Facing the upcoming era of Internet-of-Things and connected intelligence, efficient information processing, computation, and communication design becomes a key challenge in large-scale intelligent systems. Recently, O...
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Facing the upcoming era of Internet-of-Things and connected intelligence, efficient information processing, computation, and communication design becomes a key challenge in large-scale intelligent systems. Recently, Over-the-Air (OtA) computation has been proposed for data aggregation and distributed computation of functions over a large set of network nodes. Theoretical foundations for this concept exist for a long time, but it was mainly investigated within the context of wireless sensor networks. There are still many open questions when applying OtA computation in different types of distributed systems where modern wireless communication technology is applied. In this article, we provide a comprehensive overview of the OtA computation principle and its applications in distributed learning, control, and inference systems, for both server-coordinated and fully decentralized architectures. Particularly, we highlight the importance of the statistical heterogeneity of data and wireless channels, the temporal evolution of model updates, and the choice of performance metrics, for the communication design in OtA federated learning (FL) systems. Several key challenges in privacy, security, and robustness aspects of OtA FL are also identified for further investigation.
We propose a distributed optical fiber sensing event recognition scheme based on Markov Transition Field (MTF) and knowledge distillation. The event recognition algorithm has the advantages of being lightweight, fast,...
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We propose a distributed optical fiber sensing event recognition scheme based on Markov Transition Field (MTF) and knowledge distillation. The event recognition algorithm has the advantages of being lightweight, fast, and high accuracy. The event data are converted into images by the MTF algorithm, which keeps the event signals correlated in the time domain while highlighting the visual differences between different events. A two-stage knowledge distillation model compression method is proposed, which effectively compresses the large-scale model into a lightweight model with optimal learning capability, ensuring the lightweight and efficient recognition of events by the compressed model (student model). The experimental results show that the student model improves the recognition rate of six events by 5.2% and achieves 96.6% event recognition accuracy by the two-stage knowledge distillation method. The size of the student model is only 1.4 MB, the number of parameters is only 0.35 M, and the FLOPs are only 0.17 G. The student model recognizes a single event in 0.129s on a low configuration device, which can meet the requirements of deployment and real-time monitoring of resource-limited devices.
This paper investigates the uplink reception in the cloud radio access network (C-RAN) with finite-capacity fronthaul links. The latter is an emerging network that transfers the computing load from the radio heads (RH...
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ISBN:
(纸本)9781538682098
This paper investigates the uplink reception in the cloud radio access network (C-RAN) with finite-capacity fronthaul links. The latter is an emerging network that transfers the computing load from the radio heads (RHs) to the central processor (CP) unit. Due to the prohibitive complexity of computations, the most efficient uplink C-RAN schemes are challenging to be implemented in practical systems. Using deep neural networks (DNNs), we propose a new and low complex distributed processing for uplink C-RAN subject to some quantification rules. The objective of our architecture, called TDNet, is to optimize the processing jointly at the RHs and the CP side. Our goal is not to solve signal detection in multi-antenna systems. Instead, our work aims to find a helpful transformation scheme at the RH side before quantization. A correspondent decoding scheme at the CP side considers the quantization scheme. Inspired by the projected gradient descent algorithm, TDNet is designed as a distributed DNN with sparse connections. Numerical results are provided and show that our scheme outperforms linear receivers such as the zero-forcing (ZF). It also achieves near-optimal performance compared to the sphere decoder (SD) algorithm, especially for a low-to-moderate number of quantization bits.
This article delves into the distributed precoding for network massive multiinput-multioutput (NM-MIMO) systems where no user data stream is shared among the base stations (BSs). Aiming to navigate the challenge of mi...
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This article delves into the distributed precoding for network massive multiinput-multioutput (NM-MIMO) systems where no user data stream is shared among the base stations (BSs). Aiming to navigate the challenge of minimizing information exchange for weighted sum-rate(WSR) maximization, which inherently places substantial demands on signaling overheads, we begin by reformulating the original problem as a BS-specific format. Inspired by such a rewritten form, we propose a virtualWSR as the objective function to derive an approximation of this reformulated problem. For a specific BS, the calculation of this virtual WSR is solely contingent upon the precoders within its own cell and low-dimensional virtual covariance matrices as initial values. Through an iterative approach facilitated by the minorization-maximization (MM) algorithm, we attain a stationary point for the maximization of nonconcave virtualWSR. With the locally generated virtual covariance matrices exchanged as initial values, the precoding matrix within each cell can be optimized independently and concurrently. This method eliminates the need for additional external exchanges throughout the iterative process. The simulation indicates the efficacy of our proposed approach in achieving a favorable WSR.
In the evolution of 6th Generation (6G) technology, the emergence of cell-free networking presents a paradigm shift, revolutionizing user experiences within densely deployed networks where distributed access points co...
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ISBN:
(纸本)9798350317657;9798350317640
In the evolution of 6th Generation (6G) technology, the emergence of cell-free networking presents a paradigm shift, revolutionizing user experiences within densely deployed networks where distributed access points collaborate. However, the integration of intelligent mechanisms is crucial for optimizing the efficiency, scalability, and adaptability of these 6G cell-free networks. One application aiming to optimize spectrum usage is Automatic Modulation Classification (AMC), a vital component for classifying and dynamically adjusting modulation schemes. This paper explores different distributed solutions for AMC in cell-free networks, addressing the training, computational complexity, and accuracy of two practical approaches. The first approach addresses scenarios where signal sharing is not feasible due to privacy concerns or fronthaul limitations. Our findings reveal that maintaining comparable accuracy is remarkably achievable, yet it comes with increased computational demand. The second approach considers a central model and multiple distributed models collaboratively classifying the modulation. This hybrid model leverages diversity gain through signal combining and requires synchronization and signal sharing. The hybrid model demonstrates superior performance, achieving a 2.5% improvement in accuracy with equivalent total computational load. Notably, the hybrid model distributes the computational load across multiple devices, resulting in a lower individual computational load.
Emergent distributed environments going beyond cloud computing offer new kinds of processing for massive and heterogeneous data, but also pose new challenges for data management. These challenges include among others:...
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ISBN:
(纸本)9798400704222
Emergent distributed environments going beyond cloud computing offer new kinds of processing for massive and heterogeneous data, but also pose new challenges for data management. These challenges include among others: load balancing, dynamic data allocation on computing nodes, fault tolerance, handling low quality data, data and processes replication, resource provisioning, buffer management, query processing and optimization on different types of hardware, transaction management, and minimization of power usage. The BiDEDE workshop addresses these and other challenges related to building post-cloud distributed environments for data processing.
In the geospatial sector big data concept also has already impact. Several studies facing originally computer science techniques applied in GIS processing of huge amount of geospatial data. In other research studies g...
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ISBN:
(纸本)9781629935201
In the geospatial sector big data concept also has already impact. Several studies facing originally computer science techniques applied in GIS processing of huge amount of geospatial data. In other research studies geospatial data is considered as it were always been big data (Lee and Kang, 2015). Nevertheless, we can prove data acquisition methods have been improved substantially not only the amount, but the resolution of raw data in spectral, spatial and temporal aspects as well. A significant portion of big data is geospatial data, and the size of such data is growing rapidly at least by 20% every year (Dasgupta, 2013). The produced increasing volume of raw data, in different format, representation and purpose the wealth of information derived from this data sets represents only valuable results. However, the computing capability and processing speed rather tackle with limitations, even if semi-automatic or automatic procedures are aimed on complex geospatial data (Kristof et al., 2014). In late times, distributed computing has reached many interdisciplinary areas of computer science inclusive of remote sensing and geographic information processing approaches. Cloud computing even more requires appropriate processing algorithms to be distributed and handle geospatial big data. Map-Reduce programming model and distributed file systems have proven their capabilities to process non GIS big data. But sometimes it's inconvenient or inefficient to rewrite existing algorithms to Map-Reduce programming model, also GIS data can not be partitioned as text-based data by line or by bytes. Hence, we would like to find an alternative solution for data partitioning, data distribution and execution of existing algorithms without rewriting or with only minor modifications. This paper focuses on technical overview of currently available distributed computing environments, as well as GIS data (raster data) partitioning, distribution and distributed processing of GIS algorithms. A
This paper introduces the concept of distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectu...
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ISBN:
(纸本)9798350344998;9798350345001
This paper introduces the concept of distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures. Additionally, the DISAC framework integrates novel waveform design with new semantic and goal-oriented communication paradigms, enabling ISAC technologies to transition from traditional data fusion to the semantic composition of diverse sensed and shared information. This progress facilitates large-scale, energy-efficient support for high-precision spatial-temporal processing, optimizing ISAC resource utilization, and enabling effective multi-modal sensing performance. Addressing key challenges such as efficient data management and connect-compute resource utilization, 6G-DISAC stands to revolutionize applications in diverse sectors including transportation, healthcare, and industrial automation. Our study encapsulates the project's vision, methodologies, and potential impact, marking a significant stride towards a more connected and intelligent world.
Mobile communications have been undergoing a generational change every ten years. While 5G network deployments are maturing, significant efforts are being made to standardize 6G, which is expected to be commercially i...
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Mobile communications have been undergoing a generational change every ten years. While 5G network deployments are maturing, significant efforts are being made to standardize 6G, which is expected to be commercially introduced by 2030. This article provides unique perspectives on the 6G network (radio and core) architecture(s) from the anticipated 6G use cases to meet the necessary performance requirements. To cater to key 6G use cases, the 6G architecture must integrate different network-level functions in a multiplicity of virtual cloud environments, leveraging the advancements of distributed processing and artificial intelligence, and securely integrating different sub-networks - for example - terrestrial and non-terrestrial networks, into the overall 6G network. This article characterizes the impact of 6G architectures from a deployment perspective with backwards compatibility in mind.
Being aware of the channel and its properties is critical for coherent transmission in massive multiple-input multiple-output (M-MIMO) systems due to the large channel dimension in the space domain. In cell-free (CF) ...
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Being aware of the channel and its properties is critical for coherent transmission in massive multiple-input multiple-output (M-MIMO) systems due to the large channel dimension in the space domain. In cell-free (CF) systems, the channel dimension increases further as each user is served by multiple access points, with a significant burden on signal processing. Angle domain transmission and channel maps promise to alleviate this burden by reducing channel dimensions in the angle domain and providing a priori channel information through channel measurements and modeling, respectively. In this paper, we propose a channel map-based angle domain multiple access scheme for the uplink CF M-MIMO communications. First, we propose an angle domain data reception scheme constituting receive combining and large-scale fading decoding to maximize spectral efficiency. Then, we derive an initial access criterion utilizing the angle domain channel similarity between users, based on which we propose pilot assignment and access point selection schemes for better trade-offs between spectral and energy efficiency. Finally, we construct two channel map-based transmission mechanisms by wielding different levels of channel information, where a tailored data reception scheme with a newly derived spectral efficiency upper bound is also proposed for quantitative evaluation. Simulation results show that the proposed channel map-based angle domain schemes outperform their space domain alternatives and the schemes without using channel maps regarding spectral and energy efficiency.
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