Pub Date : 2024-11-07DOI: 10.1109/OJCOMS.2024.3493417
David Franco;Marivi Higuero;Ane Sanz;Juanjo Unzilla;Maider Huarte
The rapid emergence of new applications and services, and their increased demand for Quality of Service (QoS), have a significant impact on the development of today’s communication networks. As a result, communication networks are constantly evolving towards new architectures, such as the 6th Generation (6G) of communication systems, currently being studied in academic and research environments. One of the most critical aspects of designing communication networks is meeting the restricted delay and packet loss requirements. In this context, although link failure recovery has been widely addressed in the literature, it remains one of the main causes of packet losses and delays in the network. The failure recovery time in currently deployed technologies is still far from the sub-millisecond delay required in 6G networks. The time required for distributed network architectures to converge to a common network state after a link failure is excessive. In contrast, centralized architectures such as Software-Defined Networking (SDN) solve this problem but still need to notify the failure to a centralized controller, which increases the recovery time. This paper proposes a very Fast Failure Recovery (vFFR) strategy that can recover from link failures in sub-millisecond timescales by reacting directly from the data plane of the network devices while maintaining a synchronized state with the centralized controller. We first analyze current failure recovery strategies and classify them according to the techniques used to optimize failure recovery time. Afterward, we describe the design of a vFFR strategy that combines three data plane recovery algorithms to reduce latency and packet loss under varying network conditions. Our vFFR strategy has been modeled in P4 language and tested on an emulation platform to validate the three data plane recovery algorithms under different conditions. The results show that latency varies according to the alternate path selected in the recovery algorithm, and the packet loss rate remains constant even when the background traffic reaches 90% of the link capacity. In addition, the vFFR strategy has been implemented on Intel Tofino devices, achieving a failure recovery time lower than $500~mu s$ and a total frame loss rate below 0.005% in all cases, including those with a 35 Gbps load.
{"title":"vFFR: A Very Fast Failure Recovery Strategy Implemented in Devices With Programmable Data Plane","authors":"David Franco;Marivi Higuero;Ane Sanz;Juanjo Unzilla;Maider Huarte","doi":"10.1109/OJCOMS.2024.3493417","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3493417","url":null,"abstract":"The rapid emergence of new applications and services, and their increased demand for Quality of Service (QoS), have a significant impact on the development of today’s communication networks. As a result, communication networks are constantly evolving towards new architectures, such as the 6th Generation (6G) of communication systems, currently being studied in academic and research environments. One of the most critical aspects of designing communication networks is meeting the restricted delay and packet loss requirements. In this context, although link failure recovery has been widely addressed in the literature, it remains one of the main causes of packet losses and delays in the network. The failure recovery time in currently deployed technologies is still far from the sub-millisecond delay required in 6G networks. The time required for distributed network architectures to converge to a common network state after a link failure is excessive. In contrast, centralized architectures such as Software-Defined Networking (SDN) solve this problem but still need to notify the failure to a centralized controller, which increases the recovery time. This paper proposes a very Fast Failure Recovery (vFFR) strategy that can recover from link failures in sub-millisecond timescales by reacting directly from the data plane of the network devices while maintaining a synchronized state with the centralized controller. We first analyze current failure recovery strategies and classify them according to the techniques used to optimize failure recovery time. Afterward, we describe the design of a vFFR strategy that combines three data plane recovery algorithms to reduce latency and packet loss under varying network conditions. Our vFFR strategy has been modeled in P4 language and tested on an emulation platform to validate the three data plane recovery algorithms under different conditions. The results show that latency varies according to the alternate path selected in the recovery algorithm, and the packet loss rate remains constant even when the background traffic reaches 90% of the link capacity. In addition, the vFFR strategy has been implemented on Intel Tofino devices, achieving a failure recovery time lower than \u0000<inline-formula> <tex-math>$500~mu s$ </tex-math></inline-formula>\u0000 and a total frame loss rate below 0.005% in all cases, including those with a 35 Gbps load.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7121-7146"},"PeriodicalIF":6.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671990","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 : 2024-11-06DOI: 10.1109/OJCOMS.2024.3492695
Lorenzo Giorgi;Carlo Puliafito;Antonio Virdis;Enzo Mingozzi
Edge computing brings computation facilities in close proximity to users, hence paving the way to a plethora of applications characterized by stringent requirements. Edge systems are highly dynamic, and clients may have to access their edge services at different locations over time. When this happens, it is fundamental to guarantee seamless service continuity, i.e., letting endpoints reach each other transparently and with no or negligible impact on performance. In this work, we propose a service-continuity solution for edge environments that leverages an ecosystem of distributed edge proxies as its core element. Edge proxies mediate between client applications and edge services and are dynamically reconfigured by a system orchestrator to ensure service continuity when the proxy for a client needs to change. Our proxies exploit HTTP Alternative Services, an extension of the HTTP standard, to inform clients of the new proxy to reach. Our approach is fully transparent to the application logic and does not require any non-standard protocol modification. We implemented a Proof-of-Concept and used it to assess our solution over a small-scale testbed. We considered different experimental scenarios and variants of the proposed strategy, comparing it against alternative approaches, namely one where the edge proxy does not change and one based on DNS resolution. Experimental results show the validity and superior performance of the proposed methodology.
边缘计算将计算设施带到用户附近，从而为大量具有严格要求的应用铺平了道路。边缘系统具有高度动态性，客户可能需要在不同时间、不同地点访问边缘服务。在这种情况下，保证服务的无缝连续性至关重要，即让端点透明地相互连接，并且对性能没有影响或影响微乎其微。在这项工作中，我们为边缘环境提出了一种服务连续性解决方案，其核心要素是利用分布式边缘代理生态系统。边缘代理在客户端应用程序和边缘服务之间进行调解，并由系统协调器动态地重新配置，以确保在客户端的代理需要更改时服务的连续性。我们的代理利用 HTTP 替代服务（HTTP 标准的扩展）通知客户端要访问的新代理。我们的方法对应用逻辑完全透明，不需要修改任何非标准协议。我们实施了一个概念验证，并在一个小规模测试平台上对我们的解决方案进行了评估。我们考虑了不同的实验场景和拟议策略的变体，并将其与其他方法（即边缘代理不发生变化的方法和基于 DNS 解析的方法）进行了比较。实验结果表明了所提方法的有效性和优越性能。
{"title":"Service Continuity in Edge Computing Through Edge Proxies and HTTP Alternative Services","authors":"Lorenzo Giorgi;Carlo Puliafito;Antonio Virdis;Enzo Mingozzi","doi":"10.1109/OJCOMS.2024.3492695","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3492695","url":null,"abstract":"Edge computing brings computation facilities in close proximity to users, hence paving the way to a plethora of applications characterized by stringent requirements. Edge systems are highly dynamic, and clients may have to access their edge services at different locations over time. When this happens, it is fundamental to guarantee seamless service continuity, i.e., letting endpoints reach each other transparently and with no or negligible impact on performance. In this work, we propose a service-continuity solution for edge environments that leverages an ecosystem of distributed edge proxies as its core element. Edge proxies mediate between client applications and edge services and are dynamically reconfigured by a system orchestrator to ensure service continuity when the proxy for a client needs to change. Our proxies exploit HTTP Alternative Services, an extension of the HTTP standard, to inform clients of the new proxy to reach. Our approach is fully transparent to the application logic and does not require any non-standard protocol modification. We implemented a Proof-of-Concept and used it to assess our solution over a small-scale testbed. We considered different experimental scenarios and variants of the proposed strategy, comparing it against alternative approaches, namely one where the edge proxy does not change and one based on DNS resolution. Experimental results show the validity and superior performance of the proposed methodology.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7057-7074"},"PeriodicalIF":6.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10745557","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671120","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 : 2024-11-05DOI: 10.1109/OJCOMS.2024.3491947
Orangel Azuaje;Ana Aguiar
Swarms of mobile wireless-connected sensors are increasingly deployed for applications such as monitoring, surveillance, and safety-critical operations. Quantifying end-to-end (e2e) delay performance guarantees in these scenarios is paramount. In this paper, we present a theoretical approach using Stochastic Network Calculus (SNC) with Moment Generating Functions (MGFs) to characterize e2e delay bounds in Mobile Wireless Sensor Networks (MWSNs). Our study focuses on a network composed of two segments: the first segment includes multiple nodes connected via a contention-based channel using the Distributed Coordination Function (DCF), while the second segment consists of a link prone to disconnections due to the mobility of nodes in the first segment. We model the first segment by calculating the expected per-packet service time in a non-saturated homogeneous contention channel and the second segment using a Discrete Time Markov Chain (DTMC). Initially, we derive a mathematical expression that correlates the offered load with the saturation status of each node’s queue in a non-saturated contention channel with homogeneous nodes. We then provide numerical e2e delay bounds for an illustrative example of a first responder network, quantifying the effects of non-saturated traffic, communication range on the head-sink link, and scheduling algorithms across different network sizes. Finally, we compare the derived e2e delay bounds with network simulations to assess their accuracy and reliability.
{"title":"Delay Guarantees for a Swarm of Mobile Sensors in Safety-Critical Applications","authors":"Orangel Azuaje;Ana Aguiar","doi":"10.1109/OJCOMS.2024.3491947","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3491947","url":null,"abstract":"Swarms of mobile wireless-connected sensors are increasingly deployed for applications such as monitoring, surveillance, and safety-critical operations. Quantifying end-to-end (e2e) delay performance guarantees in these scenarios is paramount. In this paper, we present a theoretical approach using Stochastic Network Calculus (SNC) with Moment Generating Functions (MGFs) to characterize e2e delay bounds in Mobile Wireless Sensor Networks (MWSNs). Our study focuses on a network composed of two segments: the first segment includes multiple nodes connected via a contention-based channel using the Distributed Coordination Function (DCF), while the second segment consists of a link prone to disconnections due to the mobility of nodes in the first segment. We model the first segment by calculating the expected per-packet service time in a non-saturated homogeneous contention channel and the second segment using a Discrete Time Markov Chain (DTMC). Initially, we derive a mathematical expression that correlates the offered load with the saturation status of each node’s queue in a non-saturated contention channel with homogeneous nodes. We then provide numerical e2e delay bounds for an illustrative example of a first responder network, quantifying the effects of non-saturated traffic, communication range on the head-sink link, and scheduling algorithms across different network sizes. Finally, we compare the derived e2e delay bounds with network simulations to assess their accuracy and reliability.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7147-7159"},"PeriodicalIF":6.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742911","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672016","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}
Integrated sensing and communication enhances the spectral efficiency by using shared resources, eliminating the need for separate bandwidth allocations. Unmanned aerial vehicle (UAVs) play a key role in this, offering mobility, flexibility, and extended coverage for serving multiple users, especially in scenarios like disaster response and environmental monitoring. This paper explores multiple UAVs with integrated sensing and communication capabilities, using the Age of Information (AoI) metric to optimize resource allocation for timely data transmission. We propose two algorithms, Variable Particle Swarm Optimization (VPSO) and Twin Variable Neighborhood Particle Swarm Optimization (TVPSO), to jointly optimize power, bandwidth, and UAV trajectories to minimize AoI. Numerical results show the effects of the sensing and communication power ratio, the number of UAVs and the number of users on AoI and energy consumption. Furthermore, TVPSO is shown to outperform other PSO variants and the Deep Q Networks (DQN)-based approach, offering faster convergence and superior performance.
{"title":"Optimizing Multi-UAV Multi-User System Through Integrated Sensing and Communication for Age of Information (AoI) Analysis","authors":"Yulin Zhou;Aziz Altaf Khuwaja;Xiaoting Li;Nan Zhao;Yunfei Chen","doi":"10.1109/OJCOMS.2024.3489873","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3489873","url":null,"abstract":"Integrated sensing and communication enhances the spectral efficiency by using shared resources, eliminating the need for separate bandwidth allocations. Unmanned aerial vehicle (UAVs) play a key role in this, offering mobility, flexibility, and extended coverage for serving multiple users, especially in scenarios like disaster response and environmental monitoring. This paper explores multiple UAVs with integrated sensing and communication capabilities, using the Age of Information (AoI) metric to optimize resource allocation for timely data transmission. We propose two algorithms, Variable Particle Swarm Optimization (VPSO) and Twin Variable Neighborhood Particle Swarm Optimization (TVPSO), to jointly optimize power, bandwidth, and UAV trajectories to minimize AoI. Numerical results show the effects of the sensing and communication power ratio, the number of UAVs and the number of users on AoI and energy consumption. Furthermore, TVPSO is shown to outperform other PSO variants and the Deep Q Networks (DQN)-based approach, offering faster convergence and superior performance.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6918-6931"},"PeriodicalIF":6.3,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650591","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}
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™. 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.
{"title":"Segment-Encoded Explicit Trees (SEETs) for Stateless Multicast: P4-Based Implementation and Performance Study","authors":"Steffen Lindner;Thomas Stüber;Maximilian Bertsch;Toerless Eckert;Michael Menth","doi":"10.1109/OJCOMS.2024.3490433","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3490433","url":null,"abstract":"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™. 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.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6903-6917"},"PeriodicalIF":6.3,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650586","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 : 2024-10-31DOI: 10.1109/OJCOMS.2024.3489424
Mohammed Salih Mohammed Gismalla;Suhail I. Al-Dharrab;Saleh A. Alawsh;Ali H. Muqaibel
This paper investigates the performance of hovering unmanned aerial vehicles (UAVs) using free space optical (FSO) communications with decode-and-forward (DF) relaying. Multi-hop UAV relays are considered. The channel is modeled considering the atmospheric attenuation due to the sandstorm and relative humidity (RH), in addition to the atmospheric turbulence, angle-of-arrival (AOA) fluctuations, and pointing errors (PE). We first obtain a simplified expression for end-to-end (E2E) outage probability considering atmospheric attenuation conditions, then the impact of various parameters such as transmitted power, field of view (FOV) and optical beamwidth on E2E outage probability is incorporated. We also derive a closed form expression for the ergodic capacity and characterize the effect of different system parameters. Monte Carlo simulation is utilized to prove the analytical results. The asymptotic bound for E2E outage probability is validated to offer insights into the effects of different channel coefficients. Analysis of the results indicates that the impact of low visibility due to sandstorms is comparable to that of high RH on FSO links.
{"title":"Performance Analysis of Multi-Hop UAVs Using FSO Communications Under Humidity and Sandstorms Conditions","authors":"Mohammed Salih Mohammed Gismalla;Suhail I. Al-Dharrab;Saleh A. Alawsh;Ali H. Muqaibel","doi":"10.1109/OJCOMS.2024.3489424","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3489424","url":null,"abstract":"This paper investigates the performance of hovering unmanned aerial vehicles (UAVs) using free space optical (FSO) communications with decode-and-forward (DF) relaying. Multi-hop UAV relays are considered. The channel is modeled considering the atmospheric attenuation due to the sandstorm and relative humidity (RH), in addition to the atmospheric turbulence, angle-of-arrival (AOA) fluctuations, and pointing errors (PE). We first obtain a simplified expression for end-to-end (E2E) outage probability considering atmospheric attenuation conditions, then the impact of various parameters such as transmitted power, field of view (FOV) and optical beamwidth on E2E outage probability is incorporated. We also derive a closed form expression for the ergodic capacity and characterize the effect of different system parameters. Monte Carlo simulation is utilized to prove the analytical results. The asymptotic bound for E2E outage probability is validated to offer insights into the effects of different channel coefficients. Analysis of the results indicates that the impact of low visibility due to sandstorms is comparable to that of high RH on FSO links.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6987-7001"},"PeriodicalIF":6.3,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650587","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 : 2024-10-30DOI: 10.1109/OJCOMS.2024.3488503
Kai-Kit Wong
This paper proposes a transmitter channel state information (CSI)-free, massive connectivity scheme in the downlink that requires no optimization from the base station (BS) side. Inter-user interference is dealt with solely by the mobile users themselves, each equipped with a position-flexible fluid antenna system (FAS). Specifically, we adopt the compact ultra massive antenna (CUMA) receiver architecture at the FAS of each user, which protects the desired user’s signal from interference without the need of the interferers’ CSI. Different from previous work, we incorporate the return loss and isolation parameters into the channel model to properly reflect realistic mutual coupling countermeasures and deploy randomized reconfigurable intelligent surfaces (RISs) in the environment to restore rich scattering channel conditions. Our simulation results demonstrate that under these conditions at $26~{textrm {GHz}}$ , CUMA can accommodate 50 users per channel use to achieve a rate of $45~{textrm {bps/Hz}}$ , without power control and precoding at the BS and also no successive interference cancellation (SIC) at each user. The rate will be increased to $54~{textrm {bps/Hz}}$ if 100 users are served, revealing the possibility of massive connectivity without the CSI at the BS.
{"title":"Transmitter CSI-Free RIS-Randomized CUMA for Extreme Massive Connectivity","authors":"Kai-Kit Wong","doi":"10.1109/OJCOMS.2024.3488503","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3488503","url":null,"abstract":"This paper proposes a transmitter channel state information (CSI)-free, massive connectivity scheme in the downlink that requires no optimization from the base station (BS) side. Inter-user interference is dealt with solely by the mobile users themselves, each equipped with a position-flexible fluid antenna system (FAS). Specifically, we adopt the compact ultra massive antenna (CUMA) receiver architecture at the FAS of each user, which protects the desired user’s signal from interference without the need of the interferers’ CSI. Different from previous work, we incorporate the return loss and isolation parameters into the channel model to properly reflect realistic mutual coupling countermeasures and deploy randomized reconfigurable intelligent surfaces (RISs) in the environment to restore rich scattering channel conditions. Our simulation results demonstrate that under these conditions at \u0000<inline-formula> <tex-math>$26~{textrm {GHz}}$ </tex-math></inline-formula>\u0000, CUMA can accommodate 50 users per channel use to achieve a rate of \u0000<inline-formula> <tex-math>$45~{textrm {bps/Hz}}$ </tex-math></inline-formula>\u0000, without power control and precoding at the BS and also no successive interference cancellation (SIC) at each user. The rate will be increased to \u0000<inline-formula> <tex-math>$54~{textrm {bps/Hz}}$ </tex-math></inline-formula>\u0000 if 100 users are served, revealing the possibility of massive connectivity without the CSI at the BS.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6890-6902"},"PeriodicalIF":6.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600383","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}
The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, we investigate how machine learning can be applied to time series data of beam measurements in the Fifth Generation (5G) New Radio (NR) system to improve the HO procedure. This paper introduces the Early-Scheduled Handover Preparation scheme designed to enhance the robustness and efficiency of the HO procedure, particularly in scenarios involving high mobility and dense small cell deployments. Early-Scheduled Handover Preparation focuses on optimizing the timing of the HO preparation phase by leveraging machine learning techniques to predict the earliest possible trigger points for HO events. We identify a new early trigger for HO preparation and demonstrate how it can beneficially reduce the required time for HO execution reducing channel quality degradation. These insights enable a new HO preparation scheme that offers a novel, user-aware, and proactive HO decision making in MIMO scenarios incorporating mobility.
切换（HO）程序是蜂窝网络中最关键的功能之一，由服务小区和邻近小区的用户信道测量驱动。整个切换过程的成功率在很大程度上受到准备阶段的影响。由于具有大型天线阵列的大规模多输入多输出 (MIMO) 系统可以解决信道行为的更多细节问题，我们研究了如何将机器学习应用于第五代 (5G) 新无线电 (NR) 系统中波束测量的时间序列数据，以改进 HO 程序。本文介绍了 "早期计划切换准备 "方案，该方案旨在提高 HO 程序的稳健性和效率，尤其是在涉及高移动性和密集小基站部署的场景中。早期计划移交准备主要通过利用机器学习技术来预测最早可能的移交事件触发点，从而优化移交准备阶段的时间安排。我们确定了一个新的HO准备早期触发点，并演示了它如何有效缩短HO执行所需的时间，减少信道质量下降。这些见解促成了一种新的 HO 准备方案，该方案可在包含移动性的 MIMO 场景中提供新颖、用户感知和主动的 HO 决策。
{"title":"Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems","authors":"Dino Pjanić;Alexandros Sopasakis;Andres Reial;Fredrik Tufvesson","doi":"10.1109/OJCOMS.2024.3488594","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3488594","url":null,"abstract":"The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, we investigate how machine learning can be applied to time series data of beam measurements in the Fifth Generation (5G) New Radio (NR) system to improve the HO procedure. This paper introduces the Early-Scheduled Handover Preparation scheme designed to enhance the robustness and efficiency of the HO procedure, particularly in scenarios involving high mobility and dense small cell deployments. Early-Scheduled Handover Preparation focuses on optimizing the timing of the HO preparation phase by leveraging machine learning techniques to predict the earliest possible trigger points for HO events. We identify a new early trigger for HO preparation and demonstrate how it can beneficially reduce the required time for HO execution reducing channel quality degradation. These insights enable a new HO preparation scheme that offers a novel, user-aware, and proactive HO decision making in MIMO scenarios incorporating mobility.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6959-6971"},"PeriodicalIF":6.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650589","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}
Reconfigurable intelligent surfaces (RISs) and multiuser gigantic multiple-input multiple-output (MU-gMIMO) systems are key technologies for enabling sixth-generation (6G) networks. Their numerous advantages include minimal path losses, high energy efficiency (EE), high spectrum efficiency (SE), high data rates, and compatibility with line-of-sight (LoS) and non-LoS (NLoS) paths. However, RIS-gMIMO faces numerous challenges, including pilot overhead during beam training due to a combined radiation field, high training overhead due to the cascaded channels between transceivers, inaccurate channel state information (CSI) due to the rapidly changing RIS-user equipment (UE) channel, and low-accuracy channel estimation caused by semipassive RISs. With semipassive RIS-gMIMO communications, we present a novel quantized deep learning (qDL) channel model. This proposed channel model is constructed via a radio frequency (RF) chain matrix, a combined radiation field, and a truncated activation output. To enhance the feature extraction performance and reduce the loss of the model, a novel qDL-based channel estimation scheme is also proposed to concurrently utilize denoising multilayer perceptron (DnMLP) units to satisfy the imposed sparsity constraint. The qDL scheme outperforms the previously developed benchmark schemes in terms of accuracy and performance according to the normalized mean squared error (NMSE) of the simulation results.
{"title":"Quantized Deep Learning Channel Model and Estimation for RIS-gMIMO Communication","authors":"Joydev Ghosh;César Vargas-Rosales;Van Nhan Vo;Chakchai So-In","doi":"10.1109/OJCOMS.2024.3487847","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3487847","url":null,"abstract":"Reconfigurable intelligent surfaces (RISs) and multiuser gigantic multiple-input multiple-output (MU-gMIMO) systems are key technologies for enabling sixth-generation (6G) networks. Their numerous advantages include minimal path losses, high energy efficiency (EE), high spectrum efficiency (SE), high data rates, and compatibility with line-of-sight (LoS) and non-LoS (NLoS) paths. However, RIS-gMIMO faces numerous challenges, including pilot overhead during beam training due to a combined radiation field, high training overhead due to the cascaded channels between transceivers, inaccurate channel state information (CSI) due to the rapidly changing RIS-user equipment (UE) channel, and low-accuracy channel estimation caused by semipassive RISs. With semipassive RIS-gMIMO communications, we present a novel quantized deep learning (qDL) channel model. This proposed channel model is constructed via a radio frequency (RF) chain matrix, a combined radiation field, and a truncated activation output. To enhance the feature extraction performance and reduce the loss of the model, a novel qDL-based channel estimation scheme is also proposed to concurrently utilize denoising multilayer perceptron (DnMLP) units to satisfy the imposed sparsity constraint. The qDL scheme outperforms the previously developed benchmark schemes in terms of accuracy and performance according to the normalized mean squared error (NMSE) of the simulation results.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6932-6958"},"PeriodicalIF":6.3,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650588","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 : 2024-10-29DOI: 10.1109/OJCOMS.2024.3487912
Alejandro De La Fuente;Guillem Femenias;Felip Riera-Palou;Giovanni Interdonato
Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging technology for beyond fifth-generation (5G) systems aimed at enhancing the energy and spectral efficiencies of future mobile networks while providing nearly uniform quality of service to all users. Moreover, multicasting has garnered increasing attention in recent years, as physical-layer multicasting proves to be an efficient approach for serving multiple users simultaneously, all with identical service demands while sharing radio resources. A multicast service is typically delivered using either unicast or a single multicast transmission. In contrast, this work introduces a subgroup-centric multicast CF-mMIMO framework that splits the users into several multicast subgroups. The subgroup creation is based on the similarities in the spatial channel characteristics of the multicast users. This framework benefits from efficiently sharing the pilot sequence used for channel estimation and the precoding filters used for data transmission. The proposed framework relies on two scalable precoding strategies, namely, the centralized improved partial MMSE (IP-MMSE) and the distributed conjugate beamforming (CB). Numerical results demonstrate that the centralized IP-MMSE precoding strategy outperforms the CB precoding scheme in terms of sum SE when multicast users are uniformly distributed across the service area. In contrast, in cases where users are spatially clustered, multicast subgrouping significantly enhances the sum spectral efficiency (SE) of the multicast service compared to both unicast and single multicast transmission. Interestingly, in the latter scenario, distributed CB precoding outperforms IP-MMSE, particularly in terms of per-user SE, making it the best solution for delivering multicast content. Heterogeneous scenarios that combine uniform and clustered distributions of users validate multicast subgrouping as the most effective solution for improving both the sum and per-user SE of a multicast CF-mMIMO service.
无小区大规模多输入多输出（CF-mMIMO）是超越第五代（5G）系统的新兴技术，旨在提高未来移动网络的能效和频谱效率，同时为所有用户提供几乎一致的服务质量。此外，近年来，多播技术日益受到关注，因为物理层多播技术被证明是同时为多个用户提供服务的有效方法，所有用户都有相同的服务需求，同时共享无线电资源。组播服务通常采用单播或单次组播传输方式提供。相比之下，这项工作引入了一种以子组为中心的多播 CF-mMIMO 框架，可将用户分成多个多播子组。子组的创建基于组播用户空间信道特性的相似性。该框架可有效共享用于信道估计的先导序列和用于数据传输的预编码滤波器。所提出的框架依赖于两种可扩展的预编码策略，即集中式改进部分 MMSE（IP-MMSE）和分布式共轭波束成形（CB）。数值结果表明，当组播用户均匀分布在整个服务区域时，集中式 IP-MMSE 预编码策略的 SE 总和优于 CB 预编码方案。相反，在用户空间集群的情况下，与单播和单一组播传输相比，组播分组能显著提高组播服务的总频谱效率（SE）。有趣的是，在后一种情况下，分布式 CB 预编码优于 IP-MMSE，特别是在每用户 SE 方面，使其成为传输组播内容的最佳解决方案。结合用户均匀分布和聚类分布的异构场景验证了多播分组是提高多播 CF-mMIMO 服务总和与单用户 SE 的最有效解决方案。
{"title":"Subgroup-Centric Multicast Cell-Free Massive MIMO","authors":"Alejandro De La Fuente;Guillem Femenias;Felip Riera-Palou;Giovanni Interdonato","doi":"10.1109/OJCOMS.2024.3487912","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3487912","url":null,"abstract":"Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging technology for beyond fifth-generation (5G) systems aimed at enhancing the energy and spectral efficiencies of future mobile networks while providing nearly uniform quality of service to all users. Moreover, multicasting has garnered increasing attention in recent years, as physical-layer multicasting proves to be an efficient approach for serving multiple users simultaneously, all with identical service demands while sharing radio resources. A multicast service is typically delivered using either unicast or a single multicast transmission. In contrast, this work introduces a subgroup-centric multicast CF-mMIMO framework that splits the users into several multicast subgroups. The subgroup creation is based on the similarities in the spatial channel characteristics of the multicast users. This framework benefits from efficiently sharing the pilot sequence used for channel estimation and the precoding filters used for data transmission. The proposed framework relies on two scalable precoding strategies, namely, the centralized improved partial MMSE (IP-MMSE) and the distributed conjugate beamforming (CB). Numerical results demonstrate that the centralized IP-MMSE precoding strategy outperforms the CB precoding scheme in terms of sum SE when multicast users are uniformly distributed across the service area. In contrast, in cases where users are spatially clustered, multicast subgrouping significantly enhances the sum spectral efficiency (SE) of the multicast service compared to both unicast and single multicast transmission. Interestingly, in the latter scenario, distributed CB precoding outperforms IP-MMSE, particularly in terms of per-user SE, making it the best solution for delivering multicast content. Heterogeneous scenarios that combine uniform and clustered distributions of users validate multicast subgrouping as the most effective solution for improving both the sum and per-user SE of a multicast CF-mMIMO service.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6872-6889"},"PeriodicalIF":6.3,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600338","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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