We develop a new algorithm for activity detection for grant-free multiple access in distributed multiple-input multiple-output (MIMO). The algorithm is a distributed version of the approximate message passing (AMP) based on a soft combination of likelihood ratios computed independently at multiple access points. The underpinning theoretical basis of our algorithm is a new observation that we made about the state evolution in the AMP. Specifically, with a minimum mean-square error denoiser, the state maintains a block-diagonal structure whenever the covariance matrices of the signals have such a structure. We show by numerical examples that the algorithm outperforms competing schemes from the literature.

Grant-free multiple access (GFMA) mitigates the uplink handshake overhead to support low-latency communication by transmitting payload data together with the pilot (preamble). However, the channel capacity with random access is limited by the number of available orthogonal pilots and the incoordination among devices. We consider a delay-constrained GFMA system, where each device with randomly generated data traffic needs to deliver its data packets before some pre-determined deadline. The pilot selection problem is formulated to minimize the average packet drop rate of the worst user. A priority-sorting based centralized policy is derived by introducing a fairness promoting function. For decentralization, we propose a multi-agent policy optimization algorithm with improved sample efficiency by exploring the model structure. Simulation results show that our proposed scheme facilitates near-optimal coordination between devices by using only partial state information.

Internet of Things (IoT) has attracted considerable attention in recent years due to its potential of interconnecting a large number of heterogeneous wireless devices. However, it is usually challenging to provide reliable and efficient random access control when massive IoT devices are trying to access the network simultaneously. In this article, we investigate methods to introduce intelligent random access management for a massive cellular IoT network to reduce access latency and access failures. Toward this end, we introduce two novel frameworks, namely, local device selection (LDS) and intelligent preamble selection (IPS). LDS enables local communication between neighboring devices to provide cluster-wide cooperative congestion control, which leads to a better distribution of the access intensity under bursty traffics. Taking advantage of the capability of reinforcement learning in developing cooperative multiagent policies, IPS is introduced to enable the optimization of the preamble selection policy in each IoT clusters. To handle the exponentially growing action space in IPS, we design a novel reinforcement learning structure, named branching actor-critic, to ensure that the output size of the underlying neural networks only grows linearly with the number of action dimensions. Simulation results indicate that the introduced mechanism achieves much lower access delays with fewer access failures in various realistic scenarios of interests.