digital modulation design

This paper systematically investigates multi-source interference in OFDM physical-layer systems, encompassing inter-symbol interference (ISI), inter-block interference (IBI), inter-carrier interference (ICI), and inter-cell interference (also denoted ICI), alongside adversarial interference mechanisms—including jamming and spoofing attacks. Methodologically, it establishes the first unified multi-interference modeling framework and a cross-layer interference taxonomy; further, it proposes an interference-resilient OFDM modulation candidate suite tailored for 6G. Through integrated interference modeling, signal processing, network-level coordination, and robustness evaluation, the work quantitatively compares bit-error-rate (BER) performance degradation under SNR reduction across 12 modulation schemes. The results establish a theoretical benchmark and validate technical feasibility for 6G physical-layer design, positioning the proposed framework as a foundational contribution to next-generation resilient waveform development.

To address insufficient signal transmission stability and reliability under complex time-varying channels, this paper proposes a closed-loop radio-frequency (RF) mirror modulation system. The method introduces real-time RF mirror feedback into media modulation for the first time, integrating dynamic complex-weight control, media-driven constellation initialization, and Rayleigh fading channel modeling to enable online optimization of the signal constellation. Its core contribution lies in significantly enlarging the minimum Euclidean distance among constellation points at the receiver via feedback, thereby enhancing channel robustness. Experimental results demonstrate that the proposed scheme substantially reduces bit error rate (BER), enabling media modulation performance to approach the theoretical limit of an ideal additive white Gaussian noise (AWGN) channel—thus overcoming the fundamental performance ceiling of conventional open-loop modulation schemes.

This work proposes a novel multidimensional signal constellation, termed SCOPT, to enhance the energy efficiency of high-speed communication systems without increasing transmit power or employing additional coding. By extending the normalized signal duration to enlarge the minimum Euclidean distance between signals, SCOPT achieves reliable communication below the conventional Shannon limit within a geometric framework—a first in the field—while preserving a simple structure compatible with standard modulation schemes such as QAM and APSK. Both theoretical analysis and simulations demonstrate that SCOPT substantially improves energy efficiency and significantly reduces the required signal duration, offering both theoretical novelty and practical relevance.

To address the 6G requirements of low power consumption and high spectral efficiency, this paper tackles the limitations of conventional spatial modulation—namely, high transmitter-side power consumption and poor flexibility due to hardware-intensive antenna activation at the transmitter. We propose, for the first time, Reconfigurable Intelligent Surface (RIS)-assisted Adaptive Spatial Modulation at the receiver (RASM). RASM leverages an RIS to dynamically tailor the wireless channel and enables joint, adaptive selection of the optimal antenna subset at the receiver. This simultaneously enhances the signal-to-noise ratio (SNR) of target antennas and implicitly encodes extra information in the selected antenna indices. Theoretical bit error rate (BER) analysis and simulations consistently demonstrate that RASM achieves superior BER performance while significantly improving spectral efficiency—without requiring any transmitter hardware modifications and with substantially lower power consumption than transmitter-based schemes. This work establishes a novel paradigm for integrating RIS with spatial modulation, exhibiting strong practical potential for 6G systems.

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.

This work addresses the limitations of conventional channel knowledge graphs (CKGs), which capture only static environments and thus struggle to model time-varying channels induced by dynamic scatterers, terminal orientation changes, and radio-frequency impairments—leading to prohibitively high overhead in acquiring high-dimensional channel state information. To overcome this, the paper proposes a Dynamic Channel Knowledge Graph (Dynamic CKG), establishing for the first time a systematic theoretical framework that serves as an intermediate representation layer bridging static environmental priors and physical-layer signal processing. This framework enables joint pilot design, interference mitigation, and integrated sensing and communication. By integrating geospatial data, time-varying channel modeling, and machine learning–driven graph construction, the approach achieves co-design of CKG and signal processing, significantly reducing channel acquisition overhead while enhancing both communication efficiency and sensing performance, thereby offering a novel paradigm for 6G systems.

This work addresses the fundamental trade-off between communication and sensing performance in integrated sensing and communication (ISAC) systems by proposing a semi-analytical amplitude phase-shift keying (APSK) signal design framework. Leveraging i.i.d. uniformly distributed discrete inputs, the approach links the communication capacity gap to the minimum Euclidean distance and quantifies sensing performance via symbol energy variance. A multi-ring parametric constellation family enables flexible control over the communication–sensing trade-off. Theoretical analysis yields explicit scaling laws for key design parameters, demonstrating that the proposed scheme maintains a constant capacity gap across all signal-to-noise ratios and, for the first time under discrete input constraints, asymptotically approaches the Pareto boundary achievable with continuous inputs. Simulations confirm that the designed APSK constellations achieve near-optimal joint performance, closely approaching theoretical limits.

This study addresses the signal-dependent noise and channel memory induced by laser relative intensity noise (RIN) in intensity-modulated direct-detection optical communication systems. Starting from a continuous-time waveform, the authors develop a discrete-time channel model incorporating RIN effects and analyze its achievable information rates using a mismatched decoding framework. The analysis reveals that RIN causes the conditional noise variance to depend polynomially on the transmitted symbol, thereby violating the conventional constant-variance assumption. Furthermore, neglecting channel memory leads to saturation of the generalized mutual information (GMI) with increasing constellation order, elucidating the performance degradation observed in high-order modulation formats. Numerical results confirm that this phenomenon stems from the asymmetric and non-vanishing contributions of individual symbols to the GMI.