In millimeter wave (mmWave) vehicular communications, multi-hop relay disconnection by line-of-sight (LOS) blockage is a critical problem, particularly in the early diffusion phase of mmWave-available vehicles, where not all vehicles have mmWave communication devices. This paper proposes a distributed position control method to establish long relay paths through road side units (RSUs). This is realized by a scheme via which autonomous vehicles change their relative positions to communicate with each other via LOS paths. Even though vehicles with the proposed method do not use all the information of the environment and do not cooperate with each other, they can decide their action (e.g., lane change and overtaking) and form long relays only using information of their surroundings (e.g., surrounding vehicle positions). The decision-making problem is formulated as a Markov decision process such that autonomous vehicles can learn a practical movement strategy for making long relays by a reinforcement learning (RL) algorithm. This paper designs a learning algorithm based on a sophisticated deep reinforcement learning algorithm, asynchronous advantage actor-critic (A3C), which enables vehicles to learn a complex movement strategy quickly through its deep-neural-network architecture and multi-agent-learning mechanism. Once the strategy is well trained, vehicles can move independently to establish long relays and connect to the RSUs via the relays. Simulation results confirm that the proposed method can increase the relay length and coverage even if the traffic conditions and penetration ratio of mmWave communication devices in the learning and operation phases are different.

The end-to-end packet error rate (PER) performance of a multi-hop cooperative relaying system is discussed in this paper. In this system, the end-to-end PER performance improves with the number of hops under certain conditions. The PER performance of multi-hop cooperative networks is analyzed with the state transition technique. The theoretical analysis reveals that the PER performance can be kept almost constant, or even improved, as the number of hops is increased. Computer simulation results agree closely with the analysis results. Moreover, to confirm this performance characteristic in an actual setup, an in-lab experiment using a fading emulator was conducted. The experimental results confirm the theoretical end-to-end PER performance of this system.

Generalized selection combining (GSC) was recently proposed as a low-complexity diversity combining technique for diversity-rich environments. This letter proposes a multi-hop Decode-and-Forward Relaying (MDFR) scheme in conjunction with GSC and describes its performance in terms of average bit error probability. We have shown that the proposed protocol offers a remarkable diversity advantage over direct transmission as well as the conventional decode-and-forward relaying (CDFR) scheme. Simulation results are also given to verify the analytical results.

This letter provides a study on the end-to-end performance of multi-hop wireless communication systems equipped with re-generative (decode-and-forward) relays over Rayleigh fading channels. More specifically, the probability density function (pdf) of the tightly approximated end-to-end signal-to-noise ratio (SNR) of the systems is derived. Using this approximation allows us to avoid considering all possible combinations of correct and erroneous decisions at the relays for which the end-to-end transmission is error-free. The proposed analysis offers a simple and unifying approach as well as reduces computation burden in evaluating important multi-hop system's performance metrics. Simulations are performed to verify the accuracy and to show the tightness of the theoretical analysis.