Abstract
In this paper we investigate the problem of joint active user detection (AUD) and channel estimation (CE) in massive machine-type communications (mMTC) scenario with a massive multiple-input multiple-output (MIMO) base station. Besides sporadic traffic of massive access, the block sparsity of angular domain channel in massive MIMO is additionally considered to reduce the pilot overhead. By formulating the joint AUD and CE problem by sparse Bayesian learning with the double-sparsity feature in the angular domain, we introduce the modified pattern-coupled prior model to capture the non-uniform block structure. Then an algorithm based on variational Bayesian inference is proposed to recover the angular-domain channel. Moreover, a novel AUD scheme using the recovered angular-domain channel is developed by exploiting the high array gain in massive MIMO systems. Numerical results demonstrate that our proposed method can significantly improve the channel estimation quality and detection accuracy especially in the low pilot-length region.
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