The success of the smart grid majorly depends on the advanced communication architectures. An advanced smart grid network should satisfy the future demands of the electric systems in terms of reliability and latency. The latest 4th-generation (4G) wireless technology, the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE), is a promising choice for smart grid wide area networks (WAN), due to its higher data rates, lower latency and larger coverage. However, LTE is not a dedicated technology invented for smart grid, and it does not provide Quality of Service (QoS) guarantee to the smart grid applications. In this paper, we propose an optimal LTE uplink scheduling scheme to provide scheduling timeguarantee at the LTE base station for different class of traffic, with a minimal number of total resource blocks. A lightweight heuristic algorithm is proposed to obtain the optimal allocation of resource blocks for each class of traffic. In the simulation, we compare the proposed optimal scheduling scheme and two existing scheduling schemes, the Large-Metric-First scheduling scheme and the Guaranteed Bit Rate (GBR) /Non-GBR scheduling scheme. The comparison results demonstrate that the proposed optimal scheduling can use less resource blocks to satisfy the scheduling time requirements than the other two existing scheduling schemes.

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Long Term Evolution (LTE) is a promising option for the 4th generation communications because of its higher data rates, lower latency and larger coverage. However, in a multi-cell LTE network, the network performance may be deteriorated by load imbalance. The unbalanced load among multiple cells leads to a higher delay and a higher packet drop rate in the overloaded cell, or an underutilization of resources in the under-loaded cell. In order to solve this problem, we propose a practical load balancing algorithm to find the optimal handover operations between the overloaded cell and possible target cells. The simulation results demonstrate that the proposed algorithm can reduce network overload and increase the network bandwidth efficiency.