Exploiting Transmission Control for Joint User Identification and Channel Estimation in Massive Connectivity

Sun, Zhuo; Wei, Zhiqiang; Yang, Lei; Yuan, Jinhong; Cheng, Xingqing; Wan, Lei

doi:10.1109/tcomm.2019.2919021

Cited by 43 publications

(35 citation statements)

References 37 publications

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“…where (b) is due to (3), and (c) is due to that h n ∼ CN 0, [7], and (e) is due to the distributions ofĥ n and ∆h n , as M → ∞ [1]. Then, by [8],…”

Section: Analysis Of Sstrmentioning

confidence: 99%

“…The asymptotic performance of user activity detection and channel estimation is analyzed in [1], and the asymptotic achievable rate is analyzed in [2] (assuming perfect user activity detection). In [3], the authors propose channel-based access control and modified AMP for user activity detection, and analyze the performance of user activity detection. Note that in [1] and [3], performance analysis of the data transmission phase is not considered.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the authors propose channel-based access control and modified AMP for user activity detection, and analyze the performance of user activity detection. Note that in [1] and [3], performance analysis of the data transmission phase is not considered.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the authors in [2] optimize the pilot length to maximize the achievable rate for only one user activity realization, without considering the activity statistics, making the obtained pilot length less suitable for the case where the total number of active users has a large variance. Finally, the authors in [3] optimize the access control parameter to maximize the user identification performance, without considering the channel estimation and data transmission, making the obtained access control applicable only for limited scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Optimization of Successful Symbol Transmission Rate for Grant-free Massive Access With Massive MIMO

et al. 2019

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Grant-free massive access is an important technique for supporting massive machine-type communications (mMTC) for Internet-of-Things (IoT). Two important features in grantfree massive access are low-complexity devices and short-packet data transmission, making the traditional performance metric, achievable rate, unsuitable in this case. In this letter, we investigate grant-free massive access in a massive multiple-input multiple-output (MIMO) system. We consider random access control, and adopt approximate message passing (AMP) for user activity detection and channel estimation in the pilot transmission phase and small phase-shift-keying (PSK) modulation in the data transmission phase. We propose a more reasonable performance metric, namely successful symbol transmission rate (SSTR), for grant-free massive access. We obtain closed-form approximate expressions for the asymptotic SSTR in the cases of maximal ratio combining (MRC) and zero forcing (ZF) beamforming at the base station (BS), respectively. We also maximize the asymptotic SSTR with respect to the access parameter and pilot length.

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“…where (b) is due to (3), and (c) is due to that h n ∼ CN 0, [7], and (e) is due to the distributions ofĥ n and ∆h n , as M → ∞ [1]. Then, by [8],…”

Section: Analysis Of Sstrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis and Optimization of Successful Symbol Transmission Rate for Grant-free Massive Access With Massive MIMO

et al. 2019

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“…As a further challenge, the large number of connected users in MTC makes the conventional 'handshaking'-based access control impractical owing to its high communication overhead and due to the resultant signal latency [31]. As a remedy, random access control schemes dispensing with handshaking have received intense attention in NOMA scenarios [32].…”

Section: Introductionmentioning

confidence: 99%

Iterative Joint Channel Estimation, User Activity Tracking, and Data Detection for FTN-NOMA Systems Supporting Random Access

Yuan

Guo

et al. 2020

IEEE Trans. Commun.

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Estimation of user activity prior for active user detection in massive machine type communications

et al. 2023

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Exploiting Transmission Control for Joint User Identification and Channel Estimation in Massive Connectivity

Cited by 43 publications

References 37 publications

Analysis and Optimization of Successful Symbol Transmission Rate for Grant-free Massive Access With Massive MIMO

Analysis and Optimization of Successful Symbol Transmission Rate for Grant-free Massive Access With Massive MIMO

Iterative Joint Channel Estimation, User Activity Tracking, and Data Detection for FTN-NOMA Systems Supporting Random Access

Estimation of user activity prior for active user detection in massive machine type communications

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