pwc-archive/papers-with-abstracts huggingface dataset:
Joint Beamforming and Power Control for D2D-Assisted Integrated Sensing and Communication Networks
Authors: Zhenyu Xue, Yuang Chen, Hancheng Lu, Baolin Chong, Wanqing Long
Date: 2024-08-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is an emerging technology in next-generation communication networks. However, the communication performance of the ISAC system may be severely affected by interference from the radar system if the sensing task has demanding performance requirements. In this paper, we exploit device-to-device communication (D2D) to improve system communication capacity. The ISAC system in a single cell D2D assisted-network is investigated, where the base station (BS) performs target sensing and communication with multiple celluar user equipments (CUEs) as well as D2D user equipments (DUEs) simultaneously communicating with other DUEs by multiplexing the same frequency resource. To achieve the optimal communication performance in the D2D-assisted ISAC system, a joint beamforming and power control problem is formulated with the goal to maximize the sum rate of the system while guaranteeing the performance requirements of radar sensing. Due to the non-convexity of the problem, we propose the algorithm to transform the origin problem into a relaxation form and obtain the solution. We also proposed the zero-forcing (ZF) beamforming scheme to acquire the solution that can eliminate the interference of the BS on DUEs. Extensive numerical simulations demonstrated that with the assistance of the D2D communications, our proposed algorithm significantly outperforms the baseline schemes in the system sum rate.
Convolutional Neural Network Approach for Emitter Association using Channel Identification in a MIMO System based on Propagation Features
Authors: Michael F. Talley Jr., Kofi Nyarko, Willie L. Thompson II, Arlene Cole-Rhodes, Craig Scott
Date: 2024-08-19 00:00:00

In this paper, an application of a 1D deep convolutional neural network (DCNN) and 4x4 1D DCNN Multi-channel Model (DCNN-MCM) was developed to predict the probability of a channel being associated with a given transmitter for each emitter in a 4x4 multi-input multi-output (MIMO) system. Counterintuitively, compared to the traditional approach to emitter association (EA), this research argues for the identification of received RF signals based on channel features (CFs) such as the channel impulse response (CIR) and transfer function (TF). Based on the CFs there are unique properties per transmit and receive pair that can be used to identify the different transmitters. More specifically, CFs are often defined by a wide sense stationary (WSS) stochastic process which gives them unique properties such as, being statistically independent and reciprocal for emitter association. Given these CFs, the received RF signals can be used to measure the clutter and objects surrounding the emitters. Safely assuming the surrounding clutter and objects are unique to each emitter, the estimation and tracking of this clutter provides a way of identifying what emitter is transmitting the observed signal. This method successfully classifies and identifies the emitters at 97.22% and 88.89% accuracy for the DCNN and DCNN-MCM respectively. This method was further compared with the accuracy of previous methods to test the validity of the approach and future improvements.
Near-Field Sensing: A Low-Complexity Wavenumber-Domain Method
Authors: Hao Jiang, Zhaolin Wang, Yuanwei Liu
Date: 2024-08-18 00:00:00

A novel low-complexity wavenumber-domain method is proposed for near-field sensing (NISE). Specifically, the power-concentrated region of the wavenumber-domain channels is related to the target position in a non-linear manner. Based on this observation, a bi-directional convolutional neural network (BiCNN)-based approach is proposed to capture such a relationship, thereby facilitating low-complexity target localization. This method enables direct and gridless target localization using only a limited bandwidth and a single antenna array. Simulation results demonstrate that: 1) during the offline training phase, the proposed BiCNN method can learn to localize the target with fewer trainable parameters compared to the naive neural network architectures; and 2) during the online implementation phase, the BiCNN method can spend 100x less time while maintaining comparable performance to the conventional two-dimensional multiple signal classification (MUSIC) algorithms.
Channel Estimation, Interpolation and Extrapolation in Doubly-dispersive Channels
Authors: Zijun Gong, Fan Jiang, Yuhui Song, Cheng Li, Xiaofeng Tao
Date: 2024-08-18 00:00:00

The OTFS (Orthogonal Time Frequency Space) is widely acknowledged for its ability to combat Doppler spread in time-varying channels. In this paper, another advantage of OTFS over OFDM (Orthogonal Frequency Division Multiplexing) will be demonstrated: much reduced channel training overhead. Specifically, the sparsity of the channel in delay-Doppler (D-D) domain implies strong correlation of channel gains in time-frequency (T-F) domain, which can be harnessed to reduce channel training overhead through interpolation. An immediate question is how much training overhead is needed in doubly-dispersive channels? A conventional belief is that the overhead is only dependent on the product of delay and Doppler spreads, but we will show that it's also dependent on the T-F window size. The finite T-F window leads to infinite spreading in D-D domain, and aliasing will be inevitable after sampling in T-F domain. Two direct consequences of the aliasing are increased channel training overhead and interference. Another factor contributing to channel estimation error is the inter-symbol-carrier-interference (ISCI), resulting from the uncertainty principle. Both aliasing and ISCI are considered in channel modelling, a low-complexity algorithm is proposed for channel estimation and interpolation through FFT. A large T-F window is necessary for reduced channel training overhead and aliasing, but increases processing delay. Fortunately, we show that the proposed algorithm can be implemented in a pipeline fashion. Further more, we showed that data-aided channel tracking is possible in D-D domain to further reduce the channel estimation frequency, i.e., channel extrapolation. The impacts of aliasing and ISCI on channel interpolation error are analyzed.
Interference-Free Backscatter Communications for OFDM-Based Symbiotic Radio
Authors: Muhammad Bilal Janjua, Alphan Şahin, Hüseyin Arslan
Date: 2024-08-17 00:00:00

This study proposes an orthogonal frequency division multiplexing (OFDM) based scheme to achieve interference-free backscatter communications (BC) in a symbiotic radio system. In specific, we propose three frequency shift keying (FSK) based backscatter modulation schemes to shift the primary signal, i.e., the OFDM symbols transmitted from a base station (BS), in the frequency domain to transmit its information. Symbiotically, the BS empties specific subcarriers within the band so that the received frequency-shifted signals from the backscatter device and the primary signal are always orthogonal. The first scheme relies on the combination of on-off keying (OOK) within the FSK modulation while the second and the third schemes are based on the conventional FSK modulation with different in-band null-subcarrier allocation. These schemes allow the use of non-coherent detection at the receiver which addresses the channel estimation challenge for the signals arriving from a backscatter device. We derive the bit-error rate performance of the detector theoretically. The comprehensive simulations show that the proposed approach achieves a lower bit-error rate up to 10-4 at 30 dB with BC by eliminating direct link interference.
Secrecy Energy Efficiency Maximization in RIS-Aided Wireless Networks with Statistical CSI
Authors: Robert Kuku Fotock, Agbotiname Lucky Imoize, Alessio Zappone, Marco Di Renzo, Roberto Garello
Date: 2024-08-17 00:00:00

This work studies the problem of secrecy energy efficiency maximization in multi-user wireless networks aided by reconfigurable intelligent surfaces, in which an eavesdropper overhears the uplink communication. A provably convergent optimization algorithm is proposed which optimizes the user's transmit power, metasurface reflection coefficients, and base station receive filters. The complexity of the proposed method is analyzed and numerical results are provided to show the performance of the proposed optimization method.
RIS-based Over-the-air Diffractional Channel Coding
Authors: Yingzhe Hui, Shuyi Chen, Yifan Qin, Weixiao Meng, Qiushi Zhang, Wei Jin
Date: 2024-08-17 00:00:00
Tasks: Decoder

Reconfigurable Intelligent Surfaces (RIS) are programmable metasurfaces utilizing sub-wavelength meta-atoms and a controller for precise electromagnetic wave manipulation. This work introduces an innovative channel coding scheme, termed RIS-based diffractional channel coding (DCC), which capitalizes on diffraction between two RIS layers for signal-level encoding. Contrary to traditional methods, DCC expands signal dimensions through diffraction, presenting a novel countermeasure to channel effects. This paper focuses on the operational principles of DCC, including encoder and decoder designs, and explores its possibilities to construct block and trellis codes, demonstrating its potential as both an alternative and a supplementary conventional coding scheme. Key advantages of DCC include eliminating extra power requirements for encoding, achieving computation at the speed of light, and enabling adjustable code distance, making it a progressive solution for efficient wireless communication, particularly in systems with large-scale data or massive MIMO.
FAS vs. ARIS: Which Is More Important for FAS-ARIS Communication Systems?
Authors: Junteng Yao, Liaoshi Zhou, Tuo Wu, Ming Jin, Chongwen Huang, Chau Yuen
Date: 2024-08-17 00:00:00

In this paper, we investigate the question of which technology, fluid antenna systems (FAS) or active reconfigurable intelligent surfaces (ARIS), plays a more crucial role in FAS-ARIS wireless communication systems. To address this, we develop a comprehensive system model and explore the problem from an optimization perspective. We introduce an alternating optimization (AO) algorithm incorporating majorization-minimization (MM), successive convex approximation (SCA), and sequential rank-one constraint relaxation (SRCR) to tackle the non-convex challenges inherent in these systems. Specifically, for the transmit beamforming of the BS optimization, we propose a closed-form rank-one solution with low-complexity. For the optimization the positions of fluid antennas (FAs) of the BS, the Taylor expansions and MM algorithm are utilized to construct the effective lower bounds and upper bounds of the objective function and constraints, transforming the non-convex optimization problem into a convex one. Furthermore, we use the SCA and SRCR to optimize the reflection coefficient matrix of the ARIS and effectively solve the rank-one constraint. Simulation results reveal that the relative importance of FAS and ARIS varies depending on the scenario: FAS proves more critical in simpler models with fewer reflecting elements or limited transmission paths, while ARIS becomes more significant in complex scenarios with a higher number of reflecting elements or transmission paths. Ultimately, the integration of both FAS and ARIS creates a win-win scenario, resulting in a more robust and efficient communication system. This study underscores the importance of combining FAS with ARIS, as their complementary use provides the most substantial benefits across different communication environments.
Symbol-Level Precoding for Near-Field ISAC
Authors: Nithin Babu, Alva Kosasih, Christos Masouros, Emil Björnson
Date: 2024-08-16 00:00:00
Tasks: ISAC

The forthcoming 6G and beyond wireless networks are anticipated to introduce new groundbreaking applications, such as Integrated Sensing and Communications (ISAC), potentially leveraging much wider bandwidths at higher frequencies and using significantly larger antenna arrays at base stations. This puts the system operation in the radiative near-field regime of the BS antenna array, characterized by spherical rather than flat wavefronts. In this paper, we refer to such a system as near-field ISAC. Unlike the far-field regime, the near-field regime allows for precise focusing of transmission beams on specific areas, making it possible to simultaneously determine a target's direction and range from a single base station and resolve targets located in the same direction. This work designs the transmit symbol vector in near-field ISAC to maximize a weighted combination of sensing and communication performances subject to a total power constraint using symbol-level precoding (SLP). The formulated optimization problem is convex, and the solution is used to estimate the angle and range of the considered targets using the 2D MUSIC algorithm. The simulation results suggest that the SLP-based design outperforms the block-level-based counterpart. Moreover, the 2D MUSIC algorithm accurately estimates the targets' parameters.
ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis
Authors: Yubao Zhao, Jiaju Kang, Tian Zhang, Puyu Han, Tong Chen
Date: 2024-08-16 00:00:00
Tasks: Contrastive Learning, Diagnostic, Language Modeling, Language Modelling, Medical Report Generation, Retrieval, Text Generation

The success of Multimodal Large Language Models (MLLMs) in the medical auxiliary field shows great potential, allowing patients to engage in conversations using physiological signal data. However, general MLLMs perform poorly in cardiac disease diagnosis, particularly in the integration of ECG data analysis and medical report generation, mainly due to the complexity of ECG data analysis and the gap between text and ECG signal modalities. To address these issues, we propose ECG-Chat, a multitask MLLMs focused on ECG medical report generation, providing multimodal conversational capabilities based on cardiology knowledge. We propose a contrastive learning approach that integrates ECG waveform data with text reports, aligning ECG features with reports in a fine-grained manner. This method also results in an ECG encoder that excels in zero-shot report retrieval tasks. Additionally, expanding existing datasets, we constructed a 19k ECG diagnosis dataset and a 25k multi-turn dialogue dataset for training and fine-tuning ECG-Chat, which provides professional diagnostic and conversational capabilities. Furthermore, ECG-Chat can generate comprehensive ECG analysis reports through an automated LaTeX generation pipeline. We established a benchmark for the ECG report generation task and tested our model on multiple baselines. ECG-Chat achieved the best performance in classification, retrieval, and medical report generation tasks. Our code is available at https://github.com/YubaoZhao/ECG-Chat.
Intra-symbol Differential Amplitude Shift Keying-aided Blind Detector for Ambient Backscatter Communication Systems
Authors: Shuaijun Ma, Peng Wei, Sa Xiao, Jianquan Wang, Wanbin Tang, Wei Xiang
Date: 2024-08-16 00:00:00

Ambient backscatter communications (AmBC) are a promising technology for addressing the energy consumption challenge in wireless communications through the reflection or absorption of surrounding radio frequency (RF) signals. However, it grapples with the intricacies of ambient RF signal and the round-trip path loss. For traditional detectors, the incorporation of pilot sequences results in a reduction in spectral efficiency. Furthermore, traditional energy-based detectors are inherently susceptible to a notable error floor issue, attributed to the co-channel direct link interference (DLI). Consequently, this paper proposes a blind symbol detector without the prior knowledge of the channel state information, signal variance, and noise variance. By leveraging the intra-symbol differential amplitude shift keying (IDASK) scheme, this detector effectively redirects the majority of the DLI energy towards the largest eigenvalue of the received sample covariance matrix, thereby utilizing the second largest eigenvalue for efficient symbol detection. In addition, this paper conducts theoretical performance analyses of the proposed detector in terms of the false alarm probability, missed detection probability, and the bit-error rate (BER) lower bound. Simulation results demonstrate that the proposed blind detector exhibits a significant enhancement in symbol detection performance compared to its traditional counterparts.
Optimal Linear Precoding Under Realistic Satellite Communications Scenarios
Authors: Geoffrey Eappen, Jorge Luis Gonzalez, Vibhum Singh, Rakesh Palisetty, Alireza Haqiqtnejad, Liz Martinez Marrero, Jevgenij Krivochiza, Jorge Querol, Nicola Maturo, Juan Carlos Merlano Duncan, Eva Lagunas, Stefano Andrenacci, Symeon Chatzinotas
Date: 2024-08-16 00:00:00

In this paper, optimal linear precoding for the multibeam geostationary earth orbit (GEO) satellite with the multi-user (MU) multiple-input-multiple-output (MIMO) downlink scenario is addressed. Multiple-user interference is one of the major issues faced by the satellites serving the multiple users operating at the common time-frequency resource block in the downlink channel. To mitigate this issue, the optimal linear precoders are implemented at the gateways (GWs). The precoding computation is performed by utilizing the channel state information obtained at user terminals (UTs). The optimal linear precoders are derived considering beamformer update and power control with an iterative per-antenna power optimization algorithm with a limited required number of iterations. The efficacy of the proposed algorithm is validated using the In-Lab experiment for 16X16 precoding with multi-beam satellite for transmitting and receiving the precoded data with digital video broadcasting satellite-second generation extension (DVB- S2X) standard for the GW and the UTs. The software defined radio platforms are employed for emulating the GWs, UTs, and satellite links. The validation is supported by comparing the proposed optimal linear precoder with full frequency reuse (FFR), and minimum mean square error (MMSE) schemes. The experimental results demonstrate that with the optimal linear precoders it is possible to successfully cancel the inter-user interference in the simulated satellite FFR link. Thus, optimal linear precoding brings gains in terms of enhanced signal-to-noise-and-interference ratio, and increased system throughput and spectral efficiency.
Fast Network Recovery from Large-Scale Disasters: A Resilient and Self-Organizing RAN Framework
Authors: M. Yaser Yagan, Sefa Kayraklık, Samed Kesir, Gizem Sumen, Ibrahim Hokelek, Mehmet Basaran, George C. Alexandropoulos, Ertugrul Basar, Cicek Cavdar, Huseyin Arslan, Ali Gorcin
Date: 2024-08-16 00:00:00

Extreme natural phenomena are occurring more frequently everyday in the world, challenging, among others, the infrastructure of communication networks. For instance, the devastating earthquakes in Turkiye in early 2023 showcased that, although communications became an imminent priority, existing mobile communication systems fell short with the operational requirements of harsh disaster environments. In this article, we present a novel framework for robust, resilient, adaptive, and open source sixth generation (6G) radio access networks (Open6GRAN) that can provide uninterrupted communication services in the face of natural disasters and other disruptions. Advanced 6G technologies, such as reconfigurable intelligent surfaces (RISs), cell-free multiple-input-multiple-output, and joint communications and sensing with increasingly heterogeneous deployment, consisting of terrestrial and non-terrestrial nodes, are robustly integrated. We advocate that a key enabler to develop service and management orchestration with fast recovery capabilities will rely on an artificial-intelligence-based radio access network (RAN) controller. To support the emergency use case spanning a larger area, the integration of aerial and space segments with the terrestrial network promises a rapid and reliable response in the case of any disaster. A proof-of-concept that rapidly reconfigures an RIS for performance enhancement under an emergency scenario is presented and discussed.
Generalized code index modulation-aided frequency offset realign multiple-antenna spatial modulation approach for next-generation green communication systems
Authors: Bang Huang, Jiajie Xu, Mohamed-Slim Alouini
Date: 2024-08-16 00:00:00

For next-generation green communication systems, this article proposes an innovative communication system based on frequency-diverse array-multiple-input multiple-output (FDA-MIMO) technology, which aims to achieve high data rates while maintaining low power consumption. This system utilizes frequency offset index realign modulation, multiple-antenna spatial index modulation, and spreading code index modulation techniques. In the proposed generalized code index modulation-aided frequency offset realign multiple-antenna spatial modulation (GCIM-FORMASM) system, the coming bits are divided into five parts: spatial modulation bits by activating multiple transmit antennas, frequency offset index bits of the FDA antennas, including frequency offset combination bits and frequency offset realign bits, spreading code index modulation bits, and modulated symbol bits. Subsequently, this paper utilizes the orthogonal waveforms transmitted by the FDA to design the corresponding transmitter and receiver structures and provide specific expressions for the received signals. Meanwhile, to reduce the decoding complexity of the maximum likelihood (ML) algorithm, we propose a three-stage despreading-based low complexity (DBLC) algorithm leveraging the orthogonality of the spreading codes. Additionally, a closed-form expression for the upper bound of the average bit error probability (ABEP) of the DBLC algorithm has been derived. Analyzing metrics such as energy efficiency and data rate shows that the proposed system features low power consumption and high data transmission rates, which aligns better with the concept of future green communications. The effectiveness of our proposed methods has been validated through comprehensive numerical results.
Deep multi-intentional inverse reinforcement learning for cognitive multi-function radar inverse cognition
Authors: HanCong Feng, Kaili Jiang, Bin Tang
Date: 2024-08-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Trajectory Clustering

In recent years, radar systems have advanced significantly, offering environmental adaptation and multi-task capabilities. These developments pose new challenges for electronic intelligence (Elint) and electronic support measures (ESM), which need to identify and interpret sophisticated radar behaviors. This paper introduces a Deep Multi-Intentional Inverse Reinforcement Learning (DMIIRL) method for the identification and inverse cognition of cognitive multi-function radars (CMFR). Traditional Inverse Reinforcement Learning (IRL) methods primarily target single reward functions, but the complexity of CMFRs necessitates multiple reward functions to fully encapsulate their behavior. To this end, we develop a method that integrates IRL with Expectation-Maximization (EM) to concurrently handle multiple reward functions, offering better trajectory clustering and reward function estimation. Simulation results demonstrate the superiority of the proposed method over baseline approaches.