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This paper investigates the grant-free random access with massive IoTdevices. By embedding the data symbols in the signature sequences, joint deviceactivity detection and data decoding can be achieved, which, however,significantly increases the computational complexity. Coordinate descentalgorithms that enjoy a low per-iteration complexity have been employed tosolve the detection problem, but previous works typically employ a randomcoordinate selection policy which leads to slow convergence. In this paper, wedevelop multi-armed bandit approaches for more efficient detection viacoordinate descent, which make a delicate trade-off between exploration andexploitation in coordinate selection. Specifically, we first propose a banditbased strategy, i.e., Bernoulli sampling, to speed up the convergence rate ofcoordinate descent, by learning which coordinates will result in moreaggressive descent of the objective function. To further improve theconvergence rate, an inner multi-armed bandit problem is established to learnthe exploration policy of Bernoulli sampling. Both convergence rate analysisand simulation results are provided to show that the proposed bandit basedalgorithms enjoy faster convergence rates with a lower time complexity comparedwith the state-of-the-art algorithm. Furthermore, our proposed algorithms areapplicable to different scenarios, e.g., massive random access withlow-precision analog-to-digital converters (ADCs).

Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach