Novel Adaptive Transmission for Effective URLLC Support in 5G and Beyond Wireless Systems: Reinforcement Learning based Designs
Date
2023-08-31
Authors
Saatchi, Negin Sadat
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Abstract
The Industrial Internet of Things (IIoT) has transformed industrial processes by
connecting devices and enabling real-time data exchange. However, the increasing
demands of future IIoT applications necessitate a trustworthy, ultra-reliable, and
low-latency communication (URLLC) service to support critical and time-sensitive
operations. This requires the development of advanced wireless technologies capable
of delivering data reliably while meeting stringent latency requirements.
In this work, we first propose a novel adaptive transmission design for the fifthgeneration
New Radio (5G NR) technology to enhance its URLLC provision capability.
Our approach involves jointly selecting numerology, mini-slot size, and
modulation and coding scheme (MCS) for each transmission attempt. By considering
the prevailing channel conditions and the available latency budget, we aim
to maximize the probability of successful data delivery while strictly adhering to
latency constraints. We formulate the problem as a sequential decision-making process,
which we cast as a finite-step Markov Decision Process (MDP). Our objective
is to derive an optimal policy that guides the selection of transmission parameters
at each step, ensuring efficient resource allocation and adaptive decision-making. To
achieve this, we apply a model-based reinforcement learning and model-free deep
reinforcement learning techniques to obtain the optimal policy.
Through selected numerical examples, we demonstrate the superior performance
of our proposed joint design compared to conventional schemes. The numerical
results highlight the significant performance gains achieved across a wide range of
transmission scenarios, particularly in situations with stringent latency budgets and
poor channel quality. While our proposed joint design is demonstrated within the
context of 5G NR, its applicability extends to future generations of wireless systems
that adopt similar reliability and latency mechanisms.
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Keywords
URLLC, probability of successful transmission, adaptive modulation and coding, deep reinforcement learning, reinforcement learning, Markov decision process