Pub Date : 2024-09-11DOI: 10.1109/OJCOMS.2024.3458802
Federico Chiariotti;Beatriz Soret;Petar Popovski
Over the past decade, the deep learning revolution has led to ever-increasing demands for computing power and working memory to support larger and larger neural networks. As this coincided with the end of Moore’s law, distributed solutions have emerged as a natural answer: in particular, the novel Coded Distributed Computing (CDC) paradigm exploits results from coding theory to divide large tasks into redundant sets of smaller subtasks to be processed across multiple workers, making the computation more robust to stragglers and malicious worker nodes. Optimizing the use of these distributed computing resources is critical, as excessive redundancy might impact on performance and energy consumption. This work considers a CDC system receiving periodic tasks, deriving the full distribution of the latency, reliability, and Peak Age of Information (PAoI) under worker diversity and random failures. The CDC system is modeled as a fork-join $D/M/(K, N)/L$ queue, where only K of the coded N subtasks are necessary to solve the overall task, and workers can hold up to L subtasks in their queues. Our results are useful for resource optimization, showing the relationship between system load, redundancy, and latency, as well as the trade-off between latency, reliability, and age performance.
过去十年间，深度学习革命导致对计算能力和工作内存的需求不断增加，以支持越来越大的神经网络。由于这与摩尔定律的终结不谋而合，分布式解决方案自然应运而生：特别是，新颖的编码分布式计算（CDC）范例利用编码理论的结果，将大型任务划分为冗余的较小子任务集，由多个工作者处理，从而使计算对落伍者和恶意工作者节点更具鲁棒性。优化使用这些分布式计算资源至关重要，因为过多的冗余可能会影响性能和能耗。本研究考虑了一个接收周期性任务的 CDC 系统，推导出了工人多样性和随机故障下的延迟、可靠性和峰值信息年龄（PAoI）的完整分布。CDC 系统被建模为叉接 $D/M/(K, N)/L$ 队列，其中只有编码的 N 个子任务中的 K 个是解决整个任务所必需的，而工人的队列中最多可容纳 L 个子任务。我们的结果有助于资源优化，显示了系统负载、冗余和延迟之间的关系，以及延迟、可靠性和年龄性能之间的权衡。
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Pub Date : 2024-09-10DOI: 10.1109/OJCOMS.2024.3457507
Kengo Furuta;Takumi Takahashi;Hideki Ochiai
This paper proposes a novel beam-domain channel estimation (CE) algorithm via sparse Bayesian learning (SBL) using complex t-prior for massive multi-user multiple-input multiple-output (MIMO) systems. Due to the sidelobe leakage and insufficient observation resolution resulting from physical constraints, the equivalent channel after digital beamforming at the receiver has a structure with many small but non-zero elements, which cannot be modeled strictly as a sparse signal. To fully capture this pseudo-sparse structure characterized by the signal strength variations among elements, we design a novel SBL algorithm that incorporates a complex t-distribution using a hierarchical Bayesian model. By utilizing a high degree of adaptability of this heavy-tailed prior, it is possible to efficiently learn the signal strength, accounting for elements with non-zero but small values, which is verified by the regularization analysis based on an equivalent optimization problem. The efficacy of the proposed CE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) sparse signal recovery (SSR)-based algorithms but also achieves the performance of a genie-aided scheme over a wide signal-to-noise ratio (SNR) range in both sub-6 GHz and millimeter-wave (mmWave) wireless communication scenarios.
本文针对大规模多用户多输入多输出（MIMO）系统，提出了一种新型波束域信道估计（CE）算法，该算法通过稀疏贝叶斯学习（SBL），使用复杂的t-prior。由于物理限制导致的侧叶泄漏和观测分辨率不足，接收器数字波束成形后的等效信道具有许多小但非零元素的结构，不能严格地将其建模为稀疏信号。为了充分捕捉这种以元素间信号强度变化为特征的伪稀疏结构，我们设计了一种新颖的 SBL 算法，利用分层贝叶斯模型将复杂的 t 分布纳入其中。通过利用这种重尾先验的高度适应性，可以高效地学习信号强度，同时考虑到信号强度值不为零但较小的元素，这一点在基于等效优化问题的正则化分析中得到了验证。数值仿真证实了所提出的 CE 算法的有效性，表明所提出的方法不仅大大优于基于稀疏信号恢复（SSR）的最先进（SotA）算法，而且在 6 GHz 以下和毫米波（mmWave）无线通信场景中，在较宽的信噪比（SNR）范围内实现了精灵辅助方案的性能。
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Pub Date : 2024-09-10DOI: 10.1109/ojcoms.2024.3457023
Zhiwei Wei, Jingxin Mao, Bing Li, Rongqing Zhang
{"title":"Privacy-Preserving Hierarchical Reinforcement Learning Framework for Task Offloading in Low-Altitude Vehicular Fog Computing","authors":"Zhiwei Wei, Jingxin Mao, Bing Li, Rongqing Zhang","doi":"10.1109/ojcoms.2024.3457023","DOIUrl":"https://doi.org/10.1109/ojcoms.2024.3457023","url":null,"abstract":"","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"13 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209504","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 integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a groundbreaking paradigm for intelligent edge devices. With their capacity for human-like language processing and generation, LLMs empower edge computing with a powerful set of tools, paving the way for a new era of decentralized intelligence. Yet, a notable research gap exists in obtaining a thorough comprehension of LLM-based EI architectures, which should incorporate crucial elements such as security, optimization, and responsible development. This survey aims to bridge this gap by providing a comprehensive resource for both researchers and practitioners. We explore LLM-based EI architectures in-depth, carefully analyzing state-of-the-art paradigms and design decisions. To facilitate efficient and scalable edge deployments, we perform a comparative analysis of recent optimization and autonomy techniques specifically designed for resource-constrained edge environments. Additionally, we shed light on the extensive potential of LLM-based EI by demonstrating its varied practical applications across a wide range of domains. Acknowledging the utmost importance of security, our survey thoroughly investigates potential vulnerabilities inherent in LLM-based EI deployments. We explore corresponding defense mechanisms to protect the integrity and confidentiality of data processed at the edge. In conclusion, highlighting the essential aspect of trustworthiness, we outline best practices and guiding principles for the responsible development and deployment of these systems. By conducting a comprehensive review of these key components, our survey aims to support the ethical development and strategic implementation of LLM-driven EI, paving the way for its transformative impact on diverse applications.
大型语言模型（LLM）与边缘智能（EI）的整合为智能边缘设备引入了一种开创性的模式。大型语言模型具有类似人类语言处理和生成的能力，为边缘计算提供了一套强大的工具，为分散智能的新时代铺平了道路。然而，在全面了解基于 LLM 的电子智能架构方面还存在明显的研究空白，这些架构应包含安全、优化和负责任的开发等关键要素。本调查旨在为研究人员和从业人员提供全面的资源，从而弥合这一差距。我们深入探讨了基于 LLM 的 EI 架构，仔细分析了最先进的范例和设计决策。为了促进高效、可扩展的边缘部署，我们对最近专为资源受限的边缘环境设计的优化和自主技术进行了比较分析。此外，我们还展示了基于 LLM 的 EI 在广泛领域中的各种实际应用，从而揭示了它的巨大潜力。考虑到安全的极端重要性，我们的调查深入研究了基于 LLM 的 EI 部署中固有的潜在漏洞。我们探讨了相应的防御机制，以保护边缘处理数据的完整性和保密性。最后，我们强调了可信性这一重要方面，概述了负责任地开发和部署这些系统的最佳实践和指导原则。通过对这些关键要素进行全面审查，我们的调查旨在为 LLM 驱动的电子信息基础设施的道德发展和战略实施提供支持，为其在各种应用中产生变革性影响铺平道路。
{"title":"LLM-Based Edge Intelligence: A Comprehensive Survey on Architectures, Applications, Security and Trustworthiness","authors":"Othmane Friha;Mohamed Amine Ferrag;Burak Kantarci;Burak Cakmak;Arda Ozgun;Nassira Ghoualmi-Zine","doi":"10.1109/OJCOMS.2024.3456549","DOIUrl":"10.1109/OJCOMS.2024.3456549","url":null,"abstract":"The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a groundbreaking paradigm for intelligent edge devices. With their capacity for human-like language processing and generation, LLMs empower edge computing with a powerful set of tools, paving the way for a new era of decentralized intelligence. Yet, a notable research gap exists in obtaining a thorough comprehension of LLM-based EI architectures, which should incorporate crucial elements such as security, optimization, and responsible development. This survey aims to bridge this gap by providing a comprehensive resource for both researchers and practitioners. We explore LLM-based EI architectures in-depth, carefully analyzing state-of-the-art paradigms and design decisions. To facilitate efficient and scalable edge deployments, we perform a comparative analysis of recent optimization and autonomy techniques specifically designed for resource-constrained edge environments. Additionally, we shed light on the extensive potential of LLM-based EI by demonstrating its varied practical applications across a wide range of domains. Acknowledging the utmost importance of security, our survey thoroughly investigates potential vulnerabilities inherent in LLM-based EI deployments. We explore corresponding defense mechanisms to protect the integrity and confidentiality of data processed at the edge. In conclusion, highlighting the essential aspect of trustworthiness, we outline best practices and guiding principles for the responsible development and deployment of these systems. By conducting a comprehensive review of these key components, our survey aims to support the ethical development and strategic implementation of LLM-driven EI, paving the way for its transformative impact on diverse applications.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5799-5856"},"PeriodicalIF":6.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1109/OJCOMS.2024.3455769
Daniel Mawunyo Doe;Jing Li;Dusit Niyato;Yuqing Hu;Jun Li;Zhen Gao;Xiao-Ping Zhang;Zhu Han
In this paper, we address key challenges in Proof-of-Stake (PoS) blockchains, with a particular focus on Ethereum 2.0. We introduce an innovative mechanism that combines Tullock contests and signaling games to optimize weight assignments based on security deposits from heterogeneous nodes. While Tullock contests motivate participants to allocate resources for potential rewards, signaling games enable efficient information transfer, thereby enriching decision-making. This approach enhances network security, efficiency, and resilience by incentivizing resource investment and facilitating effective information exchange. Our framework significantly outperforms existing methods, achieving a 45.43% increase in blockchain utility and a 47.92% rise in node utility. Additionally, it yields marked improvements in user participation rates (26.89 − 32.21%) and service coverage (24 − 29.54%), and also proves to be resilient against attacks from selfish nodes.
{"title":"Harnessing Tullock Contests and Signaling Games: A Novel Weight Assignment Strategy for Ethereum 2.0","authors":"Daniel Mawunyo Doe;Jing Li;Dusit Niyato;Yuqing Hu;Jun Li;Zhen Gao;Xiao-Ping Zhang;Zhu Han","doi":"10.1109/OJCOMS.2024.3455769","DOIUrl":"10.1109/OJCOMS.2024.3455769","url":null,"abstract":"In this paper, we address key challenges in Proof-of-Stake (PoS) blockchains, with a particular focus on Ethereum 2.0. We introduce an innovative mechanism that combines Tullock contests and signaling games to optimize weight assignments based on security deposits from heterogeneous nodes. While Tullock contests motivate participants to allocate resources for potential rewards, signaling games enable efficient information transfer, thereby enriching decision-making. This approach enhances network security, efficiency, and resilience by incentivizing resource investment and facilitating effective information exchange. Our framework significantly outperforms existing methods, achieving a 45.43% increase in blockchain utility and a 47.92% rise in node utility. Additionally, it yields marked improvements in user participation rates (26.89 − 32.21%) and service coverage (24 − 29.54%), and also proves to be resilient against attacks from selfish nodes.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5782-5798"},"PeriodicalIF":6.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10668813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1109/OJCOMS.2024.3455763
Mohit K. Sharma;Ibrahim Farhat;Chen-Feng Liu;Nassim Sehad;Wassim Hamidouche;Mérouane Debbah
Over the past decade, the use of Unmanned Aerial Vehicles (UAVs) has grown significantly due to their agility, maneuverability, and rapid deployability. An important application is the use of UAV-mounted 360-degree cameras for real-time streaming of Omnidirectional Videos (ODVs), enabling immersive experiences with up to six Degrees-of-freedom (6DoF) for applications like remote surveillance and gaming. However, streaming high-resolution ODVs with low latency (below 1 second) over an air-to-ground (A2G) wireless channel faces challenges due to its inherent non-stationarity, impacting the Quality-of-experience (QoE). Limited onboard energy availability and energy consumption variability based on flight parameters add to the complexity. This paper conducts a thorough survey of challenges and research efforts in UAV-based immersive video streaming. First, we outline the end-to-end 360-degree video transmission pipeline, covering coding, packaging, and streaming with a focus on standardization for device and service interoperability. Next, we review the research on optimizing video streaming over UAV-to-ground wireless channels, and present a real testbed demonstrating 360-degree video streaming from a UAV with remote control over a 5G network. To assess performance, a high-resolution 360-degree video dataset captured from UAVs under different conditions is introduced. Encoding schemes like AVC/H.264, HEVC/H.265, VVC/H.266, VP9, and AV1 are evaluated for encoding latency and QoE. Results show that HEVC‘s hardware implementation achieves a good QoE-latency trade-off, while AV1’s software implementation provides superior QoE. The paper concludes with discussions on open challenges and future directions for efficient and low-latency immersive video streaming via UAVs.
{"title":"Real-Time Immersive Aerial Video Streaming: A Comprehensive Survey, Benchmarking, and Open Challenges","authors":"Mohit K. Sharma;Ibrahim Farhat;Chen-Feng Liu;Nassim Sehad;Wassim Hamidouche;Mérouane Debbah","doi":"10.1109/OJCOMS.2024.3455763","DOIUrl":"10.1109/OJCOMS.2024.3455763","url":null,"abstract":"Over the past decade, the use of Unmanned Aerial Vehicles (UAVs) has grown significantly due to their agility, maneuverability, and rapid deployability. An important application is the use of UAV-mounted 360-degree cameras for real-time streaming of Omnidirectional Videos (ODVs), enabling immersive experiences with up to six Degrees-of-freedom (6DoF) for applications like remote surveillance and gaming. However, streaming high-resolution ODVs with low latency (below 1 second) over an air-to-ground (A2G) wireless channel faces challenges due to its inherent non-stationarity, impacting the Quality-of-experience (QoE). Limited onboard energy availability and energy consumption variability based on flight parameters add to the complexity. This paper conducts a thorough survey of challenges and research efforts in UAV-based immersive video streaming. First, we outline the end-to-end 360-degree video transmission pipeline, covering coding, packaging, and streaming with a focus on standardization for device and service interoperability. Next, we review the research on optimizing video streaming over UAV-to-ground wireless channels, and present a real testbed demonstrating 360-degree video streaming from a UAV with remote control over a 5G network. To assess performance, a high-resolution 360-degree video dataset captured from UAVs under different conditions is introduced. Encoding schemes like AVC/H.264, HEVC/H.265, VVC/H.266, VP9, and AV1 are evaluated for encoding latency and QoE. Results show that HEVC‘s hardware implementation achieves a good QoE-latency trade-off, while AV1’s software implementation provides superior QoE. The paper concludes with discussions on open challenges and future directions for efficient and low-latency immersive video streaming via UAVs.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5680-5705"},"PeriodicalIF":6.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10668820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1109/ojcoms.2024.3454700
Marziyeh Karkhaneh, Sajedeh Norouzi, Mohammad R. Abedi, Nader Mokari, Mohammad R. Javan, Hamid Saeedi, Eduard A. Jorswieck
{"title":"Implementation Insights of Robust Dynamic Spectrum Sharing for Heterogeneous Services in Non-Standalone 5G","authors":"Marziyeh Karkhaneh, Sajedeh Norouzi, Mohammad R. Abedi, Nader Mokari, Mohammad R. Javan, Hamid Saeedi, Eduard A. Jorswieck","doi":"10.1109/ojcoms.2024.3454700","DOIUrl":"https://doi.org/10.1109/ojcoms.2024.3454700","url":null,"abstract":"","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"55 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209507","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}
Pub Date : 2024-09-04DOI: 10.1109/OJCOMS.2024.3454454
Luca Rugini;Keya Sardar;Giuseppe Baruffa
This paper proposes a Long Range (LoRa) chirp transmission scheme with multiple transmit and receive antennas. The main goal is to increase the data rate of LoRa using multiple-input multipleoutput (MIMO) spatial multiplexing. Several coherent detectors are proposed and compared in terms of performance, assuming a channel with flat Rayleigh fading and additive white Gaussian noise. By leveraging on a convenient matrix-vector model, we show that the maximum-likelihood (ML) detector can be obtained with low complexity, when the number of transmit antennas is two or three. When the number of transmit antennas is four or five, we propose a transmission scheme that permits a near- ML detection with reduced complexity. We also propose linear and widely linear detectors that exploit the signal sparsity in the chirp domain. Simulation results confirm the effectiveness of the proposed MIMO LoRa transmission schemes and detectors. Simulated results also include the effect of imperfect synchronization and channel estimation errors on the proposed coherent detection.
本文提出了一种具有多个发射和接收天线的长距离（LoRa）啁啾传输方案。其主要目标是利用多输入多输出（MIMO）空间复用技术提高 LoRa 的数据传输速率。假设信道具有平坦的瑞利衰落和加性白高斯噪声，提出了几种相干检测器并对其性能进行了比较。通过利用方便的矩阵-矢量模型，我们证明了当发射天线数量为两个或三个时，最大似然（ML）检测器的复杂度较低。当发射天线数量为四或五根时，我们提出了一种传输方案，允许以较低的复杂度实现接近 ML 的检测。我们还提出了利用啁啾域信号稀疏性的线性和广义线性检测器。仿真结果证实了所提出的 MIMO LoRa 传输方案和检测器的有效性。仿真结果还包括不完美同步和信道估计误差对拟议相干检测的影响。
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In this paper, we propose a novel spectrum allocation design, leveraging machine learning, for multiuser communication systems operating at the terahertz (THz) band. In this design, we propose to (i) change the bandwidth of sub-bands and (ii) underutilize edge spectra of transmission windows (TWs) where the molecular absorption (MA) coefficient is very high. Different from existing studies, our design is not limited to the scenario where the MA coefficient in the spectrum designated for allocation can be accurately modeled by simply using a piecewise exponential function. We establish a constrained optimization problem and introduce an unsupervised learning approach for its solution. Through offline training, we learn a deep neural network (DNN) using a loss function inspired by the Lagrangian of the established problem. The trained DNN is then employed to derive solutions when multiuser distance parameters are given. Based on numerical analysis, we show that when the MA coefficient in the spectrum designated for allocation exhibits highly non-linear variations, our proposed approach can achieve a higher data rate than that of existing approaches which only attain approximate solutions.
在本文中，我们利用机器学习，为工作在太赫兹（THz）频段的多用户通信系统提出了一种新颖的频谱分配设计。在这一设计中，我们建议：(i) 改变子频带的带宽；(ii) 不充分利用分子吸收（MA）系数非常高的传输窗口（TW）的边缘光谱。与现有研究不同的是，我们的设计并不局限于指定分配的频谱中的 MA 系数可以通过简单地使用片断指数函数来精确建模的情况。我们建立了一个约束优化问题，并引入了一种无监督学习方法来解决该问题。通过离线训练，我们利用受到既定问题拉格朗日启发的损失函数学习深度神经网络（DNN）。然后，在给出多用户距离参数的情况下，利用训练好的 DNN 推导出解决方案。基于数值分析，我们表明，当指定用于分配的频谱中的 MA 系数表现出高度非线性变化时，与只能获得近似解的现有方法相比，我们提出的方法可以实现更高的数据传输率。
{"title":"Spectrum Allocation for Multiuser Terahertz Communication Systems: A Machine Learning Approach","authors":"Akram Shafie;Nan Yang;Chunhui Li;Xiangyun Zhou;Trung Q. Duong","doi":"10.1109/OJCOMS.2024.3454479","DOIUrl":"10.1109/OJCOMS.2024.3454479","url":null,"abstract":"In this paper, we propose a novel spectrum allocation design, leveraging machine learning, for multiuser communication systems operating at the terahertz (THz) band. In this design, we propose to (i) change the bandwidth of sub-bands and (ii) underutilize edge spectra of transmission windows (TWs) where the molecular absorption (MA) coefficient is very high. Different from existing studies, our design is not limited to the scenario where the MA coefficient in the spectrum designated for allocation can be accurately modeled by simply using a piecewise exponential function. We establish a constrained optimization problem and introduce an unsupervised learning approach for its solution. Through offline training, we learn a deep neural network (DNN) using a loss function inspired by the Lagrangian of the established problem. The trained DNN is then employed to derive solutions when multiuser distance parameters are given. Based on numerical analysis, we show that when the MA coefficient in the spectrum designated for allocation exhibits highly non-linear variations, our proposed approach can achieve a higher data rate than that of existing approaches which only attain approximate solutions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5857-5873"},"PeriodicalIF":6.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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