Inter-Network Localization Frameworks for Heterogeneous Networks With Multi-Connectivity

The ubiquity of many different networking options with diversified wireless radio access technologies creates an exciting opportunity to use heterogeneous networks (HetNets) for localization systems. Toward this motivation, in this paper, we propose two different inter-network localization frameworks based on maximum likelihood (ML) estimator for HetNets with multi-connectivity by considering the propagation characteristics of individual network tiers in a realistic manner. The first is inter-network noncooperative (INN) based on the weighted superposition of the location estimates of individual network tiers along with their reliability. The second is inter-network cooperative (INC) grounded on the joint utilization of raw received power measurements from all network tiers. We also derive Cramér-Rao bound (CRB) expressions for both frameworks as a theoretical performance benchmark. Furthermore, we obtain another performance criteria called as spatial quantization error bound to analytically determine the effect of grid search algorithm, which is used for solving the INN and INC ML cost functions, on localization performance. Extensive simulations validate our proposed localization frameworks.