49,146 research outputs found
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe
Amplify-and-Forward in Wireless Relay Networks
A general class of wireless relay networks with a single source-destination
pair is considered. Intermediate nodes in the network employ an
amplify-and-forward scheme to relay their input signals. In this case the
overall input-output channel from the source via the relays to the destination
effectively behaves as an intersymbol interference channel with colored noise.
Unlike previous work we formulate the problem of the maximum achievable rate in
this setting as an optimization problem with no assumption on the network size,
topology, and received signal-to-noise ratio. Previous work considered only
scenarios wherein relays use all their power to amplify their received signals.
We demonstrate that this may not always maximize the maximal achievable rate in
amplify-and-forward relay networks. The proposed formulation allows us to not
only recover known results on the performance of the amplify-and-forward
schemes for some simple relay networks but also characterize the performance of
more complex amplify-and-forward relay networks which cannot be addressed in a
straightforward manner using existing approaches.
Using cut-set arguments, we derive simple upper bounds on the capacity of
general wireless relay networks. Through various examples, we show that a large
class of amplify-and-forward relay networks can achieve rates within a constant
factor of these upper bounds asymptotically in network parameters.Comment: Minor revision: fixed a typo in eqn. reference, changed the
formatting. 30 pages, 8 figure
Outcome contingency selectively affects the neural coding of outcomes but not of tasks
Value-based decision-making is ubiquitous in every-day life, and critically depends on the contingency between choices and their outcomes. Only if outcomes are contingent on our choices can we make meaningful value-based decisions. Here, we investigate the effect of outcome contingency on the neural coding of rewards and tasks. Participants performed a reversal-learning paradigm in which reward outcomes were contingent on trial-by-trial choices, and performed a ‘free choice’ paradigm in which rewards were random and not contingent on choices. We hypothesized that contingent outcomes enhance the neural coding of rewards and tasks, which was tested using multivariate pattern analysis of fMRI data. Reward outcomes were encoded in a large network including the striatum, dmPFC and parietal cortex, and these representations were indeed amplified for contingent rewards. Tasks were encoded in the dmPFC at the time of decision-making, and in parietal cortex in a subsequent maintenance phase. We found no evidence for contingency-dependent modulations of task signals, demonstrating highly similar coding across contingency conditions. Our findings suggest selective effects of contingency on reward coding only, and further highlight the role of dmPFC and parietal cortex in value-based decision-making, as these were the only regions strongly involved in both reward and task coding
A biomimetic basis for auditory processing and the perception of natural sounds
Biomimicry is a powerful science that aims to take advantage of nature's
remarkable ability to devise innovative solutions to challenging problems. In
the setting of hearing, mimicking how humans hear is the foremost strategy in
designing effective artificial hearing approaches. In this work, we explore the
mathematical foundations for the exchange of design inspiration and features
between biological hearing systems, artificial sound-filtering devices, and
signal processing algorithms. Our starting point is a concise asymptotic
analysis of subwavelength acoustic metamaterials. We are able to fine tune this
structure to mimic the biomechanical properties of the cochlea, at the same
scale. We then turn our attention to developing a biomimetic signal processing
algorithm. We use the response of the cochlea-like structure as an initial
filtering layer and then add additional biomimetic processing stages, designed
to mimic the human auditory system's ability to recognise the global properties
of natural sounds
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