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2024 IEEE conference on Standards for communications and Networking, CSCN 2024

作者： Ayan, Onur Pappas, Nikolaos Estevez, Miguel Angel Gutierrez An, Xueli Kellerer, Wolfgang Huawei Technologies Munich Germany Linköping University Linköping Sweden Technical University of Munich Munich Germany

ISBN: (纸本)9798331507428

Networked control systems (NCSs), which are feedback control loops closed over a communication network, have been a popular research topic over the past decades. Numerous works in the literature propose novel algorithms and protocols with joint consideration of communication and control. However, the vast majority of the recent research results, which have shown remarkable performance improvements if a cross-layer methodology is followed, have not been widely adopted by the industry. In this work, we review the shortcomings of today's mobile networks that render cross-layer solutions, such as semantic and goal-oriented communications, very challenging in practice. To tackle this, we propose a new framework for 6 G user plane design that simplifies the adoption of recent research results in networked control, thereby facilitating the joint communication and control design in next-generation mobile networks. © 2024 IEEE.

关键词： Networked control systems

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Integrating Optical and Mobile networks: A Comprehensive End-to-End Simulation Platform 24

Integrating Optical and Mobile Networks: A Comprehensive End...

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24th International conference on Transparent Optical networks (ICTON)

作者： Rodriguez, Micaela Pirri, Maximo Ingles, Lucas Rattaro, Claudina Castro, Alberto Univ Republica Sch Engn Montevideo Uruguay

ISBN: (纸本)9798350377330;9798350377323

communications are crucial in human interactions, the economy, education, and accessing and democratizing various services and information. With a vast physical infrastructure deployed, optical networks have been fundamental in end-to-end broadband communications. They are considered the foundation for future communications in modern society. next-generation optical networks are expected to provide broadband services with massive capacity, lower latencies, and improved reliability, supporting various ultra-high bandwidth applications: cloud/edge networking, 8K video services, and digital twins. Optical networks have been playing an increasingly important role in mobile communications. They will be key for 5G/6G networks, given their bandwidth, coverage, synchronization, and low latency requirements. Various technologies are being developed for the convergence of optical-mobile networks to enhance performance, improve energy efficiency, and simplify the network's design, implementation, and operation. Researchers, vendors, and operators must be able to evaluate these new solutions effectively. They require a robust simulation framework that seamlessly implements the mobile and optical domains and their interactions. As far as we know, no open-source simulator has fulfilled these requirements to date. For this reason, in this paper, we present an optical-mobile unified simulation platform that addresses the convergence of 5G/6G technology and optical networks. This tool enables academia and industry members to test new optical-mobile network architectures and end-to-end (multi-technology domain) algorithms for dynamic resource allocation or restoration, advancing the capabilities and understanding of next-generation network technologies.

关键词： 5G 6G Network Simulation Optical-Mobile networks

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Collaborative Multi-Agent Deep Reinforcement Learning for Energy-Efficient Resource Allocation in Heterogeneous Mobile Edge Computing networks

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IEEE TRANSACTIONS ON WIRELESS communications 2024年第6期23卷 6653-6668页

作者： Xiao, Yang Song, Yuqian Liu, Jun Beijing Univ Posts & Telecommun BUPT Intelligent Percept & Comp Res Ctr Sch Artificial Intelligence Beijing 100876 Peoples R China

Mobile edge computing (MEC) is an enabling technology for next-generation network architectures to deliver more diversified communication services and meet more demanding quality-of-service (QoS) requirements. However, owing to the growing scale and heterogeneity of the networks, energy-efficient resource allocation in heterogeneous MEC (Het-MEC) networks faces great challenges. As an emerging research area, multi-agent deep reinforcement learning (MADRL) is expected to realize autonomous resource allocation in Het-MEC networks by learning from trial and error. Nevertheless, existing MADRL-based solutions are usually not applicable to practical scenarios by the limitations of centralized control schemes or massive signaling overhead. To address the issue, we first formulate the energy-efficient resource allocation problem in Het-MEC networks as a time-variant mixed-integer nonlinear programming (MINLP) problem. Then, we propose a fully decentralized collaborative MADRL-based algorithm featuring the multi-actor shared-critic (MASC) architecture and the regional training distributed execution (RTDE) framework, which effectively stabilizes model training and reduces information exchange, respectively. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithm. Numerical results demonstrate that the proposed algorithm comprehensively outperforms several mainstream baseline methods in terms of convergence performance, scalability, and robustness.

关键词： Resource management Training Task analysis Power control Scalability Heuristic algorithms Collaboration Heterogeneous mobile edge computing (Het-MEC) network resource allocation multi-agent deep reinforcement learning (MADRL) energy efficiency

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Large Language Model-Driven Curriculum Design for Mobile networks

Large Language Model-Driven Curriculum Design for Mobile Net...

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IEEE/CIC International conference on communications in China (ICCC)

作者： Erak, Omar Alhussein, Omar Naser, Shimaa Alabbasi, Nouf Mi, De Muhaidat, Sami Khalifa Univ Dept Comp Sci KU 6G Res Ctr Abu Dhabi U Arab Emirates Khalifa Univ Dept Comp & Informat Engn KU 6G Res Ctr Abu Dhabi U Arab Emirates Birmingham City Univ Coll Comp Future Informat Networks Res Cluster Birmingham W Midlands England Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

ISBN: (纸本)9798350378412

This study introduces an innovative framework that employs large language models (LLMs) to automate the design and generation of curricula for reinforcement learning (RL). As mobile networks evolve towards the 6G era, managing their increasing complexity and dynamic nature poses significant challenges. Conventional RL approaches often suffer from slow convergence and poor generalization due to conflicting objectives and the large state and action spaces associated with mobile networks. To address these shortcomings, we introduce curriculum learning, a method that systematically exposes the RL agent to progressively challenging tasks, improving convergence and generalization. However, curriculum design typically requires extensive domain knowledge and manual human effort. Our framework mitigates this by utilizing the generative capabilities of LLMs to automate the curriculum design process, significantly reducing human effort while improving the RL agent's convergence and performance. We deploy our approach within a simulated mobile network environment and demonstrate improved RL convergence rates, generalization to unseen scenarios, and overall performance enhancements. As a case study, we consider autonomous coordination and user association in mobile networks. Our obtained results highlight the potential of combining LLM-based curriculum generation with RL for managing next-generation wireless networks, marking a significant step towards fully autonomous network operations.

关键词： Curriculum learning large language models mobile networks reinforcement learning resource management

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Analysis of Link Failure performance in Low Earth Orbit Satellite networks

Analysis of Link Failure Performance in Low Earth Orbit Sate...

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IEEE/CIC International conference on communications in China (ICCC)

作者： Song, Zhengyi Liu, Yan Xi, Leifang Ma, Buyun Ma, Xiao Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China China Acad Space Technol Dept Satellite Network Syst Xian 710119 Peoples R China

ISBN: (纸本)9798350378412

As global communication needs grow, Low Earth Orbit (LEO) satellites are rapidly becoming a key infrastructure for next-generation communications networks. Especially in network designs that utilize inter-satellite laser links (ISLs), the stability and reliability of these links have a decisive impact on the overall performance of the network. However, laser links can be disrupted by various factors, leading to service interruptions or degraded performance. Therefore, it is essential to analyze link failures. This paper discusses the failure characteristics of laser links in LEO satellite networks and introduces a mathematical model based on alternating renewal processes and Markov chains. This model aims to analyze the failure probabilities of inter-satellite laser links to enhance network stability and reliability. Simulations were performed using SpaceX's Starlink project as a case study. These simulation results, which align closely with theoretical analyses, confirm that the proposed model can accurately reflect the probability distribution of network disrupted states under various parameter configurations.

关键词： Low Earth orbit satellite networks inter-satellite laser links link failure analysis alternating renewal process Markov chain

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Resource Allocation for FeMBB and eURLLC Coexistence in RSMA-Based Cellular networks 25

Resource Allocation for FeMBB and eURLLC Coexistence in RSMA...

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IEEE Wireless communications and Networking conference (IEEE WCNC)

作者： Taskou, Shiva Kazemi Rasti, Mehdi Amirkabir Univ Technol Dept Comp Engn Tehran Iran Univ Oulu Ctr Wireless Commun CWC Oulu Finland

ISBN: (纸本)9798350303582;9798350303599

This paper studies the Joint Resource block (RB) allocation and Power control (JRP) problem for the coexistence of further enhanced mobile broadband (FeMBB) and extreme ultra-reliable low latency (eURLLC) services in rate splitting multiple access-based next-generation (e.g., 5G-Advanced and 6G) wireless networks. In the JRP problem, for each FeMBB user, a minimum data rate requirement is considered, and eURLLC users should meet their latency and reliability requirements. To address the JRP problem, we propose a hybrid deep reinforcement learning (HDRL-JRP) algorithm, in which a double dueling deep Q-network is employed for RB allocation and a deep deterministic policy gradient is used for power control. Via simulation results, the performance of the HDRL-JRP algorithm is demonstrated in terms of total data rate.

关键词： 5G-Advanced and 6G further enhanced mobile broadband (FeMBB) extreme ultra-reliable low latency (eURLLC) puncturing rate splitting multiple access (RSMA) hybrid deep reinforcement learning

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Efficient Mirror Element Assignment in OIRS-Aided UAV-Based FSO networks with Rate Adaptation 12

Efficient Mirror Element Assignment in OIRS-Aided UAV-Based ...

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12th IEEE International Black Sea conference on communications and Networking (BlackSeaCom)

作者： Singh, Priyanka Salameh, Haythem A. Bany Bohara, Vivek Ashok Srivastava, Anand Ayyash, Moussa Indraprastha Inst Informat Technol Delhi India Al Ain Univ Al Ain U Arab Emirates Chicago State Univ Chicago IL USA

ISBN: (纸本)9798350351866;9798350351859

Optical intelligent reflective surfaces (OIRS) offer a promising solution for next-generation optical wireless communication (OWC) networks by mitigating the need for a line of sight (LoS) between ground stations and users. Unmanned aerial vehicles (UAVs) combined with OIRS can address non-availability of LoS links. This work proposes integrating an OWC-based network with an OIRS-aided UAV to support high data rates and broad coverage in next-generation communication networks. A key challenge is optimally assigning OIRS mirror elements to users to ensure their quality of service requirements. In this paper, we assign OIRS mirror elements to users to ensure that their rate demands are met. Specifically, we propose a channel quality-based mirror-element assignment (CMA) scheme to maximize the network sum-rate under hardware and rate demand constraints. Simulation results reveal that, compared to the traditional equal-mirror element assignment (EMA) scheme, CMA demonstrates superior performance in terms of sum-rate and coverage probability.

关键词： Unmanned aerial vehicles (UAV)

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Handover Optimization Framework for next-generation Wireless networks: 5G, 5G-Advanced and 6G

Handover Optimization Framework for Next-Generation Wireless...

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IEEE International conference on Automatic control and Intelligent Systems (I2CACIS)

作者： Mbulwa, Abbas Ibrahim Yew, Hoe Tung Chekima, Ali Dargham, Jamal Ahmad Univ Malaysia Sabah Fac Engn Kota Kinabalu Sabah Malaysia

ISBN: (纸本)9798350372113;9798350372106

Mobile communications have been undergoing a generational evolution at least every ten years, currently in the fifth-generation (5G) and 5G-Advanced decade. As 5G and 5G-Advanced become a commercial reality, there is already significant interest in systems beyond 5G i.e., the sixth generation (6G) of wireless systems, anticipated to officially launch in 2030. The applications of 5G, 5G-Advanced, and 6G require more spectrum featuring a diverse range of radio frequency bands, ranging from below 6 GHz up to 1 THz. This includes low-band spectrum for coverage (macro cells) offering low data rates, mid-band for both coverage and capacity (small cells) offering high data rates, and high-band and beyond spectrum for capacity only, offering ultra-high data rates. By utilizing the capacity layer (small cells), a mobile user will take a shorter time to exit the cell. However, this exposes the user to very high handover requests, ping-pong effects, and handover failures, which negatively impact the quality of service (QoS) and quality of experience (QoE). Additionally, the integration of Terrestrial networks (TNs) and Non-Terrestrial networks (NTNs) envisioned for 5G, 5G-Advanced and 6G networks has made mobility (handover) management far more challenging due to mobile end-devices and infrastructure dynamics. To address the handover challenge, we proposed an infrastructure-agnostic handover framework for auto-tuning self-optimization in the handover measurement stage and handover decision-making stage. This framework aims to optimize handover performance and improve QoS and QoE for mobile users in 5G, 5G-Advanced, and 6G wireless networks.

关键词： Handover Handover optimization next-generation wireless networks 5G 5G-Advanced 6G Terrestrial networks Non-Terrestrial networks

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Experimental Hardware Implementation of Trigger Based Scheduled MAC for next generation Wi-Fi 59

Experimental Hardware Implementation of Trigger Based Schedu...

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59th Annual IEEE International conference on communications (IEEE ICC)

作者： Seijo, Oscar Perez-Ramirez, Javier Val, Inaki Rastorfer, Drake Akhmetov, Dmitry Das, Dibakar Banjade, Vesh Raj Sharma Cavalcanti, Dave Intel Corp Portland OR USA Basque Res & Technol Alliance Ikerlan Technol Res Ctr Arrasate Mondragon Spain

ISBN: (纸本)9781728190549

Emerging applications in the wireless domain such as factory automation or extended reality require high reliable and deterministic communications. New scheduling mechanisms introduced in current state-of-the-art Wi-Fi (i.e. 802.11ax, 802.11be) offer better performance, but in many cases not sufficient to meet the requirements of the aforementioned applications. In this paper, we present a hardware implementation of Scheduled Time-Sensitive Transmission Opportunity (S-TXOP) over Wi-Fi. S-TXOP provides accurate time synchronization and time-aware scheduling to enable highly efficient deterministic wireless communications. We describe a field-programmable gate array software-defined radio S-TXOP implementation, and show numerical and experimental results supporting low cycle time transmissions with determinism.

关键词： next generation Wi-Fi industrial wireless networks factory communications IEEE 802.11 wireless time-sensitive networking low latency communications extended reality field-programmable gate array software-defined radio

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An eBPF-XDP Hardware-Based Network Slicing Architecture for Future 6G Front- to Back-Haul networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第2期21卷 2224-2239页

作者： Salva-Garcia, Pablo Ricart-Sanchez, Ruben Alcaraz-Calero, Jose M. Wang, Qi Herrera-Ruiz, Octavio Univ West Scotland Sch Comp Engn & Phys Sci Paisley PA1 2BE Renfrew Scotland Netronome Mira Netronome SmartNIC Support Cranberry Township PA 16066 USA Netronome Mira Mira Network Secur Dept Cranberry Township PA 16066 USA

The heterogeneous requirements imposed by different vertical businesses have motivated a networking paradigm shift in the next generation of mobile networks (beyond 5G and 6G), leading to critical operation competitiveness of improved productivity, performance and efficiency. Furthermore, with the global digital revolution, such as Industry 4.0, and a connected world, network virtualisation together with high reliability and high performance communications have become crucial elements for mobile network operators. To minimise the negative effects that could affect critical services, network slicing is widely recognised as a key technology with the objective of meeting the Service-Level Agreements (SLAs) and Key performance Indicators (KPIs) in future 6G networks. In this context, it is essential to introduce a programmable data plane able to enforce flexible Quality of Service (QoS) commitments, while providing high-performance packet processing and real-time monitoring capabilities. To this end, this paper is focused on designing, prototyping and evaluating a novel framework that leverages a set of hardware-based technologies including eXpress Data Path (XDP), extended Barkeley Packet Filter (eBPF) and Smart Network Interface Cards (SmartNICs) to offload network functionality with the objective of providing high-performance pre-6G front-, mid- and back-haul network communications and thus, decreasing the overhead incurs by the Linux Kernel. The proposed solution is implemented based on bypassing the Linux Kernel and accelerating the communication, while providing network slice control and real-time monitoring capabilities. The main aim of this framework is to ensure network communications in forthcoming 6G infrastructures by guaranteeing 6G KPIs and avoiding system overload. The empirical validation of this solution for Industry 4.0 services as an example use case demonstrates key performance improvements in terms of packet processing as high as about 25Gbps, 20M p

关键词： 6G programmable data plane eBPF XDP network slicing

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