In vehicular ad hoc networks (VANETs), network topology and communication links frequently change due to the high mobility of vehicles. Key challenges include how to shorten transmission delays and increase the stability of transmissions. When establishing routing paths, most research focuses on detecting traffic and selecting roads with higher vehicle densities in order to transmit packets, thus avoiding carry-and-forward scenarios and decreasing transmission delays; however, such approaches may not obtain accurate real-time traffic densities by periodically monitoring each road because vehicle densities change so rapidly. In this paper, we propose a novel routing information system called the machine learning-assisted route selection (MARS) system to estimate necessary information for routing protocols. In MARS, road information is maintained in roadside units with the help of machine learning. We use machine learning to predict the moves of vehicles and then choose some suitable routing paths with better transmission capacity to transmit packets. Further, MARS can help to decide the forwarding direction between two RSUs according to the predicted location of the destination and the estimated transmission delays in both forwarding directions. Our proposed system can provide in-time routing information for VANETs and greatly enhance network performance.

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The growing demand of real-time services, such as video streaming and VoIP with high constraints on delays and bandwidth brings new challenges in the design of wireless communication system. So how to use radio resource efficiently becomes an important point for the LTE system. In our paper, we propose a new LTE downlink packet scheduler, which adopts the packet delay to prioritize packet transmissions and uses packet prediction mechanism to solve the burst transmission situation. We also use the channel quality indicator that users report to the eNodeB to classify users' mobility, and then we utilize different resource allocation schemes for uses with different mobility in every transmission time interval. The simulations are conducted by LTE-Sim R5 to analyze the performance of proposed method. And the simulation results show that the proposed method outperforms other packet scheduling schemes in terms of goodput, packet delays, and packet loss rates. Our proposed method can improve the spectrum efficiency and satisfy the QoS requirements of real-time traffic.

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In vehicular ad hoc networks (VANETs), due to highly mobile and frequently changing topology, available resources and transmission opportunities are restricted. To address this, we propose a burst transmission and frame aggregation (FAB) scheme to enhance transmission opportunity (TXOP) efficiency of IEEE 802.11p. Aggregation and TXOP techniques are useful for improving transmission performance. FAB aggregates frames in the relay node and utilizes the TXOP to transmit these frames to the next hop with a burst transmission. Simulation results show that the proposed FAB scheme can significantly improve the performance of inter-vehicle communications.