LoRa Network Planning and Sizing

Posted by: In: Products, Services 06 Jul 2016 Tags: , , , , , , ,

LoRa simulation using ATDI’s RF Solution

General overview of LoRa, and network planning approach in ATDI’s RF engineering tools.

LPWAN (Low-power wide area network)

Low-power WAN (LPWAN) is a wireless wide area network technology that is specialized for interconnecting devices with low-bandwidth connectivity, focusing on range and power efficiency.

It is designed for machine-to-machine (M2M) networking environments. Such network requires decreased power, longer range and lower cost compare to commercial mobile network. The networks can also support more devices over a larger coverage area than consumer mobile technologies and have better bi-directionality.

LPWAN is a key factor for large-scale communication networks typically required in Smart-City applications and Internet of Things, known as IoT. Recently, narrowband and spread-spectrum technologies surfaced as cost-effective candidate technologies to fulfil low throughput and long-range communication.

Bluetooth, ZigBee and Wi-Fi are adequate for consumer-level IoT implementations. The need for a technology such as LPWAN is much greater in industrial IoT, civic and commercial applications. In these environments, the huge numbers of connected devices can only be supported if communications are efficient and power costs low.

The most critical factors in a LPWAN are:

  • Network architecture
  • Communication range
  • Battery lifetime or low power
  • Robustness to interference
  • Network capacity (maximum number of nodes in a network)
  • Network security
  • One-way vs two-way communication
  • Variety of applications served


Local Area Network (LAN) Low Power Wide Area Cellular Network
Short Range Communication Internet of Things Traditional M2M
Bluetooth, WiFi, ZigBee LoRa/NB GSM, 3G, 4G
Well established standard in building Lowe power consumption, Low cost positioning Existing coverage, High data rate
Battery live, Provisioning

Network cost and dependencies

Low data rate,

Emerging standards


Total cost of ownership is high

Lora® and LoraWan™

LoRaWAN™ is a LPWAN specification intended for wireless battery operated Things in regional, national or global network. LoRaWAN target key requirements of internet of things such as secure bi-directional communication, mobility and localization services. This standard has been widely spread in IoT industries with its strong interoperability among smart Things with the benefit of low demand in complex local installations.

LoRa® is the wireless modulation to create long range communication link. It is based on chirp spread spectrum modulation, which maintains the same low power characteristics but significantly increases the communication range.[1]

The main advantage of LoRa® is in its long range capability, which allows a single gateway or base station to cover entire cities or hundreds of square kilometres. LoRa® and LoraWAN™ provides larger link budget compare to other technologies in environment or obstruction of a given location. [2]

LoRa® physical layer

LoRa is a spread-spectrum modulation scheme that that uses wideband linear frequency modulated pulses whose frequency increases or decreases over a certain amount of time to encode information.

The main advantages of this approach are twofold: a substantial increase in receiver sensitivity due to the processing gain of the spread spectrum technique and a high tolerance to TX and RX frequencies misalign.

Network model

LoRa is simply a transmission modulation; the technology can be applied to any network model such as Mesh, P2P and P-MP such as star topology. The network components are the Gateway and Endpoints. The end points can be sensors for different applications.

Key challenges

Regardless whether LoRa or NB wireless system – there are RF limitations inherited from the use-case itself. The End-points (EP) can be deep-indoor, in the basement and even under-ground. Most noticeable challenge is the EP and/or the gateway can be as little as 2m height pushing additional pressure to RF simulators and link-level predictions.

End-Point installation location and variable height requirement

Classical mobile coverage planning has typically addressed outdoor and indoor planning requirements separately by deploying macro-sites, micro-sites and DAS for in-building. Accordingly, statistical propagation models have evolved to assist in area planning. M2M communication networks are P-MP in nature with sensors installed at different floors including basement. These requirements push network planners to adopt 3D digital maps and apply path-specific full deterministic propagation models.

[1] Section 2, Technical Overview of LoRa® and LoRaWAN™, Technical Marketing Workgroup 1.0, Lora Alliance Org. (Nov. 2015)

[2] Section 2, Technical Overview of LoRa® and LoRaWAN™, Technical Marketing Workgroup 1.0, Lora Alliance Org. (Nov. 2015)


Building impact in urban environment

Smart-City means large buildings and skyscrapers. Such environment has been hard to manage due to its traffic density nature and short signal travel distance. Planning GW station location can be a challenge due to vast obstructions in the vicinity of the transmitter leaving network planners completely blind to the overall coverage footprint in such environment. Every candidate location can produce a completely varying coverage. Hence, LoRa network simulation model must support high-resolution 3D building layer and be able to predict outdoor diffraction and indoor signals with high precision.

Link adaptation modelling

LoRa supports multiple spreading factors from 6 to 12. With 12 being the most robust but also the longest in terms of air-time occupancy. Having large number of End-Points with such link mode can completely exhaust the GW resources and hence deliver the wrong network design. Network dimensioning should rely on accurate signal level prediction to work out the SF distribution amongst target End-Points.

Interference calculations and dimensioning

Interference protection is not guaranteed with LoRa targeting unlicensed bands. “Polite transmission protocols” are encouraged for all devices. With LoRa promising many kms of coverage – practical range can be less than what is expected due to current band utilization and overall spectral noise density.

Co-existence with 2G/3G/4G

Lack of international and/or regional harmonization for ISM bands can be a threat to budget receiving devices. Manufacturers have to support US, Europe, AUS and APAC markets. Improving receiver’s selectivity for a specific market is an additional challenge for these devices before they undergo and large scale deployment. For instance, US ISM band on 900 MHz extend to GSM900 in Singapore creating an overlap that can potentially overload small devices even if operating on adjacent band due to poor selectivity and high sensitivity.


Propagation models

Empirical models and their requested tunning

Empirical models model the environment as a series of random variables. These models are the least accurate but require the least information about the environment and use much less processing power to generate predictions. An example of these types of model are the Stanford University Interim (SUI) channel models developed under the Institute of Electrical and Electronic Engineers (IEEE) 802.16 working group. These models are not available on purpose in ICS Telecom: medium resolution cartography can indeed be processed very easily (from SRTM/Landsat data for instance), making this propagation modeling without detailed cartography not accurate enough with regards to the results that could be obtained using other models. Other examples of empirical models is COST-231 Hata model. Although empirical propagation models for mobile systems have been comprehensively validated (mainly macrocell 2G/3G planning, but not for IoT analysis), it has not been established if they are appropriate for M2M systems.

These models are less dependent on the quality of the cartography: they try to re-create the urban environment and the resulting mean path loss using typical inputs such as the distance, the average building height (giving by the clutter file), the average street width…

The cartographic dataset loaded in ICS Telecom will differentiate the signal propagation between downtown Hong Kong or in a medium size French city using a deterministic model, whereas it is the tuning of the empirical model itself that will make the difference. Requiring less cartographic input is a major asset for the empirical models, but their main drawback is the fact they require tuning in order to be accurate. And this model-tuning phase cannot be achieved without accurate measurements, that need to be performed according to the same technology and the same urban environment as the one that will be simulated afterwards.

Deterministic models and planning margins

The deterministic models make use of the laws governing electromagnetic wave propagation to determine the received signal power at a particular location. They require a 3-D map of the propagation environment: the more compatible the accuracy of the cartography with a certain technology to simulate, the better the coverage accuracy (for a given set of technical parameters for the base stations / Terminals / CPEs). Typical examples are the ITU-R 525/526 models, used with appropriate additional propagation effects (diffraction, sub-path attenuation and 3D ray tracing).

Depending on the type of technology to simulate, the receiver can be placed either above the urban clutter codes (Fixed Wireless Access type of networking), or “dug” into the clutter. In this case, attenuation associated to the signal strength received at each pixel will be attenuated based upon the selected diffraction model.

The limitation of statistical models with HR data

Empirical models are used in order to simulate by mathematical terms topographical characteristics that are not available on the cartographic dataset used as a basis for the propagation calculation, such as the average height of the buildings in the area, the width of the streets… All of these are already available in a High Resolution cartographic dataset, making the characteristics of the empirical model redundant with the cartographic dataset itself. The urban environment is described as close to reality as possible, making deterministic models much more efficient in terms of accuracy than empirical models when HR data is used.



Dense urban environment: ATDI uses full set of 3D GIS input and deterministic propagation models which are proved in numerous use cases over three decades and validated its accuracy in field measurements for urban environment. These models are described as path-specific, which is a perfect candidate for P-MP non-mobile scenarios, typically deployed for IoT applications.


ATDI’s LoRa® network planning approach


ATDI adopts a set of full 3D and deterministic propagation models proven in case-studies and validated by field measurements for urban environment. These models are described as path-specific. Unlike classical models such as Hata which is typically used for macro-coverage predictions and street level mobile receivers. Deterministic model such as ITU-R P.525 + Deygout94 is governed by rules of physics and require full descriptive RF environment such as digital terrain model, clutter and buildings.

Path specific models are the perfect candidate for P-MP non-mobile scenarios typically deployed for the IoT.



Building impact prediction

Buildings have two-fold impact; shadowing and absorption. Thanks to our dedicated building layer – the tool is able to distinguish outdoor and indoor receivers and switch accordingly. Signal crossing multiple buildings can undergo diffraction on few buildings and absorption on the last building where the EP is installed. Such effect is only possible with fully-deterministic model and separate building layer model. See Figure 1 for full predictions at every level. Separate clutter classes can be defined for under-ground installations. Also see Figure 2 for building impact on outdoor signal level.


LoRa Gateway reserves SF12 mode for first transmitted packet and worst links to guarantee service initiation and maintain the session for poor RF links. While this is tolerable for first few packets; maintaining the link with higher SF means longer transmission duration, longer queues and poor handling of resources. This will also effect the overall capacity of the serving Gateway. After first transmission, the gateway signals the sensor to switch to better spreading-factors if permissible by the RF link-level. The network design would ideally target lower spreading factors to increase efficiency and to cater for the daily traffic demand. Urban environment, see Figure 2, can be heavily shadowed outdoor with varying signal levels that would require high spreading-factors especially indoor. With most of the sensor nodes being indoor or outdoor in fixed locations; it is of high importance to predict the path-specific link level for every node taking into account all obstacles within the path as described in the section “Deterministic propagation models”.



Link adaptation modeling

LoRa overcome some of the urban challenges by adopting 7 different spreading factors (SF) and 4 coding-rate scheme (see AN1200.22). Varying SF result in different SNR and packet duration. While such scheme has its merits and de-merits – it’s necessary to simulate this behaviour while trialling the network in preparation to the full-scale deployment. Figure 3 and Figure 4 show coverage ranges and adaptive link performance for suburban and urban environments respectively.


Figure 3 shows LoRa spreading-factor adaptation outdoor and indoor in a suburban environment taking into account 3D building layer. The Gateway is installed 5m AGL with End-Points being 3m AGL. Coverage include interference margin and reliability calculations and outdoor to indoor penetration when applicable.



Figure 4 shows LoRa spreading-factor adaptation outdoor and indoor in a heavy urban environment taking into account 3D building layer. The Gateway is installed 5m AGL with End-Points being 3m AGL. Coverage include interference margin and reliability calculations and outdoor to indoor penetration when applicable.


Co-existence analysis

LoRa’s blocking immunity is roughly 82.5dB, 86.5dB and 89dB for 1, 2 and 10 MHz respectively (see sx1272 datasheet). The immunity is measured in reference to LoRa’s receiver sensitivity plus 3dB. The presence of strong near-by transmitter can compromise the performance of the receiver by overloading the RF front. The receiver is not necessarily optimized for the hosting band-plan given the discrepancy in band plan adoption amongst regions and countries.

See calculations below for LoRa 125kHz, SF=12 co-existing with 2G carrier being at +/- 2MHz:

·         Interference threshold due to blocking =  Lora Sensitivity + 3dB  + LoRa_Blocking_Immunity

·         Interference threshold due to blocking =  -137dBm + 3dB  +  86.5

=  -47.5 dBm

With GSM transmitting typically in the order of 100W EIRP with 86dB MCL for urban environment. 3GPP estimates 70dB MCL including antenna gain (4.5.1 3GPP TR 36.942). Hence GSM power received is (50dBm – 86 dB) = -36dBm.

Additional protection required =   -36dBm – (-47.5dB) = 11.5dB


More information


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