Statistical Device Activity Detection for OFDM-Based Massive Grant-Free Access

Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of Things (IoT), mainly concentrate on narrow band systems under flat fading. In contrast, this paper investigates massive grant-free access in a wideband system under frequency-selective fading. First, we present an orthogonal frequency division multiplexing (OFDM)-based massive grant-free access scheme. Then, we propose two different but equivalent models for the received pilot signal. Specifically, one directly models the received signal for actual devices, whereas the other can be interpreted as a signal model for virtual devices. The two signal models are insightful and essential for designing various device activity detection and channel estimation methods for OFDM-based massive grant-free access. Next, we systematically investigate statistical device activity detection under frequency-selective Rayleigh fading based on the two signal models. In particular, in the case without prior knowledge of device activities, we model device activities as deterministic but unknown binary constants and propose three maximum likelihood (ML) estimation-based device activity detection methods with different detection accuracies and computation times. In the case with prior knowledge of device activities, we model device activities as realizations of Bernoulli random variables with a known joint distribution, which appropriately incorporates the prior knowledge, and propose three maximum a posterior probability (MAP) estimation-based device activity methods, which further enhance the accuracies of the corresponding ML estimation-based methods at the cost of increased computational complexities. The proposed methods can meet diverse practical needs for OFDM-based massive grant-free access.