Cooperative Limited Feedback Design for Massive Machine-Type Communications
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Multiuser multiple-input multiple-output (MIMO) systems have been in the spotlight since it is expected to support high connection density in internet of things (IoT) networks. Considering the massive connectivity in IoT networks, the challenge for the multiuser MIMO systems is to obtain accurate channel state information (CSI) at the transmitter in order that the sum-rate throughput can be maximized. However, current communication mechanisms relying upon frequency division duplexing (FDD) might not fully support massive number of machine-type devices due to the rate-constrained limited feedback and complicated time-consuming scheduling. In this paper, we develop a cooperative feedback strategy to maximize the benefits of massive connectivity under limited resource constraint for the feedback link. In the proposed algorithm, two neighboring users form a single cooperation unit to improve the channel quantization performance by sharing some level of channel information. To satisfy the low-latency requirement in IoT networks, the cooperation process is conducted without any transmitter intervention. In addition, we analyze the sum-rate throughput of the multiuser MIMO systems relying upon the proposed feedback strategy to study a cooperation decision-making framework. Based on the analytical studies, we develop a network-adapted cooperation algorithm to turn the user cooperation mode on and off according to network conditions.
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