372 citations · 668 across the 43 of their papers we have counts for
9 papers · 1 filter
Telling Neural Network What We Know: Weight Configuration for RNN and Its Application to NextG Real-Time Neural Receiver
Ummay Sumaya Khan, Lizhong Zheng, Lingjia Liu
AI-enabled receivers offer a promising approach to adaptive physical-layer processing, but conventional neural receivers often require extensive offline training and computationall…
Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection
Jiarui Xu, Karim Said, Lizhong Zheng +1
Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach h…
Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG
Jiarui Xu, Shashank Jere, Yifei Song +3
Integration of artificial intelligence (AI) and machine learning (ML) into the air interface has been envisioned as a key technology for next-generation (NextG) cellular networks.…
2D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detection
Jiarui Xu, Karim Said, Lizhong Zheng +1
Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach h…
Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing
Shashank Jere, Karim Said, Lizhong Zheng +1
Deep learning has seen a rapid adoption in a variety of wireless communications applications, including at the physical layer. While it has delivered impressive performance in task…
Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing
Shashank Jere, Lizhong Zheng, Karim Said +1
Recurrent neural networks (RNNs) are known to be universal approximators of dynamic systems under fairly mild and general assumptions. However, RNNs usually suffer from the issues…
Learning to Estimate: A Real-Time Online Learning Framework for MIMO-OFDM Channel Estimation
Lianjun Li, Sai Sree Rayala, Jiarui Xu +2
In this paper we introduce StructNet-CE, a novel real-time online learning framework for MIMO-OFDM channel estimation, which only utilizes over-the-air (OTA) pilot symbols for onli…
Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing
Jiarui Xu, Lianjun Li, Lizhong Zheng +1
The limited over-the-air (OTA) pilot symbols in multiple-input-multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for detecti…
Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection
Zhou Zhou, Shashank Jere, Lizhong Zheng +1
In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system. Building on a reservoir computing (RC)-based approach towards symbo…