2026· IEEE Transactions on Wireless Communications· Vol 25, pp. 21433-21448· 5 citations· 61 references
Computer Science
Abstract
The unprecedented growth of machine-type devices has underscored the need for fundamental solutions to support emerging massive connectivity. In particular, unsourced random access (URA) has emerged as a promising paradigm, reframing the massive connectivity problem as a coding-theoretic challenge with favorable energy and spectral efficiency. Among the widely studied URA models, the Gaussian multiple-access channel (GMAC) and multi-input multi-output (MIMO) systems are of particular significance. Sparse code design is well-suited for URA, offering scalable solutions while retaining many advantages of legacy access protocols. However, existing sparse code designs often suffer from limited sparsity control, inefficient interference cancellation, and a strong dependence on specific channel code designs, posing challenges for long-term adaptability as more powerful channel codes continue to evolve. In MIMO-URA systems, additional activity detection and channel estimation phases typically lead to increased missed detection (MD) and false alarm (FA) errors compared with the GMAC model, which does not require these phases. While prior studies have predominantly focused on minimizing MD errors, the effective mitigation of FA errors remains an open problem. To address this challenge, we propose a sparse code with slotted transmission under the GMAC model, combined with an analytical power division strategy to enhance interference cancellation. Furthermore, we introduce a novel MIMO receiver framework based on joint pattern–data–channel (JPDC) estimation, which significantly reduces FA errors by leveraging the intrinsic correlation between user activity and transmitted data. Notably, the proposed method achieves improved overall system performance without requiring additional transmission overhead or complex algorithms.
This paper investigates the design of the on-off division multiple access (ODMA) transmission scheme for multiple-input multiple-output (MIMO) massive unsourced random access (URA) systems with soft-output (SO) polar codes. First, a three-segment pilot-uncoupled coding scheme is introduced under the ODMA framework, which reduces the coding rate of the data segment without increasing the transmission overhead, improving the overall system performance. Building upon this architecture, a hierarchical pattern detection framework is developed. Specifically, a coarse-grained candidate set of transmission patterns is first identified through correlation operations. Based on this, a message-passing (MP)-based pattern detection algorithm is developed to iteratively estimate the posterior probabilities of transmission patterns, followed by the \textit{maximum a posteriori} (MAP) estimation to obtain the precise pattern detection result. Furthermore, a joint pattern detection and data decoding algorithm based on the bit-wise SO information of polar decoder is investigated, where the posterior probability information provided by the polar decoder is exploited to refine the pattern detection and contribute to an improved accuracy. In addition, by leveraging bit-wise SO information of the successive cancellation list polar decoder, an MP-based iterative decoding algorithm is developed to significantly enhance the decoding performance. The proposed scheme simultaneously exploits the transmission gain of uncoupled-ODMA framework, the coding gain of polar codes in the short-blocklength regime, and the iterative decoding gain enabled by SO information, while the computational complexity is significantly reduced through the hierarchical detection framework. Simulation results demonstrate that the proposed scheme achieves strong robustness ...
Tianya Li, Xiao-Ran Zhang, Nan Hu et al.· IEEE Transactions on Wireles...· 0 citations
Current wireless systems combat inter-symbol interference (ISI) by diagonalizing or sparsifying the channel matrix, yet they remain vulnerable to selective fading. To address this, we propose a universal random precoding (RP) transmission framework based on the universality class. RP leverages random transforms to statistically exploit all subchannels and construct an equivalent channel belonging to the universality class, thereby enhancing diversity gain while maintaining backward compatibility with existing waveforms. Low-complexity implementations include the randomly permuted fast transform (FT-RP) and the interleaved block-sparse fast transform (IBSFT-RP). A cross-domain OAMP/MAMP (CD-OAMP/MAMP) detector is designed for RP systems, which is replica maximum \textit{a posteriori} (MAP)-optimal according to state evolution (SE). Simulation results on MIMO systems demonstrate that RP with CD-OAMP/MAMP achieves near-RM performance with much lower complexity, with additional benefits of flexible compression ratios for spectral efficiency.
Jiazhen Dong, Lei Liu, Xiaojun Yuan et al.· 0 citations
Grant-free random access (GFRA) is a promising solution for massive machine-type communications (mMTC) in future wireless networks. However, reliable user activity detection and channel estimation are critical challenges, particularly when orthogonal time-frequency space (OTFS) modulation is integrated with GFRA to address doubly selective channels induced by high mobility. In this paper, we propose an OTFS-based GFRA framework that exploits the inherent structured sparsity of delay-Doppler channels. By adopting a basis expansion model (BEM), we formulate joint user activity detection and channel estimation as a structured compressive sensing problem. A bi-level sparsity structure is identified, consisting of common sparsity across multiple receive antennas and activation sparsity across mMTC users. To effectively leverage this structure, we construct a two-layer factor graph and develop a structured sparsity expectation propagation (SS-EP) algorithm for efficient Bayesian inference. Simulation results demonstrate that the proposed scheme significantly outperforms existing benchmarks.
Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for next-generation integrated sensing and communication (ISAC) systems. However, it becomes challenging to improve spectral efficiency while simultaneously obtaining accurate channel and sensing-related parameters, particularly in doubly-dispersive channels with fractional delays and fractional Doppler shifts. To tackle this challenge, by formulating the channel estimation task as a multiple measurement vectors (MMV) off-grid sparse recovery problem, we propose a data-aided grid-evolution sparse Bayesian learning (D-GESBL) scheme for channel estimation and sensing under a superimposed pilot framework. Specifically, we develop an efficient data-aided iterative receiver, in which reliably decoded data symbols are fed back as additional pseudo-pilot information to assist channel estimation and sensing. To mitigate off-grid mismatch and improve the overall estimation accuracy, we develop a grid evolution procedure that iteratively adjusts the virtual grids in the discrete affine Fourier (DAF) domain according to the estimated off-grid components. Furthermore, by integrating the generalized approximate message passing (GAMP) algorithm into the proposed SBL framework, we also develop a low-complexity data-aided GAMP–based grid-evolution SBL (D-GAMP-GESBL) algorithm. Finally, the numerical results validate the effectiveness of our proposed schemes and demonstrate their superiority over existing state-of-the-art methods.
Yi-Rui Luo, Yong-Liang Guan, Yao Ge et al.· IEEE Transactions on Communi...· 0 citations
Unsourced random access (URA) supports massive sporadic connectivity but remains limited by multiuser interference, distributed channel uncertainty, and local fading. This paper proposes a fluid-antenna-assisted distributed joint decoder for cell-free massive MIMO URA. Single-radio-frequencychain access points sequentially sound correlated ports; the central processor then performs correlation-aware expectationmaximization approximate message passing, selects one common payload port per access point, and combines MIMO iterative Gaussian approximation with full-packet channel re-estimation and successive interference cancellation. Fixed-port ablations confirm the complementary benefits of distributed reception, reestimation, and iterative cancellation, while fluid-antenna simulations show improved average and lower-tail received power over fixed-port reception. The framework provides a feasible integration of fluid antennas and cell-free URA and motivates end-to-end evaluation under pilot collisions and switching overhead.
This work investigates unsourced random access (URA) over quasi-static fading channels, which is well suited for massive connectivity. In most existing URA schemes for fading channels, message recovery typically requires channel estimation, and these schemes therefore rely on pilot-assisted designs, resulting in limited spectral efficiency and degraded performance. In this context, on–off division multiple access (ODMA) has emerged as a promising URA framework due to its super-sparse structure; however, existing ODMA-based schemes still rely on a single large pattern book and exhibit high computational complexity. To overcome these limitations, this paper proposes a novel URA scheme that integrates signal scrambling with an ODMA-based framework, referred to as SS-ODMA. The key idea of SS-ODMA is to introduce a user-specific polarity scrambling pattern as an auxiliary signature, which decouples the original large pattern book into two compact components and mitigates the exponential growth in detection complexity. Building on this structure, we develop a low-complexity three-stage iterative detection algorithm that enables activity detection, joint channel estimation and scrambling pattern detection, and multi-user data recovery without relying on pilot signals. Simulation results demonstrate that the proposed SS-ODMA scheme outperforms existing URA schemes over a wide range of active users while reducing computational complexity by approximately two orders of magnitude.
Jianxiang Yan, Ying Li, Guanghui Song et al.· IEEE Transactions on Wireles...· 0 citations
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