arXiv - CS - Information Theory最新文献_第9页

Cell segmentation of in situ transcriptomics data using signed graph partitioning 利用符号图分割技术对原位转录组学数据进行细胞分割

arXiv - CS - Information Theory Pub Date : 2023-12-07 DOI: arxiv-2312.04181

Axel Andersson, Andrea Behanova, Carolina Wählby, Filip Malmberg

{"title":"Cell segmentation of in situ transcriptomics data using signed graph partitioning","authors":"Axel Andersson, Andrea Behanova, Carolina Wählby, Filip Malmberg","doi":"arxiv-2312.04181","DOIUrl":"https://doi.org/arxiv-2312.04181","url":null,"abstract":"The locations of different mRNA molecules can be revealed by multiplexed in\u0000situ RNA detection. By assigning detected mRNA molecules to individual cells,\u0000it is possible to identify many different cell types in parallel. This in turn\u0000enables investigation of the spatial cellular architecture in tissue, which is\u0000crucial for furthering our understanding of biological processes and diseases.\u0000However, cell typing typically depends on the segmentation of cell nuclei,\u0000which is often done based on images of a DNA stain, such as DAPI. Limiting cell\u0000definition to a nuclear stain makes it fundamentally difficult to determine\u0000accurate cell borders, and thereby also difficult to assign mRNA molecules to\u0000the correct cell. As such, we have developed a computational tool that segments\u0000cells solely based on the local composition of mRNA molecules. First, a small\u0000neural network is trained to compute attractive and repulsive edges between\u0000pairs of mRNA molecules. The signed graph is then partitioned by a mutex\u0000watershed into components corresponding to different cells. We evaluated our\u0000method on two publicly available datasets and compared it against the current\u0000state-of-the-art and older baselines. We conclude that combining neural\u0000networks with combinatorial optimization is a promising approach for cell\u0000segmentation of in situ transcriptomics data.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138553773","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}

引用次数: 0

Enumerating Complexity Revisited 重新审视枚举复杂性

arXiv - CS - Information Theory Pub Date : 2023-12-07 DOI: arxiv-2312.04187

Alexander Shekhovtsov, Georgii Zakharov

引用次数: 0

Reed-Muller codes have vanishing bit-error probability below capacity: a simple tighter proof via camellia boosting 里德-穆勒码在容量以下误码概率消失：通过山茶花提升的简单严密证明

arXiv - CS - Information Theory Pub Date : 2023-12-07 DOI: arxiv-2312.04329

Emmanuel Abbe, Colin Sandon

引用次数: 0

A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems 大规模多输入多输出系统的低开销、基于并入-外推法的少量 CSI 反馈框架

arXiv - CS - Information Theory Pub Date : 2023-12-07 DOI: arxiv-2312.04062

Binggui Zhou, Xi Yang, Jintao Wang, Shaodan Ma, Feifei Gao, Guanghua Yang

{"title":"A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems","authors":"Binggui Zhou, Xi Yang, Jintao Wang, Shaodan Ma, Feifei Gao, Guanghua Yang","doi":"arxiv-2312.04062","DOIUrl":"https://doi.org/arxiv-2312.04062","url":null,"abstract":"Accurate channel state information (CSI) is essential for downlink precoding\u0000at the base station (BS), especially for frequency FDD wideband massive MIMO\u0000systems with OFDM. In FDD systems, CSI is attained through CSI feedback from\u0000the user equipment (UE). However, large-scale antennas and large number of\u0000subcarriers significantly increase CSI feedback overhead. Deep learning-based\u0000CSI feedback methods have received tremendous attention in recent years due to\u0000their great capability of compressing CSI. Nonetheless, large amounts of\u0000collected samples are required to train deep learning models, which is severely\u0000challenging in practice. Besides, with the rapidly increasing number of\u0000antennas and subcarriers, most of these deep learning methods' CSI feedback\u0000overhead also grow dramatically, owing to their focus on full-dimensional CSI\u0000feedback. To address this issue, in this paper, we propose a low-overhead\u0000Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for\u0000massive MIMO systems. To further reduce the feedback overhead, a\u0000low-dimensional eigenvector-based CSI matrix is first formed with the\u0000incorporation process at the UE, and then recovered to the full-dimensional\u0000eigenvector-based CSI matrix at the BS via the extrapolation process. After\u0000that, to alleviate the necessity of the extensive collected samples and enable\u0000few-shot CSI feedback, we further propose a knowledge-driven data augmentation\u0000method and an artificial intelligence-generated content (AIGC) -based data\u0000augmentation method by exploiting the domain knowledge of wireless channels and\u0000by exploiting a novel generative model, respectively. Numerical results\u0000demonstrate that the proposed IEFSF can significantly reduce CSI feedback\u0000overhead by 16 times compared with existing CSI feedback methods while\u0000maintaining higher feedback accuracy using only several hundreds of collected\u0000samples.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"103 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138553968","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}

引用次数: 0

Reversible Entanglement Beyond Quantum Operations 超越量子操作的可逆纠缠

arXiv - CS - Information Theory Pub Date : 2023-12-07 DOI: arxiv-2312.04456

Xin Wang, Yu-Ao Chen, Lei Zhang, Chenghong Zhu

引用次数: 0

New ternary self-orthogonal codes and related LCD codes from weakly regular plateaued functions 来自弱正则高原函数的新三元自正交码及相关 LCD 码

arXiv - CS - Information Theory Pub Date : 2023-12-07 DOI: arxiv-2312.04261

Dengcheng Xie, Shixin Zhu, Yang Li

引用次数: 0

Enhanced data Detection for Massive MIMO with 1-Bit ADCs 利用 1 位 ADC 增强大规模 MIMO 的数据检测功能

arXiv - CS - Information Theory Pub Date : 2023-12-07 DOI: arxiv-2312.04183

Amin Radbord, Italo Atzeni, Antti Tolli

{"title":"Enhanced data Detection for Massive MIMO with 1-Bit ADCs","authors":"Amin Radbord, Italo Atzeni, Antti Tolli","doi":"arxiv-2312.04183","DOIUrl":"https://doi.org/arxiv-2312.04183","url":null,"abstract":"We present new insightful results on the uplink data detection for massive\u0000multiple-input multiple-output systems with 1-bit analog-to-digital converters.\u0000The expected values of the soft-estimated symbols (i.e., after the linear\u0000combining and prior to the data detection) have been recently characterized for\u0000multiple user equipments (UEs) and maximum ratio combining (MRC) receiver at\u0000the base station. In this paper, we first provide a numerical evaluation of the\u0000expected value of the soft-estimated symbols with zero-forcing (ZF) and minimum\u0000mean squared error (MMSE) receivers for a multi-UE setting with correlated\u0000Rayleigh fading. Then, we propose a joint data detection (JD) strategy, which\u0000exploits the interdependence among the soft-estimated symbols of the\u0000interfering UEs, along with its low-complexity variant. These strategies are\u0000compared with a naive approach that adapts the maximum-likelihood data\u0000detection to the 1-bit quantization. Numerical results show that ZF and MMSE\u0000provide considerable gains over MRC in terms of symbol error rate. Moreover,\u0000the proposed JD and its low-complexity variant provide a significant boost in\u0000comparison with the single-UE data detection.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138553765","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}

引用次数: 0

A Scalable and Generalizable Pathloss Map Prediction 可扩展和通用的路径损耗图预测

arXiv - CS - Information Theory Pub Date : 2023-12-06 DOI: arxiv-2312.03950

Ju-Hyung Lee, Andreas F. Molisch

{"title":"A Scalable and Generalizable Pathloss Map Prediction","authors":"Ju-Hyung Lee, Andreas F. Molisch","doi":"arxiv-2312.03950","DOIUrl":"https://doi.org/arxiv-2312.03950","url":null,"abstract":"Large-scale channel prediction, i.e., estimation of the pathloss from\u0000geographical/morphological/building maps, is an essential component of wireless\u0000network planning. Ray tracing (RT)-based methods have been widely used for many\u0000years, but they require significant computational effort that may become\u0000prohibitive with the increased network densification and/or use of higher\u0000frequencies in B5G/6G systems. In this paper, we propose a data-driven,\u0000model-free pathloss map prediction (PMP) method, called PMNet. PMNet uses a\u0000supervised learning approach: it is trained on a limited amount of RT (or\u0000channel measurement) data and map data. Once trained, PMNet can predict\u0000pathloss over location with high accuracy (an RMSE level of $10^{-2}$) in a few\u0000milliseconds. We further extend PMNet by employing transfer learning (TL). TL\u0000allows PMNet to learn a new network scenario quickly (x5.6 faster training) and\u0000efficiently (using x4.5 less data) by transferring knowledge from a pre-trained\u0000model, while retaining accuracy. Our results demonstrate that PMNet is a\u0000scalable and generalizable ML-based PMP method, showing its potential to be\u0000used in several network optimization applications.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138553762","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}

引用次数: 0

Densifying MIMO: Channel Modeling, Physical Constraints, and Performance Evaluation for Holographic Communications 密集型多输入多输出（MIMO）：全息通信的信道建模、物理约束和性能评估

arXiv - CS - Information Theory Pub Date : 2023-12-06 DOI: arxiv-2312.03255

Y. Liu, M. Zhang, T. Wang, A. Zhang, M. Debbah

{"title":"Densifying MIMO: Channel Modeling, Physical Constraints, and Performance Evaluation for Holographic Communications","authors":"Y. Liu, M. Zhang, T. Wang, A. Zhang, M. Debbah","doi":"arxiv-2312.03255","DOIUrl":"https://doi.org/arxiv-2312.03255","url":null,"abstract":"As the backbone of the fifth-generation (5G) cellular network, massive\u0000multiple-input multiple-output (MIMO) encounters a significant challenge in\u0000practical applications: how to deploy a large number of antenna elements within\u0000limited spaces. Recently, holographic communication has emerged as a potential\u0000solution to this issue. It employs dense antenna arrays and provides a\u0000tractable model. Nevertheless, some challenges must be addressed to actualize\u0000this innovative concept. One is the mutual coupling among antenna elements\u0000within an array. When the element spacing is small, near-field coupling becomes\u0000the dominant factor that strongly restricts the array performance. Another is\u0000the polarization of electromagnetic waves. As an intrinsic property, it was not\u0000fully considered in the previous channel modeling of holographic communication.\u0000The third is the lack of real-world experiments to show the potential and\u0000possible defects of a holographic communication system. In this paper, we\u0000propose an electromagnetic channel model based on the characteristics of\u0000electromagnetic waves. This model encompasses the impact of mutual coupling in\u0000the transceiver sides and the depolarization in the propagation environment.\u0000Furthermore, by approximating an infinite array, the performance restrictions\u0000of large-scale dense antenna arrays are also studied theoretically to exploit\u0000the potential of the proposed channel. In addition, numerical simulations and a\u0000channel measurement experiment are conducted. The findings reveal that within\u0000limited spaces, the coupling effect, particularly for element spacing smaller\u0000than half of the wavelength, is the primary factor leading to the inflection\u0000point for the performance of holographic communications.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138556816","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}

引用次数: 0

Channel-Transferable Semantic Communications for Multi-User OFDM-NOMA Systems 面向多用户 OFDM-NOMA 系统的信道可传输语义通信

arXiv - CS - Information Theory Pub Date : 2023-12-06 DOI: arxiv-2312.03299

Lan Lin, Wenjun Xu, Fengyu Wang, Yimeng Zhang, Wei Zhang, Ping Zhang

{"title":"Channel-Transferable Semantic Communications for Multi-User OFDM-NOMA Systems","authors":"Lan Lin, Wenjun Xu, Fengyu Wang, Yimeng Zhang, Wei Zhang, Ping Zhang","doi":"arxiv-2312.03299","DOIUrl":"https://doi.org/arxiv-2312.03299","url":null,"abstract":"Semantic communications are expected to become the core new paradigms of the\u0000sixth generation (6G) wireless networks. Most existing works implicitly utilize\u0000channel information for codecs training, which leads to poor communications\u0000when channel type or statistical characteristics change. To tackle this issue\u0000posed by various channels, a novel channel-transferable semantic communications\u0000(CT-SemCom) framework is proposed, which adapts the codecs learned on one type\u0000of channel to other types of channels. Furthermore, integrating the proposed\u0000framework and the orthogonal frequency division multiplexing systems\u0000integrating non-orthogonal multiple access technologies, i.e., OFDM-NOMA\u0000systems, a power allocation problem to realize the transfer from additive white\u0000Gaussian noise (AWGN) channels to multi-subcarrier Rayleigh fading channels is\u0000formulated. We then design a semantics-similar dual transformation (SSDT)\u0000algorithm to derive analytical solutions with low complexity. Simulation\u0000results show that the proposed CT-SemCom framework with SSDT algorithm\u0000significantly outperforms the existing work w.r.t. channel transferability,\u0000e.g., the peak signal-to-noise ratio (PSNR) of image transmission improves by\u00004.2-7.3 dB under different variances of Rayleigh fading channels.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138547528","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}

引用次数: 0