Abstract
In this paper, an asynchronous sparse Bayesian learning (ASBL) algorithm-based receiver for uplink (UL) grant-free transmission is proposed. The time-domain channel estimation is performed by the ASBL algorithm to obtain the channel impulse response (CIR) for each user. Then, the support vector machine (SVM) algorithm is adopted to classify the CIRs of all users. Therefore, the sporadic feature of the massive machine-type communication (mMTC) devices is exploited for identification purpose. The proposed algorithm is verified over asynchronous multipath fading channels and compared with previously compressed sensing (CS)-based algorithms, including the orthogonal matching pursuit (OMP) algorithm and the detecting-based OMP (DOMP) algorithm. Compared with the traditional CS algorithms, the proposed algorithms can reduce the false alarm rate by 70% and obtain a more accurate set of active users.
Highlights
The fifth generation (5G) wireless communication network proposes a massive machine-type communication scenario which provides wireless connectivity for a large number of low-complexity and low-power devices
In this paper, we propose that the traditional frequency domain channel estimation can be converted to the time domain
This paper proposes a new receiver for UL grant-free asynchronous non-orthogonal multiple access (NOMA) transmission
Summary
The fifth generation (5G) wireless communication network proposes a massive machine-type communication (mMTC) scenario which provides wireless connectivity for a large number of low-complexity and low-power devices. The authors in [11] and [12] propose that SBL algorithm can be applied to solve the blind active user detection problem in UL grant-free NOMA transmission. Taking asynchronous access and multipath fading into account, the fading block structure is not appropriate any more To solve this problem, in this paper, we propose that the traditional frequency domain channel estimation can be converted to the time domain. This paper utilizes the estimated time-domain channel responses as training samples to build a SVM classifier, and perform the active user classification by separates channel responses into two categories. The receiver can intercept the complete pilot sequence without ISI when TCP ≥ τd + τmax
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