75 citations · 157 across the 23 of their papers we have counts for
26 papers
Connecting Base-Extension Semantics, Beth Semantics, and Ecumenical Logic
Victor Barroso-Nascimento, Joaquim T. Waddington, Elaine Pimentel
We investigate the relationship between base-extension semantics, Beth semantics, and Prawitz's ecumenical logic. We first present a base-extension semantics for the propositional…
UL-Assisted Parametric CSI Feedback and Prediction for FDD Systems
Hao Chang, Mingcheng Nie, Shuangyang Li +4
In frequency-division duplex (FDD) systems, the uplink (UL) and downlink (DL) channels are not fully reciprocal. Therefore, DL channel state information (CSI) estimated by each use…
Channel Estimation for OFDM via Delay-Doppler Refinement
Mingcheng Nie, Hao Chang, Xiaoqi Zhang +4
In this paper, we propose a novel channel estimation (CE) algorithm for orthogonal frequency division multiplexing (OFDM) systems that exploits the unique characteristics of the de…
Graph Neural Network-based End-to-End Learning for Multi-User MIMO Systems
Hao Chang, Hoang Triet Vo, Alva Kosasih +2
End-to-end (E2E) learning has recently been proposed to jointly design the modulator and symbol detector by using deep neural networks (DNNs). However, existing schemes lack suffic…
Graph-based Untrained Neural Network Detector for OTFS Systems
Hao Chang, Branka Vucetic, Wibowo Hardjawana
Inter-carrier interference (ICI) caused by mobile reflectors significantly degrades the conventional orthogonal frequency division multiplexing (OFDM) performance in high-mobility…
Opportunistic Scheduling Using Statistical Information of Wireless Channels
Zhouyou Gu, Wibowo Hardjawana, Branka Vucetic
This paper considers opportunistic scheduler (OS) design using statistical channel state information~(CSI). We apply max-weight schedulers (MWSs) to maximize a utility function of…
Graph Representation Learning for Contention and Interference Management in Wireless Networks
Zhouyou Gu, Branka Vucetic, Kishore Chikkam +2
Restricted access window (RAW) in Wi-Fi 802.11ah networks manages contention and interference by grouping users and allocating periodic time slots for each group's transmissions. W…
Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems
Hao Chang, Alva Kosasih, Wibowo Hardjawana +2
The orthogonal time frequency space (OTFS) symbol detector design for high mobility communication scenarios has received numerous attention lately. Current state-of-the-art OTFS de…
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?
Yuhong Liu, Changyang She, Yi Zhong +3
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. To obtain time…
Graph Neural Network Aided MU-MIMO Detectors
Alva Kosasih, Vincent Onasis, Vera Miloslavskaya +3
Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplin…
Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation
Alva Kosasih, Xinwei Qu, Wibowo Hardjawana +2
The orthogonal time-frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications. The existing low complexity OTFS detecto…
GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection
Hengtao He, Alva Kosasih, Xianghao Yu +4
Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput…
Practical Considerations of DER Coordination with Distributed Optimal Power Flow
Daniel Gebbran, Sleiman Mhanna, Archie C. Chapman +3
The coordination of prosumer-owned, behind-the-meter distributed energy resources (DER) can be achieved using a multiperiod, distributed optimal power flow (DOPF), which satisfies…
Graph Neural Network Aided Expectation Propagation Detector for MU-MIMO Systems
Alva Kosasih, Vincent Onasis, Wibowo Hardjawana +4
Multiuser massive multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. In an uplink MUMIMO system, a base st…
Density Evolution Analysis of the Iterative Joint Ordered-Statistics Decoding for NOMA
Chentao Yue, Mahyar Shirvanimoghaddam, Alva Kosasih +5
In this paper, we develop a density evolution (DE) framework for analyzing the iterative joint decoding (JD) for non-orthogonal multiple access (NOMA) systems, where the ordered-st…
NOMA Joint Decoding based on Soft-Output Ordered-Statistics Decoder for Short Block Codes
Chentao Yue, Alva Kosasih, Mahyar Shirvanimoghaddam +5
In this paper, we design the joint decoding (JD) of non-orthogonal multiple access (NOMA) systems employing short block length codes. We first proposed a low-complexity soft-output…
Bayesian-based Symbol Detector for Orthogonal Time Frequency Space Modulation Systems
Xinwei Qu, Alva Kosasih, Wibowo Hardjawana +2
Recently, the orthogonal time frequency space (OTFS) modulation is proposed for 6G wireless system to deal with high Doppler spread. The high Doppler spread happens when the transm…
A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems
Alva Kosasih, Wibowo Hardjawana, Branka Vucetic +1
Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multiple-input-multiple…
Improving Cell-Free Massive MIMO Detection Performance via Expectation Propagation
Alva Kosasih, Vera Miloslavskaya, Wibowo Hardjawana +2
Cell-free (CF) massive multiple-input multiple-output (M-MIMO) technology plays a prominent role in the beyond fifth-generation (5G) networks. However, designing a high performance…
A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems
Alva Kosasih, Vera Miloslavskaya, Wibowo Hardjawana +3
The stringent requirements on reliability and processing delay in the fifth-generation (G) cellular networks introduce considerable challenges in the design of massive multiple-…
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
Zhouyou Gu, Changyang She, Wibowo Hardjawana +4
In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-se…
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G
Rui Dong, Changyang She, Wibowo Hardjawana +2
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 n…
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
Changyang She, Rui Dong, Zhouyou Gu +6
In the future 6th generation networks, ultra-reliable and low-latency communications (URLLC) will lay the foundation for emerging mission-critical applications that have stringent…
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin
Rui Dong, Changyang She, Wibowo Hardjawana +2
In this work, we consider a mobile edge computing system with both ultra-reliable and low-latency communications services and delay tolerant services. We aim to minimize the normal…