  Future 6G-enabled vehicular networks face the challenge of ensuring
ultra-reliable low-latency communication (URLLC) for delivering safety-critical
information in a timely manner. Existing resource allocation schemes for
vehicle-to-everything (V2X) communication systems primarily rely on traditional
optimization-based algorithms. However, these methods often fail to guarantee
the strict reliability and latency requirements of URLLC applications in
dynamic vehicular environments due to the high complexity and communication
overhead of the solution methodologies. This paper proposes a novel deep
reinforcement learning (DRL) based framework for the joint power and block
length allocation to minimize the worst-case decoding-error probability in the
finite block length (FBL) regime for a URLLC-based downlink V2X communication
system. The problem is formulated as a non-convex mixed-integer nonlinear
programming problem (MINLP). Initially, an algorithm grounded in optimization
theory is developed based on deriving the joint convexity of the decoding error
probability in the block length and transmit power variables within the region
of interest. Subsequently, an efficient event-triggered DRL-based algorithm is
proposed to solve the joint optimization problem. Incorporating event-triggered
learning into the DRL framework enables assessing whether to initiate the DRL
process, thereby reducing the number of DRL process executions while
maintaining reasonable reliability performance. Simulation results demonstrate
that the proposed event-triggered DRL scheme can achieve 95% of the performance
of the joint optimization scheme while reducing the DRL executions by up to 24%
for different network settings.
