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Found 7 papers, 1 papers with code
Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications
The rise of deep learning has marked significant progress in fields such as computer vision, natural language processing, and medical imaging, primarily through the adaptation of pre-trained models for specific tasks.
Deep Learning Based Uplink Multi-User SIMO Beamforming Design
The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensive coverage, minimal latency and energy-efficient performance.
A Wideband Signal Recognition Dataset
Signal recognition is a spectrum sensing problem that jointly requires detection, localization in time and frequency, and classification.
Wideband Signal Localization with Spectral Segmentation
We define the signal localization task, present the metrics of precision and recall, and establish baselines for traditional energy detection on this task.
Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks
Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system.
Physical Layer Communications System Design Over-the-Air Using Adversarial Networks
This paper presents a novel method for synthesizing new physical layer modulation and coding schemes for communications systems using a learning-based approach which does not require an analytic model of the impairments in the channel.
Over the Air Deep Learning Based Radio Signal Classification
We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals.
