Geometric path-loss (GPL) in the radio propagation channel dominantly decides the signal strength and hence yields a significant and multi-faceted influence on wireless communications and networking. In this paper, the GPL is reported to be (truncated) Pareto distributed in wireless random networks, which applies to both of the two considered cases: locally random network (LRN) and homogeneous random network (HRN). Note that most literature assume (infinite) HRNs for scenarios of the fourth or earlier generation wireless communication where most base stations (BSs) have the omni-directional coverage. This might easily be violated when there exist node clusters and, more importantly, for 5G or beyond scenarios with mmWave or THz communication that are more narrow directional on the beamforming and hence coverage. However, the node clusters can reasonably be modelled by the LRNs, thus solving the above issue. To cope with that, the LRN is considered to be more generic and hence easily reduces to the HRN. Two applications on the path-loss exponent (PLE) self-estimation and the expected interference are also presented to demonstrate the significance of this contribution. The findings in this paper might cast light upon the future of random network analysis and designing new kinds of techniques.

In this letter, we address the differential received signal strength based problem with model parameter errors. To deal with the model parameter errors, we adopt the robust weighted least squares criterion, leading to a minimax optimization problem. By assuming the model parameter errors lie in a ball, the minimax problem is transformed into a tractable reformulation via the S-Lemma. Confronted with the nonconvexity of the reformulated problem, we approximately solve it by applying the semidefinite relaxation. The proposed approach only requires the knowledge of the upper bounds of the model parameter errors, which are practically easy to acquire. Simulation results show that the proposed method is robust to the model parameter errors and outperforms the existing methods.

The path-loss exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction. In this paper, we are the first to introduce two directional maximum likelihood (ML) self-estimators for the PLE in wireless networks. They can individually estimate the PLE in any direction merely by locally collecting the related received signal strength (RSS) measurements. The corresponding Cramér-Rao lower bound (CRLB) is also obtained. Simulation results show that the performance of the proposed estimators is very close to the CRLB. Additionally, also for the first time, the RSSs based on only a geometric path loss are found to follow a truncated Pareto distribution in wireless random networks. This might be of great help in the analysis of wireless communications and networking.

Since the global positioning system (GPS) is not applicable underwater, source localization using wireless sensor networks (WSNs) is gaining popularity in oceanographic applications. Unlike terrestrial WSNs (TWSNs) which uses electromagnetic signaling, underwater WSNs (UWSNs) require underwater acoustic (UWA) signaling. Received signal strength (RSS)-based source localization is considered in this paper due to its practical simplicity and the constraint of low-cost sensor devices, but this area received little attention so far because of the complicated UWA transmission loss (TL) phenomena. In this paper, we address this issue and propose two novel semidefinite programming (SDP) approaches which can be solved more efficiently. The numerical results validate our proposed SDP solvers in underwater environments, and indicate that the placement of the anchor nodes influences the RSS-based localization accuracy similarly as in the terrestrial counterpart. We also highlight that adopting traditional terrestrial RSS-based localization methods will fail in underwater scenarios.