Determination of optimized sleep interval for 10 gigabit-passive optical network using learning intelligence

The overall aim of this project is to investigate the application of a machine learning method in finding the optimized length of asleep time interval (TAS) in a cyclic sleep mechanism (CSM). Since past decade, the implementations of CSM in the optical network unit (ONU) to reduce the energy consumption in 10 gigabit-passive optical network (XG-PON) were extensively researched. However, the newest era sees the emergence of various network traffic with stringent demands that require further improvements on the TAS selection. Since conventional methods utilize complex algorithm, this paper presents the employment of an artificial neural network (ANN) to facilitate ONU to determine the optimized TAS values using learning from past experiences. Prior to simulation, theoretical analysis was done using the M/G/1 queueing system. The ANN was than trained and tested for the XG-PON network for optimal TAS decisions. Results have shown that towards higher network load, a decreasing TAS trend was observed from both methods. A wider TAS range was recorded from the ANN network as compared to the theoretical values. Therefore, these findings will benefit the network operators to have a flexibility measure in determining the optimal TAS values at current network conditions

Performance of wimax in radio over fiber gigabit passive optical network architecture

The integration of wireless and optical network is a promising solution to support the growth of traffic demands in future access networks. The integrated network would provide high bandwidth (BW), flexibility, mobility and reliability. To meet the demand of future networks and provide wider service coverage, Gigabit Passive Optical Network (GPON) is chosen as the backbone of wireless distribution networks due to its high network capacity to be combined with WiMAX, today’s most promising wireless network. In this GPON network, a Radio-over-Fiber (RoF) technology as the wireless transmission technique is deployed in the proposed optical-wireless hybrid architecture. This thesis focuses on the design, simulation and analysis works of WiMAX on RoFGPON architecture based on physical and network layer simulation. At the physical level, to investigate the power and noise related measures, the network has been designed and simulated in the OptiSystem. The network was found to perform well at 30km of fiber with Bit Error Rate (BER) that was lower than 10-10. Next, a model was developed at the network layer to analyze the performance of wireless IEEE 802.16 medium access control (MAC) scheme when transmitting in the optical network architecture. The research also addressed the additional fiber delay imposed on existing MAC timing scheme which was done in the Network Simulator-2. Due to the fiber delay, analysis of the throughput, packet losses and end-to-end delay performances showed that throughput degradation was found to be 10% at 30km of fiber. In comparison to mathematical analysis, the network layer simulation can support up to 9000 users simultaneously with 1:32 GPON splitting ratio; which is almost 50% lower than the physical layer capacity due to the effect of real network characteristics such as packet losse

Determination of optimized sleep interval for 10 gigabit-passive optical network using learning intelligence

The overall aim of this project is to investigate the application of a machine learning method in finding the optimized length of asleep time interval (TAS) in a cyclic sleep mechanism (CSM). Since past decade, the implementations of CSM in the optical network unit (ONU) to reduce the energy consumption in 10 gigabit-passive optical network (XG-PON) were extensively researched. However, the newest era sees the emergence of various network traffic with stringent demands that require further improvements on the TAS selection. Since conventional methods utilize complex algorithm, this paper presents the employment of an artificial neural network (ANN) to facilitate ONU to determine the optimized TAS values using learning from past experiences. Prior to simulation, theoretical analysis was done using the M/G/1 queueing system. The ANN was than trained and tested for the XG-PON network for optimal TAS decisions. Results have shown that towards higher network load, a decreasing TAS trend was observed from both methods. A wider TAS range was recorded from the ANN network as compared to the theoretical values. Therefore, these findings will benefit the network operators to have a flexibility measure in determining the optimal TAS values at current network conditions