With the continuously decreasing cost of launching satellites, low-Earth-orbit (LEO) optical satellite networks (OSNs) have emerged as an important research topic. By using laser communication, 10 Gbps laser inter-satellite links (LISLs) have been fully deployed, while 100 Gbps LISLs are rapidly advancing and are expected to become key components of future networks. With the expected growth in satellite-delivered service demand, OSNs will inevitably enter a hybrid stage in which 10 and 100 Gbps LISLs coexist. However, such coexistence may cause traffic bottlenecks of 10 Gbps LISLs and underutilization of 100 Gbps LISLs in LEO OSNs. From a network planning perspective, this paper focuses on developing efficient hybrid 10/100 Gbps LISL planning algorithms. We first propose a uniformly connected 100 Gbps LISL planning algorithm (UC-100GPA) to ensure the widespread deployment of 100 Gbps LISLs across the network. Based on this, a genetic algorithm for 100 Gbps LISL planning optimization (GA-100GPO) is proposed to further determine the sub-optimal deployment ratio of 100 Gbps LISLs in both the intra-orbit plane (intra-OP) and the inter-orbit plane (inter-OP). Simulation results show that UC-100GPA reduces the blocking ratio by 30.32% and 14.88% compared to deployments without 100 Gbps LISLs and with intra-OP 100 Gbps LISLs, respectively. When the traffic load is 800 Erlang, GA-100GPO achieves a blocking ratio of only 0.56% under a sub-optimal deployment ratio of 63.38% intra-OP and 36.62% inter-OP.
{"title":"Hybrid 10/100 Gbps laser inter-satellite link planning in optical satellite networks","authors":"Lihan Zhao;Yongli Zhao;Wei Wang;Kunpeng Zheng;Hua Wang;Zijian Cui;Jie Zhang","doi":"10.1364/JOCN.583119","DOIUrl":"https://doi.org/10.1364/JOCN.583119","url":null,"abstract":"With the continuously decreasing cost of launching satellites, low-Earth-orbit (LEO) optical satellite networks (OSNs) have emerged as an important research topic. By using laser communication, 10 Gbps laser inter-satellite links (LISLs) have been fully deployed, while 100 Gbps LISLs are rapidly advancing and are expected to become key components of future networks. With the expected growth in satellite-delivered service demand, OSNs will inevitably enter a hybrid stage in which 10 and 100 Gbps LISLs coexist. However, such coexistence may cause traffic bottlenecks of 10 Gbps LISLs and underutilization of 100 Gbps LISLs in LEO OSNs. From a network planning perspective, this paper focuses on developing efficient hybrid 10/100 Gbps LISL planning algorithms. We first propose a uniformly connected 100 Gbps LISL planning algorithm (UC-100GPA) to ensure the widespread deployment of 100 Gbps LISLs across the network. Based on this, a genetic algorithm for 100 Gbps LISL planning optimization (GA-100GPO) is proposed to further determine the sub-optimal deployment ratio of 100 Gbps LISLs in both the intra-orbit plane (intra-OP) and the inter-orbit plane (inter-OP). Simulation results show that UC-100GPA reduces the blocking ratio by 30.32% and 14.88% compared to deployments without 100 Gbps LISLs and with intra-OP 100 Gbps LISLs, respectively. When the traffic load is 800 Erlang, GA-100GPO achieves a blocking ratio of only 0.56% under a sub-optimal deployment ratio of 63.38% intra-OP and 36.62% inter-OP.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 3","pages":"263-276"},"PeriodicalIF":4.3,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic routing, modulation, and spectrum assignment (RMSA) in elastic optical networks (EONs) requires joint optimization considering complex physical layer impairments. While deep reinforcement learning (DRL) has shown promise for RMSA, existing methods face two fundamental limitations: (i) rigid distance-adaptive modulation rules that underutilize spectrum resources and (ii) value estimation bias in continuing tasks that prevents convergence to optimal policies. This paper proposes a physical layer-aware DRL framework that addresses both limitations. First, we incorporate reward centering to eliminate value estimation bias in continuing tasks, enabling the agent to distinguish fine-grained policy differences. Second, the framework enables autonomous joint optimization of routing and modulation selection, removing reliance on distance-based rules. Simulations on NSFNET and COST239 demonstrate two key results: (i) reward centering reduces service blocking probability by 16% compared to standard DRL under identical constraints, and (ii) autonomous modulation selection reduces blocking by up to 77% in high-load regimes where distance-adaptive methods saturate at approximately 16%. Physical layer analysis reveals that performance gains are achieved by operating closer to transmission limits, with the average GSNR margin reduced from 7.1 to 2.7 dB.
{"title":"Physical layer-aware deep reinforcement learning with advantage function stabilization for dynamic RMSA in elastic optical networks","authors":"Haojie Wang;Yixin Wang;Yongli Zhao;Jie Zhang","doi":"10.1364/JOCN.577029","DOIUrl":"https://doi.org/10.1364/JOCN.577029","url":null,"abstract":"Dynamic routing, modulation, and spectrum assignment (RMSA) in elastic optical networks (EONs) requires joint optimization considering complex physical layer impairments. While deep reinforcement learning (DRL) has shown promise for RMSA, existing methods face two fundamental limitations: (i) rigid distance-adaptive modulation rules that underutilize spectrum resources and (ii) value estimation bias in continuing tasks that prevents convergence to optimal policies. This paper proposes a physical layer-aware DRL framework that addresses both limitations. First, we incorporate reward centering to eliminate value estimation bias in continuing tasks, enabling the agent to distinguish fine-grained policy differences. Second, the framework enables autonomous joint optimization of routing and modulation selection, removing reliance on distance-based rules. Simulations on NSFNET and COST239 demonstrate two key results: (i) reward centering reduces service blocking probability by 16% compared to standard DRL under identical constraints, and (ii) autonomous modulation selection reduces blocking by up to 77% in high-load regimes where distance-adaptive methods saturate at approximately 16%. Physical layer analysis reveals that performance gains are achieved by operating closer to transmission limits, with the average GSNR margin reduced from 7.1 to 2.7 dB.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 3","pages":"250-262"},"PeriodicalIF":4.3,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Time-division multiplexing passive optical networks (TDM-PONs) are emerging as one of the most promising access network technologies for the industrial Internet due to their advantages of high bandwidth, low cost, and strong resistance to electromagnetic interference. Various applications within the industrial Internet, such as mobile robots and safety emergency stop applications, require TDM-PON to possess deterministic transmission capabilities. In response, a series of solutions, such as cooperative DBA (Co-DBA) and deterministic DBA (det-DBA), were proposed to enhance the deterministic network transmission capacity of TDM-PON. However, they were limited by focusing on some customized optimization problems but lacked a comprehensive analytical model to evaluate the inherent absolute performance upper bound under worst-case conditions and the practically guaranteed performance upper bound under specific scheduling strategies, which is crucial for describing determinism. To the best of our knowledge, we are the first to apply network calculus in TDM-PON to theoretically evaluate its performance. Our analytical model considers two fundamental bounds: the adversarial transmission window scheduling (A-TWS) bound, which describes the inherent performance bound under the most unfavorable scheduling, and the strategy-specific transmission window scheduling (SS-TWS) bound, which provides achievable performance guarantees under a particular allocation strategy. Based on this, we first established the arrival curve models for traffic flows and the service curve models for three bandwidth allocation schemes. Subsequently, we conducted a performance analysis of the delay and buffer size. Our results quantitatively compare the A-TWS and SS-TWS bounds of each bandwidth allocation scheme, intuitively contrasting the gap between the theoretical worst-case scenario and the practically achievable performance, which contributes to the performance evaluation of TDM-PON.
{"title":"Network-calculus-based analytical model for deterministic TDM-PON","authors":"Lizhu Liu;Chen Su;Zhiqun Gu;Yuefeng Ji;Jiawei Zhang","doi":"10.1364/JOCN.572958","DOIUrl":"https://doi.org/10.1364/JOCN.572958","url":null,"abstract":"Time-division multiplexing passive optical networks (TDM-PONs) are emerging as one of the most promising access network technologies for the industrial Internet due to their advantages of high bandwidth, low cost, and strong resistance to electromagnetic interference. Various applications within the industrial Internet, such as mobile robots and safety emergency stop applications, require TDM-PON to possess deterministic transmission capabilities. In response, a series of solutions, such as cooperative DBA (Co-DBA) and deterministic DBA (det-DBA), were proposed to enhance the deterministic network transmission capacity of TDM-PON. However, they were limited by focusing on some customized optimization problems but lacked a comprehensive analytical model to evaluate the inherent absolute performance upper bound under worst-case conditions and the practically guaranteed performance upper bound under specific scheduling strategies, which is crucial for describing determinism. To the best of our knowledge, we are the first to apply network calculus in TDM-PON to theoretically evaluate its performance. Our analytical model considers two fundamental bounds: the adversarial transmission window scheduling (A-TWS) bound, which describes the inherent performance bound under the most unfavorable scheduling, and the strategy-specific transmission window scheduling (SS-TWS) bound, which provides achievable performance guarantees under a particular allocation strategy. Based on this, we first established the arrival curve models for traffic flows and the service curve models for three bandwidth allocation schemes. Subsequently, we conducted a performance analysis of the delay and buffer size. Our results quantitatively compare the A-TWS and SS-TWS bounds of each bandwidth allocation scheme, intuitively contrasting the gap between the theoretical worst-case scenario and the practically achievable performance, which contributes to the performance evaluation of TDM-PON.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 3","pages":"234-249"},"PeriodicalIF":4.3,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolas Jara;Hermann Pempelfort;Erick Viera;Patricia Morales;Gerardo Rubino;Alejandra Beghelli
ZR+ pluggables have the potential to transform the optical core landscape, paving the way for new, to our knowledge, network services enabled by a reconfigurable infrastructure. However, reconfigurability brings challenges. One of the challenges among them is the fast and accurate planning tools for networks with stochastic demands. In this paper, we introduce SPRINT: a fast and accurate planning engine for ZR+-enabled hop-by-hop IPoWDM networks. By leveraging a novel, to the best of our knowledge, recurrence-based approach, SPRINT can determine the location and number of ZR+ pluggables needed across an IPoWDM network to meet a target quality of service at a very low computational cost. We compared SPRINT to conventional simulation-based dimensioning techniques in real-world network topologies exhibiting different structural, spatial, and spectral features under different traffic loads. Results show SPRINT is up to four orders of magnitude faster while maintaining a high average accuracy of 90%. This speedup is critical when evaluating a number of envisaged scenarios, while accuracy is key to avoid the high cost of overprovisioning. SPRINT supports both uniform and non-uniform dimensioning strategies, providing flexibility in resource allocation and cost reductions in network planning. By addressing the lack of open fast dimensioning tools for hop-by-hop IPoWDM networks, this work contributes to the advancement of the design of next-generation ZR+-based optical networks.
{"title":"SPRINT: a fast planning engine for ZR+-enabled hop-by-hop IPoWDM networks","authors":"Nicolas Jara;Hermann Pempelfort;Erick Viera;Patricia Morales;Gerardo Rubino;Alejandra Beghelli","doi":"10.1364/JOCN.576513","DOIUrl":"https://doi.org/10.1364/JOCN.576513","url":null,"abstract":"ZR+ pluggables have the potential to transform the optical core landscape, paving the way for new, to our knowledge, network services enabled by a reconfigurable infrastructure. However, reconfigurability brings challenges. One of the challenges among them is the fast and accurate planning tools for networks with stochastic demands. In this paper, we introduce SPRINT: a fast and accurate planning engine for ZR+-enabled hop-by-hop IPoWDM networks. By leveraging a novel, to the best of our knowledge, recurrence-based approach, SPRINT can determine the location and number of ZR+ pluggables needed across an IPoWDM network to meet a target quality of service at a very low computational cost. We compared SPRINT to conventional simulation-based dimensioning techniques in real-world network topologies exhibiting different structural, spatial, and spectral features under different traffic loads. Results show SPRINT is up to four orders of magnitude faster while maintaining a high average accuracy of 90%. This speedup is critical when evaluating a number of envisaged scenarios, while accuracy is key to avoid the high cost of overprovisioning. SPRINT supports both uniform and non-uniform dimensioning strategies, providing flexibility in resource allocation and cost reductions in network planning. By addressing the lack of open fast dimensioning tools for hop-by-hop IPoWDM networks, this work contributes to the advancement of the design of next-generation ZR+-based optical networks.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 3","pages":"222-233"},"PeriodicalIF":4.3,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuanjian Zhang;Yongli Zhao;Xiaodan Yan;Jian Yang;Ang Cao;Ruiqi Liu
Recent conflicts underscore the need for robust privacy in cross-border communications over optical satellite networks (OSNs). While optical inter-satellite links (O-ISLs) resist eavesdropping, constellation-level route determinism and beam scheduling increase path observability, enabling traffic correlation. We present AROA, an area-aware, randomized O-ISL anonymization algorithm integrated into an anonymous optical satellite-network (AOSN) architecture for high-value users. AROA combines source-routed paths, multi-hop encryption, and geo-fenced relay randomization to balance privacy and latency, leveraging low-latency, high-throughput O-ISLs while avoiding reliance on local operators. We evaluate AOSN in two national-level confrontation scenarios. Under large-scale, short-term intrusions, AOSN reduces the median privacy-leakage probability by up to 94.62%; under small-scale, long-term intrusions, it keeps the adversary’s 90th-percentile monitored time below 10.2% of the observation window when three satellites are compromised. Experiments on three satellite-computing payloads demonstrate 80 Mbps per channel and ${approx} 15.67,{rm ms}$ per-payload processing latency.
{"title":"AROA: optical-ISL-overlap-aware anonymous path selection in optical satellite networks","authors":"Yuanjian Zhang;Yongli Zhao;Xiaodan Yan;Jian Yang;Ang Cao;Ruiqi Liu","doi":"10.1364/JOCN.578174","DOIUrl":"https://doi.org/10.1364/JOCN.578174","url":null,"abstract":"Recent conflicts underscore the need for robust privacy in cross-border communications over optical satellite networks (OSNs). While optical inter-satellite links (O-ISLs) resist eavesdropping, constellation-level route determinism and beam scheduling increase path observability, enabling traffic correlation. We present AROA, an area-aware, randomized O-ISL anonymization algorithm integrated into an anonymous optical satellite-network (AOSN) architecture for high-value users. AROA combines source-routed paths, multi-hop encryption, and geo-fenced relay randomization to balance privacy and latency, leveraging low-latency, high-throughput O-ISLs while avoiding reliance on local operators. We evaluate AOSN in two national-level confrontation scenarios. Under large-scale, short-term intrusions, AOSN reduces the median privacy-leakage probability by up to 94.62%; under small-scale, long-term intrusions, it keeps the adversary’s 90th-percentile monitored time below 10.2% of the observation window when three satellites are compromised. Experiments on three satellite-computing payloads demonstrate 80 Mbps per channel and <tex>${approx} 15.67,{rm ms}$</tex> per-payload processing latency.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 3","pages":"206-221"},"PeriodicalIF":4.3,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid escalation of Internet traffic and increasingly heterogeneous service demands impose stringent requirements on optical networks for dynamic resource scheduling, efficient spectrum utilization, and automated operation. Elastic optical networks (EONs) are regarded as a promising solution, yet their performance remains constrained by two critical challenges: routing, modulation format, and spectrum allocation (RMSA) and spectrum defragmentation (SD). Existing approaches predominantly focus on optimizing one of these tasks, which may lead to limited adaptability and suboptimal network efficiency. To address this gap, we propose an asynchronous cooperative multi-agent deep reinforcement learning framework, termed MADRL-JRASD, for the joint optimization of RMSA and proactive SD. The framework incorporates an RMSA agent with dynamic allocation capability and an SD agent with autonomous decision-making ability, coordinated through an asynchronous architecture that enables adaptive responses to environmental changes. Invalid-action masking and carefully designed reward functions are further integrated to enhance training stability and convergence. Comprehensive evaluations over three representative topologies demonstrate that MADRL-JRASD reduces the blocking probability by up to 81% compared with RMSA heuristics without SD and achieves an 85% reduction in overhead relative to heuristic algorithms combining RMSA and SD that attain similar blocking performance. Moreover, the sensitivity analysis shows that the SD agent improves spectrum utilization and that multi-agent cooperation enhances global decision coordination, while action masking and reward design jointly strengthen the convergence and efficiency of MADRL-JRASD.
{"title":"Asynchronous cooperative multi-agent deep reinforcement learning for joint RMSA and spectrum defragmentation in optical fiber communication networks","authors":"Xiao Zhang;Qinghua Tian;Xiangjun Xin;Yiqun Pan;Haipeng Yao;Fu Wang;Ze Dong;Xiaolong Pan;Sitong Zhou;Feng Tian;Ran Gao","doi":"10.1364/JOCN.579970","DOIUrl":"https://doi.org/10.1364/JOCN.579970","url":null,"abstract":"The rapid escalation of Internet traffic and increasingly heterogeneous service demands impose stringent requirements on optical networks for dynamic resource scheduling, efficient spectrum utilization, and automated operation. Elastic optical networks (EONs) are regarded as a promising solution, yet their performance remains constrained by two critical challenges: routing, modulation format, and spectrum allocation (RMSA) and spectrum defragmentation (SD). Existing approaches predominantly focus on optimizing one of these tasks, which may lead to limited adaptability and suboptimal network efficiency. To address this gap, we propose an asynchronous cooperative multi-agent deep reinforcement learning framework, termed MADRL-JRASD, for the joint optimization of RMSA and proactive SD. The framework incorporates an RMSA agent with dynamic allocation capability and an SD agent with autonomous decision-making ability, coordinated through an asynchronous architecture that enables adaptive responses to environmental changes. Invalid-action masking and carefully designed reward functions are further integrated to enhance training stability and convergence. Comprehensive evaluations over three representative topologies demonstrate that MADRL-JRASD reduces the blocking probability by up to 81% compared with RMSA heuristics without SD and achieves an 85% reduction in overhead relative to heuristic algorithms combining RMSA and SD that attain similar blocking performance. Moreover, the sensitivity analysis shows that the SD agent improves spectrum utilization and that multi-agent cooperation enhances global decision coordination, while action masking and reward design jointly strengthen the convergence and efficiency of MADRL-JRASD.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 3","pages":"180-194"},"PeriodicalIF":4.3,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in wavelength-selective switch (WSS) technology have significantly enhanced the functionality of reconfigurable optical add/drop multiplexers (ROADMs), particularly in core networks. However, metro and access networks face unique constraints, where cost, space, and energy efficiency are paramount. As one approach, filter-less optical networks have been proposed, in which WSSs are replaced by passive splitters and combiners operating across the entire spectrum band. While this strategy reduces equipment cost and power consumption, the lack of wavelength-selective elements prevents wavelength reuse, leading to increased planning complexity and limited scalability. To address these challenges, we propose a streamlined colorless directionless (CD)-ROADM architecture that employs a single multi-input-port (MIP)-WSS with joint switching. By consolidating ROADM functionality into a single-WSS device, the proposed architecture reduces the equipment cost, physical footprint, power consumption, and amount of intra-node fiber wiring while retaining essential CD-ROADM capabilities. The joint switching mechanism effectively mitigates wavelength contention inside the MIP-WSS, thereby preserving routing flexibility. Moreover, the reduced insertion loss achieved using this architecture extends the transmission reach and enhances network capacity. This paper makes three main contributions. First, we present the operating principle and design of a single MIP-WSS CD-ROADM while emphasizing how joint switching enables efficient resource utilization. Second, we analyze WSS characteristics and review conventional ROADM designs based on single- and multi-input-port WSSs, positioning our work within the broader design landscape. Third, we experimentally show the feasibility of the proposed architecture through a three-degree CD-ROADM implementation using a commercial liquid-crystal-on-silicon-based ${1} times {9}$ WSS configured as a ${4} times {4}$ MIP-WSS. The experimental results confirm that the proposed approach simplifies node architectures while delivering sufficient functionality and performance for deployment.
{"title":"Minimal-hardware colorless-directionless ROADM based on a single multi-input-port-WSS with joint switching","authors":"Masahiko Jinno;Ryunosuke Sasaki;Takuma Izumi;Masahiro Kitada;Takashi Miyamura;Satoru Okamoto;Naoaki Yamanaka","doi":"10.1364/JOCN.580878","DOIUrl":"https://doi.org/10.1364/JOCN.580878","url":null,"abstract":"Recent advances in wavelength-selective switch (WSS) technology have significantly enhanced the functionality of reconfigurable optical add/drop multiplexers (ROADMs), particularly in core networks. However, metro and access networks face unique constraints, where cost, space, and energy efficiency are paramount. As one approach, filter-less optical networks have been proposed, in which WSSs are replaced by passive splitters and combiners operating across the entire spectrum band. While this strategy reduces equipment cost and power consumption, the lack of wavelength-selective elements prevents wavelength reuse, leading to increased planning complexity and limited scalability. To address these challenges, we propose a streamlined colorless directionless (CD)-ROADM architecture that employs a single multi-input-port (MIP)-WSS with joint switching. By consolidating ROADM functionality into a single-WSS device, the proposed architecture reduces the equipment cost, physical footprint, power consumption, and amount of intra-node fiber wiring while retaining essential CD-ROADM capabilities. The joint switching mechanism effectively mitigates wavelength contention inside the MIP-WSS, thereby preserving routing flexibility. Moreover, the reduced insertion loss achieved using this architecture extends the transmission reach and enhances network capacity. This paper makes three main contributions. First, we present the operating principle and design of a single MIP-WSS CD-ROADM while emphasizing how joint switching enables efficient resource utilization. Second, we analyze WSS characteristics and review conventional ROADM designs based on single- and multi-input-port WSSs, positioning our work within the broader design landscape. Third, we experimentally show the feasibility of the proposed architecture through a three-degree CD-ROADM implementation using a commercial liquid-crystal-on-silicon-based <tex>${1} times {9}$</tex> WSS configured as a <tex>${4} times {4}$</tex> MIP-WSS. The experimental results confirm that the proposed approach simplifies node architectures while delivering sufficient functionality and performance for deployment.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 3","pages":"195-205"},"PeriodicalIF":4.3,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laser communication, with advantages in transmission distance, capacity, confidentiality, and anti-interference, has become a key development direction for future satellite communications, especially inter-satellite communications. However, satellite user service demands are unevenly distributed, with service hotspots mostly in the northern hemisphere, leading to traffic aggregation in developed areas. This issue is more prominent in low Earth orbit (LEO) constellations due to the large number of satellites and small ground service coverage. Therefore, effective routing algorithms are needed to alleviate congestion caused by traffic aggregation. In this paper, an advance planning-path conflict avoidance (AP-PCA) algorithm based on topology switching and traffic distribution awareness is proposed to avoid path conflict, considering the impact of dynamic topology switching and the performance difference of satellite–ground links. The influence of other original source–destination node pairs on critical links in the calculation of link weights to avoid conflicts in path selection is also considered. Compared with the three benchmark algorithms, the simulation results verify that our proposed solution reduces the blocking probability of traffic requests while ensuring the transmission delay and bandwidth utilization.
{"title":"Path conflict avoidance scheme based on topology switching and traffic distribution awareness in optical satellite networks","authors":"Xin Li;Zijian Cui;Lin Li;Yongli Zhao;Wei Wang;Jie Zhang","doi":"10.1364/JOCN.578259","DOIUrl":"https://doi.org/10.1364/JOCN.578259","url":null,"abstract":"Laser communication, with advantages in transmission distance, capacity, confidentiality, and anti-interference, has become a key development direction for future satellite communications, especially inter-satellite communications. However, satellite user service demands are unevenly distributed, with service hotspots mostly in the northern hemisphere, leading to traffic aggregation in developed areas. This issue is more prominent in low Earth orbit (LEO) constellations due to the large number of satellites and small ground service coverage. Therefore, effective routing algorithms are needed to alleviate congestion caused by traffic aggregation. In this paper, an advance planning-path conflict avoidance (AP-PCA) algorithm based on topology switching and traffic distribution awareness is proposed to avoid path conflict, considering the impact of dynamic topology switching and the performance difference of satellite–ground links. The influence of other original source–destination node pairs on critical links in the calculation of link weights to avoid conflicts in path selection is also considered. Compared with the three benchmark algorithms, the simulation results verify that our proposed solution reduces the blocking probability of traffic requests while ensuring the transmission delay and bandwidth utilization.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 3","pages":"165-179"},"PeriodicalIF":4.3,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
OFC 2025 invited papers, tutorials, and those with the highest marks were invited to provide an extended paper to JOCN, and this Special Issue brings this remarkable array of papers together. Two of the main themes are passive optical networks (PONs) and the use of AI across virtually all aspects of optical networks. This introduction picks out just some of the highlights in this Special Issue, and the reader is encouraged to explore the entire collection.
{"title":"Introduction to the OFC 2025 Special Issue","authors":"Andrew Lord","doi":"10.1364/JOCN.591160","DOIUrl":"https://doi.org/10.1364/JOCN.591160","url":null,"abstract":"OFC 2025 invited papers, tutorials, and those with the highest marks were invited to provide an extended paper to JOCN, and this Special Issue brings this remarkable array of papers together. Two of the main themes are passive optical networks (PONs) and the use of AI across virtually all aspects of optical networks. This introduction picks out just some of the highlights in this Special Issue, and the reader is encouraged to explore the entire collection.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 2","pages":"OFC1-OFC1"},"PeriodicalIF":4.3,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370435","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing demand for high-bandwidth, zero-trouble services is imposing unprecedented challenges on optical communication networks. Traditional human-centric network management approaches are increasingly inadequate for addressing the complexity, scalability, and reliability requirements of modern optical networks. This tutorial provides a comprehensive overview of the evolution toward autonomous optical networks (AONs), where large language model (LLM)-based artificial intelligence (AI) agents are utilized. We systematically introduce the fundamental concepts and architectural frameworks for AI agent-enabled AONs. Key agentic technologies are examined, including domain adaptation strategies for LLMs, advanced prompting techniques, and the construction of agentic AI systems. Furthermore, we analyze the toolsets that support the operational effectiveness of AI agents in AONs. The monitoring and analytics toolset provides accurate awareness of the network state and predicts future changes. The digital twin (DT) construction toolset enables high-fidelity modeling of optical networks. The intelligent management and control toolset is employed for service provisioning, failure management, and continuous network optimization. By integrating these agentic technologies and toolsets, AI agents can deliver end-to-end autonomous network lifecycle management. Key challenges remain in areas such as reliability, proper utilization of the LLM reasoning capabilities, and cost-effectiveness.
{"title":"AI agent for autonomous optical networks: architectures, technologies, and prospects [Invited Tutorial]","authors":"Yihao Zhang;Qizhi Qiu;Xiaomin Liu;Xiaoshu Yu;Dianxuan Fu;Xingyu Liu;Zihang Wang;Hao Lin;Yuli Chen;Lilin Yi;Weisheng Hu;Qunbi Zhuge","doi":"10.1364/JOCN.576017","DOIUrl":"https://doi.org/10.1364/JOCN.576017","url":null,"abstract":"The growing demand for high-bandwidth, zero-trouble services is imposing unprecedented challenges on optical communication networks. Traditional human-centric network management approaches are increasingly inadequate for addressing the complexity, scalability, and reliability requirements of modern optical networks. This tutorial provides a comprehensive overview of the evolution toward autonomous optical networks (AONs), where large language model (LLM)-based artificial intelligence (AI) agents are utilized. We systematically introduce the fundamental concepts and architectural frameworks for AI agent-enabled AONs. Key agentic technologies are examined, including domain adaptation strategies for LLMs, advanced prompting techniques, and the construction of agentic AI systems. Furthermore, we analyze the toolsets that support the operational effectiveness of AI agents in AONs. The monitoring and analytics toolset provides accurate awareness of the network state and predicts future changes. The digital twin (DT) construction toolset enables high-fidelity modeling of optical networks. The intelligent management and control toolset is employed for service provisioning, failure management, and continuous network optimization. By integrating these agentic technologies and toolsets, AI agents can deliver end-to-end autonomous network lifecycle management. Key challenges remain in areas such as reliability, proper utilization of the LLM reasoning capabilities, and cost-effectiveness.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"18 2","pages":"A159-A178"},"PeriodicalIF":4.3,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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