Method and system for routing in low density parity check (LDPC) decoders

Abstract

An approach is provided for decoding a low density parity check (LDPC) coded signal. Edge values associated with a structured parity check matrix used to generate the LDPC coded signal are retrieved from memory. The edge values specify the relationship of bit nodes and check nodes, and are stored within memory according to a predetermined scheme that permits concurrent retrieval of a set of the edge values. A decoded signal corresponding to the LDPC coded signal is output based on the retrieved edge values.

Description

FIELD OF THE INVENTION

The present invention relates to communication systems, and more particularly to coded systems.

BACKGROUND OF THE INVENTION

Communication systems employ coding to ensure reliable communication across noisy communication channels. These communication channels exhibit a fixed capacity that can be expressed in terms of bits per symbol at certain signal to noise ratio (SNR), defining a theoretical upper limit (known as the Shannon limit). As a result, coding design has aimed to achieve rates approaching this Shannon limit. One such class of codes that approach the Shannon limit is Low Density Parity Check (LDPC) codes.

Traditionally, LDPC codes have not been widely deployed because of a number of drawbacks. One drawback is that the LDPC encoding technique is highly complex. Encoding an LDPC code using its generator matrix would require storing a very large, non-sparse matrix. Additionally, LDPC codes require large blocks to be effective; consequently, even though parity check matrices of LDPC codes are sparse, storing these matrices is problematic.

From an implementation perspective, a number of challenges are confronted. For example, storage is an important reason why LDPC codes have not become widespread in practice. Also, a key challenge in LDPC code implementation has been how to achieve the connection network between several processing engines (nodes) in the decoder. Further, the computational load in the decoding process, specifically the check node operations, poses a problem.

Therefore, there is a need for a LDPC communication system that employs simple encoding and decoding processes. There is also a need for using LDPC codes efficiently to support high data rates, without introducing greater complexity. There is also a need to improve performance of LDPC encoders and decoders. There is also a need to minimize storage requirements for implementing LDPC coding. There is a further need for a scheme that simplifies the communication between processing nodes in the LDPC decoder.

SUMMARY OF THE INVENTION

These and other needs are addressed by the present invention, wherein an approach for decoding a structured Low Density Parity Check (LDPC) codes is provided. Structure of the LDPC codes is provided by restricting portion part of the parity check matrix to be lower triangular and/or satisfying other requirements such that the communication between bit nodes and check nodes of the decoder is simplified. Edge values associated with the structured parity check matrix used to generate the LDPC coded signal are retrieved from memory. The edge values specify the relationship of bit nodes and check nodes, and according to one embodiment of the present invention, are stored within the memory according to a predetermined scheme (e.g., contiguous physical memory locations) that permits concurrent retrieval of a set of the edge values. According to another embodiment of the present invention, the edge values having bit nodes of n degrees are stored in a first portion of the memory, and edge values having bit nodes of greater than n degrees are stored in a second portion of the memory. The storage arrangement of the edge values advantageously allows fast retrieval of the edge values during the decoding process.

Also, the approach can advantageously exploit the unequal error protecting capability of LDPC codes on transmitted bits to provide extra error protection to more vulnerable bits of high order modulation constellations (such as 8-PSK (Phase Shift Keying)). The decoding process involves iteratively regenerating signal constellation bit metrics into an LDPC decoder after each decoder iteration or several decoder iterations. The above arrangement provides a computational efficient approach to decoding LDPC codes.

According to one aspect of an embodiment of the present invention, a method for decoding a low density parity check (LDPC) coded signal is disclosed. The method includes retrieving edge values associated with a structured parity check matrix used to generate the LDPC coded signal, wherein the edge values specify relationship of bit nodes and check nodes, and are stored according to a predetermined scheme that permits concurrent retrieval of a set of the edge values. The method also includes outputting a decoded signal corresponding to the LDPC coded signal based on the retrieved edge values.

According to another aspect of an embodiment of the present invention, a decoder for decoding a low density parity check (LDPC) coded signal is disclosed. The decoder includes means for retrieving edge values associated with a structured parity check matrix used to generate the LDPC coded signal. The decoder also includes memory for storing the edge values according to a predetermined scheme that permits concurrent retrieval of a set of the edge values, wherein the edge values specify relationship of bit nodes and check nodes. Further, the decoder includes means for outputting a decoded signal corresponding to the LDPC coded signal based on the retrieved edge values.

According to another aspect of an embodiment of the present invention, a memory accessible by a low density parity check (LDPC) decoder for decoding a LDPC coded signal is disclosed. The memory includes a first portion storing a first group of edge values associated with a structured parity check matrix used to generate the LDPC coded signal, the first group of edges being connected to bit nodes of n degrees. Additionally, the memory includes a second portion storing a second group of edge values associated with the structured parity check matrix used to generate the LDPC coded signal, the second group of edges being connected to bit nodes of greater than n degrees, wherein a set of edge values from the first group or the second group is retrieved to output a decoded signal.

Still other aspects, features, and advantages of the present invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the present invention. The present invention is also capable of other and different embodiments, and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the present invention. Accordingly, the drawing and description are to be regarded as illustrative in nature, and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which:

FIG. 1 is a diagram of a communications system configured to utilize Low Density Parity Check (LDPC) codes, according to an embodiment of the present invention;

FIG. 2 is a diagram of an exemplary transmitter in the system of FIG. 1;

FIG. 3 is a diagram of an exemplary receiver in the system of FIG. 1;

FIG. 4 is a diagram of a sparse parity check matrix, in accordance with an embodiment of the present invention;

FIG. 5 is a diagram of a bipartite graph of an LDPC code of the matrix of FIG. 4;

FIG. 6 is a diagram of a sub-matrix of a sparse parity check matrix, wherein the sub-matrix contains parity check values restricted to the lower triangular region, according to an embodiment of the present invention;

FIG. 7 is a graph showing performance between codes utilizing unrestricted parity check matrix (H matrix) versus restricted H matrix having a sub-matrix as in FIG. 6;

FIGS. 8A and 8B are, respectively, a diagram of a non-Gray 8-PSK modulation scheme, and a Gray 8-PSK modulation, each of which can be used in the system of FIG. 1;

FIG. 9 is a graph showing performance between codes utilizing Gray labeling versus non-Gray labeling;

FIG. 10 is a flow chart of the operation of the LDPC decoder using non-Gray mapping, according to an embodiment of the present invention;

FIG. 11 is a flow chart of the operation of the LDPC decoder of FIG. 3 using Gray mapping, according to an embodiment of the present invention;

FIGS. 12A-12C are diagrams of the interactions between the check nodes and the bit nodes in a decoding process, according to an embodiment of the present invention;

FIGS. 13A and 13B are flowcharts of processes for computing outgoing messages between the check nodes and the bit nodes using, respectively, a forward-backward approach and a parallel approach, according to various embodiments of the present invention;

FIGS. 14A-14C are graphs showing simulation results of LDPC codes generated in accordance with various embodiments of the present invention;

FIGS. 15A and 15B are diagrams of the top edge and bottom edge, respectively, of memory organized to support structured access as to realize randomness in LDPC coding, according to an embodiment of the present invention; and

FIG. 16 is a diagram of a computer system that can perform the processes of encoding and decoding of LDPC codes, in accordance with embodiments of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

A system, method, and software for efficiently decoding structured Low Density Parity Check (LDPC) codes are described. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It is apparent, however, to one skilled in the art that the present invention may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.

FIG. 1 is a diagram of a communications system configured to utilize Low Density Parity Check (LDPC) codes, according to an embodiment of the present invention. A digital communications system 100 includes a transmitter 101 that generates signal waveforms across a communication channel 103 to a receiver 105. In this discrete communications system 100, the transmitter 101 has a message source that produces a discrete set of possible messages; each of the possible messages has a corresponding signal waveform. These signal waveforms are attenuated, or otherwise altered, by communications channel 103. To combat the noise channel 103, LDPC codes are utilized.

The LDPC codes that are generated by the transmitter 101 enables high speed implementation without incurring any performance loss. These structured LDPC codes output from the transmitter 101 avoid assignment of a small number of check nodes to the bit nodes already vulnerable to channel errors by virtue of the modulation scheme (e.g., 8-PSK).

Such LDPC codes have a parallelizable decoding algorithm (unlike turbo codes), which advantageously involves simple operations such as addition, comparison and table look-up. Moreover, carefully designed LDPC codes do not exhibit any sign of error floor.

According to one embodiment of the present invention, the transmitter 101 generates, using a relatively simple encoding technique, LDPC codes based on parity check matrices (which facilitate efficient memory access during decoding) to communicate with the receiver 105. The transmitter 101 employs LDPC codes that can outperform concatenated turbo+RS (Reed-Solomon) codes, provided the block length is sufficiently large.

FIG. 2 is a diagram of an exemplary transmitter in the system of FIG. 1. A transmitter 200 is equipped with an LDPC encoder 203 that accepts input from an information source 201 and outputs coded stream of higher redundancy suitable for error correction processing at the receiver 105. The information source 201 generates k signals from a discrete alphabet, X. LDPC codes are specified with parity check matrices. On the other hand, encoding LDPC codes require, in general, specifying the generator matrices. Even though it is possible to obtain generator matrices from parity check matrices using Gaussian elimination, the resulting matrix is no longer sparse and storing a large generator matrix can be complex.

Encoder 203 generates signals from alphabet Y to a modulator 205 using a simple encoding technique that makes use of only the parity check matrix by imposing structure onto the parity check matrix. Specifically, a restriction is placed on the parity check matrix by constraining certain portion of the matrix to be triangular. The construction of such a parity check matrix is described more fully below in FIG. 6. Such a restriction results in negligible performance loss, and therefore, constitutes an attractive trade-off.

Modulator 205 maps the encoded messages from encoder 203 to signal waveforms that are transmitted to a transmit antenna 207, which emits these waveforms over the communication channel 103. Accordingly, the encoded messages are modulated and distributed to a transmit antenna 207. The transmissions from the transmit antenna 207 propagate to a receiver, as discussed below.

FIG. 3 is a diagram of an exemplary receiver in the system of FIG. 1. At the receiving side, a receiver 300 includes a demodulator 301 that performs demodulation of received signals from transmitter 200. These signals are received at a receive antenna 303 for demodulation. After demodulation, the received signals are forwarded to a decoder 305, which attempts to reconstruct the original source messages by generating messages, X′, in conjunction with a bit metric generator 307. With non-Gray mapping, the bit metric generator 307 exchanges probability information with the decoder 305 back and forth (iteratively) during the decoding process, which is detailed in FIG. 10. Alternatively, if Gray mapping is used (according to one embodiment of the present invention), one pass of the bit metric generator is sufficient, in which further attempts of bit metric generation after each LDPC decoder iteration are likely to yield limited performance improvement; this approach is more fully described with respect to FIG. 11. To appreciate the advantages offered by the present invention, it is instructive to examine how LDPC codes are generated, as discussed in FIG. 4.

FIG. 4 is a diagram of a sparse parity check matrix, in accordance with an embodiment of the present invention. LDPC codes are long, linear block codes with sparse parity check matrix H_(n−k)xn. Typically the block length, n, ranges from thousands to tens of thousands of bits. For example, a parity check matrix for an LDPC code of length n=8 and rate ½ is shown in FIG. 4. The same code can be equivalently represented by the bipartite graph, per FIG. 5.

FIG. 5 is a diagram of a bipartite graph of an LDPC code of the matrix of FIG. 4. Parity check equations imply that for each check node, the sum (over GF (Galois Field)(2)) of all adjacent bit nodes is equal to zero. As seen in the figure, bit nodes occupy the left side of the graph and are associated with one or more check nodes, according to a predetermined relationship. For example, corresponding to check node m₁, the following expression exists n₁+n₄+n₅+n₈=0 with respect to the bit nodes.

Returning the receiver 303, the LDPC decoder 305 is considered a message passing decoder, whereby the decoder 305 aims to find the values of bit nodes. To accomplish this task, bit nodes and check nodes iteratively communicate with each other. The nature of this communication is described below.

From check nodes to bit nodes, each check node provides to an adjacent bit node an estimate (“opinion”) regarding the value of that bit node based on the information coming from other adjacent bit nodes. For instance, in the above example if the sum of n₄, n₅ and n₈ “looks like” 0 to m₁, then m₁ would indicate to n₁ that the value of n₁ is believed to be 0 (since n₁+n₄+n₅+n₈=0); otherwise m₁ indicate to n₁ that the value of n₁ is believed to be 1. Additionally, for soft decision decoding, a reliability measure is added.

From bit nodes to check nodes, each bit node relays to an adjacent check node an estimate about its own value based on the feedback coming from its other adjacent check nodes. In the above example n₁ has only two adjacent check nodes m₁ and m₃. If the feedback coming from m₃ to n₁ indicates that the value of n₁ is probably 0, then n₁ would notify m₁ that an estimate of n₁'s own value is 0. For the case in which the bit node has more than two adjacent check nodes, the bit node performs a majority vote (soft decision) on the feedback coming from its other adjacent check nodes before reporting that decision to the check node it communicates. The above process is repeated until all bit nodes are considered to be correct (i.e., all parity check equations are satisfied) or until a predetermined maximum number of iterations is reached, whereby a decoding failure is declared.

FIG. 6 is a diagram of a sub-matrix of a sparse parity check matrix, wherein the sub-matrix contains parity check values restricted to the lower triangular region, according to an embodiment of the present invention. As described previously, the encoder 203 (of FIG. 2) can employ a simple encoding technique by restricting the values of the lower triangular area of the parity check matrix. According to an embodiment of the present invention, the restriction imposed on the parity check matrix is of the form:H_(n−k)xn=[A_(n−k)xkB_{(n−k)x(n−k)}],where B is lower triangular.

Any information block i=(i₀, i₁, . . . , i_k−1) is encoded to a codeword C=(i₀, i₁, . . . , i_k−1, p₀, p₁, . . . , p_n-k−1) using Hc^T=0, and recursively solving for parity bits; for example,a₀₀i₀+a₀₁i₁+ . . . +a_0,k−1i_k−1+p₀=0Solve p₀,a₁₀i₀+a₁₁i₁+ . . . +a_1,k−1i_k−1+b₁₀p₀+p₁=0Solve p₁ and similarly for p₂,p₃, . . . ,p_n−k−1.

FIG. 7 is a graph showing performance between codes utilizing unrestricted parity check matrix (H matrix) versus restricted H matrix of FIG. 6. The graph shows the performance comparison between two LDPC codes: one with a general parity check matrix and the other with a parity check matrix restricted to be lower triangular to simplify encoding. The modulation scheme, for this simulation, is 8-PSK. The performance loss is within 0.1 dB. Therefore, the performance loss is negligible based on the restriction of the lower triangular H matrices, while the gain in simplicity of the encoding technique is significant. Accordingly, any parity check matrix that is equivalent to a lower triangular or upper triangular under row and/or column permutation can be utilized for the same purpose.

FIGS. 8A and 8B are, respectively, a diagram of a non-Gray 8-PSK modulation scheme, and a Gray 8-PSK modulation, each of which can be used in the system of FIG. 1. The non-Gray 8-PSK scheme of FIG. 8A can be utilized in the receiver of FIG. 3 to provide a system that requires very low Frame Erasure Rate (FER). This requirement can also be satisfied by using a Gray 8-PSK scheme, as shown in FIG. 8B, in conjunction with an outer code, such as Bose, Chaudhuri, and Hocquenghem (BCH), Hamming, or Reed-Solomon (RS) code.

Under this scheme, there is no need to iterate between the LDPC decoder 305 (FIG. 3) and the bit metric generator 307, which may employ 8-PSK modulation. In the absence of an outer code, the LDPC decoder 305 using Gray labeling exhibit an earlier error floor, as shown in FIG. 9 below.

FIG. 9 is a graph showing performance between codes utilizing Gray labeling versus non-Gray labeling of FIGS. 8A and 8B. The error floor stems from the fact that assuming correct feedback from LDPC decoder 305, regeneration of 8-PSK bit metrics is more accurate with non-Gray labeling since the two 8-PSK symbols with known two bits are further apart with non-Gray labeling. This can be equivalently seen as operating at higher Signal-to-Noise Ratio (SNR). Therefore, even though error asymptotes of the same LDPC code using Gray or non-Gray labeling have the same slope (i.e., parallel to each other), the one with non-Gray labeling passes through lower FER at any SNR.

On the other hand, for systems that do not require very low FER, Gray labeling without any iteration between LDPC decoder 305 and 8-PSK bit metric generator 307 may be more suitable because re-generating 8-PSK bit metrics before every LDPC decoder iteration causes additional complexity. Moreover, when Gray labeling is used, re-generating 8-PSK bit metrics before every LDPC decoder iteration yields only very slight performance improvement. As mentioned previously, Gray labeling without iteration may be used for systems that require very low FER, provided an outer code is implemented.

The choice between Gray labeling and non-Gray labeling depends also on the characteristics of the LDPC code. Typically, the higher bit or check node degrees, the better it is for Gray labeling, because for higher node degrees, the initial feedback from LDPC decoder 305 to 8-PSK (or similar higher order modulation) bit metric generator 307 deteriorates more with non-Gray labeling.

When 8-PSK (or similar higher order) modulation is utilized with a binary decoder, it is recognized that the three (or more) bits of a symbol are not received “equally noisy”. For example with Gray 8-PSK labeling, the third bit of a symbol is considered more noisy to the decoder than the other two bits. Therefore, the LDPC code design does not assign a small number of edges to those bit nodes represented by “more noisy” third bits of 8-PSK symbol so that those bits are not penalized twice.

FIG. 10 is a flow chart of the operation of the LDPC decoder using non-Gray mapping, according to an embodiment of the present invention. Under this approach, the LDPC decoder and bit metric generator iterate one after the other. In this example, 8-PSK modulation is utilized; however, the same principles apply to other higher modulation schemes as well. Under this scenario, it is assumed that the demodulator 301 outputs a distance vector, d, denoting the distances between received noisy symbol points and 8-PSK symbol points to the bit metric generator 307, whereby the vector components are as follows:

$d_i=-\frac{E_s}{N_0}\left\{{\left(r_x-s_{i,x}\right)}^2+{\left(r_y-s_{i,y}\right)}^2\right\}i=0,1,\dots 7.$

The 8-PSK bit metric generator 307 communicates with the LDPC decoder 305 to exchange a priori probability information and a posteriori probability information, which respectively are represented as u, and a. That is, the vectors u and a respectively represent a priori and a posteriori probabilities of log likelihood ratios of coded bits.

The 8-PSK bit metric generator 307 generates the a priori likelihood ratios for each group of three bits as follows. First, extrinsic information on coded bits is obtained:e_j=a_j−u_jj=0,1,2.Next, 8-PSK symbol probabilities, p_i i=0,1, . . . ,7, are determined.*y_j=−ƒ(0, e_j) j=0,1,2 where ƒ(a,b)=max(a,b)+LUT_f(a,b) with LUT_f(a,b)=1n(1+e^−|a−b|)*x_j=y_j+e_j j=0,1,2*p₀=x₀+x₁+x₂ p₄=y₀+x₁+x₂ p₁=x₀+x₁+y₂ p₅=y₀+x₁+y₂ p₂=x₀+y₁+x₂ p₆=y₀+y₁+x₂ p₃=x₀+y₁+y₂ p₇=y₀+y₁+y₂

Next, the bit metric generator 307 determines a priori log likelihood ratios of the coded bits as input to LDPC decoder 305, as follows:u₀=ƒ(d₀+p₀,d₁+p₁d₂+p₂,d₃+p₃)−ƒ(d₄+p₄,d₅+p₅,d₆+p₆,d₇+p₇)−e₀ u₁=ƒ(d₀+p₀,d₁+p₁,d₄+p₄,d₅+p₅)−ƒ(d₂+p₂,d₃+p₃,d₆+p₆,d₇+p₇)−e₁ u₂=ƒ(d₀+p₀,d₂+p₂,d₄+p₄,d₆+p₆)−ƒ(d₁+p₁,d₃+p₃,d₅+p₅,d₇+p₇)−e₂

It is noted that the function ƒ(.) with more than two variables can be evaluated recursively; e.g. ƒ(a,b,c)=ƒ(ƒ(a,b),c).

The operation of the LDPC decoder 305 utilizing non-Gray mapping is now described. In step 1001, the LDPC decoder 305 initializes log likelihood ratios of coded bits, v, before the first iteration according to the following (and as shown in FIG. 12A):

$v_{n\to k_i}=u_n,$ n=0,1, . . . , N−1,i=1,2, . . . , deg(bit node n)

Here,

$v_{n\to k_i}$ denotes the message that goes from bit node n to its adjacent check node k_i, u_n denotes the demodulator output for the bit n and N is the codeword size.

In step 1003, a check node, k, is updated, whereby the input v yields the output w. As seen in FIG. 12B, the incoming messages to the check node k from its d_c adjacent bit nodes are denoted by

$v_{n_1\to k},v_{n_2\to k},\dots ,v_{n_{dc}\to k}.$ The goal is to compute the outgoing messages from the check node k back to d_c adjacent bit nodes. These messages are denoted by

$w_{k\to n_1},w_{k\to n_2},\dots ,w_{k\to n_{dc}},\mathrm{where}$ $w_{k\to n_i}=g\left(v_{n_1\to k},v_{n_2\to k},\dots ,v_{n_{i-1}\to k},v_{n_{i+1}\to k},\dots ,v_{n_{dc}\to k}\right).$ The function g( ) is defined as follows:g(a,b)=sign(a)×sign(b)×{min(|a|,|b|)}+LUT_g(a,b),where LUT_g(a,b)=1n(1+e^−|a+b|)−1n(1+e^−|a−b|). Similar to function ƒ, function g with more than two variables can be evaluated recursively.

Next, the decoder 305, per step 1205, outputs a posteriori probability information (FIG. 12C), such that:

$a_n=u_n+\sum _j{w}_{k_j\to n}.$

Per step 1007, it is determined whether all the parity check equations are satisfied. If these parity check equations are not satisfied, then the decoder 305, as in step 1009, re-derives 8-PSK bit metrics and channel input u_n. Next, the bit node is updated, as in step 1011. As shown in FIG. 14C, the incoming messages to the bit node n from its d_v adjacent check nodes are denoted by

$w_{k_1\to n},w_{k_2\to n},\dots ,w_{k_{dv}\to n}$ The outgoing messages from the bit node n are computed back to d_v adjacent check nodes; such messages are denoted by

$v_{n\to k_1},v_{n\to k_2},\dots ,v_{n\to k_{dv}},$ and computed as follows:

$v_{n\to k_i}=u_n+\sum _{j\ne i}{w}_{k_j\to n}$ In step 1013, the decoder 305 outputs the hard decision (in the case that all parity check equations are satisfied):

${\hat{c}}_n=\{\begin{array}{cc}0,& a_n\ge 0\\ 1,& a_n<0\end{array}\mathrm{Stop}\mathrm{if}H{\hat{c}}^T=0$

The above approach is appropriate when non-Gray labeling is utilized. However, when Gray labeling is implemented, the process of FIG. 11 is executed.

FIG. 11 is a flow chart of the operation of the LDPC decoder of FIG. 3 using Gray mapping, according to an embodiment of the present invention. When Gray labeling is used, bit metrics are advantageously generated only once before the LDPC decoder, as re-generating bit metrics after every LDPC decoder iteration may yield nominal performance improvement. As with steps 1001 and 1003 of FIG. 10, initialization of the log likelihood ratios of coded bits, v, are performed, and the check node is updated, per steps 1101 and 1103. Next, the bit node n is updated, as in step 1105. Thereafter, the decoder outputs the a pasteriori probability information (step 1107). In step 1109, a determination is made whether all of the parity check equations are satisfied; if so, the decoder outputs the hard decision (step 1111). Otherwise, steps 1103-1107 are repeated.

FIG. 13A is a flowchart of process for computing outgoing messages between the check nodes and the bit nodes using a forward-backward approach, according to an embodiment of the present invention. For a check node with d_c adjacent edges, the computation of d_c(d_c−1) and numerous g(.,.) functions are performed. However, the forward-backward approach reduces the complexity of the computation to 3(d_c−2), in which d_c−1 variables are stored.

Referring to FIG. 12B, the incoming messages to the check node k from d_c adjacent bit nodes are denoted by

$v_{n_1\to k},v_{n_2\to k},\dots ,v_{n_{dc}\to k}.$ It is desired that the outgoing messages are computed from the check node k back to d_c adjacent bit nodes; these outgoing messages are denoted by

$w_{k\to n_1},w_{k\to n_2},\dots ,w_{k\to n_{dc}}.$

Under the forward-backward approach to computing these outgoing messages, forward variables, ƒ₁, ƒ₂, . . . , ƒ_dc, are defined as follows:ƒ₁=v_1→k ƒ₂=g(ƒ₁,v_2→k)ƒ₃=g(ƒ₂,v_3→k): : :ƒ_dc=g(ƒ_dc−1,v_dc→k)In step 1301, these forward variables are computed, and stored, per step 1303.

Similarly, backward variables, b₁, b₂, . . . , b_dc, are defined by the following:b_dc=v_dc→k b_dc−1=g(b_dc,v_dc−1→k): : :b_i=g(b₂,v_dc→k)In step 1305, these backward variables are then computed. Thereafter, the outgoing messages are computed, as in step 1307, based on the stored forward variables and the computed backward variables. The outgoing messages are computed as follows:w_k→1=b₂ w_k→i=g(ƒ_i−1,v_i+1)=2,3, . . . ,d_c−1w_k→dc=ƒ_dc−1

Under this approach, only the forward variables, ƒ₂, ƒ₃, . . . , ƒ_dc, are required to be stored. As the backward variables b_i are computed, the outgoing messages, w_k→i, are simultaneously computed, thereby negating the need for storage of the backward variables.

The computation load can be further enhance by a parallel approach, as next discussed.

FIG. 13B is a flowchart of process for computing outgoing messages between the check nodes and the bit nodes using a parallel approach, according to an embodiment of the present invention. For a check node k with inputs

$v_{n_1\to k},v_{n_2\to k},\dots ,v_{n_{dc}\to k}$ from d_c adjacent bit nodes, the following parameter is computed, as in step 1311:

${\gamma }_{k}g=\left(v_{n_1\to k},v_{n_2\to k},\dots ,v_{n_{dc}\to k}\right).$

It is noted that the g(.,.) function can also be expressed as follows:

$g\left(a,b\right)=\ln \frac{1+e^{a+b}}{e^a+e^b}.$

Exploiting the recursive nature of the g(.,.) function, the following expression results:

$\begin{array}{c}{\gamma }_k=\ln \frac{1+e^{g\left(v_{n_1\to k},\dots ,v_{n_{i-1}\to k},v_{n_{i+1}\to k},\dots ,v_{n_i\to k}\right)+v_{n_i\to k}}}{e^{g\left(v_{n_1\to k},\dots ,v_{n_{i-1}\to k},v_{n_{i+1}\to k},\dots ,v_{n_i\to k}\right)}+e^{v_{n_i\to k}}}\\ =\ln \frac{1+e^{w_{k\to n_i}+v_{n_i\to k}}}{e^{w_{k\to n_i}}+e^{v_{n_i\to k}}}\end{array}$

Accordingly,

$w_{k\to n_i}$ can be solved in the following manner:

$w_{k\to n_i}=\ln \frac{e^{v_{n_i\to k}+{\gamma }_k}-1}{e^{v_{n_i\to k}-{\gamma }_k}-1}-{\gamma }_k$

The 1n(.) term of the above equation can be obtained using a look-up table LUT_x that represents the function in |e^x−1| (step 1313). Unlike the other look-up tables LUT_f or LUT_g, the table LUT_x would likely requires as many entries as the number of quantization levels. Once γ_k is obtained, the calculation of

$w_{k\to n_i}$ for all n_i can occur in parallel using the above equation, per step 1315.

The computational latency of γ_k is advantageously log₂(d_c).

FIGS. 14A-14C are graphs showing simulation results of LDPC codes generated in accordance with various embodiments of the present invention. In particular, FIGS. 14A-14C show the performance of LDPC codes with higher order modulation and code rates of ¾ (QPSK, 1.485 bits/symbol), ⅔ (8-PSK, 1.980 bits/symbol), and ⅚ (8-PSK, 2.474 bits/symbol).

Two general approaches exist to realize the interconnections between check nodes and bit nodes: (1) a fully parallel approach, and (2) a partially parallel approach. In fully parallel architecture, all of the nodes and their interconnections are physically implemented. The advantage of this architecture is speed.

The fully parallel architecture, however, may involve greater complexity in realizing all of the nodes and their connections. Therefore with fully parallel architecture, a smaller block size may be required to reduce the complexity. In that case, for the same clock frequency, a proportional reduction in throughput and some degradation in FER versus Es/No performance may result.

The second approach to implementing LDPC codes is to physically realize only a subset of the total number of the nodes and use only these limited number of “physical” nodes to process all of the “functional” nodes of the code. Even though the LDPC decoder operations can be made extremely simple and can be performed in parallel, the further challenge in the design is how the communication is established between “randomly” distributed bit nodes and check nodes. The decoder 305 (of FIG. 3), according to one embodiment of the present invention, addresses this problem by accessing memory in a structured way, as to realize a seemingly random code. This approach is explained with respect to FIGS. 15A and 15B.

FIGS. 15A and 15B are diagrams of the top edge and bottom edge, respectively, of memory organized to support structured access as to realize randomness in LDPC coding, according to an embodiment of the present invention. Structured access can be achieved without compromising the performance of a truly random code by focusing on the generation of the parity check matrix. In general, a parity check matrix can be specified by the connections of the check nodes with the bit nodes. For example, the bit nodes can be divided into groups of a fixed size, which for illustrative purposes is 392. Additionally, assuming the check nodes connected to the first bit node of degree 3, for instance, are numbered as a, b and c, then the check nodes connected to the second bit node are numbered as a+p, b+p and c+p, the check nodes connected to the third bit node are numbered as a+2p, b+2p and c+2p etc.; where p=(number of check nodes)/392. For the next group of 392 bit nodes, the check nodes connected to the first bit node are different from a, b, c so that with a suitable choice of p, all the check nodes have the same degree. A random search is performed over the free constants such that the resulting LDPC code is cycle-4 and cycle-6 free. Because of the structural characteristics of the parity check matrix of the present invention, the edge information can stored to permit concurrent access to a group of relevant edge values during decoding.

In other words, the approach of the present invention facilitates memory access during check node and bit node processing. The values of the edges in the bipartite graph can be stored in a storage medium, such as random access memory (RAM). It is noted that for a truly random LDPC code during check node and bit node processing, the values of the edges would need to be accessed one by one in a random fashion. However, such a conventional access scheme would be too slow for a high data rate application. The RAM of FIGS. 15A and 15B are organized in a manner, whereby a large group of relevant edges can be fetched in one clock cycle; accordingly, these values are placed “together” in memory, according to a predetermined scheme or arrangement. It is observed that, in actuality, even with a truly random code, for a group of check nodes (and respectively bit nodes), the relevant edges can be placed next to one another in RAM, but then the relevant edges adjacent to a group of bit nodes (respectively check nodes) will be randomly scattered in RAM. Therefore, the “togetherness,” under the present invention, stems from the design of the parity check matrices themselves. That is, the check matrix design ensures that the relevant edges for a group of bit nodes and check nodes are simultaneously placed together in RAM.

As seen in FIGS. 15A and 15B, each box contains the value of an edge, which is multiple bits (e.g., 6). Edge RAM, according to one embodiment of the present invention, is divided into two parts: top edge RAM 1501 (FIG. 15A) and bottom edge RAM 1503 (FIG. 15B). Bottom edge RAM contains the edges between bit nodes of degree 2, for example, and check nodes. Top edge RAM contains the edges between bit nodes of degree greater than 2 and check nodes. Therefore, for every check node, 2 adjacent edges are stored in the bottom edge RAM 1503, and the rest of the edges are stored in the top edge RAM 1501. For example, the size of the top edge RAM 1501 and bottom edge RAM 1503 for various code rates are given in Table 1

	TABLE 1
	1/2	2/3	3/4	5/6
Top Edge	400 × 392	440 × 392	504 × 392	520 × 392
RAM
Bottom	160 × 392	110 × 392	72 × 392	52 × 392
Edge RAM

Based on Table 1, an edge RAM of size 576×392 is sufficient to store the edge metrics for all the code rates of ½, ⅔, ¾, and ⅚.

As noted, under this exemplary scenario, a group of 392 bit nodes and 392 check nodes are selected for processing at a time. For 392 check node processing, q=d_c−2 consecutive rows are accessed from the top edge RAM 1501, and 2 consecutive rows from the bottom edge RAM 1503. The value of d_c depends on the specific code, for example d_c=7 for rate ½, d_c=10 for rate ⅔, d_c=16 for rate ¾ and d_c=22 for rate ⅚ for the above codes. Of course other values of d_c for other codes are possible. In this instance, q+2 is the degree of each check node.

For bit node processing, if the group of 392 bit nodes has degree 2, their edges are located in 2 consecutive rows of the bottom edge RAM 1503. If the bit nodes have degree d>2, their edges are located in some d rows of the top edge RAM 1501. The address of these d rows can be stored in non-volatile memory, such as Read-Only Memory (ROM). The edges in one of the rows correspond to the first edges of 392 bit nodes, the edges in another row correspond to the second edges of 392 bit nodes, etc. Moreover for each row, the column index of the edge that belongs to the first bit node in the group of 392 can also be stored in ROM. The edges that correspond to the second, third, etc. bit nodes follow the starting column index in a “wrapped around” fashion. For example, if the j^th edge in the row belongs to the first bit node, then the (j+1)st edge belongs to the second bit node, (j+2)nd edge belongs to the third bit node, . . . , and (j−1)st edge belongs to the 392^th bit node.

In Tables 2-5, the row index and the starting column index of top edge RAM 1501 are specified for every group of 392 bit nodes of degree 3 or larger, for the respective code rates of ⅔, ⅚, ½, and ¾. Each row in the Tables 2-5 represents a group of 392 bit nodes. The first number denotes the row index and the second number denotes the starting column index. For example in Table 2, the first row completely determines the addresses of adjacent edges for the first group of 392 bit nodes of degree 13. Specifically, the entry 0/0 indicates that the first adjacent edges for all of the 392 bit nodes are stored in row number 0. Moreover in that row, the column indexed 0 carries the information for the first adjacent edge of the first bit node, column indexed 1 carries the information for the first adjacent edge of the second bit node etc., and finally column indexed 391 carries the information for the first adjacent edge of the 392th bit node.

Similarly the entry 433/323 specifies that the second adjacent edges for all of the 392 bit nodes are stored in row number 433. Moreover in that row, the column indexed 323 carries the information for the second adjacent edge of the first bit node, column indexed 324 carries the information for the second adjacent edge of the second bit node etc. The column indexed 322 carries the information for the second adjacent edge of the 392th bit node.

Similarly, other entries in the first row of Table 2 determine the addresses of the remaining adjacent edges for the first group of 392 bit nodes. Likewise, the entries in the second row of Table 2 determine the addresses of the adjacent edges for the second group of 392 bit nodes, etc.

TABLE 2

Row Index/Starting Column Index (Rate 2/3)

0/0

433/323

242/150

91/117

323/112

147/93

35/105

227/232

196/311

292/180

52/244

180/250

20/335

8/0

121/326

178/109

299/157

195/338

99/232

251/107

411/263

364/199

28/218

276/370

108/80

84/130

16/0

281/359

18/112

83/180

115/264

163/149

355/321

11/206

268/100

436/79

252/316

420/280

380/335

24/0

345/122

146/365

107/40

283/363

123/368

379/340

3/156

124/15

220/187

356/127

188/71

156/82

32/0

425/177

234/46

267/219

67/224

171/275

219/306

387/87

372/56

140/31

36/339

116/36

316/288

40/0

417/214

122/188

339/58

235/72

187/26

75/302

19/362

164/285

132/109

148/189

60/65

412/303

48/0

89/312

362/214

43/21

419/219

427/378

395/10

347/167

68/221

260/310

396/54

308/268

388/176

56/0

73/69

434/266

155/277

435/360

363/183

51/165

331/181

12/232

404/193

172/175

324/349

348/98

64/0

177/354

34/172

243/141

139/362

259/151

179/166

307/56

76/367

244/121

100/299

428/12

284/133

72/0

145/264

194/335

131/362

403/326

315/180

275/137

203/86

204/303

4/5

228/360

300/76

92/17

80/0

377/382

394/243

27/109

59/237

371/175

211/358

291/353

340/161

212/94

332/333

44/117

236/200

88/0

65/365

378/142

96/0

57/285

226/108

104/0

97/161

250/133

112/0

129/184

114/44

120/0

337/130

50/178

128/0

401/389

170/258

136/0

25/330

82/372

144/0

321/309

162/170

152/0

185/38

386/128

160/0

49/376

90/331

168/0

265/293

314/166

176/0

297/86

282/193

184/0

217/117

42/210

192/0

201/124

306/86

200/0

313/377

138/97

208/0

193/247

202/163

216/0

209/377

186/212

224/0

233/238

26/22

232/0

329/152

410/271

240/0

9/245

106/170

248/0

409/190

58/289

256/0

113/375

154/44

264/0

33/232

274/268

272/0

153/339

218/145

280/0

289/319

98/4

288/0

41/209

130/23

296/0

385/42

210/267

304/0

17/7

258/227

312/0

169/166

290/330

320/0

241/107

66/111

328/0

137/39

418/182

336/0

249/137

354/218

344/0

161/73

2/79

352/0

105/280

266/282

360/0

257/69

298/51

368/0

81/185

338/118

376/0

369/228

370/202

384/0

225/71

74/136

392/0

1/314

346/289

400/0

353/286

322/166

408/0

305/81

330/301

416/0

273/170

402/282

424/0

393/227

10/312

432/0

361/379

426/364

5/0

350/140

263/166

13/0

102/110

87/335

21/0

174/333

215/219

29/0

422/227

31/273

37/0

406/168

175/11

45/0

254/42

279/201

53/0

230/347

47/291

61/0

214/139

55/92

69/0

358/131

199/344

77/0

86/374

183/298

85/0

118/118

407/25

93/0

318/221

39/66

101/0

54/256

79/202

109/0

374/195

119/162

117/0

238/89

207/243

125/0

366/78

95/96

133/0

46/216

351/9

141/0

326/99

127/87

149/0

134/75

319/102

157/0

158/154

15/65

165/0

286/158

143/362

173/0

190/146

191/205

181/0

62/4

343/262

189/0

94/239

271/38

197/0

198/207

231/297

205/0

22/32

167/205

213/0

246/385

303/246

221/0

390/368

439/220

229/0

334/207

247/262

237/0

398/378

63/211

245/0

150/340

359/100

253/0

294/75

415/189

261/0

222/321

391/78

269/0

166/343

159/105

277/0

126/93

239/166

285/0

110/113

151/373

293/0

302/144

71/18

301/0

262/368

111/193

309/0

414/332

375/389

317/0

142/256

103/242

325/0

278/22

7/154

333/0

342/192

423/330

341/0

14/181

431/16

349/0

38/367

383/16

357/0

270/91

223/195

365/0

182/211

287/313

373/0

310/170

135/230

381/0

78/15

295/220

389/0

430/353

335/91

397/0

30/141

367/216

405/0

382/36

311/98

413/0

206/377

255/372

421/0

438/225

399/148

429/0

70/182

327/105

437/0

6/277

23/94

TABLE 3

Row Index/Starting Column Index (Rate 5/6)

0/0

221/158

442/14

503/323

283/150

104/117

384/112

165/93

45/105

266/232

226/311

347/180

67/244

20/0

101/369

162/326

323/359

23/112

124/180

144/264

205/149

405/321

6/206

306/100

507/79

287/316

40/0

201/285

302/12

63/134

243/68

264/238

344/375

105/259

345/213

246/75

66/148

327/100

167/220

60/0

381/141

422/112

443/125

223/47

204/375

504/214

145/188

385/58

206/72

166/26

87/302

7/362

80/0

461/383

82/80

143/61

463/106

284/196

4/94

85/104

285/235

386/3

426/218

27/38

107/161

100/0

41/310

482/66

343/376

403/166

324/265

404/236

245/230

445/63

186/343

486/88

427/202

267/362

120/0

61/31

502/317

123/25

163/139

424/269

164/309

25/56

505/260

406/279

346/148

367/315

47/382

140/0

421/362

462/206

263/297

83/384

244/287

184/132

225/140

125/14

506/216

106/311

447/87

487/264

160/0

441/191

382/360

423/282

203/2

84/58

64/347

425/249

5/267

466/232

46/275

127/385

187/26

180/0

501/296

222/324

3/73

43/6

364/319

444/204

185/82

65/259

26/90

286/155

307/181

147/366

200/0

301/325

102/119

383/285

103/84

304/121

484/352

365/102

485/107

86/9

366/76

387/229

467/52

220/0

341/331

322/242

483/275

303/293

464/166

44/283

305/232

465/86

126/193

146/184

207/38

407/117

240/0

21/314

362/289

363/211

183/120

24/286

224/166

325/186

265/144

446/81

326/301

227/4

247/199

260/0

161/91

142/78

280/0

241/209

2/119

300/0

141/87

342/147

320/0

281/55

42/46

340/0

261/213

182/145

360/0

181/264

62/88

380/0

1/96

262/184

400/0

361/30

282/126

420/0

81/202

202/206

440/0

481/156

242/263

460/0

401/170

22/126

480/0

321/42

402/21

500/0

121/272

122/337

8/0

289/369

190/223

28/0

369/313

130/127

48/0

189/92

290/241

68/0

509/124

210/56

88/0

489/23

430/101

108/0

309/208

510/162

128/0

349/147

330/242

148/0

29/263

490/54

168/0

69/312

250/377

188/0

249/315

270/116

208/0

49/176

170/58

228/0

109/337

10/55

248/0

469/65

110/187

268/0

209/105

470/362

288/0

229/164

150/80

308/0

9/293

410/374

328/0

329/122

310/152

348/0

149/124

390/382

368/0

389/160

230/92

388/0

169/357

370/368

408/0

449/296

90/377

428/0

269/32

70/212

448/0

409/59

450/257

468/0

89/291

30/234

488/0

429/130

350/95

508/0

129/276

50/38

11/0

292/349

133/372

31/0

492/271

253/248

51/0

192/149

273/378

71/0

352/265

153/37

91/0

332/244

293/199

111/0

152/354

393/243

131/0

312/144

213/184

151/0

92/219

173/11

171/0

392/182

473/325

191/0

232/219

193/30

211/0

372/157

13/63

231/0

12/108

333/359

251/0

112/33

513/88

271/0

72/207

413/9

291/0

272/100

93/357

311/0

432/166

233/272

331/0

412/265

33/210

351/0

132/155

493/50

371/0

512/292

453/214

391/0

172/387

53/114

411/0

32/233

433/177

431/0

252/113

373/52

451/0

212/347

353/90

471/0

52/89

73/198

491/0

452/285

313/233

511/0

472/103

113/84

14/0

55/43

36/361

34/0

355/70

116/287

54/0

115/137

196/57

74/0

95/161

416/206

94/0

295/273

336/209

114/0

255/184

296/287

134/0

435/11

376/38

154/0

155/356

16/379

174/0

135/251

76/10

194/0

235/314

256/293

214/0

75/296

216/326

234/0

275/314

356/116

254/0

455/133

156/165

274/0

375/292

436/283

294/0

515/227

456/337

314/0

315/111

176/155

334/0

175/98

276/334

354/0

335/7

476/87

374/0

415/161

136/15

394/0

395/338

496/98

414/0

495/82

396/269

434/0

195/312

516/187

454/0

475/100

56/356

474/0

15/163

316/195

494/0

35/197

96/145

514/0

215/301

236/381

17/0

18/321

459/4

37/0

398/236

139/8

57/0

338/117

439/84

77/0

438/97

499/93

97/0

298/292

19/215

117/0

218/224

419/275

137/0

98/229

299/27

157/0

378/133

339/232

177/0

118/191

359/271

197/0

478/272

159/386

217/0

498/262

119/219

237/0

418/282

519/297

257/0

238/33

379/339

277/0

258/230

179/350

297/0

158/27

59/188

317/0

518/249

399/229

337/0

278/333

279/330

357/0

138/276

239/49

377/0

178/34

219/304

397/0

458/344

199/181

417/0

58/312

479/158

437/0

358/377

259/364

457/0

78/157

319/380

477/0

318/75

99/57

497/0

38/296

79/26

517/0

198/115

39/342

TABLE 4
Row Index/Starting Column Index (Rate 1/2)
240/0	306/249	387/194	98/132	268/80	219/33	64/252	108/54
245/0	146/169	37/233	183/243	233/207	9/336	54/91	363/391
250/0	196/123	242/31	63/103	118/277	344/177	339/46	173/219
255/0	216/36	287/288	318/43	83/327	34/28	354/114	53/84
260/0	316/69	377/8	323/110	308/250	314/209	214/101	298/134
265/0	256/186	257/166	23/196	68/68	234/41	144/249	333/11
270/0	201/192	32/38	213/255	203/124	84/285	264/12	263/134
275/0	36/100	247/220	388/286	273/339	89/334	154/192	223/148
280/0	396/141	132/112	283/125	163/47	79/375	14/214	138/188
285/0	31/189	82/65	18/303	313/92	299/317	129/18	373/356
290/0	381/332	332/312	258/214	43/21	364/219	274/378	198/10
295/0	121/66	217/124	338/346	48/380	189/155	199/79	278/224
300/0	71/260	22/248	193/240	288/248	284/237	224/268	38/263
305/0	46/232	282/193	293/175	378/349	169/98	184/165	168/31
310/0	156/116	87/62	208/390	113/287	69/354	269/172	343/141
315/0	311/12	192/133	143/43	58/75	124/176	324/24	383/346
320/0	16/118	372/259	368/265	133/59	309/321	289/272	104/80
325/0	336/114	7/90	123/190	228/181	114/324	319/240	244/246
330/0	226/71	112/218	358/348	398/83	179/121	119/366	394/197
335/0	21/383	142/80	158/61	218/106	329/196	4/94	49/104
340/0	221/97	2/252	178/174	13/190	164/166	99/130	204/9
345/0	126/76	67/120	78/183	243/53	134/140	149/197	359/239
350/0	96/221	392/290	28/163	353/297	279/147	294/343	374/314
355/0	176/230	367/63	73/343	393/88	174/202	259/362	249/256
360/0	241/60	202/21	348/66	328/351	139/144	94/258	384/41
365/0	26/374	262/54	303/391	153/132	254/145	209/307	74/126
370/0	91/25	382/139	238/269	128/309	239/56	24/260	369/279
375/0	151/213	317/133	253/161	188/92	399/371	194/116	39/302
380/0	296/140	307/14	93/216	148/311	389/87	334/264	109/335
385/0	286/76	222/17	33/116	8/191	304/360	19/282	379/2
390/0	106/217	212/188	103/68	248/264	29/48	44/174	349/274
395/0	281/269	162/333	3/243	88/320	159/75	59/300	229/136
0/0	346/176	157/302
5/0	171/47	42/1
10/0	86/124	267/2
15/0	321/291	197/8
20/0	236/149	147/50
25/0	1/168	347/191
30/0	386/257	252/12
35/0	301/64	397/176
40/0	341/340	272/97
45/0	101/201	122/134
50/0	136/201	57/343
55/0	131/169	292/299
60/0	166/389	352/216
65/0	76/132	297/33
70/0	211/261	167/45
75/0	161/323	12/150
80/0	116/93	107/105
85/0	246/180	322/244
90/0	261/190	342/297
95/0	366/385	177/103
100/0	391/240	77/328
105/0	266/327	182/182
110/0	181/73	47/322
115/0	191/126	72/135
120/0	251/115	227/161
125/0	276/85	172/213
130/0	371/17	327/236
135/0	66/326	62/109
140/0	141/232	357/107
145/0	271/218	207/370
150/0	56/252	127/20
155/0	61/143	97/305
160/0	11/383	237/214
165/0	376/359	337/112
170/0	186/149	277/321
175/0	206/79	92/316
180/0	291/315	362/135
185/0	51/93	232/326
190/0	6/197	102/103
195/0	331/142	187/122
200/0	361/363	27/368
205/0	326/15	152/187
210/0	111/82	52/214
215/0	41/385	312/150
220/0	356/387	137/254
225/0	81/175	302/84
230/0	351/11	17/303
235/0	231/55	117/265

TABLE 5

Row Index/Starting Column Index (Rate 3/4)

0/0

113/334

100/308

423/175

493/163

32/370

116/20

467/48

243/275

370/284

356/114

77/201

7/214

14/0

29/350

44/366

185/335

3/40

494/155

144/324

383/185

229/96

230/376

188/182

427/304

385/269

28/0

435/215

366/165

101/329

17/221

46/276

74/130

341/4

313/169

314/11

272/267

21/376

273/122

42/0

155/306

240/253

353/325

451/355

312/33

88/27

47/23

327/90

286/87

34/201

483/221

175/39

56/0

197/263

492/185

283/223

367/316

60/241

228/91

145/175

439/3

454/168

202/98

133/214

203/82

70/0

463/384

352/298

269/9

129/294

256/303

214/387

5/316

285/257

90/282

48/376

399/317

329/102

84/0

1/159

170/317

409/245

255/173

270/11

438/179

271/224

89/131

300/144

328/199

343/321

231/338

98/0

211/266

450/256

199/279

171/358

242/192

466/378

187/100

19/70

62/98

384/313

35/382

245/164

112/0

141/386

128/357

87/172

465/64

424/35

354/238

117/300

257/174

146/154

496/182

161/232

91/355

126/0

15/196

296/183

395/218

73/356

452/367

158/342

173/70

131/251

258/268

76/176

455/172

119/109

140/0

57/265

58/45

437/175

59/369

284/357

102/53

103/286

33/318

412/49

160/25

105/120

371/188

154/0

323/272

198/11

31/140

227/330

410/150

298/113

61/249

495/207

244/190

426/233

63/30

189/283

168/0

477/41

408/85

311/63

45/301

326/13

200/292

159/218

481/99

20/171

174/192

217/102

315/178

182/0

43/340

212/289

381/152

115/273

172/111

368/2

75/34

369/291

132/92

482/375

413/195

301/219

196/0

225/338

436/232

479/161

339/50

340/372

396/293

355/218

397/80

468/212

342/375

497/351

259/314

210/0

253/84

30/254

297/89

241/165

382/65

18/60

299/186

425/104

440/255

398/62

441/191

469/14

224/0

449/109

478/333

325/82

143/94

186/39

130/44

453/22

411/329

6/168

118/357

287/119

357/258

238/0

169/152

310/308

213/159

157/365

480/361

4/64

201/245

215/92

104/185

216/189

147/125

49/310

252/0

267/180

380/44

266/0

71/132

184/228

280/0

281/48

268/91

294/0

393/59

254/241

308/0

379/129

86/21

322/0

127/319

114/57

336/0

85/227

282/298

350/0

491/101

324/74

364/0

309/378

226/317

378/0

239/220

2/201

392/0

407/135

156/221

406/0

365/360

394/114

420/0

183/335

422/129

434/0

421/105

464/120

448/0

295/245

142/160

462/0

351/37

338/29

476/0

337/16

72/305

490/0

99/220

16/347

8/0

23/384

346/305

22/0

415/118

444/373

36/0

65/28

24/211

50/0

261/130

80/113

64/0

275/316

220/366

78/0

205/109

206/255

92/0

51/110

38/74

106/0

373/262

304/363

120/0

345/250

472/134

134/0

37/173

388/301

148/0

359/272

430/234

162/0

429/71

150/189

176/0

93/332

94/299

190/0

163/385

416/307

204/0

289/144

164/50

218/0

457/140

290/145

232/0

79/42

360/26

246/0

387/59

262/196

260/0

233/93

66/21

274/0

303/116

136/28

288/0

121/176

276/279

302/0

485/235

332/69

316/0

443/336

178/353

330/0

499/298

458/45

344/0

191/240

234/244

358/0

9/13

248/94

372/0

219/36

402/112

386/0

331/192

52/58

400/0

471/294

500/144

414/0

317/186

318/150

428/0

107/69

108/346

442/0

149/139

486/346

456/0

135/170

192/65

470/0

401/2

10/281

484/0

177/326

374/2

498/0

247/143

122/242

11/0

474/49

433/281

25/0

292/134

335/294

39/0

404/29

265/296

53/0

320/345

111/194

67/0

208/221

13/84

81/0

264/133

419/95

95/0

54/157

83/51

109/0

166/363

195/303

123/0

194/389

377/15

137/0

460/36

447/169

151/0

306/23

279/311

165/0

40/133

153/233

179/0

236/53

27/257

193/0

446/121

293/259

207/0

250/350

167/310

221/0

68/104

209/119

235/0

334/224

251/323

249/0

432/83

503/117

263/0

180/192

125/201

277/0

362/183

391/267

291/0

110/347

405/288

305/0

222/22

223/10

319/0

502/80

489/249

333/0

12/100

139/370

347/0

390/229

321/44

361/0

376/295

97/70

375/0

124/166

69/108

389/0

418/73

349/223

403/0

96/321

237/242

417/0

26/23

181/237

431/0

82/7

55/264

445/0

348/347

461/381

459/0

138/244

41/239

473/0

488/356

475/320

487/0

278/80

307/248

501/0

152/153

363/334

With the organization shown in FIGS. 15A and 15B, speed of memory access is greatly enhanced during LDPC coding.

FIG. 16 illustrates a computer system upon which an embodiment according to the present invention can be implemented. The computer system 1600 includes a bus 1601 or other communication mechanism for communicating information, and a processor 1603 coupled to the bus 1601 for processing information. The computer system 1600 also includes main memory 1605, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 1601 for storing information and instructions to be executed by the processor 1603. Main memory 1605 can also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 1603. The computer system 1600 further includes a read only memory (ROM) 1607 or other static storage device coupled to the bus 1601 for storing static information and instructions for the processor 1603. A storage device 1609, such as a magnetic disk or optical disk, is additionally coupled to the bus 1601 for storing information and instructions.

The computer system 1600 may be coupled via the bus 1601 to a display 1611, such as a cathode ray tube (CRT), liquid crystal display, active matrix display, or plasma display, for displaying information to a computer user. An input device 1613, such as a keyboard including alphanumeric and other keys, is coupled to the bus 1601 for communicating information and command selections to the processor 1603. Another type of user input device is cursor control 1615, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor 1603 and for controlling cursor movement on the display 1611.

According to one embodiment of the invention, generation of LDPC codes is provided by the computer system 1600 in response to the processor 1603 executing an arrangement of instructions contained in main memory 1605. Such instructions can be read into main memory 1605 from another computer-readable medium, such as the storage device 1609. Execution of the arrangement of instructions contained in main memory 1605 causes the processor 1603 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the instructions contained in main memory 1605. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiment of the present invention. Thus, embodiments of the present invention are not limited to any specific combination of hardware circuitry and software.

The computer system 1600 also includes a communication interface 1617 coupled to bus 1601. The communication interface 1617 provides a two-way data communication coupling to a network link 1619 connected to a local network 1621. For example, the communication interface 1617 may be a digital subscriber line (DSL) card or modem, an integrated services digital network (ISDN) card, a cable modem, or a telephone modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 1617 may be a local area network (LAN) card (e.g. for Ethernet™ or an Asynchronous Transfer Model (ATM) network) to provide a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation, communication interface 1617 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information. Further, the communication interface 1617 can include peripheral interface devices, such as a Universal Serial Bus (USB) interface, a PCMCIA (Personal Computer Memory Card International Association) interface, etc.

The network link 1619 typically provides data communication through one or more networks to other data devices. For example, the network link 1619 may provide a connection through local network 1621 to a host computer 1623, which has connectivity to a network 1625 (e.g. a wide area network (WAN) or the global packet data communication network now commonly referred to as the “Internet”) or to data equipment operated by service provider. The local network 1621 and network 1625 both use electrical, electromagnetic, or optical signals to convey information and instructions. The signals through the various networks and the signals on network link 1619 and through communication interface 1617, which communicate digital data with computer system 1600, are exemplary forms of carrier waves bearing the information and instructions.

The computer system 1600 can send messages and receive data, including program code, through the network(s), network link 1619, and communication interface 1617. In the Internet example, a server (not shown) might transmit requested code belonging to an application program for implementing an embodiment of the present invention through the network 1625, local network 1621 and communication interface 1617. The processor 1603 may execute the transmitted code while being received and/or store the code in storage device 169, or other non-volatile storage for later execution. In this manner, computer system 1600 may obtain application code in the form of a carrier wave.

The term “computer-readable medium” as used herein refers to any medium that participates in providing instructions to the processor 1603 for execution. Such a medium may take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as storage device 1609. Volatile media include dynamic memory, such as main memory 1605. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise bus 1601. Transmission media can also take the form of acoustic, optical, or electromagnetic waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape, optical mark sheets, any other physical medium with patterns of holes or other optically recognizable indicia, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read.

Various forms of computer-readable media may be involved in providing instructions to a processor for execution. For example, the instructions for carrying out at least part of the present invention may initially be borne on a magnetic disk of a remote computer. In such a scenario, the remote computer loads the instructions into main memory and sends the instructions over a telephone line using a modem. A modem of a local computer system receives the data on the telephone line and uses an infrared transmitter to convert the data to an infrared signal and transmit the infrared signal to a portable computing device, such as a personal digital assistance (PDA) and a laptop. An infrared detector on the portable computing device receives the information and instructions borne by the infrared signal and places the data on a bus. The bus conveys the data to main memory, from which a processor retrieves and executes the instructions. The instructions received by main memory may optionally be stored on storage device either before or after execution by processor.

Accordingly, the various embodiments of the present invention provide an approach for generating structured Low Density Parity Check (LDPC) codes, as to simplify the encoder and decoder. Structure of the LDPC codes is provided by restricting the parity check matrix to be lower triangular. Also, the approach can advantageously exploit the unequal error protecting capability of LDPC codes on transmitted bits to provide extra error protection to more vulnerable bits of high order modulation constellations (such as 8-PSK (Phase Shift Keying)). The decoding process involves iteratively regenerating signal constellation bit metrics into an LDPC decoder after each decoder iteration or several decoder iterations. The above approach advantageously yields reduced complexity without sacrificing performance.

While the present invention has been described in connection with a number of embodiments and implementations, the present invention is not so limited but covers various obvious modifications and equivalent arrangements, which fall within the purview of the appended claims.

Claims

1. A method comprising: storing a first edge value in a first segment of memory; andstoring a second edge value in a second segment of memory, wherein the edge values are associated with a structured parity check matrix used to generate a low density parity check coded signal, the edge values specifying relationship of bit nodes and check nodes, wherein the first segment is contiguous with the second segment, and the first edge value and the second edge value are read during a single processor clock cycle for decoding the low density parity check coded signal.

2. A method according to claim 1, wherein the memory is partitioned according to degrees of the bit nodes.

3. A method according to claim 2, wherein edge values having bit nodes of n degrees are stored in a first portion of the memory, and edge values having bit nodes of greater than n degrees are stored in a second portion of the memory, n being an integer.

4. A method according to claim 1, wherein addresses of the memory is stored in a Read-Only memory.

5. A method according to claim 1, wherein M bit nodes or M check nodes correspond to the edge values, and M parallel processing engines are utilized to decode the low density parity check coded signal.

6. A computer-readable storage medium bearing instructions that are arranged, upon execution, to cause one or more processors to perform the method of claim 1.

7. A memory device comprising: a first segment configured to store a first edge value; anda second segment contiguous with the first segment and configured to store a second edge value, wherein the edge values are associated with a structured parity check matrix used to generate a low density parity check coded signal, the edge values specifying relationship of bit nodes and check nodes, wherein the first edge value and the second edge value are read during a single processor clock cycle for decoding the low density parity check coded signal.

8. A device according to claim 7, wherein the memory is partitioned according to degrees of the bit nodes.

9. A device according to claim 8, wherein edge values having bit nodes of n degrees are stored in a first portion of the memory, and edge values having bit nodes of greater than n degrees are stored in a second portion of the memory, n being an integer.

10. A device according to claim 7, wherein addresses of the memory is stored in a Read-Only memory.

11. A device according to claim 7, wherein M bit nodes or M check nodes correspond to the edge values, and M parallel processing engines are utilized to decode the low density parity check coded signal.

12. A system comprising: a memory including, a first segment configured to store a first edge value, anda second segment contiguous with the first segment and configured to store a second edge value, wherein the edge values are associated with a structured parity checkmatrix used to generate a low density parity check coded signal, the edge values specifying relationship of bit nodes and check nodes; anda plurality of processing engines configured to read the edge values during a single clock cycle for decoding the low density parity check coded signal.

13. A system according to claim 12, wherein the memory is partitioned according to degrees of the bit nodes.

14. A system according to claim 13, wherein edge values having bit nodes of n degrees are stored in a first portion of the memory, and edge values having bit nodes of greater than n degrees are stored in a second portion of the memory, n being an integer.

15. A system according to claim 12, wherein addresses of the memory is stored in a Read-Only memory.

16. A method comprising: reading, during a single processor clock cycle, a first edge value stored in a first segment of memory; andreading, during the single processor clock cycle, a second edge value in a second segment of memory, wherein the edge values are associated with a structured parity check matrix used to generate a low density parity check coded signal, the edge values specifying relationship of bit nodes and check nodes, wherein the first segment is contiguous with the second segment for decoding the low density parity check coded signal.

17. A method according to claim 16, wherein the memory is partitioned according to degrees of the bit nodes.

18. A method according to claim 17, wherein edge values having bit nodes of n degrees are stored in a first portion of the memory, and edge values having bit nodes of greater than n degrees are stored in a second portion of the memory, n being an integer.

19. A method according to claim 16, wherein addresses of the memory is stored in a Read-Only memory.

20. A method according to claim 16, wherein M bit nodes or M check nodes correspond to the edge values, and M parallel processing engines are utilized to decode the low density parity check coded signal.

21. A computer-readable storage medium bearing instructions that are arranged, upon execution, to cause one or more processors to perform the method of claim 16.