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Found 24 papers, 2 papers with code
Blind Federated Learning via Over-the-Air q-QAM
Indeed, we propose a new federated edge learning framework in which edge devices use digital modulation for over-the-air uplink transmission to the edge server while they have no access to the channel state information.
ChannelComp: A General Method for Computation by Communications
However, when we use digital modulations for AirComp, a general belief is that the superposition property of the radio waves returns a meaningless overlapping of the digital signals.
Optimal Receive Filter Design for Misaligned Over-the-Air Computation
The fundamental idea of OAC is to exploit signal superposition to compute functions of multiple simultaneously transmitted signals.
A Comparison of Neural Networks for Wireless Channel Prediction
The performance of modern wireless communications systems depends critically on the quality of the available channel state information (CSI) at the transmitter and receiver.
Blind Asynchronous Goal-Oriented Detection for Massive Connectivity
Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users.
Computing Functions Over-the-Air Using Digital Modulations
Over-the-air computation (AirComp) is a known technique in which wireless devices transmit values by analog amplitude modulation so that a function of these values is computed over the communication channel at a common receiver.
Federated Learning Using Three-Operator ADMM
We propose FedTOP-ADMM, which generalizes FedADMM and is based on a three-operator ADMM-type technique that exploits a smooth cost function on the edge server to learn a global model parallel to the edge devices.
A-LAQ: Adaptive Lazily Aggregated Quantized Gradient
This paper proposes Adaptive Lazily Aggregated Quantized Gradient (A-LAQ), which is a method that significantly extends LAQ by assigning an adaptive number of communication bits during the FL iterations.
Blind Asynchronous Over-the-Air Federated Edge Learning
Federated Edge Learning (FEEL) is a distributed machine learning technique where each device contributes to training a global inference model by independently performing local computations with their data.
EVM Mitigation with PAPR and ACLR Constraints in Large-Scale MIMO-OFDM Using TOP-ADMM
Although signal distortion-based peak-to-average power ratio (PAPR) reduction is a feasible candidate for orthogonal frequency division multiplexing (OFDM) to meet standard/regulatory requirements, the error vector magnitude (EVM) stemming from the PAPR reduction has a deleterious impact on the performance of high data-rate achieving multiple-input multiple-output (MIMO) systems.
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning
We propose an iteration-termination method that trade-offs the training performance and networking costs.
Over-the-Air Federated Learning with Retransmissions (Extended Version)
Finally, we propose a heuristic for selecting the optimal number of retransmissions, which can be calculated before training the ML model.
Wireless for Control: Over-the-Air Controller
Numerical results show that our proposed over-the-air controller achieves a huge widening of the stability region in terms of sampling time and delay, and a significant reduction of the computation error of the control signal.
Simultaneous Wireless Information and Power Transfer for Federated Learning
We investigate the trade-off between the number of communication rounds and communication round time while harvesting energy to compensate the energy expenditure.
Cost-efficient SVRG with Arbitrary Sampling
We show that our approach can substantially outperform vanilla SVRG and its variants in terms of both convergence rate and total cost of running the algorithm.
Dynamic Clustering in Federated Learning
To overcome these limitations, in this paper, we propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division.
EVM-Constrained and Mask-Compliant MIMO-OFDM Spectral Precoding
In this paper we propose a novel spectral precoding approach which constrains the EVM while complying with the mask requirements.
Wireless for Machine Learning
As data generation increasingly takes place on devices without a wired connection, machine learning (ML) related traffic will be ubiquitous in wireless networks.
The Internet of Things as a Deep Neural Network
To ensure communication efficiency, this article proposes to model the measurement compression at IoT nodes and the inference at the base station or cloud as a deep neural network (DNN).
A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks
Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization and providing a larger bandwidth to individual user equipment at the expense of increasing inter-operator interference.
Distributed control of DC grids: integrating prosumers motives
In this paper, a novel distributed control strategy addressing a (feasible) psycho-social-physical welfare problem in islanded Direct Current (DC) smart grids is proposed.
Bayesian Model Selection for Change Point Detection and Clustering
We address the new problem of estimating a piece-wise constant signal with the purpose of detecting its change points and the levels of clusters.
Learning Kolmogorov Models for Binary Random Variables
We summarize our recent findings, where we proposed a framework for learning a Kolmogorov model, for a collection of binary random variables.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
A Unified Framework for Training Neural Networks
The lack of mathematical tractability of Deep Neural Networks (DNNs) has hindered progress towards having a unified convergence analysis of training algorithms, in the general setting.
