Pub Date : 2025-01-20DOI: 10.1109/JSAC.2025.3531578
Shicong Liu;Xianghao Yu;Zhen Gao;Jie Xu;Derrick Wing Kwan Ng;Shuguang Cui
Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, which leads to unique challenges in channel estimation (CE). In this paper, we propose a new sensing-enhanced uplink CE scheme for near-field XL-MIMO, which notably reduces the required quantity of baseband samples and the dictionary size. In particular, we first propose a sensing method that can be accomplished in a single time slot. It employs power sensors embedded within the antenna elements to measure the received power pattern rather than baseband samples. A time inversion algorithm is then proposed to precisely estimate the locations of users and scatterers, which offers a substantially lower computational complexity. Based on the estimated locations from sensing, a novel dictionary is then proposed by considering the eigen-problem based on the near-field transmission model, which facilitates efficient near-field CE with less baseband sampling and a more lightweight dictionary. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Simulation results unveil that the proposed time inversion algorithm achieves accurate localization with power measurements only, and remarkably outperforms various widely-adopted algorithms in terms of computational complexity. Furthermore, the proposed eigen-dictionary considerably improves the accuracy in CE with a compact dictionary size and a drastic reduction in baseband samples by up to 66%.
{"title":"Sensing-Enhanced Channel Estimation for Near-Field XL-MIMO Systems","authors":"Shicong Liu;Xianghao Yu;Zhen Gao;Jie Xu;Derrick Wing Kwan Ng;Shuguang Cui","doi":"10.1109/JSAC.2025.3531578","DOIUrl":"10.1109/JSAC.2025.3531578","url":null,"abstract":"Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, which leads to unique challenges in channel estimation (CE). In this paper, we propose a new sensing-enhanced uplink CE scheme for near-field XL-MIMO, which notably reduces the required quantity of baseband samples and the dictionary size. In particular, we first propose a sensing method that can be accomplished in a single time slot. It employs power sensors embedded within the antenna elements to measure the received power pattern rather than baseband samples. A time inversion algorithm is then proposed to precisely estimate the locations of users and scatterers, which offers a substantially lower computational complexity. Based on the estimated locations from sensing, a novel dictionary is then proposed by considering the eigen-problem based on the near-field transmission model, which facilitates efficient near-field CE with less baseband sampling and a more lightweight dictionary. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Simulation results unveil that the proposed time inversion algorithm achieves accurate localization with power measurements only, and remarkably outperforms various widely-adopted algorithms in terms of computational complexity. Furthermore, the proposed eigen-dictionary considerably improves the accuracy in CE with a compact dictionary size and a drastic reduction in baseband samples by up to 66%.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 3","pages":"628-643"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Optical intelligent reflecting surface (OIRS) has attracted increasing attention due to its capability of overcoming signal blockages in visible light communication (VLC), an emerging technology for the next-generation advanced transceivers. However, current works on OIRS predominantly assume known channel state information (CSI), while its estimation problem has not been studied yet. To bridge such a gap, this paper proposes a new and customized OIRS channel estimation protocol with joint space-time sampling under the alignment-based OIRS channel model. First, we unveil the spatial and temporal coherence characteristics and derive OIRS coherence distance and coherence time in closed form. Next, to achieve dynamic beam alignment for pilot transmission within the coherence time, we propose to tune the rotation angles of the OIRS reflecting elements following a geometric optics-based non-uniform codebook. Then, given the beam alignment within the considered coherence time, a sequential OIRS channel estimation method is proposed, where the OIRS is divided into multiple subarrays based on the coherence distance. The CSI for each subarray is estimated sequentially, followed by a space-time interpolation to retrieve full CSI for other non-aligned transceiver antennas. Numerical results validate our theoretical analyses and demonstrate the efficacy of the proposed OIRS channel estimation protocol as compared to benchmark schemes.
{"title":"Channel Estimation for Optical Intelligent Reflecting Surface-Assisted VLC System: A Joint Space-Time Sampling Approach","authors":"Shiyuan Sun;Fang Yang;Weidong Mei;Jian Song;Zhu Han;Rui Zhang","doi":"10.1109/JSAC.2025.3531529","DOIUrl":"10.1109/JSAC.2025.3531529","url":null,"abstract":"Optical intelligent reflecting surface (OIRS) has attracted increasing attention due to its capability of overcoming signal blockages in visible light communication (VLC), an emerging technology for the next-generation advanced transceivers. However, current works on OIRS predominantly assume known channel state information (CSI), while its estimation problem has not been studied yet. To bridge such a gap, this paper proposes a new and customized OIRS channel estimation protocol with joint space-time sampling under the alignment-based OIRS channel model. First, we unveil the spatial and temporal coherence characteristics and derive OIRS coherence distance and coherence time in closed form. Next, to achieve dynamic beam alignment for pilot transmission within the coherence time, we propose to tune the rotation angles of the OIRS reflecting elements following a geometric optics-based non-uniform codebook. Then, given the beam alignment within the considered coherence time, a sequential OIRS channel estimation method is proposed, where the OIRS is divided into multiple subarrays based on the coherence distance. The CSI for each subarray is estimated sequentially, followed by a space-time interpolation to retrieve full CSI for other non-aligned transceiver antennas. Numerical results validate our theoretical analyses and demonstrate the efficacy of the proposed OIRS channel estimation protocol as compared to benchmark schemes.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 3","pages":"867-882"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/JSAC.2025.3528806
Marco Polverini;Antonio Cianfrani;Tommaso Caiazzi;Mariano Scazzariello
The rise of new applications, such as interactive remote presence, online gaming, and video-assisted remote control of industrial machinery, necessitates enhanced requirements in terms of throughput and delay stability. Many efforts have been made to address these needs, with Deterministic Networking (DetNet) being one such initiative. DetNet aims to guarantee delivery with low latency and minimal jitter, ensuring high reliability and performance for time-sensitive applications. However, DetNet applicability in real-world scenarios is limited due to the need of a lower-layer protocol supporting resource reservation procedures (e.g., MPLS), and the lack of publicly available implementations. In this work, we present SRv6 Live-Live, an easy-to-deploy and highly scalable implementation of DetNet functions using the Segment Routing over IPv6 (SRv6) model. The SRv6 Live-Live behavior replicates packets of a selected flow across multiple paths at the ingress of the SRv6 domain and drops redundant replicas at the egress. After discussing insights about the paths’ selection strategy, we provide a SRv6 Live-Live implementation for programmable data planes using P4. We also propose the use of SRv6 Live-Live for best path selection at line rate, in SD-WAN scenarios. The main results obtained in the extensive performance evaluation are that SRv6 Live-Live preserves the throughput in case of congestion and reduces the tail end-to-end delay with a marginal impact on best-effort flows.
{"title":"SRv6 Meets DetNet: A New Behavior for Low Latency and High Reliability","authors":"Marco Polverini;Antonio Cianfrani;Tommaso Caiazzi;Mariano Scazzariello","doi":"10.1109/JSAC.2025.3528806","DOIUrl":"10.1109/JSAC.2025.3528806","url":null,"abstract":"The rise of new applications, such as interactive remote presence, online gaming, and video-assisted remote control of industrial machinery, necessitates enhanced requirements in terms of throughput and delay stability. Many efforts have been made to address these needs, with Deterministic Networking (DetNet) being one such initiative. DetNet aims to guarantee delivery with low latency and minimal jitter, ensuring high reliability and performance for time-sensitive applications. However, DetNet applicability in real-world scenarios is limited due to the need of a lower-layer protocol supporting resource reservation procedures (e.g., MPLS), and the lack of publicly available implementations. In this work, we present SRv6 Live-Live, an easy-to-deploy and highly scalable implementation of DetNet functions using the Segment Routing over IPv6 (SRv6) model. The SRv6 Live-Live behavior replicates packets of a selected flow across multiple paths at the ingress of the SRv6 domain and drops redundant replicas at the egress. After discussing insights about the paths’ selection strategy, we provide a SRv6 Live-Live implementation for programmable data planes using P4. We also propose the use of SRv6 Live-Live for best path selection at line rate, in SD-WAN scenarios. The main results obtained in the extensive performance evaluation are that SRv6 Live-Live preserves the throughput in case of congestion and reduces the tail end-to-end delay with a marginal impact on best-effort flows.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 2","pages":"448-458"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/JSAC.2025.3528818
Yi Liu;Nan Geng;Mingwei Xu;Yuan Yang;Enhuan Dong;Chenyi Liu;Qiaoyin Gan;Qing Li;Jianping Wu
Traffic engineering (TE) has attracted extensive attention over the years. Operators expect to design a TE scheme that accommodates traffic dynamics well and achieves good TE performance with little overhead. Some approaches like oblivious routing compute an optimal static routing based on a large traffic matrix (TM) range, which usually leads to much performance loss. Many approaches compute routing solutions based on one or a few representative TMs obtained from observed historical TMs. However, they may suffer from performance degradation for unexpected TMs and usually induce much overhead of system operating. In this paper, we propose ALTE, an adaptive and low-cost TE scheme based on TM classification. We develop a novel clustering algorithm to properly group a set of historical TMs into several clusters and compute a candidate routing solution for each TM cluster. A machine learning classifier is trained to infer the proper candidate routing solution online based on the features extracted from some easily measured statistics. We implement a system prototype of ALTE and do extensive simulations and experiments using both real and synthetic traffic traces. The results show that ALTE achieves near-optimal performance for dynamic traffic and introduces little overhead of routing updates.
{"title":"Adaptive and Low-Cost Traffic Engineering: A Traffic Matrix Clustering Perspective","authors":"Yi Liu;Nan Geng;Mingwei Xu;Yuan Yang;Enhuan Dong;Chenyi Liu;Qiaoyin Gan;Qing Li;Jianping Wu","doi":"10.1109/JSAC.2025.3528818","DOIUrl":"10.1109/JSAC.2025.3528818","url":null,"abstract":"Traffic engineering (TE) has attracted extensive attention over the years. Operators expect to design a TE scheme that accommodates traffic dynamics well and achieves good TE performance with little overhead. Some approaches like oblivious routing compute an optimal static routing based on a large traffic matrix (TM) range, which usually leads to much performance loss. Many approaches compute routing solutions based on one or a few representative TMs obtained from observed historical TMs. However, they may suffer from performance degradation for unexpected TMs and usually induce much overhead of system operating. In this paper, we propose ALTE, an adaptive and low-cost TE scheme based on TM classification. We develop a novel clustering algorithm to properly group a set of historical TMs into several clusters and compute a candidate routing solution for each TM cluster. A machine learning classifier is trained to infer the proper candidate routing solution online based on the features extracted from some easily measured statistics. We implement a system prototype of ALTE and do extensive simulations and experiments using both real and synthetic traffic traces. The results show that ALTE achieves near-optimal performance for dynamic traffic and introduces little overhead of routing updates.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 2","pages":"510-523"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/JSAC.2025.3528815
Minghao Ye;Junjie Zhang;Zehua Guo;H. Jonathan Chao
Distributed Traffic Engineering (TE) aims to optimize network performance by generating individual routing strategies at each router without a global view of the network. A major challenge for these TE solutions is handling performance degradation caused by unexpected traffic fluctuations and unpredictable link failures. Recently, Machine Learning (ML) techniques have introduced new opportunities to enhance distributed TE. In this paper, we propose Path-Based Graph Neural Network (PathGNN), which leverages the emerging GNN architecture to quickly infer robust and resilient routing strategies in a distributed manner to accommodate unexpected network conditions. PathGNN adopts a novel path-link bipartite graph modeling approach to capture the dynamics of link resources shared by routing paths. It then performs efficient GNN message exchanges among routers to make adaptive local routing decisions for better load balancing. Additionally, PathGNN leverages Supervised Learning (SL) to directly learn from optimal routing strategies through efficient offline training. Evaluation results on four real-world network topologies demonstrate PathGNN’s strong generalization capability. Compared to state-of-the-art distributed TE solutions, PathGNN improves the load balancing performance by at least 24.4% with lower end-to-end delay under dynamic traffic scenarios, and also boosts performance by up to 35.3% under multiple link failures.
{"title":"Path-Based Graph Neural Network for Robust and Resilient Routing in Distributed Traffic Engineering","authors":"Minghao Ye;Junjie Zhang;Zehua Guo;H. Jonathan Chao","doi":"10.1109/JSAC.2025.3528815","DOIUrl":"10.1109/JSAC.2025.3528815","url":null,"abstract":"Distributed Traffic Engineering (TE) aims to optimize network performance by generating individual routing strategies at each router without a global view of the network. A major challenge for these TE solutions is handling performance degradation caused by unexpected traffic fluctuations and unpredictable link failures. Recently, Machine Learning (ML) techniques have introduced new opportunities to enhance distributed TE. In this paper, we propose Path-Based Graph Neural Network (PathGNN), which leverages the emerging GNN architecture to quickly infer robust and resilient routing strategies in a distributed manner to accommodate unexpected network conditions. PathGNN adopts a novel path-link bipartite graph modeling approach to capture the dynamics of link resources shared by routing paths. It then performs efficient GNN message exchanges among routers to make adaptive local routing decisions for better load balancing. Additionally, PathGNN leverages Supervised Learning (SL) to directly learn from optimal routing strategies through efficient offline training. Evaluation results on four real-world network topologies demonstrate PathGNN’s strong generalization capability. Compared to state-of-the-art distributed TE solutions, PathGNN improves the load balancing performance by at least 24.4% with lower end-to-end delay under dynamic traffic scenarios, and also boosts performance by up to 35.3% under multiple link failures.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 2","pages":"422-436"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/JSAC.2025.3528817
Lidia Ruiz;Juan Carlos García-Escartín
Quantum Key Distribution has become a mature quantum technology that has outgrown dedicated links and is ready to be incorporated into the classical infrastructure. In this scenario with multiple potential nodes, it is crucial having efficient ways to allocate the network resources between all the potential users. We propose a simple method for routing and wavelength assignment in wavelength multiplexed networks in which classical and quantum channels coexist. The proposed heuristics take into account the specific requirements of quantum key distribution and focus on keeping at bay the contamination of the quantum channels by photons coming from the classical signals from non-linear processes, among others. These heuristics reduce the shared path between classical and quantum channels and improve the signal-to-noise ratio in the quantum channels, improving their quantum key rate. We compare the results to the usual classical RWA approach.
{"title":"Routing and Wavelength Assignment in Hybrid Networks With Classical and Quantum Signals","authors":"Lidia Ruiz;Juan Carlos García-Escartín","doi":"10.1109/JSAC.2025.3528817","DOIUrl":"10.1109/JSAC.2025.3528817","url":null,"abstract":"Quantum Key Distribution has become a mature quantum technology that has outgrown dedicated links and is ready to be incorporated into the classical infrastructure. In this scenario with multiple potential nodes, it is crucial having efficient ways to allocate the network resources between all the potential users. We propose a simple method for routing and wavelength assignment in wavelength multiplexed networks in which classical and quantum channels coexist. The proposed heuristics take into account the specific requirements of quantum key distribution and focus on keeping at bay the contamination of the quantum channels by photons coming from the classical signals from non-linear processes, among others. These heuristics reduce the shared path between classical and quantum channels and improve the signal-to-noise ratio in the quantum channels, improving their quantum key rate. We compare the results to the usual classical RWA approach.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 2","pages":"412-421"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10839041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974657","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 : 2025-01-13DOI: 10.1109/JSAC.2025.3528807
Gao Zheng;Ning Wang;Peng Qian;David Griffin;Regius Rahim Tafazolli
Supporting ubiquitous deployment of built-in Internet service with Software Defined Networking (SDN), Network Function Virtualization (NFV), and Low Earth Orbit (LEO) satellite constellations has been widely accepted as one of the key technologies for the next-generation communication services. By integrating terrestrial and space network capabilities, new design features are introduced to existing network ecosystems. For instance, terrestrial Virtual Network Functions (VNFs) can now be hosted on satellites, utilizing satellite highways. This requires expanding the roles of the control units, originally responsible for terrestrial data planes, to include space-based counterparts. As a result, seamless integration of Service Function Chains (SFCs) across satellite constellations and terrestrial control units becomes a challenge due to the topology dynamics caused by high-speed LEO satellites. In this paper, we propose the Geosynchronous Service Function Chaining (GSFC) scheme to facilitate programmable, Internet SFC operations based on LEO satellite network environments. The key idea is to cluster adjacent LEO satellites to represent logical VNF containers at the fixed positions, where the initial VNFs at the region are continuously filled up by the traversing satellite payload functions in a predictable manner. With this design, the ground-based controllers can maintain the space-terrestrial SFCs without being affected by the constantly shifting satellite VNFs, and thereby large-scale and complex recalculation for the routing policies is avoided. The design principle introduces a ground-breaking approach to space Internet protocol stacks, facilitating robust routing for SFC operations across integrated space and terrestrial networks. Our simulation results verify the feasibility of the proposed GSFC-based VNF orchestration mechanism and reveal the trade-offs in both data and control plane performance.
{"title":"SDN-Based Service Function Chaining in Integrated Terrestrial and LEO Satellite-Based Space Internet","authors":"Gao Zheng;Ning Wang;Peng Qian;David Griffin;Regius Rahim Tafazolli","doi":"10.1109/JSAC.2025.3528807","DOIUrl":"10.1109/JSAC.2025.3528807","url":null,"abstract":"Supporting ubiquitous deployment of built-in Internet service with Software Defined Networking (SDN), Network Function Virtualization (NFV), and Low Earth Orbit (LEO) satellite constellations has been widely accepted as one of the key technologies for the next-generation communication services. By integrating terrestrial and space network capabilities, new design features are introduced to existing network ecosystems. For instance, terrestrial Virtual Network Functions (VNFs) can now be hosted on satellites, utilizing satellite highways. This requires expanding the roles of the control units, originally responsible for terrestrial data planes, to include space-based counterparts. As a result, seamless integration of Service Function Chains (SFCs) across satellite constellations and terrestrial control units becomes a challenge due to the topology dynamics caused by high-speed LEO satellites. In this paper, we propose the Geosynchronous Service Function Chaining (GSFC) scheme to facilitate programmable, Internet SFC operations based on LEO satellite network environments. The key idea is to cluster adjacent LEO satellites to represent logical VNF containers at the fixed positions, where the initial VNFs at the region are continuously filled up by the traversing satellite payload functions in a predictable manner. With this design, the ground-based controllers can maintain the space-terrestrial SFCs without being affected by the constantly shifting satellite VNFs, and thereby large-scale and complex recalculation for the routing policies is avoided. The design principle introduces a ground-breaking approach to space Internet protocol stacks, facilitating robust routing for SFC operations across integrated space and terrestrial networks. Our simulation results verify the feasibility of the proposed GSFC-based VNF orchestration mechanism and reveal the trade-offs in both data and control plane performance.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 2","pages":"537-550"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/JSAC.2025.3528816
Hsin-Tsung Lin;Wei-Cheng Chen;Pi-Chung Wang
Packet classification is a key mechanism that classifies incoming packets into flows to enable software-defined networking as well as a variety of networking services. Currently, ternary content addressable memory (TCAM) has been widely used for high-speed and low-latency packet classification. However, both range expansion and update performance are the fundamental issues for TCAM-based packet classification. A rule containing ranges could be replicated to multiple rules after converting its ranges into prefixes or ternary strings to occupy more than one TCAM entry. Many range encoding algorithms have been proposed to alleviate or avoid the problem of range expansion. These algorithms can be classified into database-independent (DI) and database-dependent (DD). While database-independent algorithms can accommodate new ranges without re-encoding the existing ranges, they may still cause rule replication. In contrast, database-dependent algorithms could avoid rule replication by adaptively encoding ranges, but new ranges may result in updates of the existing ranges. Accordingly, both types of algorithms may multiply the cost of TCAM updates. In this paper, we propose a DD range-encoding algorithm, Longest Enclosure Prefix Range Encoding (LEPRE), which can ensure that any new range does not cause any rule replication and re-encoding of the existing ranges. LEPRE employs the original fields as a part of range encoding to significantly decrease the requirements of extra bits for range encoding. Our experiment results show that LEPRE can maximize the TCAM storage efficiency. LEPRE also fully supports incremental updates to minimize the latency of TCAM updates.
{"title":"LEPRE: An Updatable Database-Dependent Range Encoding Algorithm","authors":"Hsin-Tsung Lin;Wei-Cheng Chen;Pi-Chung Wang","doi":"10.1109/JSAC.2025.3528816","DOIUrl":"10.1109/JSAC.2025.3528816","url":null,"abstract":"Packet classification is a key mechanism that classifies incoming packets into flows to enable software-defined networking as well as a variety of networking services. Currently, ternary content addressable memory (TCAM) has been widely used for high-speed and low-latency packet classification. However, both range expansion and update performance are the fundamental issues for TCAM-based packet classification. A rule containing ranges could be replicated to multiple rules after converting its ranges into prefixes or ternary strings to occupy more than one TCAM entry. Many range encoding algorithms have been proposed to alleviate or avoid the problem of range expansion. These algorithms can be classified into database-independent (DI) and database-dependent (DD). While database-independent algorithms can accommodate new ranges without re-encoding the existing ranges, they may still cause rule replication. In contrast, database-dependent algorithms could avoid rule replication by adaptively encoding ranges, but new ranges may result in updates of the existing ranges. Accordingly, both types of algorithms may multiply the cost of TCAM updates. In this paper, we propose a DD range-encoding algorithm, Longest Enclosure Prefix Range Encoding (LEPRE), which can ensure that any new range does not cause any rule replication and re-encoding of the existing ranges. LEPRE employs the original fields as a part of range encoding to significantly decrease the requirements of extra bits for range encoding. Our experiment results show that LEPRE can maximize the TCAM storage efficiency. LEPRE also fully supports incremental updates to minimize the latency of TCAM updates.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 2","pages":"551-562"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/JSAC.2025.3528813
David Zenati;Tzalik Maimon;Kobi Cohen
In the rapidly evolving landscape of wireless networks, achieving enhanced throughput with low latency for data transmission is crucial for future communication systems. While low complexity OSPF-type solutions have shown effectiveness in lightly-loaded networks, they often falter in the face of increasing congestion. Recent approaches have suggested utilizing backpressure and deep learning techniques for route optimization. However, these approaches face challenges due to their high implementation and computational complexity, surpassing the capabilities of networks with limited hardware devices. A key challenge is developing algorithms that improve throughput and reduce latency while keeping complexity levels compatible with OSPF. In this collaborative research between Ben-Gurion University and Ceragon Networks Ltd., we address this challenge by developing a novel approach, dubbed Regularized Routing Optimization (RRO). The RRO algorithm offers both distributed and centralized implementations with low complexity, making it suitable for integration into 5G and beyond technologies, where no significant changes to the existing protocols are needed. It increases throughput while ensuring latency remains sufficiently low through regularized optimization. We analyze the computational complexity of RRO and prove that it converges with a level of complexity comparable to OSPF. Extensive simulation results across diverse network topologies demonstrate that RRO significantly outperforms existing methods.
{"title":"RRO: A Regularized Routing Optimization Algorithm for Enhanced Throughput and Low Latency With Efficient Complexity","authors":"David Zenati;Tzalik Maimon;Kobi Cohen","doi":"10.1109/JSAC.2025.3528813","DOIUrl":"10.1109/JSAC.2025.3528813","url":null,"abstract":"In the rapidly evolving landscape of wireless networks, achieving enhanced throughput with low latency for data transmission is crucial for future communication systems. While low complexity OSPF-type solutions have shown effectiveness in lightly-loaded networks, they often falter in the face of increasing congestion. Recent approaches have suggested utilizing backpressure and deep learning techniques for route optimization. However, these approaches face challenges due to their high implementation and computational complexity, surpassing the capabilities of networks with limited hardware devices. A key challenge is developing algorithms that improve throughput and reduce latency while keeping complexity levels compatible with OSPF. In this collaborative research between Ben-Gurion University and Ceragon Networks Ltd., we address this challenge by developing a novel approach, dubbed Regularized Routing Optimization (RRO). The RRO algorithm offers both distributed and centralized implementations with low complexity, making it suitable for integration into 5G and beyond technologies, where no significant changes to the existing protocols are needed. It increases throughput while ensuring latency remains sufficiently low through regularized optimization. We analyze the computational complexity of RRO and prove that it converges with a level of complexity comparable to OSPF. Extensive simulation results across diverse network topologies demonstrate that RRO significantly outperforms existing methods.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 2","pages":"437-447"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/JSAC.2025.3528814
Songshi Dou;Zehua Guo
Many new cloud services and applications have emerged recently. They account for a large share of traffic in Wide Area Networks (WANs) and provide traffic with various Quality of Service (QoS) requirements. Software-Defined Wide Area Network (SD-WAN) offers a promising opportunity for improving the performance of these applications with flexible network management. Nevertheless, SD-WANs are managed by controllers, and unpredictable controller failures may degrade flexible network management. Switches previously controlled by the failed controllers become offline, and flows traversing these offline switches lose the path programmability to route flows on available forwarding paths. Thus, these offline flows cannot be routed/rerouted on available paths to accommodate potential traffic variations, leading to severe performance degradation. Traffic Engineering (TE) is a prevalent network application, which aims to enable differentiable QoS for these numerous cloud services and applications. However, TE performance cannot be guaranteed when controller failures happen due to the loss of flexible network management. Existing recovery solutions reassign offline switches to other active controllers to recover the degraded path programmability but may not promise good TE performance since higher path programmability does not necessarily guarantee satisfactory TE performance. In this paper, we propose Ares to provide predictable TE performance under controller failures. We formulate an optimization problem, which aims to maintain predictable TE performance by jointly considering fine-grained flow-controller reassignment and flow rerouting. Given that the proposed problem is proven to be NP-hard, we further propose a heuristic algorithm to efficiently solve this problem. Specifically, when controller failures occur, Ares updates real-time network information with traffic traces and failure status to calculate optimal flow-controller reassignment and flow rerouting policies. Ares then reassigns and reroutes offline flows to maintain predictable TE performance. Extensive simulation results under two real-world topologies with traffic traces demonstrate that our problem formulation exhibits comparable load balancing performance to optimal TE solution without controller failures, and the proposed Ares can significantly improve average load balancing performance by up to 35.79% with low computation time compared with the state-of-the-art solution.
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