Search Results for author: Lingjia Liu

Found 34 papers, 0 papers with code

Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG

no code implementations5 Mar 2024 Jiarui Xu, Shashank Jere, Yifei Song, Yi-Hung Kao, Lizhong Zheng, Lingjia Liu

At the air interface, multiple-input multiple-output (MIMO) and its variants such as multi-user MIMO (MU-MIMO) and massive/full-dimension MIMO have been key enablers across successive generations of cellular networks with evolving complexity and design challenges.

Scheduling

Maximally Concentrated Sequences after Half-sample Shifts

no code implementations17 Jan 2024 Karim A. Said, Lingjia Liu, A. A., Beex

It is well known that index (discrete-time)-limited sampled sequences leak outside the support set when a band-limiting operation is applied.

2D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detection

no code implementations14 Nov 2023 Jiarui Xu, Karim Said, Lizhong Zheng, Lingjia Liu

Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios.

Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing

no code implementations8 Oct 2023 Shashank Jere, Karim Said, Lizhong Zheng, Lingjia Liu

With this groundwork, we incorporate the available domain knowledge in the form of the statistics of the wireless channel directly into the weights of the ESN model.

Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing

no code implementations4 Aug 2023 Shashank Jere, Lizhong Zheng, Karim Said, Lingjia Liu

Reservoir computing (RC), a special RNN where the recurrent weights are randomized and left untrained, has been introduced to overcome these issues and has demonstrated superior empirical performance in fields as diverse as natural language processing and wireless communications especially in scenarios where training samples are extremely limited.

Learning to Estimate: A Real-Time Online Learning Framework for MIMO-OFDM Channel Estimation

no code implementations22 May 2023 Lianjun Li, Sai Sree Rayala, Jiarui Xu, Lizhong Zheng, Lingjia Liu

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 online training and converges within one OFDM subframe.

Binary Classification

DRL meets DSA Networks: Convergence Analysis and Its Application to System Design

no code implementations18 May 2023 Ramin Safavinejad, Hao-Hsuan Chang, Lingjia Liu

In dynamic spectrum access (DSA) networks, secondary users (SUs) need to opportunistically access primary users' (PUs) radio spectrum without causing significant interference.

Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)

no code implementations26 Apr 2023 Shashank Jere, Ying Wang, Ishan Aryendu, Shehadi Dayekh, Lingjia Liu

The increased flexibility and density of spectrum access in 5G New Radio (NR) has made jamming detection and classification a critical research area.

Bayesian Inference Time Series

Distributed Learning Meets 6G: A Communication and Computing Perspective

no code implementations2 Mar 2023 Shashank Jere, Yifei Song, Yang Yi, Lingjia Liu

With the ever-improving computing capabilities and storage capacities of mobile devices in line with evolving telecommunication network paradigms, there has been an explosion of research interest towards exploring Distributed Learning (DL) frameworks to realize stringent key performance indicators (KPIs) that are expected in next-generation/6G cellular networks.

Edge-computing Federated Learning +1

Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing

no code implementations17 Aug 2022 Jiarui Xu, Lianjun Li, Lizhong Zheng, Lingjia Liu

The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols.

Clustering-based Multicast Scheme for UAV Networks

no code implementations3 Nov 2021 Hao Song, Lingjia Liu, Bodong Shang, Scott Pudlewski, Elizabeth Serena Bentley

When an unmanned aerial vehicle (UAV) network is utilized as an aerial small base station (BS), like a relay deployed far away from macro BSs, existing multicast methods based on acknowledgement (ACK) feedback and retransmissions may encounter severe delay and signaling overhead due to hostile wireless environments caused by a long-distance propagation and numerous UAVs.

Clustering Collision Avoidance

RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection

no code implementations3 Oct 2021 Jiarui Xu, Zhou Zhou, Lianjun Li, Lizhong Zheng, Lingjia Liu

The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity.

On the Efficiency of Deep Neural Networks

no code implementations29 Sep 2021 Yibin Liang, Yang Yi, Lingjia Liu

For given performance requirement, an efficient neural network should use the simplest network architecture with minimal number of parameters and connections.

Edge-computing Efficient Neural Network

Learning to Equalize OTFS

no code implementations17 Jul 2021 Zhou Zhou, Lingjia Liu, Jiarui Xu, Robert Calderbank

Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain.

Scheduling

Federated Dynamic Spectrum Access

no code implementations28 Jun 2021 Yifei Song, Hao-Hsuan Chang, Zhou Zhou, Shashank Jere, Lingjia Liu

In this article, we introduce a Federated Learning (FL) based framework for the task of DSA, where FL is a distributive machine learning framework that can reserve the privacy of network terminals under heterogeneous data distributions.

Federated Learning Multi-agent Reinforcement Learning

Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning

no code implementations6 Jun 2021 Kian Hamedani, Lingjia Liu, Jithin Jagannath, Yang, Yi

It will be shown that the utility of the defender is variant in different scenarios, based on the defender that is being used.

UAV Swarm-Enabled Aerial Reconfigurable Intelligent Surface

no code implementations10 Mar 2021 Bodong Shang, Rubayet Shafin, Lingjia Liu

In practice, multiple UAVs can form a UAV swarm to enable the ARIS cooperatively.

Towards Intelligent RAN Slicing for B5G: Opportunities and Challenges

no code implementations27 Feb 2021 EmadElDin A Mazied, Lingjia Liu, Scott F. Midkiff

To meet the diverse demands for wireless communication, fifth-generation (5G) networks and beyond (B5G) embrace the concept of network slicing by forging virtual instances (slices) of its physical infrastructure.

Scheduling

Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing

no code implementations6 Feb 2021 Zhou Zhou, Kangjun Bai, Nima Mohammadi, Yang Yi, Lingjia Liu

This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems.

Harnessing Tensor Structures -- Multi-Mode Reservoir Computing and Its Application in Massive MIMO

no code implementations25 Jan 2021 Zhou Zhou, Lingjia Liu, Jiarui Xu

In this paper, we introduce a new neural network (NN) structure, multi-mode reservoir computing (Multi-Mode RC).

Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks

no code implementations2 Dec 2020 Zhou Zhou, Yan Xin, Hao Chen, Charlie Zhang, Lingjia Liu

In this paper, we consider jointly optimizing cell load balance and network throughput via a reinforcement learning (RL) approach, where inter-cell handover (i. e., user association assignment) and massive MIMO antenna tilting are configured as the RL policy to learn.

Reinforcement Learning (RL)

Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection

no code implementations1 Dec 2020 Zhou Zhou, Shashank Jere, Lizhong Zheng, Lingjia Liu

In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system.

Binary Classification

Learning for Integer-Constrained Optimization through Neural Networks with Limited Training

no code implementations NeurIPS Workshop LMCA 2020 Zhou Zhou, Shashank Jere, Lizhong Zheng, Lingjia Liu

In this paper, we investigate a neural network-based learning approach towards solving an integer-constrained programming problem using very limited training.

Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond

no code implementations12 Oct 2020 Hao-Hsuan Chang, Lingjia Liu, Yang Yi

However, training of both DRL and RNNs is known to be challenging requiring a large amount of training data to achieve convergence.

Management

Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G

no code implementations15 Jul 2020 Shashank Jere, Qiang Fan, Bodong Shang, Lianjun Li, Lingjia Liu

Thus, in this paper, we design a novel edge computing-assisted federated learning framework, in which the communication constraints between IoT devices and edge servers and the effect of various IoT devices on the training accuracy are taken into account.

BIG-bench Machine Learning Edge-computing +1

Securing Mobile IoT with Unmanned Aerial Systems

no code implementations15 May 2020 Aly Sabri Abdalla, Bodong Shang, Vuk Marojevic, Lingjia Liu

The Internet of Things (IoT) will soon be omnipresent and billions of sensors and actuators will support our industries and well-being.

RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training

no code implementations15 Mar 2020 Zhou Zhou, Lingjia Liu, Shashank Jere, Jianzhong, Zhang, Yang Yi

In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC).

Quantization

Content-Aware User Association and Multi-User MIMO Beamforming over Mobile Edge Caching

no code implementations26 Jun 2019 Susanna Mosleh, Qiang Fan, Lingjia Liu, Jonathan D. Ashdown, Erik Perrins, Kurt Turck

In this paper, multiple-input-multiple-output (MIMO) operation and user association policy are linked to the underlying cache placement strategy to ensure a good trade-off between load balancing and backhaul traffic taking into account the underlying wireless channel and the finite cache capacity at edge servers.

Learning for Detection: MIMO-OFDM Symbol Detection through Downlink Pilots

no code implementations25 Jun 2019 Zhou Zhou, Lingjia Liu, Hao-Hsuan Chang

Reservoir computing (RC) is a special recurrent neural network which consists of a fixed high dimensional feature mapping and trained readout weights.

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

no code implementations29 Oct 2018 Rachad Atat, Lingjia Liu, Jinsong Wu, Guangyu Li, Chunxuan Ye, Yang Yi

{Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics.

Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach

no code implementations28 Oct 2018 Hao-Hsuan Chang, Hao Song, Yang Yi, Jianzhong Zhang, Haibo He, Lingjia Liu

To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics.

BIG-bench Machine Learning Q-Learning +2

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