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In this paper, the uplink adaptation for massivemultiple-input-multiple-output (MIMO) networks without the knowledge of userdensity is considered. Specifically, a novel cooperative uplink transmissionand detection scheme is first proposed for massive MIMO networks, where eachuplink frame is divided into a number of data blocks with independent codingschemes and the following blocks are decoded based on previously detected datablocks in both service and neighboring cells. The asymptoticsignal-to-interference-plus-noise ratio (SINR) of the proposed scheme is thenderived, and the distribution of interference power considering the randomnessof the users' locations is proved to be Gaussian. By tracking the mean andvariance of interference power, an online robust rate adaptation algorithmensuring a target packet outage probability is proposed for the scenario wherethe interfering channel and the user density are unknown.

Learning-based Rate Adaptation for Uplink Massive MIMO Networks with Cooperative Data-Assisted Detection