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IEEE International Conference on Communications (ICC)

作者： Delong Su Lingya Liu Jing Xu Han Wang Yiyin Wang Cunqing Hua School of Communication and Electronic Engineering East China Normal University Shanghai China National Mobile Communications Research Laboratory Southeast University China Shanghai Institute of Satellite Engineering Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The frame-based precoding oriented from the frame structure under the DVB-S2 standard for satellite communications leads to the multicast transmission in each user frame. This paper investigates the multicast-aware user framing/grouping problem to facilitate the frame-based precoding that demands users of high channel similarity in each group. We propose two alternative approaches to increase the intra-group channel similarity by taking into account the channels of all users already in the group when selecting the parallel users for it. One approach extracts the first principle component vector from the channel matrix constituted by current group members and uses it to measure the similarity to the ungrouped users for the selection of the next group member. The other one adds up the projections of the ungrouped user's channel to the channels of the current group members to measure the similarity. Numerical results demonstrate that the proposed two algorithms outperform a benchmark algorithm in various scenarios, verifying the effectiveness of exploiting the channel information of all group members to constitute multicast groups with high intra-group similarity.

关键词： multicast algorithms Satellites Precoding Current measurement Benchmark testing Vectors Standards

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Dynamic Field-of-View-Based Clustering for Efficient 360-degree multicast Streaming

Dynamic Field-of-View-Based Clustering for Efficient 360-deg...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ahmed Saadallah Sidi-Mohammed Senouci Ines El-Korbi Philippe Brunet DRIVE Lab Université de Bourgogne Nevers France

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The popularity of 360-degree video streaming has surged in recent years. However, delivering high-quality content with limited bandwidth poses a significant challenge, particularly in mobile delivery setups. Although techniques like predicting a user’s Field of View (FoV) can improve streaming efficiency, multicasting offers a superior solution for delivering content to multiple users simultaneously. However, when users focus on different zones (FoV) in 360° videos, multicast streaming becomes difficult to implement effectively. To address this challenge, we propose in this paper a novel approach that leverages userspecific FoV prediction to dynamically adapt multicast streams. We propose a dynamic architecture that clusters users for optimized streaming based on their predicted FoV. This clustering leverages a DBSCAN-inspired algorithm that incorporates user head movement data. Users with similar FoV preferences are grouped, enabling transmission of only the relevant portions of the 360° video to each cluster. This significantly reduces bandwidth consumption compared to traditional per-user delivery, thus enhancing the overall Quality of Experience (QoE) for users.

关键词： multicast algorithms Heuristic algorithms Clustering algorithms Bandwidth Streaming media multicast communication Prediction algorithms Complexity theory Quality of experience Time complexity

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Socially-Aware Tile-Based Point Cloud multicast with Registration

Socially-Aware Tile-Based Point Cloud Multicast with Registr...

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IEEE International Conference on Communications (ICC)

作者： Han-Rong Lai Ru-Jun Wang Chih-Hang Wang De-Nian Yang Wen-Tsuen Chen Jang-Ping Sheu Dept. of Computer Science National Tsing Hua University Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

With the emergence of new applications for holographic-type communication in healthcare, entertainment, and education, point cloud video transmission has become essen-tial. This paper aims to reduce the bandwidth cost by leveraging tile-based video transmission with point cloud registration in a wireless multicast network. A point cloud video is divided into multiple tiles, and each tile contains a portion of point cloud objects and can be registered by adjacent tiles with some similar objects under the registration rotation and registration overlap constraints. We formulate a new optimization problem and prove that it is NP-hard, and then we design an algorithm multicast Multi-Tile Registration (MMTR) to select multicasting and registered tiles under consideration of socially related users' preferences with the idea of a tile registration graph. A more popular tile can be multicasted to more friends to minimize the bandwidth cost. Experimental results with real datasets show that MMTR can reduce bandwidth costs by more than 20% and achieve better video quality compared to state-of-the-art point cloud transmission algorithms.

关键词： Point cloud compression Wireless communication Costs multicast algorithms Bandwidth Medical services multicast communication

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Performance Optimization for multicast Mm Wave MIMO Networks with Mobile Users

Performance Optimization for Multicast Mm Wave MIMO Networks...

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IEEE International Conference on Communications Workshops, ICC

作者： Songling Zhang Mingzhe Chen Wenjing Zhang Zhaohui Yang Danpu Liu Zhilong Zhang Kai-Kit Wong Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Zhejiang Lab Hangzhou China Zhejiang University Hangzhou China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of maximizing the sum rate of mobile users in a multi-base station (BS) cooperative millimeter-wave (mmWave) multicast communication system is studied. In the considered model, due to the real-time mobility of users, the users being served by a given BS and beamforming of BSs and users are dynamic. Multiple BSs must cooperate to serve dynamic requests of multiple mobile users. This problem is posed as an optimization framework whose goal is to maximize the sum rate of all mobile users by jointly optimizing the number of users served by all BSs and beamforming matrices of both BSs and users. To solve this non-convex optimization problem, we first introduce a value decomposition based reinforcement learning (VD- RL) algorithm to determine the users to be served by each BS. Then, we use the block diagonalization method to obtain the fully digital transmit beamforming matrices of all BSs as well as the receive beamforming matrices of the users. Finally, a fast optimization algorithm is used to optimize the hybrid beamforming matrices of both BSs and users. Simulation results show that, the proposed algorithm can achieve up to 51 % gain in terms of the sum rate of all mobile users compared to baseline multi-agent algorithms.

关键词： multicast algorithms Array signal processing Heuristic algorithms Simulation Conferences multicast communication Real-time systems

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An Ultra-Low Latency multicast Router for Large-Scale Multi-Chip Neuromorphic Processing 3

An Ultra-Low Latency Multicast Router for Large-Scale Multi-...

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IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Ding, Chen Huan, Yuxiang Jia, Hao Yan, Yulong Yang, Fanxi Zou, Zhuo Zheng, Li-Rong Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781665419130

Neuromorphic simulation is fundamental to the study of information processing mechanism of the human brain and can further inspire application development of event-driven spiking neural networks. However large-scale neuromorphic simulation requires massive parallelism on multi-chip processing and imposes great challenges on dealing with data transmission latency and congestion problems between chips, especially when the number of simulated neurons reaches to billions or even trillions level. In this paper, we propose an ultra-low-latency on-chip router together with a multicast routing algorithm that focuses on reducing global loads and balancing loads between links. Additionally, we build a large-scale neuromorphic simulation platform consisting of 64 FPGA chips and evaluate the proposed design on it. The experiment results suggest that this design benefits from the proposed multicast routing algorithm in global communication loads and simulation capacity. This work has 4.1% similar to 5.2% reduction of global loads comparing to previous works and can achieve a latency as low as 25ns and a maximum data throughput of 6.25Gbps/chip.

关键词： multicast algorithms Neuromorphics Parallel processing Routing Throughput Brain modeling System-on-chip

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An Adaptive MBSFN Resource Allocation Algorithm for multicast and Unicast Traffic

An Adaptive MBSFN Resource Allocation Algorithm for Multicas...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Ihtisham Khalid Merkebu Girmay Vasilis Maglogiannis Dries Naudts Adnan Shahid Ingrid Moerman Department of Information Technology IDLab Ghent University - imec Belgium

The need for supporting multimedia streaming services in cellular networks as standardized by 3GPP is expanding rapidly. Evolved Multimedia Broadcast multicast Service (eM-BMS) was initially introduced in Release 9 and following releases have introduced several enhancements. Multimedia Broadcast multicast Single Frequency Network (MBSFN) is one of the eMBMS enhancements targeting to reduce interference, however, its static parameter configuration yields inefficient resource allocation. Therefore, in this paper, an adaptive demand-driven MBSFN resource allocation algorithm is proposed aiming to efficiently utilize the radio resources. The algorithm flexibly assigns resources to multicast transmissions by varying MBSFN configuration parameters (the number and period of multicast subframes) and provides freed resources to unicast traffic. The proposed algorithm is implemented and evaluated using a Software Defined Radio platform which we made open source. As compared to the fixed MBSFN parameter configuration, our solution showcases an improvement of at least 24% and maximally by 40% in terms of multicast resource efficiency. Also, the total system throughput (multicast and unicast) improves by at least 4% and maximally by 24%.

关键词： Digital multimedia broadcasting multicast algorithms Unicast Software algorithms Streaming media Throughput Resource management

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SENeCA: Scalable Energy-efficient Neuromorphic Computer Architecture 4

SENeCA: Scalable Energy-efficient Neuromorphic Computer Arch...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) - Intelligent Technology in the Post-Pandemic Era

作者： Yousefzadeh, Amirreza Van Schaik, Gert-Jan Tahghighi, Mohammad Detterer, Paul Traferro, Stefano Hijdra, Martijn Stuijt, Jan Corradi, Federico Sifalakis, Manolis Konijnenburg, Mario IMEC Eindhoven Netherlands

ISBN: (数字)9781665409964

ISBN: (纸本)9781665409964

SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired Spiking Neural Networks for extreme edge applications inside or near sensors where ultra-low power and adaptivity features are required. SENeCA is optimized to exploit unstructured spatio-temporal sparsity in computations and data transfer. It is a digital IP, contains interconnected Neuron Cluster Cores, with RISC-V-based instruction set, an optimized Neuromorphic Co-Processor, and event-based communication infrastructure. SENeCA improves state of the art by: Addressing the flexibility issue in neuromorphic processors by allowing a fully programmable neuron model and learning/adaptivity algorithms;Improving the area efficiency by employing a 3-level memory hierarchy which allows using novel embedded memory technologies;Efficient deployment of advanced learning mechanisms and optimization algorithms by accelerating neural operations in three data types: int4, int8 and BrainFloat16;Efficient event communication by using a new Network-on-Chip with multicasting, a compression mechanism, and source-based routing. The implemented digital IP can be tuned for different applications to have a flexible number of cores and Neural Processing Elements (NPEs) per core and optional use of off-chip memory. Next to the hardware, the SENeCA platform includes an SDK and a hardware-aware simulator for close-loop synthesis/mapping optimization(1).

关键词： multicast algorithms Neuromorphics Memory management Neurons Neural networks Network-on-chip Sensor phenomena and characterization

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Joint Power Allocation and Passive Beamforming Design for IRS-Assisted Physical-Layer Service Integration

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第11期20卷 7286-7301页

作者： Ning, Boyu Chen, Zhi Tian, Zhongbao Wang, Xiaomei Pan, Cunhua Fang, Jun Li, Shaoqian Univ Elect Sci & Technol China UESTC Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China Univ Elect Sci & Technol China UESTC Sch Math Sci Chengdu 611731 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

Intelligent reflecting surface (IRS) has emerged as an appealing solution to enhance wireless communication performance by reconfiguring the wireless propagation environment. In this paper, we propose to apply IRS to the physical-layer service integration (PHY-SI) system, where a single-antenna access point (AP) integrates two sorts of service messages, i.e., multicast message and confidential message, via superposition coding to serve multiple single-antenna users. Our goal is to optimize the power allocation (for transmitting different messages) at the AP and the passive beamforming at the IRS to maximize the achievable secrecy rate region. To this end, we formulate this problem as a bi-objective optimization problem, which is shown equivalent to a secrecy rate maximization problem subject to the constraints on the quality of multicast service. Due to the non-convexity of this problem, we propose two customized algorithms to obtain its high-quality suboptimal solutions, thereby approximately characterizing the secrecy rate region. The resulting performance gap with the globally optimal solution is analyzed. Furthermore, we provide theoretical analysis to unveil the impact of IRS beamforming on the performance of PHY-SI. Numerical results demonstrate the advantages of leveraging IRS in improving the performance of PHY-SI and also validate our theoretical analysis.

关键词： Wireless communication Array signal processing multicast algorithms Optimization Resource management Approximation algorithms Pareto optimization Intelligent reflecting surface physical layer service integration multicasting wiretap channel secrecy rate region

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Efficient Virtual Network Embedding of Cloud-Based Data Center Networks into Optical Networks

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第11期32卷 2793-2808页

作者： Fan, Weibei Xiao, Fu Chen, Xiaobai Cui, Lei Yu, Shui Nanjing Univ Posts & Telecommun Coll Comp Nanjing 210049 Jiangsu Peoples R China Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210003 Jiangsu Peoples R China Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia

The demand for data center bandwidth has exploded due to the continuous development of cloud computing, causing the use of network resources close to saturation. Optical network has become an encouraging technology for many burgeoning networks and parallel/distributed computing applications because of its huge bandwidth. This article focuses on efficient embedding of data centers into optical networks, which aims to reduce complexity of the network topology by using the parallel transmission characteristics of optical fiber. We first present a novel virtual network embedding (VNE) mathematical model used for optical data center networks. Then we derive a priority of location VNE algorithm according to node proximity sensing and path comprehensive evaluation. Furthermore, we propose routing and wavelength assignment for DCNs into optical networks, and identify the lower bound of the required number of wavelengths. Extensive evaluations show that the proposed embedding algorithm can reduce the average waiting time of virtual network requests by 20 percent, increase the request acceptance rate and revenue-overhead ratio by 13 percent, as compared to the latest VNE algorithm.

关键词： Optical fiber networks Bandwidth Heuristic algorithms Optical sensors Approximation algorithms multicast algorithms Data centers Data center network network virtualization optical network embedding performance evaluation

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Intelligent Reflecting Surface Aided Multigroup multicast MISO Communication Systems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 3236-3251页

作者： Zhou, Gui Pan, Cunhua Ren, Hong Wang, Kezhi Nallanathan, Arumugam Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Intelligent reflecting surface (IRS) has recently been envisioned to offer unprecedented massive multiple-input multiple-output (MIMO)-like gains by deploying large-scale and low-cost passive reflection elements. By adjusting the reflection coefficients, the IRS can change the phase shifts on the impinging electromagnetic waves so that it can smartly reconfigure the signal propagation environment and enhance the power of the desired received signal or suppress the interference signal. In this paper, we consider downlink multigroup multicast communication systems assisted by an IRS. We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint. To tackle this non-convex problem, we propose two efficient algorithms under the majorization& x2013;minimization (MM) algorithm framework. Specifically, a concave lower bound surrogate objective function of each user& x0027;s rate has been derived firstly, based on which two sets of variables can be updated alternately by solving two corresponding second-order cone programming (SOCP) problems. Then, in order to reduce the computational complexity, we derive another concave lower bound function of each group& x0027;s rate for each set of variables at every iteration, and obtain the closed-form solutions under these loose surrogate objective functions. Finally, the simulation results demonstrate the benefits in terms of the spectral and energy efficiency of the introduced IRS and the effectiveness in terms of the convergence and complexity of our proposed algorithms.

关键词： multicast communication multicast algorithms Signal processing algorithms Linear programming Interference Precoding Complexity theory Intelligent reflecting surface (IRS) large intelligent surface (LIS) multigroup multicast alternating optimization majorization-minimization (MM)

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