Unsourced multiple access (UMA) is a novel approach to support a large number of devices in a massive Machine-Type Communication (mMTC) system. UMA enables devices to concurrently encode their data using the same codebook to transmit without being individually identified, resulting in reduced signaling and computational overhead at the base station. Hybrid-domain non-orthogonal multiple access (NOMA), which combines power-domain NOMA with code-domain NOMA, is another technique that enhances the spectral efficiency of mMTC. While the study of hybrid-domain NOMA has been conducted, its integration with UMA has not been thoroughly investigated. Considering that mMTC traffic primarily consists of sporadic short packets in the uplink direction, employing a fully distributed mMTC multiple access protocol can substantially decrease signaling overhead and latency. In this work, a multi-armed bandits (MAB) paradigm is adopted to create a distributed power selection policy for devices that using UMA. Particularly, an MAB algorithm called Thompson Sampling (TS) is used to allow mMTC devices to minimize the transmission power without violating the minimum receiving signal-to-noise constraint needed to correctly decode the UMA codewords back to the original messages. A mean-field modeling technique is used to approximate the learned policies. The knowledge gained from the approximated policies can be transferred to new devices by initializing their prior distribution, which is called Mean-field Transfer Thompson Sampling (MFTTS). Simulations show that the mean-field approximation is indeed accurate and effective. Interestingly, MFTTS performs better than TS without knowledge transfer as well as other distributed power allocation methods.

Distributed access control is a crucial component for massive machine type communication (mMTC). In this communication scenario, centralized resource allocation is not scalable because resource configurations have to be sent frequently from the base station to a massive number of devices. We investigate distributed reinforcement learning for resource selection without relying on centralized control. Another important feature of mMTC is the sporadic and dynamic change of traffic. Existing studies on distributed access control assume that traffic load is static or they are able to gradually adapt to the dynamic traffic. We minimize the adaptation period by training TinyQMIX, which is a lightweight multi-agent deep reinforcement learning model, to learn a distributed wireless resource selection policy under various traffic patterns before deployment. Therefore, the trained agents are able to quickly adapt to dynamic traffic and provide low access delay. Numerical results are presented to support our claims.

Inspired by the ability of human multitasking, there is a growing body of literature in the computational intelligence community dedicated to solving multiple problems concurrently. One of the research areas that have been receiving much attention in this topic is evolutionary multitasking, which is able to solve multiple complicated optimization problems together, yielding a better result than solving them in isolation. However, researches on evolutionary multitasking mostly focus on solving a small number of problems together. In an attempt to improve evolutionary multitasking, we propose Many-Task Multi-Armed Bandit Evolutionary Algorithm (Ma2BEA), including a new structure for the evolutionary multitasking algorithm. It also adopts a well-proven result of Multi-Armed Bandit (MAB) as the method to control the adaptive knowledge exchange between different tasks. In particular, the action of selecting which task to perform inter-task crossover is learned and decided by the designed MAB agent. We verify that Ma2BEA correctly learned the underlying relationship between tasks using the simple 10-task benchmarks. Besides, Ma2BEA is compared with other evolutionary many-tasking algorithms that have recently been proposed using the Single-Objective Many-task benchmark from the WCCI 2020 Competition on Evolutionary Multi-task Optimization. Empirical results show that Ma2BEA is competitive in terms of high solution quality and reasonable execution time.

Missing values appear in most multivariate time series, especially in the monitored network traffic data due to high measurement cost and unavoidable loss. In the networking fields, missing data prevents advanced analysis and downgrades downstream applications such as traffic engineering and anomaly detection. Despite the great potential, existing imputation approaches based on tensor decomposition and deep learning techniques have shown limitations in addressing missing values of traffic data due to its dynamic behavior. In this paper, we propose Graph Convolutional Recurrent Neural Network for Imputing Network Traffic (GCRINT), a combination between Recurrent Neural Network (RNN) and Graph Convolutional Neural Network, for filling the missing values of network traffic data. We use a bidirectional Long Short-Term Memory network and Graph Neural Network to efficiently learn the spatial-temporal correlations in partially observed data. We conducted extensive experiments to evaluate our model by using two different datasets and various missing scenarios. The experiment results show that GCRINT achieves significantly low imputation errors and reduces the error by 35% compared to the state-of-the-art methods. GCRINT also helps to obtain a stable performance in the traffic engineering problem.