Transceiver Architecture, Link Capacity, and Example Specifications

Author(s):  
Douglas H Morais
Keyword(s):  
2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Yassine Khlifi ◽  
Majid Alotaibi

AbstractOptical label switching is introduced for ensuring fast data transfer, quality of service (QoS) support, and better resource management. However, the important issue is how to optimize resource usage and satisfy traffic constraints for improving network performance and design. This paper proposes a dynamic approach that optimizes the resource in terms of link capacity and FDL (fiber delay line) buffering as well as a wavelength converter. The proposed approach decreases the resources usage and guarantees QoS support for various traffic demands. The optimization strategy consists of two stages: path building and traffic management. The path building assures logical topology making using the cumulative cost of available resource and traffic requirements including unicast and multicast. The traffic management solves the resource formulation problem during the traffic transfer by guaranteeing the required loss and blocking delay. Simulation work is conducted for validating the proposed approach and evaluating its performances and effectiveness. Simulation results show that our proposal minimizes effectively the use of link capacity of lightpath and light-tree. Moreover, our approach optimizes the use of buffering capacity and wavelength converter and guarantees QoS support according to traffic requirements.


Author(s):  
Jonathan Guerin ◽  
Marius Portmann ◽  
Konstanty Bialkowski ◽  
Wee Lum Tan ◽  
Steve Glass

10.5772/9841 ◽  
2010 ◽  
Author(s):  
tefica Mrvelj ◽  
Miro Cvitkovic ◽  
Ivan Markezic
Keyword(s):  

Author(s):  
Dr. D. Chitra ◽  
K. Ilakkiya

This paper considers wireless networks in which various paths are obtainable involving each source and destination. It is allowing each source to tear traffic among all of its existing paths, and it may conquer the lowest achievable number of transmissions per unit time to sustain a prearranged traffic matrix. Traffic bound in contradictory instructions in excess of two wireless hops can utilize the “reverse carpooling” advantage of network coding in order to decrease the number of transmissions used. These call such coded hops “hyper-links.” With the overturn carpooling procedure, longer paths might be cheaper than shorter ones. However, convenient is an irregular situation among sources. The network coding advantage is realized only if there is traffic in both directions of a shared path. This project regard as the problem of routing amid network coding by egotistic agents (the sources) as a potential game and develop a method of state-space extension in which extra agents (the hyper-links) decouple sources’ choices from each other by declaring a hyper-link capacity, allowing sources to split their traffic selfishly in a distributed fashion, and then altering the hyper-link capacity based on user actions. Furthermore, each hyper-link has a scheduling constraint in stipulations of the maximum number of transmissions authorized per unit time. Finally these project show that our two-level control scheme is established and verify our investigative insights by simulation.


2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Nida Nurvira ◽  
Anggun Fitrian Isnawati ◽  
Achmad Rizal Danisya

Increasing user requirements for LTE networks, data traffic from eNodeB to core network is also increases, therefore, the recommended solution for meeting this high data traffic is to use a backhaul network design. Backhaul is the path or network used to connect eNodeB with the core network. In this research, backhaul technology used is wi-fi 802.11ac backhaul and microwave backhaul. In this study begins by collecting existing data, then perform capacity calculations to find out the number of eNodeB needed and to find out the capacity of the backhaul links to be designed, then determine the antenna height to achieve LOS conditions, then calculate the desired performance standards and calculate the backhaul network link budget on microwave and wi-fi technologies. Based on the calculation results in terms of capacity, the total user target is 90,167 users and has a throughput capacity per eNodeB of 61 Mbps. In the link-capacity calculation, the total link capacity is 427 Mbps. From the simulation results that using microwave technology, the average RSL value is -30.90 dBm, the value meets the -57 dBm threshold standard and the value of availability does not meet the standard of 99.999% because the average value obtained is 99.998095%. Whereas for wi-fi technology, the average RSL value is -39.24 dBm and meet the -72 dBm threshold standard, for the average availability value meets 99.999% standard, with a value of 100%. From the results of the two technologies, can be conclude that the wi-fi technology is more suitable for the use of backhaul network design in Ciputat Sub-district.


Author(s):  
Okokpujie Kennedy ◽  
Emmanuel Chukwu ◽  
Olamilekan Shobayo ◽  
Etinosa Noma-Osaghae ◽  
Imhade Okokpujie ◽  
...  

This paper demonstrates the robustness of active queue management techniques to varying load, link capacity and propagation delay in a wireless environment. The performances of four standard   controllers used in Transmission Control Protocol/Active Queue Management (TCP/AQM) systems were compared. The active queue management controllers were the Fixed-Parameter Proportional Integral (PI), Random Early Detection (RED), Self-Tuning Regulator (STR) and the Model Predictive Control (MPC). The robustness of the congestion control algorithm of each technique was documented by simulating the varying conditions using MATLAB® and Simulink® software. From the results obtained, the MPC controller gives the best result in terms of response time and controllability in a wireless network with varying link capacity and propagation delay. Thus, the MPC controller is the best bet when adaptive algorithms are to be employed in a wireless network environment. The MPC controller can also be recommended for heterogeneous networks where the network load cannot be estimated.


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