3 research outputs found
Spatiotemporal Modelling of Multi-Gateway LoRa Networks with Imperfect SF Orthogonality
Meticulous modelling and performance analysis of Low-Power Wide-Area (LPWA)
networks are essential for large scale dense Internet-of-Things (IoT)
deployments. As Long Range (LoRa) is currently one of the most prominent LPWA
technologies, we propose in this paper a stochastic-geometry-based framework to
analyse the uplink transmission performance of a multi-gateway LoRa network
modelled by a Matern Cluster Process (MCP). The proposed model is first to
consider all together the multi-cell topology, imperfect spreading factor (SF)
orthogonality, random start times, and geometric data arrival rates. Accounting
for all of these factors, we initially develop the SF-dependent collision
overlap time function for any start time distribution. Then, we analyse the
Laplace transforms of intra-cluster and inter-cluster interference, and
formulate the uplink transmission success probability. Through simulation
results, we highlight the vulnerability of each SF to interference, illustrate
the impact of parameters such as the network density, and the power allocation
scheme on the network performance. Uniquely, our results shed light on when it
is better to activate adaptive power mechanisms, as we show that an SF-based
power allocation that approximates LoRa ADR, negatively impacts nodes near the
cluster head. Moreover, we show that the interfering SFs degrading the
performance the most depend on the decoding threshold range and the power
allocation scheme.Comment: IEEE Global Communications Conferenc
On the Scalability of Duty-Cycled LoRa Networks with Imperfect SF Orthogonality
This papers uses stochastic geometry and queuing theory to study he scalability of long-range (LoRa) networks, accounting for duty cycling restrictions and imperfect spreading factor (SFs) orthogonality. The scalability is characterised by the joint boundaries of device density and traffic intensity per device. Novel cross-correlation factors are used to quantify imperfect SForthogonality. Our results show that a proper characterisation of LoRa orthogonality extends the scalability of the network. They also highlight that for low/medium densities decreasing the SF extends the spanned spectrum of sensing applications characterised by their traffic requirements (i.e. sensing rate). However, for high density (> 104 nodes/Km2 ), the Pareto frontiers converge to a stability limit governed by the SF allocation scheme and the predefined capture thresholds. The results further evince the importance of capturing threshold distribution among the SFs to mitigate the unfair latency