Estimation of user activity prior for active user detection in massive machine type communications

Abstract

To enable massive-scale connectivity with low latency and efficient resource usage for massive machine type communications, the grant free access scheme has been proposed, which allows users to communicate with low signaling overhead and without the need for any prior resource allocation procedure. Due to the sporadic nature of packet transmission and non-orthogonal multiplexing, access points need to perform active user detection (AUD) to identify which users have sent the packets based on the received data. In this paper, we propose an enhanced AUD algorithm, which exploits each user’s activity pattern for detecting active users. We assume that each user randomly sends its own packet with different probability distributions parameterized by the user activity probability (UAP). In our work, such UAPs are inferred from the trajectory of the measurements collected over a certain period of time. Then, the estimated UAPs are incorporated into the compressed sensing-based algorithm for the joint AUD and channel estimation. Using the expectation-maximization algorithm, our method can efficiently find the maximum likelihood estimate of the UAPs for all users. We also present an on-line algorithm that sequentially updates the UAP estimates for each measurement. Our numerical evaluation demonstrates the benefit of the UAP estimation for compressed sensing-based AUD methods.