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Found 16 papers, 2 papers with code
H2O-Danube-1.8B Technical Report
We present H2O-Danube, a series of small 1. 8B language models consisting of H2O-Danube-1. 8B, trained on 1T tokens, and the incremental improved H2O-Danube2-1. 8B trained on an additional 2T tokens.
Towards Optimal Pilot Spacing and Power Control in Multi-Antenna Systems Operating Over Non-Stationary Rician Aging Channels
In particular, when the channel changes rapidly in time, channel aging degrades the performance in terms of spectral efficiency without proper pilot spacing and power control.
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.
Rapid Deforestation and Burned Area Detection using Deep Multimodal Learning on Satellite Imagery
Our method successfully achieves high-precision deforestation estimation and burned area detection on unseen images from the region.
Tensor-based modeling/estimation of static channels in IRS-assisted MIMO systems
This paper proposes a tensor-based parametric modeling and estimation framework in multiple-input multiple-output (MIMO) systems assisted by intelligent reflecting surfaces (IRSs).
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.
Low-Complexity Joint Active and Passive Beamforming Design for IRS-Assisted MIMO
In this letter, we consider an intelligent reflecting surface (IRS)-assisted multiple input multiple output (MIMO) communication and we optimize the joint active and passive beamforming by exploiting the geometrical structure of the propagation channels.
Efficient Battery Usage in Wireless-Powered Cell-Free Systems with Self-Energy Recycling
This paper investigates wireless-powered cell-free systems, in which the users send their uplink data signal while simultaneously harvesting energy from network nodes and user terminals - including the transmitting user terminal itself - by performing self-energy recycling.
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.
Reducing the Control Overhead of Intelligent Reconfigurable Surfaces Via a Tensor-Based Low-Rank Factorization Approach
We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links.
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.
IRS Phase-Shift Feedback Overhead-Aware Model Based on Rank-One Tensor Approximation
In this paper, we propose a rank-one tensor modeling approach that yields a compact representation of the optimum IRS phase-shift vector for reducing the feedback overhead.
Joint Resource Allocation and Transceiver Design for Sum-Rate Maximization under Latency Constraints in Multicell MU-MIMO Systems
This problem is motivated by the observation that multicell MU-MIMO OFDM systems serve multiple quality of service classes and the system performance depends critically on both the transceiver design and the scheduling algorithm.
Decentralized Joint Beamforming, User Scheduling and QoS Management in 5G and Beyond Systems
Fifth generation cellular systems support a broad range of services, including mobile broadband, critical and massive Internet of Things and are used in a variety of scenarios.
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.
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.
