Data center networks are considered to make use of the computing and storage resources in data centers, which include intra-datacenter and inter-datacenter networks. Both of them will depend on the optical networking due to its advantages, such as low latency, high bandwidth, and low energy consumption. Data center interconnected by flexi-grid optical networks is a promising scenario to allocate spectral resources for applications in a dynamic, tunable and efficient control manner. Due to the high burstiness and high-bandwidth characteristics of the services, optical interconnect in intra-datacenter networks has attracted much attention compared with inter-datacenter network. Many datacenter applications in the environment require lower delay and higher availability with the end-to-end guaranteed quality of service. In this paper, we propose a novel time-aware software defined networking (TaSDN) architecture for OpenFlow-based datacenter optical networks, by introducing a time-aware service scheduling (TaSS) strategy. TaSDN can arrange and accommodate the applications with required QoS considering the time factor, and enhance the responsiveness to quickly provide for datacenter demand. The overall feasibility and efficiency of the proposed architecture are experimentally verified on our testbed with OpenFlow-based intra-datacenter and inter-datacenter optical networks.
We propose a novel TDM-SCFDM-PON architecture which can combine the advantages of conventional TDM-PON and SCFDM modulation. Colourless upstream transmission for the TDM-SCFDM-PON is experimentally demonstrated.
The coverage performance for Digital Television/ Terrestrial Multimedia Broadcasting-Advance system and 3GPP "Further evolved Multimedia Broadcast Multicast Service", long-term evolution-based broadcasting system defined by release 16 (R16) with different working parameters has been evaluated through the field trial in Shenzhen with a focus on high-speed mobility car with reasonably high spectrum efficiency. The trial uses the same transmitter facility of same the RF hardware, at the same frequency and with the same power. Results show that terrain is major deterministic factor for coverage performance for each system and well-designed longer time-interleaver for R16 is indispensable to users' satisfaction under this application scenario.
An OpenFlow-based control plane for elastic lightpath provisioning in Flexi-Grid optical networks has been introduced, based on which a Spectrum Sharing Algorithm (SSA) is proposed for time-varying traffic. Experimental results show its good performance.
This paper presents an accurate fault location method based on deep neural evolution network in optical networks. Experiments indicate that the proposed method improves the accuracy of fault location when confronted with large-scale alarm sets.
In the realm of emergency disaster relief, it is paramount to attain a thorough comprehension of the semantic information associated with the local disaster scene for strategic rescue path planning and immediate rescue operations for affected individuals. Unmanned aerial vehicle (UAV) networks are widely utilized for rapid data collection in the aftermath of disasters due to their flexibility and maneuverability, assisting in rescue decision-making. However, some disasters, such as seismic events and floods have disrupted the initially structured ground shape information, leading to a disparate distribution of data collected by various UAV groups. This exposes traditional semantic segmentation models susceptible to shortcut bias, posing challenges in adapting to semantic segmentation tasks in disaster scenarios. Thus, this paper proposes a bias-compensation augmentation learning based semantic segmentation framework, which substantially enhances the extraction capability of semantic information. Initially, we exploit an artificial augmentation neural network for bias-awareness to determine the relative bias values of the collected image data. Subsequently, considering the limited computing power resources in UAV networks, we present a bias compensation computation offloading strategy to achieve a relatively balanced distribution of semantic information across UAV nodes, optimizing the trade-off between network scheduling efficiency and model accuracy. We conduct reconstruction validation on the FloodNet dataset, and a plethora of experimental results demonstrate that, compared to traditional methods, this approach greatly improves the accuracy of pixel-level semantic segmentation by over 86.5%. Moreover, the average combined processing time is also reduced by over 50%, enhancing the utilization efficiency of limited computational resources.
We demonstrate that the power penalty induced by fiber dispersion can be negligible for DSB OFDM-PON by employing higher order modulation, lower RF carrier and allocating sub-carriers dynamically according to the power penalty.
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