4 citations · 12 across the 11 of their papers we have counts for
11 papers
Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals
Mert Kalfa, Sadik Yagiz Yetim, Arda Atalik +6
Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic…
On the Rate-Distortion-Perception Function
Jun Chen, Lei Yu, Jia Wang +3
Rate-distortion-perception theory generalizes Shannon's rate-distortion theory by introducing a constraint on the perceptual quality of the output. The perception constraint comple…
On Distributed Lossy Coding of Symmetrically Correlated Gaussian Sources
Siyao Zhou, Sadaf Salehkalaibar, Jingjing Qian +4
A distributed lossy compression network with encoders and a decoder is considered. Each encoder observes a source and sends a compressed version to the decoder. The decoder pro…
Distributed Learning for Time-varying Networks: A Scalable Design
Jian Wang, Yourui Huangfu, Rong Li +2
The wireless network is undergoing a trend from "onnection of things" to "connection of intelligence". With data spread over the communication networks and computing capability enh…
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks
Jian Wang, Chen Xu, Rong Li +2
To improve the system performance towards the Shannon limit, advanced radio resource management mechanisms play a fundamental role. In particular, scheduling should receive much at…
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
Chen Xu, Jian Wang, Tianhang Yu +5
In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate. Two genie-aided heuristic…
Realistic Channel Models Pre-training
Yourui Huangfu, Jian Wang, Chen Xu +5
In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models.…
Deep Reinforcement Learning for Scheduling in Cellular Networks
Jian Wang, Chen Xu, Yourui Huangfu +3
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia. A common solution is to replace partial or even all mo…
Reinforcement Learning for Nested Polar Code Construction
Lingchen Huang, Huazi Zhang, Rong Li +2
In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques. First, an MDP environ…
AI Coding: Learning to Construct Error Correction Codes
Lingchen Huang, Huazi Zhang, Rong Li +2
In this paper, we investigate an artificial-intelligence (AI) driven approach to design error correction codes (ECC). Classic error correction code was designed upon coding theory…
-expansion: A Theoretical Framework for Fast and Recursive Construction of Polar Codes
Gaoning He, Jean-Claude Belfiore, Xiaocheng Liu +8
In this work, we introduce -expansion, a notion borrowed from number theory, as a theoretical framework to study fast construction of polar codes based on a recursive structure…