Hybrid Message Passing-Based Detectors for Uplink Grant-Free NOMA Systems (2401.14611v1)
Abstract: This paper studies improving the detector performance which considers the activity state (AS) temporal correlation of the user equipments (UEs) in the time domain under the uplink grant-free non-orthogonal multiple access (GF-NOMA) system. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally-correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared with the existing methods while keeping the same order complexity.
- X. Chen, et al., “Massive access for 5G and beyond,” IEEE J. Sel. Areas Commun., vol. 39, no. 3, pp. 615–637, Mar. 2021.
- L. Dai, et al., “Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends,” IEEE Commun. Mag., vol. 53, no. 9, pp. 74–81, Sep. 2015.
- L. Liu, et al., “Sparse signal processing for grant-free massive connectivity: A future paradigm for random access protocols in the internet of things,” IEEE Signal Process. Mag., vol. 35, no. 5, pp. 88–99, Sep. 2018.
- B. Li, et al., “Compressed sensing based multiuser detection of grant-free NOMA with dynamic user activity,” IEEE Commun. Lett., vol. 26, no. 1, pp. 143–147, Nov. 2021.
- Y. Gao, et al., “Multiuser detection of GF-NOMA with dynamic-active users and temporal-correlated channels,” IEEE Commun. Lett., vol. 26, no. 10, pp. 2380–2384, Jul. 2022.
- W. Yuan, et al., “Iterative joint channel estimation, user activity tracking, and data detection for FTN-NOMA systems supporting random access,” IEEE Trans. Commun., vol. 68, no. 5, pp. 2963–2977, Feb. 2020.
- X. Zhang, et al., “Bayesian learning-based multiuser detection for grant-free NOMA systems,” IEEE Wireless Commun. Lett., vol. 21, no. 8, pp. 6317–6328, Feb. 2022.
- X. Zhang, et al., “Generalized approximate message passing based Bayesian learning detectors for uplink grant-free NOMA,” IEEE Trans. Veh. Technol., pp. 1–6, May 2023.
- Y. Song, et al., “Hybrid message passing algorithm for downlink FDD massive MIMO-OFDM channel estimation,” IEEE Trans. Wireless Commun., pp. 1–1, Oct. 2023.
- X. Kuai, et al., “Structured turbo compressed sensing for downlink massive MIMO-OFDM channel estimation,” IEEE Trans. Wireless Commun., vol. 18, no. 8, pp. 3813–3826, Aug. 2019.
- C. I. Frison, et al., “MC-CDMA and SCMA performance and complexity comparison in overloaded scenarios,” 2019 IEEE Colombian Conference on Communications and Computing (COLCOM), pp. 1–6, Jun. 2019.
- H. R. Carvajal Mora, et al., “Mean bit error rate evaluation of MC-CDMA cellular systems employing multiuser-maximum-likelihood detector,” IEEE Trans. Veh. Technol., vol. 66, no. 11, pp. 9838–9851, Jul. 2017.
- W. Zhu, et al., “Message passing-based joint user activity detection and channel estimation for temporally-correlated massive access,” IEEE Trans. Commun., vol. 71, no. 6, pp. 3576–3591, Mar. 2023.
- N. L. Pedersen, et al., “Application of Bayesian hierarchical prior modeling to sparse channel estimation,” in Proc. IEEE Int. Conf. Commun. (ICC), Jun. 2012, pp. 3487–3492.
- L. Mo, et al., “Generalized unitary approximate message passing for double linear transformation model,” IEEE Trans. Signal Process., vol. 71, pp. 1524–1538, Apr. 2023.
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