238 citations · 802 across the 40 of their papers we have counts for
40 papers
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis
Ying Wang, Shashank Jere, Soumya Banerjee +3
Jamming and intrusion detection are critical in 5G research, aiming to maintain reliability, prevent user experience degradation, and avoid infrastructure failure. This paper intro…
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…
DRL meets DSA Networks: Convergence Analysis and Its Application to System Design
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. Sinc…
Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)
Shashank Jere, Ying Wang, Ishan Aryendu +2
The increased flexibility and density of spectrum access in 5G New Radio (NR) has made jamming detection and classification a critical research area. To detect coexisting jamming a…
5G-Advanced Towards 6G: Past, Present, and Future
Wanshi Chen, Xingqin Lin, Juho Lee +4
Since the start of 5G work in 3GPP in early 2016, tremendous progress has been made in both standardization and commercial deployments. 3GPP is now entering the second phase of 5G…
Distributed Learning Meets 6G: A Communication and Computing Perspective
Shashank Jere, Yifei Song, Yang Yi +1
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 re…
Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G
Shadab Mahboob, Lingjia Liu
Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th Generation (6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge a…
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…
Efficient Data Exchange in Unmanned Aerial Vehicle Networks Utilizing Unsupervised Learning-Based Clustering
Hao Song, Lingjia Liu, Ananth Balasubramanian
An unmanned aerial vehicle (UAV) network can serve as an aerial relay to periodically receive packets from macro base stations (BSs). Severe packet loss may happen especially when…
Clustering-based Multicast Scheme for UAV Networks
Hao Song, Lingjia Liu, Bodong Shang +2
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 a…
Optimizing Number, Placement, and Backhaul Connectivity of Multi-UAV Networks
Javad Sabzehali, Vijay K. Shah, Qiang Fan +3
Multi-Unmanned Aerial Vehicle (UAV) Networks is a promising solution to providing wireless coverage to ground users in challenging rural areas (such as Internet of Things (IoT) dev…
RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection
Jiarui Xu, Zhou Zhou, Lianjun Li +2
In this paper, we introduce a structure-based neural network architecture, namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MI…
Learning to Equalize OTFS
Zhou Zhou, Lingjia Liu, Jiarui Xu +1
Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain. The conventi…
Federated Dynamic Spectrum Access
Yifei Song, Hao-Hsuan Chang, Zhou Zhou +2
Due to the growing volume of data traffic produced by the surge of Internet of Things (IoT) devices, the demand for radio spectrum resources is approaching their limitation defined…
Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning
Kian Hamedani, Lingjia Liu, Jithin Jagannath +2
Smart grids are vulnerable to cyber-attacks. This paper proposes a game-theoretic approach to evaluate the variations caused by an attacker on the power measurements. Adversaries c…
UAV Swarm-Enabled Aerial Reconfigurable Intelligent Surface
Bodong Shang, Rubayet Shafin, Lingjia Liu
Reconfigurable intelligent surface (RIS) offers tremendous spectrum and energy efficiency in wireless networks by adjusting the amplitudes and/or phases of passive reflecting eleme…
Towards Intelligent RAN Slicing for B5G: Opportunities and Challenges
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)…
Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing
Zhou Zhou, Kangjun Bai, Nima Mohammadi +2
This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems. By modeling radio-frequen…
Differential Privacy Meets Federated Learning under Communication Constraints
Nima Mohammadi, Jianan Bai, Qiang Fan +3
The performance of federated learning systems is bottlenecked by communication costs and training variance. The communication overhead problem is usually addressed by three communi…
Harnessing Tensor Structures -- Multi-Mode Reservoir Computing and Its Application in Massive MIMO
Zhou Zhou, Lingjia Liu, Jiarui Xu
In this paper, we introduce a new neural network (NN) structure, multi-mode reservoir computing (Multi-Mode RC). It inherits the dynamic mechanism of RC and processes the forward p…
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…
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks
Zhou Zhou, Yan Xin, Hao Chen +2
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…
Learning for Integer-Constrained Optimization through Neural Networks with Limited Training
Zhou Zhou, Shashank Jere, Lizhong Zheng +1
In this paper, we investigate a neural network-based learning approach towards solving an integer-constrained programming problem using very limited training. To be specific, we in…