Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Mashhadi, Mahdi Boloursaz"'
Recent advancements in diffusion models have made a significant breakthrough in generative modeling. The combination of the generative model and semantic communication (SemCom) enables high-fidelity semantic information exchange at ultra-low rates. A
Externí odkaz:
http://arxiv.org/abs/2407.03050
Over-the-air computation (AirComp) is a promising technology converging communication and computation over wireless networks, which can be particularly effective in model training, inference, and more emerging edge intelligence applications. AirComp
Externí odkaz:
http://arxiv.org/abs/2405.15969
Generative foundation AI models have recently shown great success in synthesizing natural signals with high perceptual quality using only textual prompts and conditioning signals to guide the generation process. This enables semantic communications a
Externí odkaz:
http://arxiv.org/abs/2403.17256
Autor:
Aghashahi, Somayeh, Zeinalpour-Yazdi, Zolfa, Tadaion, Aliakbar, Mashhadi, Mahdi Boloursaz, Elzanaty, Ahmed
In this letter, we investigate the signal-to-interference-plus-noise-ratio (SINR) maximization problem in a multi-user massive multiple-input-multiple-output (massive MIMO) system enabled with multiple reconfigurable intelligent surfaces (RISs). We e
Externí odkaz:
http://arxiv.org/abs/2303.04272
In this paper, the problem of drone-assisted collaborative learning is considered. In this scenario, swarm of intelligent wireless devices train a shared neural network (NN) model with the help of a drone. Using its sensors, each device records sampl
Externí odkaz:
http://arxiv.org/abs/2303.02266
This paper studies issues that arise with respect to the joint optimization for convergence time in federated learning over wireless networks (FLOWN). We consider the criterion and protocol for selection of participating devices in FLOWN under the en
Externí odkaz:
http://arxiv.org/abs/2209.06623
We present a new deep-neural-network (DNN) based error correction code for fading channels with output feedback, called deep SNR-robust feedback (DRF) code. At the encoder, parity symbols are generated by a long short term memory (LSTM) network based
Externí odkaz:
http://arxiv.org/abs/2112.11789
Autor:
Safavi, Anahid Robert, Perotti, Alberto G., Popovic, Branislav M., Mashhadi, Mahdi Boloursaz, Gunduz, Deniz
A new deep-neural-network (DNN) based error correction encoder architecture for channels with feedback, called Deep Extended Feedback (DEF), is presented in this paper. The encoder in the DEF architecture transmits an information message followed by
Externí odkaz:
http://arxiv.org/abs/2105.01365
Autor:
Zecchin, Matteo, Mashhadi, Mahdi Boloursaz, Jankowski, Mikolaj, Gunduz, Deniz, Kountouris, Marios, Gesbert, David
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility. To reduce the search overhead of iterative beam discovery
Externí odkaz:
http://arxiv.org/abs/2104.14579
Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection. For vehicle-to-infrastructure (V2I) networks, side information from LIDAR sensors mounted
Externí odkaz:
http://arxiv.org/abs/2102.02802