No abstract

No abstract

Elastic Optical Network (EON) is expected as one of future networks in terms of spectrum flexibility. While Routing and Wavelength Assignment (RWA) problem is one of the key issues in traditional Wavelength Division Multiplexing (WDM) networks, Routing and Spectrum Assignment (RSA) problem has much impact on network performance in EONs. On the other hand, Data-Center (DC) traffic and/or mobile back-haul traffic keep on increasing. To deal with such forthcoming huge capacity of applications, Space Division Multiplexing (SDM) technologies, such as Multi-Core Fiber (MCF) and few-mode fiber, are intensively researched. From network perspective, this paper focuses on Routing, Spectrum and Core Assignment (RSCA) problem for future SDM-EON.Introducing multi-core fibers makes RSA problem more complex because fiber-core dimension is newly expanded. In addition, physical impairment caused by MCF must be taken into account. In this paper, first, the target RSCA problem is divided into the routing and SCA problems, and Kshortest path based pre-computation method is introduced as the routing solution. Next, according to whether MCF has intercore crosstalk or not, we propose SCA methods with crosstalk awareness and with prioritized area concept, respectively. Finally, the paper evaluates the effectiveness of the proposed algorithms compared with representative ones.

Numerous studies have investigated elastic optical networks (EONs) with the aim of expanding the transmission capacities of core networks. To achieve this goal, it is necessary to solve the spectrum resource wastage problem caused by spectrum fragmentation. Moreover, due to the potentially high traffic demands in future networks, it is important to handle requests that need to be reserved immediately (immediate reservation, IR) as well as those that can be reserved in advance (advance reservation, AR). In networks that support the coexistence of IR and AR requests, IR service degradation by AR requests is a challenging issue because AR requests tend to reserve future resources, which causes a lack of current resources to meet IR requests. Therefore, we address the problem of spectrum fragmentation and the service-level control of IR and AR requests by routing and spectrum allocation (RSA). First, we summarize related research into EONs and resource-allocation methods for IR and AR requests. Next, we propose a novel dynamic RSA method to reduce spectrum fragmentation and control the service level of IR and AR requests in terms of bandwidth blocking probability (BBP) in EONs considering the multiplexing effect of spatial channels. Finally, we evaluate the proposed method based on computer simulations and our results demonstrate that the proposed method can improve the BBP for the entire traffic flow by reducing spectrum fragmentation, as well as the service control of AR requests and IR requests under various network conditions.

Large language models (LLMs) have enhanced the capacity of vision-language models to caption visual text. This generative approach to image caption enrichment further makes textual captions more descriptive, improving alignment with the visual context. However, while many studies focus on benefits of generative caption enrichment (GCE), are there any negative side effects? We compare standard-format captions and recent GCE processes from the perspectives of "gender bias" and "hallucination", showing that enriched captions suffer from increased gender bias and hallucination. Furthermore, models trained on these enriched captions amplify gender bias by an average of 30.9% and increase hallucination by 59.5%. This study serves as a caution against the trend of making captions more descriptive.