Reinforcement Learning Based Adaptive Blocklength and MCS Selection for Minimization of Age Violation Probability

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2022-1

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Özkaya, Ayşenur

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As a measure of data freshness, Age of Information (AoI) is an important semantic performance metric in systems where small status update packets need to be delivered to a monitor in a timely manner. This study aims to minimize the age violation probability (AVP), which is defined as the probability that instantaneous age exceeds a certain threshold. The AVP can be considered as one of the key performance indicators in emerging 5G and beyond technologies such as massive machine-to-machine communications (mMTC) and ultra-reliable low latency communications (URLLC). This thesis focuses on two main problems regarding the adaptive transmission of short packets in time-sensitive systems. Firstly, we propose two methods for choosing the optimal blocklength for coding in short packet transmissions. We utilize finite blocklength theory approximations along with dynamic programming (DP) and reinforcement learning (RL) methods. Adopting state-aggregated value iteration and Q-learning algorithms, we present adaptive policies that dynamically select the optimal blocklength according to the state of the system. Our second problem focuses on choosing the appropriate modulation and coding scheme (MCS) for minimization of age violation probability. We construct a deep reinforcement learning (DRL) framework and employ deep Q networks (DQN) to exploit a policy for the dynamic selection of MCS among available MCSs defined in 5G standards. The performances of the proposed approaches are demonstrated in different scenarios and compared with the performances of benchmark policies and state-of-the-art algorithms.

Subject Keywords

Age of information, Dynamic programming, Reinforcement learning, Adaptive modulation and coding, Finite blocklength

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https://hdl.handle.net/11511/99608

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

A. Özkaya, “Reinforcement Learning Based Adaptive Blocklength and MCS Selection for Minimization of Age Violation Probability,” M.S. - Master of Science, Middle East Technical University, 2022.