2,241 research outputs found
Small Cell Offloading Through Cooperative Communication in Software-Defined Heterogeneous Networks
To meet the ever-growing demand for a higher communicating rate and better
communication quality, more and more small cells are overlaid under the macro
base station (MBS) tier, thus forming the heterogeneous networks. Small cells
can ease the load pressure of MBS but lack of the guarantee of performance. On
the other hand, cooperation draws more and more attention because of the great
potential of small cell densification. Some technologies matured in wired
network can also be applied to cellular networks, such as Software-defined
networking (SDN). SDN helps simplify the structure of multi-tier networks. And
it's more reasonable for the SDN controller to implement cell coordination. In
this paper, we propose a method to offload users from MBSs through small cell
cooperation in heterogeneous networks. Association probability is the main
indicator of offloading. By using the tools from stochastic geometry, we then
obtain the coverage probabilities when users are associated with different
types of base stations (BSs). All the cell association and cooperation are
conducted by the SDN controller. Then on this basis, we compare the overall
coverage probabilities, achievable rate and energy efficiency with and without
cooperation. Numerical results show that small cell cooperation can offload
more users from MBS tier. It can also increase the system's coverage
performance. As small cells become denser, cooperation can bring more gains to
the energy efficiency of the network.Comment: 12 pages, 7 figure
Energy Efficiency Analysis of Heterogeneous Cellular Networks With Extra Cell Range Expansion
The split control and user plane is key to the future heterogeneous cellular
network (HCN), where the small cells are dedicated for the most data
transmission while the macrocells are mainly responsible for the control
signaling. Adapting to this technology, we propose a general and tractable
framework of extra cell range expansion (CRE) by introducing an additional bias
factor to enlarge the range of small cells flexibly for the extra offloaded
macrousers in a two-tier HCN, where the macrocell and small cell users have
different required data rates. Using stochastic geometry, we analyze the energy
efficiency (EE) of the extra CRE with joint low power transmission and resource
partitioning, where the coverages of EE and data rate are formulated
theoretically. Numerical simulations verify that the proposed extra CRE can
improve the EE performance of HCN, and also show that deploying more small
cells can provide benefits for EE coverage, but the EE improvement becomes
saturated if the small cell density exceeds a threshold. Instead of
establishing the detail configuration, this paper can provide some valuable
insights and guidelines to the practical design of future networks, especially
for the traffic offloading in HCN.Comment: 10 pages, 8 figures, IEEE ACCES
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Joint User Association and Power Control for Load Balancing in Downlink Heterogeneous Cellular Networks
Instead of achievable rate in the conventional association, we utilize the
effective rate to design two association schemes for load balancing in
heterogeneous cellular networks (HCNs), which are both formulated as such
problems with maximizing the sum of effective rates. In these two schemes, the
one just considers user association, but the other introduces power control to
mitigate interference and reduce energy consumption while performing user
association. Since the effective rate is closely related to the load of some BS
and the achievable rate of some user, it can be used as a key factor of
association schemes for load balancing in HCNs. To solve the association
problem without power control, we design a one-layer iterative algorithm, which
converts the sum-of-ratio form of original optimization problem into a
parameterized polynomial form. By combining this algorithm with power control
algorithm, we propose a two-layer iterative algorithm for the association
problem with power control. Specially, the outer layer performs user
association using the algorithm of problem without power control, and the inner
layer updates the transmit power of each BS using a power update function
(PUF). At last, we give some convergence and complexity analyses for the
proposed algorithms. As shown in simulation results, the proposed schemes have
superior performance than the conventional association, and the scheme with
joint user association and power control achieves a higher load balancing gain
and energy efficiency than conventional scheme and other offloading scheme.Comment: 10 page
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
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
TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in Hetnet
In heterogeneous cellular network, task scheduling for computation offloading
is one of the biggest challenges. Most works focus on alleviating heavy burden
of macro base stations by moving the computation tasks on macro-cell user
equipment (MUE) to remote cloud or small-cell base stations. But the
selfishness of network users is seldom considered. Motivated by the cloud edge
computing, this paper provides incentive for task transfer from macro cell
users to small cell base stations. The proposed incentive scheme utilizes small
cell user equipment to provide relay service. The problem of computation
offloading is modelled as a two-stage auction, in which the remote MUEs with
common social character can form a group and then buy the computation resource
of small-cell base stations with the relay of small cell user equipment. A
two-stage auction scheme named TARCO is contributed to maximize utilities for
both sellers and buyers in the network. The truthful, individual rationality
and budget balance of the TARCO are also proved in this paper. In addition, two
algorithms are proposed to further refine TARCO on the social welfare of the
network. Extensive simulation results demonstrate that, TARCO is better than
random algorithm by about 104.90% in terms of average utility of MUEs, while
the performance of TARCO is further improved up to 28.75% and 17.06% by the
proposed two algorithms, respectively.Comment: 22 pages, 9 figures, Working paper, SUBMITTED to IEEE TRANSACTIONS ON
SERVICES COMPUTIN
MEC-aware Cell Association for 5G Heterogeneous Networks
The need for efficient use of network resources is continuously increasing
with the grow of traffic demand, however, current mobile systems have been
planned and deployed so far with the mere aim of enhancing radio coverage and
capacity. Unfortunately, this approach is not sustainable anymore, as 5G
communication systems will have to cope with huge amounts of traffic,
heterogeneous in terms of latency among other Qualityof- Service (QoS)
requirements. Moreover, the advent of Multiaccess Edge Computing (MEC) brings
up the need to more efficiently plan and dimension network deployment by means
of jointly exploiting the available radio and processing resources. From this
standpoint, advanced cell association of users can play a key role for 5G
systems. Focusing on a Heterogeneous Network (HetNet), this paper proposes a
comparison between state-of-the-art (i.e., radio-only) and MEC-aware cell
association rules, taking the scenario of task offloading in the Uplink (UL) as
an example. Numerical evaluations show that the proposed cell association rule
provides nearly 60% latency reduction, as compared to its standard,
radio-exclusive counterpart.Comment: 2018 IEEE Wireless Communications and Networking Conference Workshops
(WCNCW): The First Workshop on Control and management of Vertical slicing
including the Edge and Fog Systems (COMPASS
User Association for Offloading in Heterogeneous Network Based on Matern Cluster Process
Future mobile networks are converging toward heterogeneous multi-tier
networks, where various classes of base stations (BS) are deployed based on
user demand. So it is quite necessary to utilize the BSs resources rationally
when BSs are sufficient. In this paper, we develop a more realistic model that
fully considering the inter-tier dependence and the dependence between users
and BSs, where the macro base stations (MBSs) are distributed according to a
homogeneous Poisson point process (PPP) and the small base stations (SBSs)
follows a Matern cluster process (MCP) whose parent points are located in the
positions of the MBSs in order to offload the users from the over-loaded MBSs.
We also assume the users are just randomly located in the circles centered at
the MBSs. Under this model, we derive the association probability and the
average ergodic rate by stochastic geometry. An interesting result that the
density of MBS and the radius of the clusters jointly affect the association
probabilities in a joint form is obtained. We also observe that using the
clustered SBSs results in aggressive offloading compared with previous cellular
networks
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure
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