This work uses NS3 simulation to study the effect of mobility speed on the performance of three handover algorithms in Long Term Evolution (LTE) Networks. A realistic multi-cell LTE network was set up using NS3 simulation software. Mobility models were used to vary the location of the User Equipment (UE), hence triggering handover events across the network. The performance was measured using Signal Interference Noise Ratio (SINR) and number of completed handovers. Result revealed that at a speed between the ranges of 0 -3 km/h, the Integrative algorithm performed best while at 4 -60km/h, the performance of the A3RSRP algorithm was the best with an average value of 95dB. Also, at an increased speed within the range of 60 -120 km/h, the Integrative algorithm had a slightly better performance than the A3RSRP. However, at a speed above 120 km/h, the integrative algorithm performed best with an SINR of 120dB. In terms of completed handovers, the Integrative algorithm had the least number of completed handovers throughout the entire range of considered speeds. Thus, we establish that mobility speed has a significant effect on the performance of handover algorithms. The Long-Term Evolution (LTE) system was designed with the aim of providing a higher data rates and lower latency under various mobility conditions (Dimou et al., 2009). According to (3GPP TR 25.913), the LTE system is expected to provide mobility support for User Equipment (UE) up to speeds of 500 km/h while maintaining an uninterrupted provision of high data rates and services. Mobility at high speed has always been a challenge in wireless networks and LTE was designed to overcome this challenge. To accomplish this purpose, LTE must minimize delay and packet loss in voice transmission and ensure reliability in data transmission during high-speed scenarios. In lieu of this, optimizing the handover procedure to get the required performance is considered as one important issue in mobile networks (Hämäläinen, 2011). LTE Handover is a process that transfers a UE from one evolved NodeB (eNodeB) to another eNodeB or one sector to another sector within the same eNodeB due to perceived better cell coverage from the target eNodeB (Lin et al, 2011a). This goal is achieved by analyzing a periodic or event triggered downlink received signal strength (RSS) and carrier-tointerference ratio (CIR) measurements from the UEs. The eNodeB then decides based on the received parameters on whether to handover the UE to the neighboring eNodeB or keep the UE connected to it. The decision-making process is controlled by an efficient handover algorithm as it enhances the system capacity and the service quality cost effectiveness. The performance of the LTE handover scheme depends majorly on the handover algorithm in use (Hans et al, 2014). Due to this fact, researchers have channeled efforts at optimizing existing algorithms while some new ones have been developed. Three of the numerous algorithms that have become popular in LTE networks include (i) Power Budget Handover ...