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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. [ABSTRACT FROM AUTHOR]

With the recent developments on opening the terahertz (THz) spectrum for experimental purposes by the Federal Communications Commission, transceivers operating in the range of 0.1THz-10THz, which are known as THz bands, will enable ultra-high throughput wireless communications. However, actual implementation of the high-speed and high reliability THz band communication systems should start with providing extensive knowledge in regards to the propagation channel characteristics. Considering the huge bandwidth and the rapid changes in the characteristics of THz wireless channels, ray tracing and one-shot statistical modeling are not adequate to define an accurate channel model. In this work, we propose Gamma mixture based channel modeling for the THz band via the expectation-maximization (EM) algorithm. First, maximum likelihood estimation (MLE) is applied to characterize the Gamma mixture model parameters, and then EM algorithm is used to compute MLEs of the unknown parameters of the measurement data. The accuracy of the proposed model is investigated by using the Weighted relative mean difference (WMRD) error metrics, Kullback-Leibler (KL)-divergence, and Kolmogorov-Smirnov (KS) test to show the difference between the proposed model and the actual probability density functions (PDFs) that are obtained via the designed test environment. To efficiently evaluate the performance of the proposed method in more realistic scenarios, all the analysis is done by examining measurement data from a measurement campaign in the 240 GHz to 300 GHz frequency range, using a well-isolated anechoic chamber. According to WMRD error metrics, KL-divergence, and KS test results, PDFs generated by the mixture of Gamma distributions fit to the actual histogram of the measurement data. It is shown that instead of taking pseudo-average characteristics of sub-bands in the wide band, using the mixture models allows for determining channel parameters more precisely. [ABSTRACT FROM AUTHOR]