# Informed Dynamic Scheduling for Belief-Propagation Decoding of LDPC Codes

**ABSTRACT** Low-Density Parity-Check (LDPC) codes are usually decoded by running an iterative belief-propagation, or message-passing, algorithm over the factor graph of the code. The traditional message-passing schedule consists of updating all the variable nodes in the graph, using the same pre-update information, followed by updating all the check nodes of the graph, again, using the same pre-update information. Recently several studies show that sequential scheduling, in which messages are generated using the latest available information, significantly improves the convergence speed in terms of number of iterations. Sequential scheduling raises the problem of finding the best sequence of message updates. This paper presents practical scheduling strategies that use the value of the messages in the graph to find the next message to be updated. Simulation results show that these informed update sequences require significantly fewer iterations than standard sequential schedules. Furthermore, the paper shows that informed scheduling solves some standard trapping set errors. Therefore, it also outperforms traditional scheduling for a large numbers of iterations. Complexity and implementability issues are also addressed.

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**ABSTRACT:**Abstract Fountain codes have demonstrated great results for the binary erasure channel and have already been incorporated into several international standards to recover lost packets at the application layer. These include multimedia broadcast/multicast ses- sions and digital video broadcasting on global internet-protocol. The rateless property of Fountain codes holds great promise for noisy channels. These are more sophisti- cated mathematical models representing errors on communications links rather than only erasures. The practical implementation of Fountain codes for these channels, however, is hampered by high decoding cost and delay. In this work we study trapping sets in Fountain codes over noisy channels and their - SourceAvailable from: Brian K. Butler
##### Conference Paper: Error floor approximation for LDPC codes in the AWGN channel

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##### Conference Paper: Complexity reduction in BICM-ID systems through selective log-likelihood ratio updates

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**ABSTRACT:**Bit-interleaved coded modulation with iterative decoding (BICM-ID) performs well under a variety of channel conditions. Based on a factor graph representation of BICM-ID, we propose a modification of the BICM-ID receiver that can reduce the computational complexity significantly without BER degredation. The basic idea is to avoid updates of code bit log-likelihood ratios whose magnitude is already sufficiently large. Simulation results illustrate the excellent performance-complexity trade-off of our method.Signal Processing Advances in Wireless Communications (SPAWC), 2010 IEEE Eleventh International Workshop on; 07/2010

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arXiv:cs/0702111v2 [cs.IT] 28 Feb 2007

Informed Dynamic Scheduling for

Belief-Propagation Decoding of LDPC Codes

Andres I. Vila Casado, Miguel Griot and Richard D. Wesel

Department of Electrical Engineering, University of California, Los Angeles, CA 90095-1594

Email: avila@ee.ucla.edu, mgriot@ee.ucla.edu, wesel@ee.ucla.edu

Abstract— Low-Density Parity-Check (LDPC) codes are

usually decoded by running an iterative belief-propagation,

or message-passing, algorithm over the factor graph of the

code. The traditional message-passing schedule consists of

updating all the variable nodes in the graph, using the same

pre-update information, followed by updating all the check

nodes of the graph, again, using the same pre-update in-

formation.Recently several studies show that sequential

scheduling, in which messages are generated using the lat-

est available information, significantly improves the conver-

gence speed in terms of number of iterations.

scheduling raises the problem of finding the best sequence of

message updates. This paper presents practical scheduling

strategies that use the value of the messages in the graph

to find the next message to be updated.

sults show that these informed update sequences require

significantly fewer iterations than standard sequential sched-

ules. Furthermore, the paper shows that informed schedul-

ing solves some standard trapping set errors. Therefore, it

also outperforms traditional scheduling for a large numbers

of iterations. Complexity and implementability issues are

also addressed.

Sequential

Simulation re-

Index Terms— Belief propagation, message-passing sched-

ule, error-control codes, low-density parity-check codes.

I. Introduction

Belief Propagation (BP) provides Maximum-Likelihood

(ML) decoding over a cycle-free factor-graph representa-

tion of a code as shown in [1] and [2]. In some cases, BP

over loopy factor graphs of channel codes has been shown

to have near ML performance. BP performs well on the bi-

partite factor graphs composed of variable nodes and check

nodes that describe LDPC codes.

However, loopy BP is an iterative algorithm and there-

fore requires a message-passing schedule.

simultaneous scheduling, is the most popular scheduling

strategy.In every iteration flooding simultaneously up-

dates all the variable nodes (with each update using the

same set of pre-update data) and then, updates all the

check nodes (again, with each update using the same pre-

update information).Recently, several papers have ad-

dressed the effects of different types of sequential, or non-

simultaneous, scheduling strategies in BP LDPC decoding.

The idea was introduced as a sequence of check-node up-

dates in [3] and [4] and as a sequence of variable-node up-

dates in [5]. It is also presented in [6] under the name of

Layered BP (LBP), in [7] as serial schedule, in [8] as shuf-

fled BP, in [9] as row message passing, column message

passing and row-column message passing, among others.

Flooding, or

This work was supported by the state of California and ST Micro-

electronics through UC discovery grant COM 03-10142.

Simulations and theoretical tools in these works show

that sequential scheduling converges twice as fast as flood-

ing when used in LDPC decoding. It has also been shown

that sequential updating doesn’t increase the decoding

complexity per iteration, thus allowing the convergence

speed increase at no cost [9]. In [10], where a global frame-

work for the analysis of LDPC decoders is introduced, the

complexity is assumed to be independent from the sequen-

tial scheduling chosen. Furthermore, different types of se-

quential schedules such as sequential check-node updat-

ing, sequential variable-node updating and sequential mes-

sage updating have very similar performance results [9].

Given their similarities, the different types of sequential

updates will be referred in this paper as Standard Sequen-

tial Scheduling (SSS). In the simulations presented in this

paper the SSS strategy used for comparison is LBP, a se-

quence of check-node updates, as presented in [4] and [6].

Sequential updating poses the problem of finding the or-

dering of message updates that results in the best conver-

gence speed and/or code performance. The current state of

the messages in the graph can be used to dynamically up-

date the schedule, producing what we call an Informed Dy-

namic Schedule (IDS) and presented in [11]. To our knowl-

edge, the only well defined informed sequential scheduling

is the Residual Belief Propagation (RBP) algorithm pre-

sented by Elidan et al. in [12]. They proposed it for general

sequential message passing, not specifically for BP decod-

ing.

RBP is a greedy algorithm that organizes the message

updates according to how different is the message generated

in the current iteration from the message generated in the

previous iteration. The intuition is that the bigger this

difference, the further from convergence this part of the

graph is.Therefore, propagating this message first will

make BP converge at a higher speed.

Simulations show that RBP LDPC decoding has a higher

convergence speed than SSS but its error-rate performance

for a large enough number of iterations is worse. This be-

havior is commonly found in greedy algorithms, which tend

to arrive at a solution faster, but arrive at the correct solu-

tion less often. We propose a less-greedy IDS in which all

the outgoing messages of a check-node are generated simul-

taneously. We call this IDS node-wise RBP. It converges

both faster and more often than SSS.

Both RBP and node-wise RBP require the knowledge of

the message to be updated in order to pick which message

to update.This means that several messages are com-

Page 2

puted and not passed. Thus, increasing the complexity of

the decoding per iteration. We propose using the min-BP

check-node update algorithm explained in [13] and [14] to

simplify the ordering metric and significantly decrease the

complexity for both informed scheduling strategies while

maintaining the same performance. Also, an analysis of

the hardware issues that may arise in a parallel implemen-

tation of these informed sequential scheduling strategies is

presented.

This paper is organized as follows. Section II reviews

LDPC decoding and the flooding and SSS schedules. Sec-

tion III explains how to implement RBP decoding for

LDPC codes.This section also introduces and justifies

node-wise RBP. Section IV addresses some complexity and

implementability issues. The simulation results of all the

message-passing schedules are compared and discussed in

Section V. Section VI delivers the conclusions.

II. BP decoding for LDPC codes

In general, BP consists of the exchange of messages be-

tween the nodes of a graph.

propagates messages to its neighbors based on its current

incoming messages. The vector of all the messages in the

graph is denoted by m and mk denotes the k’th message

in the vector m. The function that generates mkfrom m

is denoted by fk(m).

The LDPC code graph is a bi-partite graph composed

by N variable nodes vj for j ∈ {1,...,N} that represent

the codeword bits and M check nodes cifor i ∈ {1,...,M}

that represent the parity-check equations. The exchanged

messages correspond to the Log-Likelihood Ratio (LLR)

of the probabilities of the bits. The sign of the LLR in-

dicates the most likely value of the bit and the absolute

value of the LLR gives the reliability of the message. In

this fashion, the channel information LLR of the variable

node vj is Cvj= log

p(yj|vj=1)

signal. Then, for any ciand vjthat are connected, the two

message generating functions, mvj→ci= fvj→ci(m) and

mci→vj= fci→vj(m), are:

Each node generates and

?p(yj|vj=0)

?

, where yjis the received

mvj→ci=

?

ca∈N(vj)\ci

mca→vj+ Cvj,

(1)

mci→vj= 2 × atanh

?

vb∈N(ci)\vj

tanh

?mvb→ci

2

?

, (2)

where N (vj)\cidenotes the neighbors of vj excluding ci,

and N (ci)\vjdenotes the neighbors of ciexcluding vj.

BP decoding consists of the iterative update of the mes-

sages until a stopping rule is satisfied. In flooding schedul-

ing, an iteration consists on the simultaneous update of all

the messages mv→cfollowed by the simultaneous update of

all the messages mc→v. In SSS, an iteration consists on the

sequential update of all the messages mv→c as well as all

the messages mc→vin a specific pre-defined order. The al-

gorithm stops if the decoded bits satisfy all the parity-check

equations or a maximum number of iterations is reached.

III. Residual Belief Propagation (RBP)

RBP, as introduced in [12], is an informed scheduling

strategy that updates first the message that maximizes an

ordering metric called the residual. A residual is the norm

(defined over the message space) of the difference between

the values of a message before and after an update. For a

message mk, the residual is defined as:

r(mk) = ?fk(m) − mk?.

(3)

The intuitive justification of this approach is that as

loopy BP converges, the differences between the messages

before and after an update go to zero. Therefore if a mes-

sage has a large residual, it means that it’s located in a

part of the graph that hasn’t converged yet. Therefore,

propagating that message first should speed up the pro-

cess. Elidan et al. create a priority queue, ordered by the

value of the residual, so at each step the first message in

the queue is updated and then the queue is reordered using

the new information.

A. RBP decoding for LDPC codes

In LLR BP decoding, all the message spaces are one-

dimensional (the real line). Therefore, the residuals are

the absolute values of the difference of the LLRs.

Let us analyze the behavior of RBP decoding for LDPC

codes in order to simplify the decoding algorithm. Initially,

all the messages mv→care set to the value of their corre-

spondent channel message Cv. No operations are needed

in this initialization.This implies that the residuals of

all the variable-to-check messages r(mv→c) are equal to

0. Then, without loss of generality, we assume that the

message mci→vjhas the residual r∗, which is the biggest

of the graph. After mci→vjis propagated, only residuals

r(mvj→ca) change, with ca∈ N (vj)\ci.

The new residuals r(mvj→ca) are equal to r∗, because r∗

was the change in the message mci→vjand Eq. 1 shows

that the message update operations of a variable node are

only sums. Therefore, the messages mvj→cahave now the

biggest residuals in the graph.

Assuming that propagating the messages mvj→cawon’t

generate any new residuals bigger than r∗, RBP can be

simplified. Every time a message mc→vis propagated, the

outgoing messages from the variable node v will be updated

and propagated. This facilitates the scheduling since we

need only to maintain a queue Q of messages mc→v, or-

dered by the value of their residuals, in order to find out

the next message to be propagated. RBP LDPC decoding

is formally described in Algorithm 1, the stopping rule will

be discussed in Section VI.

There is an intuitive way to see how RBP decoding works

for LDPC codes. Let us assume that at a certain moment

in the decoding, there is a check node ci with residuals

r(mci→vb) = 0 for every vb ∈ N (ci). Now let us assume

that there is a change in the value of the message mvj→ci.

The biggest change in a check-to-variable message out of

ci(therefore the largest residual) will happen in the edge

that corresponds to the incoming variable-to-checkmessage

with the lowest reliability (excluding the message mvj→ci).

Page 3

Algorithm 1 RBP decoding for LDPC codes

Initialize all mc→v= 0

2:

Initialize all mvj→ci= Cj

3:

Compute all r(mc→v) and generate Q

4:

Let mci→vjbe the first message in Q

5:

Generate and propagate mci→vj

6:

Set r(mci→vj) = 0 and re-order Q

7:

for every ca∈ N (vj)\ci do

8:

Generate and propagate mvj→ca

9:

for every vb∈ N (ca)\vj do

10:

Compute r(mca→vb) and re-order Q

11:

end for

12: end for

13: if Stopping rule is not satisfied then

14:

Position=4;

15: end if

1:

Let us denote by vkthe variable node that is the destina-

tion of the message that has the largest residual r(mci→vk).

Then, the message mvk→cihas the smallest reliability out

of all messages mvb→ci, with vb∈ N (ci)\vj.

This implies that, for this particular scenario, once

there’s a change in a variable-to-check message, RBP will

propagate first the message to the variable node with the

lowest reliability. This makes sense intuitively. In some

sense the lowest reliability variable node needs to receive

new information more than the higher reliability ones.

The negative effects of the greediness of RBP are appar-

ent in the case of unsatisfied check nodes. RBP will sched-

ule to propagate first the message that will “correct” the

variable node with the lowest reliability. This is the most

likely variable node to be in error. However, if that variable

node was already correct, changing its sign will likely gener-

ate new errors, making the BP convergence more difficult.

This analysis helps us see why RBP corrects the most likely

errors faster but has trouble correcting “difficult” errors as

will be seen in Section V. We define “difficult” errors as

the errors that need a large number of message updates to

be corrected.

B. Node-wise RBP decoding for LDPC codes

In order to obtain a better performance for all types of

errors, perhaps a less greedy scheduling strategy must be

used. As noted earlier, some of the greediness of RBP came

from the fact that it tends to propagate first the message to

the less reliable variable nodes. We propose to simultane-

ously update and propagate all the check-to-variable mes-

sages that correspond to the same check node ci, instead

of only propagating the message with the largest residual

r(mci→vj). It can be seen, using the analysis presented

earlier, that this algorithm is less likely to generate new er-

rors. We call this less greedy strategy node-wise RBP and

it’s performance can be seen in Section V. Node-wise RBP

is similar to LBP; it is a sequence of check-node updates.

However, unlike LBP, which follows a pre-determined or-

der, the check node to be updated next is chosen dynam-

ically according to the residuals of the check-to-variable

messages. Node-wise RBP is formally described in Algo-

rithm 2.

Algorithm 2 Node-wise RBP decoding for LDPC codes

Initialize all mc→v= 0

2:

Initialize all mvj→ci= Cj

3:

Compute all r(mc→v) and generate Q

4:

Let mci→vjbe the first message in Q

5:

for every vk∈ N (ci) do

6:

Generate and propagate mci→vk

7:

Set r(mci→vk) = 0 and re-order Q

8:

for every ca∈ N (vk)\ci do

9:

Generate and propagate mvk→ca

10:

for every vb∈ N (ca)\vk do

11:

Compute r(mca→vb) and re-order Q

12:

end for

13:

end for

14: end for

15: if Stopping rule is not satisfied then

16:

Position=4;

17: end if

1:

Node-wise RBP converges both faster than SSS (in terms

of number of messages updated) and better than SSS (in

terms of FER of the code for a large number of iterations).

We can explain intuitively and demonstrate experimentally

that the lower error rates are achieved because informed

scheduling allows the LDPC decoder to overcome many

”trapping sets”. Trapping sets, or near-codewords, as de-

fined in [15], are small variable-node sets such that the

induced sub-graph has a small number of odd degree neigh-

bors. In [15], Richardson also mentions that the most trou-

blesome trapping set errors are those where the odd degree

neighbors have degree 1 (in the induced sub-graph), and

the even-degree neighbors have degree 2 (in the induced

sub-graph).

It is likely that node-wise RBP solves the variable nodes

in error by sequentially updating the degree-1 check-nodes

connected to them. When a variable node in a trapping set

is corrected, the induced sub-graph of the variable-nodes-

in-error will change as follows. At least one check node

that was degree-2, becomes degree-1 after the variable node

correction. This check node is likely to be picked as the

next check-node to be updated by node-wise RBP because

its messages will have large residuals. This update will

probably correct another variable node in the trapping set.

We corroborated this analysis by studying the behavior

of the decoders for the noise realizations that the SSS de-

coder could not solve for 200 iterations and that node-wise

RBP solved in a very small number of iterations (less than

10). We found that a large majority of the SSS errors in

these cases are caused by trapping sets that node-wise RBP

solved in the manner mentioned before.

IV. Complexity and Implementation

Given the surge in popularity of LDPC codes for practi-

cal implementations, it is interesting to address some issues

Page 4

about the complexity of RBP and node-wise RBP when

compared to SSS and flooding.

A. Complexity per iteration

For an IDS we consider one iteration to have occurred

after the number of updates equals the number of updates

completed in an SSS or flooding iteration. For RBP, an

iteration will be counted after the number of check-to-

variable message updates equals the number of edges in

the LDPC graph. For node-wise RBP an iteration will be

counted after a number of check-node updates equals the

number of check-nodes of the code.

In [9], the authors prove that if the appropriate up-

date equations are chosen, the total number of operations

per iteration of all the sequential schedules is equal to the

number of operations per iterations of the flooding sched-

ule, making their complexity per iteration equal. Given

that both RBP and node-wise RBP are forms of sequential

updates, a sequence of message updates in the first case

and a sequence of check-node updates in the second, then

they also use, on average, the same number of message-

generating operations per iteration (using our definition of

iteration for informed schedules).

In order to maintain the same complexity, we use the

typical stopping rules in the decoding. Stop if at the end

of an iteration the decoded bits satisfy all the parity-check

equations, or a maximum number of iterations is reached.

On top of the message-generation complexity, informed

schedules incur two extra processes: residual computation

and ordering of the residuals. As defined in Section III, the

residual computation requires the value of the message that

would be propagated. This requires additional complexity

since there will be numerous message computations that

will only be used to calculate residuals. We propose to use

the min-BP check-node update approximation explained in

[13] to compute the approximate-residual as follows,

r(a)(mk) =

???f(a)

k

(m) − m(a)

k

???,

(4)

where the superscript (a) stands for approximate and in-

dicates the min-BP approximation. The min-BP check-

node update consists of finding the two variable-to-check

messages with the smallest reliability. Then, the small-

est reliability is assigned as the check-to-variable message

reliability for all the edges except the one where the small-

est reliability came from. The second smallest reliability

is assigned to that remaining edge.

computed for all the check-to-variable messages. Thus, re-

placing all the residual functions for approximate-residual

functions in Algorithms 1 and 2, defines Approximate RBP

(ARBP) decoding and node-wise ARBP decoding. These

significantly simpler algorithms perform as well as the ones

presented in Section III, as will be seen in Section V. Note

that we only propose to use min-BP for the residual com-

putation. For the actual propagation of messages we use

the full update equations (1) and (2).

Even for node-wise ARBP, the practical trade-off be-

tween the increase in the per-iteration complexity and the

decrease in the number of iterations (and improved FER)

The proper sign is

is difficult to address in general as it depends on specific

implementation choices. Our current research is addressing

this trade-off in detail.

B. Parallel Decoding

The possibility of having several processors computing

messages at the same time during the LDPC decoding has

become an intense area of research and an important rea-

son why LDPC codes are so successful. Furthermore, codes

with a specific structure have been shown to allow SSS de-

coding while maintaining the same parallelism degree ob-

tained for flooding decoding [4]. In principle, the idea of

having an ordered sequence of updates, that uses the most

recent information as much as possible, isn’t compatible

with the idea of simultaneously computing messages. How-

ever, since the ordering of the queue Q based on the new

results occurs after the update, it is possible that the sev-

eral parallel processors can work on different parts of the

graph while still using the most recent information.

We define the parallel node-wise ARBP scheduling strat-

egy as the node-wise ARBP strategy where instead of up-

dating only the check-node with the largest approximate

residual, p check-nodes are updated at the same time. The

p nodes that have the largest approximate residuals are

updated simultaneously. These p check nodes are not de-

signed to work in parallel, unlike the p check-nodes of a

p × p sub-matrix as defined in [4].

However, parallel processing may be implemented ex-

tending the hardware solutions presented in [16]. For in-

stance, if one or more check-nodes have in common one

or more variable nodes, they will all use the same previ-

ous information and compute the incremental variations

that are afterwards combined in the variable-node update.

There are hardware issues, such as memory clashes, that

still need to be carefully addressed when implementing par-

allel node-wise ARBP.

Parallel node-wise ARBP has a very small performance

degradation when compared with node-wise ARBP, as will

be seen in Section V. We defined and simulated the paral-

lel version of node-wise ARBP since it’s the simplest IDS

strategy and therefore, the most likely to be implemented.

V. Simulation Results

This section presents the AWGN performance of the dif-

ferent scheduling strategies presented above. All the simu-

lations are floating point and use the same rate-1/2 LDPC

code. The blocklength of the code is 1944 and it has the

same sub-matrix structure as the one presented in [4] with

sub-matrix size equal to 54x54.

The SSS results shown correspond to the sequential

check-node update introduced in [4]. This scheduling is

known as Layered Belief Propagation (LBP) and guaran-

tees a parallelism degree equal to the sub-matrix size of the

LDPC code (54 in our case). As shown in [9], different SSS

strategies produce almost identical results so its selection

doesn’t significantly affect the performance of the decoder.

Fig. 1 shows the performance of the scheduling strategies

discussed above, flooding, SSS (LBP), RBP, ARBP, node-

Page 5

010 20 304050

10

−5

10

−4

10

−3

10

FER

−2

10

−1

10

0

Iterations

Flooding

SSS (LBP)

RBP

ARBP

NW RBP

NW ARBP

Parallel NW ARBP

Fig. 1.FER Performance of flooding, SSS (LBP), RBP and node-

wise RBP vs. number of iterations for a fixed Eb/No = 1.75

dB

wise RBP, node-wise ARBP, and parallel node-wise ARBP,

as the number of iterations increases. The figure shows

that RBP has a significantly better performance than SSS

(LBP) for a small number of iterations, but a sub-par per-

formance for a larger number of iterations. Specifically, the

performance of RBP at 4 iterations is equal to the perfor-

mance of SSS (LBP) at 13 iterations, but the curves cross

over at 19 iterations. This suggests that RBP has trouble

with “difficult” errors as discussed earlier.

Node-wise RBP, while not as good as RBP for a small

number of iterations, shows consistently better perfor-

mance than SSS (LBP) across all iterations. Specifically,

the performance of node-wise RBP at 18 iterations is equal

to the performance of SSS (LBP) at 50 iterations. The re-

sults for flooding are shown for comparison purposes, and

replicate the theoretical and empirical results of [4]-[9] that

claim that flooding needs twice the number of iterations as

SSS.

Fig. 1 also shows the performance of the approximate

residual schedules and compares them with the schedules

that use the exact residuals.

ARBP and node-wise ARBP perform almost indistinguish-

ably from RBP and node-wise RBP respectively. We re-

iterate that the approximate residual diminishes the com-

plexity of residual computation significantly, thus making

ARBP, and node-wise ARBP more attractive than their

exact counterparts.

Furthermore, Fig. 1 also shows the performance of par-

allel node-wise ARBP. The relatively small loss in perfor-

mance when compared to node-wise ARBP is the price

for the throughput increase resulting from the parallelism.

The number p of check-nodes processed in parallel was

It can be seen that both

1.31.41.5 1.6

Eb/No

1.7 1.8 1.92

10

−6

10

−5

10

−4

10

−3

10

−2

10

−1

10

0

FER

Flooding

SSS (LBP)

Node−wise ARBP

15 Iterations

50 Iterations

Fig. 2.FER Performance of flooding, SSS (LBP) and node-wise

ARBP for 15 and 50 iterations vs. Eb/No

set to 54, which is equal to the parallelism guaranteed by

SSS (LBP) decoding this structured LDPC code with sub-

matrix size equal to 54x54 [4].

The FER of node-wise ARBP vs. SNR and for 15 and

50 iterations (maximum) is presented in Fig. 2. The FER

of flooding and SSS (LBP) are also presented as references.

It can be seen that the SNR gap between node-wise ARBP

and SSS (LBP) is more pronounced for a small number of

iterations and/or a large SNR.

Fig. 3 and Fig. 4 show the performance of different

scheduling strategies for the blocklength 1944 rate-1/2 and

rate 5/6 LDPC codes selected for the IEEE 802.11n stan-

dard [17]. These simulations were run for a high number of

iterations (200) and show that node-wise ARBP achieves a

better FER performance that SSS (LBP). Fig. 4 also shows

that even for high rate codes, node-wise ARBP converges

both faster and better than SSS (LBP).

VI. Conclusions

This paper shows that, while maintaining the same

message-generation functions, IDS can improve the per-

formance of BP LDPC decoding.

RBP and its simplification ARBP are appropriate for

applications that have a high target error-rate, given that

RBP achieves these error-rates using significantly fewer it-

erations than SSS. They are also appropriate for high-speed

applications that only allow a small number of iterations.

However, for applications that require lower error rates and

allow larger delays RBP and ARBP aren’t appropriate.

For such applications node-wise RBP and its simplifica-

tion node-wise ARBP perform better than SSS for any tar-

get error-rate and any number of iterations. These node-

wise strategies achieve a lower error-rates by overcoming

Page 6

0 50100150 200

10

−5

10

−4

10

−3

10

FER

−2

10

−1

10

0

Iterations

Flooding

SSS (LBP)

ARBP

Node−wise ARBP

Fig. 3. FER vs. number of iterations of the 802.11n blocklength-1944

rate-1/2 code using flooding, SSS (LBP), ARBP and node-wise

ARBP for a fixed Eb/No= 1.75 dB

trapping set errors that SSS cannot solve. Furthermore

a parallel implementation of node-wise ARBP was shown

to perform nearly as well as the original node-wise ARBP,

making this informed scheduling more attractive for prac-

tical implementations.

The improvement in performance of these informed

scheduling strategies were also shown for a high-rate code

(rate 5/6). However, they come with the cost of an increase

in complexity per iteration due to the residual computa-

tion and its ordering. The trade-off provided by node-wise

ARBP between increasing the per-iteration complexity and

reducing the number of iterations (while also reducing the

FER for a large number of iterations) requires further in-

vestigation in the context of specific implementations.

The ideas presented in this work may be extended to

other communication solutions that use iterative BP, such

as turbo codes, turbo-equalization, iterative demodulation

and decoding of high-order constellations. The extensions

of the IDS strategies may also prove beneficial for loopy BP

solutions to problems outside the communications field.

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