2D time-frequency interference modelling using stochastic geometry for performance evaluation in Low-Power Wide-Area Networks

Abstract

Abstract-In wireless networks, interferences between transmissions are modelled either in time or frequency domain. In this article, we jointly analyze interferences in the timefrequency domain using a stochastic geometry model assuming the total time-frequency resources to be a two-dimensional plane and transmissions from Internet of Things (IoT) devices timefrequency patterns on this plane. To evaluate the interference, we quantify the overlap between the information packets: provided that the overlap is not too strong, the packets are not necessarily lost due to capture effect. This flexible model can be used for multiple medium access scenarios and is especially adapted to the random time-frequency access schemes used in Low-Power Wide-Area Networks (LPWANs). By characterizing the outage probability and throughput, our approach permits to evaluate the performance of two representative LPWA technologies Sigfox R and LoRaWAN R .

A Thorough Study of LoRaWAN Performance Under Different Parameter Settings

“…To obtain more general results, [11] uses a stochastic geometry model to jointly analyze interference in the time and frequency domains. It is observed that when implementing a packet repetition strategy, i.e., transmitting each message multiple times, the failure probability reduces, but clearly the average throughput decreases because of the introduced redundancy.…”

Performance Evaluation and Optimization of LPWA IoT Networks: A Stochastic Geometry Approach

“…In [13], a novel receiver for grantfree radio access IoT networks has been designed, which benefits from oscillator imperfection of cheap IoT devices for contention resolution. In [15], outage probability in grantfree access has been studied by assuming a constant received power from all contending devices, which is not the case in practice regarding the limited transmit-power of IoT devices, as well as lack of channel state information at the deviceside for power control. The success probability in grant-free radio access has been also analyzed in [8,16] by assuming a Poisson point process (PPP) distribution of IoT devices.…”

“…From the battery lifetime analysis in (15), one sees that battery lifetime of devices may decrease in n i and P i because of the potential increase in the energy consumption per reporting period. Furthermore, when reliability of communication is lower than a threshold, increase in n i and P i may decrease the need for listening to the channel for ACK arrival and retransmissions, and hence, increasing n i and P i may increase the battery lifetime.…”

“…Here, we aim at finding the optimized operation point of the network with respect to the battery lifetime. Using the battery lifetime definition in (15), one may define the optimization problem for deriving the optimized operating point of type i IoT devices as follows:…”

Achieving Max-Min Throughput in LoRa Networks

“…In the context of LoRa networks, a mathematical analysis on the uplink coverage is conducted in [9] via stochastic geometry, which yet considers only the strongest interferer and ignores the time dependence of (partially) overlapping packets. Recent works in [10] and [11] consider channel fading, aggregate interference, and accurate packet overlapping, and yet it is difficult to obtain the exact distribution of packet success probability analytically. In summary, a tractable and accurate analytical model is highly needed for the performance evaluation in LoRa networks, which also facilitates further system optimization with low-complexity and low-overhead design.…”