检索结果-内蒙古大学图书馆

multicast Scheduling Over Multiple Channels: A Distribution-Embedding Deep Reinforcement Learning Method

IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第5期24卷 3502-3519页

作者： Li, Ran Huang, Chuan Qin, Xiaoqi Yang, Dong Nie, Xinyao Chinese Univ Hong Kong Shenzhen Future Network Intelligence Inst Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Future Network Intelligence Inst Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Guangdong Prov Key Lab Future Networks Intelligenc Shenzhen 518172 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Jiaotong Univ Sch Elect & informat Engn Beijing 100044 Peoples R China SF Technol Shenzhen 518052 Peoples R China

multicasting is an efficient technique for simultaneously transmitting common messages from the base station (BS) to multiple mobile users (MUs). multicast scheduling over multiple channels, which aims to jointly minimize the energy consumption of the BS and the latency of serving asynchronized requests from the MUs, is formulated as an infinite-horizon Markov decision process (MDP) problem with a large discrete action space, multiple time-varying constraints, and multiple time-invariant constraints. To address these challenges, this paper proposes a novel distribution-embedding multi-agent proximal policy optimization (DE-MAPPO) algorithm, which consists of one modified MAPPO and one distribution-embedding module. The former one handles the large discrete action space and time-varying constraints by modifying the structure of the actor networks and the training kernel of the conventional MAPPO;and the latter one iteratively adjusts the action distribution to satisfy the time-invariant constraints. Moreover, a performance upper bound of the considered MDP is derived by solving a two-step optimization problem. Finally, numerical results demonstrate that our proposed algorithm outperforms the existing ones in terms of applicability, effectiveness, and robustness, and achieves comparable performance to the derived upper bound.

关键词： multicast algorithms Energy consumption Optimal scheduling Streaming media Quality of service Cellular networks NOMA multicast communication Millimeter wave communication Upper bound multicast large discrete action space time-varying constraints time-invariant constraints distribution-embedding multi-agent proximal policy optimization (DE-MAPPO)

来源：评论

学校读者我要写书评

暂无评论

Low-Complexity Joint Antenna Selection and Robust Multi-Group multicast Beamforming for Massive MIMO

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 792-808页

作者： Mohamadi, Niloofar Dong, Min Shahbazpanahi, Shahram Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON L1G 0C5 Canada

We consider low-complexity design for joint antenna selection and robust multi-group multicast beamforming in massive multiple-input multiple-output (MIMO) systems. Relying on the estimated channel covariance and assuming a limited number of antennas for transmission at the base station, we aim to minimize the transmit power subject to the worst-case signal-to-interference-plus-noise-ratio (SINR) guarantee and per selected antenna power budget. Converting the worst-case SINR constraints to a set of non-convex constraints, we propose a two-phase approach to solve the problem efficiently: the antenna selection phase, followed by the robust multicast beamforming generation phase. We propose an SINR-based approach for antenna selection, where the challenging mixed-integer problem is converted into an approximate joint optimization problem via a sequence of transformation, relaxation, and SINR approximation. We develop a fast two-layered alternating direction method of multipliers (ADMM)-based algorithm to compute an approximate solution. In particular, with our ADMM construction, we obtain semi-closed-form solutions for antenna selection and beamforming subproblems at each ADMM iteration for fast updates. To further reduce the computational complexity, we propose a signal-to-leakage-ratio (SLR)-based approach using the SLR constraints in the joint optimization problem. This allows us to develop a two-layered ADMM-based algorithm, which can compute a solution more efficiently due to the SLR structure. The robust multicast beamforming solution for the selected antennas is computed using the fast algorithm we developed recently. Simulation results show the effectiveness of our two proposed approximated approaches for antenna selection and the overall two-phase approach in both overall performance and substantially low computational complexity in a massive MIMO setting.

关键词： Array signal processing Antennas multicast algorithms Massive MIMO Transmitting antennas Signal to noise ratio Interference Antenna selection robust multicast beamforming large-scale systems ADMM low complexity

来源：评论

学校读者我要写书评

暂无评论

Resource Scheduling for High-Capacity multicast Service in Ultra-Dense LEO Satellite Networks

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第2期73卷 2468-2481页

作者： Ma, Ting Qian, Bo Qin, Xiaohan Zhang, Xin Cai, Lin X. Zhou, Haibo Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Peng Cheng Lab Dept Math & Theories Shenzhen 518000 Peoples R China IIT Dept Elect & Comp Engn Chicago IL 60616 USA

Ultra-dense low earth orbit (LEO) network has been envisioned as a promising solution to improve communication coverage and reliability. In this article, we investigate the resource scheduling for the downlink multigroup multicast cooperative transmissions in an ultra-dense LEO network, where multiple LEO satellites serve as aerial base stations to cooperatively offer high-capacity multicast service for ground users (GUs) within the coverage. To obtain the system max-min fair (MMF) capacity, we formulate a weighted MMF downlink beamforming and subchannel assignment problem. As the formulated problem is NP-hard and non-convex, we adopt a many-to-many matching model to solve the subchannel assignment, and apply the successive convex approximation (SCA) coupled with Gaussian randomization and cochannel power control to obtain the downlink beamforming during the matching process. Furthermore, the matching algorithm is guaranteed to converge to a two-side exchange stable matching within finite iteration steps. Extensive numerical results are provided to demonstrate the superiority and effectiveness of the proposed resource scheduling scheme for cooperative multicast transmissions.

关键词： Low earth orbit satellites Satellites Satellite broadcasting Array signal processing Antenna arrays Downlink multicast algorithms Resource scheduling multicast ultra-dense LEO networks beamforming subchannel assignment

来源：评论

学校读者我要写书评

暂无评论

Learning to Beamform in Joint multicast and Unicast Transmission With Imperfect CSI

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第5期71卷 2711-2723页

作者： Zhang, Zhe Tao, Meixia Liu, Ya-Feng Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr CMIC Shanghai 200240 Peoples R China Chinese Acad Sci Inst Computat Math & Sci Engn Comp Acad Math & Syst Sci State Key Lab Sci & Engn Comp Beijing 100190 Peoples R China

With the rapid development of mobile Internet, the demand for multicast is growing rapidly, such as content pushing and video streaming. The multicast service is usually offered to users without interrupting their on-going unicast transmission, and thus the multicast and unicast beamformers needs to be jointly designed, which generally requires perfect channel state information (CSI). However, perfect CSI is usually unavailable due to the channel estimation error. In this paper, we propose a learning based approach to jointly design the multicast and unicast beamformers with imperfect CSI. To learn the beamforming strategy, a new graph neural network (GNN) based architecture named unicast-multicast GNN (UMGNN) is proposed, which only requires the estimated channel. UMGNN can guarantee the permutation invariance/equivalence and model the special property in the multicast transmission, i.e., the multicast rate is determined by the worst user. Moreover, by sharing the parameters across different users, UMGNN exhibits a pretty good scalability to different number of users. Numerical results show that UMGNN outperforms a fully connected neural network and a widely used sampling-based algorithm. To highlight its performance in the multicast transmission, we also show that UMGNN can find the correct worst user that determines the multicast rate.

关键词： Unicast Array signal processing Optimization multicast algorithms Graph neural networks Channel estimation Interference Joint multicast and unicast transmission beamforming imperfect CSI UMGNN

来源：评论

学校读者我要写书评

暂无评论

Enhancing Multi-Connected XR Device Performance With Soft Combining and Joint HARQ Processing in Cooperative multicast

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

引用

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 2438-2452页

作者： Ahsen, Muhammad Yanakiev, Boyan Rosa, Claudio Adeogun, Ramoni Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark Nokia Stand DK-9220 Aalborg Denmark

In this work, we investigate multi-connectivity for Extended Reality (XR) devices, which have stringent requirements in terms of delay, data rate and reliability. This approach employs one direct connection and one indirect connection via a 5th generation-Advanced (5G-A) tethering device. We investigate the Selection Combining Scheme (SCS) and Selection/Soft Combining Scheme (SSCS) between the tethering device and the XR device under point-to-multipoint (PTM) multicast transmission in downlink (DL) in a spatially consistent channel model. The SSCS is particularly effective in enhancing the XR device performance, achieving the maximum potential gain of either selection or soft combining, while limiting cooperative data exchange. This is done through various limited fraction soft bits relaying schemes, including modified ones from existing literature and newly proposed code block (CB) based indexing schemes. Additionally, we propose algorithms for joint processing of hybrid automatic repeat request (HARQ) feedback (FB) in 5G multicast sessions with cooperative users to enhance spectral efficiency by enabling the network to benefit from the cooperation of the users. Our link-level simulations demonstrate that multi-connectivity with joint HARQ enables XR devices to achieve the same spectral efficiency as with unicast but with a 0.8 dB and 4.5 dB lower average signal-to-noise ratio (SNR) with SCS and SSCS, respectively, in non-line-of-sight (NLOS) scenarios. In line-of-sight (LOS) scenarios, multi-connectivity yields a gain of 3 dB with SSCS, though no significant gain is observed with SCS.

关键词： Diversity reception 5G mobile communication Decoding Reliability Receivers multicast algorithms Collaboration Base stations Spectral efficiency Quality of service XR wireless tethering cooperative communication MBS HARQ tethered subnetwork soft combining

来源：评论

学校读者我要写书评

暂无评论

Multigroup multicast Sum-Rate Maximization Based on Reconfigurable Intelligent Surface and D2D for MIMO Communication System

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 31095-31111页

作者： Li, Jinfeng Zhu, Xiaorong Nanjing Univ Posts & Telecommun Wireless Commun Key Lab Jiangsu Prov Nanjing 210003 Jiangsu Peoples R China

Reconfigurable intelligent surface (RIS) has the characteristics of programmable wireless environments, and device-to-device (D2D) communication can fully utilize network resources. Therefore, in this article, RIS and D2D technologies are cleverly integrated into multicast multiple-input-multiple-output (MIMO) communication system. The goal is to maximize the multigroup multicast sum-rate (SR) by jointly optimizing the orthogonal resource block (ORB) reusing coefficients, beamforming vector of the access point (AP), power assignment of D2D pairs, and reflection coefficients of RIS while meeting the transmission power of the AP, rate requirements of user equipments (UEs), ORBs reusing, and reflecting elements modulus-one constraints. This is a mixed-integer nonlinear programming (MINLP) problem. To solve this problem, we propose a new block coordinate descent (BCD)-based algorithm to decouple the original optimization problem into four subproblems. First, a new directional search algorithm is proposed to effectively optimize ORB reuse coefficients. Second, based on Cauchy-Schwarz inequality, the value of the beamforming vector is calculated. Next, successive convex approximation (SCA)-based method is adopted to optimize the power assignment of D2D pairs on each ORB. Finally, we develop the auxiliary relaxation approach (ARA) to optimize the reflection coefficients of RIS. The simulation results confirm that compared to existing designs and algorithms, the proposed design can greatly improve the multicast SR, the proposed algorithm is reasonable and efficient.

关键词： Device-to-device communication MIMO communication Reconfigurable intelligent surfaces multicast algorithms 6G mobile communication Vectors Interference Device-to-device (D2D) pairs multiple-input-multiple-output (MIMO) multigroup multicast optimizing reconfigurable intelligent surface (RIS)

来源：评论

学校读者我要写书评

暂无评论

Joint View Selection, Multigroup multicast Beamforming, and DIBR for RIS-Aided Multi-View Videos

引用

IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第7期24卷 6376-6393页

作者： Lee, Chi-Han Yang, De-Nian Lee, Guang-Siang Wang, Chih-Hang Liao, Wanjiun Natl Taiwan Univ Dept Elect Engn Taipei 106319 Taiwan Acad Sinica Res Ctr Informat Technol Innovat Inst Informat Sci Taipei 115201 Taiwan Acad Sinica Inst Informat Sci Taipei 115201 Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 621301 Taiwan

The rapid development of multi-view videos (MVV) transmission is an irresistible trend. Concurrently, reconfigurable intelligent surface (RIS)-assisted wireless communication has drawn significant attention. We observe that the view selection based on the base station and the view synthesis based on depth-image-based rendering (DIBR) can effectively reduce power consumption. Therefore, this paper studies the view selection and synthesis for RIS-aided MVV in multigroup multicast beamforming. To deal with this complicated scenario, we investigate a problem, named the joint View selection, multicast beamforming, and DIBR (JVMD), to minimize the total multicast beamforming power, the view transmission operation power, and view synthesis, subject to quality-of-service (QoS), RIS phase shifts, view selection, and DIBR constraints. Unfortunately, the mathematical model is a complicated mixed discrete-continuous optimization problem. To tackle this challenging problem, we designed an algorithm, named View selection, Beamforming, RIS phase, and DIBR (VBRD) algorithm. First, we deal with the discrete optimization problem of selecting the view. VBRD uses the dual-based approximation methodology to round back a primal's integer solution. Then, in the continuous optimization problem, we apply the alternating optimization (AO) method to determine beamforming, RIS phase, and DIBR. Finally, simulation results show the performance of exploiting view synthesis for RIS-assisted wireless communication.

关键词： Array signal processing Videos Reconfigurable intelligent surfaces Optimization Mobile computing Streaming media Quality of service Electronic mail multicast algorithms Virtual reality Approximation bound depth-image-based rendering (DIBR) multicast beamforming reconfigurable intelligent surface (RIS) view selection

来源：评论

学校读者我要写书评

暂无评论

DHRL-FNMR: An Intelligent multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第5期21卷 5733-5755页

作者： Ye, Miao Zhao, Chenwei Wen, Peng Wang, Yong Wang, Xiaoli Qiu, Hongbing Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Comp & Informat Secur Guilin 541004 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

The multicast routing problem in software-defined networking (SDN) is an NP-hard problem. The existing solution methods based on deep strength learning suffer from the problems of branch redundancy, an excessively large action space and slow convergence of the intelligent models. In this paper, an intelligent multicast routing algorithm based on deep hierarchical reinforcement learning is proposed to circumvent the aforementioned problems. First, the optimal multicast tree problem is decomposed into two subproblems: fork node selection and the construction of an optimal path from a fork node to a destination node. Second, a multichannel matrix is designed as the state space for the internal and external controllers of hierarchical reinforcement learning based on the global network-aware information characteristics of SDN. Then, different action spaces are designed for the upper and lower subproblems, four action selection policies are designed for constructing multicast paths, and different reward policies are designed at different levels. Finally, a series of experiments and their results show that the designed algorithm not only searches the multicast tree efficiently but also converges faster and without redundant branches, with better performance in terms of bandwidth, delay and packet loss rate than the current mainstream solution algorithms. The codes for DHRL-FNMR are open and available at https://***/GuetYe/DHRL-FNMR.

关键词： multicast algorithms Routing Aerospace electronics Heuristic algorithms Signal processing algorithms Bandwidth Reinforcement learning Deep hierarchical reinforcement learning multicast tree deep reinforcement learning software-defined networking

来源：评论

学校读者我要写书评

暂无评论

360^° Video multicast Scheduling Optimization in Orthogonal and Nonorthogonal Scenarios

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第10期11卷 17128-17140页

作者： Gao, Nianzhen Wang, Yiming Hua, Xinhai Bi, Ting Jiang, Tao Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Res Ctr Mobile Commun 6G Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China ZTE Corp Dept Cloud Video Prod Nanjing 210012 Peoples R China

Due to the huge file size of virtual reality (VR) videos, providing high-quality and low-latency live VR videos to multiple users using existing network architectures becomes exceptionally challenging. Although some tile-based multicast schemes can reduce the amount of transmitted data, users with differentiated channel conditions and interaction behaviors still need to be effectively grouped. In this article, we first model VR video multicast scheduling in the case of orthogonal multiple access (OMA), where the objective of maximizing the sum of user experience qualities forms an NP-hard integer nonlinear programming problem. By analyzing the time complexity source of the exhaustion-based global optimal algorithm and the impact of two factors on grouping, we propose a scheduling algorithm with very low complexity based on dynamic programming in the OMA case and a field-of-view ratio sorting and grouping method for non-OMA (NOMA) scenarios. In extensive actual viewport experiments, the proposed algorithm outperforms the state-of-the-art multicast scheme adopting OMA by 17.86% with negligible time complexity, making it suitable for deployment in realistic environments. The proposed sorting method increases the system utility of NOMA's optimal solution by 13.13%.

关键词： Streaming media Bit rate Decoding NOMA multicast algorithms Static VAr compensators Heuristic algorithms 360 degrees video multicast knapsack problem orthogonal multiple access (OMA) scalable video coding (SVC) tile-based transmission

来源：评论

学校读者我要写书评

暂无评论

Segment-Encoded Explicit Trees (SEETs) for Stateless multicast: P4-Based Implementation and Performance Study

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

引用

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 6903-6917页

作者： Lindner, Steffen Stueber, Thomas Bertsch, Maximilian Eckert, Toerless Menth, Michael Univ Tubingen Chair Commun Networks D-72076 Tubingen Germany Futurewei Technol Datacom Stand Res San Jose CA 95050 USA

IP multicast (IPMC) is used to efficiently distribute one-to-many traffic within networks. It requires per-group state in core nodes and results in large signaling overhead when multicast groups change. Bit Index Explicit Replication (BIER) and its tree engineering variant BIER-TE have been introduced as a stateless transport mechanism for IPMC. To utilize BIER or BIER-TE in a large domain, domains need to be subdivided into smaller sets of receivers or smaller connected subdomains, respectively. Sending traffic to receivers in different sets or subdomains necessarily implies sending multiple packets. While efficient algorithms exist to compute sets for BIER, algorithms for computing BIER-TE subdomains are still missing. In this paper, we present a novel stateless tree encoding mechanism called Segment-Encoded Explicit Tree (SEET). It encodes an explicit multicast distribution tree within a packet header so that tree engineering is supported and sets or subdomains are not needed for large domains. SEET is designed to be implementable on low-cost switching ASICs which we underline by a prototype for the Intel Tofino (TM). If explicit distribution trees are too large to be accommodated within a single header, multiple packets with different distribution trees are sent. For this purpose, we suggest an effective optimization heuristic. A comprehensive study compares the number of sent packets and resulting overall traffic for SEET and BIER in large domains. In our experiments, SEET outperforms BIER even for large multicast groups with up to 1024 receivers.

关键词： Receivers IP networks Scalability Indexes Routing Network topology multicast algorithms Topology Unicast Switches Segment-Encoded Explicit Trees (SEETs) bit index explicit replication (BIER) multicast performance evaluation optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：