DeepAI AI Chat
Log In Sign Up

Deep Deterministic Policy Gradient to Minimize the Age of Information in Cellular V2X Communications

by   Zoubeir Mlika, et al.

This paper studies the problem of minimizing the age of information (AoI) in cellular vehicle-to-everything communications. To provide minimal AoI and high reliability for vehicles' safety information, NOMA is exploited. We reformulate a resource allocation problem that involves half-duplex transceiver selection, broadcast coverage optimization, power allocation, and resource block scheduling. First, to obtain the optimal solution, we formulate the problem as a mixed-integer nonlinear programming problem and then study its NP-hardness. The NP-hardness result motivates us to design simple solutions. Consequently, we model the problem as a single-agent Markov decision process to solve the problem efficiently using fingerprint deep reinforcement learning techniques such as deep-Q-network (DQN) methods. Nevertheless, applying DQN is not straightforward due to the curse of dimensionality implied by the large and mixed action space that contains discrete and continuous optimization decisions. Therefore, to solve this mixed discrete/continuous problem efficiently, simply and elegantly, we propose a decomposition technique that consists of first solving the discrete subproblem using a matching algorithm based on state-of-the-art stable roommate matching and then solving the continuous subproblem using DRL algorithm that is based on deep deterministic policy gradient DDPG. We validate our proposed method through Monte Carlo simulations where we show that the decomposed matching and DRL algorithm successfully minimizes the AoI and achieves almost 66 compared to the best benchmarks for various vehicles' speeds, transmission power, or packet sizes. Further, we prove the existence of an optimal value of broadcast coverage at which the learning algorithm provides the optimal AoI.


Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach

This paper studies the multi-agent resource allocation problem in vehicu...

Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networks

This paper investigates the problem of minimizing the age-of-information...

Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications

Cellular vehicle-to-everything (V2X) communication is crucial to support...

Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

The interconnection of vehicles in the future fifth generation (5G) wire...