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We propose a collision recovery scheme for symbol-synchronous slotted ALOHA based on physical layer network coding over extended Galois fields. Information is extracted from colliding bursts allowing to achieve higher maximum throughput with respect to previously proposed collision recovery schemes. An energy efficiency analysis is also performed, and it is shown that by adjusting the transmission probability, high energy efficiency can be achieved. A performance evaluation is carried out using the proposed algorithms, revealing remarkable performance in terms of normalized throughput. [Invited Paper]
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Network-Coded Diversity Protocol for Collision
Recovery in Slotted ALOHA Networks
G. Cocco, N. Alagha, C. Ibarsand S. Cioni
Centre Tecnol`
ogic de Telecomunicacions de Catalunya – CTTC
Parc Mediterrani de la Tecnologia, Av. Carl Friedrich Gauss 7 08860,
Castelldefels – Spain
European Space Agency - ESTEC, Noordwijk – The Netherlands
giuseppe.cocco@cttc.es, nader.alagha@esa.int, christian.ibars@cttc.es,
stefano.cioni@esa.int
Abstract
We propose a collision recovery scheme for symbol-synchronous slotted ALOHA (SA) based on
physical layer network coding over extended Galois fields. Information is extracted from colliding
bursts allowing to achieve higher maximum throughput with respect to previously proposed collision
recovery schemes. An energy efficiency analysis is also performed and it is shown that, by adjusting the
transmission probability, high energy efficiency can be achieved. A performance evaluation is carried out
using the proposed algorithms, revealing remarkable performance in terms of normalized throughput.
Index Terms
Multiple access, MAC, physical-layer network coding, PNC, slotted ALOHA, collision resolution
I. INTRODU CTION
The throughput of Slotted ALOHA (SA) systems is limited by the collisions that take place
when more than one node accesses the channel in the same time slot. This limitation is par-
ticularly problematic in satellite networks with random access, where the long round-trip time
(RTT) greatly limits the use of feedback from the receiver. Techniques like Diversity Slotted
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ALOHA (DSA) [1], in which each packet is transmitted more than once, have been proposed in
order to increase the probability of successful detection. In [2] a novel scheme called network-
assisted diversity multiple access (NDMA), inspired by signal separation principles borrowed
from signal processing, has been presented. In the scheme the collisions are recovered through
successive retransmissions assuming feedback from the receiver. This interprets the received
signals across successive transmission slots as a matrix which is processed in the analog domain
so that the single bursts are recovered if the matrix is full rank. In Contention Resolution Diversity
Slotted ALOHA (CRDSA) [3] the transmissions are organized in frames. The collided signals
are exploited using a successive interference cancelation (SIC) process. In CRDSA each packet
is transmitted twice and uncollided packets are subtracted from slots in which their replicas
are present. An enhanced version of CRDSA, called CRDSA++, has been presented in [4]. In
CRDSA++ more than two copies of each burst (3to 5) can be transmitted and an iterative
interference cancelation is applied, if needed, within a slot. In [5] a packet-level forward error
correction (FEC) code has been applied to CRDSA, while in [6] a convergence analysis and
optimization of CRDSA has been proposed. Another technique that allows to extract information
from colliding signals is physical layer network coding (PNC). PNC was originally proposed to
increase spectral efficiency in two-way relay communication [7] by having the relay decoding
the collision of two signals under the hypothesis of symbol, frequency and phase synchronism. In
[8] a cooperative relaying protocol that leverages on PNC and SIC has been proposed, while in
[9] PNC has been applied in the satellite context for pairwise node communication. In [10] and
[11] it has been proposed to apply PNC to determine the identity of transmitting nodes in case
of ACK collision in multicast networks by using energy detection and ad-hoc coding schemes.
In [12] an overview of the state of the art on PNC has been presented from an information
theoretical point of view. In [13] and [14] PNC has been applied for collision resolution in
multiple access systems with feedback from the receiver, under the assumption of frequency
synchronous transmitters.
In this paper we present a new scheme named Network-Coded Diversity Protocol (NCDP), that
leverages on PNC over extended Galois fields for recovering collisions in symbol-synchronous
SA systems. Once the PNC is applied to decode the collided bursts, the receiver uses common
matrix manipulation techniques over finite fields to recover the original messages, which results
in a high-throughput scheme. The proposed scheme and analysis differ from previous works on
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collision resolutions at both system level and physical level:
Unlike in [2] and [13] we assume that transmissions are organized in frames. We consider
two different setups, one in which feedback from the receiver is not allowed and another
in which feedback is allowed.
We take into account the energy consumption in the design of our solution and evaluate
jointly the spectral and the energy efficiency of the proposed scheme, comparing it with
other collision resolution schemes previously proposed in the literature.
We use extended Galois fields, i.e., GF (2n)with n > 2, instead of GF (2), which is
generally used in PNC. This allows to better exploit the diversity of the system, leading to
an increased spectral efficiency and, depending on the system load, to an increased energy
efficiency. Unlike in [2], in our scheme most of the processing is done using finite field
arithmetics, which reduces the complexity of the system.
We present results relative to implementation issues such as decoding in the presence of
frequency offsets, channel estimation and imperfect symbol synchronism for a generic num-
ber of colliding signals. Up to our knowledge such issues have been previously addressed
only for the case of two transmitters [15], [16], [17].
The rest of the paper is organized as follows. In Section II we present the system model. In
Section III the proposed scheme is described, while a theoretical analysis of its performance is
carried out in Section IV. Section V deals with practical issues such as decoding in presence of
frequency offsets, channel estimation and imperfect symbol synchronization. In Section VI we
present the numerical results, while Section VII contains the conclusions.
II. SYSTEM MODE L
Let us consider the return link (i.e, the link from a user terminal to a satellite or a base
station) of a multiple access system with Mtransmitting terminals, T1, ....., TM, and one receiver
R. Packet arrivals at each transmitter are modeled as a Poisson process with rate G
M, which is
independent from one transmitter to the other. Each packet ui= [ui(1), ...., ui(K)] consists of
Kbinary symbols of information ui(ξ)∈ {0,1}, for ξ= 1,...,K. We assume that, upon
receiving a message, each terminal Tiuses the same linear channel code of fixed rate rcc =K
N
to protect its message ui, obtaining the codeword xi= [xi(1), ..., xi(N)], where xi(l)∈ {0,1}
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for l= 1,...,N. Each codeword xiis BPSK modulated (using the mapping 0→ −1,1+1),
thus obtaining the transmitted signal
si(t) =
N
X
l=1
bi(l)g(tlTs),(1)
where Tsis the symbol period, bi(l)is the BPSK mapping of xi(l)and g(t)is the square root
raised cosine (SRRC) pulse. The signal si(t)is called burst. Higher order modulations such as
QPSK may also be used, but some preliminary results we obtained showed a consistent increase
of the FER with respect to BPSK in case random phase and frequency offsets are present. In
particular we observed that with QPSK modulation some relative phase rotations introduce an
ambiguity in the received signal that impedes correct decoding even in the absence of noise.
Further investigation is needed to study this issue and the potential countermeasures, such as
the adoption of different quaternary modulations. In the present paper we focus on BPSK which
is more robust to random phase and frequency offsets. As we will show in Section VI, even
with a real-valued modulation our method outperforms other collision resolution schemes that
use complex-valued modulations for certain code rates and SNR values.
In the following we will refer to a time division multiple access (TDMA) scheme. However,
the techniques proposed hereafter can be also applied to other access schemes, such as multi-
frequency-TDMA (MF-TDMA), in which a frame may include several carriers, or code division
multiple access (CDMA), where NCDP can be used to recover collisions in each of the code
sub-channels. The proposed technique still relies on single carrier transmission by each user
terminal. From the user terminal perspective no significant change is required.
Transmissions are organized in frames. Each frame is divided into Stime slots. The number
Sof time slots that compose a frame is fixed, i.e., it does not change from one frame to the
other. The duration of each slot is equal to about Nburst symbols. When more than one terminal
transmits its burst in the same slot a collision occurs at the receiver. A collision involving k
transmitters is said to have size k. We assume symbol-synchronous transmissions, i.e., in case
of a collision, the signals from the transmitters add up with symbol synchronism at the receiver
R. The received signal before matched filtering and sampling at R, in case of a collision of size
k(assuming, without loss of generality, the first kterminals collide), is:
y(t) = h1(t)s1(t) + ... +hk(t)sk(t) + w(t),(2)
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where si(t)is the burst transmitted by user i,w(t)is a complex additive white Gaussian noise
(AWGN) process while hi(t)takes into account the channel from terminal ito the receiver. hi(t)
can be expressed as:
hi(t) = Aiej(2πνit+ϕi),(3)
where (Ai)2=|hi(t)|2is a lognormally distributed random variable modeling the channel power
of transmitter i, while νiand ϕiare the frequency and phase offsets with respect to the
local oscillator in R, respectively. The log-normality of the satellite channel power has been
assumed in several previous works such as [3], [4] and [18] although, as indicated in [4], this
is a pessimistic assumption. As we will refer to the schemes presented in these papers, we
keep the assumption of log-normal power distribution in the following. In general a distribution
characterized by large fluctuations of the channel power are likely to affect the proposed scheme
because it is conditioned by the channel with less power. We will come back on this issue in
the following sections. We assume that the amplitude Aiand the frequency offset νiremain
constant within one frame while ϕiis a random variable uniformly distributed in [π, +π]that
changes independently from one slot to the other due to the phase noise at the transmitting
terminals as assumed in [3]. The assumption of constant phase within a burst is more accurate
for shorter burst lengths and assuming high class transmitting terminals with stable oscillators
or a high symbol rate (typically above 2Mbaud). We assume this model for ease of exposition.
Further studies are needed to characterize the sensitivity of PNC to phase noise for a generic
number of colliding signals and especially for a large burst size, but this is out of the scope of
the present paper. Assuming that the frequency offset is small compared to the symbol rate 1/Ts
(i.e., νTs1), the sample taken at time tlafter matched filtering signal y(t)is:
r(tl) = h1(tl)q1(tl) + ... +hk(tl)qk(tl) + n(tl),(4)
where q(t) = s(t)g(t),being the convolution operator, while n(tl)’s are i.i.d. zero mean
complex Gaussian random variables with variance N0in each component. Note that even in case
a BPSK modulation is used, as we are assuming in this paper, both the I and Q components of
the received signal are considered by the receiver. This is because the phases of the users have
random relative offsets and thus both components carry information relative to the useful signal.
The frequency and phase relative offsets must be taken into account by the decoder, as they can
not be eliminated by the demodulator. We consider this more in detail in Section III.
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We assume that the receiver has knowledge of the nodes that are transmitting, as well as the full
channel state information at each time slot. As we are considering a random access scheme, the
knowledge about nodes identity cannot be available a priori at the receiver. Thus, nodes identity
must be determined by Rstarting from the received signal, even in case of collision. This can
be achieved by having the transmitting nodes add a pseudonoise preamble in each transmitted
burst. For a length-Npre preamble there are 2Npr e different sequences. Using maximal length
pseudonoise signature sequences the crosscorrelation between any two different sequences is
1/Npre, which translates in a 10 dB gain for preambles that are just 10 symbols long. We
discuss the issue of node identification and channel estimation more in detail in Section V.
III. NETWOR K CODED DIVERSITY PROTO COL
In this section we present our network-coded diversity protocol (NCDP) which aims at in-
creasing the throughput and reducing packet losses in Slotted ALOHA multiple access systems.
In the first part of the section we describe the way the received signal is processed by the receiver
in case of collision, while in the rest of the section we describe the NCDP at the transmitter
and at the receiver sides.
A. Multi-User Physical Layer Network Coding
When a collision of size koccurs, i.e., kbursts collide in the same slot, the receiver tries
to decode the bit-wise XOR of the ktransmitted messages. This can be done by feeding the
decoder with the appropriate log-likelihood ratios (LLR). The calculation of the LLRs for a
collision of generic size kin case of BPSK modulation was presented in [13]. In the following
we include the effect of frequency offset in the calculation of the LLR’s, which was not taken
into account in [13].
When signals from ktransmitters collide, the received signal at Ris given by (2). Each
codeword xiis calculated from uias xi=C(ui), where C(.)is the channel encoder operator.
All nodes use the same linear code C(.). Starting from r(t), the receiver Rwants to decode
codeword xs,x1x2...xk, where denotes the bit-wise XOR. In order to do this the
decoder of Ris fed with vector L= [L(1), ..., L(N)] of LLRs for xs, where:
L(l) = ln
Pk+1
2
i=1 P(k
2i1)
m=1 e|r(tl)do(2i1,m)Th(tl)|2
2N0
Pk+1
2
i=1 P(k
2i)
m=1 e|r(tl)de(2i,m)Th(tl)|2
2N0
,(5)
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h(tl)being a column vector containing the channel coefficients of the ktransmitters at time
tl(which change at each sample due to frequency offsets), while do(2i1, m)and de(2i, m)
are column vectors containing one (the m-th) of the k
2i1or k
2ipossible permutations over k
symbols (without repetitions) of an odd or even number of symbols with value “+1”, respectively.
Equation (5) is derived considering that an even or an odd number of symbols with value +1
adding up at Rmust be interpreted by the decoder as a 0 or a 1, respectively (see [19] and [20]
for an extension to higher order modulations). If the decoding process is successful, Robtains
the message us,u1...uk. In Section V the frame error rate (FER) curves for different
collision sizes obtained using these LLR values are shown.
B. NCDP: Transmitter Side
We call active terminals the nodes that have packets to transmit in a given frame. Each
message is transmitted more than once within a frame, i.e., several replicas of the same message
are transmitted. We will give details about the number of replicas transmitted within a frame in
the next section. Assume that node ihas a message uito deliver to Rduring a given frame, i.e.,
node Tiis an active terminal. Before each transmission, node ipre-encodes uias depicted in
Fig. 1. The pre-coding process works as follows. uiis divided into L=K
nblocks of nbits each.
n bits n bits
Channel
Coding
Modulation
n bits
Fig. 1. NCDP pre-encoding, channel coding and modulation scheme at the transmitter side.
At each slot a different coefficient αij ,j∈ {1,...,S}, is drawn randomly according to a uniform
distribution in GF (2n). If αij = 0, terminal Tidoes not transmit in slot j. Each of the Lblocks
ur
i,r∈ {1,...,L}, is interpreted as an element in GF (2n)and multiplied by αij . We call u
ij the
message uiafter the multiplication by αij .u
ij is then channel encoded, generating the codeword
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xij =C(u
ij ). After channel coding, a header piis added to xij . Such header is generated using
a pseudonoise sequence generator such as the ones used in CDMA. The increase in complexity
at the transmitter side with respect to the case in which all nodes use the same preamble is not
large if maximal length pseudonoise sequences are used, as each node just needs to choose a
random seed and feed it to a shift register which is the same for all nodes and known at the
receiver. On the other hand, at the receiver side the complexity associated to the detection phase
increases in a quasi-linear fashion with the number of correlators used. However, such increase
in complexity may not be an issue if the receiver is located at the gateway station, as it is likely
to have good computational capabilities. The same header piis used for all transmissions of node
Tiwithin frame f. Once the header is attached, xij is BPSK modulated and transmitted. Note
that the multiplication of uiby αij is needed to introduce randomness in the MAC mechanism
and does not modify the number of information bits transmitted.
The choice of the coefficients and of the header is done as follows. Node Tidraws a random
number µ.µis fed to a pseudo-random number generator in GF (2n), which is the same for all
terminals and is known at R. The first Soutputs of the generator are used as coefficients. The
header is uniquely determined by µ, i.e, there is a one-to-one correspondence between the set
of values that can be assumed by µand the set of available pseudo-noise sequences. The cross-
correlation properties of the preambles allow the receiver to know which of the active terminals
in frame fis transmitting in each time slot. Moreover, as the header univocally determines µ
and thus the set of coefficients used by each node, Ris able to know which coefficient is used
by each transmitter in a given slot. As we will see in Section III-C, this is of fundamental
importance for the decoding process. As said before, the set of headers is a set of pseudo-noise
sequences, such as those usually adopted in CDMA. The fundamental difference with respect
to a CDMA system is that in the latter the (quasi-)orthogonality of the codes is used to (quasi-
)orthogonalize the channels and expand the spectrum, while in NCDP the low cross-correlation
of the preambles is used only for determining the identity of the transmitting node, which is
obtained without any spectral expansion, as the symbol rate 1/Tsis equal to the chip rate (i.e.,
the rate at which the modulated symbols are transmitted over the channel).
The discrimination capability of pseudo-noise preambles may suffer a consistent degradation
in the presence of strong Doppler shifts which are typical of Low Earth Orbit (LEO) and Medium
Earth Orbit (MEO) satellite systems. For this reason such node identification method is mostly
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suited for geostationary satellite networks as well as terrestrial multiple access systems.
C. NCDP: Receiver Side
The decoding scheme at the receiver side is illustrated with an example in Fig. 2(a) and Fig.
2(b). In the example, a frame with S= 4 slots and Ntx = 3 active terminals is considered.
In the figures only bursts with non-zero coefficients are shown. In each slot the receiver uses
the pseudo-noise preamble of each burst to determine which node is transmitting and which
coefficient has been used for that burst. As described in Section III-B, the coefficients used by a
node in each burst are univocally determined by the preamble. The preamble can be determined
at the receiver Rusing a correlator which calculates the correlation of the received signal with
the maximal length sequence for each possible shift. In order to increase the number of available
sequences of coefficients and to avoid the problems due to the eventual unsuccessful decoding
of some of the slots, the system can be designed so that for each preamble a different coefficient
is associated to each slot. In this way the sequence of coefficients associated to the preamble
changes depending on the slots where the burst that uses that preamble is transmitted. The total
number of different sequences associated to a given preamble is, thus, equal to the number of
possible dispositions of the drepetitions over the Sslots of the frame, that is, S
d. Note that
the use of a preamble is not a peculiarity of NCDP, as usually practical systems make use of
a preamble to perform channel estimation. The preamble is also used by Rto estimate the
channel for each of the transmitters. More details about the channel estimation are given in [21]
and are recalled in Section V. Once the channel has been estimated, the receiver applies PNC
decoding to calculate the bitwise XOR of the transmitted messages, as detailed in Section III-A.
According to arithmetics in Galois fields and to what is stated in Section III-B, the bitwise
XOR is interpreted as a sum in GF (2n). Thus the slots that have been correctly decoded are
interpreted as a system of linear equations in GF (2n)with coefficients αij, which are known to
the receiver through the headers (see Fig. 2(a)). In order to simplify the notation, in the figure
we indicated the vector u
ij =αij u1
i,...,αijuL
i, representing the network coded packet, as
αij ui. At this point, if the coefficient matrix Ahas full rank, Rcan recover all the original
messages using common matrix manipulation techniques in GF (2n)(see Fig. 2(b)). If Ais
not full rank, not all the transmitted packets can be recovered. However, a part of them can
still be retrieved using Gaussian elimination. The decoding process in case of rank deficient
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Decoder
Linear equation in
Received
frame
(a) Step 1: decoding at slot level (PNC). (b) Step 2: decoding at frame level (network coding).
coefficient matrix is analyzed in Section IV. Note that, while in [2] the coefficient matrix A
(called mixing matrix) is a complex matrix whose elements are the terminals’ channel gains,
in NCDP Ais a matrix in a Galois Field. In NCDP each slot is processed only once in the
complex domain (PNC decoding), while all matrix manipulations are done in GF (2n). In [2],
instead, the matrix Ais processed entirely in the complex domain. Operating in GF (2n)has
an important advantage in terms of complexity, as all the processing can be done in the digital
domain and avoids numerical problems that may derive from using a complex matrix, especially
in case of small channel gains. If, on the one hand, using a complex coefficient matrix leads
to a high probability of having full rank (which, however, also depends on the precision of the
quantization in the sampling process), on the other hand in NCDP a relatively small field size
(e.g., GF (28)) already achieves almost the same performance in terms of throughput as in the
case of a complex matrix, as we show in Section IV.
IV. THROUGHPUT AND ENE RGY EFFICIENCY ANALYSIS
A. Throughput
During each frame the terminals buffer the packets to be transmitted in the following frame.
Each terminal transmits its packet more than once within a frame, randomly choosing a new
coefficient in GF (2n)independently at each transmission. As described in the previous section,
the coefficients can be generated using a pseudo-random number generator fed with a seed
which is univocally determined by the chosen pseudo-noise preamble. Using the preamble the
receiver can build up a coefficient matrix A[GF (2n)]S×Ntx for each frame, with Aj,i =αij,
αij ∈ {1,...,2n1}. The rows of Arepresent the time slots while the columns represent the
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active terminals, i.e., the terminals that transmit in the present frame. If αij = 0, terminal Ti
does not transmit in slot j. During slot j,Rreceives the sum of the bursts with αij 6= 0. From
the received signal, Rtries to obtain the bit-wise XOR of the encoded messages as described
in Section II. The XOR is interpreted by Ras a linear equation in GF (2n), the coefficients of
which are derived through the pseudo-noise preamble as described in Section III. If Ntx is the
number of active terminals in a frame and assuming that all the received signals are decoded
correctly, a linear system of equations in GF (2n)is obtained with Sequations and Ntx variables.
Each variable corresponds to a different source message. If Ahas rank equal to Ntx , then all the
messages can be obtained by R. A necessary condition for Ato be full rank is Ntx S, i.e.,
the number of active terminals in a frame must be lower than the number of slots in a frame.
Assuming Poisson arrivals with aggregate intensity G, the probability of such event is:
P r{Ntx S}=
S
X
n=0
(GS)neGS
n!,(6)
that includes also the case in which there are no active terminals during a frame. For instance, in
case of S= 100 slots and G= 0.8the probability expressed by Eqn. (6) is on the order of 0.99.
Even if Ntx < S, however, it can still happen that Ais not full rank, i.e., not all the messages
can be recovered. The probability that Ais full rank for a given Ntx < S depends on the
MAC policy, and particularly on the probability distribution used to choose the coefficients. One
possibility is to use a uniform distribution for the coefficients (i.e., each coefficient can assume
any value in {0,...,2n1}with probability 2n). In this case the number dof transmitted
replicas is a random variable, and the probability that Ais full rank is [22]:
P(S, Ntx) =
Ntx1
Y
k=0 11
2n(Sk).(7)
Using (6) and (7) we find the expression for the normalized throughput:
Φ = 1
S
S
X
m=1
m(GS)meGS
m!P(S, m) = 1
S
S
X
m=1
(GS)meGS
(m1)!
m1
Y
k=0 11
2n(Sk)
=1
S
S1
X
m=0
(GS)m+1 eGS
(m)!
m
Y
k=0 11
2n(Sk)=G
S1
X
m=0
(GS)meGS
m!
m
Y
k=0 11
2n(Sk).
(8)
From Eqn. (8) we can see that Φgrows with n, which means that the system throughput increases
with the size of the considered finite field. The throughput achievable in case of an asymptotically
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large field size nis:
lim
n→∞ Φ = lim
n→∞ "G
S1
X
m=0
(GS)meGS
m!
m
Y
k=0 11
2n(Sk)#=G
S1
X
m=0
(GS)meGS
m!.(9)
Thus, the normalized throughput Φtends to the probability of having less than Stransmitters in
a frame as n→ ∞. Note that this is the same performance that would be achieved by a scheme
that uses coefficient matrix in the complex domain, as in [2]. Further in this section we show
that almost the same performance can be achieved by NCDP using a finite and relatively small
field size.
B. Energy
The MAC scheme we just analyzed presents one main drawback in terms of energy efficiency.
As a matter of fact, given the frame length S, a node transmits each message on average E[d] =
S×ptimes, p= (12n)being the probability to choose a non-zero coefficient, i.e., the average
number of transmissions grows linearly with S. In order to decrease the energy consumption,
the probability of choosing the zero coefficient may be increased. However, a reduction in
the transmission probability pmay affect the system throughput. In order to understand the
relationship between the probability pand the throughput Φ, we refer to some results in random
matrix theory. The problem can be formulated as follows: consider an S×Ntx ,SNtx, random
matrix Aover GF (2n)with i.i.d. entries, each of which assumes value 0with probability 1p
while with probability pit assumes values in {1,...,2n1}. We are interested in the relationship
between pand the probability that Ais full rank. In [23] the authors show that, in order to achieve
a rank Ntx O(1) with high probability, then, for Ntx large, pcannot be lower than the threshold
probability log(Ntx )
Ntx . At high loads (i.e., G1), on average Ntx S, which means that, setting
p=log(S)
S, the average number of transmissions (and so the energy consumption) for each node
is E[d] = log(S), i.e., it grows logarithmically with the number of slots in a frame. On the
other side, Smust be kept large enough, as this increases the decoding probability (see Eqn.
(9)). With reference to the example considered earlier in this section the average number of
transmissions corresponding to the minimum required pfor S= 100 is equal to about 4.6. We
evaluated numerically the effect a reduction of phas on Φfor the case S= 100 and q= 28.
We considered three cases. In the first one the transmission probability in each slot has been
set to p= 1 2n= 0.9961, which corresponds to the case studied in the first part of this
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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
G
Φ
p=0.9961 analytic
analytic asymptotic
p=0.9961 Monte Carlo
p=0.0625
p=log(S)
S= 0.0461
Fig. 2. Normalized throughput plotted against the normalized offered load for different values of the transmission probability
p. We set S= 100 slots per frame while the coefficients were chosen in GF (28). The asymptotic analytical curve (Eqn. (9))
is also plotted.
section and for which the throughput is given by Eqn. (8). In the second case we set pjust
above the threshold, i.e., p= 0.0625 >log(S)
S= 0.0461, while in the last case phas been set
exactly equal to the threshold probability. Fig. 2 shows the results together with the numerical
validation of Eqn. (8). It is interesting to note how passing from p= 0.9961 to p= 0.0628,
with a reduction in transmission probability (or, equivalently, in average energy per message) of
about 93.7%, leaves the throughput unchanged, while a further decrease of pof just another 1.5%
leads to a 10% reduction in the maximum throughput with respect to the case p= 0.9961. The
asymptotic analytical curve described by Eqn. (9) is also plotted in Fig. 2. Such curve represents
the throughput of a system where coefficients are chosen in a finite field with asymptotically
large field size. It also represents the throughput of a system derived from the NDMA scheme
proposed in [2], i.e., the coefficient matrix is complex and is processed in the complex domain.
It can be seen that the performance in terms of throughput is almost the same for NDMA and for
NCDP with p= 0.0625, i.e., NCDP does not lose significantly with respect to NDMA, while
saves in complexity by performing all the processing in a finite field instead of the complex
March 20, 2018 DRAFT
14
domain.
To further lower the energy consumption and control the number of repetitions d(which,
being a Bernoulli random variable, can theoretically assume values as large as S), an alternative
is to fix the number of transmitted replicas a priori. Although this solution may decrease the
probability of decoding all the transmitted messages (because the resulting Amatrix would be
a subset of all possible matrix of the same size), it may still be possible to recover part of them
by using Gaussian elimination.
V. IMPLEMENTATIO N ASPECT S
For each frame the receiver Rneeds to know which of the active terminals are transmitting
in each slot and must have CSI for each of the users. Both needs are addressed including a
pseudonoise preamble, such as the spreading codes used in CDMA, at the beginning of each
transmitter’s burst. In [3] frequency offset and channel amplitude are derived from the clean
bursts (i.e., bursts that did not experience collision) and assumed to remain constant over the
whole frame. The need for an orthogonal preamble has been removed in CRDSA++ [4]. Unlike
in [3], the method we propose can not rely only on clean bursts. Thus the frequency offset and
the amplitude of each transmitter must be estimated using the collided bursts.
Channel estimation can be performed using the Estimate Maximize (EM) algorithm as we
have shown in [21]. We have adopted the approach described in [24], where the EM algorithm
is used to estimate parameters from superimposed signals. We have applied the same approach
to estimate amplitudes, phases and frequency offsets from the baseband samples of the received
signal in case of a collision of size k. The details on how the EM algorithm is applied can be
found in [21].
In Fig. 3 the FER curves for different collision sizes obtained using the LLR values calculated
in Section III-A are shown. The FER curve for the case of estimated channels using the EM
algorithm are also shown. These results have the purpose of showing the feasibility of channel
estimation from the collided messages. We are currently working on the enhancement of channel
estimation in order to further improve the performance of PNC in terms of FER. In the simulation
all channel amplitudes were assumed to be equal. We performed some preliminary simulations
with unbalanced channels and observed a certain degradation in terms of FER. More specifically,
the performance of PNC decoding in the case of a collision with different channel gains is
March 20, 2018 DRAFT
15
0 5 10 15 20 25
10−3
10−2
10−1
100
Eb/No in dB
FER
k=5
k=5 (estimated channels)
k=1
Fig. 3. FER curves for the XOR of transmitted messages for a collision of size 5.Ebis the energy per information bit for each
node. A tail-biting duo-binary turbo code with rate rcc = 1/2and codeword length 256 symbols is used by each node. Phase
offsets are uniformly distributed in (π, +π), frequency offsets are uniformly distributed in (0,νmax )with νmax equal
to 1% of the symbol rate on the channel. Amplitudes are constant and equal to 1. The FER curves for the case of estimated
channels using the EM algorithm are also shown [21].
slightly better than that of the weakest channel. This may require the adoption of appropriate
countermeasure depending on the deployment setup. Further analysis on the effect of channel
unbalance is out of the scope of the present paper and will be tackled as future work.
In order to detect eventual decoding errors, a cyclic redundancy check (CRC) can be used.
The CRC operations are done in GF (2) and, by the linearity of the channel encoder, the CRC
field in the message obtained by decoding a collision of size kis a good CRC for us, which
is the bitwise XOR of the messages encoded in the kcollided signals. This allows to detect
decoding errors, within the limits of the CRC capabilities, also in collided bursts.
Another important issue is the imperfect symbol synchronism. In [21] we have proposed
several techniques, based on oversampling, aiming at reducing the impairments brought in by
the lack of perfect synchronism. The different methods we proposed in [21] are all based on
oversampling nd show a loss of about 1dB at F E R = 102with respect to the case with ideal
synchronism. We do not report here the results for a matter of space.
The lack of timing and phase synchronization determines a certain degradation in the sequence-
detection performance of the code. However, such degradation can be usually tolerated in
practical systems such as CDMA ones. We refer to [25] for further details on this issue.
March 20, 2018 DRAFT
16
VI. NUM ERICA L RESULTS
Our performance metrics are the throughput Φ, defined as the average number of bits per
second per Hertz (bit/s/Hz) that can be correctly decoded, and the packet loss rate (PLR) Υ, i.e.,
the ratio between the number of lost packets and the total number of packets that arrive at the
transmitters. The relationship between these metrics is given by Φ = rdecG(1 Υ),rdec being
the rate in bits per second per Hz (bit/s/Hz) used by the transmitter and Gbeing the average
rate at which the new messages (i.e., messages that are being transmitted for the first time) are
injected in the network. Note that Gis independent from the number of times a message is
repeated within a slot. Also note that rdec is the number of bits per second per Hertz. Thus, for
instance, if a QPSK modulation is adopted, rdec corresponds to twice the rate of the channel
encoder indicated with rcc in Section II.
We consider two benchmarks. The first one is the CRDSA++ scheme with three repetitions.
In CRDSA++ a node transmits three copies of a burst (twin bursts) in different slots randomly
chosen within a frame. Each of the twin bursts contains information about the position of the
other twin bursts in the frame. If one of the twin bursts does not experience a collision (i.e., it
is clean) and can be correctly decoded, the positions of the other twin bursts are known. These
bursts may or may not experience a collision with other bursts. If a collision occurs, these are
removed through interference cancelation using the knowledge of the decoded bursts. In order
to do this Rmemorizes the whole frame, decodes the clean bursts, reconstructs the modulated
signals including the effect of each user’s channel, and subtracts them from the slots in which
their replicas are located. The SIC process is iterated for a number Niter of times, at each time
decoding the bursts that appear to be “clean” after the previous SIC iteration. If at the end of
the SIC process not all the bursts have been decoded, the receiver tries to decode each of the
collided bursts considering the interfering bursts as noise. If a burst is successfully decoded all
its replicas are subtracted from the frame and the SIC process starts back.
The second benchmark we consider is a slotted ALOHA system. In a SA system each burst
is transmitted only once. Reception is successful if only one burst is transmitted within a given
slot, while, in case two or more bursts collide, they are discarded. The capture effect is not
considered in the SA scheme.
In both NCDP and CRDSA++ the performance at the physical layer plays an important role
March 20, 2018 DRAFT
17
in both system throughput and packet loss rate. In order to take this into account in the most
general way we adopt an information theoretical approach assuming Gaussian codebooks for
CRDSA++ and Lattice codes for NCDP. We also assume channels are symmetric. We evaluate
the performance of CRDSA++ as in [26]. In case of complex-valued channel symbols and
collision of size k, a burst can be correctly decoded if [27]
rdec log21 + SN R
1 + (k1) ·SNR,(10)
where rdec is the rate in bits per second per Hertz while SNR is the signal to noise ratio of the
channel (i.e., Es/N0).
As for NCDP, we refer to a result in [28] (Theorem 4) according to which the bitwise XOR
of kcolliding signals using the same rate rdec and real-valued channel symbols can be correctly
decoded if
rdec 1
2log21
k+SN R.(11)
We consider two different simulation setups. In the first one the nodes do not receive any
feedback from the receiver, while in the second setup Rgives some feedback to the active
terminals. For this last case we consider an automatic repeat request (ARQ) scheme, in which
a node receives an acknowledgement (ACK) or a negative acknowledgement (NACK) from the
receiver in case a message is or is not correctly received, respectively. The amount of feedback
is limited to one ACK/NACK message per node and per frame1. A node that receives a NACK
enters a backlog state. Backlogged nodes retransmit the message for which they received the
NACK in another frame, uniformly chosen at random among the next Bframes. We call Bthe
maximum backlog time. The process goes on until the message is acknowledged [29]. In this
setup we also consider the average energy consumption per received message ηas performance
metric, defined as the average number of transmissions needed for a message to be correctly
received by R.
In both setups we assume a very large population of users and a frame size of S= 100.
1An alternative to the NACK is to have the transmitters using a counter for each transmitted packet, indicating the time
elapsed since it has been transmitted. If the timer exceeds a threshold value (which depends on the system’s RTT), the message
is declared to be lost.
March 20, 2018 DRAFT
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0 0.5 1 1.5
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Φ(bits/s/Hz)
G
NCDP, rdec = 0.94
NCDPp=0.0 625,rdec = 0.94
CRDSA++, rde c = 0.94
Slotted ALOHA, rdec = 0.94
NCDP, rdec = 0.5714
NCDPp=0.0 625,rdec = 0.5714
CRDSA++, rde c = 0.5714
Slotted ALOHA, rdec = 0.57 14
Fig. 4. Normalized throughput Φvs normalized traffic load G. The field size for the coefficients of NCDP is 28. No feedback
is assumed from the receiver.
In the first setup, in which no feedback is provided by the receiver, the average amount of
energy spent by a node for each message which is correctly received does not change with
the system load G, and is equal to the average number of times a message is repeated within
a frame. In Fig. 4 the normalized throughput Φis plotted against the normalized traffic load
G. In the figure the throughput curves of NCDP and CRDSA++ schemes for d= 3 replicas
and rates rdec = 0.94 and rdec = 0.5714 (4/7) are shown. The throughput curve for NCDP in
case of a constant retransmission probability p= 0.0625 is also shown (NCDPp= 0.0625).
Note that this probability is above the threshold value we mentioned in Section IV, as for
S= 100 we have log(S)/S = 0.0461. The scheme with p= 0.0625 outperforms all the others
in terms of throughput, achieving a peak value of more than 0.7bit/s/Hz for a rate rdec = 0.94.
The precoding coefficients of NCDP (indicated as αij in Section III-B) are drawn uniformly in
GF (28). The normalized throughput for NCDP with d= 3 and GF (2) (not plotted in the figure)
has a peak value of about 0.4888, with a loss of about 15% with respect to the case in which
GF (28)is used. In the figure we see how, depending on the rate and for the same number of
repetitions, either NCDP or CRDSA++ achieve the highest throughput peak. This is due to the
fact that the packet loss rate of CRDSA++ increases when passing from rdec = 0.5714 bpcu
March 20, 2018 DRAFT
19
0 0.5 1 1.5
10−5
10−4
10−3
10−2
10−1
100
Υ(PLR)
G
NCDP, rdec = 0.94
NCDPp=0.0 625,rdec = 0.94
CRDSA++, rde c = 0.94
NCDP, rdec = 0.5714
NCDPp=0.0 625,rdec = 0.5714
CRDSA++, rde c = 0.5714
Slotted ALOHA
Fig. 5. Packet loss rate Υvs normalized traffic load G. The field size for the coefficients of NCDP is 28. No feedback is
assumed from the receiver. The PLR curve for SA is the same for both the considered code rates.
to rdec = 0.94 bit/s/Hz, as confirmed by Fig. 5, where the packet loss rates of the considered
schemes are shown. From the figure we also see that the PLR of NCDP is the same for both
the considered rates and the difference in the peak throughput is only due to the difference in
the rate at the physical level. For both the considered rates NCDP achieves a PLR as low as
103for a network load of 0.655, while, for the same PLR, CRDSA++ achieves a throughput
of 1.14 and 0.46 for rdec = 0.5714 and rdec = 0.94, respectively. Interestingly NCDPp= 0.0625,
although achieving the highest peak throughput, never gets to a PLR lower than 103, which is
due partly because of the probability that a node chooses not to transmit in a given frame (that
happens with probability (1 p)S).
It is interesting to note how, in NCDP, increasing the number of transmissions per message
(and so the energy consumption) leads to an increase in the peak throughput of the system.
In the second setup, in which feedback is allowed, we evaluate jointly the throughput Φ
and the energy consumption ηof the schemes under study. In Fig. 6, Φis plotted against G
for a maximum backlog time B= 50 frames. The precoding coefficients of NCDP are drawn
uniformly in GF (28). The figure shows how Φincreases linearly with Gup to a threshold load
value. Such threshold increases with the (average) number of repetitions and corresponds to the
March 20, 2018 DRAFT
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0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
G
Φ(bits/s/Hz)
NCDP, rdec = 0.94
NCDPp=0.0 625,rdec = 0.94
CRDSA++, rde c = 0.94
Slotted ALOHA, rdec = 0.94
NCDP, rdec = 0.5714
NCDPp=0.0 625,rdec = 0.5714
CRDSA++, rde c = 0.5714
Slotted ALOHA, rdec = 0.57 14
Fig. 6. Normalized throughput Φvs normalized traffic load Gin a system with retransmission.
maximum network load for which the throughput in the setup without feedback (Fig. 4) has an
almost linear behavior, i.e., the PLR is low. This indicates that, if the load is such that a non
negligible fraction of the messages are not decoded at the first attempt, the retransmissions
saturate the channel, blocking both the iterative cancelation process of CRDSA++ and the
Gaussian elimination decoding in NCDP. Note that this does not happen in the SA system,
coherently to what shown in [29] for the case of large backlog time. In order to compare
jointly the spectral and the energy efficiency of the different schemes, we plot the curves for
the normalized throughput vs the average energy consumption per received message η, which
is shown in Fig. 7. The increase in the number of repetitions corresponds to an increase in
throughput but also to a higher energy consumption for a given transmitter in a given frame.
However, as shown in Fig. 7, this does not necessarily imply a loss in energy efficiency. From
the figure it can be seen that that there is not a scheme that outperforms the others in terms of
both energy and spectral efficiency, but which scheme is best depends on the maximum target
throughput. SA achieves a higher throughput with a lower energy consumption with respect
to the other schemes in the region Φ<0.35, while in the region Φ>0.35 both NCDP and
CRDSA++ achieve a higher throughput with lower energy consumption with respect to SA.
March 20, 2018 DRAFT
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100101102103104105
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
η
Φ(bits/s/Hz)
NCDP, rdec = 0.94
NCDPp=0.0625,rdec = 0.94
CRDSA++, rdec = 0.94
Slotted ALOHA, rdec = 0.94
NCDP, rdec = 0.5714
NCDPp=0.0625,rdec = 0.5714
CRDSA++, rdec = 0.5714
Slotted ALOHA, rdec = 0.5714
Fig. 7. Normalized throughput vs average energy consumption per decoded message.
NCDP achieves a maximum Φof 0.653 for rdec = 0.94, slightly higher than CRDSA++, for
which the peak value is 0.628, for rdec = 0.5714. In the NCDP scheme with a retransmission
probability of p= 0.0625 a peak throughput of 0.655 bit/s/Hz is achieved in correspondence of
an average energy consumption of η= 6.25. The maximum average throughput that is achieved
in correspondence to a packet loss rate of 103is similar in the two schemes, with CRDSA++
achieving a slightly higher throughput (0.64) than NCDP (0.61).
The simulations show that, for the same number of repetitions, which scheme between NCDP
and CRDSA++ performs better (in terms of both throughput and packet loss rate) depends on
the rate rdec. It is not straightforward to find which scheme performs better than the other for
any given (rdec, SNR)pair. However, in the following we derive a subset of the region in the
(rdec, SNR)plane where NCDP outperforms CRDSA++. We start by deriving an upper bound
on the minimum SNR (SNRmin ) required by NCDP in order to decode correctly (at the physical
level, i.e., applying physical layer network coding) a collision of any size. From Eqn. (11) follows
that for a collision of size k, the decoding at physical level of PNC with real-valued modulation
is successful if:
SN R 4rdec 1
k.(12)
March 20, 2018 DRAFT
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Thus, an upper bound on the minimum SNR is:
SN Rub
min = 4rdec ,(13)
which does not depend on the collision size. Let us now consider CRDSA++. If the iterative
decoding stops, in CRDSA++ the receiver tries to decode each burst considering the others as
noise in each slot. In case of symmetric channels and complex-valued modulation the maximum
rate rdec in bit/s/Hz such that the decoding is still possible must satisfy
rdec log21 + SN R
1 + SN R ,(14)
that corresponds to the case in which there is only one interferer. The SNR below which the
decoding (i.e., the collision resolution at physical level within a slot) stops is, thus:
SN R 2rdec 1
22rdec .(15)
From Eqn. (13) and Eqn. (15) we find that NCDP can perform fully while CRDSA++ is limited
to decoding and canceling clean bursts (as in CRDSA) if
4rdec SN R 2rdec 1
22rdec .(16)
Now it is sufficient to note that, if PNC decoding is successful in each slot, NCDP can perform
in the digital domain the equivalent of the SIC of CRDSA in case the coefficient matrix A
is not full rank. This implies that, when Eqn. (16) holds, NCDP performs at least as well as
CRDSA++. We plot the region defined by Eqn. (13) and Eqn. (15) in Fig. (8). In the region at
the right of the picture where the required SNR of CRDSA++ is higher than that of NCDP, the
latter scheme outperforms the former. This confirms the results of the simulations shown before,
in which, for the same number of repetitions, when (rdec = 0.5714, SNR = 10dB)CRDSA++
outperforms NCDP, while if (rdec = 0.94, SNR = 10dB)NCDP performs better. Interestingly
NCDP outperforms CRDSA++ at higher rates. We must also note that NCDP is limited to BPSK
modulations, while CRDSA++ can be applied also with complex modulations and in principle
may achieve higher rates in bit/s/Hz. However, from Fig. 8 we see that in order to work well at
rates approaching 1bit per channel symbol, CRDSA++ needs an asymptotically large SNR.
Further studies are needed to address a fair comparison in case of asymmetric channels.
An important issue in the collision recovery mechanisms considered in this paper is their
complexity. NCDP needs a more strict synchronization with respect to CRDSA++ (symbol level
March 20, 2018 DRAFT
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0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
−10
−5
0
5
10
15
20
25
30
rdec (bits/s/Hz)
SNR (dB)
SN Rmin NCDP (real-valued modulation)
SN Rmin CRDSA++ (complex-valued modulation)
Fig. 8. Curves in the (rdec , SN R)plane describing the upper bound on the minimum SNR of NCDP and the minimum SNR
for CRDSA++ required to solve collisions at the physical layer. In the region on the right of the picture where the required
SNR of CRDSA is higher than that of NCDP, the latter scheme outperforms the former.
versus slot level). However, NCDP may be an argument in favor of symbol synchronous multiple
access systems which are currently debated in the satellite communications arena. As for the
decoding complexity, although NCDP has a higher complexity in the physical layer decoder
with respect to a typical point to point system (that has been studied in our previous work [21]),
our scheme has the advantage that, after the PNC decoding, all the operations at the receiver
are done in a finite field, which is particularly suited for implementation in the digital domain.
CRDSA++, instead, applies a SIC, which requires to store and process the whole frame in the
analog domain, requiring by far more memory than in case of a digital frame processing. A fair
complexity comparison between the two schemes is not straightforward and can not be addressed
exhaustively in the present paper for a matter of space.
VII. CO NCLUS IONS
We have proposed a new collision recovery scheme for symbol-synchronous slotted ALOHA
systems based on PNC over extended Galois fields. The adoption of an EGF allows to better
exploit the diversity of the system, leading to an increased spectral efficiency and, depending
March 20, 2018 DRAFT
24
on the system load, to an increased energy efficiency. We have compared the proposed scheme
with two benchmarks in two different setups, one without feedback and the other with feedback
from the receiver. In the second setup we have evaluated jointly the spectral efficiency and the
energy consumption of the proposed scheme. Once the PNC is applied to decode the collided
bursts, the receiver applies common matrix manipulation techniques over finite fields, which
results in a high-throughput scheme. We showed that NCDP achieves a higher or comparable
spectral efficiency with respect to the considered benchmarks, while there is not a single scheme
that outperforms the others in terms of both energy and spectral efficiency, but the best scheme
depends on the maximum achievable throughput. For completeness, we also reported our previous
results related to several physical layer issues related to multi-user PNC, namely decoding in the
presence of offsets and channel estimation. As a final remark, we underline that there is room for
significant improvement in the performance of the PNC decoder by using lattice codes, according
to recent result in information theory [30].
ACKNOWLEDGEME NT S
This work was partially supported by the European Commission under project ICT-FP7-
258512 (EXALTED), by the Spanish Government under project TEC2010-17816 (JUNTOS)
and project TEC2010-21100 (SOFOCLES), and by Generalitat de Catalunya under grant 2009-
SGR-940. Giuseppe Cocco was partially supported by the European Space Agency under the
Networking/Partnering Initiative.
Part of the content of this paper has been presented in [31].
This is the pre-peer reviewed version of the following article: G. Cocco, N. Alagha, C. Ibars,
S. Cioni, Network-Coded Diversity Protocol for Collision Recovery in Slotted ALOHA Networks,
in Wiley’s International Journal of Satellite Communications and Networking, which has been
published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1002/sat.1056.
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March 20, 2018 DRAFT
... The throughput that is achieved by this approach is significantly better than that of other approaches. In [89] Network-Coded Diversity Protocol (NCDP) has been devised. The key idea of NCDP is to apply PNC on top of the CRDSA packet repetition(s) at random locations within the frame composed by S slots. ...
... After the PNC is applied to decode the collided bursts, the receiver uses common matrix manipulation techniques over finite fields to recover the original messages. See Ref. [89] for more details on the NCDP demodulator operations. To operate with high level of MAC load, the NCDP scheme requires the use of orthogonal preambles. ...
... This implies a more complex gateway demodulator preamble acquisition unit compared to CRDSA single preamble approach. In [89], it was found that the NCDP scheme performs best with BPSK modulation and its performance is negatively impacted by packet power unbalance. With perfectly power balanced packets, the NCDP performance is found to be inferior to CRDSA with 3 replicas. ...
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In this survey paper we review the Random Access (RA) techniques with particular emphasis on the issues and the possible solutions applicable to satellite networks. RA dates back to the 1970's when the ALOHA protocol was developed to solve the problem of interconnecting university computers located in different Hawaiian islands. Since then, several evolutions of the ALOHA protocol have been developed. In particular, solutions were devised to mitigate the problem of packet collisions severely degrading the RA protocols performance. The approach followed for many years has been to avoid the occurrence of collisions rather than solving them. More recently, techniques tackling the RA packet collision problem have appeared triggered by the need of improving RA performance in satellite and terrestrial wireless networks. In particular, satellite networks large propagation delay does not allow the adoption of enhanced terrestrial RA techniques based on channel sensing. Adopting conventional demand assignment multiple access protocols is not suitable for supporting a large number of sensors or devices transmitting small size low duty cycle packets as required for Machine-to-Machine (M2M) communications. This provided the stimulus to exploit Successive Interference Cancellation (SIC) schemes to solve packet collision issues. The use of SIC in RA is relatively new and has opened up a promising research area. We provide an extensive review of recent high performance RA techniques achieving more than three orders of magnitude throughput increase compared to the original ALOHA at low packet loss rate. In this survey we cover both slotted and unslotted techniques. Finally, we review the use of RA in satellite systems and related standards including recent proposals for M2M applications.
... The k information packets of each user are then encoded into n h coded packets via its own chosen code c h . After encoding, a preamble P m and a pointer are attached to each of the n h coded packets, where the preamble is unique [194][195][196][197] and the pointer indicates the location of all other (n h − 1) coded packets for each user [34]. Moreover, each of n h coded packets is equipped with the information about the code chosen by the The channel is a multiple access erasure channel, where M noncooperative users communicate their data blocks to one receiver and the channel from each user to the receiver is the erasure channel with an erasure rate . ...
Preprint
The thesis is dedicated to studying methods to improve the efficiency of random access schemes and to facilitate their deployment in machine-type communications (MTC). First, a joint user activity identification and channel estimation scheme is designed for grant-free random access systems. We propose a decentralized transmission control and design a compressed sensing (CS)-based user identification and channel estimation scheme. We analyze the packet delay and throughput of the proposed scheme. We also optimize the transmission control scheme to maximize the system throughput. Second, a random access scheme, i.e., the coded slotted ALOHA (CSA) scheme, is designed for erasure channels to improve the system throughput. By deriving the extrinsic information transfer (EXIT) functions and optimizing their convergence behavior, we design the code probability distributions for CSA schemes with repetition codes and maximum distance separable (MDS) codes to maximize the expected traffic load, under packet erasure and slot erasure channels. We derive the asymptotic throughput of CSA schemes over the erasure channels for an infinite frame length, which is verified to well approximate the throughput for a practical frame length. Third, an efficient data decoding algorithm for the CSA scheme is proposed to further improve the system efficiency. We present a low-complexity physical-layer network coding (PNC) method to obtain linear combinations of collided packets and design an enhanced message-level successive interference cancellation (SIC) algorithm to exploit the linear combinations of collided packets. We propose an analytical framework and derive the system throughput for the proposed scheme. The CSA scheme is further optimized to maximize the system throughput and energy efficiency.
... This way, more slots become decodable, and the process is repeated until all slots are decoded. Physical layer network coding (PLNC) takes this idea 1 step further: it tries to decode all the messages even if all the slots experience collisions, by combining them into a system of linear Equation (4). ...
Article
Machine-Type Communications represent a major challenge for the upcoming 5G technology. Future cellular systems, in fact, will be in charge of supporting a huge number of devices generating sporadic small packets at random times. In this context, the Random Access Channel protocol is generally used to initiate the communication sessions, aimed at delivering this kind of traffic. But, occasional peaks of requests, generated when many devices react to the same event, may severely degrade network performance (i.e., by increasing the collision probability). This letter investigates, through computer simulations, the performance of well known procedures for the Random Access Channel, designed for the current 4G technology and the upcoming 5G system in challenging scenarios never seen before. Specifically, the evaluation targets access peaks caused by emergency situations, including every phase of the protocol from the initial contention to the transmission of the application payload. Obtained results highlights pros and cons of available solutions, while showing challenging issues that should be carefully addressed in future research activities.
... The Coded Slotted ALOHA (CSA) [22] scheme represents a further generalization of the IRSA scheme. The application of physical layer network coding (PNC) techniques to the resolution of slotted RA collisions has been investigated in [25] where the Network-Coded Diversity Protocol (NCDP) has been devised on top of burst repetition at random locations within the frame. NCDP leverages on PNC over extended Galois fields for recovering collisions in symbolsynchronous SA systems. ...
Chapter
The last few years had witnessed a growing interest for aspects related to Random Access (RA) protocol enhancements for terrestrial and satellite applications. Emerging machine-to-machine applications require efficient RA-like communication protocols for supporting network composed by a large number of sensors generating a low duty cycle packet traffic. Until recently, satellite networks were penalized by the poor RA protocols performance. This was mainly related to the satellite systems inherent large propagation delays. To overcome these issues, high-performance RA schemes with affordable complexity for satellite networks such as the Contention Resolution Diversity Slotted ALOHA (CRDSA) and the Enhanced-Spread Spectrum ALOHA (E-SSA) were recently proposed, demonstrated, and adopted in satellite standards such as ETSI DVB-RCS2 and ETSI S-MIM. Recent trends of RA for satellite networks, key design parameters, and ongoing research are covered by this chapter.
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Chapter
Half-title pageSeries pageTitle pageCopyright pageDedicationPrefaceAcknowledgementsContentsList of figuresHalf-title pageIndex
Article
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Chapter
Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H), and what is the ultimate transmission rate of communication (answer: the channel capacity C). For this reason some consider information theory to be a subset of communication theory. We will argue that it is much more. Indeed, it has fundamental contributions to make in statistical physics (thermodynamics), computer science (Kolmogorov complexity or algorithmic complexity), statistical inference (Occam's Razor: “The simplest explanation is best”) and to probability and statistics (error rates for optimal hypothesis testing and estimation). The relationship of information theory to other fields is discussed. Information theory intersects physics (statistical mechanics), mathematics (probability theory), electrical engineering (communication theory) and computer science (algorithmic complexity). We describe these areas of intersection in detail.
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