No abstract

No abstract

In heterogeneous networks (HetNets), load balancing among different tiers can be effectively achieved by a biased user association scheme with which each user chooses to associate with one base station (BS) based on the biased received power. In contrast to previous studies where a BS always has packets to transmit, we assume in this paper that incoming packets intended for all the associated users form a queue in the BS. In order to find the delay limit of the network to support realtime service, we focus on the delay optimization problem by properly tuning the biasing factor of each tier. By adopting a thinned Poisson point process (PPP) model to characterize the locations of BSs in the busy state, an explicit expression of the average traffic intensity of each tier is obtained. On that basis, an optimization problem is formulated to minimize a lower bound of the network mean queuing delay. By showing that the optimization problem is convex, the optimal biasing factor of each tier can be obtained numerically. When the mean packet arrival rate of each user is small, a closed-form solution is derived. The simulation results demonstrate that the network queuing performance can be significantly improved by properly tuning the biasing factor. It is further shown that the network mean queuing delay might be improved at the cost of a deterioration of the network signal-to-interference ratio (SIR) coverage, which indicates a performance tradeoff between real-time and non-realtime traffic in HetNets.Index Terms-heterogeneous network, biasing factor, average traffic intensity, network mean queuing delay

Abstract. SINS/GPS integrated navigation requires solving a set of nonlinear equations. In this case, the new method based on wavelet multi-resolution analysis (WMRA) aided adaptive Kalman filter (AKF) for SINS / GPS integration for aircraft navigation are proposed to perform better than the classical. The WMRA is used to compare the SINS and GPS position outputs at different resolution levels. These differences represent, in general, the SINS errors, which are used to correct for the SINS outputs during GPS outages. The proposed scheme combines the estimation capability of AKF and the learning capability of WMRA thus resulting in improved adaptive and estimation performance. The simulations show that good results in SINS/GPS positioning accuracy can be obtained by applying the new method based on WMRA and AKF.

No abstract