1,305 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 survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Joint and Competitive Caching Designs in Large-Scale Multi-Tier Wireless Multicasting Networks
Caching and multicasting are two promising methods to support massive content
delivery in multi-tier wireless networks. In this paper, we consider a random
caching and multicasting scheme with caching distributions in the two tiers as
design parameters, to achieve efficient content dissemination in a two-tier
large-scale cache-enabled wireless multicasting network. First, we derive
tractable expressions for the successful transmission probabilities in the
general region as well as the high SNR and high user density region,
respectively, utilizing tools from stochastic geometry. Then, for the case of a
single operator for the two tiers, we formulate the optimal joint caching
design problem to maximize the successful transmission probability in the
asymptotic region, which is nonconvex in general. By using the block successive
approximate optimization technique, we develop an iterative algorithm, which is
shown to converge to a stationary point. Next, for the case of two different
operators, one for each tier, we formulate the competitive caching design game
where each tier maximizes its successful transmission probability in the
asymptotic region. We show that the game has a unique Nash equilibrium (NE) and
develop an iterative algorithm, which is shown to converge to the NE under a
mild condition. Finally, by numerical simulations, we show that the proposed
designs achieve significant gains over existing schemes.Comment: 30 pages, 6 pages, submitted to IEEE GLOBECOM 2017 and IEEE Trans.
Commo
Edge Caching in Dense Heterogeneous Cellular Networks with Massive MIMO Aided Self-backhaul
This paper focuses on edge caching in dense heterogeneous cellular networks
(HetNets), in which small base stations (SBSs) with limited cache size store
the popular contents, and massive multiple-input multiple-output (MIMO) aided
macro base stations provide wireless self-backhaul when SBSs require the
non-cached contents. Our aim is to address the effects of cell load and hit
probability on the successful content delivery (SCD), and present the minimum
required base station density for avoiding the access overload in an arbitrary
small cell and backhaul overload in an arbitrary macrocell. The massive MIMO
backhaul achievable rate without downlink channel estimation is derived to
calculate the backhaul time, and the latency is also evaluated in such
networks. The analytical results confirm that hit probability needs to be
appropriately selected, in order to achieve SCD. The interplay between cache
size and SCD is explicitly quantified. It is theoretically demonstrated that
when non-cached contents are requested, the average delay of the non-cached
content delivery could be comparable to the cached content delivery with the
help of massive MIMO aided self-backhaul, if the average access rate of cached
content delivery is lower than that of self-backhauled content delivery.
Simulation results are presented to validate our analysis.Comment: Accepted to appear in IEEE Transactions on Wireless Communication
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