Similar articles

Abstract: The combination of terahertz and massive multiple-input multiple-output (MIMO) is promising for meeting the increasing data rate demand of future wireless communication systems thanks to the significant bandwidth and spatial degrees of freedom. However, unique channel features, such as the near-field beam split effect, make channel estimation particularly challenging in terahertz massive MIMO systems. On one hand, adopting the conventional angular domain transformation dictionary designed for low-frequency far-field channels will result in degraded channel sparsity and destroyed sparsity structure in the transformed domain. On the other hand, most existing compressive sensing based channel estimation algorithms cannot achieve high performance and low complexity simultaneously. To alleviate these issues, in this study, we first adopt frequency-dependent near-field dictionaries to maintain good channel sparsity and sparsity structure in the transformed domain under the near-field beam split effect. Then, a deep unfolding based wideband terahertz massive MIMO channel estimation algorithm is proposed. In each iteration of the approximate message passing-sparse Bayesian learning algorithm, the optimal update rule is learned by a deep neural network (DNN), whose architecture is customized to effectively exploit the inherent channel patterns. Furthermore, a mixed training method based on novel designs of the DNN architecture and the loss function is developed to effectively train data from different system configurations. Simulation results validate the superiority of the proposed algorithm in terms of performance, complexity, and robustness.

基于深度展开的宽带太赫兹近场大规模天线信道估计

[1]Chen YH, Yan LF, Han C, 2021. Hybrid spherical- and planar-wave modeling and DCNN-powered estimation of terahertz ultra-massive MIMO channels. IEEE Trans Commun, 69(10):7063-7076.

[2]Cui MY, Dai LL, 2022. Channel estimation for extremely large-scale MIMO: far-field or near-field? IEEE Trans Commun, 70(4):2663-2677.

[3]Cui MY, Dai LL, 2023. Near-field wideband channel estimation for extremely large-scale MIMO. Sci China Inform Sci, 66(7):172303.

[4]Cui MY, Dai LL, Wang ZC, et al., 2023a. Near-field rainbow: wideband beam training for XL-MIMO? IEEE Trans Wirel Commun, 22(6):3899-3912.

[5]Cui MY, Tan JB, Dai LL, 2023b. Wideband hybrid precoding for THz massive MIMO with angular spread. Sci Sin Inform, 53(4):772-786.

[6]Elbir AM, Shi W, Papazafeiropoulos AK, et al., 2023. Near-field terahertz communications: model-based and model-free channel estimation. IEEE Access, 11:36409-36420.

[7]Gao JB, Hu M, Zhong CJ, et al., 2022. An attention-aided deep learning framework for massive MIMO channel estimation. IEEE Trans Wirel Commun, 21(3):1823-1835.

[8]Gao JB, Zhong CJ, Li GY, 2023a. AMP-SBL unfolding for wideband mmWave massive MIMO channel estimation. IEEE Int Conf on Communications Workshops, p.60-65.

[9]Gao JB, Zhong CJ, Li GY, et al., 2023b. Deep learning-based channel estimation for wideband hybrid mmWave massive MIMO. IEEE Trans Commun, 71(6):3679-3693.

[10]Hu XL, Liu CX, Peng MG, et al., 2023. IRS-based integrated location sensing and communication for mmWave SIMO systems. IEEE Trans Wirel Commun, 22(6):4132-4145.

[11]Lu Y, Dai LL, 2023. Near-field channel estimation in mixed LoS/NLoS environments for extremely large-scale MIMO systems. IEEE Trans Commun, 71(6):3694-3707.

[12]Luo M, Guo QH, Jin M, et al., 2021. Unitary approximate message passing for sparse Bayesian learning. IEEE Trans Signal Process, 69:6023-6039.

[13]Nayir H, Karakoca E, Görçin A, et al., 2022. Hybrid-field channel estimation for massive MIMO systems based on OMP cascaded convolutional autoencoder. Proc IEEE 96^th Vehicular Technology Conf, p.1-6.

[14]Qin ZJ, Ye H, Li GY, et al., 2019. Deep learning in physical layer communications. IEEE Wirel Commun, 26(2):93-99.

[15]Srivastava S, Mishra A, Rajoriya A, et al., 2019. Quasi-static and time-selective channel estimation for block-sparse millimeter wave hybrid MIMO systems: sparse Bayesian learning (SBL) based approaches. IEEE Trans Signal Process, 67(5):1251-1266.

[16]Wan ZW, Gao Z, Gao FF, et al., 2021. Terahertz massive MIMO with holographic reconfigurable intelligent surfaces. IEEE Trans Commun, 69(7):4732-4750.

[17]Wei XH, Dai LL, 2022. Channel estimation for extremely large-scale massive MIMO: far-field, near-field, or hybrid-field? IEEE Commun Lett, 26(1):177-181.

[18]Yu WT, Shen YF, He HT, et al., 2022. Hybrid far- and near-field channel estimation for THz ultra-massive MIMO via fixed point networks. IEEE Global Communications Conf, p.5384-5389.

[19]Zhang XY, Wang ZN, Zhang HY, et al., 2023. Near-field channel estimation for extremely large-scale array communications: a model-based deep learning approach. IEEE Commun Lett, 27(4):1155-1159.

[20]Zhu YF, Guo HY, Lau VKN, 2021. Bayesian channel estimation in multi-user massive MIMO with extremely large antenna array. IEEE Trans Signal Process, 69:5463-5478.