mechanism converges and characterize the equilibrium therein.
Numerical results reveal that the proposed social recommendation aided DSA
mechanisms can achieve superior performance using real social data traces and
its performance loss in the weak network information case is insignificant,
compared with the strong network information case.Comment: Xu Chen, Xiaowen Gong, Lei Yang, and Junshan Zhang, "Amazon in the
White Space: Social Recommendation Aided Distributed Spectrum Access,"
IEEE/ACM Transactions Networking, 201
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
Distributed Cooperation Under Uncertainty in Drone-Based Wireless Networks: A Bayesian Coalitional Game
We study the resource sharing problem in a drone-based wireless network. We
consider a distributed control setting under uncertainty (i.e. unavailability
of full information). In particular, the drones cooperate in serving the users
while pooling their spectrum and energy resources in the absence of prior
knowledge about different system characteristics such as the amount of
available power at the other drones. We cast the aforementioned problem as a
Bayesian cooperative game in which the agents (drones) engage in a coalition
formation process, where the goal is to maximize the overall transmission rate
of the network. The drones update their beliefs using a novel technique that
combines the maximum likelihood estimation with Kullback-Leibler divergence. We
propose a decision-making strategy for repeated coalition formation that
converges to a stable coalition structure. We analyze the performance of the
proposed approach by both theoretical analysis and simulations
- …