48 citations · 86 across the 24 of their papers we have counts for
17 papers · 1 filter
TransCoder: A Neural-Enhancement Framework for Channel Codes
Anastasiia Kurmukova, Selim F. Yilmaz, Emre Ozfatura +1
Reliable communication over noisy channels requires the design of specialized error-correcting codes (ECCs) tailored to specific system requirements. Recently, neural network-based…
Transformer-aided Wireless Image Transmission with Channel Feedback
Haotian Wu, Yulin Shao, Emre Ozfatura +2
This paper presents a novel wireless image transmission paradigm that can exploit feedback from the receiver, called DeepJSCC-ViT-f. We consider a block feedback channel model, whe…
Do not Interfere but Cooperate: A Fully Learnable Code Design for Multi-Access Channels with Feedback
Emre Ozfatura, Chenghong Bian, Deniz Gunduz
Data-driven deep learning based code designs, including low-complexity neural decoders for existing codes, or end-to-end trainable auto-encoders have exhibited impressive results,…
Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes
Emre Ozfatura, Yulin Shao, Amin Ghazanfari +3
Deep neural network (DNN)-assisted channel coding designs, such as low-complexity neural decoders for existing codes, or end-to-end neural-network-based auto-encoder designs are ga…
All you need is feedback: Communication with block attention feedback codes
Emre Ozfatura, Yulin Shao, Alberto Perotti +2
Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do no…
AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications
Yulin Shao, Emre Ozfatura, Alberto Perotti +2
Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this…
Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning
Baturalp Buyukates, Emre Ozfatura, Sennur Ulukus +1
Distributed implementations are crucial in speeding up large scale machine learning applications. Distributed gradient descent (GD) is widely employed to parallelize the learning t…
Gradient Coding with Dynamic Clustering for Straggler Mitigation
Baturalp Buyukates, Emre Ozfatura, Sennur Ulukus +1
In distributed synchronous gradient descent (GD) the main performance bottleneck for the per-iteration completion time is the slowest \textit{straggling} workers. To speed up GD it…
Coded Distributed Computing with Partial Recovery
Emre Ozfatura, Sennur Ulukus, Deniz Gunduz
Coded computation techniques provide robustness against straggling workers in distributed computing. However, most of the existing schemes require exact provisioning of the straggl…
Age-Based Coded Computation for Bias Reduction in Distributed Learning
Emre Ozfatura, Baturalp Buyukates, Deniz Gunduz +1
Coded computation can be used to speed up distributed learning in the presence of straggling workers. Partial recovery of the gradient vector can further reduce the computation tim…
Straggler-aware Distributed Learning: Communication Computation Latency Trade-off
Emre Ozfatura, Sennur Ulukus, Deniz Gunduz
When gradient descent (GD) is scaled to many parallel workers for large scale machine learning problems, its per-iteration computation time is limited by the straggling workers. St…
Gradient Coding with Clustering and Multi-message Communication
Emre Ozfatura, Deniz Gunduz, Sennur Ulukus
Gradient descent (GD) methods are commonly employed in machine learning problems to optimize the parameters of the model in an iterative fashion. For problems with massive datasets…
Speeding Up Distributed Gradient Descent by Utilizing Non-persistent Stragglers
Emre Ozfatura, Deniz Gunduz, Sennur Ulukus
Distributed gradient descent (DGD) is an efficient way of implementing gradient descent (GD), especially for large data sets, by dividing the computation tasks into smaller subtask…
Delay-Aware Coded Caching for Mobile Users
Emre Ozfatura, Thomas Rarris, Deniz Gunduz +1
In this work, we study the trade-off between the cache capacity and the user delay for a cooperative Small Base Station (SBS) coded caching system with mobile users. First, a delay…
Mobility-Aware Coded Storage and Delivery
Emre Ozfatura, Deniz Gunduz
Content caching at small-cell base stations (SBSs) is a promising method to mitigate the excessive backhaul load and delay, particularly for on-demand video streaming applications.…
Uncoded Caching and Cross-level Coded Delivery for Non-uniform File Popularity
Emre Ozfatura, Deniz Gunduz
Proactive content caching at user devices and coded delivery is studied considering a non-uniform file popularity distribution. A novel centralized uncoded caching and coded delive…
Mobility and Popularity-Aware Coded Small-Cell Caching
Emre Ozfatura, Deniz Gündüz
In heterogeneous cellular networks with caching capability, due to mobility of users and storage constraints of small-cell base stations (SBSs), users may not be able to download a…