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Performance Analysis of LoRa and LR-FHSS in 3GPP LEO Satellite and HAPS Channel Scenarios for Remote Area IoT Coverage
Jean Michel de Souza Sant'Ana, Nurul Huda Mahmood, Vinicius Pozzobon Borin
TL;DR
Remote-area IoT coverage is challenging, and this paper compares LoRa and LR-FHSS for LEO satellite and HAPS gateways using 3GPP channel models. HAPS outperforms LEO in the reported comparisons, with lower LoRa spreading factors usable in HAPS scenarios; the paper identifies LoRa as a possible HAPS fit and LR-FHSS as a better LEO option.
Problem
Method
The study compares LoRa and LR-FHSS using 3GPP channel models while analyzing gateway altitude and coverage area.
Source: §V · p. 5Results
HAPS outperforms LEO in all compared cases, and lower LoRa spreading factors can achieve outage probabilities below 10^-1 across much of the HAPS network.
Source: §IV · pp. 3–4Takeaways & Limitations
Given LR-FHSS's complexity and energy cost, the paper suggests LoRa may suit HAPS communication while LR-FHSS appears a better option for LEO.
Source: §V · p. 5
Abstract
from arXiv · showhide
In this paper, we analyze and compare the performance of LoRa and LR-FHSS in a remote area Internet-of-Things (IoT) connectivity scenario using a low-earth orbit (LEO) satellites and high-altitude platform station (HAPS)-mounted gateway. We conducted a link budget analysis to evaluate communication stability to assess the ability of the technologies to support the different distances and behaviors using a 3GPP channel model for non-terrestrial networks. Several parameters are considered, such as altitude, minimum elevation angle as well as the impact of different types of loss, such as shadow and fast fading, ionosphere scintillation loss, for example. Our findings highlight the performance gap between LR-FHSS and LoRa in terms of both connectivity. We also highlight how lower HAPS altitudes can benefit certain LoRa settings compared to LEO satellites. We show that HAPS can enable the use of lower LoRa spreading factors, which improves scalability and energy efficiency, while LR-FHSS and a higher spreading factor LoRa remain the solution for LEO satellites.
I. INTRODUCTION
Remote-area IoT connectivity is a challenge, and satellite links are one proposed way to extend access. HAPS may offer lower-loss, region-specific coverage, while the use of LR-FHSS with HAPS remains unstudied in the cited related work.
I. INTRODUCTION
- Remote-area IoT connectivity is a major challenge, and integrating terrestrial and non-terrestrial networks has been proposed to extend access.Devices may transmit directly or through an aggregator to a LEO satellite.Source: §I · p. 1
- HAPS offer lower path loss and improved line-of-sight relative to LEO satellites, while still covering large areas and allowing targeted regional deployment.The passage gives HAPS altitudes of 8 to 50 km and notes their potential relevance to power-limited devices and emergency coverage.Source: §I · p. 1
- Prior work examined LoRa in HAPS settings, but the cited literature had not studied LR-FHSS in a HAPS scenario.The cited LoRa studies considered path loss and altitude, with one also using a simplified collision scheme for scalability.Source: §I · p. 1
A. Contributions
The study compares LoRa and LR-FHSS uplinks for LEO and HAPS IoT scenarios. It varies gateway distance, elevation angle, and altitude, and uses a shadowing-plus-fading model.
A. Contributions
- The study compares LoRa and LR-FHSS feasibility and performance for LEO and HAPS IoT scenarios across device-to-gateway distances, elevation angles, and altitudes.The analysis uses a shadowing-plus-fading model that accounts for elevation angle.Source: A. Contributions · p. 1
- Unlike the cited earlier LoRa-HAPS studies, the analysis includes elevation-dependent shadowing and fading and evaluates LR-FHSS.The passage contrasts the adopted model with, and identifies the added LR-FHSS analysis.Source: A. Contributions · p. 1
- UAVs are excluded because the adopted model gives their short-distance, strong-line-of-sight links zero outage probability, preventing meaningful metric-based comparisons.Source: A. Contributions · p. 1
B. Organization
The paper proceeds from an overview of LoRa and LR-FHSS to the system model, numerical evaluation, and conclusion. Table I presents parameters for the two technologies.
B. Organization
- Section II overviews LoRa and LR-FHSS.Source: B. Organization · p. 1
- Section III presents the system model and communication assumptions, including the condition for unsuccessful communication.Source: B. Organization · pp. 1–2
- Section IV evaluates numerical results, Section V concludes, and Table I lists LoRa and LR-FHSS parameters.Source: B. Organization · p. 2, Table I · p. 2
II. LORA AND LR-FHSS
The paper considers LoRaWAN uplink communication from devices to a HAPS-mounted gateway and describes the LoRa and LR-FHSS PHY trade-offs. LoRa spreading factors trade data rate against sensitivity, while LR-FHSS adds frequency hopping, replication, and coding.
II. LORA AND LR-FHSS
- The paper considers LoRaWAN uplink communication from a device to a HAPS-mounted gateway, not downlink.Source: §II · p. 2
- LoRa offers six spreading factors, SF7 to SF12: smaller factors increase transmission rate and shorten time-on-air but reduce noise resilience and radio sensitivity.Higher spreading factors are often used for long non-terrestrial links, while smaller factors offer greater Doppler immunity but shorter range.Source: §II · p. 2
- LR-FHSS uses frequency hopping with GMSK, and successful reception requires at least one decoded header plus enough payload fragments to meet its coding rate.The passage specifies 488 Hz physical channels, repeated headers, and payload coding in 50-bit chunks.Source: §II · p. 2
- LR-FHSS provides a link budget slightly higher than LoRa SF12 and adds header and payload diversity, but its longer transmissions may increase energy use and collision probability.The passage notes that energy consumption is not explored in this paper; increasing LR-FHSS header replication or lowering its coding rate also lengthens packet duration.Source: §II · p. 2
III. SYSTEM MODEL
The model relates gateway altitude and minimum elevation angle to the coverage footprint and device-to-gateway geometry. It uses a 3GPP non-terrestrial channel model to estimate received power under specified propagation losses and comparison assumptions.
III. SYSTEM MODEL
- Device-to-gateway distance and elevation angle are modeled using spherical-Earth geometry, with the minimum elevation angle setting the footprint-radius relationship to gateway height.The minimum elevation angle occurs at the edge of the coverage footprint.Source: §III · p. 2
- Received power combines transmit power and antenna gains with free-space path loss, shadowing, clutter, scintillation, and fast-fading losses in the 3GPP non-terrestrial channel model.LoRa and LR-FHSS bandwidths are assumed equivalent for comparison because their difference is less than 10%.Source: §III · pp. 2–3
- Shadowing is modeled as log-normal, with a zero-mean normal distribution in dB and standard deviation of 4 dB for LOS or 8 dB for NLOS; LOS assignment follows Table II probabilities.The provided text identifies fast fading as a loss component but does not specify its model.Source: §III · p. 3
- The channel parameters use S-band values and rural-scenario assumptions, while ionospheric scintillation is included only for LEO, at weak S4 level 0.2, with 9.60 dB loss for LEO and 0 dB for HAPS.The S-band choice is described as closest to the ISM band shown in the 3GPP documents.Source: §III · p. 3
A. Condition for unsuccessful communication
The section measures communication failure as the probability that received power falls below the sensitivity threshold. The threshold varies by LoRa spreading factor but is fixed across LR-FHSS configurations.
A. Condition for unsuccessful communication
- Unsuccessful communication is measured by the probability that received signal power falls below the sensitivity threshold, relating the measure to the technologies’ ability to communicate over long distances.The condition is also affected by gateway altitude and device position relative to the gateway, including elevation angle.Source: A. Condition for unsuccessful… · p. 3
- LoRa’s sensitivity threshold changes with spreading factor, whereas LR-FHSS uses the same threshold for every configuration.Source: A. Condition for unsuccessful… · p. 3
- The probability also depends on gateway altitude and device-to-gateway positioning, including elevation angle.Source: A. Condition for unsuccessful… · p. 3
IV. NUMERICAL RESULTS
The simulations compare LoRa and LR-FHSS across HAPS and LEO scenarios, finding better connectivity for HAPS and a tradeoff between HAPS reliability and coverage footprint. Lower spreading factors work better at lower HAPS altitudes, while LEO performance remains poor even for high spreading factors and LR-FHSS.
IV. NUMERICAL RESULTS
- Across distance-dependent C1 curves, HAPS SF7 and SF8 achieve outage probabilities below 10^-1 over most of the network, whereas LEO performance is much worse.Distance and elevation angle are related; elevation-angle changes alter LOS probability and fading parameters, and scintillation loss is present in the LEO scenario.Source: §IV · p. 3, Figure 2 · p. 4, Figure 3 · p. 4
- At altitude H = 1000 km, outage remains above 10^-1 even for SF12 and LR-FHSS, so neither configuration brings outage below that threshold under this condition.Source: §IV · p. 4
- With a HAPS-sized network radius, HAPS at 30 km outperforms a 500 km LEO satellite in every case, while lower spreading factors perform well only with HAPS.The comparison assumes devices choose an appropriate transmission time for the LEO satellite.Source: §IV · p. 4, Figure 4 · p. 5
- At the network border, lower HAPS altitudes improve reliability for smaller spreading factors but reduce the coverage footprint.At 30 km, SF7 performance falls below 0.1; for performance below 10^-2, SF12 reaches around 20 km altitude.Source: §IV · pp. 4–5, Figure 5 · p. 5
V. CONCLUSIONS
The paper compares LoRa and LR-FHSS for remote-area coverage through LEO satellites and HAPS-mounted gateways using 3GPP channel models. It concludes that LoRa is better suited to HAPS, while LR-FHSS appears better suited to LEO satellite scenarios.
V. CONCLUSIONS
- The study compares LoRa and LR-FHSS for remote-area coverage using LEO satellites and HAPS-mounted gateways, examining altitude and coverage-area effects with 3GPP channel models.The authors expect these parameters to be explored further in research and deployments to enhance reliability and coverage.Source: §V · p. 5
- Lower spreading factors can be employed in HAPS scenarios; LoRa may be better suited there because LR-FHSS brings complexity and energy costs, while LR-FHSS appears better for LEO.Source: §V · p. 5
- Further research and deployments should explore these parameters to enhance reliability and coverage.Source: §V · p. 5