Enhancing Radio Access Network Performance over LTE-A for Machine-to-Machine Communications under Massive Access

The expected tremendous growth of machine-to-machine (M2M) devices will require solutions to improve random access channel (RACH) performance. Recent studies have shown that radio access network (RAN) performance is degraded under the high density of devices. In this paper, we propose three methods to enhance RAN performance for M2M communications over the LTE-A standard. The first method employs a different value for the physical RACH configuration index to increase random access opportunities. The second method addresses a heterogeneous network by using a number of picocells to increase resources and offload control traffic from the macro base station. The third method involves aggregation points and addresses their effect on RAN performance. Based on evaluation results, our methods improved RACH performance in terms of the access success probability and average access delay

Enhancing Radio Access Network Performance over LTE-A for Machine-to-Machine Communications under Massive Access

The expected tremendous growth of machine-to-machine (M2M) devices will require solutions to improve random access channel (RACH) performance. Recent studies have shown that radio access network (RAN) performance is degraded under the high density of devices. In this paper, we propose three methods to enhance RAN performance for M2M communications over the LTE-A standard. The first method employs a different value for the physical RACH configuration index to increase random access opportunities. The second method addresses a heterogeneous network by using a number of picocells to increase resources and offload control traffic from the macro base station. The third method involves aggregation points and addresses their effect on RAN performance. Based on evaluation results, our methods improved RACH performance in terms of the access success probability and average access delay

Impact of smart metering data aggregation on distribution system state estimation

Pseudo MV/LV (Medium/Low Voltage) transformer loads are usually used as partial inputs to the Distribution System State Estimation (DSSE) in MV systems. Such pseudo load can be represented by the aggregation of Smart Metering (SM) data. This follows the government restriction that Distribution Network Operators (DNOs) can only use aggregated SM data. Therefore, we assess the subsequent performance of DSSE, which shows the impact of this restriction - it affects the voltage angle estimation significantly. The possibilities for improving the DSSE accuracy under this restriction are further studied. First, two strategies that can potentially relax this restriction’s impact are studied: the correlations among pseudo loads’ errors are taken into account in the DSSE process; a power loss estimation method is proposed. Second, the investments (i.e., either adding measurement devices or increasing the original devices’ accuracy) for the satisfactory DSSE results are assessed. All these are for addressing DNOs’ concerns on this restriction