Resistive Random-Access Memory (ReRAM)—a core component of nanoscale mixed-signal storage circuits—faces a unique data-dependent challenge, sneak path (SP) interference, which severely limits its use in high-density integrated systems. With the rapid advancement of high-density ReRAM crossbar architectures, independently designed constrained coding or error-correcting coding (ECC) often introduces excessive hardware redundancy. It fails to synergistically optimize SP suppression and channel noise correction, reducing the integration and energy efficiency of ReRAM-based circuits. To address this, this paper proposes a unified multi-module collaborative storage error-control framework, achieving “suppression-correction” dual optimization for SP interference in one system. Constrained codes built on the maximum mutual information principle significantly lower SP occurrence probability while preserving channel capacity. An optimized adaptive belief propagation (BP) detection algorithm enables precise estimation of ReRAM array-stored data, and polar codes efficiently correct residual errors. Simulation results show the unified framework greatly improves array data discrimination accuracy. This work not only offers a new paradigm for balancing reliability and efficiency in practical ReRAM storage circuit design but also connects information theory with nanoscale mixed-signal circuit implementation.

An analytical framework integrating performance characterization and coding theory is proposed to mitigate sneak path (SP) interference in resistive random-access memory (ReRAM) crossbar arrays. The core innovation is identified in the mathematical decomposition of ReRAM’s non-ergodic data-dependent channel into multiple stationary memoryless subchannels. Through information-theoretic analysis, an approximate finite-length characterization of the theoretical lower bound for decoding word error probability (WEP) is established. This is achieved by systematically analyzing the SP occurrence rate in constrained array geometries combined with comprehensive evaluation of both mutual information and dispersion metrics across the decomposed channel components. Building upon this decomposition paradigm, a systematic code construction methodology is developed using density evolution principles for sparse-graph code design. The designed codes not only exhibit capacity-approaching decoding thresholds but also yield word error rate simulation results that are close to the derived WEP bound under practical crossbar configurations.

Due to the duality between source coding and channel coding, high-performance channel codes are often adopted for addressing source coding problems. In this paper, we investigate the design and analysis of protograph low-density parity-check (P-LDPC) codes in lossy source coding systems. First, we propose a novel lossy source coding architecture based on P-LDPC codes, and empirically verify that although high-performance channel codes perform exceptionally well in their intended domain, they are not inherently optimal for lossy compression. To facilitate analysis, we introduce the lossy compression protograph extrinsic information transfer (LC-PEXIT) algorithm, which aids in evaluating the rate-distortion (RD) performance of P-LDPC codes. Additionally, the LC-PEXIT algorithm allows for the prediction of key parameters, such as the prior coefficientPand the reinforcement rateR, both of which critically influence system performance. We further propose a design algorithm for lossy P-LDPC codes and provide two sets of design examples. Experimental results show strong alignment between the predicted and actual values ofPandRf, with the designed P-LDPC codes exhibiting superior RD performance compared to classical P-LDPC and state-of-the-art ultra-sparse LDPC (US-LDPC) codes. In particular, the designed P-LDPC codes over benchmark codes increases the system coding efficiency of up to approximate 70% and achieves a performance gain of 1.69 ~ 3.26 dB, making it highly effective for lossy compression applications.

This letter proposes a high-order probabilistic shaped (PS) system for joint source-channel coding (JSCC) using polar codes, which are employed for both source compression and channel coding. The modulation is based on a selection rule derived from the proposed bit classification distribution matching (BCDM) algorithm. An external binary random sequence is used to select and redundantly insert the codeword sequence, which makes the modulation symbol probability approach the Gaussian distribution, instead of being uniformly distributed as in the conventional constellation mapping. At the receiver, a joint belief propagation (JBP) decoder, which consists of a source and a channel BP decoder, is applied. Simulation results demonstrate that the proposed PS joint source-channel polar-coded modulation (PS-JSCPCM) system yields a significant enhancement to the bit error rate (BER) performance compared with the traditional unshaped joint source-channel code-modulation (JSCCM) system.

This paper investigates the possibilities of optimizing existing protograph low-density parity-check (PLDPC) codes and designing optimal PLDPC codes on both AWGN and Rayleigh channels under binary modulation with unequal power allocation (UPA) for nonuniform sources. While the binary UPA modulation achieves a power gain, it also results in unequal error protection on the coded bits in systematic codewords during transmission. For irregular codes, it is possible to best protect the information bits by optimizing the classification of variable nodes (VNs). Those VNs represent the coded bits in protograph that allows exploiting jointly the degree irregularity and the power differentiation. An algorithm for UPA-based protograph extrinsic information transfer (UPA-PEXIT) analysis on the Rayleigh channel is proposed with the concept of surrogate channel which circumvents the channel asymmetry problem with UPA. With the RJA code as example, it is shown that classification optimization does improve the decoding threshold when the source entropy is small. To design optimal codes, a UPA-based differential evolution (UPA-DE) algorithm is proposed in conjunction with the UPA-PEXIT analysis. A family of optimized codes are illustrated for three typical nonuniform source distributions and compared with the RJA and NND codes by simulations in error rate performance.