680 research outputs found
Optimal Dynamic Multicast Scheduling for Cache-Enabled Content-Centric Wireless Networks
Caching and multicasting at base stations are two promising approaches to
support massive content delivery over wireless networks. However, existing
scheduling designs do not make full use of the advantages of the two
approaches. In this paper, we consider the optimal dynamic multicast scheduling
to jointly minimize the average delay, power, and fetching costs for
cache-enabled content-centric wireless networks. We formulate this stochastic
optimization problem as an infinite horizon average cost Markov decision
process (MDP). It is well-known to be a difficult problem due to the curse of
dimensionality, and there generally only exist numerical solutions. By using
relative value iteration algorithm and the special structures of the request
queue dynamics, we analyze the properties of the value function and the
state-action cost function of the MDP for both the uniform and nonuniform
channel cases. Based on these properties, we show that the optimal policy,
which is adaptive to the request queue state, has a switch structure in the
uniform case and a partial switch structure in the nonuniform case. Moreover,
in the uniform case with two contents, we show that the switch curve is
monotonically non-decreasing. Then, by exploiting these structural properties
of the optimal policy, we propose two low-complexity optimal algorithms.
Motivated by the switch structures of the optimal policy, to further reduce the
complexity, we also propose a low-complexity suboptimal policy, which possesses
similar structural properties to the optimal policy, and develop a
low-complexity algorithm to compute this policy.Comment: 17 double-column pages; Shorter version appears in ISIT 201
Stochastic Content-Centric Multicast Scheduling for Cache-Enabled Heterogeneous Cellular Networks
Caching at small base stations (SBSs) has demonstrated significant benefits
in alleviating the backhaul requirement in heterogeneous cellular networks
(HetNets). While many existing works focus on what contents to cache at each
SBS, an equally important problem is what contents to deliver so as to satisfy
dynamic user demands given the cache status. In this paper, we study optimal
content delivery in cache-enabled HetNets by taking into account the inherent
multicast capability of wireless medium. We consider stochastic content
multicast scheduling to jointly minimize the average network delay and power
costs under a multiple access constraint. We establish a content-centric
request queue model and formulate this stochastic optimization problem as an
infinite horizon average cost Markov decision process (MDP). By using
\emph{relative value iteration} and special properties of the request queue
dynamics, we characterize some properties of the value function of the MDP.
Based on these properties, we show that the optimal multicast scheduling policy
is of threshold type. Then, we propose a structure-aware optimal algorithm to
obtain the optimal policy. We also propose a low-complexity suboptimal policy,
which possesses similar structural properties to the optimal policy, and
develop a low-complexity algorithm to obtain this policy.Comment: Accepted to IEEE Trans. on Wireless Communications (June 6, 2016).
Conference version appears in ACM CoNEXT 2015 Workshop on Content Caching and
Delivery in Wireless Networks (CCDWN
Content-Centric Multicast Beamforming in Cache-Enabled Cloud Radio Access Networks
Multicast transmission and wireless caching are effective ways of reducing
air and backhaul traffic load in wireless networks. This paper proposes to
incorporate these two key ideas for content-centric multicast transmission in a
cloud radio access network (RAN) where multiple base stations (BSs) are
connected to a central processor (CP) via finite-capacity backhaul links. Each
BS has a cache with finite storage size and is equipped with multiple antennas.
The BSs cooperatively transmit contents, which are either stored in the local
cache or fetched from the CP, to multiple users in the network. Users
requesting a same content form a multicast group and are served by a same
cluster of BSs cooperatively using multicast beamforming. Assuming fixed cache
placement, this paper investigates the joint design of multicast beamforming
and content-centric BS clustering by formulating an optimization problem of
minimizing the total network cost under the quality-of-service (QoS)
constraints for each multicast group. The network cost involves both the
transmission power and the backhaul cost. We model the backhaul cost using the
mixed -norm of beamforming vectors. To solve this non-convex
problem, we first approximate it using the semidefinite relaxation (SDR) method
and concave smooth functions. We then propose a difference of convex functions
(DC) programming algorithm to obtain suboptimal solutions and show the
connection of three smooth functions. Simulation results validate the advantage
of multicasting and show the effects of different cache size and caching
policies in cloud RAN.Comment: IEEE Globecom 201
Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN
This paper presents a content-centric transmission design in a cloud radio
access network (cloud RAN) by incorporating multicasting and caching. Users
requesting a same content form a multicast group and are served by a same
cluster of base stations (BSs) cooperatively. Each BS has a local cache and it
acquires the requested contents either from its local cache or from the central
processor (CP) via backhaul links. We investigate the dynamic content-centric
BS clustering and multicast beamforming with respect to both channel condition
and caching status. We first formulate a mixed-integer nonlinear programming
problem of minimizing the weighted sum of backhaul cost and transmit power
under the quality-of-service constraint for each multicast group. Theoretical
analysis reveals that all the BSs caching a requested content can be included
in the BS cluster of this content, regardless of the channel conditions. Then
we reformulate an equivalent sparse multicast beamforming (SBF) problem. By
adopting smoothed -norm approximation and other techniques, the SBF
problem is transformed into the difference of convex (DC) programs and
effectively solved using the convex-concave procedure algorithms. Simulation
results demonstrate significant advantage of the proposed content-centric
transmission. The effects of three heuristic caching strategies are also
evaluated.Comment: To appear in IEEE Trans. on Wireless Communication
Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Wireless Networks
Caching and multicasting at base stations are two promising approaches to
support massive content delivery over wireless networks. However, existing
analysis and designs do not fully explore and exploit the potential advantages
of the two approaches. In this paper, we consider the analysis and optimization
of caching and multicasting in a large-scale cache-enabled wireless network. We
propose a random caching and multicasting scheme with a design parameter. By
carefully handling different types of interferers and adopting appropriate
approximations, we derive a tractable expression for the successful
transmission probability in the general region, utilizing tools from stochastic
geometry. We also obtain a closed-form expression for the successful
transmission probability in the high signal-to-noise ratio (SNR) and user
density region. Then, we consider the successful transmission probability
maximization, which is a very complex non-convex problem in general. Using
optimization techniques, we develop an iterative numerical algorithm to obtain
a local optimal caching and multicasting design in the general region. To
reduce complexity and maintain superior performance, we also derive an
asymptotically optimal caching and multicasting design in the asymptotic
region, based on a two-step optimization framework. Finally, numerical
simulations show that the asymptotically optimal design achieves a significant
gain in successful transmission probability over some baseline schemes in the
general region.Comment: 31 pages, 6 figures, 1 table. Transactions on Wireless Communication
(submitted in July 2015, now under 2nd revision
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
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
Caching at the Wireless Edge: Design Aspects, Challenges and Future Directions
Caching at the wireless edge is a promising way of boosting spectral
efficiency and reducing energy consumption of wireless systems. These
improvements are rooted in the fact that popular contents are reused,
asynchronously, by many users. In this article, we first introduce methods to
predict the popularity distributions and user preferences, and the impact of
erroneous information. We then discuss the two aspects of caching systems,
namely content placement and delivery. We expound the key differences between
wired and wireless caching, and outline the differences in the system arising
from where the caching takes place, e.g., at base stations, or on the wireless
devices themselves. Special attention is paid to the essential limitations in
wireless caching, and possible tradeoffs between spectral efficiency, energy
efficiency and cache size.Comment: Published in IEEE Communications Magazin
Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Heterogeneous Wireless Networks
Heterogeneous wireless networks (HetNets) provide a powerful approach to meet
the dramatic mobile traffic growth, but also impose a significant challenge on
backhaul. Caching and multicasting at macro and pico base stations (BSs) are
two promising methods to support massive content delivery and reduce backhaul
load in HetNets. In this paper, we jointly consider caching and multicasting in
a large-scale cache-enabled HetNet with backhaul constraints. We propose a
hybrid caching design consisting of identical caching in the macro-tier and
random caching in the pico-tier, and a corresponding multicasting design. By
carefully handling different types of interferers and adopting appropriate
approximations, we derive tractable expressions for the successful transmission
probability in the general region as well as the high signal-to-noise ratio
(SNR) and user density region, utilizing tools from stochastic geometry. Then,
we consider the successful transmission probability maximization by optimizing
the design parameters, which is a very challenging mixed discrete-continuous
optimization problem due to the sophisticated structure of the successful
transmission probability. By using optimization techniques and exploring the
structural properties, we obtain a near optimal solution with superior
performance and manageable complexity. This solution achieves better
performance in the general region than any asymptotically optimal solution,
under a mild condition. The analysis and optimization results provide valuable
design insights for practical cache-enabled HetNets.Comment: 37 pages, 7 figures. arXiv admin note: text overlap with
arXiv:1512.0617
Cost-optimal caching for D2D networks with user mobility: Modeling, analysis, and computational approaches
Caching popular files at user equipments (UEs) provides an effective way to
alleviate the burden of the backhaul networks. Generally, popularity-based
caching is not a system-wide optimal strategy, especially for user mobility
scenarios. Motivated by this observation, we consider optimal caching with
presence of mobility. A cost-optimal caching problem (COCP) for
device-to-device (D2D) networks is modelled, in which the impact of user
mobility, cache size, and total number of encoded segments are all accounted
for. Compared with the related studies, our investigation guarantees that the
collected segments are non-overlapping, takes into account the cost of
downloading from the network, and provides a rigorous problem complexity
analysis. The hardness of the problem is proved via a reduction from the
satisfiability problem. Next, a lower-bounding function of the objective
function is derived. By the function, an approximation of COCP (ACOCP)
achieving linearization is obtained, which features two advantages. First, the
ACOCP approach can use an off-the-shelf integer linear programming algorithm to
obtain the global optimal solution, and it can effectively deliver solutions
for small-scale and mediumscale system scenarios. Second, and more importantly,
based on the ACOCP approach, one can derive the lower bound of global optimum
of COCP, thus enabling performance benchmarking of any suboptimal algorithm. To
tackle large scenarios with low complexity, we first prove that the optimal
caching placement of one user, giving other users' caching placements, can be
derived in polynomial time. Then, based on this proof, a mobility aware
user-by-user (MAUU) algorithm is developed. Simulation results verify the
effectivenesses of the two approaches by comparing them to the lower bound of
global optimum and conventional caching algorithms
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