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We present a novel scheme for Slotted ALOHA random access systems (RAS) that combines physical-layer network coding (PLNC) with multiuser detection (MUD). PLNC and MUD are applied jointly at the physical (PHY) layer in order to extract any linear combination of messages experiencing a collision within a slot. The set of combinations extracted from a whole frame is then processed by the receiver to recover the original packets. A simple pre-coding stage at the transmitting terminals allows the receiver to further decrease the packet loss rate. We present results for the decoding at the PHY layer as well as several performance measures at frame level, namely throughput, packet loss rate and energy efficiency. The results we present are promising and suggest that a cross layer approach leveraging on the joint use of PLNC and MUD can significantly improve the performance of RA systems in the presence of slow fading.
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Seek and Decode: Random Access with
Physical-Layer Network Coding and Multiuser
Detection
Giuseppe Cocco, Stephan Pfletschinger∗† and Monica Navarro
German Aerospace Center – DLR
Offenburg University of Applied Sciences
Centre Tecnol`
ogic de Telecomunicacions de Catalunya (CTTC)
mailto: giuseppe.cocco@dlr.de
Abstract
We present a novel scheme for Slotted ALOHA random access systems (RAS) that combines
physical-layer network coding (PLNC) with multiuser detection (MUD). PLNC and MUD are applied
jointly at the physical (PHY) layer in order to extract any linear combination of messages experiencing
a collision within a slot. The set of combinations extracted from a whole frame is then processed by the
receiver to recover the original packets. A simple pre-coding stage at the transmitting terminals allows
the receiver to further decrease the packet loss rate. We present results for the decoding at the PHY layer
as well as several performance measures at frame level, namely throughput, packet loss rate and energy
efficiency. The results we present are promising and suggest that a cross layer approach leveraging
on the joint use of PLNC and MUD can significantly improve the performance of RA systems in the
presence of slow fading.
I. INTRODUCTION
Random access systems (RAS) can be regarded as an opportunity and a challenge at the same
time. On the one side RAS require little coordination among the transmitters. This, among other
Part of the results presented in this article have been presented at ICC 2014 [1] and NetCod 2014 [2] conferences.
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advantages, makes it possible to live together with large delays that are typical, for instance, of
satellite communication networks. As a drawback, the lack of coordination brings about the issue
of signals from different transmitters interfering at the receiver. So far different ways of tackling
the problem of collisions in RAS have been proposed. These include exploiting the difference
in the power of the received signals [3] or applying multiuser detection (MUD) methods as in
code-division multiple-access (CDMA) systems [4]. Multi-packet reception, i.e., the capability
for the receiver to decode more than one packet from a collision, has been and still is an active
research field. In [5] an overview of the main MUD techniques is presented. The impact of
multi-packets reception in Slotted ALOHA systems has been studied in [6]. Another approach
proposed in the literature consists in having each transmitter sending multiple replicas of the
same packet within a frame. The receiver tries to decode the packets that do not experience
collision as proposed in [7] or, once such clean packets have been decoded, it subtracts the
decoded packets from the slots where their replicas are [8] [9]. Recently, the possibility of
decoding functions of colliding signals has been studied in [10] and [11]. In these works the
linearity of error correction codes has been applied in the two-way relay channel (TWRC) to
decode the XOR of messages experiencing a collision. Starting from the sum of the physical
signals and assuming the same channel code at both end nodes is used, the corresponding XOR
is calculated and exploited, through an adequate MAC protocol, as side information to recover
the single messages. This approach is one of the possible implementations of the wider concept
of physical-layer network coding (PLNC). The performance limits for the decoding of the sum
of colliding signals have been studied from an information theoretical perspective and assuming
lattice codes in [12] [13]. Most of the literature about PLNC focuses on the TWRC. In [14]
[15] a quaternary decoding approach for the MAC phase of the two-way relay channel has
been proposed, showing that there is an advantage in computing the XOR by combining the
previously estimated individual messages, rather than directly decoding the sum from the analog
signal. In [16] PLNC has been applied to random access systems by decoding the XOR of all
colliding signals within a slot and then trying to recover all transmitted packets within a frame
using matrix manipulations in F2. In [17] and [18], an enhanced scheme based on PLNC over
extension fields has been proposed. An information theoretical analysis of the performance of
physical-layer network coding in random access systems has been presented in [19]. Recent
variants of coded random access schemes are presented in [20], focusing on MAC aspects and
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their asymptotic performance. Details on the theoretical analysis of the different proposals based
on the coded Slotted ALOHA paradigm (with the respective frame and frameless approaches)
can be found in the references therein, together with discussions on practical implementation
aspects. In [21] a theoretical analysis of coded Slotted ALOHA systems is presented. In [22]
and [23] a practical implementation of a system that makes use of both PLNC and MUD in the
multiple-access channel of a wireless local area network is presented. Specifically, in [23] the
case of two colliding signals is considered, a relaying setup is assumed and a joint detection
(but not joint decoding) is performed. Practical solutions for the detection of active users and
the estimation of channel state information parameters are also being actively investigated [24].
Some of these techniques are based on compressed-sensing or sparse multi-user detection. Since
this is a common problem for coded random access solutions in general, we are not providing
a complete overview of recent work. Some examples and related bibliography can be found in
[25] [26].
In the present paper we propose two novel schemes to enhance throughput and packet loss
rate (PLR) of Slotted ALOHA networks that leverage on a combination of PLNC and MUD. In
the proposed schemes each information message undergoes a pre-coding stage at the transmitter
before the channel encoding. The pre-coding consists in a simple multiplication by a coefficient
drawn at random from an extension field. The receiver tries to decode at the physical (PHY) layer
any linear combination in F2from the set of colliding bursts within each slot. Once the whole
frame has been processed at the PHY layer, the receiver uses the set of linear combinations
available to retrieve all messages transmitted within the frame by using matrix manipulation
techniques over the same extension field of the pre-coding stage. The use of an extension field
in the pre-coding stage decreases the PLR of the system. At the PHY layer the receiver employs
a hybrid between a PLNC decoder and a MUD. Two different MUD schemes are considered in
combination with PLNC. One is a joint decoder (JD), in which all signals are decoded jointly
1. The other MUD technique we combine with PLNC is successive interference cancellation
(SIC). We present numerical results for the number of innovative (i.e., linearly independent)
1This differs significantly from a parallel interference cancellation scheme (PIC), since in this last one several decoders are
employed in parallel estimating a different message each, while in a JD just one decoder is used, which decodes jointly all of
the messages.
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messages decoded within a slot as well as for throughput, packet loss rate and energy efficiency
in a framed slotted ALOHA-like scenario. Our results show that, unlike the scheme presented in
[18], the joint use of PLNC and joint decoding is robust against slow fading, which characterizes
many scenarios of practical relevance.
The rest of the paper is organized as follows. In Section II we introduce the system model.
In Section III the proposed approach is described while in Section IV we focus on the different
decoding alternatives at the PHY layer. Section V contains the numerical results, while the
conclusions are presented in Section VI.
II. SYSTEM MODEL
Let us consider a random multiple-access network with an infinite population of terminals and
one receiver Rx. Time is divided into slots. Transmissions are organized in frames of Sslots
each. We define a packet uas a block of RN information bits. The user population generates
an aggregated offered traffic which is modelled as a Poisson process of intensity Gpackets per
slot. Each time a packet ui= [ui,1,...,ui,RN ]is generated at terminal Ti, it is channel encoded
using an encoder of rate R, thus creating a codeword ci= [ci,1,...,ci,N ]of Nbits. The same
channel code is used by all transmitting terminals. The codeword ciis then mapped to a binary
phase-shift keying (BPSK)-modulated burst xiand transmitted over the channel. We consider
BPSK modulation for simplicity, but other kinds of modulations can also be used. It is worth
noting that the specific modulation considered can have a significant impact on the packet loss
rate performance of PLNC. In [27] and [28] it was shown that finding the modulation which
minimizes the message error rate is not trivial even for an uncoded system and collisions of size
2. How to optimally design the modulation constellation in a coded system and for a generic
collision size in case a joint PLNC and MUD receiver is used is a challenging open problem
which is out of the scope of the present paper. We also point out that the schemes proposed in
the following rely on channel codes and modulations already in use in commercial standards and
have the advantage of requiring little modification at the transmitter side. Most of the additional
complexity if moved at the receiver which usually has less constraints in terms of computational
capabilities with respect to the user terminals.
We assume that the burst duration is approximately equal to that of a slot. Let us now consider
one of the Sslots of the frame. In case of a collision of Kpackets (namely, collision of size
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K) the n-th sample of the received signal can then be written as
yn=
K
X
k=1
hkxk,n +wn, wn∼ N (0,1) ,(1)
where the fading coefficients are real-valued and follow a certain probability distribution with
E{|hk|2}=SNR,E{x}being the mean value of x. The fading coefficients are estimated at the
receiver but are not known at the transmitters and are assumed to change in an independently
and identically distributed (i.i.d.) fashion across terminals and time slots. We further assume
that the transmitters are synchronized such that all signals transmitted within a slot add up with
symbol synchronism at the receiver. At the receiver side, Rx first processes the frame at the
physical level one slot at a time. The PHY processing consists into applying a combination of
MUD and PLNC in order to decode as many linearly independent messages as possible. Once
the processing at the PHY level is completed, Rx applies a second step of decoding, which takes
place at the frame level. The linear combinations recovered from the PHY layer processing are
then treated as a system of equations in Fq,qbeing an extension field of the kind q= 2nbc ,
nbc N. The decoding process at frame level is detailed in Section III while the details of how
different combinations are extracted from the same collision are given in Section IV.
III. RANDOM ACCESS WITH PLNC AND MUD
In the present section we describe the proposed random access scheme named Seek and
Decode (S&D). The transmitter side is the same as in [18]. The main innovation is in the
decoding process at both slot level and frame level. We briefly recall the operations at the
transmitter side presented in [18] and then move on to the description of the receiver side.
A. Transmitter Side
Each burst is transmitted more than once within a frame, i.e., several replicas of the same
burst are transmitted. Assume that terminal ihas a message uito deliver to Rx during a given
frame, i.e., terminal Tiis an active terminal in that frame. Before each transmission, terminal i
pre-encodes uias depicted in Fig. 1. The message to be transmitted is divided into groups of
nbc bits each. Each group of bits is mapped to a symbol in Fq,q= 2nbc , and then multiplied by
a coefficient αi,j Fq. The coefficient αi,j,j∈ {1,...,S}, is chosen at random in each time
slot jwhile it is fixed for all symbols within a message. Note that the pre-coding does not have
6
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ߙ,א ॲ
,
ܰ ܴܰ ܰ
Fig. 1. Pre-coding, channel coding and modulation at the transmitter side. Pre-coding consists in mapping the message to a
vector in Fq,q= 2nbc , multiply each element of the vector by the same coefficient αi,j randomly chosen in Fqand apply an
inverse mapping (Fq)1from Fqto {0,1}. The sub index jindicates the slot within a frame in which the replica of message
uiis transmitted. A different coefficient αi,j is used for each replica.
any impact on the decoding process at the PHY layer and requires little increase in complexity
with respect to a traditional scheme. The multiplication of uiby αi,j helps the decoding at
frame level, which will be described later in this section. After the multiplication, the message
is channel-encoded (block Gin Fig. 1), a header is attached and the modulation takes place
(block µin Fig. 1). Pseudo-noise sequences with good cross-correlation properties can be used in
the header in order to identify the user within the frame. Such identification allows the receiver
to deduce the pre-coding coefficients used by each transmitter as described in the following. The
coefficients αi,j can be generated using a pseudo-random number generator. In a given frame the
active terminal chooses a seed for the generator and takes from it as many outputs as the number
of replicas to be transmitted. Each seed is associated to a certain header, which is detected by
the receiver using the cross-correlation properties of the header2. The same header is used within
a given frame by an active terminal. In this way the receiver can detect which slots a certain
terminal is transmitting in and derive the coefficients used in the different replicas from the
header. The header is also used to perform the channel estimation of each of the transmitters. A
more detailed analysis of the issues related to header detection and channel estimation can be
found in [18], [29], [30].
2Other PHY layer signatures can also be used by the terminals to allow Rx to identify the transmitters. This is a subject
which has been extensively studied in literature and further discussion on this is out of the scope of the present work.
7
B. Receiver Side: Decoding at Frame Level
According to the literature related to random access systems, when two or more signals
interfere at the receiver, this can either use some kind of interference cancelation or, as in
physical-layer network coding, try to decode a function of the colliding signals. Most of the
MUD techniques found in literature can be categorized as PIC or SIC. Often such methods are
iterative and alternate a detection phase to an estimation phase. In the proposed scheme the
receiver applies a joint decoder which tries to recover simultaneously all messages involved in
the collision. An FFT-based belief propagation decoder over the vectorial combination of all
message bits, which is described in detail in [31], is adopted. The decoder jointly estimates all
the single messages and then calculates the XOR of any subset of the estimated messages. It is
important to notice that, as shown in [15], the sum in F2of a set of estimated messages can be
correct even if the estimated messages taken individually contain errors. A cyclic redundancy
check (CRC) can be used for error detection. Thanks to the linearity of the channel code, the
XOR of the CRCs relative to a set of messages is a valid CRC for the XOR of the messages
in the set. Here we assume ideal error detection at the receiver for ease of exposition. Given a
slot with a collision of size K, the receiver tries to decode Kindependent linear combinations
in F2of the colliding signals. The total number of linear combinations that the decoder can
try to recover is PK
i=1 K
i= 2K1. Assuming the receiver is able to reliably estimate the
random coefficients and the identity of the transmitters in each slot through the packet headers
[18] [29], each decoded linear combination in F2can be interpreted at the receiver, according
to arithmetics of extension fields, as an equation in Fq,q= 2nbc . Stacking together all equations
the receiver ends up with a linear system having the form
ATU=b,(2)
where Ais the coefficient matrix having Ntx rows and a number of columns that depends on
the number of combinations decoded at PHY layer, U= [u1,...,uNtx ]Tis a vector containing
the information messages transmitted by the Ntx active terminals in the frame, bis a vector
containing the output of the decoding at PHY layer and Tis the transpose operator.
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IV. DECODING AT SLOT LEVEL
In Section III we described the proposed scheme assuming a joint decoder is applied at the
PHY layer. As described in the previous section the joint decoder first estimates all the single
messages involved in a certain collision. Afterwards, the S&D variant of the joint decoding is
applied, in which the sum in F2of any subset of the estimated messages is calculated. Since
in some cases the sum of a set of estimated messages can be correct even if the estimated
messages taken individually contain errors, the S&D approach increases the average number of
useful packets decoded from a collision with respect to a normal joint decoder.
This approach is only one of the many in which MUD can be combined with PLNC. In fact
other kinds of MUD can be adapted to the S&D scheme rather than joint decoding. Although
some of them may lose in terms of performance with respect to the joint decoding approach,
they can be attractive from a practical perspective for their lower complexity.
For completeness we recall that PLNC can be applied to recover the sum of all the collided
packets for a generic collision size directly to the received signal, i.e., without first trying to
estimate the individual messages. We do not report here the details for a matter of space and
remaind the interested reader to [32] for further details.
In the present section we describe several alternative schemes while in Section V we numer-
ically compare their performance in terms of the number of innovative packets decoded from
a collision. Here we focus only on the decoding within a slot, while the performance at frame
level is assessed in Section V.
A. Separate Decoding
The simplest approach is to decode each packet separately, considering all other packets as
interference. As for all other schemes to follow, we assume channel state information (CSI) at
the receiver for all transmitting terminals as well as knowledge at the receiver of the transmit
alphabet, i.e. BPSK constellation 3. With this, and assuming Kbursts collide in a slot, we
can write the log-likelihood value (L-value) of user i, i ∈ {1,2,...,K}and symbol position
n, n ∈ {1,2,...,N}, as:
3A further simplification would be to consider the interference as Gaussian noise, which would result in reduced performance
and is therefore not considered here.
9
Li,n ,ln P[cki,n = 1 |yn]
P[ci,n = 0 |yn]= ln P[xi,n = 1 |yn]
P[xi,n =1|yn].(3)
According to Eqn. (1), ynis a weighted sum of the n-th symbols of all Kcolliding signals.
Since the received symbol yndepends on all symbols, we need to marginalize over all other
users’ symbols. For this, we define the sets X(b)
i,x=µ(d) : dFK
2, di=bfor bF2,
with cardinalityX(b)
i= 2K1. We can think of the variable das the vector of the coded bits
of all users at the same position, i.e. dn= [c1,n, c2,n ,...,cK,n]T. We obtain for the L-values
Li,n = ln Px∈X (1)
i
P[x|yn]
Px∈X (0)
i
P[x|yn]= ln Px∈X (1)
i
p(yn|x)
Px∈X (0)
i
p(yn|x)
= ln Px∈X (1)
i
exp ynhTx2
Px∈X (0)
i
exp (ynhTx)2
= jacln
x∈X (1)
inynhTx2o
jacln
x∈X (0)
inynhTx2o
(4)
where jacln {x1,...,xn},ln Pn
j=1 exp (xj)denotes the Jacobian logarithm, which can be
computed recursively and for which computationally efficient approximations exist [33]. These
L-values are input to a soft-input decoder, which typically is a Viterbi, a turbo or an LDPC
decoder.
B. Successive Interference Cancellation (SIC)
A straightforward and well-known extension of basic single-user decoding is SIC: if a packet
ukis successfully decoded, its corresponding codeword ckand symbol sequence xkare known
and can be subtracted from the received signal yn, creating a multiple-access channel (as defined
in [34]) within a slot with K1terminals. This process can be repeated until decoding of all
remaining packets fails. To avoid unneccessary computations, we can exploit the knowledge of
the instantaneous SNRs and order the users accordingly: let πbe a permutation of {1,2,...,K}
such that
hπ(1) hπ(2) · · · hπ(K).(5)
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Then decoding starts with user π(1). Apart from reducing computational complexity, this ordering
is also useful to reduce the probability of undetected errors. To check the correct decoding of
a packet, usually an additional error detection code, e.g. a CRC, has to be introduced into each
message uk. Since there is a non-zero probability that an erroneous decoding is not detected,
the number of decoding attempts with low probability of success should be kept to a minimum.
C. Seek & Decode with Successive Interference Cancellation (S&D+SIC)
For a coded Slotted ALOHA system, a further decoding step after SIC is possible. Assume
that after the SIC procedure described above, KK1packets have been correctly decoded, hence
leaving K1∈ {2,...,K}packets for which decoding failed. In this situation, the receiver can try
to decode a combined packet, which is given by the sum of two or more of the packets that have
not yet been decoded. In a typical SIC the decoding process would stop here. In the proposed
S&D approach, instead, the receiver can try to decode the sum of a subset of {1,2,...,K1},
e.g. given by K={k1, k2, . . . , k} ⊂ {1,2,...,K1}. If the decoding is successful, such decoded
packet can be exploited as a side information in order to help the decoding of other packets
within the same collision. The way in which such side information is exploited resembles the
SIC process, even though the cancellation is not applied directly on the sampled signal. In
the following we detail such mechanism more in depth. Let us assume that no user in the set
{1,2,...,K1}could be decoded with the normal SIC. Then the receiver can try to decode the
sum of a subset of {1,2, . . . , K1}, e.g. given by K={k1, k2,...,k} ⊂ {1,2,...,K1}. For this
subset we define the sets of constellation symbols for 2as
X(b)
,(x=µ(d) : dF
2with
X
i=1
di=b), b F2,(6)
µ() being the mapping function from bits to constellation symbols, and obtain the corresponding
L-values as
LK
n= ln P
xX(1)
exp (yn[hk1hk2···hk]x)2
P
xX(0)
exp (yn[hk1hk2···hk]x)2.(7)
These L-values LK
1, LK
2, . . . , LK
Nare fed to the soft-input decoder, which, if successful, finds
the corresponding codeword Pk∈K ckor message Pk∈K uk. Note that the sum of messages or
codewords is defined in the finite field F2, which is the same as the bit-wise XOR. This concept
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of packet combining is closely related to inter-flow network coding and it exploits the linearity
of the code, which can be seen by the relation
X
k∈K
ck=X
k∈K
ukG.(8)
For error detection, since CRC codes are also binary linear codes, the same CRC can be used.
For K1undecoded packets, there exist
K1
X
=2 K1
= 2K1K11
combinations of two or more packets, for which a decoding attempt is possible from the L-values
defined by (7). With this definition, note that the subsets X(b)
only depend on band on the
number of packets but not on their indices k1,...,k. After successful decoding of a packet
sum, a subsequent idea is to re-apply interference cancellation with the packet combination.
This, however, is not directly possible since the combined codeword cK=Pk∈K ckdoes not
correspond to any received symbol sequence xkin (1) and the sum of codewords and symbol
sequences are taken over different fields, namely F2and R. However, knowledge of a combined
packet cKmight still be useful for another decoding attempt: the cardinality of the sets X(b)
can
be reduced by a factor of two by introducing the additional constraint of the known combined
packet. Then, the L-values can be recomputed and new decoding attempts (including = 1 for
individual packets) can be undertaken. This approach brings about a slight additional complexity
due to the constraint on the decoded combination. In this case, the sets X(b)
will additionally
depend on nand hence have to be computed for each coded bit.
It is interesting how such approach has strong similarities with [35]. In [35] the decoder first
tries to decode linear combinations of a subset of the colliding messages, and then uses the
knowledge of such combination to help recovering others. In [35] as well as in the approach
just presented, the knowledge of the first combination decoded can not be exploited by just
subtracting it from the received signal, since it does not contain a waveform corresponding to
the decoded combination. However, in both cases such side information can be exploited by the
decoder. Although there are significant differences between the channel models in the two cases,
a joint study of the two models may lead to interesting results from a practical perspective. The
need for an in-depth analysis of the subject does not allow for an adequate assessment in the
present paper and is left as a promising matter of study for future works.
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D. Seek & Decode with Joint Decoding (S&D+JD)
From (1) we can observe that, for what concerns the detection, the received samples yn
depend on all coded bits ck,n at the same bit position but are independent of bits at other
positions. The optimum decoding approach is therefore to consider the vectorial symbols dn,
[c1,n, c2,n ,...,cK,n]Tjointly. This can be done with a joint decoder which operates on the vectors
dnor on an equivalent integer representation ¯
dnsuch that dn= bin( ¯
dn). The notation bin(b)
denotes the binary representation of the non-negative integer b. For LDPC and for convolutional
codes, such joint decoders are described in [31], [36]. The decoder input is given by the
probability vector
pn,
pn(0)
pn(1)
.
.
.
pn(2K1)
R2K,(9)
where
pn(b),P[d= bin (b)|yn]p(yn|x=µ(bin(b))) ,(10)
for b= 0,1,...,2K1. Let ¯
xb=µ(bin(b)), then
pn=α
exp ynhT¯
x02
exp ynhT¯
x12
.
.
.
exp ynhT¯
x2K12
,(11)
where αis a scaling factor which is irrelevant for the decoding algorithm. The decoder output
is an estimate of all messages (or equivalenty of all codewords),
ˆ
Uslot =
ˆ
u1
ˆ
u2
.
.
.
ˆ
uK
.(12)
Note that ˆ
Uslot in Eqn. (12) refers to the packets transmitted within a slot, while ˆ
Uin (2)
refers to the packets transmitted in a whole frame. Making use of an error detecting code,
the receiver checks all possible packet combinations, i.e. all 2K1non-empty subsets of
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{ˆ
u1,ˆ
u2,...,ˆ
uK}and builds the binary matrix Aslot F(2K1)×K
2. Matrix Aslot is such that
its rows a= [a1, a2,...,aK]indicate the user indices which are contained in successfully
decoded combinations. For instance, if the combined packet c1+c3+c4is correctly decoded,
the corresponding row is a= [1,0,1,1,0,0] for K= 6. From this matrix, the number of
innovative packets decoded from the collision of size Kis calculated as its rank. This joint
decoding approach reverses the order of the S&D+SIC method: while in S&D+SIC the packet
combination is determined first and then a decoding attempt is carried out, joint decoding first
tries to decode all packets jointly and then the receiver checks which combinations are correct.
In order to assess the performance of the different schemes considered so far, we count the
number of innovative packets per slot. Innovative packets are either individually decoded packets
or combinations of packets which cannot be obtained by combining other decoded packets.
The number of innovative packets is the same as the number of linearly independent packet
combinations, i.e., the rank of Aslot in F2arithmetic.
Another benchmark we consider is joint decoding (JD), which consists in applying the joint
decoder without PLNC. We adopt JD and SIC as benchmarks since they allow to measure the
gains of the joint use of PLNC and MUD with respect to MUD only. The main features of the
schemes presented in this section are summarized in Table I.
It is worth noting that many other MUD methods have been proposed and proved to achieve
good performance with respect to simple SIC, such as Turbo MUD [37]. For a matter of space
all such methods can not be compared in the present paper, and a full comparison is out of the
scope of this work. Besides, our choice of the MUD schemes is motivated by the following. The
JD is the optimal decoder, in that it jointly decodes the received messages, thus achieving better
performance (average rank of Aslot) than any other decoder. The SIC has been selected due to its
low implementation complexity and practical importance. As a matter of facts, SIC is nowadays
included in commercial communications standards such as the Digital Video Broadcasting -
Return Channel to Satellite (DVB-RCS) [38].
E. Example
In the following we illustrate the S&D scheme with a toy example. Let us consider a frame
with S= 2 slots and Ntx = 4 active terminals. Let us assume that terminals 1and 2transmit in
14
both slots, each time choosing at random their pre-coding coefficients. Terminal 3only transmits
in the first slot while terminal 4transmits only in the second, as illustrated in Fig. 2. The S&D
decoder is applied at the physical layer in one of the variants presented previously in the present
section. As explained, the S&D decoder consists of a combination of PLNC and MUD and,
depending on the channel state of each of the transmitters, it may be able to decode from a
single collision a number of linearly independent combinations up to the collision size. In the
following example we assume that the decoder is able to output only two linear combinations
from each of the two slots as shown in the picture. Starting from these combinations, the receiver
tries then to recover all information messages u1,...,u4by applying another decoding stage,
this time at packet level rather than at the PHY level. The decoding is possible if the coefficient
S&D
decoder
S&D
decoder
Fig. 2. Example of decoding at the PHY layer in S&D with a two-slots frame and four active terminals. Terminals 1and 2
transmit in both slots, each time choosing at random their pre-coding coefficients. Terminal 3only transmits in the first slot
while terminal 4transmits only in the second. We recall that, as shown in 1, uirepresents the mapping of the information
message uiform an RN-dimensional vector in F2to an RN/nbc -dimensional vector in Fq.
15
matrix Ain Fq(shown below) has rank equal to the number of active terminals
AT=
α1,1α2,10 0
α1,10α3,10
α1,2α2,20 0
0α2,20α4,2
.
In order to further clarify how the PHY decoder is able to obtain the system in Eqn. (2) starting
from the analog superposition of the interfering signals, let us consider the decoding of slot 1 in
the example of Fig. 2. The physical signal seen by the receiver is y1, which is the superposition
of signals x1,1,x2,1and x3,1transmitted by terminal 1, 2 and 3, respectively, each weighted
by the corresponding fading coefficient. By applying the S&D decoder described previously in
this section the decoder outputs the bit-wise XOR of two different pairs of messages, namely
u
1,1u
2,1and u
1,1u
3,1. We recall that u
i,1is the packet that is transmitted by terminal Ti
in slot 1 after channel encoding and modulation. Due to the pre-coding (multiplication times a
random coefficient αi,1) we have u
i,1=αi,1ui, where the multiplication is done in Fq,q= 2nbc .
According to the arithmetics of extension fields, the bit-wise XOR (sum in F2) of u
1,1and u
2,1is
equivalent to the equation α1,1u1+α2,1u2in Fq. The pre-coding process adds little complexity
to the transmitters and allows to achieve better results in terms of packet loss rate (PLR) as
shown in the numerical results presented in Section V.
We recall from Section III that the coefficients in the matrix ATabove are chosen at random
by the four transmitters (see Fig. 1). Specifically, transmitter i, i = 1,...,4, chooses coefficients
αi,j ,j= 1,2. We see in the present example that coefficient α1,1is present twice in the first
column of matrix AT. This is because the first two rows of the matrix correspond to equations
obtained from the same slot. Note also that matrix Ais rank deficient if coefficients are chosen
in F2(i.e., all coefficients shown in the matrix above are equal to 1), while it can be full rank
if coefficients are chosen in some larger extension field, since the probability of obtaining a full
rank matrix increases with the field size [39]. This motivates the inclusion of the pre-coding
stage. We also note that in the example the average number of packets decoded per slot, if A
is full rank, is 2.
Note that Acontains information about the packet combinations within a given frame, but it
is not the same as the matrix Aslot defined in Section IV. Matrix Ais obtained by the receiver
16
combining the Aslot matrices from all the slots in the frame and using the information relative
to the pre-coding coefficients.
We stress the fact that the proposed scheme does much more than simply applying a MUD,
since any linear combination of the colliding signals decoded at the PHY layer can be exploited in
the second decoding phase at the frame level. We also note that, in principle, it would be possible
to use the soft information extracted from each slot and combine it at the frame level. Although
such approach would perform better than S&D, its complexity and memory requirements would
be much larger with respect to the S&D scheme, which has the advantage of processing each
slot only once and allows a lower complexity decoding at the frame level, since all operations
are performed over a GF of size 2nbc , which is suited to a digital implementation.
To conclude this section we recall that up to now we made the assumption that the receiver
is able to estimate the fading coefficients starting from the preambles of the colliding signals.
The practical feasibility of the channel estimation, as well as other practical issues, have been
discussed in [29] and so are not dealt with explicitly here. The channel estimation based on the
estimate-maximize algorithm presented in [29] has been enhanced in [30] exploiting the cross-
correlation properties of the preamble as well as considering the presence of pilot symbols, that
are foreseen by many standards, showing that the average channel estimation error in a MUD
context can be kept reasonably low in a practical setup.
F. Complexity Considerations and Possible Combined Approaches
An important aspect in the different decoding approaches at the PHY layer is their performance-
complexity tradeoff. For the basic separate decoding scheme, complexity can be reduced by
ordering users according to their instantaneous SNR and stop decoding after the decoding of
one user has failed. This will obviously cause a slight performance loss which depends mainly on
the SNR differences and on the applied coding scheme, i.e. basically on the packet length. The
same idea can be applied to both SIC techniques, while for S&D+SIC, a packet combination can
be checked for linear independency before the decoding attempt. The complexity of S&D+SIC
in the worst case is proportional to 2K1decoding attempts. The complexity of joint decoding
using LDPC codes is proportional to K·2Kfor belief propagation with transform-based check-
node processing [40], [41]. This complexity can be reduced on the one hand by applying joint
decoding after SIC and on the other hand by applying reduced-complexity decoding algorithms
17
[42].
V. NUMERICAL RESULTS
In this section we evaluate numerically the performance of the proposed schemes. First we
compare the different PHY layer decoding approaches presented in Section IV in terms of number
of innovative packets decoded from a single slot, then we move to the comparison of throughput,
packet loss rate and energy efficiency at frame level for the S&D scheme and several benchmark
systems.
A. Performance at Slot Level
We recall that innovative packets are either individually decoded packets or combinations of
packets which cannot be obtained by combining other decoded packets. Figures 3 and 4 show the
achieved number of innovative packets per slot with the described decoding techniques with 4
and 8users, that correspond to the average rank of the matrix Aslot defined in Section IV. We can
see that for both cases, S&D+JD performs best and its gain with respect to the others increases
with the number of users. For a high number of users, the advantage of S&D+JD to all other
techniques is dramatic. On the other hand, we point out that, unlike S&D+JD, the S&D+SIC
scheme has the advantage that is does not require any modification at the decoder, since only
the LLR calculation is modified with respect to a standard receiver. We further note that the
advantage of S&D+SIC over pure SIC decreases with the number of users. For sufficiently high
SNR, all methods benefit from collided packets, which can be most clearly seen in Fig. 3 for four
users. At low SNR the average number of recovered packets per slot is close to the single-user
case, while for medium to high SNR, on average more than one packet is recovered from a
single slot. For all considered cases, the number of innovative packets tends to Kas the SNR
grows, i.e. for high SNR nearly all collided packets can be decoded.
B. Performance at Frame Level
We define the normalized throughput Tas the average number of packets decoded within a
slot averaged across the realizations. We further define the PLR as the ratio of the number of
lost packets to the total number of packets transmitted (not counting repetitions). The following
18
−5 0 5 10 15 20 25 30
0
0.5
1
1.5
2
2.5
3
3.5
4
SNR [dB]
Innovative packets per slot
S&D+JD
S&D+SIC
SIC
Sep. dec.
Single user
Fig. 3. Innovative packets decoded per slot versus average SNR in Rayleigh fading channel for a collision of size K= 4.
holds:
T=G(1 P LR).(13)
Note that G, which represents the logical load of the network [8] [9], is independent of the number
of times a message is repeated within a frame. The physical load on the network is larger than
or equal to G. In particular, if 2 copies of the same packet are sent by each active terminal,
then the physical load is twice as large as the logical load. Since the interaction between the
frame and the PHY layers are of fundamental importance in the schemes considered here, in the
simulations the whole decoding process has been implemented. The actual decoded combinations
at the physical layer have been used as input to the decoder at the frame level. As suggested
in Section III, if rank(A)< Ntx , i.e. not all messages can be decoded in a frame, the receiver
19
10 15 20 25 30 35 40
0
1
2
3
4
5
6
7
8
SNR [dB]
Innovative packets per slot
S&D+JD
S&D+SIC
SIC
Sep. dec.
Single user
Fig. 4. Innovative packets decoded per slot versus average SNR in Rayleigh fading channel for a collision of size K= 8.
applies Gaussian elimination on Ain order to extract as many packets as possible. In Fig. 5,
6 and 7 T, PLR and the energy efficiency are plotted against the network load G, respectively.
The energy efficiency is defined as the ratio of the number of repetitions (which is proportional
to the total amount of energy used to transmit a packet) to the number of decoded packets (not
counting repetitions). Two repetitions and a frame with S= 10 slots have been considered for all
schemes. A Rayleigh block fading channel with 15 dB average SNR has been considered. The
LDPC code of the WiMAX standard with parameters N= 576,R= 1/2, and BPSK modulation
have been adopted. A maximum collision size of K= 7 has been set, i.e., collisions of more
than 7signals are discarded. The introduction of a maximum decodable collision size is justified
by practical issues such as complexity and power saturation at the receiver. In Fig. 5 it can be
20
0.5 1 1.5 2 2.5
0
0.5
1
1.5
2
T
G
S&D+JD, nbc=8
S&D+SIC, nbc=8
S&D+JD, nbc=2
S&D+SIC, nbc=2
Joint dec.
SIC
Sep. dec.
Slotted ALOHA
Fig. 5. Throughput in Rayleigh block fading channel, SNR=15 dB. The channel code is the WiMAX LDPC with parameters
N = 576, R= 1/2, BPSK modulation. 2 replicas of the same packet are transmitted by each user. A maximum collision size
of K= 7 has been set. Collisions of higher order are discarded. The frame size Shas been set to S= 10 slots.
seen how S&D provides significant gains in terms of throughput with respect to the schemes
that apply MUD only. The use of a larger field size in the pre-coding stage slightly increases the
peak throughput and enhances the PLR performance at low network loads, as shown in Fig. 5
and 6, respectively. In order to quantify such enhancement, we evaluated through Monte Carlo
simulations the probability that, once the iterative decoding stops, the rest of the packets can
be decoded through matrix inversion. In correspondence to a load of G= 2.1(for which the
peak throughput with the configuration of Fig. 5 is achieved), such probability is around 4% for
coefficients in F28and 0.06% for coefficients in F2, i.e., the probability to decode the remaining
packets is about sixty times larger when the field with higher cardinality is used. However, since
both probabilities are relatively small, the overall improvement on the throughput is limited.
21
0.5 1 1.5 2 2.5
10−3
10−2
10−1
100
packet loss rate
G
S&D+JD, nbc=8
S&D+SIC nbc=8
S&D+JD, nbc=2
S&D+SIC, nbc=2
Joint dec.
SIC
Sep. dec.
Slotted ALOHA
Fig. 6. Packet loss rate in Rayleigh block fading channel, SNR=15 dB. The channel code is the LDPC used in WiMAX standard
with parameters N = 576, R= 1/2, BPSK modulation. 2 replicas of the same packet are transmitted by each user. A maximum
collision size of K= 7 has been set. Collisions of higher order are discarded. The frame size Shas been set to S= 10 slots.
In figures 8, 9 and 10 the throughput, packet loss rate and energy efficiency for an average
SNR of 10 dB are plotted, respectively. The rest of parameters are the same as in Fig. 5. By
comparing the two sets of figures it can be seen how the channel SNR impacts the decoding at
the PHY layer, which leads to a higher throughput and lower PLR when the SNR is higher, as
expected. At both SNR values the JD scheme performs better than all others non-S&D schemes
and at 10 dB closely approaches the S&D+SIC for lower network loads, outperforming it in the
region G > 1.5. Such good performance is due to the fact that the decoding of all messages
is done jointly rather than separately as in the SIC or the separate decoding schemes. The
22
0.5 1 1.5 2 2.5
0
2
4
6
8
10
12
14
16
18
20
G
Energy/decoded packet
S&D+JD, nbc=8
S&D+SIC nbc=8
S&D+JD, nbc=2
S&D+SIC, nbc=2
Joint dec.
SIC
Sep. dec.
Slotted ALOHA
Fig. 7. Energy efficiency plotted against load in Rayleigh block fading channel, SNR=15 dB. The channel code is the WiMAX
LDPC with parameters N = 576, R= 1/2, BPSK modulation. 2 replicas of the same packet are transmitted by each user. A
maximum collision size of K= 7 has been set. Collisions of higher order are discarded. The frame size Shas been set to
S= 10 slots.
introduction of PLNC significantly increases the performance of the JD scheme of up to a 13
% at both SNR values, as can be seen in Fig. 5 and Fig. 8. In all figures the Slotted ALOHA
scheme is also shown as a benchmark. In Slotted ALOHA all terminals transmit only one replica
of their message, while in all other schemes two replicas are used, i.e., twice the energy is used.
In order to compare the energy efficiency of the different schemes, in Fig. 7 and Fig. 10 we
show the average energy consumption per decoded message plotted against the load Gfor an
SNR of 10 dB and 15 dB, respectively. Slotted ALOHA shows a more efficient energy use at
low network loads up to about 0.7. This is due mainly to the fact that in Slotted ALOHA each
23
0.5 1 1.5 2 2.5
0
0.5
1
1.5
2
T
G
S&D+JD, nbc=8
S&D+SIC, nbc=8
S&D+JD, nbc=2
S&D+SIC, nbc=2
Joint dec.
SIC
Sep. dec.
Slotted ALOHA
Fig. 8. Throughput in Rayleigh block fading channel, SNR=10 dB. The channel code is the WiMAX LDPC with parameters
N = 576, R= 1/2, BPSK modulation. 2 replicas of the same packet are transmitted by each user. A maximum collision size
of K= 7 has been set. Collisions of higher order are discarded. The frame size Shas been set to S= 10 slots.
terminal transmits half of the power used in the other schemes. However, for G > 0.7these,
and most of all S&D+JD, perform significantly better than Slotted ALOHA in terms of energy
efficiency, confirming the effectiveness of the proposed approach in situations characterized by a
relatively high logical network load. The combined decoder is capable of extracting much more
information from collisions than each of the two techniques taken individually. Furthermore, the
solution presented here is robust against power unbalance (actually benefiting from it), which
constitutes an issue if PLNC is applied with no MUD as in [18]. It is worth pointing out the
fact that the joint decoding approach is optimal within each slot if this is treated as an isolated
channel. If, instead, the slot is regarded as part of a frame and multiple replicas of the same
packet are transmitted, using PLNC jointly with joint decoder brings a significant advantage.
24
This is not in contrast with the intuition that the joint decoder is optimal, since if it were applied
over the whole frame at once, it would lead to the best possible performance. However, the
huge increase in complexity makes such approach impractical. The advantage of our proposed
approach is that it brings significant advantages with respect to the joint decoder applied at slot
level with a limited increase in complexity, since the whole frame is processed only once at the
physical layer, while the rest of operations are done over a finite field. Our approach is not an
0.5 1 1.5 2 2.5
10−2
10−1
100
packet loss rate
G
S&D+JD, nbc=8
S&D+SIC nbc=8
S&D+JD, nbc=2
S&D+SIC, nbc=2
Joint dec.
SIC
Sep. dec.
Slotted ALOHA
Fig. 9. Packet loss rate in Rayleigh block fading channel, SNR=10 dB. The channel code is the WiMAX LDPC with parameters
N = 576, R= 1/2, BPSK modulation. 2 replicas of the same packet are transmitted by each user. A maximum collision size
of K= 7 has been set. Collisions of higher order are discarded. The frame size Shas been set to S= 10 slots.
alternative to other diversity schemes proposed for slotted ALOHA such as Irregular Repetition
Slotted ALOHA (IRSA) [9]. As a matter of fact the S&D approach can be used on top of IRSA.
The proposed scheme would allow either to increase the throughput for a given frame size or
25
0.5 1 1.5 2 2.5
0
2
4
6
8
10
12
14
16
18
20
22
G
Energy/decoded packet
S&D+JD, nbc=8
S&D+SIC nbc=8
S&D+JD, nbc=2
S&D+SIC, nbc=2
Joint dec.
SIC
Sep. dec.
Slotted ALOHA
Fig. 10. Energy efficiency plotted against load in Rayleigh block fading channel, SNR=10 dB. The channel code is the WiMAX
LDPC with parameters N = 576, R= 1/2, BPSK modulation. 2 replicas of the same packet are transmitted by each user. A
maximum collision size of K= 7 has been set. Collisions of higher order are discarded. The frame size Shas been set to
S= 10 slots.
to reduce the frame size while guaranteeing the same throughput. Similar considerations have
been presented in [43], where MUD is applied to IRSA. In order to show the gain deriving from
applying S&D on top of IRSA, we compare the throughput and PLR curves of the two schemes
for the case of a frame with 200 slots and Rayleigh fading channels with average SNR 15 dB.
In the simulation the number of replicas transmitted by a given user is chosen according to the
following degree distribution [9]:
Λ(x) = 0.5465x2+ 0.1623x3+ 0.2912x6.
26
Unlike in [43], the results shown in figures 11 and 12 have been obtained applying a combination
0 0.5 1 1.5 2
0
0.5
1
1.5
2
G
T
S&D+JD, nbc=8 on IRSA
IRSA
Fig. 11. Throughput in Rayleigh block fading channel, SNR=15 dB. The channel code is the WiMAX LDPC with parameters
N = 576, R= 1/2, BPSK modulation. A maximum collision size of K= 7 has been set for S&D only. The frame size Shas
been set to S= 200 slots.
of PLNC and MUD to IRSA rather than MUD alone. Note that the results for S&D could be
further enhanced by first running the IRSA cancellation in the analog domain and then applying
S&D on the remaining collisions. Since the S&D would work on collisions that on average have
a lower size, its performance would enhance.
We also point out that better performance can be obtained with an approach based on a frame-
level joint detection and decoding of all of the packets rather than using a slot-based approach
as we proposed in this paper. However, such approach would imply a considerable increase in
the complexity of the decoder with respect to our method.
27
0 0.5 1 1.5 2 2.5
10−6
10−4
10−2
100
G
packet loss rate
S&D+JD on IRSA
IRSA
Fig. 12. Packet loss rate in Rayleigh block fading channel, SNR=15 dB. The channel code is the WiMAX LDPC with parameters
N = 576, R= 1/2, BPSK modulation. A maximum collision size of K= 7 has been set for S&D only. The frame size Shas
been set to S= 200 slots.
C. Discussion
In order to have a complete picture of what are the performance limits of the proposed
scheme as well as how the different parameters impact the behavior of the scheme it would
be desirable to have an analytical expression for the throughput or, equivalently, for the packet
loss rate. An approximate semi-analytical expression for the throughput has been derived in [1]
under the assumption that each active user accesses the channel in each slot with probability
12nbc . Such expression is based on a bound on the probability to decode the sum of a
subset of colliding messages. Deriving a formula for the general case is quite challenging, since
it requires an analytical characterization of the packet error rate of finite length channel codes
over fading channel with no channel state information at the transmitter and in the presence of
28
interference. Furthermore, such characterization should also provide information on the behavior
of the specific code when it is applied in a MUD or PLNC context. It is in general very difficult
to model the performance of a specific error correcting code over a general discrete memoryless
channel (unless the code is particularly short, or embeds a very strong structure, as in the
case of convolutional and Reed Solomon codes). For LDPC and turbo codes, in general, rather
than modeling the performance of a specific code, ensemble-based arguments are used, which
nevertheless mostly assume maximum-likelihood (rather than iterative) decoding, and hence fail
to provide a realistic model [44]. From the simulations we carried out and from the available
bounds, we can say that larger nbc lead to a higher probability of having a full rank matrix,
although, as we showed previously in the present section, the gain when going from nbc = 2 to
nbc = 8 is limited. The analysis presented in [19] can be regarded as a starting point, although the
codes and the channel model are highly abstracted and thus particular care should be used when
transposing such results to practical setups. As a final remark, we point out that the proposed
method can be applied for channel codes of any packet length, although the performance of the
S&D decoder in general depends, apart from the specific channel code that is consider, also on
the codeword length.
VI. CONCLUSIONS
We proposed a novel cross-layer approach to random access systems that uses a hybrid PLNC-
MUD decoder at the PHY layer and a frame level decoder based on matrix manipulation over
extension fields. In the proposed scheme each terminal transmits several channel-coded replicas
of the same message within a frame after a pre-multiplication by a random coefficient in an
extension field Fq. At the PHY layer the receiver decodes as many linear combination as possible
in F2of the signals colliding in each slot. In the second decoding stage, which is carried out at
frame level, the set of combinations is treated by the receiver as a single system of equations in
Fq. We presented simulation results for throughput, packet loss rate and energy efficiency over
a block fading channel. The whole decoding process at both PHY and frame level has been
implemented in the simulations. Our results show that a significant enhancement in throughput
and PLR can be achieved by combining PLNC and MUD. The combined decoder is capable
of extracting much more information from collisions than each of the two techniques taken
individually. In particular, we showed that the combination of PLNC and JD together with the
29
frame-level decoding stage, considerably enhance the JD method, despite the fact that the latter
is optimal when applied to slots in isolation. Furthermore, unlike in previously proposed schemes
based on PLNC only, the approach presented in this paper is robust against block fading.
As future work we plan to optimize the multiple-access scheme taking into account the decoder
performance, which is a function of the collision size and the specific linear combination within
a collision, with the aim of maximizing the system throughput and minimizing the PLR also
taking energy efficiency into account.
As a final remark, we would like to point out that evaluating the impact of the joint use of
PLNC and MUD in random access systems is a challenging task and far from being concluded.
The present work can be regarded as a further step towards a full exploitation of these two
techniques in the Slotted ALOHA scenario.
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32
TABLE I
DECODING STRATEGIES AT PHY LAYER.
Method Description Requires Requires
pre-coding joint decoding
Separate dec. joint detection, no no
separate decoding
SIC joint detection, no no
separate decoding,
then interference
cancellation
S&D+SIC as in SIC, yes no
then detect/decode
combinations
JD joint detection, no yes
joint decoding
S&D+JD as in JD, yes yes
then combine
estimated messages
... We focus on the coded random access scheme from [15], which was adapted and proposed for massive access connectivity within European project [20], [21]. We generally adopt the corresponding numerology, though all considerations in this paper can be straightforwardly extended to different parameter values. ...
... There are 2 |T ′ | − |T ′ | − 1 such weight vectors to be considered, since we exclude the all-zero vector and the |T ′ | singleton vectors (which are not decodable due to the SIC routine having terminated). This sum decoding is done by a variation of the BP decoder (see Section 4.4 in [15] and Section III in [23] for details). The usefulness of sum decoding is best visualized by the histogram in Figure 2, which makes it evident that even when SIC fails, a large amount of codeword combinations can still be reliably decoded and are potentially helpful for frame-level decoding. ...
Preprint
Full-text available
Hinging on ideas from physical-layer network coding, some promising proposals of coded random access systems seek to improve system performance (while preserving low complexity) by means of packet repetitions and decoding of linear combinations of colliding packets, whenever the decoding of individual packets fails. The resulting linear combinations are then temporarily stored in the hope of gathering enough linearly independent combinations so as to eventually recover all individual packets through the resolution of a linear system at the end of the contention frame. However, it is unclear which among the numerous linear combinations---whose number grows exponentially with the degree of collision---will have low probability of decoding error. Since no analytical framework exists to determine which combinations are easiest to decode, this makes the case for a machine learning algorithm to assist the receiver in deciding which linear combinations to target. For this purpose, we train neural networks that approximate the error probability for every possible linear combination based on the estimated channel gains and demonstrate the effectiveness of our approach by numerical simulations.
... It is seen that the same NC is extracted for both methods. The proposed method in He and Liew 13 makes X R after 2 time slots using (1), while our proposed method extracts the same X R in 1 time slot based on PLNC-NWRC using (5). Considering Figure 2, the upper bound of probability of error for X R in He and Liew 13 can be calculated as follows: ...
Article
In this manuscript, fundamental limits of physical layer network coding with modulo‐sum mapping (to avoid of large cardinality), in terms of the number of superimposed signals that still render an acceptable probability of error, are studied. It is expected that (1) for a given bit error rate or throughput, an upper bound on the number of superimposed signals may exist and (2) if a large network is considered as a set of small subnetworks, and the proposed network code is used in each subnetwork, a significant increase in network throughput can be achieved. Answering these questions sheds light on the scalability of multiuser physical layer network coding with bit error rate or throughput constraints. Theoretical calculations, as well as simulation results, indicate the extraction of network coded signal from more than 2 (which is special case known as 2‐way relay channel) superimposed signals can be practical and lucrative. Fundamental limits of Physical Layer Network Coding (PLNC) with modulo‐sum mapping, in terms of the number of superimposed signals that still render an acceptable probability of error, are studied. For a given bit error rate or throughput, an upper bound on the number of superimposed signals is extracted. If a large network is considered as a set of small subnetworks utilizing the proposed network code in each subnetwork, a significant increase in network throughput can be achieved.
... It is seen that the same NC is extracted for both methods. The proposed method in He and Liew 13 makes X R after 2 time slots using (1), while our proposed method extracts the same X R in 1 time slot based on PLNC-NWRC using (5). Considering Figure 2, the upper bound of probability of error for X R in He and Liew 13 can be calculated as follows: ...
... If different users select the same time slot for transmission, a packet collision is experienced. While collided packets were irremediably lost in early versions of SA, recent studies have shown that collisions can be resolved by network diversity, multiuser detection, network coding strategies [3]–[5], or by successive interference cancellation (SIC) techniques [6] which substantially improves the system throughput. The key concept behind SIC is that each user might send repetitions of the same message in different slots. ...
Article
Full-text available
Motivated by the analogy between successive interference cancellation and iterative belief-propagation on erasure channels, irregular repetition slotted ALOHA (IRSA) strategies have received a lot of attention in the design of medium access control protocols. The IRSA schemes have been mostly analyzed for theoretical scenarios for homogenous sources, where they are shown to substantially improve the system performance compared to classical slotted ALOHA protocols. In this work, we consider generic systems where sources in different importance classes compete for a common channel. We propose a new prioritized IRSA algorithm and derive the probability to correctly resolve collisions for data from each source class. We then make use of our theoretical analysis to formulate a new optimization problem for selecting the transmission strategies of heterogenous sources. We optimize both the replication probability per class and the source rate per class, in such a way that the overall system utility is maximized. We then propose a heuristic-based algorithm for the selection of the transmission strategy, which is built on intrinsic characteristics of the iterative decoding methods adopted for recovering from collisions. Experimental results validate the accuracy of the theoretical study and show the gain of well-chosen prioritized transmission strategies for transmission of data from heterogenous classes over shared wireless channels.
Conference Paper
Full-text available
We present a novel decoding scheme for slotted ALOHA which is based on concepts from physical-layer network coding (PNC) and multi-user detection (MUD). In addition to recovering individual user packets from a packet collision as it is usually done with MUD, the receiver applies PNC to decode packet combinations that can be used to retrieve the original packets using information available from other slots. We evaluate the novel scheme and compare it with another scheme based on PNC that has been proposed recently and show that both attain important gains compared to basic successive interference cancellation. This suggests that combining PNC and MUD can lead to significant gains with respect to previously proposed methods on either one or the other.
Conference Paper
Full-text available
We present a novel random multiple access scheme that combines joint multiuser detection (MUD) with physical-layer network coding (PLNC) over extended Galois fields (EGF). We derive an analytical bound to the throughput at the system level and present simulation results for the decoding at the physical level in both fast fading and block fading channels. We adopt a cross layer approach in which a non-binary joint multiuser decoder is used in combination with PLNC at slot level, while the use of EGF increases the system diversity at frame level. The results we present are encouraging and suggest that the combination of these two interference management techniques can significantly enhance the performance of random multiple access systems.
Article
Full-text available
The emergence of Machine-to-Machine (M2M) communication requires new Medium Access Control (MAC) schemes and physical (PHY) layer concepts to support a massive number of access requests. The concept of coded random access, introduced recently, greatly outperforms other random access methods and is inherently capable to take advantage of the capture effect from the PHY layer. Furthermore, at the PHY layer, compressive sensing based multi-user detection (CS-MUD) is a novel technique that exploits sparsity in multi-user detection to achieve a joint activity and data detection. In this paper, we combine coded random access with CS-MUD on the PHY layer and show very promising results for the resulting protocol.
Article
The rise of machine-to-machine communications has rekindled interest in random access protocols as a support for a massive number of uncoordinatedly transmitting devices. The legacy ALOHA approach is developed under a collision model, where slots containing collided packets are considered as waste. However, if the common receiver (e.g. base station) is able to store the collision slots and use them in a transmission recovery process based on successive interference cancellation, the design space for access protocols is radically expanded. We present the paradigm of coded random access, in which the structure of the access protocol can be mapped to a structure of an erasure-correcting code defined on a graph. This opens the possibility to use coding theory and tools for designing efficient random access protocols, offering markedly better performance than ALOHA. Several instances of coded random access protocols are described, as well as a case study on how to upgrade a legacy ALOHA system using the ideas of coded random access.
Article
This paper presents a first real-time network-coded multiple access (NCMA) system that jointly exploits physical (PHY)-layer network coding (PNC) and multiuser decoding (MUD) to boost the throughput of a wireless local area network (WLAN). NCMA is a new design paradigm for multipacket reception wireless networks, in which the access point can receive and decode several packets simultaneously transmitted by multiple users. Conventionally, multipacket reception is realized using MUD only, whereas the key idea of NCMA is to use PNC together with MUD to realize multipacket reception. Although the feasibility of NCMA has previously been studied by the authors, our previous NCMA prototype was a version with offline signal processing. In addition, our previous investigation left open a number of theoretical and implementation issues, the resolution of which is critical to the adoption of NCMA in real practice. The current investigation makes the following state-of-the-art contributions toward NCMA: 1) we demonstrate a first NCMA system with integrated real-time PHY- and MAC-layer decoding; 2) we construct a new unified framework for MAC-layer decoding that yields higher throughput with faster decoding—the faster decoding is one of the key enablers of our real-time implementation; and 3) we design new PHY-layer decoding techniques that overcome the poor performance of the first-generation NCMA prototype at low SNR. Experimental results show that, compared with the previous NCMA prototype, our new NCMA prototype improves real-time throughput by more than 100% at medium-high SNR ( $geq$ 8 dB).
Article
This paper considers random multiple access in a network where only a small portion of users have data to forward and transmit packets in each time slot because the user activity ratio is not high in practice. For this reason, the access point (AP) has to not only identify the users who transmitted but also decode the received data codewords. Exploiting the sparsity of transmitting users, Lasso, which is a well-known practical compressed sensing algorithm, is applied for efficient user identification. The compressed sensing algorithm enables the AP to handle more users than the conventional random multiple access schemes do. We develop distributed scheduling methods for maximizing the system sum throughput, and we analyze the corresponding optimal throughput for three different cases of channel knowledge, i.e., the channel state information at the transmitter (CSIT), the channel state information at the receiver (CSIR), and the imperfect channel state information at the receiver (ImCSIR). We also derive the closed-form expressions of asymptotically optimal scheduling parameters and the corresponding maximum sum throughput for each CSI assumption. The results show the effects of system parameters on the sum throughput and provide useful insights on using compressed sensing for throughput maximization in random multiple access schemes.
Conference Paper
We develop a belief-propagation (BP) decoder for the joint decoding of multiple codewords which belong to the same non-binary LDPC code. Decoding is based on soft information in form of joint channel-posterior probabilities of all codeword symbols. We extend the BP algorithm for q-ary LDPC codes such that the FFT-based check node processing is preserved and the complexity remains manageable. This joint decoding is useful in settings in which multiple codewords are transmitted in a non-orthogonal way over the same channel, including multiple-access with packet collisions, physical-layer network coding and multi-resolution broadcasting. We show in an example that joint decoding can be far superior to separate decoding.
Article
This paper investigates various subtleties of applying linear physical-layer network coding (PNC) with q-level pulse amplitude modulation (q-PAM) in two-way relay channels (TWRC). A critical issue is how the PNC system performs when the received powers from the two users at the relay are imbalanced. In particular, how would the PNC system perform under slight power imbalance that is inevitable in practice, even when power control is applied? To answer these questions, this paper presents a comprehensive analysis of q-PAM PNC. Our contributions are as follows: 1) We give a systematic way to obtain the analytical relationship between the minimum distance of the signal constellation induced by the superimposed signals of the two users (a key performance determining factor) and the channel-gain ratio of the two users, for all q. In particular, we show how the minimum distance changes in a piecewise linear fashion as the channel-gain ratio varies. 2) We show that the performance of q-PAM PNC is highly sensitive to imbalanced received powers from the two users at the relay, even when the power imbalance is slight (e.g., the residual power imbalance in a power-controlled system). This sensitivity problem is exacerbated as q increases, calling into question the robustness of high-order modulated PNC. 3) We propose an asynchronized PNC system in which the symbol arrival times of the two users at the relay are deliberately made to be asynchronous. We show that such asynchronized PNC, when operated with a belief propagation (BP) decoder, can remove the sensitivity problem, allowing a robust high-order modulated PNC system to be built.