bit-error-rate measurement

Conventional metrics such as floating-point operations per second (FLOPs) fail to capture the intrinsic performance characteristics of emerging computing paradigms—including low-precision, analog, quantum, and reversible logic—due to their hardware- and precision-specific assumptions. Method: This paper proposes a general, information-theoretic framework for computational performance evaluation, modeling computation as an information-transformation channel from input to output and using mutual information as the core metric to quantify a system’s capacity to encode, process, and preserve semantically meaningful information. Contribution/Results: It is the first work to systematically integrate Shannon’s mutual information into computational performance assessment, thereby decoupling evaluation from underlying hardware implementations and numerical representations. The framework enables paradigm-agnostic, implementation-independent performance analysis across heterogeneous computing models. It establishes a foundational theoretical basis and provides a scalable, principled metric for rigorously assessing both the effectiveness and efficiency of next-generation heterogeneous computing systems.

Interference is ubiquitous in communication systems, yet receivers often employ suboptimal decoding due to limited channel knowledge or high computational complexity—rendering conventional capacity and mutual information metrics inadequate for characterizing practical performance. To address this, we establish a precise performance evaluation framework for interference-limited scenarios based on mismatched decoding theory. Specifically, we are the first to incorporate the generalized mutual information (GMI) into the bit-interleaved coded modulation (BICM) demodulation process, demonstrating its high accuracy in throughput prediction. We further propose an interference-resilient decoding metric and develop a GMI-driven joint precoding design for multi-user multiple-input single-output (MU-MISO) systems. The derived matched/mismatched capacity bounds, validated via simulations, show that our framework significantly improves spectral efficiency and assessment reliability under interference. This work establishes a new design paradigm for BICM and multi-antenna systems operating under non-ideal decoding conditions.

This work addresses the challenge of blind code rate recovery for linear block codes under high-noise conditions in non-cooperative communications. The authors propose a novel metric based on parity-check matrix rank estimation, which, for the first time, yields an analytically tractable closed-form expression for quantifying code rate recovery accuracy. Leveraging this expression, they derive optimal estimation strategies and algorithmic parameters tailored to high-noise scenarios. Theoretical analysis and simulations using LDPC codes demonstrate the effectiveness of the proposed metric and confirm its significantly superior estimation performance compared to existing methods in high-noise environments. Furthermore, the study explicitly identifies the optimal configuration of recovery parameters, offering practical guidance for implementation.

This work investigates the trade-off between error exponent and computational complexity for mismatched noise guessing decoding over additive memoryless channels. It analyzes matched, α-tilted, and a proposed universal decoding metric based on the empirical entropy of noise sequences, under both deterministic and stochastic decoding frameworks. Theoretically, it is shown that under deterministic decoding, all considered metrics are equivalent and achieve optimal performance. In contrast, under stochastic decoding, the matched metric is not complexity-optimal; instead, optimality requires either tuning the α parameter according to the code rate or employing the proposed universal metric. Notably, this universal approach attains simultaneously optimal error and complexity exponents across all coding rates and channel conditions without requiring prior knowledge of the channel statistics.

This paper addresses performance degradation in short-block-length transmission systems caused by unknown channel state information (CSI) and low-density pilot signals. We propose a joint detection and channel estimation framework for bit-interleaved coded modulation (BICM). Our key contributions are: (1) a novel joint BICM metric enabling end-to-end joint decoding and estimation assisted by training signals; (2) the first demonstration in OFDM systems of near-ideal coherent reception performance with only a 4-symbol detection window; and (3) an adaptive Demodulation Reference Signal (DMRS) power allocation scheme that jointly optimizes channel estimation accuracy and coding gain under low-overhead constraints. Evaluated on a full 5G link—featuring Polar/LDPC coding, BPSK/QPSK modulation, and OFDM—the scheme achieves significantly lower bit error rates (BER) than conventional separate-receiver architectures for ultra-short blocks (<64 bits), delivering up to 1.8 dB coding gain and approaching the perfect-CSI performance bound even with sparse DMRS placement.

Soft-decision decoding remains challenging in terms of universality, efficiency, and the need for signal-to-noise ratio (SNR) estimation. This work proposes a novel framework that, for the first time, formulates error-correcting code decoding as a continuous-time denoising process via a score-matching-based neural probability flow ordinary differential equation (ODE). The approach trains directly on raw signed channel observations without requiring SNR conditioning and leverages ODE solvers to flexibly trade off decoding latency against accuracy. By incorporating parity-check constraints and employing Euler and DPM solvers, the method achieves the lowest bit error rate in 39 out of 42 code–SNR configurations, yielding an average SNR gain of 0.17 dB (up to 0.46 dB). Switching to the DPM solver further reduces decoding time by 8.86% on average (up to 12.82%) while preserving performance.

This work addresses the challenge of simultaneously achieving semantic fidelity and robust error correction in short-blocklength communication over noisy wireless channels. The authors propose a semantic-aware short-blocklength coding framework that segments sentences into short blocks for independent transmission and employs a BART-based bidirectional-autoregressive Transformer at the receiver to perform context-aware semantic error correction. The framework integrates semantic list decoding and a confidence-guided HARQ mechanism without CRC overhead (termed SHARQ). By deeply embedding semantic information into the short-blocklength system, the approach significantly enhances performance while maintaining low latency: it achieves a 0.8 dB gain in block error rate (BLER) over conventional short codes, reduces decoding latency by 90% compared to 5G LDPC long codes while substantially improving semantic fidelity, and yields an additional 1.5 dB gain through the SHARQ mechanism.

This work addresses the fundamental trade-off between estimation accuracy and data rate in fast-fading channels, where conventional training-sequence-based channel estimation is inherently limited. The authors propose a non-iterative, modulation-, coding-, and decoding-agnostic cooperative mechanism that leverages decoded codewords as soft pilots to continuously refine channel estimates in real time—without incurring any additional overhead. The approach is universally applicable to diverse forward error correction systems and its theoretical performance limits are characterized from an information-theoretic perspective. By integrating soft-output decoding with joint time–frequency domain coding, the scheme substantially enhances transmission efficiency under rapid fading: in the frequency domain, soft information improves estimation accuracy; in the time domain, shorter codes at the same code rate outperform longer ones. Extensive simulations confirm the superiority of the proposed method.

This work addresses the fundamental trade-off between microsecond-scale latency and high decoding accuracy that hinders the practical deployment of neural decoders in quantum error correction. Under explicit accuracy–latency constraints, the authors unify and reconstruct five representative surface code neural decoder architectures, proposing an end-to-end compression pipeline and demonstrating FPGA deployment supporting code distances up to $d=9$. Their findings reveal that recent gains in decoding performance are primarily driven by training data volume rather than architectural complexity, that effective inductive bias is crucial for achieving high accuracy, and that INT4 quantization is essential to meet microsecond-level latency requirements. This study provides a viable technical pathway and empirical foundation for scalable, real-time neural quantum error correction.

This work addresses the suboptimal utilization of extrinsic information in Chase-Pyndiah decoding by proposing a dynamic extrinsic information scaling mechanism based on component decoder confidence. During iterative decoding, the method adaptively adjusts the weight of extrinsic information according to local reliability metrics, thereby enabling more effective propagation of trustworthy soft information. The proposed scheme introduces negligible computational overhead while achieving approximately 0.1 dB gain in error-rate performance over the original Chase-Pyndiah decoder on standard product codes, significantly enhancing both decoding efficiency and reliability.