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

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