Long Term Evolution (LTE) a Third Generation Partnership Project (3GPP) is developed for multimedia applications on mobile user equipment with very high data rates of the order 75/300 Mbps and low latency of 10msec. The high data rates are achieved by using SC-FDMA radio access mechanism for uplink communication and OFDM access mechanism for downlink. The performance can be further improved by scheduling the user data in an efficient manner considering channel characteristics as well as its QOS parameters, thereby allocating the resources to maximize the throughput.
The Packet Scheduler helps in handling the LTE data traffic by allocating the resources both in time and frequency dimension. In this paper, we propose a novel scheduling algorithm that allocates maximum resources for the random users depending on their channel SNR condition with main focus on the data flow behavior. This is then compared with the two distinct algorithms that focus mainly on flow level dynamics- Fair Fixed Assignment (FFA) and Maximum added value (MAV) algorithms. It is shown that our algorithm outperforms the other two algorithms in terms of mean flow transfer time.