Telecommunications System & Management

The evolution in technology is important only when it is harmonized with our mother nature. So, while developing any technology, environment should be the utmost priority. Wireless Communication is the most emergent, prolific and accepted area of communication field. So far, research efforts focus on Spectrum efficiency, transmission reliability, data rate and services provided to users. However, most of the recent research efforts have disregarded the implication of wireless network’s environmental responsibility, e.g., energy efficiency and environmental impact. Recently, it has been shown that the accumulation of greenhouse gases in the atmosphere is growing more rapidly than initially predicted. This understanding has led to a push towards green wireless communications that strives for improving energy efficiency as well as reducing environmental impact. Reduction of the green house gases produced or caused by the telecommunication sector is referred to as greening of telecommunication. Green telecommunication has many facets. It can be classified broadly in terms of greening of telecommunication networks, green telecommunication equipment manufacture, atmosphere friendly design of telecommunication buildings and safe telecommunication waste disposal. As network equipments have become more IP-based, the energy consumption required has progressively increased. Green wireless communication can be achieved with the use of Green handover, Green codes, Green electronics, Green power amplification systems, Green antennas and Green base transceiver stations using renewable energy sources. This paper includes the various aspects for the development of green wireless communication to preserve the nature.

This paper discusses the modulation recognition for OFDM signals in different Signal to Noise Ratio (SNR) and multipath channels. In this paper, the Mel Frequency Cepstral Coefficients (MFCCs) used for feature extraction and the Support Vector Machine (SVM) as classifier or Artificial Neural Network (ANN). Simulation results indicate that the proposed feature classifier have good performances in different SNR and multipath channels for both recognition rate and CPU time from the Artificial Neural Network (ANN), and the SVM classifier’s generalizing ability proves to be good.