Next-generation (5G/6G) wireless systems demand low-power mm-wave phased-array ICs. Variable-gain LNAs (VGLNAs) are key building blocks enabling hardware complexity reduction, performance enhancement and functionality extension. This paper reports a performance benchmarking of two low-power 30GHz VG-LNAs for phased-array ICs, which provide a 7.5dB gain control for 18dB Taylor taper in a 30GHz 8x8 antenna array, for a comprehensive validation of the new class of VGLNAs and its design methodology. In particular, this paper reports a second and implementation (VG-LNA2) with a reduced number (four) of gain-control back-gate voltages and super-low-Vt MOSFETs, with respect to the previous first implementation (VG-LNA1) with six gain-control back-gate voltages and regular- Vt MOSFETs, both in the same 22nm FDSOI CMOS technology. The results show that VG-LNA2 exhibits performance comparable to those of VG-LNA1, with a slightly lower power consumption. Overall, the performance benchmarking shows that the design methodology adopted for the new class of VG-LNAs leads to record low-power consumption and small form factor solutions reaching the targeted performances, regardless of the arrangements of the back-gate voltages for gain control and transistor sets, resulting in a comprehensive validation of the innovative design features and effective design methodology.
{"title":"Benchmarking and Validation of Sub-mW 30GHz VG-LNAs in 22nm FDSOI CMOS for 5G/6G Phased-Array Receivers","authors":"Domenico Zito, Michele Spasaro","doi":"arxiv-2409.07069","DOIUrl":"https://doi.org/arxiv-2409.07069","url":null,"abstract":"Next-generation (5G/6G) wireless systems demand low-power mm-wave\u0000phased-array ICs. Variable-gain LNAs (VGLNAs) are key building blocks enabling\u0000hardware complexity reduction, performance enhancement and functionality\u0000extension. This paper reports a performance benchmarking of two low-power 30GHz\u0000VG-LNAs for phased-array ICs, which provide a 7.5dB gain control for 18dB\u0000Taylor taper in a 30GHz 8x8 antenna array, for a comprehensive validation of\u0000the new class of VGLNAs and its design methodology. In particular, this paper\u0000reports a second and implementation (VG-LNA2) with a reduced number (four) of\u0000gain-control back-gate voltages and super-low-Vt MOSFETs, with respect to the\u0000previous first implementation (VG-LNA1) with six gain-control back-gate\u0000voltages and regular- Vt MOSFETs, both in the same 22nm FDSOI CMOS technology.\u0000The results show that VG-LNA2 exhibits performance comparable to those of\u0000VG-LNA1, with a slightly lower power consumption. Overall, the performance\u0000benchmarking shows that the design methodology adopted for the new class of\u0000VG-LNAs leads to record low-power consumption and small form factor solutions\u0000reaching the targeted performances, regardless of the arrangements of the\u0000back-gate voltages for gain control and transistor sets, resulting in a\u0000comprehensive validation of the innovative design features and effective design\u0000methodology.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175962","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}
Graph filters are a staple tool for processing signals over graphs in a multitude of downstream tasks. However, they are commonly designed for graphs with a fixed number of nodes, despite real-world networks typically grow over time. This topological evolution is often known up to a stochastic model, thus, making conventional graph filters ill-equipped to withstand such topological changes, their uncertainty, as well as the dynamic nature of the incoming data. To tackle these issues, we propose an online graph filtering framework by relying on online learning principles. We design filters for scenarios where the topology is both known and unknown, including a learner adaptive to such evolution. We conduct a regret analysis to highlight the role played by the different components such as the online algorithm, the filter order, and the growing graph model. Numerical experiments with synthetic and real data corroborate the proposed approach for graph signal inference tasks and show a competitive performance w.r.t. baselines and state-of-the-art alternatives.
{"title":"Online Graph Filtering Over Expanding Graphs","authors":"Bishwadeep Das, Elvin Isufi","doi":"arxiv-2409.07204","DOIUrl":"https://doi.org/arxiv-2409.07204","url":null,"abstract":"Graph filters are a staple tool for processing signals over graphs in a\u0000multitude of downstream tasks. However, they are commonly designed for graphs\u0000with a fixed number of nodes, despite real-world networks typically grow over\u0000time. This topological evolution is often known up to a stochastic model, thus,\u0000making conventional graph filters ill-equipped to withstand such topological\u0000changes, their uncertainty, as well as the dynamic nature of the incoming data.\u0000To tackle these issues, we propose an online graph filtering framework by\u0000relying on online learning principles. We design filters for scenarios where\u0000the topology is both known and unknown, including a learner adaptive to such\u0000evolution. We conduct a regret analysis to highlight the role played by the\u0000different components such as the online algorithm, the filter order, and the\u0000growing graph model. Numerical experiments with synthetic and real data\u0000corroborate the proposed approach for graph signal inference tasks and show a\u0000competitive performance w.r.t. baselines and state-of-the-art alternatives.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175987","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}
Lu Liu, Rang Liu, Ly V. Nguyen, A. Lee Swindlehurst
This paper focuses on precoding design in multi-antenna systems with improper Gaussian interference (IGI), characterized by correlated real and imaginary parts. We first study block level precoding (BLP) and symbol level precoding (SLP) assuming the receivers apply a pre-whitening filter to decorrelate and normalize the IGI. We then shift to the scenario where the base station (BS) incorporates the IGI statistics in the SLP design, which allows the receivers to employ a standard detection algorithm without pre-whitenting. Finally we address the case where the channel and statistics of the IGI are unknown, and we formulate robust BLP and SLP designs that minimize the worst case performance in such settings. Interestingly, we show that for BLP, the worst-case IGI is in fact proper, while for SLP the worst case occurs when the interference signal is maximally improper, with fully correlated real and imaginary parts. Numerical results reveal the superior performance of SLP in terms of symbol error rate (SER) and energy efficiency (EE), especially for the case where there is uncertainty in the non-circularity of the jammer.
{"title":"Symbol Level Precoding for Systems with Improper Gaussian Interference","authors":"Lu Liu, Rang Liu, Ly V. Nguyen, A. Lee Swindlehurst","doi":"arxiv-2409.07034","DOIUrl":"https://doi.org/arxiv-2409.07034","url":null,"abstract":"This paper focuses on precoding design in multi-antenna systems with improper\u0000Gaussian interference (IGI), characterized by correlated real and imaginary\u0000parts. We first study block level precoding (BLP) and symbol level precoding\u0000(SLP) assuming the receivers apply a pre-whitening filter to decorrelate and\u0000normalize the IGI. We then shift to the scenario where the base station (BS)\u0000incorporates the IGI statistics in the SLP design, which allows the receivers\u0000to employ a standard detection algorithm without pre-whitenting. Finally we\u0000address the case where the channel and statistics of the IGI are unknown, and\u0000we formulate robust BLP and SLP designs that minimize the worst case\u0000performance in such settings. Interestingly, we show that for BLP, the\u0000worst-case IGI is in fact proper, while for SLP the worst case occurs when the\u0000interference signal is maximally improper, with fully correlated real and\u0000imaginary parts. Numerical results reveal the superior performance of SLP in\u0000terms of symbol error rate (SER) and energy efficiency (EE), especially for the\u0000case where there is uncertainty in the non-circularity of the jammer.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175957","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}
Seyed Alireza Hosseini, Tam Thuc Do, Gene Cheung, Yuichi Tanaka
An image denoiser can be used for a wide range of restoration problems via the Plug-and-Play (PnP) architecture. In this paper, we propose a general framework to build an interpretable graph-based deep denoiser (GDD) by unrolling a solution to a maximum a posteriori (MAP) problem equipped with a graph Laplacian regularizer (GLR) as signal prior. Leveraging a recent theorem showing that any (pseudo-)linear denoiser $boldsymbol Psi$, under mild conditions, can be mapped to a solution of a MAP denoising problem regularized using GLR, we first initialize a graph Laplacian matrix $mathbf L$ via truncated Taylor Series Expansion (TSE) of $boldsymbol Psi^{-1}$. Then, we compute the MAP linear system solution by unrolling iterations of the conjugate gradient (CG) algorithm into a sequence of neural layers as a feed-forward network -- one that is amenable to parameter tuning. The resulting GDD network is "graph-interpretable", low in parameter count, and easy to initialize thanks to $mathbf L$ derived from a known well-performing denoiser $boldsymbol Psi$. Experimental results show that GDD achieves competitive image denoising performance compared to competitors, but employing far fewer parameters, and is more robust to covariate shift.
图像去噪器可通过即插即用（PnP）架构用于各种修复问题。在本文中，我们提出了一个通用框架，通过对最大后验（MAP）问题的解进行滚动，并将图谱拉普拉奇正则化器（GLR）作为信号先验来构建可解释的基于图谱的深度去噪器（GDD）。最近的一个定理表明，在温和条件下，任何（伪）线性去噪器$boldsymbol Psi$都可以映射为使用GLR正则化的MAP去噪问题的解，利用该定理，我们首先初始化了$boldsymbol Psi^{-1}$的图拉普拉斯矩阵$mathbf L$ viatruncated Taylor Series Expansion (TSE)。然后，我们通过将共轭梯度（CG）算法的迭代展开到神经层序列中来计算 MAP 线性系统解，将其作为一个前馈网络--一个可以进行参数调整的网络。由此产生的GDD网络是 "可解释图 "的，参数数量少，并且易于初始化，这要归功于从已知性能良好的去噪器$boldsymbolPsi$中提取的$mathbf L$。实验结果表明，与竞争者相比，GDD能实现具有竞争力的图像去噪性能，但使用的参数要少得多，而且对协变量偏移具有更强的鲁棒性。
{"title":"Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer","authors":"Seyed Alireza Hosseini, Tam Thuc Do, Gene Cheung, Yuichi Tanaka","doi":"arxiv-2409.06676","DOIUrl":"https://doi.org/arxiv-2409.06676","url":null,"abstract":"An image denoiser can be used for a wide range of restoration problems via\u0000the Plug-and-Play (PnP) architecture. In this paper, we propose a general\u0000framework to build an interpretable graph-based deep denoiser (GDD) by\u0000unrolling a solution to a maximum a posteriori (MAP) problem equipped with a\u0000graph Laplacian regularizer (GLR) as signal prior. Leveraging a recent theorem\u0000showing that any (pseudo-)linear denoiser $boldsymbol Psi$, under mild\u0000conditions, can be mapped to a solution of a MAP denoising problem regularized\u0000using GLR, we first initialize a graph Laplacian matrix $mathbf L$ via\u0000truncated Taylor Series Expansion (TSE) of $boldsymbol Psi^{-1}$. Then, we\u0000compute the MAP linear system solution by unrolling iterations of the conjugate\u0000gradient (CG) algorithm into a sequence of neural layers as a feed-forward\u0000network -- one that is amenable to parameter tuning. The resulting GDD network\u0000is \"graph-interpretable\", low in parameter count, and easy to initialize thanks\u0000to $mathbf L$ derived from a known well-performing denoiser $boldsymbol\u0000Psi$. Experimental results show that GDD achieves competitive image denoising\u0000performance compared to competitors, but employing far fewer parameters, and is\u0000more robust to covariate shift.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175998","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 quality of audio recordings in outdoor environments is often degraded by the presence of wind. Mitigating the impact of wind noise on the perceptual quality of single-channel speech remains a significant challenge due to its non-stationary characteristics. Prior work in noise suppression treats wind noise as a general background noise without explicit modeling of its characteristics. In this paper, we leverage ultrasound as an auxiliary modality to explicitly sense the airflow and characterize the wind noise. We propose a multi-modal deep-learning framework to fuse the ultrasonic Doppler features and speech signals for wind noise reduction. Our results show that DeWinder can significantly improve the noise reduction capabilities of state-of-the-art speech enhancement models.
{"title":"DeWinder: Single-Channel Wind Noise Reduction using Ultrasound Sensing","authors":"Kuang Yuan, Shuo Han, Swarun Kumar, Bhiksha Raj","doi":"arxiv-2409.06137","DOIUrl":"https://doi.org/arxiv-2409.06137","url":null,"abstract":"The quality of audio recordings in outdoor environments is often degraded by\u0000the presence of wind. Mitigating the impact of wind noise on the perceptual\u0000quality of single-channel speech remains a significant challenge due to its\u0000non-stationary characteristics. Prior work in noise suppression treats wind\u0000noise as a general background noise without explicit modeling of its\u0000characteristics. In this paper, we leverage ultrasound as an auxiliary modality\u0000to explicitly sense the airflow and characterize the wind noise. We propose a\u0000multi-modal deep-learning framework to fuse the ultrasonic Doppler features and\u0000speech signals for wind noise reduction. Our results show that DeWinder can\u0000significantly improve the noise reduction capabilities of state-of-the-art\u0000speech enhancement models.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142176000","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}
Dong Han, Jihye Moon, Luís Roberto Mercado Díaz, Darren Chen, Devan Williams, Eric Y. Ding, Khanh-Van Tran, David D. McManus, Ki H. Chon
Most deep learning models of multiclass arrhythmia classification are tested on fingertip photoplethysmographic (PPG) data, which has higher signal-to-noise ratios compared to smartwatch-derived PPG, and the best reported sensitivity value for premature atrial/ventricular contraction (PAC/PVC) detection is only 75%. To improve upon PAC/PVC detection sensitivity while maintaining high AF detection, we use multi-modal data which incorporates 1D PPG, accelerometers, and heart rate data as the inputs to a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) model to detect three arrhythmia classes. We used motion-artifact prone smartwatch PPG data from the NIH-funded Pulsewatch clinical trial. Our multimodal model tested on 72 subjects achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. These results outperformed the best state-of-the-art model by 20.81% for PAC/PVC and 2.55% for AF detection even while our model was computationally more efficient (14 times lighter and 2.7 faster).
{"title":"Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings","authors":"Dong Han, Jihye Moon, Luís Roberto Mercado Díaz, Darren Chen, Devan Williams, Eric Y. Ding, Khanh-Van Tran, David D. McManus, Ki H. Chon","doi":"arxiv-2409.06147","DOIUrl":"https://doi.org/arxiv-2409.06147","url":null,"abstract":"Most deep learning models of multiclass arrhythmia classification are tested\u0000on fingertip photoplethysmographic (PPG) data, which has higher signal-to-noise\u0000ratios compared to smartwatch-derived PPG, and the best reported sensitivity\u0000value for premature atrial/ventricular contraction (PAC/PVC) detection is only\u000075%. To improve upon PAC/PVC detection sensitivity while maintaining high AF\u0000detection, we use multi-modal data which incorporates 1D PPG, accelerometers,\u0000and heart rate data as the inputs to a computationally efficient 1D\u0000bi-directional Gated Recurrent Unit (1D-Bi-GRU) model to detect three\u0000arrhythmia classes. We used motion-artifact prone smartwatch PPG data from the\u0000NIH-funded Pulsewatch clinical trial. Our multimodal model tested on 72\u0000subjects achieved an unprecedented 83% sensitivity for PAC/PVC detection while\u0000maintaining a high accuracy of 97.31% for AF detection. These results\u0000outperformed the best state-of-the-art model by 20.81% for PAC/PVC and 2.55%\u0000for AF detection even while our model was computationally more efficient (14\u0000times lighter and 2.7 faster).","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175996","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}
In this letter, we investigate the design of multiple reconfigurable intelligent sensing surfaces (RISSs) that enhance both communication and sensing tasks. An RISS incorporates additional active elements tailored to improve sensing accuracy. Our initial task involves optimizing placement of RISSs to mitigate signal interference. Subsequently, we establish power allocation schemes for sensing and communication within the system. Our final consideration involves examining how sensing results can be utilized to enhance communication, alongside an evaluation of communication performance under the impact of sensing inaccuracies. Numerical results reveal that the sensing task reaches its optimal performance with a finite number of RISSs, while the communication task exhibits enhanced performance with an increasing number of RISSs. Additionally, we identify an optimal communication spot under user movement.
{"title":"Optimizing Placement and Power Allocation in Reconfigurable Intelligent Sensing Surfaces for Enhanced Sensing and Communication Performance","authors":"Cheng Luo, Jie Hu, Luping Xiang, Kun Yang, Bo Lei","doi":"arxiv-2409.06188","DOIUrl":"https://doi.org/arxiv-2409.06188","url":null,"abstract":"In this letter, we investigate the design of multiple reconfigurable\u0000intelligent sensing surfaces (RISSs) that enhance both communication and\u0000sensing tasks. An RISS incorporates additional active elements tailored to\u0000improve sensing accuracy. Our initial task involves optimizing placement of\u0000RISSs to mitigate signal interference. Subsequently, we establish power\u0000allocation schemes for sensing and communication within the system. Our final\u0000consideration involves examining how sensing results can be utilized to enhance\u0000communication, alongside an evaluation of communication performance under the\u0000impact of sensing inaccuracies. Numerical results reveal that the sensing task\u0000reaches its optimal performance with a finite number of RISSs, while the\u0000communication task exhibits enhanced performance with an increasing number of\u0000RISSs. Additionally, we identify an optimal communication spot under user\u0000movement.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175995","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}
Zichun Shi, Pu Miao, Peng Chen, Lei Xue, Li-Yang Zheng, Laiyuan Wang, Gaojie Chen
Differential spatial modulation (DSM) exploits the time dimension to facilitate the differential modulation, which can perfectly avoid the challenge in acquiring of heavily entangled channel state information of visible light communication (VLC) system. However, it has huge search space and high complexity for large number of transmitters. In this paper, a novel vector correction (VC)-based orthogonal matching pursuit (OMP) detection algorithm is proposed to reduce the complexity, which exploits the sparsity and relativity of all transmitters, and then employs a novel correction criterion by correcting the index vectors of the error estimation for improving the demodulation performance. To overcome the local optimum dilemma in the atoms searching, an OMP-assisted genetic algorithm is also proposed to further improve the bit error rate (BER) performance of the VLC-DSM system. Simulation results demonstrate that the proposed schemes can significantly reduce the computational complexity at least by 62.5% while achieving an excellent BER performance as compared with traditional maximum likelihood based receiver.
{"title":"Compressed Sensing based Detection Schemes for Differential Spatial Modulation in Visible Light Communication Systems","authors":"Zichun Shi, Pu Miao, Peng Chen, Lei Xue, Li-Yang Zheng, Laiyuan Wang, Gaojie Chen","doi":"arxiv-2409.06577","DOIUrl":"https://doi.org/arxiv-2409.06577","url":null,"abstract":"Differential spatial modulation (DSM) exploits the time dimension to\u0000facilitate the differential modulation, which can perfectly avoid the challenge\u0000in acquiring of heavily entangled channel state information of visible light\u0000communication (VLC) system. However, it has huge search space and high\u0000complexity for large number of transmitters. In this paper, a novel vector\u0000correction (VC)-based orthogonal matching pursuit (OMP) detection algorithm is\u0000proposed to reduce the complexity, which exploits the sparsity and relativity\u0000of all transmitters, and then employs a novel correction criterion by\u0000correcting the index vectors of the error estimation for improving the\u0000demodulation performance. To overcome the local optimum dilemma in the atoms\u0000searching, an OMP-assisted genetic algorithm is also proposed to further\u0000improve the bit error rate (BER) performance of the VLC-DSM system. Simulation\u0000results demonstrate that the proposed schemes can significantly reduce the\u0000computational complexity at least by 62.5% while achieving an excellent BER\u0000performance as compared with traditional maximum likelihood based receiver.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175989","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}
Mohammad Shehab, Mustafa Kishk, Maurilio Matracia, Mehdi Bennis, Mohamed-Slim Alouini
Civilian communication during disasters such as earthquakes, floods, and military conflicts is crucial for saving lives. Nevertheless, several challenges exist during these circumstances such as the destruction of cellular communication and electricity infrastructure, lack of line of sight (LoS), and difficulty of localization under the rubble. In this article, we discuss key enablers that can boost communication during disasters, namely, satellite and aerial platforms, redundancy, silencing, and sustainable networks aided with wireless energy transfer (WET). The article also highlights how these solutions can be implemented in order to solve the failure of communication during disasters. Finally, it sheds light on unresolved challenges, as well as future research directions.
{"title":"Five Key Enablers for Communication during and after Disasters","authors":"Mohammad Shehab, Mustafa Kishk, Maurilio Matracia, Mehdi Bennis, Mohamed-Slim Alouini","doi":"arxiv-2409.06822","DOIUrl":"https://doi.org/arxiv-2409.06822","url":null,"abstract":"Civilian communication during disasters such as earthquakes, floods, and\u0000military conflicts is crucial for saving lives. Nevertheless, several\u0000challenges exist during these circumstances such as the destruction of cellular\u0000communication and electricity infrastructure, lack of line of sight (LoS), and\u0000difficulty of localization under the rubble. In this article, we discuss key\u0000enablers that can boost communication during disasters, namely, satellite and\u0000aerial platforms, redundancy, silencing, and sustainable networks aided with\u0000wireless energy transfer (WET). The article also highlights how these solutions\u0000can be implemented in order to solve the failure of communication during\u0000disasters. Finally, it sheds light on unresolved challenges, as well as future\u0000research directions.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175960","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}
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Chaotic signals offer promising characteristics for wireless communications due to their wideband nature, low cross-correlation, and sensitivity to initial conditions. Although classical chaotic modulation schemes like Chaos Shift Keying (CSK) can theoretically match the performance of traditional modulation techniques (i.e., bit error rate), practical challenges, such as the difficulty in generating accurate signal replicas at the receiver, limit their effectiveness. Besides, chaotic signals are often considered unpredictable despite their deterministic nature. In this paper, we challenge this view by introducing a novel modulation scheme for chaotic communications that leverages the deterministic behavior of chaotic signals. The proposed approach eliminates the need for synchronized replicas of transmitted waveforms at the receiver. Moreover, to enhance noise robustness, we employ M-ary Frequency Shift Keying (FSK) modulation on the chaotic samples. Experimental results show that the proposed scheme significantly outperforms CSK when perfect replicas are unavailable, with the best performance achieved for low-order modulations, and resulting in minimal delay increase.
{"title":"Predictive Demodulation for Chaotic Communications","authors":"Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas","doi":"arxiv-2409.06630","DOIUrl":"https://doi.org/arxiv-2409.06630","url":null,"abstract":"Chaotic signals offer promising characteristics for wireless communications\u0000due to their wideband nature, low cross-correlation, and sensitivity to initial\u0000conditions. Although classical chaotic modulation schemes like Chaos Shift\u0000Keying (CSK) can theoretically match the performance of traditional modulation\u0000techniques (i.e., bit error rate), practical challenges, such as the difficulty\u0000in generating accurate signal replicas at the receiver, limit their\u0000effectiveness. Besides, chaotic signals are often considered unpredictable\u0000despite their deterministic nature. In this paper, we challenge this view by\u0000introducing a novel modulation scheme for chaotic communications that leverages\u0000the deterministic behavior of chaotic signals. The proposed approach eliminates\u0000the need for synchronized replicas of transmitted waveforms at the receiver.\u0000Moreover, to enhance noise robustness, we employ M-ary Frequency Shift Keying\u0000(FSK) modulation on the chaotic samples. Experimental results show that the\u0000proposed scheme significantly outperforms CSK when perfect replicas are\u0000unavailable, with the best performance achieved for low-order modulations, and\u0000resulting in minimal delay increase.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175994","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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