Performance evaluation of Frame Slotted.pdf

Performance evaluation of Frame Slotted-ALOHA
with Intra-Frame and Inter-Frame Successive
Interference Cancellation
A. Hernández†, F. Vázquez-Gallego∗, L. Alonso†, and J. Alonso-Zarate∗
∗Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Castelldefels, Barcelona, Spain
{francisco.vazquez, jesus.alonso}@cttc.es
†Universitat Politècnica de Catalunya (UPC), Castelldefels, Barcelona, Spain
ana.cristina.hernandez@estudiant.upc.edu, luisg@tsc.upc.edu
Abstract—Machine-to-Machine (M2M) networks allow end-
devices to communicate without human intervention. Due to
the high density of M2M networks, efficient Medium Access
Control (MAC) protocols are required to manage the access
to the channel. In this paper, we consider an M2M area
network composed of hundreds of end-devices that periodically
transmit data to a gateway. We evaluate the performance of
two Medium Access Control (MAC) protocols based on Frame
Slotted-ALOHA (FSA) with Successive Interference Cancellation
(SIC): Intra-frame SIC-FSA and Inter-frame SIC-FSA. By means
of computer-based simulations, we have compared the delay and
energy performance with respect to conventional FSA. Results
show that the average delay can be reduced in a 78% and
94% by using Intra-frame SIC-FSA and Inter-frame SIC-FSA,
respectively, and the average energy consumed per end-device
is reduced in 18% by using Intra-frame SIC-FSA, while Inter-
frame SIC-FSA decreases the energy consumed per end-device
in a 22%.
Index Terms—Successive Interference Cancellation, Frame
Slotted-ALOHA, M2M networks, data collection.
I. INTRODUCTION
Machine-to-Machine (M2M) networks allow communica-
tions between a huge number of end-devices without human
intervention. In order to reduce the maintenance costs of
M2M networks, it is crucial to prolong the lifetime of the
end-devices. Consequently, energy efficiency is one of the
main challenges in the deployment of M2M networks, and
the communication protocols need to be tailored to minimize
energy consumption. In this paper, we focus on data collection
scenarios where a group of end-devices transmit data to a
gateway periodically upon request. While the traffic load
generated by each end-device may be low, the total number of
end-devices that may attempt to get access simultaneously can
be large. Therefore, efficient Medium Access Control (MAC)
protocols are needed in order to manage the access to the
wireless channel and reduce collisions.
This work was supported by the Research Projects ADVANTAGE
(FP7-MCA-607774), CellFive (TEC2014-60130-P), P2P-SmarTest (H2020-
646469), GEOCOM (TEC2011-27723-C02-01) and by the Generalitat de
Catalunya under Grant 2014 SGR 1551.
Random access protocols are typically used in M2M net-
works because their distributed operation makes them easy
to be implemented in low-cost and simple end-devices. In
fact, many standards used in M2M (e.g., IEEE 802.11, IEEE
802.15.4, or ISO-18000-7) rely on random access protocols
such as Carrier Sense Multiple Access (CSMA), ALOHA, and
Frame Slotted-ALOHA (FSA). Unfortunately, random access
suffers from degraded performance when the number of end-
devices increases or when the traffic load is very high. Since
the end-devices must compete for the channel, the higher the
number of end-devices, the higher the probability of collision,
thus increasing the delay and energy consumption.
Recently, several research works, e.g., [1]–[7], have shown
that collisions can be resolved by using Successive Inter-
ference Cancellation (SIC) techniques that improve the perfor-
mance of ALOHA-based protocols. In order to perform SIC,
each end-device transmits a number of replicas of a data packet
in different slots. A gateway (or coordinator) might potentially
solve collisions if the replicas of the collided packets have been
decoded in different slots.
The works in [1] and [2] proposed ALOHA-based protocols
with SIC named Contention Resolution Diversity Slotted-
ALOHA (CRDSA) and Irregular Repetition Slotted-ALOHA
(IRSA), respectively. Results show that CRDSA and IRSA in-
crease the system throughput in a 20% with respect to slotted-
ALOHA. Similar results were obtained in [3] considering
variants of FSA with SIC in Radio Frequency Identification
(RFID) systems.
The performance enhancement provided by the SIC-FSA
protocol has been evaluated in terms of delay and energy
consumption in M2M networks for data collection [5]. In SIC-
FSA, also referred to as Intra-frame SIC FSA, the coordinator
executes the SIC algorithm within each frame. Results show
that Intra-frame SIC-FSA can reduce the delay and energy
consumption of the gateway in more than 50%, and the energy
consumed per end-device in more than 10%, with respect
to FSA. While the research in [5] considers that each end-
device transmits a fixed number of replicas of every data
packet in each frame, the work in [2] analyzes the throughput
of Intra-frame SIC-FSA using a random number of replicas.

Results show that the throughput is maximized by using
certain probability density functions.
The works in [6] and [7] proposed the Inter-frame SIC
technique on Pseudo-Random ALOHA. In Inter-frame SIC,
the replicas of a data packet which are decoded in a given
frame are used to resolve collisions that were caused by the
other replicas of the same data packet in slots of previous
frames. The works in [6] and [7] analyzed and compared
the benefits of the Inter-frame SIC technique, in terms of
throughput, with respect to Intra-frame SIC. Results show that
Inter-frame SIC slightly improves the throughput with respect
to Intra-frame SIC.
Up to our knowledge, the evaluation of Inter-frame SIC and
Intra-frame SIC in FSA using random numbers of replicas
has never received attention in data collection scenarios. This
is the main motivation of this paper, where we present the
performance evaluation of Intra-frame SIC-FSA and Inter-
frame SIC-FSA, and compare with conventional FSA, in terms
of delay and energy consumed by the coordinator and end-
devices. The remainder of this paper is organized as follows.
In Section II, we describe the system model. In Section III,
we describe the MAC protocols. Section IV is devoted to the
description of the energy and delay models. In Section V, we
evaluate the performance of the protocols through computer-
based simulations. Finally, Section VI concludes the paper.
II. SYSTEM MODEL
We consider a wireless network composed of n end-devices
which periodically transmit data to a coordinator (also referred
to as gateway) in Data Collection Rounds (DCR). Every DCR
starts when the coordinator broadcasts a Request for Data
(RFD) packet. We assume that each end-device has exactly one
data packet to transmit in every DCR. In addition, we assume
that all the end-devices are listening to the channel when the
coordinator transmits a RFD. After decoding a RFD, the end-
devices get synchronized to a common time frame pattern and
contend for the channel according to the rules of the adopted
MAC protocol, as described in Section III.
The RFD packet is followed by a sequence of frames
divided into m slots. The duration of a slot is fixed and equal
to the duration of a data packet. In every frame, the end-
devices choose randomly the slots to transmit its data packet
without performing carrier sense. Consequently, each slot can
be in one of three states: (i) empty, when no end-device has
transmitted in this slot; (ii) success, when only one end-device
has selected this slot to transmit its data packet; and (iii)
failure, when one or more end-devices have transmitted in
this slot but none of the data packets can be decoded by the
coordinator due to collision or channel error. We assume that
all data packets are transmitted without errors induced by the
channel and there is no capture effect. Thus, no data packet
involved in a collision can be decoded by the coordinator. The
inclusion of capture effect and transmission errors constitutes
part of our future work. In addition, we assume that the
coordinator has perfect knowledge of the wireless channel with
each end-device.
At the end of each frame the coordinator sends a Feedback
Packet (FBP) to inform the end-devices about the data packets
that have been successfully decoded. Once an end-device has
succeeded in transmitting its data packet in a given frame, it
switches into sleep mode in order to save energy. However,
the end-devices that have not succeeded will contend again in
subsequent frames. A DCR finishes when the coordinator has
decoded the data packets of all the end-devices.
The coordinator and the end-devices can be in fives modes
of operation: (i) transmitting a packet, (ii) receiving a packet,
(iii) idle listening, (iv) standby, and (v) sleeping. The asso-
ciated power consumptions are ρtx, ρrx, ρσ, ρstby, and ρsleep,
respectively. We assume that ρrx=ρσ. The Inter-Frame Spaces
(IFS) are included in every frame to compensate propagation
delays and the time required by the end-devices to switch
between reception and transmission modes.
III. MEDIUM ACCESS CONTROL PROTOCOLS
In this section, we describe the MAC protocols considered:
FSA, Intra-frame SIC-FSA, and Inter-frame SIC-FSA. In FSA,
each device transmits its data packet in one randomly selected
slot of every frame until it succeeds. In Intra-frame SIC-
FSA and Inter-frame SIC-FSA the i-th end-device transmits
ki replicas of its data packet in ki randomly selected slots of
every frame, where ki ∈ {1, 2, ..., k} is a random number that
follows a given probability density function (PDF), and k is
the maximum number of replicas. The MAC header of each
replica of a data packet includes a pointer to the position of
the other ki − 1 replicas.
A. Frame Slotted-ALOHA with Intra-frame SIC
In Intra-frame SIC-FSA, the coordinator uses the informa-
tion from every successfully decoded replica to cancel the
interference that the remaining replicas of the same data packet
may cause in other slots of the current frame. The coordinator
subtracts the decoded packet to the signal received in the slots
where the replicas have caused collisions. Due to the fact that
every decoded data packet can be used to decode other collided
packets, the SIC process is iterated until the coordinator is
unable to decode more data packets, i.e, there are only empty
slots or slots with collisions that cannot be solved. Figure 1
shows an example of a DCR using Intra-frame SIC-FSA with
m = 6 slots and n = 5 end-devices that can transmit a random
number of replicas with k ∈ {1, 2, 3}. In this example, the
DCR finishes after four SIC iterations within one frame. In
the first iteration of the SIC process, the coordinator decodes
the data packet of end-device 2 in slot 2 (packet 2) and the
data packet transmitted by end-device 4 in slot 5 (packet 4).
In the second iteration, the coordinator subtracts packet 2 to
the signal received in slot 3 and packet 3 is decoded. In the
third iteration, the coordinator decodes one replica of packet
5 by subtracting packets 2, 3 and 4 to the signal received in
slot 4. Finally, the fourth iteration allows the coordinator to
decode packet 1 by subtracting packet 5 to the signal received
in slot 1.

Figure 1. Example of operation of Intra-frame SIC-FSA.
B. Frame Slotted-ALOHA with Inter-Frame SIC
In Inter-frame SIC-FSA, the SIC process is executed by
using the information of a successfully decoded replica in a
given frame to cancel the interference that other replicas of the
same data packet may cause in slots of previous frames. The
Inter-frame SIC process is divided into three sub-processes.
Firstly, the coordinator executes Intra-frame SIC at the end
of frame j. If there are slots with unsolved collisions, the
coordinator subtracts the decoded packets to the collision
signals from previous frames by following the Backward and
Forward SIC algorithms. The parameter s denotes the number
of frames in which Backward and Forward SIC are executed.
Secondly, the coordinator executes the Backward SIC al-
gorithm. In this stage, the packets decoded in frame j are
used to resolve collisions in frame j − 1. Subsequently, the
packets decoded in frame j − 1 by means of Backward SIC
are used to resolve collisions in frame j − 2. This process is
performed iteratively and stops in frame j − s + 1. Therefore,
the coordinator has to store in memory all the signals received
in each collided slot of every s consecutive frames and knows
the position of the replicas of each data packet in every frame.
Thirdly, considering that the Backward SIC algorithm ends
in frame j − 2, i.e., s = 3, the Forward SIC algorithm uses
the information of successfully decoded packets in j − 2 to
resolve collisions in frames j − 1 and j.
Figure 2 shows an example of a DCR using Inter-frame
SIC-FSA with s = 2 frames, m = 6 slots, and n = 6 end-
devices that transmit a fixed number of replicas k = 2. In
frame j − 1, the coordinator decodes packet 4 in slot 4, and
stores the signals of the collided slots in frame j − 1. The
Intra-frame SIC algorithm is executed in frame j −1, but only
packet 4 has been decoded. In frame j, the coordinator decodes
packet 1 in slot 4. Using packet 1 decoded in frame j, the
coordinator performs Backward SIC and consequently solves
the collisions in slots 2 and 3 of frame j − 1, thus decoding
packets 2 and 3. Then, Forward SIC is executed, using packets
2 and 3 decoded in frame j − 1 to solve collisions in frame j
and decode packets 5 and 6, respectively.
Figure 2. Example of operation of Inter-frame SIC-FSA.
IV. DELAY AND ENERGY CONSUMPTION MODELS
In this section, we formulate the delay and energy consump-
tion models of FSA, Intra-frame SIC-FSA and Inter-frame
SIC-FSA. These models will be used to compute the average
delay to finish a DCR, the average energy consumed by the
coordinator and the average energy consumed per end-device.
The average delay required to finish a DCR can be formu-
lated as:
TDCR = CF(mTSLOT + 2TIF S + TF BP ), (1)
where TSLOT , TIF S and TF BP are the duration of a slot, an
IFS and the time required to transmit a FBP, respectively, and
CF is the average number of frames required to complete a
DCR.
The average energy consumed by the coordinator to
successfully receive one data packet from each end-device
during a DCR is denoted by Ecoord. The coordinator executes
the following operations in every frame: (i) it listens to the
channel in the m slots and stores in memory the signals
received in each slot, (ii) it performs a number of SIC
iterations, and (iii) it transmits a FBP. Since we focus on the
energy devoted to communications (i.e., we do not consider
the energy required to execute the SIC process), Ecoord can
be formulated as:
Ecoord = CF(mρrxTSLOT + 2ρσTIF S + ρtxTF BP ), (2)
The average energy consumed per end-device is denoted by
Edevice. An end-device executes the following operations in
every frame as long as it has not succeeded: (i) it transmits ki
replicas of the data packet in ki of the m slots, (ii) it keeps
in standby mode for the remaining m − ki slots, and (iii) it
receives a FBP from the coordinator.

The energy consumed by an end-device in a frame where
it contends can be formulated as
Etx = kiρtxTSLOT + (m − ki)ρstbyTSLOT +
2ρσTIF S + ρrxTF BP ,
(3)
where ki represents the average number of replicas that an
end-device transmits in every frame.
Once an end-device succeeds in transmitting its data packet
in a given frame, it sleeps for the remaining of the DCR. The
energy consumption of an end-device during a frame where it
sleeps can be formulated as
Esleep = ρsleep(mTSLOT + 2TIF S + TF BP ). (4)
Finally, the average energy consumed per end-device can
be formulated as:
Edevice = CFtxEtx + CFsleepEsleep, (5)
where CFtx is the average number of frames where an end-
device contends and CFsleep is the average number of frames
where the end-device sleeps until the DCR ends.
All the parameters in the expressions of TDCR, Ecoord and
Edevice have deterministic values, except for the values of
CF, CFtx and CFsleep which are obtained by computer-based
simulations in the next section.
V. PERFORMANCE EVALUATION
The performance of FSA, Intra-frame SIC-FSA, and Inter-
frame SIC-FSA is evaluated and compared in this section by
means of computer-based simulations using MATLAB. The
results of 1,000 simulation samples have been averaged to
obtain the delay and energy consumption results for each
protocol. The system parameters are summarized in Table 1.
They were selected according to the IEEE 802.11 standard [8]
and from the technical specifications of the RN-131 module
[9], a low-power Wi-Fi device from Roving Networks. The
FBP packet has been set to attach 16 bits per slot that
include the information about the status of each slot and the
identification of the end-devices whose data packet has been
decoded. The payload length of each data packet has been set
to 1024 bytes.
For Intra-frame SIC-FSA and Inter-frame SIC-FSA, we use
different PDFs for the random number of replicas in every
frame. We consider the irregular PDFs proposed in [2] to
improve the performance of the ALOHA-based protocols. The
PDFs are formulated in Equation (6), where each coefficient of
the polynomials represents the probability that an end-device
Table I
SYSTEM PARAMETERS
Parameter Value Parameter Value
MAC header 30 bytes Data-rate 54 Mbps
Data payload 1024 bytes TSLOT 176.74 μs
FBP payload 16 bits/slot TFBP FBP·8/data-rate
CRC 4 bytes TIFS 10 μs
ρtx 210 mA·3V ρsleep 3.4 μW
ρrx = ρσ 40 mA·3V ρstby 15 mA·3V
Figure 3. Average delay using FSA, Intra-frame SIC-FSA, and Inter-frame
SIC-FSA over the maximum number of replicas.
selects a number i of replicas, where i is indicated by the
exponent of each term and the maximum number k of replicas
is defined by the order of the polynomials.
PDF =
⎧
⎪
⎪
⎨
⎪
⎪
⎩
0.5102x2
+ 0.48982x4
, if k = 4
0.5631x2
+ 0.0436x3
+ 0.3933x5
, if k = 5
0.5465x2
+ 0.1623x3
+ 0.2912x6
, if k = 6
0.5x2
+ 0.28x3
+ 0.22x8
, if k = 8
(6)
In the following sections, we evaluate the average delay and
energy consumed by the coordinator and the end-devices as
a function of the maximum number k of replicas and frame
length m, considering the selection of a random and fixed
number of replicas. Finally, we evaluate the delay and energy
consumption as a function of the number n of end-devices.
A. Optimum Number of Slots per Frame and Number of
Replicas
Figure 3, Figure 4 and Figure 5 show the average delay, the
average energy consumed by the coordinator and the average
energy consumed per end-device, respectively. They have been
evaluated as a function of the maximum number k of replicas,
considering n = 100 end-devices, 3 different frame lengths
(m = n, m = 1.5n and m = 2n), and s = 3 for Inter-frame
SIC-FSA.
It is worth to evaluate the performance of Intra-frame
SIC-FSA and Inter-frame SIC-FSA when only one replica is
transmitted, i.e., k = 1. Due to the fact that Intra-frame SIC-
FSA requires more than one replica to resolve collisions in
a frame, it does not improve the performance with respect
to FSA if k = 1. On the contrary, Inter-frame SIC-FSA
outperforms FSA because it can resolve collisions between
consecutive frames, although only one replica is sent.
As it can be observed in Figure 3, if the number of replicas
is fixed, i.e., it is not random, the delay increases when the

Figure 4. Average energy consumed by the coordinator using Intra-frame and
Inter-frame SIC-FSA over the maximum number of replicas.
number of replicas increases for both Intra-frame SIC-FSA and
Inter-frame SIC-FSA. On the contrary, the use of a random
number of replicas keeps the delay almost insensitive to the
maximum number of replicas. This fact can be explained by
computing the average the number of replicas transmitted in
every frame. Results show that the average number of replicas
remains in the range [3,4] , therefore, there is not a significant
variation in delay.
The work in [5] demonstrated that the transmission of a
fixed number of replicas can minimize delay when the frame
length is set to m = 2n and k = 3 for Intra-frame SIC-
FSA. However, Figure 3 shows that the delay using Intra-frame
SIC-FSA can be minimized when the number of replicas is
a random number that follows the PDF in Equation (6) with
k = 4 and m = 1.5n. Under these conditions, Intra-frame
SIC-FSA reduces the delay in a 55% with respect to FSA.
On the contrary, as it can be observed in Figure 3, the use of
a random number of replicas in Inter-frame SIC-FSA does not
reduce the delay with respect to fixed k. The minimum average
delay using in Inter-frame SIC-FSA is reached with a fixed
number of replicas k = 3, with m = 1.5n and s = 3. Under
these conditions, Inter-frame SIC-FSA reduces the delay in a
63% respect to FSA.
As it could be expected from the analytical delay and energy
models, and as it can be observed in Figure 4, the coordinator
energy consumption shows a similar trend to the average delay.
Again, Intra-frame SIC-FSA minimizes the energy consumed
by the coordinator when the number of replicas is random
and follows the PDF with k = 4 and m = 1.5n. Under these
conditions, Intra-frame SIC-FSA reduces the energy consumed
by the coordinator in a 55% with respect to FSA. Inter-frame
SIC-FSA provides up to a 63% of energy saving when the
number of replicas is fixed in k = 3, m = 1.5n and s = 3.
The energy consumed per end-device is represented in Fig-
Figure 5. Average energy consumed per end-device using FSA, Intra-frame
SIC-FSA and Inter-frame SIC-FSA over the maximum number of replicas.
ure 5. As it can be observed, Intra-frame SIC-FSA reaches its
minimum value with a random number of replicas with k = 4
and m = 1.5n . Under these conditions, the energy consumed
per end-device is reduced in a 18% with respect to FSA.
And finally, Inter-frame SIC-FSA improves the performance
of FSA by decreasing the energy consumed per end-device in
a 22% when the number of replicas is fixed with k = 3 and
m = 1.5n.
B. Delay and Energy consumption in Dense M2M Networks
In this section, we analyze the performance of Intra-frame
SIC-FSA and Inter-frame SIC-FSA as a function of the
number n of end-devices. In all cases, we have selected the
Figure 6. Average delay using FSA, Intra-frame SIC-FSA and Inter-frame
SIC-FSA over the number of end-devices.

Figure 7. Average energy consumed by the coordinator using FSA, Intra-
frame SIC-FSA, and Inter-frame SIC-FSA over the number of end-devices.
optimum values of k and m. According to the discussion of the
previous section, this corresponds to: (i) Intra-frame SIC-FSA
with random k = 4 and m = 1.5n; (ii) Inter-frame SIC-FSA
with fixed k = 3, s = 3 and m = 1.5n; and (iii) FSA with
m = n [10].
Figures 6 and 7 show the average delay and the energy
consumed by the coordinator. As it can be observed, the delay
and the energy consumed by the coordinator increase almost
linearly with the number of end-devices. As it could expected,
Intra-frame and Inter-frame SIC-FSA outperform FSA in all
cases. When n < 50, both protocols produce similar results to
FSA. However, when n > 500, Intra-frame SIC-FSA reduces
the average delay and energy consumption of the coordinator
in a 79% with respect to FSA, while Inter-frame SIC-FSA
provides a 94% of delay reduction and energy saving.
Figure 8 shows the energy consumed per end-device as a
function of the number of end-devices involved in the data
collection process. Although both Intra-frame and Inter-frame
SIC-FSA reduce the energy consumption with respect to FSA
when using their optimum configuration, Inter-frame SIC-FSA
outperforms Intra-frame SIC-FSA. When n > 500, Intra-frame
SIC-FSA reduces the energy consumed per end-device in a
18% with respect to FSA, while Inter-frame SIC-FSA reduces
in a 22% the energy consumed per end-device.
VI. CONCLUSION
In this paper, we have evaluated the performance of the
Intra-frame SIC-FSA and Inter-frame SIC-FSA protocols in
data collection scenarios. We have evaluated the average delay
and the average energy consumed by the end-devices and
the coordinator. We have determined the optimum number of
replicas and the optimum number of slots per frame to mini-
mize delay and energy consumption. Considering the optimum
configuration, results show that Intra-frame SIC-FSA reduces
Figure 8. Average energy consumed per end-device using FSA, Intra-frame
SIC-FSA, and Inter-frame SIC-FSA over the number of end-devices.
in more than a 75% the delay and the energy consumed by
the coordinator with respect to FSA, and in a 18% the energy
consumed per end-device. Inter-frame SIC-FSA reduces the
delay and energy consumed by the coordinator in a 94% with
respect to FSA, while the energy consumption per end-device
is reduced in a 22%. Future work aims at evaluating the
performance analytically and at including the capture effect
in the analysis.
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