Axel Andersson, Andrea Behanova, Carolina Wählby, Filip Malmberg
The locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify many different cell types in parallel. This in turn enables investigation of the spatial cellular architecture in tissue, which is crucial for furthering our understanding of biological processes and diseases. However, cell typing typically depends on the segmentation of cell nuclei, which is often done based on images of a DNA stain, such as DAPI. Limiting cell definition to a nuclear stain makes it fundamentally difficult to determine accurate cell borders, and thereby also difficult to assign mRNA molecules to the correct cell. As such, we have developed a computational tool that segments cells solely based on the local composition of mRNA molecules. First, a small neural network is trained to compute attractive and repulsive edges between pairs of mRNA molecules. The signed graph is then partitioned by a mutex watershed into components corresponding to different cells. We evaluated our method on two publicly available datasets and compared it against the current state-of-the-art and older baselines. We conclude that combining neural networks with combinatorial optimization is a promising approach for cell segmentation of in situ transcriptomics data.
{"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}
We reduce the best-known upper bound on the length of a program that enumerates a set in terms of the probability of it being enumerated by a random program. We prove a general result that any linear upper bound for finite sets implies the same linear bound for infinite sets. So far, the best-known upper bound was given by Solovay. He showed that the minimum length of a program enumerating a subset $S$ of natural numbers is bounded by minus three binary logarithms of the probability that a random program will enumerate $S$. Later, Vereshchagin showed that the constant can be improved from three to two for finite sets. In this work, using an improvement of the method proposed by Solovay, we demonstrate that any bound for finite sets implies the same for infinite sets, modulo logarithmic factors. Using Vereshchagin's result, we improve the current best-known upper bound from three to two.
{"title":"Enumerating Complexity Revisited","authors":"Alexander Shekhovtsov, Georgii Zakharov","doi":"arxiv-2312.04187","DOIUrl":"https://doi.org/arxiv-2312.04187","url":null,"abstract":"We reduce the best-known upper bound on the length of a program that\u0000enumerates a set in terms of the probability of it being enumerated by a random\u0000program. We prove a general result that any linear upper bound for finite sets\u0000implies the same linear bound for infinite sets. So far, the best-known upper bound was given by Solovay. He showed that the\u0000minimum length of a program enumerating a subset $S$ of natural numbers is\u0000bounded by minus three binary logarithms of the probability that a random\u0000program will enumerate $S$. Later, Vereshchagin showed that the constant can be\u0000improved from three to two for finite sets. In this work, using an improvement\u0000of the method proposed by Solovay, we demonstrate that any bound for finite\u0000sets implies the same for infinite sets, modulo logarithmic factors. Using\u0000Vereshchagin's result, we improve the current best-known upper bound from three\u0000to two.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138556757","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}
This paper shows that a class of codes such as Reed-Muller (RM) codes have vanishing bit-error probability below capacity on symmetric channels. The proof relies on the notion of `camellia codes': a class of symmetric codes decomposable into `camellias', i.e., set systems that differ from sunflowers by allowing for scattered petal overlaps. The proof then follows from a boosting argument on the camellia petals with second moment Fourier analysis. For erasure channels, this gives a self-contained proof of the bit-error result in Kudekar et al.'17, without relying on sharp thresholds for monotone properties Friedgut-Kalai'96. For error channels, this gives a shortened proof of Reeves-Pfister'23 with an exponentially tighter bound, and a proof variant of the bit-error result in Abbe-Sandon'23. The control of the full (block) error probability still requires Abbe-Sandon'23 for RM codes.
{"title":"Reed-Muller codes have vanishing bit-error probability below capacity: a simple tighter proof via camellia boosting","authors":"Emmanuel Abbe, Colin Sandon","doi":"arxiv-2312.04329","DOIUrl":"https://doi.org/arxiv-2312.04329","url":null,"abstract":"This paper shows that a class of codes such as Reed-Muller (RM) codes have\u0000vanishing bit-error probability below capacity on symmetric channels. The proof\u0000relies on the notion of `camellia codes': a class of symmetric codes\u0000decomposable into `camellias', i.e., set systems that differ from sunflowers by\u0000allowing for scattered petal overlaps. The proof then follows from a boosting\u0000argument on the camellia petals with second moment Fourier analysis. For\u0000erasure channels, this gives a self-contained proof of the bit-error result in\u0000Kudekar et al.'17, without relying on sharp thresholds for monotone properties\u0000Friedgut-Kalai'96. For error channels, this gives a shortened proof of\u0000Reeves-Pfister'23 with an exponentially tighter bound, and a proof variant of\u0000the bit-error result in Abbe-Sandon'23. The control of the full (block) error\u0000probability still requires Abbe-Sandon'23 for RM codes.","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":"138556972","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}
Binggui Zhou, Xi Yang, Jintao Wang, Shaodan Ma, Feifei Gao, Guanghua Yang
Accurate channel state information (CSI) is essential for downlink precoding at the base station (BS), especially for frequency FDD wideband massive MIMO systems with OFDM. In FDD systems, CSI is attained through CSI feedback from the user equipment (UE). However, large-scale antennas and large number of subcarriers significantly increase CSI feedback overhead. Deep learning-based CSI feedback methods have received tremendous attention in recent years due to their great capability of compressing CSI. Nonetheless, large amounts of collected samples are required to train deep learning models, which is severely challenging in practice. Besides, with the rapidly increasing number of antennas and subcarriers, most of these deep learning methods' CSI feedback overhead also grow dramatically, owing to their focus on full-dimensional CSI feedback. To address this issue, in this paper, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems. To further reduce the feedback overhead, a low-dimensional eigenvector-based CSI matrix is first formed with the incorporation process at the UE, and then recovered to the full-dimensional eigenvector-based CSI matrix at the BS via the extrapolation process. After that, to alleviate the necessity of the extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively. Numerical results demonstrate that the proposed IEFSF can significantly reduce CSI feedback overhead by 16 times compared with existing CSI feedback methods while maintaining higher feedback accuracy using only several hundreds of collected samples.
{"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}
We introduce a reversible theory of exact entanglement manipulation by establishing a necessary and sufficient condition for state transfer under trace-preserving transformations that completely preserve the positivity of partial transpose (PPT). Under these free transformations, we show that logarithmic negativity emerges as the pivotal entanglement measure for determining entangled states' transformations, analogous to the role of entropy in the second law of thermodynamics. Previous results have proven that entanglement is irreversible under quantum operations that completely preserve PPT and leave open the question of reversibility for quantum operations that do not generate entanglement asymptotically. However, we find that going beyond the complete positivity constraint imposed by standard quantum mechanics enables a reversible theory of exact entanglement manipulation, which may suggest a potential incompatibility between the reversibility of entanglement and the fundamental principles of quantum mechanics.
{"title":"Reversible Entanglement Beyond Quantum Operations","authors":"Xin Wang, Yu-Ao Chen, Lei Zhang, Chenghong Zhu","doi":"arxiv-2312.04456","DOIUrl":"https://doi.org/arxiv-2312.04456","url":null,"abstract":"We introduce a reversible theory of exact entanglement manipulation by\u0000establishing a necessary and sufficient condition for state transfer under\u0000trace-preserving transformations that completely preserve the positivity of\u0000partial transpose (PPT). Under these free transformations, we show that\u0000logarithmic negativity emerges as the pivotal entanglement measure for\u0000determining entangled states' transformations, analogous to the role of entropy\u0000in the second law of thermodynamics. Previous results have proven that\u0000entanglement is irreversible under quantum operations that completely preserve\u0000PPT and leave open the question of reversibility for quantum operations that do\u0000not generate entanglement asymptotically. However, we find that going beyond\u0000the complete positivity constraint imposed by standard quantum mechanics\u0000enables a reversible theory of exact entanglement manipulation, which may\u0000suggest a potential incompatibility between the reversibility of entanglement\u0000and the fundamental principles of quantum mechanics.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"166 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138556989","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}
A linear code is said to be self-orthogonal if it is contained in its dual. Self-orthogonal codes are of interest because of their important applications, such as for constructing linear complementary dual (LCD) codes and quantum codes. In this paper, we construct several new families of ternary self-orthogonal codes by employing weakly regular plateaued functions. Their parameters and weight distributions are completely determined. Then we apply these self-orthogonal codes to construct several new families of ternary LCD codes. As a consequence, we obtain many (almost) optimal ternary self-orthogonal codes and LCD codes.
{"title":"New ternary self-orthogonal codes and related LCD codes from weakly regular plateaued functions","authors":"Dengcheng Xie, Shixin Zhu, Yang Li","doi":"arxiv-2312.04261","DOIUrl":"https://doi.org/arxiv-2312.04261","url":null,"abstract":"A linear code is said to be self-orthogonal if it is contained in its dual.\u0000Self-orthogonal codes are of interest because of their important applications,\u0000such as for constructing linear complementary dual (LCD) codes and quantum\u0000codes. In this paper, we construct several new families of ternary\u0000self-orthogonal codes by employing weakly regular plateaued functions. Their\u0000parameters and weight distributions are completely determined. Then we apply\u0000these self-orthogonal codes to construct several new families of ternary LCD\u0000codes. As a consequence, we obtain many (almost) optimal ternary\u0000self-orthogonal codes and LCD codes.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138557141","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}
We present new insightful results on the uplink data detection for massive multiple-input multiple-output systems with 1-bit analog-to-digital converters. The expected values of the soft-estimated symbols (i.e., after the linear combining and prior to the data detection) have been recently characterized for multiple user equipments (UEs) and maximum ratio combining (MRC) receiver at the base station. In this paper, we first provide a numerical evaluation of the expected value of the soft-estimated symbols with zero-forcing (ZF) and minimum mean squared error (MMSE) receivers for a multi-UE setting with correlated Rayleigh fading. Then, we propose a joint data detection (JD) strategy, which exploits the interdependence among the soft-estimated symbols of the interfering UEs, along with its low-complexity variant. These strategies are compared with a naive approach that adapts the maximum-likelihood data detection to the 1-bit quantization. Numerical results show that ZF and MMSE provide considerable gains over MRC in terms of symbol error rate. Moreover, the proposed JD and its low-complexity variant provide a significant boost in comparison with the single-UE data detection.
{"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}
Large-scale channel prediction, i.e., estimation of the pathloss from geographical/morphological/building maps, is an essential component of wireless network planning. Ray tracing (RT)-based methods have been widely used for many years, but they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in B5G/6G systems. In this paper, we propose a data-driven, model-free pathloss map prediction (PMP) method, called PMNet. PMNet uses a supervised learning approach: it is trained on a limited amount of RT (or channel measurement) data and map data. Once trained, PMNet can predict pathloss over location with high accuracy (an RMSE level of $10^{-2}$) in a few milliseconds. We further extend PMNet by employing transfer learning (TL). TL allows PMNet to learn a new network scenario quickly (x5.6 faster training) and efficiently (using x4.5 less data) by transferring knowledge from a pre-trained model, while retaining accuracy. Our results demonstrate that PMNet is a scalable and generalizable ML-based PMP method, showing its potential to be used in several network optimization applications.
{"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}
As the backbone of the fifth-generation (5G) cellular network, massive multiple-input multiple-output (MIMO) encounters a significant challenge in practical applications: how to deploy a large number of antenna elements within limited spaces. Recently, holographic communication has emerged as a potential solution to this issue. It employs dense antenna arrays and provides a tractable model. Nevertheless, some challenges must be addressed to actualize this innovative concept. One is the mutual coupling among antenna elements within an array. When the element spacing is small, near-field coupling becomes the dominant factor that strongly restricts the array performance. Another is the polarization of electromagnetic waves. As an intrinsic property, it was not fully considered in the previous channel modeling of holographic communication. The third is the lack of real-world experiments to show the potential and possible defects of a holographic communication system. In this paper, we propose an electromagnetic channel model based on the characteristics of electromagnetic waves. This model encompasses the impact of mutual coupling in the transceiver sides and the depolarization in the propagation environment. Furthermore, by approximating an infinite array, the performance restrictions of large-scale dense antenna arrays are also studied theoretically to exploit the potential of the proposed channel. In addition, numerical simulations and a channel measurement experiment are conducted. The findings reveal that within limited spaces, the coupling effect, particularly for element spacing smaller than half of the wavelength, is the primary factor leading to the inflection point for the performance of holographic communications.
{"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}
Semantic communications are expected to become the core new paradigms of the sixth generation (6G) wireless networks. Most existing works implicitly utilize channel information for codecs training, which leads to poor communications when channel type or statistical characteristics change. To tackle this issue posed by various channels, a novel channel-transferable semantic communications (CT-SemCom) framework is proposed, which adapts the codecs learned on one type of channel to other types of channels. Furthermore, integrating the proposed framework and the orthogonal frequency division multiplexing systems integrating non-orthogonal multiple access technologies, i.e., OFDM-NOMA systems, a power allocation problem to realize the transfer from additive white Gaussian noise (AWGN) channels to multi-subcarrier Rayleigh fading channels is formulated. We then design a semantics-similar dual transformation (SSDT) algorithm to derive analytical solutions with low complexity. Simulation results show that the proposed CT-SemCom framework with SSDT algorithm significantly outperforms the existing work w.r.t. channel transferability, e.g., the peak signal-to-noise ratio (PSNR) of image transmission improves by 4.2-7.3 dB under different variances of Rayleigh fading channels.
{"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}
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