Capacity-achieving Codes Research Articles

A Lower Bound on the Probability of Error of Polar Codes over BMS Channels

Polar codes are a family of capacity-achieving codes that have explicit and low-complexity construction, encoding, and decoding algorithms. Decoding of polar codes is based on the successive-cancellation decoder, which decodes in a bit- wise manner. A decoding error occurs when at least one bit is erroneously decoded. The various codeword bits are correlated, yet performance analysis of polar codes ignores this dependence: the upper bound is based on the union bound, and the lower bound is based on the worst-performing bit. Improvement of the lower bound is afforded by considering error probabilities of two bits simultaneously. These are difficult to compute explicitly due to the large alphabet size inherent to polar codes. In this research we propose a method to lower-bound the error probabilities of bit pairs. We develop several transformations on pairs of synthetic channels that make the resultant synthetic channels amenable to alphabet reduction. Our method yields lower bounds that significantly improve upon currently known lower bounds for polar codes under successive-cancellation decoding.

A Modified-SCD for Optimize Performance Rate at Polar Code

The first verified capacity-achieving codes were developed by Arikan called “polar codes.” It has gained significant attention due to their low encoding and decoding process complexity and optimum capacity-achievement process. In the original form of polar codes, the decoder has the inferior performance with finite ‘block-lengths' as compared to the LDPC codes, therefore, the successive cancellation decoder (“SCD”) for the polar codes is a very important element in order to provide a capacity theorem, but it comprises complexity at a larger number of iterations that is not applicable for the point of view of lower latency application. Therefore, in this article we proposed a modified SCD that rearranges the stages but it can cause decoder performance degradation, so we applied a novel design rule of the polar codes in order to match with our MSCD model. In the result analysis section, we show the effectiveness of our proposed model that tends to have higher performance rate as compared to standard SCD.

Adversarial Network Coding

A combinatorial framework for adversarial network coding is presented. Channels are described by specifying the possible actions that one or more (possibly coordinated) adversaries may take. Upper bounds on three notions of capacity—the one-shot capacity, the zero-error capacity, and the compound zero-error capacity—are obtained for point-to-point channels, and generalized to corresponding capacity regions appropriate for multi-source networks. A key result of this paper is a general method by which bounds on these capacities in point-to-point channels may be ported to networks. This technique is illustrated in detail for Hamming-type channels with multiple adversaries operating on specific coordinates, which correspond, in the context of networks, to multiple adversaries acting on specific network edges. Capacity-achieving coding schemes are described for some of the considered adversarial models.

Polar codes with the unequal error protection property

The Internet of Things (IoT) is expected to support a wide variety of devices with different transmission reliability requirements. Channel codes with the unequal error protection (UEP) property are rather appealing for such applications. As the first provably capacity-achieving codes, polar codes have excellent error-correcting performances over a wide range of block lengths with low encoding and decoding complexities, which makes these codes well-suited for the energy-constrained IoT applications. Polar codes with unequal error protection are desirable. However, the UEP property of polar codes has not been explored explicitly in existing works. In this paper, a construction method for UEP polar codes is proposed based upon a novel selection rule of bit-channels. Refreezing and reselecting operations are employed to enable the UEP property for polar codes. The upper and lower bounds on the block error rate (BLER) of each data block are derived. Simulation results show that the proposed codes can provide a good performance of unequal error protection.

Duality of Channels and Codes

For any given channel $W$ with classical inputs and possibly quantum outputs, a dual classical-input channel $W^\perp$ can be defined by embedding the original into a channel $\mathcal N$ with quantum inputs and outputs. Here we give new uncertainty relations for a general class of entropies that lead to very close relationships between the original channel and its dual. Moreover, we show that channel duality can be combined with duality of linear codes, whereupon the uncertainty relations imply that the performance of a given code over a given channel is entirely characterized by the performance of the dual code on the dual channel. This has several applications. In the context of polar codes, it implies that the rates of polarization to ideal and useless channels must be identical. Duality also relates the tasks of channel coding and privacy amplification, implying that the finite blocklength performance of extractors and codes is precisely linked, and that optimal rate extractors can be transformed into capacity-achieving codes, and vice versa. Finally, duality also extends to the EXIT function of any channel and code. Here it implies that for any channel family, if the EXIT function for a fixed code has a sharp transition, then it must be such that the rate of the code equals the capacity at the transition. This may give a different route to proving a code family achieves capacity by establishing sharp EXIT function transitions.

AWGN-Goodness Is Enough: Capacity-Achieving Lattice Codes Based on Dithered Probabilistic Shaping

In this paper, we show that any sequence of infinite lattice constellations which is good for the unconstrained Gaussian channel can be shaped into a capacity-achieving sequence of codes for the power-constrained Gaussian channel under lattice decoding and non-uniform signaling. Unlike previous results in the literature, our scheme holds with no extra condition on the lattices (e.g., quantization-goodness or vanishing flatness factor), thus establishing a direct implication between AWGN-goodness, in the sense of Poltyrev and capacity-achieving codes. Our analysis uses properties of the discrete Gaussian distribution in order to obtain precise bounds on the probability of error and achievable rates. In particular, we obtain a simple characterization of the finite-blocklength behavior of the scheme, showing that it approaches the optimal dispersion coefficient for high signal-to-noise ratio. We further show that for low signal-to-noise ratio, the discrete Gaussian over centered lattice constellations cannot achieve capacity, and thus a shift (or “dither”) is essentially necessary.

Efficient Soft Cancelation Decoder Architectures for Polar Codes

The flooding belief propagation (FO-BP) and the soft-cancelation (SCAN) algorithms are the two most popular soft-output BP algorithms for the decoding of capacity-achieving polar codes. The FO-BP algorithm has high throughput at the cost of performance degradation in high signal-to-noise ratio (SNR) region or with large block length. The SCAN algorithm has much better decoding performance while suffering from long decoding latency and low throughput. In this paper, an improved BP algorithm, named reduced complexity soft-cancelation (RCSC) algorithm, is proposed. Compared with the SCAN algorithm, the number of memory entries required by the RCSC algorithm is reduced by more than 50% in general, while achieving comparable or even better (e.g., when block size $N=2^{15}$ ) decoding performance. When block size is large (e.g., $N\geq 2^{15}$ ), the proposed RCSC algorithm reduces the required memory entries by more than 23% compared with the state-of-the-art FO-BP algorithm. The numerical results show that the error performance improvement of the RCSC algorithm is more significant when the SNR increases. For a different tradeoff, a reduced latency soft-cancelation (RLSC) algorithm is proposed to reduce the decoding latency and increase the throughput of the RCSC algorithm while slightly sacrificing decoding performance. Finally, the optimized VLSI architectures are presented for the RCSC and RLSC algorithms, respectively. The synthesis results demonstrate the efficiency of the proposed algorithms and architectures.

An Efficient List Successive Cancellation Decoder for Polar Codes

Polar codes are one of the most favorable capacity-achieving codes due to their simple structure and low decoding complexity. However, because of the disappointing decoding performance realized using conventional successive cancellation (SC) decoders, polar codes cannot be used directly in practical applications. In contrast to conventional SC decoders, list SC (SCL) decoders with large list sizes (e.g. 32) achieve performances very close to those of maximum-likelihood (ML) decoders. In SCL decoders with large list sizes, however, hardware increase is a severe problem because an SCL decoder with list size L consists of L copies of an SC decoder. In this paper, we present a low-area SCL decoder architecture that applies the proposed merged processing element-sharing (MPES) algorithm. A merged processing element (MPE) is the basic processing unit in SC decoders, and the required number of MPEs is L(N-1) in conventional SCL decoders. Using the proposed algorithm reduces the number of MPEs by about 70% compared with conventional SCL decoders when the list size is larger than 32.

Output Distributions of Capacity-Achieving Codes for Gaussian Multiple Access Channels

This letter considers communication over the Gaussian multiple access channel (MAC) with multiple sources. A sequence of length- $n$ codes for the Gaussian MAC is said to be capacity-achieving if the codes achieve the sum-rate capacity. It is shown that for any sequence of capacity-achieving codes, the normalized relative entropy between the output distribution resulted from the interaction between the length- $n$ code and the Gaussian MAC, denoted by $p_{Y^n}$ , and the $n$ -fold product of the capacity-achieving output distribution $p_Y^*$ , denoted by $p_{Y^n}^*$ , converges to zero as the blocklength grows, i.e., $\frac{1}{n}D(p_{Y^n}\Vert p_{Y^n}^*)\rightarrow 0$ . Applications of the convergence result are also discussed.

Fault-Tolerant Associative Memories Based on $c$-Partite Graphs

Associative memories allow the retrieval of previously stored messages given a part of their content. In this paper, we are interested in associative memories based on $c$ -partite graphs that were recently introduced. These memories are almost optimal in terms of the amount of storage they require (efficiency) and allow retrieving messages with low complexity. We propose a generic implementation model for the retrieval algorithm that can be readily mapped to an integrated circuit and study the retrieval performance when hardware components are affected by faults. We show using analytical and simulation results that these associative memories can be made resilient to circuit faults with a minor modification of the retrieval algorithm. In one example, the memory retains 88% of its efficiency when 1% of the storage cells are faulty, or 98% when 0.1% of the binary outputs of the retrieval algorithm are faulty. When considering storage faults, the fault tolerance exhibited by the proposed associative memory can be comparable to using a capacity-achieving error correction code for protecting the stored information.

Simple capacity‐achieving coded modulation strategy

A simple system that approaches the capacity of the additive white Gaussian noise channel (AWGN) is proposed. It is well known that this capacity is achieved by a Gaussian input, which is hard to obtain in practice. However, if the capacity is <1/2, then the use of a binary input incurs little capacity loss. A parallel between the AWGN and the multiple access channel (MAC) is exploited, which allows for the transformation, without loss of capacity, of the AWGN channel into several parallel channels with capacity <1/2. In this proposal, each channel uses a binary input and a capacity-achieving code, resulting in a system with simple encoding that operates close to capacity. A decoding scheme based on successive interference cancellation is also proposed. As a result, the receiver consists of a series of simple binary receivers. It is shown that the proposed system works at a small gap to the capacity of the AWGN, and that this gap may be attributed to the gap to capacity of the underlying binary code. Similar method has been proposed but the results have shown that this method is better than the iterative method (bit interleaved coded modulation).

Code Design for Iterative Decoding of Multilevel Codes

The code design problem for multilevel coded modulation with iterative decoding (MLCM-ID) has been left unsolved over a decade. In this paper, we define the code design criterion for MLCM-ID based on a novel concept—parametrically-mapped EXIT (PM-EXIT) function. We derive the EXIT functions for MLCM-ID, and present the mathematical work by which the three area theorems of PM-EXIT function are proved. This gives theoretical support of the curve-fitting techniques used in MLCM-ID, and also provides firm proof that the PM-EXIT function design rule allows in principle capacity-achieving code assignment. The simulation results perfectly match the theoretical analysis, and also confirm that the design rule proposed fully exploits the flexibility of MLCM-ID in the choice of codes. Both in theory and simulations, our code design is verified.

On distributed polarising relay transmission system with successive interference cancelation decoding in frequency selective fading channels

Motivated by the channel polarisation that shows an occurrence of capacity-achieving coding sequences, we address a system design issue by switching to polarising frequency selective fading FSF channels while transmitting symbols in a source-relay-destination MIMO-OFDM system. A polar-and-forward PF relay scheme is proposed to provide an alternative solution for transmitting symbols with a higher reliability than the conventional relay schemes. The OFDM modulator is implemented at source node, while coding operations, such as time reversion, complex conjugation and polarisation, are implemented at relay nodes. It is implemented with the symmetrical polarising systems, i.e., the down-polarising and up-polarising systems, which result in different capacities for the FSF channels. We analyse the bit error rate BER performance with the fixed polarised system for four OFDM blocks, which is an idea approach to select signal sequences that tend to polarising in terms of the reliability with channel combining and splitting operations. The polarised system has a salient recursiveness feature, and thus the transmitted symbols embedded in the polar code can be decoded with a low-complexity receiver.

Zero-Error Capacity of a Class of Timing Channels

We analyze the problem of zero-error communication through timing channels that can be interpreted as discrete-time queues with bounded waiting times. The channel model includes the following assumptions: 1) Time is slotted, 2) at most $ N $ "particles" are sent in each time slot, 3) every particle is delayed in the channel for a number of slots chosen randomly from the set $ \{0, 1, \ldots, K\} $, and 4) the particles are identical. It is shown that the zero-error capacity of this channel is $ \log r $, where $ r $ is the unique positive real root of the polynomial $ x^{K+1} - x^{K} - N $. Capacity-achieving codes are explicitly constructed, and a linear-time decoding algorithm for these codes devised. In the particular case $ N = 1 $, $ K = 1 $, the capacity is equal to $ \log \phi $, where $ \phi = (1 + \sqrt{5}) / 2 $ is the golden ratio, and the constructed codes give another interpretation of the Fibonacci sequence.

Communication Over Finite-Chain-Ring Matrix Channels

Though network coding is traditionally performed over finite fields, recent work on nested-lattice-based network coding suggests that, by allowing network coding over certain finite rings, more efficient physical-layer network coding schemes can be constructed. This paper considers the problem of communication over a finite-ring matrix channel $Y = AX + BE$, where $X$ is the channel input, $Y$ is the channel output, $E$ is random error, and $A$ and $B$ are random transfer matrices. Tight capacity results are obtained and simple polynomial-complexity capacity-achieving coding schemes are provided under the assumption that $A$ is uniform over all full-rank matrices and $BE$ is uniform over all rank-$t$ matrices, extending the work of Silva, Kschischang and K\"{o}tter (2010), who handled the case of finite fields. This extension is based on several new results, which may be of independent interest, that generalize concepts and methods from matrices over finite fields to matrices over finite chain rings.

Constrained Codes for Joint Energy and Information Transfer

In various wireless systems, such as sensor RFID networks and body area networks with implantable devices, the transmitted signals are simultaneously used both for information transmission and for energy transfer. In order to satisfy the conflicting requirements on information and energy transfer, this paper proposes the use of constrained run-length limited (RLL) codes in lieu of conventional unconstrained (i.e., random-like) capacity-achieving codes. The receiver's energy utilization requirements are modeled stochastically, and constraints are imposed on the probabilities of battery underflow and overflow at the receiver. It is demonstrated that the codewords' structure afforded by the use of constrained codes enables the transmission strategy to be better adjusted to the receiver's energy utilization pattern, as compared to classical unstructured codes. As a result, constrained codes allow a wider range of trade-offs between the rate of information transmission and the performance of energy transfer to be achieved.

Update-Efficiency and Local Repairability Limits for Capacity Approaching Codes

Motivated by distributed storage applications, we investigate the degree to which capacity achieving codes can be efficiently updated when a single information symbol changes, and the degree to which such codes can be efficiently repaired when a single encoded symbol is lost. Specifically, we first develop conditions under which optimum error-correction and update-efficiency are possible. We establish that the number of encoded bits that should change in response to a change in a single information bit must scale logarithmically in the block-length of the code, if we are to achieve any nontrivial rate with vanishing probability of error over the binary erasure or binary symmetric channels. Moreover, we show that there exist capacity-achieving codes with this scaling. With respect to local repairability, we develop tight upper and lower bounds on the number of remaining encoded bits that are needed to recover a single lost encoded bit. In particular, we show that when the rate of an optimal code is ε below capacity, the maximum number of codeword symbols required to recover one lost symbol must scale as log1/ε. Several variations on-and extensions of-these results are also developed, including to the problem of rate-distortion coding.

Message Passing Algorithm for Distributed Downlink Regularized Zero-Forcing Beamforming with Cooperative Base Stations

Base station (BS) cooperation can turn unwanted interference to useful signal energy for enhancing system performance. In the cooperative downlink, zero-forcing beamforming (ZFBF) with a simple scheduler is well known to obtain nearly the performance of the capacity-achieving dirty-paper coding. However, the centralized ZFBF approach is prohibitively complex as the network size grows. In this paper, we devise message passing algorithms for realizing the regularized ZFBF (RZFBF) in a distributed manner using belief propagation. In the proposed methods, the overall computational cost is decomposed into many smaller computation tasks carried out by groups of neighboring BSs and communication is only required between neighboring BSs. More importantly, some exchanged messages can be computed based on channel statistics rather than instantaneous channel state information, leading to significant reduction in computational complexity. Simulation results demonstrate that the proposed algorithms converge quickly to the exact RZFBF and much faster compared to conventional methods.

On Probabilistic Weight Distribution of Polar Codes

Polar code is a low-complexity and capacity-achieving code with applications in channel coding, source coding and secrecy. One way to evaluate the error performance of the finite-length polar 'channel' code, for instance, is to use Bonferroni-type (e.g., union bound) bounds requiring the Hamming weight distribution. Currently, there are no low-complexity algorithms for the polar code's spectra. In this paper, we propose a recursive probabilistic weight distribution (PWD) expression for polar codes. As this expression is complex, two polynomial-time approximations and a tree-based approach have been presented for a faster implementation of the PWD method with much accuracy.

On Convexity of Error Rates in Digital Communications

Convexity properties of error rates of a class of decoders, including the ML/min-distance one as a special case, are studied for arbitrary constellations, bit mapping and coding. Earlier results obtained for the AWGN channel are extended to a wide class of noise densities, including unimodal and spherically-invariant noise. Under these broad conditions, symbol and bit error rates are shown to be convex functions of the SNR in the high-SNR regime with an explicitly-determined threshold, which depends only on the constellation dimensionality and minimum distance, thus enabling an application of the powerful tools of convex optimization to such digital communication systems in a rigorous way. It is the decreasing nature of the noise power density around the decision region boundaries that insures the convexity of symbol error rates in the general case. The known high/low SNR bounds of the convexity/concavity regions are tightened and no further improvement is shown to be possible in general. The high SNR bound fits closely into the channel coding theorem: all codes, including capacity-achieving ones, whose decision regions include the hardened noise spheres (from the noise sphere hardening argument in the channel coding theorem) satisfies this high SNR requirement and thus has convex error rates in both SNR and noise power. We conjecture that all capacity-achieving codes have convex error rates. Convexity properties in signal amplitude and noise power are also investigated. Some applications of the results are discussed. In particular, it is shown that fading is convexity-preserving and is never good in low dimensions under spherically-invariant noise, which may also include any linear diversity combining.