1. Introduction
Since the emergence of the Internet of Things concept, several communication technologies were proposed to provide various IoT services. LoRaWAN is the one of the LPWA network technologies at present and is considered as the basis of IoT application in many industries [
1]. LoRaWAN provides three different classes for EDs to address the various needs of applications: Class-A, Class-B, and Class-C. Class-A is a default mode which should be implemented in all EDs. When an ED has an uplink packet to transmit, it immediately selects a frequency channel randomly and transmits the packet. Uplink transmission is followed by two short downlink receive windows: window RX1 after the receive delay 1 and window RX2 after the receive delay 2, respectively. When a GW has a downlink packet to transmit, it transmits the packet only through these two windows. The GW transmits the downlink packet preferentially through window RX1 over the same channel as the uplink channel. If the GW fails to transmit through RX1, it can re-transmit the packet through window RX2 over another downlink channel dedicated for the downlink receive window. It is noticed that an ED of Class-A sleeps at all times except uplink and two short downlink receive windows, which provides an ED with the highest energy efficiency among all classes of LoRaWAN.
In LoRaWAN, unlike cellular networks, it is not easy to realize the elaborate access control and QoS support for EDs because of the limited available bandwidth and processing capability. Moreover, as the network size is getting larger, the control of large-scale network becomes difficult. In order to efficiently control the transmission collision of uplink packet originating from numerous uncontrollable EDs in LoRaWAN, it is essential to evaluate the performance of the uplink packet transmission taking into account the characteristics of Class-A of LoRaWAN.
Many research studies on LoRaWAN have been conducted until now. Some studies measured the performance of LoRaWAN ED considering the distance between an ED and a GW or evaluating performance of LoRaWAN system as a function of the number of EDs [
2,
3,
4,
5,
6]. In addition, various efforts have been made to improve the network system capacity. In Reference [
7], considering that the operation of Class-A in LoRaWAN is based on pure-Aloha approach, the author proposed the algorithm to improve the system capacity by employing slotted-Aloha approach in the standard. Analytical models for performance metrics for uplink packet transmission were provided in References [
8,
9]. To overcome the performance degradation of pure Aloha in LoRaWAN as the network size grows, listen before talk medium access strategy was proposed in Reference [
10]. In this paper, we analyze the performance of uplink packet transmission of LoRaWAN Class-A mode using Markov model and evaluate the uplink packet transmission performance in terms of throughput and packet loss probability.
2. LoRaWAN System Model
A typical LoRa physical layer provides configuration parameters, including carrier frequency, spreading factor, bandwidth, and coding rate. Actual packet transmission time in LoRaWAN is determined by spreading factor, bandwidth, and coding rate.
The carrier frequency (CF) is the center frequency that can be programmed in units of 61 Hz between 137 MHz and 1024 MHZ. Spreading Factor (SF) is a ratio between a symbol rate and a chip rate, and has a value ranging from 6 to 12. The number of chips per symbol is defined as . As SF increases, the transmission radius of an ED increases due to the increase of SNR. However, in this case, the packet transmission time also increases; therefore, the energy consumption of the ED increases as a consequence. Bandwidth (BW) is the frequency width of the transmission band and the available bandwidth is 500 kHz, 250 kHz, and 125 kHz in LoRaWAN. As BW increases, the packet transmission rate increases, but the SNR decreases due to an additional noise. LoRaWAN includes a forward error correction (FEC) code that is used for controlling errors in data transmission. Coding rate (CR) of the FEC is the proportion of the useful information and the total data bits and it can be set to either , , , and in LoRaWAN. As CR increases, the information protection function improves, but the transmission time increases due to large amount of redundant bits.
In LoRaWAN, the packet transmission time is denoted by a time-on-air (ToA), which is the sum of the length of the preamble (
) and the length of the actual packet payload (
) [
11], which is given by
Let
,
,
,
, and
be the number of preamble symbols for LoRaWAN, the number of payload bits, the duration of a symbol, the spreading factor, and the coding rate, respectively. Then
and
are calculated by
where
,
, and
is a ceiling function. In (
2), CRC = 1 if cyclic redundancy check functionality is enabled, or CRC = 0 otherwise. IH specifies the presence of PHY header; IH = 1 (0) for implicit (explicit) operation mode. DE indicates the using of data rate optimization; DE = 1 if enabled, or DE = 0 otherwise.
is the symbol rate composed of the BW and the number of chips per symbol, which is calculated by
PL is the number of bytes in the payload, and is calculated as
where
,
,
,
,
,
,
, and
are the MAC header length, the frame header address field length, the frame control field length, the frame counter field length, the optional field’s length, the port identifier length, the frame payload length, and the message integrity code length, respectively [
11]. Here, all parameters except
have fixed value; therefore, (
4) is re-written by
By combining (
1), (
2), (
3), and (
5), we have
where
for
, and
for
. In LoRaWAN, SF is determined by signal to noise ratio (SNR) given by
Here,
is calculated by
where
,
,
,
,
,
, and
are tx output power, tx antenna gain, transmitter losses (coax, connectors), path loss, miscellaneous losses (fading margin, body loss, polarization mismatch), rx antenna gain, and receiver losses, respectively.
We simplify (
8) by combining all general gains and losses as GL, which results in
LoRaWAN uses a log-distance path loss model, which is modeled for inside a building or densely populated areas. In a log-distance path loss model, the path loss,
, is defined [
12] as
where
is the path loss at the reference distance
,
is the path loss exponent, and
is a normal random variable with zero mean, reflecting the attenuation caused by flat fading, and
d is the distance between the ED and the GW, respectively. The noise power
is defined as a function of the bandwidth (BW) and is given by [
13]
and
are set to 14 dBm and 0, respectively, by referring to the LoRa module specification. We use
= 127.41 dB,
= 1 km, and
as in Reference [
12].
Table 1 shows the measured distance between a GW and an ED obtained by combining (
7), (
9), (
10), and (
11), and this table is used to determine the SF of an ED for uplink transmission.
3. Markov Chain Model
In LoRaWAN, time, frequency, and spreading factor are orthogonal factors to each other, so packet collision occurs only when two or more EDs transmit a packet at the same time using the same frequency channel and same spreading factor. Suppose that there are N EDs associated with a gateway. We define as the number of EDs who uses the spreading factor , and the carrier frequency The state of the uplink channel is divided into three states: a success state during when an uplink packet is successfully transmitted, a collision state during when a collision happens, and an idle state in which there are no packet transmission activities. Let be the stochastic process representing the time-slot counter for the uplink packet transmission of an ED with and at slot time t, where S is the number of time slots required to transmit an uplink packet. In this model, a discrete and integer-type time scale is adopted, and the time-slot counter used for the uplink packet transmission decreases at the beginning of each slot time (It is noticed that this discrete time-scale does not directly relates to the system time).
For given
i and
j, we define
as the stochastic process representing the number of EDs with
and
which transmit or try to transmit a uplink packet at time
t. Here, we construct the discrete-time bi-dimensional Markov chain
as depicted in
Figure 1. It is noticed that this Markov chain describes only the success and collision states except idle state in order to simplify the Markov chain model. Hereafter, considering orthogonalities of spreading factor and carrier frequency, we simply denote the above Markov chain as
, omitting the lower index for given
i and
j.
ITU-T describes that many IoT traffic models have a common characteristic of frequent short-packet transmission with a Poisson distribution, based on the assumption that the reporting from EDs are uncorrelated [
14]. Therefore, we approximate the distributions of the length of uplink packet transmission interval using exponential distribution. Let
p be the probability of an ED to try to transmit uplink packet at a time slot. Then,
p is given by
where
is the length of uplink packet transmission interval of a node. In this Markov chain, the one-step transition probabilities are calculated by
Let
be the stationary distribution of
, defined by
for
for
,
. Then, we can express
as
In addition,
for
can be expressed by
Notice that starting in state
will be in state
after S steps because the length of uplink packet transmission interval is extremely larger than the length of
. From the chain regularity, we have
From (
14)–(
16), we have
where
. Thus,
is expressed as a function of the probability of an ED to try to transmit uplink packet at a time slot
p, which is given by
is the probability of the uplink channel state going into success state under the condition that there are only the success and collision states except idle state.
Let
and
be the probability of an ED being in an uplink packet transmission and the probability of an ED being in sleep and RX windows, respectively. Then,
and
are expressed by
Let
and
be the probabilities of the
j-th carrier frequency channel being in idle and busy states in view of EDs with given
, respectively. Then, we have
We call
as the normalized throughput of the channel using the
j-th carrier frequency and spreading factor
i, which is defined as the fraction of time during when the channel is used to transmit packets without collisions. Then, it is calculated by
The packet loss probability
is defined as the percentage of packets lost with respect to packets sent, and then it is calculated by
4. Performance Evaluation
For performance evaluation, we employ the EU (863-870MHz) standard which has been mostly studied. In
Table 2, various transmission options from 1 to 7 used in the EU standard are shown including spreading factor, bandwidth, coding rate, and frame payload size. Combining these parameter sets and (
1) results in ToA values, which are calculated in the rightmost column in this table. It is noted that frame payload size is set to the maximum size available defined in the EU standard. We set
as 30 s, and the length of the receive delay 1 as 1 second. We assume
N EDs associated with a GW. The geographical position of each ED is randomly determined within the maximum transmission radius of a GW. SF of an ED is determined according to SINR of the ED based on the distance between the ED and the fading channel gain.
denotes the set with nodes using the transmission option
i in
Table 2.
denotes the set of all nodes in a network and is expressed as
.
Figure 2 shows the normalized throughput for
and
. Here, the normalized throughput for
is the average throughput of EDs in
which is obtained by the sum of the throughput of all EDs in
divided by the cardinality of
. The result shows that the analysis result coincides with the simulation result. As the number of EDs in a GW increases, traffic load increases accordingly, which results in the increase of the normalized throughput at the initial phase for each region
. However, the normalized throughput decreases gradually after it reaches its peak performance because of increased packet collisions and packet retransmissions. We can see that the peak value of the throughput is reached quickly as the length of ToA increases.
Figure 3 shows the packet loss probability of
and
as a function of
N. As the number of EDs in a GW increases, packet loss probability increases steadily because of increased packet collisions and packet retransmissions. We can see that the packet loss probability increases more rapidly as the length of ToA increases, and the overall packet loss probability
increases as the number of EDs in a GW increases.