721,460 research outputs found
Random Access Protocol with Channel Oracle Enabled by a Reconfigurable Intelligent Surface
The widespread adoption of Reconfigurable Intelligent Surfaces (RISs) in
future practical wireless systems is critically dependent on the design and
implementation of efficient access protocols, an issue that has received less
attention in the research literature. In this paper, we propose a grant-free
random access (RA) protocol for a RIS-assisted wireless communication setting,
where a massive number of users' equipment (UEs) try to access an access point
(AP). The proposed protocol relies on a channel oracle, which enables the UEs
to infer the best RIS configurations that provide opportunistic access to UEs.
The inference is based on a model created during a training phase with a
greatly reduced set of RIS configurations. Specifically, we consider a system
whose operation is divided into three blocks: i) a downlink training block,
which trains the model used by the oracle, ii) an uplink access block, where
the oracle infers the best access slots, and iii) a downlink acknowledgment
block, which provides feedback to the UEs that were successfully decoded by the
AP during access. Numerical results show that the proper integration of the RIS
into the protocol design is able to increase the expected end-to-end throughput
by approximately 40% regarding the regular repetition slotted ALOHA protocol.Comment: 30 pages, 7 figures, journal pape
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control
with a significantly lower number of samples compared to usual, time-triggered
methods. These frameworks are often based on a mathematical model of the system
and specific designs of controller and event trigger. In this paper, we show
how deep reinforcement learning (DRL) algorithms can be leveraged to
simultaneously learn control and communication behavior from scratch, and
present a DRL approach that is particularly suitable for ETC. To our knowledge,
this is the first work to apply DRL to ETC. We validate the approach on
multiple control tasks and compare it to model-based event-triggering
frameworks. In particular, we demonstrate that it can, other than many
model-based ETC designs, be straightforwardly applied to nonlinear systems
Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition
The success of self-attention in NLP has led to recent applications in
end-to-end encoder-decoder architectures for speech recognition. Separately,
connectionist temporal classification (CTC) has matured as an alignment-free,
non-autoregressive approach to sequence transduction, either by itself or in
various multitask and decoding frameworks. We propose SAN-CTC, a deep, fully
self-attentional network for CTC, and show it is tractable and competitive for
end-to-end speech recognition. SAN-CTC trains quickly and outperforms existing
CTC models and most encoder-decoder models, with character error rates (CERs)
of 4.7% in 1 day on WSJ eval92 and 2.8% in 1 week on LibriSpeech test-clean,
with a fixed architecture and one GPU. Similar improvements hold for WERs after
LM decoding. We motivate the architecture for speech, evaluate position and
downsampling approaches, and explore how label alphabets (character, phoneme,
subword) affect attention heads and performance.Comment: Accepted to ICASSP 201
APHRODITE: an Anomaly-based Architecture for False Positive Reduction
We present APHRODITE, an architecture designed to reduce false positives in
network intrusion detection systems. APHRODITE works by detecting anomalies in
the output traffic, and by correlating them with the alerts raised by the NIDS
working on the input traffic. Benchmarks show a substantial reduction of false
positives and that APHRODITE is effective also after a "quick setup", i.e. in
the realistic case in which it has not been "trained" and set up optimall
Channel Estimation for Diffusive Molecular Communications
In molecular communication (MC) systems, the \textit{expected} number of
molecules observed at the receiver over time after the instantaneous release of
molecules by the transmitter is referred to as the channel impulse response
(CIR). Knowledge of the CIR is needed for the design of detection and
equalization schemes. In this paper, we present a training-based CIR estimation
framework for MC systems which aims at estimating the CIR based on the
\textit{observed} number of molecules at the receiver due to emission of a
\textit{sequence} of known numbers of molecules by the transmitter. Thereby, we
distinguish two scenarios depending on whether or not statistical channel
knowledge is available. In particular, we derive maximum likelihood (ML) and
least sum of square errors (LSSE) estimators which do not require any knowledge
of the channel statistics. For the case, when statistical channel knowledge is
available, the corresponding maximum a posteriori (MAP) and linear minimum mean
square error (LMMSE) estimators are provided. As performance bound, we derive
the classical Cramer Rao (CR) lower bound, valid for any unbiased estimator,
which does not exploit statistical channel knowledge, and the Bayesian CR lower
bound, valid for any unbiased estimator, which exploits statistical channel
knowledge. Finally, we propose optimal and suboptimal training sequence designs
for the considered MC system. Simulation results confirm the analysis and
compare the performance of the proposed estimation techniques with the
respective CR lower bounds.Comment: to be appeared in IEEE Transactions on Communications. arXiv admin
note: text overlap with arXiv:1510.0861
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