A Novel Massive Random Access in Cell-Free Massive MIMO Systems for High-Speed Mobility with OTFS Modulation

TL;DR

This paper tackles massive random access for machine-type devices in high-speed mobility by integrating OTFS modulation with a cell-free massive MIMO framework. It introduces a hybrid preamble scheme that combines a rough AUD stage using superimposed preambles with an embedded preamble for accurate AUD and joint CE, thereby reducing the dimensionality of the sparse recovery problem. A novel GAMP-PCSBL-La algorithm is developed to recover a 2-D block-sparse DD-domain channel matrix, leveraging DCT sparsity and pattern-coupled Laplacian priors to achieve high-precision AUD and CE with manageable complexity. The approach is evaluated through CF-mMIMO OTFS uplink simulations, showing superior AUD/CE performance and favorable DER/NMSE trends over competing methods, and revealing practical insights such as an optimal power split for the hybrid preamble. Overall, the proposed framework enables scalable, low-overhead access for massive MTCDs in high-mobility environments, with potential improvements in coverage, reliability, and efficiency for future wireless networks.

Abstract

In the research of next-generation wireless communication technologies, orthogonal time frequency space (OTFS) modulation is emerging as a promising technique for high-speed mobile environments due to its superior efficiency and robustness in doubly selective channels. Additionally, the cell-free architecture, which eliminates the issues associated with cell boundaries, offers broader coverage for radio access networks. By combining cell-free network architecture with OTFS modulation, the system may meet the demands of massive random access required by machine-type communication devices in high-speed scenarios. This paper explores a massive random access scheme based on OTFS modulation within a cell-free architecture. A transceiver model for uplink OTFS signals involving multiple access points (APs) is developed, where channel estimation with fractional channel parameters is approximated as a block sparse matrix recovery problem. Building on existing superimposed and embedded preamble schemes, a hybrid preamble scheme is proposed. This scheme leverages superimposed and embedded preambles to respectively achieve rough and accurate active user equipment (UEs) detection (AUD), as well as precise channel estimation, under the condition of supporting a large number of access UEs. Moreover, this study introduces a generalized approximate message passing and pattern coupling sparse Bayesian learning with Laplacian prior (GAMP-PCSBL-La) algorithm, which effectively captures block sparse features after discrete cosine transform (DCT), delivering precise estimation results with reduced computational complexity. Simulation results demonstrate that the proposed scheme is effective and provides superior performance compared to other existing schemes.

Figures (11)

• Figure 1: Massive random access in cell-free massive MIMO system.
• Figure 2: The system's signal processing flow.
• Figure 3: Structure of UPA equipped at AP.
• Figure 4: Symbols arrangement for data, guard and embedded preamble.
• Figure 5: The hybrid preamble transmission block structure.