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Recent growth in the Internet of Things (IoT) has been remarkable. Among the solutions to accommodate such a growth is Cellular IoT (C-IoT), comprising a group of technologies extended from legacy cellular infrastructures. One of the key goals of C-IoT technologies is to extend the battery life of UEs (User Equipment) in the network. However, this often comes at the cost of degrading network performance. This work attempts to identify, categorize, and analyze the available literature on this problem. The literature is broadly categorized into three sections: scheduling, data processing, and sleep modes. In each of these sections, the literature is further sub categorized. Finally, a direction for future research is identified and discussed.more » « less
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null (Ed.)We consider an LTE downlink scheduling system where a base station allocates resource blocks (RBs) to users running delay-sensitive applications. We aim to find a scheduling policy that minimizes the queuing delay experienced by the users. We formulate this problem as a Markov Decision Process (MDP) that integrates the channel quality indicator (CQI) of each user in each RB, and queue status of each user. To solve this complex problem involving high dimensional state and action spaces, we propose a Deep Reinforcement Learning based scheduling framework that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm to minimize the queuing delay experienced by the users. Our extensive experiments demonstrate that our approach outperforms state-of-the-art benchmarks in terms of average throughput, queuing delay, and fairness, achieving up to 55% lower queuing delay than the best benchmark.more » « less
