Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Information about uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this paper proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure

Joint Design of Digital and Analog Processing for Downlink C-RAN with Large-Scale Antenna Arrays

In millimeter-wave communication systems with large-scale antenna arrays, conventional digital beamforming may not be cost-effective. A promising solution is the implementation of hybrid beamforming techniques, which consist of low-dimensional digital beamforming followed by analog radio frequency (RF) beamforming. This work studies the optimization of hybrid beamforming in the context of a cloud radio access network (C-RAN) architecture. In a C-RAN system, digital baseband signal processing functionalities are migrated from remote radio heads (RRHs) to a baseband processing unit (BBU) in the "cloud" by means of finite-capacity fronthaul links. Specifically, this work tackles the problem of jointly optimizing digital beamforming and fronthaul quantization strategies at the BBU, as well as RF beamforming at the RRHs, with the goal of maximizing the weighted downlink sum-rate. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraints on the RF beamforming matrices. An iterative algorithm is proposed that is based on successive convex approximation and on the relaxation of the constant modulus constraint. The effectiveness of the proposed scheme is validated by numerical simulation results

Hierarchy Representation of Data in Machine Learnings

When there are models with clear-cut judgment results for several data points, it is possible that most models exhibit a relationship where if they correctly judge one target, they also correctly judge another target. Conversely, if most models incorrectly judge one target, they may also incorrectly judge another target. We propose a method for visualizing this hierarchy among targets. This information is expected to be beneficial for model improvement

Analyzing Planar Galactic Halo Distributions with Fuzzy/Cold Dark Matter Models

We numerically make a comparison between the fuzzy dark matter model and the cold dark matter model, focusing on formations of satellite galaxy planes around massive galaxies. We demonstrate that satellite galaxies in the fuzzy dark matter side have a tendency to form more flattened and corotating satellite systems than in the cold dark matter side due to the dissipation by the gravitational cooling effect of the fuzzy dark matter. We also show that, even with the same set of initial conditions, the number of satellites surviving becomes smaller in the fuzzy dark matter model than the cold dark matter counterpart

Force-induced acoustic phonon transport across single-digit nanometre vacuum gaps

Heat transfer between bodies separated by nanoscale vacuum gap distances has been extensively studied for potential applications in thermal management, energy conversion and data storage. For vacuum gap distances down to 20 nm, state-of-the-art experiments demonstrated that heat transport is mediated by near-field thermal radiation, which can exceed Planck's blackbody limit due to the tunneling of evanescent electromagnetic waves. However, at sub-10-nm vacuum gap distances, current measurements are in disagreement on the mechanisms driving thermal transport. While it has been hypothesized that acoustic phonon transport across single-digit nanometre vacuum gaps (or acoustic phonon tunneling) can dominate heat transfer, the underlying physics of this phenomenon and its experimental demonstration are still unexplored. Here, we use a custom-built high-vacuum shear force microscope (HV-SFM) to measure heat transfer between a silicon (Si) tip and a feedback-controlled platinum (Pt) nanoheater in the near-contact, asperity-contact, and bulk-contact regimes. We demonstrate that in the near-contact regime (i.e., single-digit nanometre or smaller vacuum gaps before making asperity contact), heat transfer between Si and Pt surfaces is dominated by force-induced acoustic phonon transport that exceeds near-field thermal radiation predictions by up to three orders of magnitude. The measured thermal conductance shows a gap dependence of $d^{-5.7\pm1.1}$ in the near-contact regime, which is consistent with acoustic phonon transport modelling based on the atomistic Green's function (AGF) framework. Our work suggests the possibility of engineering heat transfer across single-digit nanometre vacuum gaps with external force stimuli, which can make transformative impacts to the development of emerging thermal management technologies.Comment: 9 pages with 4 figures (Main text), 13 pages with 7 figures (Methods), and 13 pages with 6 figures and 1 table (Supplementary Information