88 research outputs found
Green OFDMA Resource Allocation in Cache-Enabled CRAN
Cloud radio access network (CRAN), in which remote radio heads (RRHs) are
deployed to serve users in a target area, and connected to a central processor
(CP) via limited-capacity links termed the fronthaul, is a promising candidate
for the next-generation wireless communication systems. Due to the
content-centric nature of future wireless communications, it is desirable to
cache popular contents beforehand at the RRHs, to reduce the burden on the
fronthaul and achieve energy saving through cooperative transmission. This
motivates our study in this paper on the energy efficient transmission in an
orthogonal frequency division multiple access (OFDMA)-based CRAN with multiple
RRHs and users, where the RRHs can prefetch popular contents. We consider a
joint optimization of the user-SC assignment, RRH selection and transmit power
allocation over all the SCs to minimize the total transmit power of the RRHs,
subject to the RRHs' individual fronthaul capacity constraints and the users'
minimum rate constraints, while taking into account the caching status at the
RRHs. Although the problem is non-convex, we propose a Lagrange duality based
solution, which can be efficiently computed with good accuracy. We compare the
minimum transmit power required by the proposed algorithm with different
caching strategies against the case without caching by simulations, which show
the significant energy saving with caching.Comment: Presented in IEEE Online Conference on Green Communications (Online
GreenComm), Nov. 2016 (Invited Paper
Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G
Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
Cooperative video caching and transcoding in mobile edge computing (MEC)
networks is a new paradigm for future wireless networks, e.g., 5G and 5G
beyond, to reduce scarce and expensive backhaul resource usage by prefetching
video files within radio access networks (RANs). Integration of this technique
with other advent technologies, such as wireless network virtualization and
multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible
video delivery opportunities, which leads to enhancements both for the
network's revenue and for the end-users' service experience. In this regard, we
propose a two-phase RAF for a parallel cooperative joint multi-bitrate video
caching and transcoding in heterogeneous virtualized MEC networks. In the cache
placement phase, we propose novel proactive delivery-aware cache placement
strategies (DACPSs) by jointly allocating physical and radio resources based on
network stochastic information to exploit flexible delivery opportunities.
Then, for the delivery phase, we propose a delivery policy based on the user
requests and network channel conditions. The optimization problems
corresponding to both phases aim to maximize the total revenue of network
slices, i.e., virtual networks. Both problems are non-convex and suffer from
high-computational complexities. For each phase, we show how the problem can be
solved efficiently. We also propose a low-complexity RAF in which the
complexity of the delivery algorithm is significantly reduced. A Delivery-aware
cache refreshment strategy (DACRS) in the delivery phase is also proposed to
tackle the dynamically changes of network stochastic information. Extensive
numerical assessments demonstrate a performance improvement of up to 30% for
our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure
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