3,646 research outputs found
Energy-Efficient Resource Assignment and Power Allocation in Heterogeneous Cloud Radio Access Networks
Taking full advantages of both heterogeneous networks (HetNets) and cloud
access radio access networks (CRANs), heterogeneous cloud radio access networks
(H-CRANs) are presented to enhance both the spectral and energy efficiencies,
where remote radio heads (RRHs) are mainly used to provide high data rates for
users with high quality of service (QoS) requirements, while the high power
node (HPN) is deployed to guarantee the seamless coverage and serve users with
low QoS requirements. To mitigate the inter-tier interference and improve EE
performances in H-CRANs, characterizing user association with RRH/HPN is
considered in this paper, and the traditional soft fractional frequency reuse
(S-FFR) is enhanced. Based on the RRH/HPN association constraint and the
enhanced S-FFR, an energy-efficient optimization problem with the resource
assignment and power allocation for the orthogonal frequency division multiple
access (OFDMA) based H-CRANs is formulated as a non-convex objective function.
To deal with the non-convexity, an equivalent convex feasibility problem is
reformulated, and closedform expressions for the energy-efficient resource
allocation solution to jointly allocate the resource block and transmit power
are derived by the Lagrange dual decomposition method. Simulation results
confirm that the H-CRAN architecture and the corresponding resource allocation
solution can enhance the energy efficiency significantly.Comment: 13 pages, 7 figures, accepted by IEEE TV
Interference Management in NOMA-based Fog-Radio Access Networks via Joint Scheduling and Power Adaptation
Non-Orthogonal Multiple Access (NOMA) and Fog Radio Access Networks (FRAN)
are promising candidates within the 5G and beyond systems. This work examines
the benefit of adopting NOMA in an FRAN architecture with constrained capacity
fronthaul. The paper proposes methods for optimizing joint scheduling and power
adaptation in the downlink of a NOMA-based FRAN with multiple resource blocks
(RB). We consider a mixed-integer optimization problem which maximizes a
network-wide rate-based utility function subject to fronthaul-capacity
constraints, so as to determine i) the user-to-RB assignment, ii) the allocated
power to each RB, and iii) the power split levels of the NOMA users in each RB.
The paper proposes a feasible decoupled solution for such non-convex
optimization problem using a three-step hybrid centralized/distributed
approach. The proposed solution complies with FRAN operation that aims to
partially shift the network control to the FAPs, so as to overcome delays due
to fronthaul rate constraints. The paper proposes and compares two distinct
methods for solving the assignment problem, namely the Hungarian method, and
the Multiple Choice Knapsack method. The power allocation and the NOMA power
split optimization, on the other hand, are solved using the alternating
direction method of multipliers (ADMM). Simulations results illustrate the
advantages of the proposed methods compared to different baseline schemes
including the conventional Orthogonal Multiple Access (OMA), for different
utility functions and different network environments
Edge Computing Aware NOMA for 5G Networks
With the fast development of Internet of things (IoT), the fifth generation
(5G) wireless networks need to provide massive connectivity of IoT devices and
meet the demand for low latency. To satisfy these requirements, Non-Orthogonal
Multiple Access (NOMA) has been recognized as a promising solution for 5G
networks to significantly improve the network capacity. In parallel with the
development of NOMA techniques, Mobile Edge Computing (MEC) is becoming one of
the key emerging technologies to reduce the latency and improve the Quality of
Service (QoS) for 5G networks. In order to capture the potential gains of NOMA
in the context of MEC, this paper proposes an edge computing aware NOMA
technique which can enjoy the benefits of uplink NOMA in reducing MEC users'
uplink energy consumption. To this end, we formulate a NOMA based optimization
framework which minimizes the energy consumption of MEC users via optimizing
the user clustering, computing and communication resource allocation, and
transmit powers. In particular, similar to frequency Resource Blocks (RBs), we
divide the computing capacity available at the cloudlet to computing RBs.
Accordingly, we explore the joint allocation of the frequency and computing RBs
to the users that are assigned to different order indices within the NOMA
clusters. We also design an efficient heuristic algorithm for user clustering
and RBs allocation, and formulate a convex optimization problem for the power
control to be solved independently per NOMA cluster. The performance of the
proposed NOMA scheme is evaluated via simulations
Machine-to-Machine (M2M) Communications in Virtualized Cellular Networks with MEC
As an important part of the Internet-of-Things (IoT), machine-to-machine
(M2M) communications have attracted great attention. In this paper, we
introduce mobile edge computing (MEC) into virtualized cellular networks with
M2M communications, to decrease the energy consumption and optimize the
computing resource allocation as well as improve computing capability.
Moreover, based on different functions and quality of service (QoS)
requirements, the physical network can be virtualized into several virtual
networks, and then each MTCD selects the corresponding virtual network to
access. Meanwhile, the random access process of MTCDs is formulated as a
partially observable Markov decision process (POMDP) to minimize the system
cost, which consists of both the energy consumption and execution time of
computing tasks. Furthermore, to facilitate the network architecture
integration, software-defined networking (SDN) is introduced to deal with the
diverse protocols and standards in the networks. Extensive simulation results
with different system parameters reveal that the proposed scheme could
significantly improve the system performance compared to the existing schemes
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
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
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions
The fifth generation (5G) wireless network technology is to be standardized
by 2020, where main goals are to improve capacity, reliability, and energy
efficiency, while reducing latency and massively increasing connection density.
An integral part of 5G is the capability to transmit touch perception type
real-time communication empowered by applicable robotics and haptics equipment
at the network edge. In this regard, we need drastic changes in network
architecture including core and radio access network (RAN) for achieving
end-to-end latency on the order of 1 ms. In this paper, we present a detailed
survey on the emerging technologies to achieve low latency communications
considering three different solution domains: RAN, core network, and caching.
We also present a general overview of 5G cellular networks composed of software
defined network (SDN), network function virtualization (NFV), caching, and
mobile edge computing (MEC) capable of meeting latency and other 5G
requirements.Comment: Accepted in IEEE Communications Surveys and Tutorial
Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks
In this paper, by considering multiple slices, a downlink transmission of a
sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is
investigated. In this regard, by supposing multiple input and single output
(MISO) transmission technology, a novel robust radio resource allocation is
proposed where considering uncertain channel state information (CSI), the worst
case approach is applied. The main goal of the proposed radio resource
allocation is to, maximize the system sum rate with maximum available power at
radio remote head (RRH), minimum rate requirement of each slice, maximum
frounthaul capacity of each RRH, user association, and SCMA constraints. To
solve the proposed optimization problem in an efficient manner, an iterative
method is deployed where in each iteration, beamforming and joint codebook
allocation and user association subproblem are solved separately. By
introducing some auxiliary variables, the joint codebook allocation and user
association subproblem is transformed into an integer linear programming, and
to solve the beamforming optimization problem, minorization-maximization
algorithm (MMA) is applied. Via numerical results, the performance of the
proposed system model versus different system parameters and for different
channel models are investigated.Comment: 11 pages, 8 figure
User-centric Performance Optimization with Remote Radio Head Cooperation in C-RAN
In a cloud radio access network (C-RAN), distributed remote radio heads
(RRHs) are coordinated by baseband units (BBUs) in the cloud. The
centralization of signal processing provides flexibility for coordinated
multi-point transmission (CoMP) of RRHs to cooperatively serve user equipments
(UEs). We target enhancing UEs' capacity performance, by jointly optimizing the
selection of RRHs for serving UEs, i.e., resource allocation (and CoMP
selection). We analyze the computational complexity of the problem. Next, we
prove that under fixed CoMP selection, the optimal resource allocation amounts
to solving a so-called iterated function. Towards user-centric network
optimization, we propose an algorithm for the joint optimization problem,
aiming at maximumly scaling up the capacity for any target UE group of
interest. The proposed algorithm enables network-level performance evaluation
for quality of experience
Distributed Resource Allocation in 5G Cellular Networks
The 5G cellular wireless systems will have a multi-tier architecture
consisting of macrocells, different types of licensed small cells and D2D
networks to serve users with different quality-of-service (QoS) requirements in
a spectrum efficient manner. Distributed resource allocation and interference
management is one of the fundamental research challenges for such multi-tier
heterogeneous networks. In this chapter, we consider the radio resource
allocation problem in a multi-tier orthogonal frequency division multiple
access (OFDMA)-based cellular (e.g., 5G LTE-A) network. In particular, we
present three novel approaches for distributed resource allocation in such
networks utilizing the concepts of stable matching, factor-graph based message
passing, and distributed auction. We illustrate each of the modeling schemes
with respect to a practical radio resource allocation problem. In particular,
we consider a multi-tier network consisting a macro base station (MBS), a set
of small cell base stations (SBSs) and corresponding small cell user equipments
(SUEs), as well as D2D user equipments (DUEs). There is a common set of radio
resources (e.g., resource blocks [RBs]) available to the network tiers (e.g.,
MBS, SBSs and DUEs). The SUEs and DUEs use the available resources (e.g., RB
and power level) in an underlay manner as long as the interference caused to
the macro tier (e.g., macro user equipments [MUEs]) remains below a given
threshold. Followed by a brief theoretical overview of the modeling tools
(e.g., stable matching, message passing and auction algorithm), we present the
distributed solution approaches for the resource allocation problem in the
aforementioned network setup. We also provide a brief qualitative comparison in
terms of various performance metrics such as complexity, convergence, algorithm
overhead etc.Comment: Book chapter in "Towards 5G: Applications, Requirements and Candidate
Technologies'', Wiley, 2015, Eds. Rath Vannithamby and Shilpa Telwa
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