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The massiveness of devices in crowded Machine-to-Machine (M2M) communicationsbrings new challenges to existing random-access (RA) schemes, such as heavysignaling overhead and severe access collisions. In order to reduce thesignaling overhead, we propose a fixed-symbol aided RA scheme where activedevices access the network in a grant-free method, i.e., data packets aredirectly transmitted in randomly chosen slots. To further address the accesscollision which impedes the activity detection, one fixed symbol is insertedinto each transmitted data packet in the proposed scheme. An iterative messagepassing based activity detection (MP-AD) algorithm is performed upon thereceived signal of this fixed symbol to detect the device activity in eachslot. In addition, the deep neural network-aided MP-AD (DNN-MP-AD) algorithm isfurther designed to alleviate the correlation problem of the iterative messagepassing process. In the DNN-MP-AD algorithm, the iterative message passingprocess is transferred from a factor graph to a DNN. Weights are imposed on themessages in the DNN and further trained to improve the accuracy of the deviceactivity detection. Finally, numerical simulations are provided for thethroughput of the proposed RA scheme, the accuracy of the proposed MP-ADalgorithm, as well as the improvement brought by the DNN-MP-AD algorithm.

Fixed-Symbol Aided Random Access Scheme for Machine-to-Machine Communications