Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods

Thompson Sampling-Based Channel Selection through Density Estimation aided by Stochastic Geometry

We propose a sophisticated channel selection scheme based on multi-armed bandits and stochastic geometry analysis. In the proposed scheme, a typical user attempts to estimate the density of active interferers for every channel via the repeated observations of signal-to-interference power ratio (SIR), which demonstrates the randomness induced by randomized interference sources and fading effects. The purpose of this study involves enabling a typical user to identify the channel with the lowest density of active interferers while considering the communication quality during exploration. To resolve the trade-off between obtaining more observations on uncertain channels and using a channel that appears better, we employ a bandit algorithm called Thompson sampling (TS), which is known for its empirical effectiveness. We consider two ideas to enhance TS. First, noticing that the SIR distribution derived through stochastic geometry is useful for updating the posterior distribution of the density, we propose incorporating the SIR distribution into TS to estimate the density of active interferers. Second, TS requires sampling from the posterior distribution of the density for each channel, while it is significantly more complicated for the posterior distribution of the density to generate samples than well-known distribution. The results indicate that this type of sampling process is achieved via the Markov chain Monte Carlo method (MCMC). The simulation results indicate that the proposed method enables a typical user to determine the channel with the lowest density more efficiently than the TS without density estimation aided by stochastic geometry, and ε-greedy strategies

Heteroepitaxial growth and optoelectronic properties of layered iron oxyarsenide, LaFeAsO

Epitaxial thin films of LaFeAsO were fabricated on MgO (001) and mixed-perovskite (La, Sr)(Al, Ta)O3 (001) single-crystal substrates by pulsed laser deposition using a Nd:YAG second harmonic source and a 10 at.% F-doped LaFeAsO disk target. Temperature dependences of the electrical resistivities showed no superconducting transition in the temperature range of 2-300 K, and were similar to those of undoped polycrystalline bulk samples. The transmittance spectrum exhibited a clear peak at ~0.2 eV, which is explained by ab-initio calculations.Comment: Submission: 31st July 2008, Accepted for publication in Appl. Phys. Let

Single-atomic-layered quantum wells built in wide-gap semiconductors LnCuOCh (Ln=lanthanide, Ch=chalcogen)

LnCuOCh (Ln=lanthanide, Ch=chalcogen) layered oxychalcogenides are wide-gap p-type semiconductors composed of alternately stacked (Ln2O2)2+ oxide layers and (Cu2Ch2)2- chalcogenide layers. Energy band calculations revealed that Cu-Ch hybridized bands only spread in the (Cu2Ch2)2- layers, which suggests that hole carriers in these bands are confined by the potential barriers formed by the (Ln2O2)2+ layers. Stepwise absorption spectra of a series of LnCuOCh experimentally verified that an exciton in the (Cu2Ch2)2- layers shows a two-dimensional behavior. These theoretical and experimental results indicate that LnCuOCh has “natural multiple quantum wells” built into its layered structure