In the context of the Internet-of-Things (IoT), wireless connectivity of wide spread sensor networks for infrastructure and environment monitoring is one of the essential factors for implementation. Low-power wide-area networks (LPWAN) are cost-effective solutions for such applications. Their high maximum coupling loss makes them also appealing for deep indoor-to-outdoor applications. However, no suitable channel models for these scenarios are available, especially taking the long-term effects of stationary transmitters and receivers into account. Such long-term effects are e.g. generated by the opening or closing of doors. The consideration of these long-term effects are important for LPWAN as many use-cases only require a single successful transmission per day. To develop a suitable channel model we performed extensive long-term measurements in a suburban, a commercial and an industrial area. For these we used up to 50 special transmitters that enabled us to measure the channel characteristics over a period of several weeks with a temporal resolution of five minutes. Using thousands of measurement data we developed a generic indoor-to-outdoor channel model for LPWANs operated in license-exempt frequency bands based on statistical analyses of the obtained data.

Low Power Wide Area Networks (LPWAN) are an interesting technology to provide wireless connectivity to low-power and low-cost devices over large distances in license exempt frequency bands. This goal is mainly achieved by very low payload bit-rates. However, the low rates result in long on-air times that make the LPWAN signals vulnerable to interference from other devices also operating in these frequency bands. This problem is especially relevant for LPWAN base-stations that are typically equipped with highly exposed antennas, resulting in high interferer signal levels. The IEEE 802.15 Interest Group Low Power Wide Area has therefore developed an interference model to analyze the impact of interferer signals on LPWAN transmissions. This paper presents the interference model as well as the underlying approach. Furthermore, it shows simulation results using the developed open source implementation of the interference model. These results highlight the relevance of interference in these networks.

This paper proposes an analytical framework for the prediction of decoding error probabilities in heterogeneous wireless environments, where transmissions from various radio nodes with distinct Poisson-arrival rates and packet lengths populate the channel. Random channel access without feedback is assumed, where partial packet collisions can lead to the loss of packets. The analysis is based on the modeling of the collision length distribution between competing nodes. With recent results from information theory in the finite block length regime, we provide bounds on achievable decoding error probabilities for a given interference scenario. The new framework enables jointly considering inter- and intra-system interference, which is an important aspect in unlicensed radio bands. The framework's applicability to optimize system designs is demonstrated for a typical low-power wide area network scenario. We study the tradeoff between reliability and code rate for a point-to-point link and present achievable throughput regions. The analysis reveals the superior performance of coded time-hopping spread spectrum systems. They reach very low error probability even under strong interference for wide ranges of practically relevant load regions.

This paper studies coverage probabilities of randomly deployed radio nodes in single-destination networks. Such networks are typically operated for data aggregation in machine-to-machine (M2M) communication scenarios. In large-scale deployments, diverse environments usually result in heterogeneous propagation channels. We address the question to which extent an increased medium access can compensate for impaired propagation conditions. Based on superimposed Poisson Point Processes and a strongest interferer approximation, we propose a closed-form solution for the probability of a successful packet transmission under heterogeneous channel conditions. The applicability of the framework is demonstrated for an exemplary network configuration with a given latency constraint. Jointly optimising the medium access probabilities can either increase the network coverage area (e.g. by factor two) or reduce the overall networks power consumption by up to more than 30% in sufficiently dense deployments.

This paper analyzes the reliability and throughput of the telegram-splitting concept in the presence of inter-system and intra-system interference. Using telegram splitting, the data of one telegram is split into multiple sub-packets, which are then transmitted with temporal spacing. As a result, collisions with other telegrams destroy only some sub-packets that can be recovered using forward error correction. Consequently, the reliability of the data transmission in case of inter- and intra-system interference - a significant problem in license-exempt bands - is highly improved. In this paper, probabilities for the resulting telegram error rate of random media access systems using telegram splitting are derived. Furthermore, the achievable throughput is analyzed. The theoretical considerations in addition to simulation results prove the significant gain of the telegram-splitting concept for telemetry systems.