Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega
Choosing an appropriate frequency definition and norm is critical in graph signal sampling and reconstruction. Most previous works define frequencies based on the spectral properties of the graph and use the same frequency definition and $ell_2$-norm for optimization for all sampling sets. Our previous work demonstrated that using a sampling set-adaptive norm and frequency definition can address challenges in classical bandlimited approximation, particularly with model mismatches and irregularly distributed data. In this work, we propose a method for selecting sampling sets tailored to the sampling set adaptive GFT-based interpolation. When the graph models the inverse covariance of the data, we show that this adaptive GFT enables localizing the bandlimited model mismatch error to high frequencies, and the spectral folding property allows us to track this error in reconstruction. Based on this, we propose a sampling set selection algorithm to minimize the worst-case bandlimited model mismatch error. We consider partitioning the sensors in a sensor network sampling a continuous spatial process as an application. Our experiments show that sampling and reconstruction using sampling set adaptive GFT significantly outperform methods that used fixed GFTs and bandwidth-based criterion.
{"title":"Graph-Based Signal Sampling with Adaptive Subspace Reconstruction for Spatially-Irregular Sensor Data","authors":"Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega","doi":"arxiv-2409.09526","DOIUrl":"https://doi.org/arxiv-2409.09526","url":null,"abstract":"Choosing an appropriate frequency definition and norm is critical in graph\u0000signal sampling and reconstruction. Most previous works define frequencies\u0000based on the spectral properties of the graph and use the same frequency\u0000definition and $ell_2$-norm for optimization for all sampling sets. Our\u0000previous work demonstrated that using a sampling set-adaptive norm and\u0000frequency definition can address challenges in classical bandlimited\u0000approximation, particularly with model mismatches and irregularly distributed\u0000data. In this work, we propose a method for selecting sampling sets tailored to\u0000the sampling set adaptive GFT-based interpolation. When the graph models the\u0000inverse covariance of the data, we show that this adaptive GFT enables\u0000localizing the bandlimited model mismatch error to high frequencies, and the\u0000spectral folding property allows us to track this error in reconstruction.\u0000Based on this, we propose a sampling set selection algorithm to minimize the\u0000worst-case bandlimited model mismatch error. We consider partitioning the\u0000sensors in a sensor network sampling a continuous spatial process as an\u0000application. Our experiments show that sampling and reconstruction using\u0000sampling set adaptive GFT significantly outperform methods that used fixed GFTs\u0000and bandwidth-based criterion.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Selen Gecgel Cetin, Angeles Vazquez-Castro, Gunes Karabulut Kurt
The Moon and its surrounding cislunar space have numerous unknowns, uncertainties, or partially charted phenomena that need to be investigated to determine the extent to which they affect cislunar communication. These include temperature fluctuations, spacecraft distance and velocity dynamics, surface roughness, and the diversity of propagation mechanisms. To develop robust and dynamically operative Cislunar space networks (CSNs), we need to analyze the communication system by incorporating inclusive models that account for the wide range of possible propagation environments and noise characteristics. In this paper, we consider that the communication signal can be subjected to both Gaussian and non-Gaussian noise, but also to different fading conditions. First, we analyze the communication link by showing the relationship between the brightness temperatures of the Moon and the equivalent noise temperature at the receiver of the Lunar Gateway. We propose to analyze the ergodic capacity and the outage probability, as they are essential metrics for the development of reliable communication. In particular, we model the noise with the additive symmetric alpha-stable distribution, which allows a generic analysis for Gaussian and non-Gaussian signal characteristics. Then, we present the closed-form bounds for the ergodic capacity and the outage probability. Finally, the results show the theoretically and operationally achievable performance bounds for the cislunar communication. To give insight into further designs, we also provide our results with comprehensive system settings that include mission objectives as well as orbital and system dynamics.
{"title":"Cislunar Communication Performance and System Analysis with Uncharted Phenomena","authors":"Selen Gecgel Cetin, Angeles Vazquez-Castro, Gunes Karabulut Kurt","doi":"arxiv-2409.09426","DOIUrl":"https://doi.org/arxiv-2409.09426","url":null,"abstract":"The Moon and its surrounding cislunar space have numerous unknowns,\u0000uncertainties, or partially charted phenomena that need to be investigated to\u0000determine the extent to which they affect cislunar communication. These include\u0000temperature fluctuations, spacecraft distance and velocity dynamics, surface\u0000roughness, and the diversity of propagation mechanisms. To develop robust and\u0000dynamically operative Cislunar space networks (CSNs), we need to analyze the\u0000communication system by incorporating inclusive models that account for the\u0000wide range of possible propagation environments and noise characteristics. In\u0000this paper, we consider that the communication signal can be subjected to both\u0000Gaussian and non-Gaussian noise, but also to different fading conditions.\u0000First, we analyze the communication link by showing the relationship between\u0000the brightness temperatures of the Moon and the equivalent noise temperature at\u0000the receiver of the Lunar Gateway. We propose to analyze the ergodic capacity\u0000and the outage probability, as they are essential metrics for the development\u0000of reliable communication. In particular, we model the noise with the additive\u0000symmetric alpha-stable distribution, which allows a generic analysis for\u0000Gaussian and non-Gaussian signal characteristics. Then, we present the\u0000closed-form bounds for the ergodic capacity and the outage probability.\u0000Finally, the results show the theoretically and operationally achievable\u0000performance bounds for the cislunar communication. To give insight into further\u0000designs, we also provide our results with comprehensive system settings that\u0000include mission objectives as well as orbital and system dynamics.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xingzhi Sun, Charles Xu, João F. Rocha, Chen Liu, Benjamin Hollander-Bodie, Laney Goldman, Marcello DiStasio, Michael Perlmutter, Smita Krishnaswamy
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergraphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer's disease.
{"title":"Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics","authors":"Xingzhi Sun, Charles Xu, João F. Rocha, Chen Liu, Benjamin Hollander-Bodie, Laney Goldman, Marcello DiStasio, Michael Perlmutter, Smita Krishnaswamy","doi":"arxiv-2409.09469","DOIUrl":"https://doi.org/arxiv-2409.09469","url":null,"abstract":"In many data-driven applications, higher-order relationships among multiple\u0000objects are essential in capturing complex interactions. Hypergraphs, which\u0000generalize graphs by allowing edges to connect any number of nodes, provide a\u0000flexible and powerful framework for modeling such higher-order relationships.\u0000In this work, we introduce hypergraph diffusion wavelets and describe their\u0000favorable spectral and spatial properties. We demonstrate their utility for\u0000biomedical discovery in spatially resolved transcriptomics by applying the\u0000method to represent disease-relevant cellular niches for Alzheimer's disease.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Speech restoration aims at restoring full-band speech with high quality and intelligibility, considering a diverse set of distortions. MaskSR is a recently proposed generative model for this task. As other models of its kind, MaskSR attains high quality but, as we show, intelligibility can be substantially improved. We do so by boosting the speech encoder component of MaskSR with predictions of semantic representations of the target speech, using a pre-trained self-supervised teacher model. Then, a masked language model is conditioned on the learned semantic features to predict acoustic tokens that encode low level spectral details of the target speech. We show that, with the same MaskSR model capacity and inference time, the proposed model, MaskSR2, significantly reduces the word error rate, a typical metric for intelligibility. MaskSR2 also achieves competitive word error rate among other models, while providing superior quality. An ablation study shows the effectiveness of various semantic representations.
{"title":"Joint Semantic Knowledge Distillation and Masked Acoustic Modeling for Full-band Speech Restoration with Improved Intelligibility","authors":"Xiaoyu Liu, Xu Li, Joan Serrà, Santiago Pascual","doi":"arxiv-2409.09357","DOIUrl":"https://doi.org/arxiv-2409.09357","url":null,"abstract":"Speech restoration aims at restoring full-band speech with high quality and\u0000intelligibility, considering a diverse set of distortions. MaskSR is a recently\u0000proposed generative model for this task. As other models of its kind, MaskSR\u0000attains high quality but, as we show, intelligibility can be substantially\u0000improved. We do so by boosting the speech encoder component of MaskSR with\u0000predictions of semantic representations of the target speech, using a\u0000pre-trained self-supervised teacher model. Then, a masked language model is\u0000conditioned on the learned semantic features to predict acoustic tokens that\u0000encode low level spectral details of the target speech. We show that, with the\u0000same MaskSR model capacity and inference time, the proposed model, MaskSR2,\u0000significantly reduces the word error rate, a typical metric for\u0000intelligibility. MaskSR2 also achieves competitive word error rate among other\u0000models, while providing superior quality. An ablation study shows the\u0000effectiveness of various semantic representations.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haozhou Hu, Harpreet S. Dhillon, R. Michael Buehrer
Despite significant algorithmic advances in vision-based positioning, a comprehensive probabilistic framework to study its performance has remained unexplored. The main objective of this paper is to develop such a framework using ideas from stochastic geometry. Due to limitations in sensor resolution, the level of detail in prior information, and computational resources, we may not be able to differentiate between landmarks with similar appearances in the vision data, such as trees, lampposts, and bus stops. While one cannot accurately determine the absolute target position using a single indistinguishable landmark, obtaining an approximate position fix is possible if the target can see multiple landmarks whose geometric placement on the map is unique. Modeling the locations of these indistinguishable landmarks as a Poisson point process (PPP) $Phi$ on $mathbb{R}^2$, we develop a new approach to analyze the localizability in this setting. From the target location $mathbb{x}$, the measurements are obtained from landmarks within the visibility region. These measurements, including ranges and angles to the landmarks, denoted as $f(mathbb{x})$, can be treated as mappings from the target location. We are interested in understanding the probability that the measurements $f(mathbb{x})$ are sufficiently distinct from the measurement $f(mathbb{x}_0)$ at the given location, which we term localizability. Expressions of localizability probability are derived for specific vision-inspired measurements, such as ranges to landmarks and snapshots of their locations. Our analysis reveals that the localizability probability approaches one when the landmark intensity tends to infinity, which means that error-free localization is achievable in this limiting regime.
{"title":"Foundations of Vision-Based Localization: A New Approach to Localizability Analysis Using Stochastic Geometry","authors":"Haozhou Hu, Harpreet S. Dhillon, R. Michael Buehrer","doi":"arxiv-2409.09525","DOIUrl":"https://doi.org/arxiv-2409.09525","url":null,"abstract":"Despite significant algorithmic advances in vision-based positioning, a\u0000comprehensive probabilistic framework to study its performance has remained\u0000unexplored. The main objective of this paper is to develop such a framework\u0000using ideas from stochastic geometry. Due to limitations in sensor resolution,\u0000the level of detail in prior information, and computational resources, we may\u0000not be able to differentiate between landmarks with similar appearances in the\u0000vision data, such as trees, lampposts, and bus stops. While one cannot\u0000accurately determine the absolute target position using a single\u0000indistinguishable landmark, obtaining an approximate position fix is possible\u0000if the target can see multiple landmarks whose geometric placement on the map\u0000is unique. Modeling the locations of these indistinguishable landmarks as a\u0000Poisson point process (PPP) $Phi$ on $mathbb{R}^2$, we develop a new approach\u0000to analyze the localizability in this setting. From the target location\u0000$mathbb{x}$, the measurements are obtained from landmarks within the\u0000visibility region. These measurements, including ranges and angles to the\u0000landmarks, denoted as $f(mathbb{x})$, can be treated as mappings from the\u0000target location. We are interested in understanding the probability that the\u0000measurements $f(mathbb{x})$ are sufficiently distinct from the measurement\u0000$f(mathbb{x}_0)$ at the given location, which we term localizability.\u0000Expressions of localizability probability are derived for specific\u0000vision-inspired measurements, such as ranges to landmarks and snapshots of\u0000their locations. Our analysis reveals that the localizability probability\u0000approaches one when the landmark intensity tends to infinity, which means that\u0000error-free localization is achievable in this limiting regime.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega
This paper develops fast graph Fourier transform (GFT) algorithms with O(n log n) runtime complexity for rank-one updates of the path graph. We first show that several commonly-used audio and video coding transforms belong to this class of GFTs, which we denote by DCT+. Next, starting from an arbitrary generalized graph Laplacian and using rank-one perturbation theory, we provide a factorization for the GFT after perturbation. This factorization is our central result and reveals a progressive structure: we first apply the unperturbed Laplacian's GFT and then multiply the result by a Cauchy matrix. By specializing this decomposition to path graphs and exploiting the properties of Cauchy matrices, we show that Fast DCT+ algorithms exist. We also demonstrate that progressivity can speed up computations in applications involving multiple transforms related by rank-one perturbations (e.g., video coding) when combined with pruning strategies. Our results can be extended to other graphs and rank-k perturbations. Runtime analyses show that Fast DCT+ provides computational gains over the naive method for graph sizes larger than 64, with runtime approximately equal to that of 8 DCTs.
{"title":"Fast DCT+: A Family of Fast Transforms Based on Rank-One Updates of the Path Graph","authors":"Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega","doi":"arxiv-2409.08970","DOIUrl":"https://doi.org/arxiv-2409.08970","url":null,"abstract":"This paper develops fast graph Fourier transform (GFT) algorithms with O(n\u0000log n) runtime complexity for rank-one updates of the path graph. We first show\u0000that several commonly-used audio and video coding transforms belong to this\u0000class of GFTs, which we denote by DCT+. Next, starting from an arbitrary\u0000generalized graph Laplacian and using rank-one perturbation theory, we provide\u0000a factorization for the GFT after perturbation. This factorization is our\u0000central result and reveals a progressive structure: we first apply the\u0000unperturbed Laplacian's GFT and then multiply the result by a Cauchy matrix. By\u0000specializing this decomposition to path graphs and exploiting the properties of\u0000Cauchy matrices, we show that Fast DCT+ algorithms exist. We also demonstrate\u0000that progressivity can speed up computations in applications involving multiple\u0000transforms related by rank-one perturbations (e.g., video coding) when combined\u0000with pruning strategies. Our results can be extended to other graphs and rank-k\u0000perturbations. Runtime analyses show that Fast DCT+ provides computational\u0000gains over the naive method for graph sizes larger than 64, with runtime\u0000approximately equal to that of 8 DCTs.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmet Sacid Sümer, Mehmet Mert Şahin, Hüseyin Arslan
Rate-Splitting Multiple Access (RSMA) is a promising strategy for ensuring robust transmission in multi-antenna wireless systems. In this paper, we investigate the performance of RSMA in a downlink Decode-and-Forward (DF) relay scenario under two phases with imperfect Channel State Information (CSI) at the transmitter and the relay. In particular, in the first phase, the Base Station (BS) initially transmits to both BS Users (BUs) and the relay. In the second phase, the relay decodes and forwards the received signals to Relay Users (RUs) outside the BS coverage area. Furthermore, we investigate a scenario where the relay broadcasts a common stream intended for the RUs in the second phase. Due to the broadcast nature of the transmission, this stream is inadvertently received by both the RUs and the BUs. Concurrently, the BS utilizes Spatial Division Multiple Access (SDMA) to transmit private streams to the BUs, resulting in BUs experiencing residual interference from the common stream transmitted from relay. Incorporating this residual common stream interference into our model results in a significant enhancement of the overall sum-rate achieved at the BUs. We derive a tractable lower bound on the ergodic sum-rates, enables us to develop closed-form solutions for power allocation that maximize the overall sum-rate in both phases. Extensive simulations validate that our proposed power allocation algorithm, in conjunction with a low-complexity precoder, significantly improves the sum-rate performance of DF relay RSMA networks compared to the SDMA-based benchmark designs under imperfect CSI at the transmitter and relay.
速率分割多路访问（RSMA）是确保多天线无线系统中稳健传输的一种有前途的策略。在本文中，我们研究了 RSMA 在下行链路解码前向（DF）中继场景下的性能，该场景分为两个阶段，发射机和中继站的信道状态信息（CSI）均不完善。具体来说，在第一阶段，基站（BS）首先向 BS 用户（BU）和中继发送信息。在第二阶段，中继解码并将接收到的信号转发给基站覆盖区域外的中继用户（RU）。此外，我们还研究了一种情况，即中继在第二阶段为 RU 广播一个公共流。由于传输的广播性质，RU 和 BU 都会无意中接收到该数据流。与此同时，BS 利用空间分割多路访问 (SDMA) 向 BU 传输专用流，导致 BU 受到中继传输的公共流的残余干扰。将这种残余公共流干扰纳入我们的模型，可显著提高 BU 达到的总和速率。我们推导出了一个可控的遍历总和率下限，使我们能够开发出功率分配的闭式解决方案，最大限度地提高两个阶段的总和率。大量仿真验证了我们提出的功率分配算法与低复杂度前置编码器相结合，与基于 SDMA 的基准设计相比，在发射端和中继端 CSI 不完美的情况下，能显著提高 DFrelay RSMA 网络的总和速率性能。
{"title":"An Efficient Low-Complexity RSMA Scheme for Multi-User Decode-and-Forward Relay Systems","authors":"Ahmet Sacid Sümer, Mehmet Mert Şahin, Hüseyin Arslan","doi":"arxiv-2409.08880","DOIUrl":"https://doi.org/arxiv-2409.08880","url":null,"abstract":"Rate-Splitting Multiple Access (RSMA) is a promising strategy for ensuring\u0000robust transmission in multi-antenna wireless systems. In this paper, we\u0000investigate the performance of RSMA in a downlink Decode-and-Forward (DF) relay\u0000scenario under two phases with imperfect Channel State Information (CSI) at the\u0000transmitter and the relay. In particular, in the first phase, the Base Station\u0000(BS) initially transmits to both BS Users (BUs) and the relay. In the second\u0000phase, the relay decodes and forwards the received signals to Relay Users (RUs)\u0000outside the BS coverage area. Furthermore, we investigate a scenario where the\u0000relay broadcasts a common stream intended for the RUs in the second phase. Due\u0000to the broadcast nature of the transmission, this stream is inadvertently\u0000received by both the RUs and the BUs. Concurrently, the BS utilizes Spatial\u0000Division Multiple Access (SDMA) to transmit private streams to the BUs,\u0000resulting in BUs experiencing residual interference from the common stream\u0000transmitted from relay. Incorporating this residual common stream interference\u0000into our model results in a significant enhancement of the overall sum-rate\u0000achieved at the BUs. We derive a tractable lower bound on the ergodic\u0000sum-rates, enables us to develop closed-form solutions for power allocation\u0000that maximize the overall sum-rate in both phases. Extensive simulations\u0000validate that our proposed power allocation algorithm, in conjunction with a\u0000low-complexity precoder, significantly improves the sum-rate performance of DF\u0000relay RSMA networks compared to the SDMA-based benchmark designs under\u0000imperfect CSI at the transmitter and relay.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhang Wei, Chen Ding, Bin Zhou, Yi Jiang, Zhiyong Bu
Strong self-interference due to the co-located transmitter is the bottleneck for implementing an in-band full-duplex (IBFD) system. If not adequately mitigated, the strong interference can saturate the receiver's analog-digital converters (ADCs) and hence void the digital processing. This paper considers utilizing a reconfigurable intelligent surface (RIS), together with a receiving (Rx) phase shifter network (PSN), to mitigate the strong self-interference through jointly optimizing their phases. This method, named self-interference mitigation using RIS and PSN (SIMRP), can suppress self-interference to avoid ADC saturation effectively and therefore improve the sum rate performance of communication systems, as verified by the simulation studies.
{"title":"SIMRP: Self-Interference Mitigation Using RIS and Phase Shifter Network","authors":"Zhang Wei, Chen Ding, Bin Zhou, Yi Jiang, Zhiyong Bu","doi":"arxiv-2409.08600","DOIUrl":"https://doi.org/arxiv-2409.08600","url":null,"abstract":"Strong self-interference due to the co-located transmitter is the bottleneck\u0000for implementing an in-band full-duplex (IBFD) system. If not adequately\u0000mitigated, the strong interference can saturate the receiver's analog-digital\u0000converters (ADCs) and hence void the digital processing. This paper considers\u0000utilizing a reconfigurable intelligent surface (RIS), together with a receiving\u0000(Rx) phase shifter network (PSN), to mitigate the strong self-interference\u0000through jointly optimizing their phases. This method, named self-interference\u0000mitigation using RIS and PSN (SIMRP), can suppress self-interference to avoid\u0000ADC saturation effectively and therefore improve the sum rate performance of\u0000communication systems, as verified by the simulation studies.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alejandro Lancho, Amir Weiss, Gary C. F. Lee, Tejas Jayashankar, Binoy Kurien, Yury Polyanskiy, Gregory W. Wornell
This paper addresses the critical problem of interference rejection in radio-frequency (RF) signals using a novel, data-driven approach that leverages state-of-the-art AI models. Traditionally, interference rejection algorithms are manually tailored to specific types of interference. This work introduces a more scalable data-driven solution and contains the following contributions. First, we present an insightful signal model that serves as a foundation for developing and analyzing interference rejection algorithms. Second, we introduce the RF Challenge, a publicly available dataset featuring diverse RF signals along with code templates, which facilitates data-driven analysis of RF signal problems. Third, we propose novel AI-based rejection algorithms, specifically architectures like UNet and WaveNet, and evaluate their performance across eight different signal mixture types. These models demonstrate superior performance exceeding traditional methods like matched filtering and linear minimum mean square error estimation by up to two orders of magnitude in bit-error rate. Fourth, we summarize the results from an open competition hosted at 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024) based on the RF Challenge, highlighting the significant potential for continued advancements in this area. Our findings underscore the promise of deep learning algorithms in mitigating interference, offering a strong foundation for future research.
{"title":"RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge","authors":"Alejandro Lancho, Amir Weiss, Gary C. F. Lee, Tejas Jayashankar, Binoy Kurien, Yury Polyanskiy, Gregory W. Wornell","doi":"arxiv-2409.08839","DOIUrl":"https://doi.org/arxiv-2409.08839","url":null,"abstract":"This paper addresses the critical problem of interference rejection in\u0000radio-frequency (RF) signals using a novel, data-driven approach that leverages\u0000state-of-the-art AI models. Traditionally, interference rejection algorithms\u0000are manually tailored to specific types of interference. This work introduces a\u0000more scalable data-driven solution and contains the following contributions.\u0000First, we present an insightful signal model that serves as a foundation for\u0000developing and analyzing interference rejection algorithms. Second, we\u0000introduce the RF Challenge, a publicly available dataset featuring diverse RF\u0000signals along with code templates, which facilitates data-driven analysis of RF\u0000signal problems. Third, we propose novel AI-based rejection algorithms,\u0000specifically architectures like UNet and WaveNet, and evaluate their\u0000performance across eight different signal mixture types. These models\u0000demonstrate superior performance exceeding traditional methods like matched\u0000filtering and linear minimum mean square error estimation by up to two orders\u0000of magnitude in bit-error rate. Fourth, we summarize the results from an open\u0000competition hosted at 2024 IEEE International Conference on Acoustics, Speech,\u0000and Signal Processing (ICASSP 2024) based on the RF Challenge, highlighting the\u0000significant potential for continued advancements in this area. Our findings\u0000underscore the promise of deep learning algorithms in mitigating interference,\u0000offering a strong foundation for future research.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The short-time Fourier transform (STFT) represents a window of audio samples as a set of complex coefficients. These are advantageously viewed as magnitudes and phases and the overall distribution of phases is very often assumed to be uniform. We show that when audio signal STFT phase distributions are analyzed per-frequency or per-magnitude range, they can be far from uniform. That is, the uniform phase distribution assumption obscures significant important details. We explain the significance of the nonuniform phase distributions and how they might be exploited, derive their source, and explain why the choice of the STFT window shape influences the nonuniformity of the resulting phase distributions.
{"title":"Why some audio signal short-time Fourier transform coefficients have nonuniform phase distributions","authors":"Stephen D. Voran","doi":"arxiv-2409.08981","DOIUrl":"https://doi.org/arxiv-2409.08981","url":null,"abstract":"The short-time Fourier transform (STFT) represents a window of audio samples\u0000as a set of complex coefficients. These are advantageously viewed as magnitudes\u0000and phases and the overall distribution of phases is very often assumed to be\u0000uniform. We show that when audio signal STFT phase distributions are analyzed\u0000per-frequency or per-magnitude range, they can be far from uniform. That is,\u0000the uniform phase distribution assumption obscures significant important\u0000details. We explain the significance of the nonuniform phase distributions and\u0000how they might be exploited, derive their source, and explain why the choice of\u0000the STFT window shape influences the nonuniformity of the resulting phase\u0000distributions.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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