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Fixed Point Quantization of Deep Convolutional Networks
TLDR
This paper proposes a quantizer design for fixed point implementation of DCNs, formulate and solve an optimization problem to identify optimal fixed point bit-width allocation across DCN layers, and demonstrates that fine-tuning can further enhance the accuracy of fixed point DCNs beyond that of the original floating point model.
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The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM
TLDR
A systematic probabilistic framework that leads to both optimal and near-optimal OFDM detection schemes in the presence of unknown PHN is presented and it is pointed out that the expectation-maximization algorithm is a special case of the variational-inference-based joint estimator.
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Joint estimation of channel response, frequency offset, and phase noise in OFDM
TLDR
An optimal (maximum a posteriori) joint estimator for the channel impulse response (CIR), CFO, and PHN is introduced, utilizing prior statistical knowledge of PHN that can be obtained from measurements or data sheets.
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Overcoming Challenges in Fixed Point Training of Deep Convolutional Networks
TLDR
This work is an attempt to draw a theoretical connection between low numerical precision and training algorithm stability, and propose and verify through experiments methods that are able to improve the training performance of deep convolutional networks in fixed point.
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Subspace-based active user identification for a collision-free slotted ad hoc network
We propose a novel spreading code scheme, transmitter-receiver-based code, for wireless ad hoc networks. The design facilitates collision resolution using multiuser detection at each node, and is…
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A Variational Inference Framework for Soft-In Soft-Out Detection in Multiple-Access Channels
TLDR
It is shown that, with some loss of optimality, variational inference avoids the exponential complexity of a posteriori probability (APP) detection by optimizing a closely related, but much more manageable, objective function called variational free energy.
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OFDM phase noise cancellation via approximate probabilistic inference
TLDR
A systematic probabilistic framework to address the phase noise (PHN) problem in OFDM is proposed and a series of suboptimal approaches to blindly cancel the effect of PHN without the aid of pilot symbols are introduced.
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A variational free energy minimization interpretation of multiuser detection in CDMA
TLDR
By condensing the design of multiuser detectors into the selection of a few key probability distributions, namely p(b, p(r|b) and Q(b), this work provides rigorous justifications for numerous detectors that were proposed on heuristic grounds and recommends new and improved designs.
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Optimal OFDM channel estimation with carrier frequency offset and phase noise
TLDR
An optimal training-based OFDM channel impulse response (CIR) estimation algorithm that addresses the phase noise (PHN) and carrier frequency offset (CFO) problem and an efficient realization using the fast Fourier transform with only minor performance degradation is proposed.
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Breast Mass Detection in Mammograms via Blending Adversarial Learning
TLDR
A generative model is trained to simulate the growth of mass on normal tissue by blending mass patches into healthy breast images by regularizing the imbalanced data with synthetically generated abnormal samples to make the training of deep learning based mass detector more stable and the resulting model more robust.
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