Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-Free Massive Random Access


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In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. Recently, grant-free random access has emerged as a promising mechanism for this challenging scenario, but its potential has not been fully unleashed. In particular, the available auxiliary information has not been fully exploited, including the common sparsity pattern in the received pilot and data signal, as well as the channel decoding information. This article develops advanced receivers in a holistic manner to improve the massive access performance by jointly designing activity detection, channel estimation, and data decoding. To tackle the algorithmic and computational challenges, a turbo structure is adopted at the joint receiver. For performance enhancement, all the received symbols are utilized to jointly estimate the channel state, user activity, and soft data symbols, which effectively exploits the common sparsity pattern. Meanwhile, the extrinsic information from the channel decoder will assist the joint channel estimation and data detection. To reduce the complexity, a low-cost side information (SI)-aided receiver is also proposed, where the channel decoder provides SI to update the estimates on whether a user is active or not. Simulation results show that the turbo receiver is able to reduce the activity detection, channel estimation, and data decoding errors effectively, supporting twice as many active users compared with a separate design that disregards the common sparsity. In addition, the SI-aided receiver notably outperforms the conventional methods with a relatively low complexity.
joint activity detection,channel estimation,access,grant-free
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