60 research outputs found
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
Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise
Over-the-air computation (AirComp)-based federated learning (FL) enables
low-latency uploads and the aggregation of machine learning models by
exploiting simultaneous co-channel transmission and the resultant waveform
superposition. This study aims at realizing secure AirComp-based FL against
various privacy attacks where malicious central servers infer clients' private
data from aggregated global models. To this end, a differentially private
AirComp-based FL is designed in this study, where the key idea is to harness
receiver noise perturbation injected to aggregated global models inherently,
thereby preventing the inference of clients' private data. However, the
variance of the inherent receiver noise is often uncontrollable, which renders
the process of injecting an appropriate noise perturbation to achieve a desired
privacy level quite challenging. Hence, this study designs transmit power
control across clients, wherein the received signal level is adjusted
intentionally to control the noise perturbation levels effectively, thereby
achieving the desired privacy level. It is observed that a higher privacy level
requires lower transmit power, which indicates the tradeoff between the privacy
level and signal-to-noise ratio (SNR). To understand this tradeoff more fully,
the closed-form expressions of SNR (with respect to the privacy level) are
derived, and the tradeoff is analytically demonstrated. The analytical results
also demonstrate that among the configurable parameters, the number of
participating clients is a key parameter that enhances the received SNR under
the aforementioned tradeoff. The analytical results are validated through
numerical evaluations.Comment: 6 pages, 4 figure
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