Exploiting Simultaneous Low-Rank and Sparsity in Delay-Angular Domain for Millimeter-Wave/Terahertz Wideband Massive Access

Shao, Xiaodan; Chen, Xiaoming; Zhong, Caijun; Zhang, Zhaoyang

doi:10.1109/twc.2021.3111225

Cited by 20 publications

(19 citation statements)

References 28 publications

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“…Since the number of paths is usually limited, λ k is essentially sparse, with each nonzero value being the corresponding composite path gain √ µ k ǫ k i . The exploitation of such a sparse structure has been shown to bring improved channel estimation performance and reduced complexity [11], [26]- [28].…”

Section: A Channel Modelsmentioning

confidence: 99%

Section: A Element-wise Sparsity and Low-rank Inducing Probabilistic ...mentioning

confidence: 99%

“…Firstly, to apply the Bayesian learning framework, a probabilistic model, which encodes the knowledge of problem (11), needs to be established. To strike the trade-off between the flexibility of knowledge encoding and the tractability of the inference algorithm, we propose a novel probabilistic model by interpreting each term in problem (11) via some probability density functions (pdfs) [23]. We start with the last regularization term in the objective function of (11), which can be modeled as a circularly-symmetric complex Gaussian prior distribution 1 of the columns in matrix G, i.e., K k=1 CN G(:, k)|0, (η k ) −1 I .…”

Section: A Element-wise Sparsity and Low-rank Inducing Probabilistic ...mentioning

confidence: 99%

“…To strike the trade-off between the flexibility of knowledge encoding and the tractability of the inference algorithm, we propose a novel probabilistic model by interpreting each term in problem (11) via some probability density functions (pdfs) [23]. We start with the last regularization term in the objective function of (11), which can be modeled as a circularly-symmetric complex Gaussian prior distribution 1 of the columns in matrix G, i.e., K k=1 CN G(:, k)|0, (η k ) −1 I . On the other hand, note that the l 0 -norm constraint in (11) is imposed such that the elements in each column of channel are also sparse.…”

Section: A Element-wise Sparsity and Low-rank Inducing Probabilistic ...mentioning

confidence: 99%

“…Consequently, the signals received from the devices are weak such that performing active device separation is a challenging task for the BS. To overcome this challenge, the use of millimeter-wave (mmWave)/terahertz (THz) frequencies with massive multiple-input-multiple output (MIMO) was proposed to enhance the detection accuracy of grant-free random access [11]. However, it significantly increases the hardware and energy cost as well as signal processing complexity due to the deployment of a large number of radio frequency (RF) chains.…”

Section: Introductionmentioning

confidence: 99%

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Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning

Shao¹,

Liu²,

Chen³

et al. 2022

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This paper investigates a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme for the sixth-generation (6G) wireless networks with massive sporadic traffic devices. First of all, this paper proposes a novel joint active device separation (the message recovery of active device) and channel estimation architecture for the RIS-aided URA. Specifically, the RIS passive reflection is optimized before the successful device separation. Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences. However, the inherent coupling among multiple sparse device-RIS channels, together with the unknown number of active devices make the detection problem at hand deviate from the widely-used coupled tensor decomposition format. To overcome this challenge, this paper judiciously devises a probabilistic model that captures both the element-wise sparsity from the angular channel model and the low-rank property due to the sporadic nature of URA. Then, based on such a probabilistic model, a iterative detection algorithm is developed under the framework of sparse variational inference, where each update iteration is obtained in a closed-form and the number of active devices can be automatically estimated for effectively avoiding the overfitting of noise. Extensive simulation results confirm the excellence of the proposed URA algorithm, especially for the case of a large number of reflecting elements for accommodating a significantly large number of devices.

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Section: A Channel Modelsmentioning

confidence: 99%