Search Results for author: Wibowo Hardjawana

Found 9 papers, 1 papers with code

Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?

no code implementations11 Jul 2022 Yuhong Liu, Changyang She, Yi Zhong, Wibowo Hardjawana, Fu-Chun Zheng, Branka Vucetic

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.

Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation

no code implementations27 Jun 2022 Alva Kosasih, Xinwei Qu, Wibowo Hardjawana, Chentao Yue, Branka Vucetic

The orthogonal time-frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications.

Bayesian Inference

Graph Neural Network Aided MU-MIMO Detectors

1 code implementation19 Jun 2022 Alva Kosasih, Vincent Onasis, Vera Miloslavskaya, Wibowo Hardjawana, Victor Andrean, Branka Vucetic

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks.

Bayesian-based Symbol Detector for Orthogonal Time Frequency Space Modulation Systems

no code implementations27 Oct 2021 Xinwei Qu, Alva Kosasih, Wibowo Hardjawana, Vincent Onasis, Branka Vucetic

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

no code implementations27 Oct 2021 Alva Kosasih, Vera Miloslavskaya, Wibowo Hardjawana, Victor Andrean, Branka Vucetic

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.

Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation

no code implementations17 Sep 2020 Zhouyou Gu, Changyang She, Wibowo Hardjawana, Simon Lumb, David McKechnie, Todd Essery, Branka Vucetic

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).

Reinforcement Learning (RL) Scheduling

Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G

no code implementations29 Mar 2020 Rui Dong, Changyang She, Wibowo Hardjawana, Yonghui Li, Branka Vucetic

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.

Quantization Transfer Learning

Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks

no code implementations22 Feb 2020 Changyang She, Rui Dong, Zhouyou Gu, Zhanwei Hou, Yonghui Li, Wibowo Hardjawana, Chenyang Yang, Lingyang Song, Branka Vucetic

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.

Edge-computing Federated Learning +1

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin

no code implementations30 Jun 2019 Rui Dong, Changyang She, Wibowo Hardjawana, Yonghui Li, Branka Vucetic

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.

Association Edge-computing +1

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