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Found 12 papers, 2 papers with code
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin
We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server.
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods.
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G
To accommodate diverse Quality-of-Service (QoS) requirements in the 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions.
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
Simulation results show that our approach reduces the convergence time of DDPG significantly and achieves better QoS than existing schedulers (reducing 30% ~ 50% packet losses).
Bayesian-based Symbol Detector for Orthogonal Time Frequency Space Modulation Systems
Our simulation results show that in contrast to the state-of-the-art OTFS detectors, the proposed detector is able to achieve a BER of less than $10^{-5}$, when SNR is over $14$ dB, under high ICI environments.
Improving Cell-Free Massive MIMO Detection Performance via Expectation Propagation
The simulation results show that the proposed detector achieves significant improvements in terms of the bit-error rate and sum spectral efficiency performances as compared to the ones of the state-of-the-art CF detectors.
Graph Neural Network Aided MU-MIMO Detectors
Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks.
Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation
The orthogonal time-frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications.
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?
In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks.
Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems
In this paper, we propose an untrained DNN based on the deep image prior (DIP) and decoder architecture, referred to as D-DIP that replaces the MMSE denoiser in the iterative detector.
Graph Representation Learning for Contention and Interference Management in Wireless Networks
Additionally, we present an architecture that uses the online-measured throughput and path losses to fine-tune the decisions in response to changes in user populations and their locations.
Graph-based Untrained Neural Network Detector for OTFS Systems
Inter-carrier interference (ICI) caused by mobile reflectors significantly degrades the conventional orthogonal frequency division multiplexing (OFDM) performance in high-mobility environments.
