Pub Date : 2025-01-06DOI: 10.1109/LSP.2025.3525855
Yue Wu;Fangfang Qiang;Wujie Zhou;Weiqing Yan
Applying computer vision techniques to rail surface defect detection (RSDD) is crucial for preventing catastrophic accidents. However, challenges such as complex backgrounds and irregular defect shapes persist. Previous methods have focused on extracting salient object information from a pixel perspective, thereby neglecting valuable high- and low-frequency image information, which can better capture global structural information. In this study, we design a pixel-aware frequency conversion network (PFCNet) to explore RSDD from a frequency domain perspective. We use different attention mechanisms and frequency enhancement for high-level and shallow features to explore local details and global structures comprehensively. In addition, we design a dual-control reorganization module to refine the features across levels. We conducted extensive experiments on an industrial RGB-D dataset (NEU RSDDS-AUG), and PFCNet achieved superior performance.
{"title":"PFCNet: Enhancing Rail Surface Defect Detection With Pixel-Aware Frequency Conversion Networks","authors":"Yue Wu;Fangfang Qiang;Wujie Zhou;Weiqing Yan","doi":"10.1109/LSP.2025.3525855","DOIUrl":"https://doi.org/10.1109/LSP.2025.3525855","url":null,"abstract":"Applying computer vision techniques to rail surface defect detection (RSDD) is crucial for preventing catastrophic accidents. However, challenges such as complex backgrounds and irregular defect shapes persist. Previous methods have focused on extracting salient object information from a pixel perspective, thereby neglecting valuable high- and low-frequency image information, which can better capture global structural information. In this study, we design a pixel-aware frequency conversion network (PFCNet) to explore RSDD from a frequency domain perspective. We use different attention mechanisms and frequency enhancement for high-level and shallow features to explore local details and global structures comprehensively. In addition, we design a dual-control reorganization module to refine the features across levels. We conducted extensive experiments on an industrial RGB-D dataset (NEU RSDDS-AUG), and PFCNet achieved superior performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"606-610"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/LSP.2024.3521326
Cheng Wang;Zhen Mei;Jun Li;Kui Cai;Lingjun Kong
Accurate modeling and estimation of the threshold voltages of the flash memory can facilitate the efficient design of channel codes and detectors. However, most flash memory channel models are based on Gaussian distributions, which fail to capture certain key properties of the threshold voltages, such as their heavy-tails. To enhance the model accuracy, we first propose a piecewise student's t-distribution mixture model (PSTMM), which features degrees of freedom to control the left and right tails of the voltage distributions. We further propose an PSTMM based expectation maximization (PSTMM-EM) algorithm to estimate model parameters for flash memories by alternately computing the expected values of the missing data and maximizing the likelihood function with respect to the model parameters. Simulation results demonstrate that our proposed algorithm exhibits superior stability and can effectively extend the flash memory lifespan by 1700 program/erase (PE) cycles compared with the existing parameter estimation algorithms.
{"title":"Piecewise Student's t-distribution Mixture Model-Based Estimation for NAND Flash Memory Channels","authors":"Cheng Wang;Zhen Mei;Jun Li;Kui Cai;Lingjun Kong","doi":"10.1109/LSP.2024.3521326","DOIUrl":"https://doi.org/10.1109/LSP.2024.3521326","url":null,"abstract":"Accurate modeling and estimation of the threshold voltages of the flash memory can facilitate the efficient design of channel codes and detectors. However, most flash memory channel models are based on Gaussian distributions, which fail to capture certain key properties of the threshold voltages, such as their heavy-tails. To enhance the model accuracy, we first propose a piecewise student's t-distribution mixture model (PSTMM), which features degrees of freedom to control the left and right tails of the voltage distributions. We further propose an PSTMM based expectation maximization (PSTMM-EM) algorithm to estimate model parameters for flash memories by alternately computing the expected values of the missing data and maximizing the likelihood function with respect to the model parameters. Simulation results demonstrate that our proposed algorithm exhibits superior stability and can effectively extend the flash memory lifespan by 1700 program/erase (PE) cycles compared with the existing parameter estimation algorithms.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"451-455"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/LSP.2025.3525898
Majdoddin Esfandiari;Sergiy A. Vorobyov
A noise covariance matrix estimation approach in unknown noise field for direction finding applicable for the practically important cases of nonuniform and block-diagonal sensor noise is proposed. It is based on an alternating procedure that can be adjusted for a specific noise type. Numerical simulations are conducted in order to establish the generality and superiority of the proposed approach over the existing state-of-the-art methods, especially in challenging scenarios.
{"title":"Noise Covariance Matrix Estimation in Block-Correlated Noise Field for Direction Finding","authors":"Majdoddin Esfandiari;Sergiy A. Vorobyov","doi":"10.1109/LSP.2025.3525898","DOIUrl":"https://doi.org/10.1109/LSP.2025.3525898","url":null,"abstract":"A noise covariance matrix estimation approach in unknown noise field for direction finding applicable for the practically important cases of nonuniform and block-diagonal sensor noise is proposed. It is based on an alternating procedure that can be adjusted for a specific noise type. Numerical simulations are conducted in order to establish the generality and superiority of the proposed approach over the existing state-of-the-art methods, especially in challenging scenarios.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"531-535"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/LSP.2025.3526094
Jiayue Zhao;Shiman Li;Yi Hao;Chenxi Zhang
Color fundus photography (CFP) and optical coherence tomography (OCT) are two common modalities used in eye disease screening, providing crucial complementary information for the diagnosis of eye diseases. However, existing multimodal learning methods cannot fully leverage the information from each modality due to the large dimensional and semantic gap between 2D CFP and 3D OCT images, leading to suboptimal classification performance. To bridge the modality gap and fully exploit the information from each modality, we propose a novel feature disentanglement method that decomposes features into modality-shared and modality-specific components. We design a multi-level regularization strategy including intra-modality, inter-modality, and intra-inter-modality regularization to facilitate the effective learning of the modality Shared-Specific features. Our method achieves state-of-the-art performance on two eye disease diagnosis tasks using two publicly available datasets. Our method promises to serve as a useful tool for multimodal eye disease diagnosis.
{"title":"Bridging the Modality Gap in Multimodal Eye Disease Screening: Learning Modality Shared-Specific Features via Multi-Level Regularization","authors":"Jiayue Zhao;Shiman Li;Yi Hao;Chenxi Zhang","doi":"10.1109/LSP.2025.3526094","DOIUrl":"https://doi.org/10.1109/LSP.2025.3526094","url":null,"abstract":"Color fundus photography (CFP) and optical coherence tomography (OCT) are two common modalities used in eye disease screening, providing crucial complementary information for the diagnosis of eye diseases. However, existing multimodal learning methods cannot fully leverage the information from each modality due to the large dimensional and semantic gap between 2D CFP and 3D OCT images, leading to suboptimal classification performance. To bridge the modality gap and fully exploit the information from each modality, we propose a novel feature disentanglement method that decomposes features into modality-shared and modality-specific components. We design a multi-level regularization strategy including intra-modality, inter-modality, and intra-inter-modality regularization to facilitate the effective learning of the modality Shared-Specific features. Our method achieves state-of-the-art performance on two eye disease diagnosis tasks using two publicly available datasets. Our method promises to serve as a useful tool for multimodal eye disease diagnosis.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"586-590"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/LSP.2025.3526092
Ángel F. García-Fernández;Giorgio Battistelli
This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.
{"title":"Consensus Iterated Posterior Linearization Filter for Distributed State Estimation","authors":"Ángel F. García-Fernández;Giorgio Battistelli","doi":"10.1109/LSP.2025.3526092","DOIUrl":"https://doi.org/10.1109/LSP.2025.3526092","url":null,"abstract":"This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"561-565"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/LSP.2024.3525400
Sina Shahsavari;Piya Pal
This paper derives new non-asymptotic characterization of the Cramér-Rao Bound (CRB) of any sparse array as a function of the angular separation between two far-field narrowband sources in certain regimes characterized by a low Signal-to-Noise Ratio (SNR). The primary contribution is the derivation of matching upper and lower bounds on the CRB in a certain measurement-dependent SNR (MD-SNR) regime, where one can zoom into progressively lower SNR as the number of sensors increases. This tight characterization helps to establish that sparse arrays such as nested and coprime arrays provably exhibit lower CRB compared to Uniform Linear Arrays (ULAs) in the specified SNR regime.
{"title":"Cramér-Rao Bounds and Resolution Benefits of Sparse Arrays in Measurement-Dependent SNR Regimes","authors":"Sina Shahsavari;Piya Pal","doi":"10.1109/LSP.2024.3525400","DOIUrl":"https://doi.org/10.1109/LSP.2024.3525400","url":null,"abstract":"This paper derives new non-asymptotic characterization of the Cramér-Rao Bound (CRB) of any sparse array as a function of the angular separation between two far-field narrowband sources in certain regimes characterized by a low Signal-to-Noise Ratio (SNR). The primary contribution is the derivation of matching upper and lower bounds on the CRB in a certain measurement-dependent SNR (MD-SNR) regime, where one can zoom into progressively lower SNR as the number of sensors increases. This tight characterization helps to establish that sparse arrays such as nested and coprime arrays provably exhibit lower CRB compared to Uniform Linear Arrays (ULAs) in the specified SNR regime.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"601-605"},"PeriodicalIF":3.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/LSP.2024.3525398
Ye Liu;Tianhao Shi;Mingliang Zhai;Jun Liu
In 3D skeleton-based action recognition, the limited availability of supervised data has driven interest in self-supervised learning methods. The reconstruction paradigm using masked auto-encoder (MAE) is an effective and mainstream self-supervised learning approach. However, recent studies indicate that MAE models tend to focus on features within a certain frequency range, which may result in the loss of important information. To address this issue, we propose a frequency decoupled MAE. Specifically, by incorporating a scale-specific frequency feature reconstruction module, we delve into leveraging frequency information as a direct and explicit target for reconstruction, which augments the MAE's capability to discern and accurately reproduce diverse frequency attributes within the data. Moreover, in order to address the issue of unstable gradient updates caused by more complex optimization objectives with frequency reconstruction, we introduce a dual-path network combined with an exponential moving average (EMA) parameter updating strategy to guide the model in stabilizing the training process. We have conducted extensive experiments which have demonstrated the effectiveness of the proposed method.
{"title":"Frequency Decoupled Masked Auto-Encoder for Self-Supervised Skeleton-Based Action Recognition","authors":"Ye Liu;Tianhao Shi;Mingliang Zhai;Jun Liu","doi":"10.1109/LSP.2024.3525398","DOIUrl":"https://doi.org/10.1109/LSP.2024.3525398","url":null,"abstract":"In 3D skeleton-based action recognition, the limited availability of supervised data has driven interest in self-supervised learning methods. The reconstruction paradigm using masked auto-encoder (MAE) is an effective and mainstream self-supervised learning approach. However, recent studies indicate that MAE models tend to focus on features within a certain frequency range, which may result in the loss of important information. To address this issue, we propose a frequency decoupled MAE. Specifically, by incorporating a scale-specific frequency feature reconstruction module, we delve into leveraging frequency information as a direct and explicit target for reconstruction, which augments the MAE's capability to discern and accurately reproduce diverse frequency attributes within the data. Moreover, in order to address the issue of unstable gradient updates caused by more complex optimization objectives with frequency reconstruction, we introduce a dual-path network combined with an exponential moving average (EMA) parameter updating strategy to guide the model in stabilizing the training process. We have conducted extensive experiments which have demonstrated the effectiveness of the proposed method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"546-550"},"PeriodicalIF":3.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/LSP.2024.3524120
Yusen Wang;Xiaohong Qian;Wujie Zhou
Audio–visual segmentation (AVS) is a challenging task that focuses on segmenting sound-producing objects within video frames by leveraging audio signals. Existing convolutional neural networks (CNNs) and Transformer-based methods extract features separately from modality-specific encoders and then use fusion modules to integrate the visual and auditory features. We propose an effective Transformer-prompted network, TPNet, which utilizes prompt learning with a Transformer to guide the CNN in addressing AVS tasks. Specifically, during feature encoding, we incorporate a frequency-based prompt-supplement module to fine-tune and enhance the encoded features through frequency-domain methods. Furthermore, during audio–visual fusion, we integrate a self-supplementing cross-fusion module that uses self-attention, two-dimensional selective scanning, and cross-attention mechanisms to merge and enhance audio–visual features effectively. The prompt features undergo the same processing in cross-modal fusion, further refining the fused features to achieve more accurate segmentation results. Finally, we apply self-knowledge distillation to the network, further enhancing the model performance. Extensive experiments on the AVSBench dataset validate the effectiveness of TPNet.
{"title":"Transformer-Prompted Network: Efficient Audio–Visual Segmentation via Transformer and Prompt Learning","authors":"Yusen Wang;Xiaohong Qian;Wujie Zhou","doi":"10.1109/LSP.2024.3524120","DOIUrl":"https://doi.org/10.1109/LSP.2024.3524120","url":null,"abstract":"Audio–visual segmentation (AVS) is a challenging task that focuses on segmenting sound-producing objects within video frames by leveraging audio signals. Existing convolutional neural networks (CNNs) and Transformer-based methods extract features separately from modality-specific encoders and then use fusion modules to integrate the visual and auditory features. We propose an effective Transformer-prompted network, TPNet, which utilizes prompt learning with a Transformer to guide the CNN in addressing AVS tasks. Specifically, during feature encoding, we incorporate a frequency-based prompt-supplement module to fine-tune and enhance the encoded features through frequency-domain methods. Furthermore, during audio–visual fusion, we integrate a self-supplementing cross-fusion module that uses self-attention, two-dimensional selective scanning, and cross-attention mechanisms to merge and enhance audio–visual features effectively. The prompt features undergo the same processing in cross-modal fusion, further refining the fused features to achieve more accurate segmentation results. Finally, we apply self-knowledge distillation to the network, further enhancing the model performance. Extensive experiments on the AVSBench dataset validate the effectiveness of TPNet.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"516-520"},"PeriodicalIF":3.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/LSP.2024.3525406
Qingpeng Liang;Linsong Du;Yanzhi Wu;Zheng Ma
This letter focuses on the optimal sum secure degree of freedom (SDoF) in a two-way wiretap channel (TW-WC), wherein two legitimate full-duplex multiple-antenna nodes cooperate with each other and are wiretapped by a multiple antenna eavesdropper simultaneously. It aims to find the optimal sum SDoF pertaining to secret-key capacity for the TW-WC. First, we analyze the upper bound and lower bounds of the optimal sum SDoF by establishing their equivalence to the expression of the optimal SDoF corresponding to the secrecy rate for the TW-WC. Subsequently, in scenarios where the legitimate nodes are configured with an equal number of transmit and receive antennas, it is elucidated that the upper and lower bounds of the optimal SDoF converge. Furthermore, the findings suggest that a higher SDoF can be achieved than the existing works, thereby heralding an enhancement in secure spectral efficiency.
{"title":"Secure Degree of Freedom Bound of Secret-Key Capacity for Two-Way Wiretap Channel","authors":"Qingpeng Liang;Linsong Du;Yanzhi Wu;Zheng Ma","doi":"10.1109/LSP.2024.3525406","DOIUrl":"https://doi.org/10.1109/LSP.2024.3525406","url":null,"abstract":"This letter focuses on the optimal sum secure degree of freedom (SDoF) in a two-way wiretap channel (TW-WC), wherein two legitimate full-duplex multiple-antenna nodes cooperate with each other and are wiretapped by a multiple antenna eavesdropper simultaneously. It aims to find the optimal sum SDoF pertaining to secret-key capacity for the TW-WC. First, we analyze the upper bound and lower bounds of the optimal sum SDoF by establishing their equivalence to the expression of the optimal SDoF corresponding to the secrecy rate for the TW-WC. Subsequently, in scenarios where the legitimate nodes are configured with an equal number of transmit and receive antennas, it is elucidated that the upper and lower bounds of the optimal SDoF converge. Furthermore, the findings suggest that a higher SDoF can be achieved than the existing works, thereby heralding an enhancement in secure spectral efficiency.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"581-585"},"PeriodicalIF":3.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/LSP.2024.3524096
Zhiguo Ding
The key idea of hybrid non-orthogonal multiple access (NOMA) is to allow users to use the bandwidth resources to which they cannot have access in orthogonal multiple access (OMA) based legacy networks while still guaranteeing its compatibility with the legacy network. However, in a conventional hybrid NOMA downlink network, some users have access to more bandwidth resources than others, which leads to a potential performance loss. So what if the users can access the same amount of bandwidth resources? This letter focuses on a simple two-user scenario, and develops analytical and simulation results to reveal that for this considered scenario, conventional hybrid NOMA is still an optimal transmission strategy.
{"title":"A Study on the Optimality of Downlink Hybrid NOMA","authors":"Zhiguo Ding","doi":"10.1109/LSP.2024.3524096","DOIUrl":"https://doi.org/10.1109/LSP.2024.3524096","url":null,"abstract":"The key idea of hybrid non-orthogonal multiple access (NOMA) is to allow users to use the bandwidth resources to which they cannot have access in orthogonal multiple access (OMA) based legacy networks while still guaranteeing its compatibility with the legacy network. However, in a conventional hybrid NOMA downlink network, some users have access to more bandwidth resources than others, which leads to a potential performance loss. So what if the users can access the same amount of bandwidth resources? This letter focuses on a simple two-user scenario, and develops analytical and simulation results to reveal that for this considered scenario, conventional hybrid NOMA is still an optimal transmission strategy.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"511-515"},"PeriodicalIF":3.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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