Covariance-Based Joint Device Activity and Delay Detection in Asynchronous mMTC

Wang, Zhaorui; Liu, Ya-Feng; Liu, Liang

doi:10.1109/lsp.2022.3144853

Cited by 26 publications

(15 citation statements)

References 23 publications

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“…For instance, when the symbol duration is 5 µs (when the signal bandwidth is 200 kHz), and the oscillators result in a maximum delay of 20 µs, the value of T is 4 symbols. In the simulations of [35], [36], T is set as 4 and 5 symbols, respectively. To compare the detection performance under different T , we vary it from 0 to 8 in Section VI.…”

Section: A System Modelmentioning

confidence: 99%

“…(7) Problem ( 7) is challenging to solve due to the discontinuous and nonconvex ℓ 0 -norm constraints in (7c) caused by the asynchronous transmissions. Recently, the work [35] proposed a BCD algorithm for single-cell asynchronous activity detection. This algorithm enforces the ℓ 0 -norm constraints within the optimization process, which can guarantee its feasibility to (7c).…”

Section: B Problem Formulationmentioning

confidence: 99%

“…Therefore, the sequence generated by the proposed algorithms will not get stuck at bad stationary points caused by the highly nonconvex ℓ 0 -norm constraints. Simulation results show that the detection performance of the proposed centralized algorithm is much better than that of state-of-the-art approaches [35].…”

Section: Introductionmentioning

confidence: 96%

“…Consequently, the above activity detection methods based on the synchronous assumption suffer significant degradation when applied in asynchronous activity detection. To address this issue, the work [35] introduced ℓ 0 -norm constraints into the covariance based optimization problem, equivalently making each active device transmit only one effective signature sequence from different possible delays in each transmission. To tackle the ℓ 0 -norm constrained problem, [35] further proposed a block coordinate descent (BCD) algorithm, which shows significant performance improvement compared with a CS based method [36].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the current solution in [35] faces a major challenge, which comes from the fact that enforcing these discontinuous and nonconvex ℓ 0 -norm constraints within each iteration of the BCD algorithm may cause the solution to get stuck at bad stationary points [37], [38], which will degrade the detection performance. To tackle this challenge, this paper proposes a novel equivalent penalized reformulation for the original asynchronous activity detection problem.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms

Lin

Liu

et al. 2023

IEEE Trans. Wireless Commun.

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Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves a close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links.

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Section: A System Modelmentioning

confidence: 99%

Section: B Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%