Instrumentation & Measurement Magazine 23-9 - 29

layers and their performance. Likewise, evaluating only the
key higher layer parameters does not provide any useful information to fix possible problems at the vulnerable PHY layer.
Therefore, research activity currently aims to go beyond a single layer measurement approach to establish how and in what
measure the presence of interference and noise can influence
the overall performance of the LoRa wireless link.
To this aim, the authors of this paper propose a cross-layer
approach to assess LoRa wireless technology in presence of
Additive White Gaussian Noise (AWGN). More specifically,
the main goal of the approach is the experimental determination of the correlation between the values that characterize
major PHY layer quantities (e.g., channel power and signalto-interference ratio) and those assumed by key higher layer
parameters (e.g., packet-loss ratio and one-way delay) in the
presence of interference. In particular, the proposed method
allows the packet loss rate (PLR) for different configurations
of the LoRa signal and intentional AWGN levels to be evaluated. The AWGN can emulate interference effects occurring at
the physical (PHY) layer when the interference is due to modulated signals having wide bandwidth and noise-like spectrum,
a typical actual condition of the spectrum LoRa operates
in. Moreover, to reduce experimental effort, the cross-layer
measurements have been carried out according to a Central
Composite Design (CCD) experimental technique.

LoRa Basics
LoRa technology is a wireless transmission technology with
a very low power consumption and is used to transmit small
amounts of data over long distances (up to 15 km). LoRa uses
chirp spread spectrum (CSS) modulation for data transmission, which allows even extremely small signals, hidden in the
noise, to be demodulated by the receiver [6]. In a CCS modulation, the carrier signal is a chirp. Each chirp represents a
symbol and its duration is the symbol time. Data transmission is a chronological sequence of rising and falling chirp
pulse. The frequency bandwidth of the pulse is equivalent
to the spectral bandwidth of the signal. Unlike the direct sequence spread spectrum (DSS), CSS uses chirp pulses instead
of a pseudo-random code sequences for frequency spreading. The spreading factor (SF) is the number of bits encoded
for symbol (ranging from 7 up to 12). As an example, if the SF
is 7, then it is possible to transmit 7 bits over one symbol, 256

Table 1 - Minimum signal-to-noise ratio (SNR)
to be guaranteed for signal demodulation
based on SF values
SF
7

SNR Limit (dB)
-7.5

8

-10

9

-12.5

10

-15

11

-17.5

12

-20

December 2020	

Fig. 1. LoRa PHY Layer.

different patterns; if SF is 8 it is possible to transmit 8 bits, 512
different patterns, and so on, up to a SF equal to 12. The starting frequency of the chirp represents the values assumed by
the symbol. LoRa communication performance can be tuned
by varying several PHY setting parameters: bandwidth
(Bw); spreading factor (SF); coding rate (CR); transmission
power; preamble length; header implicit or explicit; and payload length. Bw can assume three different values: 125; 250;
and 500 kHz [7]. The higher the Bw, the higher the data rate
for transmitting packets but the lower the receiver sensitivity and communication range. The greater the SF, the lower
the signal-to-noise ratio that must be guaranteed for reliable
communications (Table 1). Therefore, as the distance between
transmitter and receiver increases, SF must be increased and
Bw reduced, at the cost of longer packets and hence a higher
energy consumption.
Coding rate (CR) protects the LoRa signal from interference bursts. The larger the CR, the greater the robustness.
However, as for the SF, the larger the CR's value, the longer the
packet and the higher energy. CR can take values such as 4/5,
4/6, 4/7, 4/8. As an example, in Table 2, duration of the packets, expressed in ms, versus Bw and SF, with CR=4/5, implicit
header, cyclic redundancy check (CRC)=1 and payload=10
bytes, are reported [8]. As can be appreciated, each unitary increment of SF corresponds to doubling of the time symbol.

The Cross-Layer Measurement Approach
A cross-layer measurement approach allows an efficient assessment of the communication networks' performance. For
an IoT device, interference can severely degrade the signal integrity at the PHY layer, which in turn can compromise the
performance of the IoT application and cause its battery life to
decrease due to the need for retransmitting the lost packets [9],
[10]. Moreover, retransmitting packets can cause the overrun
of the maximum duty-cycle, defined as the maximum percentage of time during which an end-device can occupy a channel
(value equal to 1% in EU 868 for end-devices). The cross-layer
measurement approach gives the opportunity to experimentally correlate the values that characterize major PHY layer
quantities to those assumed by key higher layer parameters,
thus providing useful information to fix possible problems at
vulnerable PHY layers [11]-[13].
The proposed cross-layer approach can be of great help
both in the design and maintenance stages of a wireless IoT

IEEE Instrumentation & Measurement Magazine	29



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