209 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
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
Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks
The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm
which incorporates the cloud computing into heterogeneous networks (HetNets),
thereby taking full advantage of cloud radio access networks (C-RANs) and
HetNets. Characterizing the cooperative beamforming with fronthaul capacity and
queue stability constraints is critical for multimedia applications to
improving energy efficiency (EE) in H-CRANs. An energy-efficient optimization
objective function with individual fronthaul capacity and inter-tier
interference constraints is presented in this paper for queue-aware multimedia
H-CRANs. To solve this non-convex objective function, a stochastic optimization
problem is reformulated by introducing the general Lyapunov optimization
framework. Under the Lyapunov framework, this optimization problem is
equivalent to an optimal network-wide cooperative beamformer design algorithm
with instantaneous power, average power and inter-tier interference
constraints, which can be regarded as the weighted sum EE maximization problem
and solved by a generalized weighted minimum mean square error approach. The
mathematical analysis and simulation results demonstrate that a tradeoff
between EE and queuing delay can be achieved, and this tradeoff strictly
depends on the fronthaul constraint
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
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