Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks

Conventional cellular wireless networks were designed with the purpose of providing high throughput for the user and high capacity for the service provider, without any provisions of energy efficiency. As a result, these networks have an enormous Carbon footprint. In this paper, we describe the sources of the inefficiencies in such networks. First we present results of the studies on how much Carbon footprint such networks generate. We also discuss how much more mobile traffic is expected to increase so that this Carbon footprint will even increase tremendously more. We then discuss specific sources of inefficiency and potential sources of improvement at the physical layer as well as at higher layers of the communication protocol hierarchy. In particular, considering that most of the energy inefficiency in cellular wireless networks is at the base stations, we discuss multi-tier networks and point to the potential of exploiting mobility patterns in order to use base station energy judiciously. We then investigate potential methods to reduce this inefficiency and quantify their individual contributions. By a consideration of the combination of all potential gains, we conclude that an improvement in energy consumption in cellular wireless networks by two orders of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843

An intelligent genetic algorithm for PAPR reduction in a multi-carrier CDMA wireless system

Abstract— A novel intelligent genetic algorithm (GA), called Minimum Distance guided GA (MDGA) is proposed for peak-average-power ratio (PAPR) reduction based on partial transmit sequence (PTS) scheme in a synchronous Multi-Carrier Code Division Multiple Access (MC-CDMA) system. In contrast to traditional GA, our MDGA starts with a balanced ratio of exploration and exploitation which is maintained throughout the process. It introduces a novel replacement strategy which increases significantly the convergence rate and reduce dramatically computational complexity as compared to the conventional GA. The simulation results demonstrate that, if compared to the PAPR reduction schemes using exhaustive search and traditional GA, our scheme achieves 99.52% and 50+% reduction in computational complexity respectively