24,107 research outputs found
Laplace Functional Ordering of Point Processes in Large-scale Wireless Networks
Stochastic orders on point processes are partial orders which capture notions
like being larger or more variable. Laplace functional ordering of point
processes is a useful stochastic order for comparing spatial deployments of
wireless networks. It is shown that the ordering of point processes is
preserved under independent operations such as marking, thinning, clustering,
superposition, and random translation. Laplace functional ordering can be used
to establish comparisons of several performance metrics such as coverage
probability, achievable rate, and resource allocation even when closed form
expressions of such metrics are unavailable. Applications in several network
scenarios are also provided where tradeoffs between coverage and interference
as well as fairness and peakyness are studied. Monte-Carlo simulations are used
to supplement our analytical results.Comment: 30 pages, 5 figures, Submitted to Hindawi Wireless Communications and
Mobile Computin
Deep Learning as a Parton Shower
We make the connection between certain deep learning architectures and the
renormalisation group explicit in the context of QCD by using a deep learning
network to construct a toy parton shower model. The model aims to describe
proton-proton collisions at the Large Hadron Collider. A convolutional
autoencoder learns a set of kernels that efficiently encode the behaviour of
fully showered QCD collision events. The network is structured recursively so
as to ensure self-similarity, and the number of trained network parameters is
low. Randomness is introduced via a novel custom masking layer, which also
preserves existing parton splittings by using layer-skipping connections. By
applying a shower merging procedure, the network can be evaluated on unshowered
events produced by a matrix element calculation. The trained network behaves as
a parton shower that qualitatively reproduces jet-based observables.Comment: 26 pages, 13 figure
Downlink and Uplink Cell Association with Traditional Macrocells and Millimeter Wave Small Cells
Millimeter wave (mmWave) links will offer high capacity but are poor at
penetrating into or diffracting around solid objects. Thus, we consider a
hybrid cellular network with traditional sub 6 GHz macrocells coexisting with
denser mmWave small cells, where a mobile user can connect to either
opportunistically. We develop a general analytical model to characterize and
derive the uplink and downlink cell association in view of the SINR and rate
coverage probabilities in such a mixed deployment. We offer extensive
validation of these analytical results (which rely on several simplifying
assumptions) with simulation results. Using the analytical results, different
decoupled uplink and downlink cell association strategies are investigated and
their superiority is shown compared to the traditional coupled approach.
Finally, small cell biasing in mmWave is studied, and we show that
unprecedented biasing values are desirable due to the wide bandwidth.Comment: 30 pages, 9 figures. Submitted to IEEE Transactions on Wireless
Communication
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