931 research outputs found
Content Placement in Cache-Enabled Sub-6 GHz and Millimeter-Wave Multi-antenna Dense Small Cell Networks
This paper studies the performance of cache-enabled dense small cell networks
consisting of multi-antenna sub-6 GHz and millimeter-wave base stations.
Different from the existing works which only consider a single antenna at each
base station, the optimal content placement is unknown when the base stations
have multiple antennas. We first derive the successful content delivery
probability by accounting for the key channel features at sub-6 GHz and mmWave
frequencies. The maximization of the successful content delivery probability is
a challenging problem. To tackle it, we first propose a constrained
cross-entropy algorithm which achieves the near-optimal solution with moderate
complexity. We then develop another simple yet effective heuristic
probabilistic content placement scheme, termed two-stair algorithm, which
strikes a balance between caching the most popular contents and achieving
content diversity. Numerical results demonstrate the superior performance of
the constrained cross-entropy method and that the two-stair algorithm yields
significantly better performance than only caching the most popular contents.
The comparisons between the sub-6 GHz and mmWave systems reveal an interesting
tradeoff between caching capacity and density for the mmWave system to achieve
similar performance as the sub-6 GHz system.Comment: 14 pages; Accepted to appear in IEEE Transactions on Wireless
Communication
A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints
We consider caching in cellular networks in which each base station is
equipped with a cache that can store a limited number of files. The popularity
of the files is known and the goal is to place files in the caches such that
the probability that a user at an arbitrary location in the plane will find the
file that she requires in one of the covering caches is maximized.
We develop distributed asynchronous algorithms for deciding which contents to
store in which cache. Such cooperative algorithms require communication only
between caches with overlapping coverage areas and can operate in asynchronous
manner. The development of the algorithms is principally based on an
observation that the problem can be viewed as a potential game. Our basic
algorithm is derived from the best response dynamics. We demonstrate that the
complexity of each best response step is independent of the number of files,
linear in the cache capacity and linear in the maximum number of base stations
that cover a certain area. Then, we show that the overall algorithm complexity
for a discrete cache placement is polynomial in both network size and catalog
size. In practical examples, the algorithm converges in just a few iterations.
Also, in most cases of interest, the basic algorithm finds the best Nash
equilibrium corresponding to the global optimum. We provide two extensions of
our basic algorithm based on stochastic and deterministic simulated annealing
which find the global optimum.
Finally, we demonstrate the hit probability evolution on real and synthetic
networks numerically and show that our distributed caching algorithm performs
significantly better than storing the most popular content, probabilistic
content placement policy and Multi-LRU caching policies.Comment: 24 pages, 9 figures, presented at SIGMETRICS'1
Pricing and Resource Allocation via Game Theory for a Small-Cell Video Caching System
Evidence indicates that downloading on-demand videos accounts for a dramatic
increase in data traffic over cellular networks. Caching popular videos in the
storage of small-cell base stations (SBS), namely, small-cell caching, is an
efficient technology for reducing the transmission latency whilst mitigating
the redundant transmissions of popular videos over back-haul channels. In this
paper, we consider a commercialized small-cell caching system consisting of a
network service provider (NSP), several video retailers (VR), and mobile users
(MU). The NSP leases its SBSs to the VRs for the purpose of making profits, and
the VRs, after storing popular videos in the rented SBSs, can provide faster
local video transmissions to the MUs, thereby gaining more profits. We conceive
this system within the framework of Stackelberg game by treating the SBSs as a
specific type of resources. We first model the MUs and SBSs as two independent
Poisson point processes, and develop, via stochastic geometry theory, the
probability of the specific event that an MU obtains the video of its choice
directly from the memory of an SBS. Then, based on the probability derived, we
formulate a Stackelberg game to jointly maximize the average profit of both the
NSP and the VRs. Also, we investigate the Stackelberg equilibrium by solving a
non-convex optimization problem. With the aid of this game theoretic framework,
we shed light on the relationship between four important factors: the optimal
pricing of leasing an SBS, the SBSs allocation among the VRs, the storage size
of the SBSs, and the popularity distribution of the VRs. Monte-Carlo
simulations show that our stochastic geometry-based analytical results closely
match the empirical ones. Numerical results are also provided for quantifying
the proposed game-theoretic framework by showing its efficiency on pricing and
resource allocation.Comment: Accepted to appear in IEEE Journal on Selected Areas in
Communications, special issue on Video Distribution over Future Interne
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