There is a constant need to minimize power consumption at user equipment (UE) of wireless communication devices. In this paper we carry out the total power consumed at the handset for Long Term Evolution (LTE) systems. In uplink transmission, Single Carrier-Frequency Division Multiple Access (SC-FDMA) has been adopted as a transmission technology by 3GPP community people instead of Orthogonal Frequency Division Multiple Access (OFDMA) used for downlink transmission technology. SC-FDMA is categorized into Localized FDMA (LFDMA) and Interleaved FDMA (IFDMA). Here, we present the power analysis of LTE system for uplink scenario. We have taken three power components namely radio, circuit and computation power. The total power consumption has been compared between IFDMA and LFDMA transmission technology at a fixed bit error rate (BER) of 10 −5 . We have studied the effect of varying the number of Resource Blocks (RBs) on the total power consumed. We demonstrate through our simulation that at smaller number of RBs (16, 32) LFDMA performances is better as compared to IFDMA in terms of total power consumption.
In the era of advanced web based applications, energy consumption needs to be analyzed for mobile devices running on batteries. In this paper, we have considered image transfer application from mobile to cloud through LTE network. We took realistic energy consumption model which includes radio energy, circuit energy along with computation energy. We have proposed an algorithm to minimize the total energy consumption per information bit for a specific bit error rate requirement. This algorithm segments an image and compress some of them based on their information entropy. We have found that optimal segmentation is beneficial as opposed to fully uncompressed or fully compressed image. We have compared wavelet and JPEG compression techniques. We have observed that same number of segments to be compressed at relatively smaller distance when required BER improves. We have seen that as the distance between transmitter and receiver increases more number of segments have to be compressed to get the minimum energy consumption. With the optimized algorithm we can save energy up to 28.26% compared to fully compressed or fully uncompressed image.
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