arXiv research

DoCoFL compresses model updates for cross-device federated learning.

BICompFL tackles bi-directional compression challenges in stochastic FL, reducing communication costs by an order of magnitude.

Paper improves communication in distributed optimization, reducing worker-to-server data exchanges.

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, CSI feedback overhead degrades the overall spectral…

DeepCMC compresses CSI for massive MIMO systems, reducing overhead and improving performance.

Study investigates FL performance over a noisy downlink, showing analog approach outperforms digital.

We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The primary contribution of our paper is an algorithm to enhance the trigger of DL CoMP using online machine learning. We use support vector machine (SVM) classifiers to enhance the user downlink…

Study improves communication efficiency in RIS-assisted downlink communication.

Mix2FLD improves FL accuracy with FD, reducing convergence time.

MARINA-P improves non-smooth federated optimization with adaptive stepsizes.

We propose a reinforcement learning (RL) based closed loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells. The main contributions of our paper are to 1) use RL to solve performance tuning problems in an indoor cellular network for voic…

Knowledge of the channel state information (CSI) at the transmitter side is one of the primary sources of information that can be used for the efficient allocation of wireless resources. Obtaining downlink (DL) CSI in Frequency Division Duplexing (FDD) systems from uplink (UL) CSI is not as straightforward as in TDD sy…

Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation. Recently, deep reinforcement learning (DRL) has…

The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at runtime, from the raw physical channel of an operative mobile network, without having to decode and/or decrypt the transmitted flows. T…

Random orthogonalization improves FL in massive MIMO systems without CSI.

Meta-DRL improves resource allocation in O-RAN networks.

This study analyzes satellite communication latency using a stochastic geometry model.

CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.

We consider a multicast scheme recently proposed for a wireless downlink in [1]. It was shown earlier that power control can significantly improve its performance. However for this system, obtaining optimal power control is intractable because of a very large state space. Therefore in this paper we use deep reinforceme…

Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the perspective of first response organisations like UNICEF. Two main challenges are rapid access to data, and the ability…

Optimizes wireless power control using graph neural networks and counterfactual optimization.

Flexible framework compresses models using LC algorithm.

Reduces multiclass and regression compression schemes to binary ones.

Proposes a link between randomness and compression in deep learning.

Paper introduces a new adaptive gradient method with gradient compression for distributed training.

Galen algorithm compresses neural networks for specific hardware with reduced latency.

The (isothermic) compressibility of lattice knots can be examined as a model of the effects of topology and geometry on the compressibility of ring polymers. In this paper, the compressibility of minimal length lattice knots in the simple cubic, face centered cubic and body centered cubic lattices are determined. Our r…

HiLLoC compresses large images losslessly using VAEs.

Neural NCD reveals LLMs don't compress well for classification.

BDC compresses both sample size and dimensionality of large datasets.

Compressing neural nets is an active research problem, given the large size of state-of-the-art nets for tasks such as object recognition, and the computational limits imposed by mobile devices. We give a general formulation of model compression as constrained optimization. This includes many types of compression: quan…

New taxonomy and evaluation of neural network compression methods.

Proposes a method to train neural networks directly on compressed text data.

We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through ex…

A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the …

Study shows LLC correlates with neural network compressibility.

Paper proposes SCALLION and SCAFCOM for compressed FL with reduced communication.

New uncertainty principle limits compression in distributed learning, suggesting optimal methods.

One of the biggest issues in deep learning theory is the generalization ability of networks with huge model size. The classical learning theory suggests that overparameterized models cause overfitting. However, practically used large deep models avoid overfitting, which is not well explained by the classical approaches…

Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size.As a result, there is a need for compression techniques that can significantly compress RNNs without negatively impacting task accuracy. This paper introduces a method to compress RNNs for resource constrained e…

Proposes a new linearity-based neural network compression method.

Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off. In this paper, we investi…

Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification…

Unified framework compresses GANs up to 47x with minimal quality loss.

Optimizes data power control in cell-free networks for better spectral efficiency.

New compression theory justifies model pruning for neural networks.

SHVC improves image compression with fewer parameters.

Model compression has been introduced to reduce the required hardware resources while maintaining the model accuracy. Lots of techniques for model compression, such as pruning, quantization, and low-rank approximation, have been suggested along with different inference implementation characteristics. Adopting model com…