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A technique to compute the Cumulative Distribution Function (CDF) of the Signal-to-Interference-plus-Noise-Ratio (SINR) for a wireless link with a multi-antenna, Linear, Minimum-Mean-Square-Error (MMSE) receiver in the presence of interferers distributed according to a non-homogenous Poisson point process on the plane, and independent Rayleigh fading between antennas is presented. This technique is used to compute the CDF of the SINR for several different models of intensity functions, in particular, power-law intensity functions, circularsymmetric Gaussian intensity functions and intensity functions described by a polynomial in a bounded domain. Additionally it is shown that if the number of receiver antennas is scaled linearly with the intensity function, the SINR converges in probability to a limit determined by the "shape" of the underlying intensity function. This work generalizes known results for homogenous Poisson networks to non-homogenous Poisson networks.

Indoor localization technology, which can provide the location information of the target object or stochastic things, is becoming essential requirement for many applications and services such as Internet-of-Things (IoT), real-time control in the development of Fifth-generation (5G) technology. Leaky coaxial cable which can be used as antennas is able to detect the location of the user in a simple way due to its potential property. In this paper, we proposes a simple method to improve the localization accuracy of 2-D indoor localization using multiple LCX cables. In addition, we also evaluate the channel capacity loss due to the localization error of the LCX-MIMO using our proposed method.

A technique is presented to characterize the Signalto-Interference-plus-Noise Ratio (SINR) on a wireless link with a multi-antenna linear Minimum-Mean-Square Error (MMSE) receiver in the presence of spatially clustered interferers. The interferers could be distributed as either a single, randomly located cluster or a Poisson cluster process. Explicit expressions are provided for both the Thomas and Matern cluster processes of interferers which can be evaluated numerically using standard numerical integration tools. These results generalize previously derived results for non-homogenous Poisson networks (where the spatial non-homogeneity is deterministic) to networks where the non-homogeneity is random.

Leaky coaxial (LCX) cable has been employed as antennas for wireless traffic over many linear-cell scenarios such as railway station, tunnels and shopping malls. In addition, LCX can be used for user localization and wireless power transfer (WPT). Compared with the equal power allocation method, the power allocation method for LCX system using positional information (PI) can improve its capacity with the same level of computational complexity. In this paper, we will investigate the level of capacity loss on the 2.4 GHz and 5 GHz band for the conventional equal power (EP) allocation method, the water-filling (WF) based power allocation, and our proposed low-complexity power allocation method for LCX system with PI. The results show that LCX system with our proposed method using PI can reduce the capacity loss due to localization error than that of others.

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A technique is presented to characterize the Signal-to-Interference-plus-Noise Ratio (SINR) of a representative link with a multiantenna linear Minimum-Mean-Square-Error receiver in a wireless network with transmitting nodes distributed according to a doubly stochastic process, which is a generalization of the Poisson point process. The cumulative distribution function of the SINR of the representative link is derived assuming independent Rayleigh fading between antennas. Several representative spatial node distributions are considered, including networks with both deterministic and random clusters, strip networks (used to model roadways, e.g.), hard-core networks and networks with generalized path-loss models. In addition, it is shown that if the number of antennas at the representative receiver is increased linearly with the nominal node density, the signal-to-interference ratio converges in distribution to a random variable that is non-zero in general, and a positive constant in certain cases. This result indicates that to the extent that the system assumptions hold, it is possible to scale such networks by increasing the number of receiver antennas linearly with the node density. The results presented here are useful in characterizing the performance of multiantenna wireless networks in more general network models than what is currently available.

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