Reconfigurable Optical Add/Drop Multiplexers (ROADMs) are going to change the landscape for future metro optical networks. In this paper, we present the detailed design layouts for next generation metro optical network equipped with the most advanced 3rd generation ROADM modules. Mathematical equations have been developed to design complex network architecture based on traffic demand and the characteristics of network equipments. Our proposed design layout for next generation network alleviates some conventional design concepts that will ultimately reduce the capital- and operational-expenditure for the overall network.
The Agile All-photonic Backbone Network (AAPN) architecture has been proposed by the telecommunication industry as a potential candidate for the ultra high speed Next Generation Optical Network (NGON) architecture. AAPN network structure is composed of adaptive optical core switches and edge routers in an overlaid star physical topology. The AAPN employs fast packet switching architecture for the network traffic, and the packet scheduling is the main part of the AAPN. The objective is to forward the packets to their destination with the lowest drop rate and delay, the bandwidth allocation can be either located at the core node or the edge switch. Two types of scheduling are considered in the AAPN architecture, namely the <i>centralized</i> and the <i>distributed</i> schemes. In the centralized scheme all decisions are made at the core node while in the distributed scheme, they are made at the edge nodes. In this paper, we want to compare both scheduling schemes. We would also like to propose a promising integrated TDM architecture that combines the good attributes of both centralized and distributed scheduling schemes. We shall characterize such architecture by various measures such as delay and loss probabilities.
Vector Quantization is one of the most powerful techniques used for speech and image compression at medium to low bit rates. Frequency Sensitive Competitive Learning algorithm (FSCL) is particularly effective for adaptive vector quantization in image compression systems. This paper presents a compression scheme for grayscale still images, by using this FSCL method. In this paper, we have generated a codebook by using five training images and this codebook is then used to decode two encoded test images. Both SNR and PSNR and certainly the visual quality of the test images that we have achieved are found better as compared to other existing methods.