32,869 research outputs found
Spectrum Sharing in RF-Powered Cognitive Radio Networks using Game Theory
We investigate the spectrum sharing problem of a radio frequency (RF)-powered
cognitive radio network, where a multi-antenna secondary user (SU) harvests
energy from RF signals radiated by a primary user (PU) to boost its available
energy before information transmission. In this paper, we consider that both
the PU and SU are rational and self-interested. Based on whether the SU helps
forward the PU's information, we develop two different operation modes for the
considered network, termed as non-cooperative and cooperative modes. In the
non-cooperative mode, the SU harvests energy from the PU and then use its
available energy to transmit its own information without generating any
interference to the primary link. In the cooperative mode, the PU employs the
SU to relay its information by providing monetary incentives and the SU splits
its energy for forwarding the PU's information as well as transmitting its own
information. Optimization problems are respectively formulated for both
operation modes, which constitute a Stackelberg game with the PU as a leader
and the SU as a follower. We analyze the Stackelberg game by deriving solutions
to the optimization problems and the Stackelberg Equilibrium (SE) is
subsequently obtained. Simulation results show that the performance of the
Stackelberg game can approach that of the centralized optimization scheme when
the distance between the SU and its receiver is large enough.Comment: Presented at PIMRC'1
NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks
With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)
Socially Trusted Collaborative Edge Computing in Ultra Dense Networks
Small cell base stations (SBSs) endowed with cloud-like computing
capabilities are considered as a key enabler of edge computing (EC), which
provides ultra-low latency and location-awareness for a variety of emerging
mobile applications and the Internet of Things. However, due to the limited
computation resources of an individual SBS, providing computation services of
high quality to its users faces significant challenges when it is overloaded
with an excessive amount of computation workload. In this paper, we propose
collaborative edge computing among SBSs by forming SBS coalitions to share
computation resources with each other, thereby accommodating more computation
workload in the edge system and reducing reliance on the remote cloud. A novel
SBS coalition formation algorithm is developed based on the coalitional game
theory to cope with various new challenges in small-cell-based edge systems,
including the co-provisioning of radio access and computing services,
cooperation incentives, and potential security risks. To address these
challenges, the proposed method (1) allows collaboration at both the user-SBS
association stage and the SBS peer offloading stage by exploiting the ultra
dense deployment of SBSs, (2) develops a payment-based incentive mechanism that
implements proportionally fair utility division to form stable SBS coalitions,
and (3) builds a social trust network for managing security risks among SBSs
due to collaboration. Systematic simulations in practical scenarios are carried
out to evaluate the efficacy and performance of the proposed method, which
shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Optimal Resource Allocation and Relay Selection in Bandwidth Exchange Based Cooperative Forwarding
In this paper, we investigate joint optimal relay selection and resource
allocation under bandwidth exchange (BE) enabled incentivized cooperative
forwarding in wireless networks. We consider an autonomous network where N
nodes transmit data in the uplink to an access point (AP) / base station (BS).
We consider the scenario where each node gets an initial amount (equal, optimal
based on direct path or arbitrary) of bandwidth, and uses this bandwidth as a
flexible incentive for two hop relaying. We focus on alpha-fair network utility
maximization (NUM) and outage reduction in this environment. Our contribution
is two-fold. First, we propose an incentivized forwarding based resource
allocation algorithm which maximizes the global utility while preserving the
initial utility of each cooperative node. Second, defining the link weight of
each relay pair as the utility gain due to cooperation (over noncooperation),
we show that the optimal relay selection in alpha-fair NUM reduces to the
maximum weighted matching (MWM) problem in a non-bipartite graph. Numerical
results show that the proposed algorithms provide 20- 25% gain in spectral
efficiency and 90-98% reduction in outage probability.Comment: 8 pages, 7 figure
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