no code implementations • 5 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.
no code implementations • 17 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.
no code implementations • 28 Nov 2023 • Ying Wang, Shashank Jere, Soumya Banerjee, Lingjia Liu, Sachin Shetty, Shehadi Dayekh
To address this, an unsupervised auto-encoder-based anomaly detection is presented with an AUC of 0. 987.
no code implementations • 14 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.
no code implementations • 8 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.
no code implementations • 4 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.
no code implementations • 22 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.
no code implementations • 18 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.
no code implementations • 26 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.
no code implementations • 2 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.
no code implementations • 17 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.
no code implementations • 3 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.
no code implementations • 3 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.
no code implementations • 29 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.
no code implementations • 17 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.
no code implementations • 28 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.
no code implementations • 6 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.
no code implementations • 10 Mar 2021 • Bodong Shang, Rubayet Shafin, Lingjia Liu
In practice, multiple UAVs can form a UAV swarm to enable the ARIS cooperatively.
no code implementations • 27 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.
no code implementations • 6 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.
no code implementations • 28 Jan 2021 • Nima Mohammadi, Jianan Bai, Qiang Fan, Yifei Song, Yang Yi, Lingjia Liu
The performance of federated learning systems is bottlenecked by communication costs and training variance.
no code implementations • 25 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).
no code implementations • 2 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.
no code implementations • 1 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.
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.
no code implementations • 12 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.
no code implementations • 15 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.
no code implementations • 15 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.
no code implementations • 30 Apr 2020 • Qiang Fan, Jianan Bai, Hongxia Zhang, Yang Yi, Lingjia Liu
Mobile IoT is composed by mobile IoT devices such as vehicles, wearable devices and smartphones.
no code implementations • 15 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).
no code implementations • 26 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.
no code implementations • 25 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.
no code implementations • 29 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.
no code implementations • 28 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.