Joint Activity and Data Detection for Massive Grant-Free Access Using Deterministic Non-Orthogonal Signatures

• Published in: IEEE transactions on wireless communications Vol. 23; no. 8; pp. 9474 - 9487
• Main Authors: Yu, Nam Yul, Yu, Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Characters; Coherence; Devices; Fields (mathematics); Fourier transforms; grant-free access; Masking; massive machine-type communications; Matrix algebra; Maximum likelihood estimation; Message passing; non-orthogonal signatures; Performance evaluation; Reliability engineering; Signatures; Simulation; Wireless communication; Wireless communications
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2024.3362933

Grant-free access is a key enabler for connecting wireless devices with low latency and low signaling overhead in massive machine-type communications (mMTC). For massive grant-free access, user-specific signatures are uniquely assigned to mMTC devices. In this paper, we first derive a sufficient condition for the successful identification of active devices through maximum likelihood (ML) estimation in massive grant-free access. The condition is represented by the coherence of a signature sequence matrix containing the signatures of all devices. Then, we present a design framework of non-orthogonal signature sequences in a deterministic fashion. The design principle relies on unimodular sequences with low correlation, which are applied as masking sequences to the columns of the discrete Fourier transform (DFT) matrix. For example constructions, we use four polyphase masking sequences represented by characters over finite fields. Leveraging algebraic techniques, we show that the signature sequence matrix of proposed non-orthogonal sequences has theoretically bounded low coherence. Simulation results demonstrate that the deterministic non-orthogonal signatures achieve excellent performance in joint activity and data detection using ML- and approximate message passing (AMP)-based algorithms for massive grant-free access in mMTC.