Signal Processing
New submissions
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New submissions for Mon, 20 May 24
- [1] arXiv:2405.10331 [pdf, other]
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Title: Detecting 5G Signal Jammers Using Spectrograms with Supervised and Unsupervised LearningSubjects: Signal Processing (eess.SP)
Cellular networks are potential targets of jamming attacks to disrupt wireless communications. Since the fifth generation (5G) of cellular networks enables mission-critical applications, such as autonomous driving or smart manufacturing, the resulting malfunctions can cause serious damage. This paper proposes to detect broadband jammers by an online classification of spectrograms. These spectrograms are computed from a stream of in-phase and quadrature (IQ) samples of 5G radio signals. We obtain these signals experimentally and describe how to design a suitable dataset for training. Based on this data, we compare two classification methods: a supervised learning model built on a basic convolutional neural network (CNN) and an unsupervised learning model based on a convolutional autoencoder (CAE). After comparing the structure of these models, their performance is assessed in terms of accuracy and computational complexity.
- [2] arXiv:2405.10507 [pdf, other]
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Title: Flexible Beamforming for Movable Antenna-Enabled Integrated Sensing and CommunicationSubjects: Signal Processing (eess.SP)
This paper investigates flexible beamforming design in an integrated sensing and communication (ISAC) network with movable antennas (MAs). A bistatic radar system is integrated into a multi-user multiple-input-single-output (MU-MISO) system, with the base station (BS) equipped with MAs. This enables array response reconfiguration by adjusting the positions of antennas. Thus, a joint beamforming and antenna position optimization problem, namely flexible beamforming, is proposed to maximize communication rate and sensing mutual information (MI). The fractional programming (FP) method is adopted to transform the non-convex objective function, and we alternatively update the beamforming matrix and antenna positions. Karush-Kuhn-Tucker (KKT) conditions are employed to derive the close-form solution of the beamforming matrix, while we propose an efficient search-based projected gradient ascent (SPGA) method to update the antenna positions. Simulation results demonstrate that MAs significantly enhance the ISAC performance when employing our proposed algorithm, achieving a 59.8% performance gain compared to fixed uniform arrays.
- [3] arXiv:2405.10510 [pdf, other]
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Title: Implementation of the Feedforward Multichannel Virtual Sensing Active Noise Control (MVANC) by Using MATLABAuthors: Boxiang WangSubjects: Signal Processing (eess.SP); Audio and Speech Processing (eess.AS)
The multichannel virtual sensing active noise control (MVANC) methodology is an advanced approach that may provide a wide area of silence at specific virtual positions that are distant from the physical error microphones. Currently, there is a scarcity of open-source programs available for the MVANC algorithm. This work presents a MATLAB code for the MVANC approach, utilizing the multichannel filtered-x least mean square (MCFxLMS) algorithm. The code is designed to be applicable to systems with any number of channels. The code can be found on GitHub.
- [4] arXiv:2405.10535 [pdf, other]
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Title: Dual-Robust Integrated Sensing and Communication: Beamforming under CSI Imperfection and Location UncertaintySubjects: Signal Processing (eess.SP)
A dual-robust design of beamforming is investigated in an integrated sensing and communication (ISAC) system.Existing research on robust ISAC waveform design, while proposing solutions to imperfect channel state information (CSI), generally depends on prior knowledge of the target's approximate location to design waveforms. This approach, however, limits the precision in sensing the target's exact location. In this paper, considering both CSI imperfection and target location uncertainty, a novel framework of joint robust optimization is proposed by maximizing the weighted sum of worst-case data rate and beampattern gain. To address this challenging problem, we propose an efficient two-layer iteration algorithm based on S-Procedure and convex hull. Finally, numerical results verify the effectiveness and performance improvement of our dual-robust algorithm, as well as the trade-off between communication and sensing performance.
- [5] arXiv:2405.10540 [pdf, ps, other]
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Title: Radar Positioning for Accurate Sensing of Pulse Waves at Multiple Sites Using a 3D Human ModelComments: 8 pages, 8 figures, 8 tables. This work is going to be submitted to the IEEE for possible publicationSubjects: Signal Processing (eess.SP)
This study proposes a sensing method using a millimeter-wave array radar and a depth camera to measure pulse waves at multiple sites on the human body. Using a three-dimensional shape model of the target human body measured by the depth camera, the method identifies reflection sites on the body through electromagnetic scattering simulation. On the basis of the simulation, the radar system can be positioned at a suitable location for measuring pulse waves depending on the posture of the target person. Through measurements using radar and depth camera systems, we demonstrate that the proposed method can estimate the body displacement waveform caused by pulse waves accurately, improving the accuracy by 14% compared with a conventional approach without a depth camera. The proposed method can be a key to realizing an accurate and noncontact sensor for monitoring blood pressure.
- [6] arXiv:2405.10553 [pdf, other]
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Title: Revealing the Trade-off in ISAC Systems: The KL Divergence PerspectiveComments: 5 pages, 5 figures; submitted to IEEE journals for possible publicationSubjects: Signal Processing (eess.SP)
Integrated sensing and communication (ISAC) is regarded as a promising technique for 6G communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler (KL) divergence performance metric. We firstly present the relationship between KL divergence and explicit ISAC performance metric, i.e., demodulation error and probability of detection. Thereafter, we investigate the impact of constellation and beamforming design on the Pareto bound via deep learning and semi-definite relaxation (SDR) techniques. Simulation results show the trade-off between sensing and communication performance in terms of bit error rate (BER) and probability of detection under different parameter set-ups.
- [7] arXiv:2405.10606 [pdf, other]
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Title: Carrier Aggregation Enabled MIMO-OFDM Integrated Sensing and CommunicationComments: 13page, 9figures, Submitted to IEEE Transactions on Wireless CommunicationsSubjects: Signal Processing (eess.SP)
In the evolution towards the forthcoming era of sixth-generation (6G) mobile communication systems characterized by ubiquitous intelligence, integrated sensing and communication (ISAC) is in a phase of burgeoning development. However, the capabilities of communication and sensing within single frequency band fall short of meeting the escalating demands. To this end, this paper introduces a carrier aggregation (CA)- enabled multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system fusing the sensing data on high and low-frequency bands by symbol-level fusion for ultimate communication experience and high-accuracy sensing. The challenges in sensing signal processing introduced by CA include the initial phase misalignment of the echo signals on high and low-frequency bands due to attenuation and radar cross section, and the fusion of the sensing data on high and lowfrequency bands with different physical-layer parameters. To this end, the sensing signal processing is decomposed into two stages. In the first stage, the problem of initial phase misalignment of the echo signals on high and low-frequency bands is solved by the angle compensation, space-domain diversity and vector crosscorrelation operations. In the second stage, this paper realizes symbol-level fusion of the sensing data on high and low-frequency bands through sensing vector rearrangement and cyclic prefix adjustment operations, thereby obtaining high-precision sensing performance. Then, the closed-form communication mutual information (MI) and sensing Cramer-Rao lower bound (CRLB) for the proposed ISAC system are derived to explore the theoretical performance bound with CA. Simulation results validate the feasibility and superiority of the proposed ISAC system.
- [8] arXiv:2405.10649 [pdf, other]
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Title: Recovery of Sparse Graph SignalsComments: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibleSubjects: Signal Processing (eess.SP); Systems and Control (eess.SY); Optimization and Control (math.OC)
This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, often referred to as the identification of the source of a diffused sparse graph signal, is seminal in the field of graph signal processing (GSP). Sparse graph signals can be used in the modeling of a variety of real-world applications in networks, such as social, biological, and power systems, and enable various GSP tasks, such as graph signal reconstruction, blind deconvolution, and sampling. In this paper, we assume double sparsity of both the graph signal and the graph topology, as well as a low-order graph filter. We propose three algorithms to reconstruct the support set of the input sparse graph signal from the graph filter output samples, leveraging these assumptions and the generalized information criterion (GIC). First, we describe the graph multiple GIC (GM-GIC) method, which is based on partitioning the dictionary elements (graph filter matrix columns) that capture information on the signal into smaller subsets. Then, the local GICs are computed for each subset and aggregated to make a global decision. Second, inspired by the well-known branch and bound (BNB) approach, we develop the graph-based branch and bound GIC (graph-BNB-GIC), and incorporate a new tractable heuristic bound tailored to the graph and graph filter characteristics. Finally, we propose the graph-based first order correction (GFOC) method, which improves existing sparse recovery methods by iteratively examining potential improvements to the GIC cost function through replacing elements from the estimated support set with elements from their one-hop neighborhood. We conduct simulations that demonstrate that the proposed sparse recovery methods outperform existing methods in terms of support set recovery accuracy, and without a significant computational overhead.
- [9] arXiv:2405.10749 [pdf, other]
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Title: Universal Joint Source-Channel Coding for Modulation-Agnostic Semantic CommunicationSubjects: Signal Processing (eess.SP)
From the perspective of joint source-channel coding (JSCC), there has been significant research on utilizing semantic communication, which inherently possesses analog characteristics, within digital device environments. However, a single-model approach that operates modulation-agnostically across various digital modulation orders has not yet been established. This article presents the first attempt at such an approach by proposing a universal joint source-channel coding (uJSCC) system that utilizes a single-model encoder-decoder pair and trained vector quantization (VQ) codebooks. To support various modulation orders within a single model, the operation of every neural network (NN)-based module in the uJSCC system requires the selection of modulation orders according to signal-to-noise ratio (SNR) boundaries. To address the challenge of unequal output statistics from shared parameters across NN layers, we integrate multiple batch normalization (BN) layers, selected based on modulation order, after each NN layer. This integration occurs with minimal impact on the overall model size. Through a comprehensive series of experiments, we validate that this modulation-agnostic semantic communication framework demonstrates superiority over existing digital semantic communication approaches in terms of model complexity, communication efficiency, and task effectiveness.
- [10] arXiv:2405.10780 [pdf, ps, other]
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Title: Intelligent Neural Interfaces: An Emerging Era in NeurotechnologySubjects: Signal Processing (eess.SP); Hardware Architecture (cs.AR); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
Integrating smart algorithms on neural devices presents significant opportunities for various brain disorders. In this paper, we review the latest advancements in the development of three categories of intelligent neural prostheses featuring embedded signal processing on the implantable or wearable device. These include: 1) Neural interfaces for closed-loop symptom tracking and responsive stimulation; 2) Neural interfaces for emerging network-related conditions, such as psychiatric disorders; and 3) Intelligent BMI SoCs for movement recovery following paralysis.
- [11] arXiv:2405.10828 [pdf, other]
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Title: Analysis of Impulsive Interference in Digital Audio Broadcasting Systems in Electric VehiclesComments: 44th Symposium on Information Theory and Signal Processing in the Benelux (SITB 2024), Delft, the NetherlandsSubjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
Recently, new types of interference in electric vehicles (EVs), such as converters switching and/or battery chargers, have been found to degrade the performance of wireless digital transmission systems. Measurements show that such an interference is characterized by impulsive behavior and is widely varying in time. This paper uses recorded data from our EV testbed to analyze the impulsive interference in the digital audio broadcasting band. Moreover, we use our analysis to obtain a corresponding interference model. In particular, we studied the temporal characteristics of the interference and confirmed that its amplitude indeed exhibits an impulsive behavior. Our results show that impulsive events span successive received signal samples and thus indicate a bursty nature. To this end, we performed a data-driven modification of a well-established model for bursty impulsive interference, the Markov-Middleton model, to produce synthetic noise realization. We investigate the optimal symbol detector design based on the proposed model and show significant performance gains compared to the conventional detector based on the additive white Gaussian noise assumption.
Cross-lists for Mon, 20 May 24
- [12] arXiv:2405.10496 (cross-list from cs.IT) [pdf, other]
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Title: Electromagnetic Information Theory for Holographic MIMO CommunicationsAuthors: Li Wei, Tierui Gong, Chongwen Huang, Zhaoyang Zhang, Wei E. I. Sha, Zhi Ning Chen, Linglong Dai, Merouane Debbah, Chau YuenSubjects: Information Theory (cs.IT); Signal Processing (eess.SP)
Holographic multiple-input multiple-output (HMIMO) utilizes a compact antenna array to form a nearly continuous aperture, thereby enhancing higher capacity and more flexible configurations compared with conventional MIMO systems, making it attractive in current scientific research. Key questions naturally arise regarding the potential of HMIMO to surpass Shannon's theoretical limits and how far its capabilities can be extended. However, the traditional Shannon information theory falls short in addressing these inquiries because it only focuses on the information itself while neglecting the underlying carrier, electromagnetic (EM) waves, and environmental interactions. To fill up the gap between the theoretical analysis and the practical application for HMIMO systems, we introduce electromagnetic information theory (EIT) in this paper. This paper begins by laying the foundation for HMIMO-oriented EIT, encompassing EM wave equations and communication regions. In the context of HMIMO systems, the resultant physical limitations are presented, involving Chu's limit, Harrington's limit, Hannan's limit, and the evaluation of coupling effects. Field sampling and HMIMO-assisted oversampling are also discussed to guide the optimal HMIMO design within the EIT framework. To comprehensively depict the EM-compliant propagation process, we present the approximate and exact channel modeling approaches in near-/far-field zones. Furthermore, we discuss both traditional Shannon's information theory, employing the probabilistic method, and Kolmogorov information theory, utilizing the functional analysis, for HMIMO-oriented EIT systems.
- [13] arXiv:2405.10513 (cross-list from cs.LG) [pdf, other]
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Title: Federated Learning With Energy Harvesting Devices: An MDP FrameworkSubjects: Machine Learning (cs.LG); Signal Processing (eess.SP)
Federated learning (FL) requires edge devices to perform local training and exchange information with a parameter server, leading to substantial energy consumption. A critical challenge in practical FL systems is the rapid energy depletion of battery-limited edge devices, which curtails their operational lifespan and affects the learning performance. To address this issue, we apply energy harvesting technique in FL systems to extract ambient energy for continuously powering edge devices. We first establish the convergence bound for the wireless FL system with energy harvesting devices, illustrating that the convergence is impacted by partial device participation and packet drops, both of which depend on the energy supply. To accelerate the convergence, we formulate a joint device scheduling and power control problem and model it as a Markov decision process (MDP). By solving this MDP, we derive the optimal transmission policy and demonstrate that it possesses a monotone structure with respect to the battery and channel states. To overcome the curse of dimensionality caused by the exponential complexity of computing the optimal policy, we propose a low-complexity algorithm, which is asymptotically optimal as the number of devices increases. Furthermore, for unknown channels and harvested energy statistics, we develop a structure-enhanced deep reinforcement learning algorithm that leverages the monotone structure of the optimal policy to improve the training performance. Finally, extensive numerical experiments on real-world datasets are presented to validate the theoretical results and corroborate the effectiveness of the proposed algorithms.
- [14] arXiv:2405.10514 (cross-list from cs.IT) [pdf, other]
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Title: Secrecy Performance Analysis of Multi-Functional RIS-Assisted NOMA NetworksComments: 14 pages, 9 figures, submitted to IEEE transactions on wireless communicationSubjects: Information Theory (cs.IT); Signal Processing (eess.SP)
Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive closed-form expressions for the secrecy outage probability (SOP) and secrecy throughput of users in the MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS; ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity; and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case.
- [15] arXiv:2405.10695 (cross-list from cs.IT) [pdf, ps, other]
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Title: On the Design of Super ConstellationsAuthors: Thrassos K. Oikonomou, Dimitrios Tyrovolas, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, Panagiotis Sarigiannidis, George K. KaragiannidisSubjects: Information Theory (cs.IT); Signal Processing (eess.SP)
In the evolving landscape of sixth-generation (6G) wireless networks, which demand ultra high data rates, this study introduces the concept of super constellation communications. Also, we present super amplitude phase shift keying (SAPSK), an innovative modulation technique designed to achieve these ultra high data rate demands. SAPSK is complemented by the generalized polar distance detector (GPD-D), which approximates the optimal maximum likelihood detector in channels with Gaussian phase noise (GPN). By leveraging the decision regions formulated by GPD-D, a tight closed-form approximation for the symbol error probability (SEP) of SAPSK constellations is derived, while a detection algorithm with O(1) time complexity is developed to ensure fast and efficient SAPSK symbol detection. Finally, the theoretical performance of SAPSK and the efficiency of the proposed O(1) algorithm are validated by numerical simulations, highlighting both its superiority in terms of SEP compared to various constellations and its practical advantages in terms of fast and accurate symbol detection.
- [16] arXiv:2405.10814 (cross-list from cs.IT) [pdf, other]
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Title: Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive NoiseComments: This work has been submitted to the IEEE for possible publicationSubjects: Information Theory (cs.IT); Machine Learning (cs.LG); Signal Processing (eess.SP)
We developed machine learning approaches for data-driven trellis-based soft symbol detection in coded transmission over intersymbol interference (ISI) channels in presence of bursty impulsive noise (IN), for example encountered in wireless digital broadcasting systems and vehicular communications. This enabled us to obtain optimized detectors based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm while circumventing the use of full channel state information (CSI) for computing likelihoods and trellis state transition probabilities. First, we extended the application of the neural network (NN)-aided BCJR, recently proposed for ISI channels with additive white Gaussian noise (AWGN). Although suitable for estimating likelihoods via labeling of transmission sequences, the BCJR-NN method does not provide a framework for learning the trellis state transitions. In addition to detection over the joint ISI and IN states we also focused on another scenario where trellis transitions are not trivial: detection for the ISI channel with AWGN with inaccurate knowledge of the channel memory at the receiver. Without access to the accurate state transition matrix, the BCJR- NN performance significantly degrades in both settings. To this end, we devised an alternative approach for data-driven BCJR detection based on the unsupervised learning of a hidden Markov model (HMM). The BCJR-HMM allowed us to optimize both the likelihood function and the state transition matrix without labeling. Moreover, we demonstrated the viability of a hybrid NN and HMM BCJR detection where NN is used for learning the likelihoods, while the state transitions are optimized via HMM. While reducing the required prior channel knowledge, the examined data-driven detectors with learned trellis state transitions achieve bit error rates close to the optimal full CSI-based BCJR, significantly outperforming detection with inaccurate CSI.
Replacements for Mon, 20 May 24
- [17] arXiv:2208.14328 (replaced) [pdf, other]
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Title: 3D Near-Field Virtual MIMO-SAR Imaging Using FMCW Radar Systems at 77 GHzAuthors: Shahrokh HamidiSubjects: Signal Processing (eess.SP)
- [18] arXiv:2303.12286 (replaced) [pdf, other]
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Title: Explainable Semantic Communication for Text TasksSubjects: Signal Processing (eess.SP)
- [19] arXiv:2403.15521 (replaced) [pdf, other]
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Title: Exploring new territory: Calibration-free decoding for c-VEP BCIComments: 6 pages, 2 figures, 9th Graz Brain-Computer Interface Conference 2024Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
- [20] arXiv:2405.09551 (replaced) [pdf, other]
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Title: Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network ApproachAuthors: David Freire-Obregón, Daniel Hernández-Sosa, Oliverio J. Santana, Javier Lorenzo-Navarro, Modesto Castrillón-SantanaComments: Second place award at the Brain Responses to Emotional Avatars Challenge held by the 18th IEEE International Conference on Automatic Face and Gesture Recognition(FG2024)Subjects: Signal Processing (eess.SP); Human-Computer Interaction (cs.HC)
- [21] arXiv:2405.09554 (replaced) [pdf, ps, other]
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Title: Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto PriorSubjects: Signal Processing (eess.SP); Information Theory (cs.IT)
- [22] arXiv:2403.01009 (replaced) [pdf, other]
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Title: Design and Performance Evaluation of SEANet, a Software-defined Networking Platform for the Internet of Underwater ThingsComments: 14 pages, 18 figuresSubjects: Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
- [23] arXiv:2405.07291 (replaced) [pdf, other]
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Title: Robust Beamforming with Gradient-based Liquid Neural NetworkAuthors: Xinquan Wang, Fenghao Zhu, Chongwen Huang, Ahmed Alhammadi, Faouzi Bader, Zhaoyang Zhang, Chau Yuen, Merouane DebbahSubjects: Information Theory (cs.IT); Signal Processing (eess.SP)
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