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The accuracy of artificial intelligence (AI) models is crucial for connected and autonomous vehicles (CAVs). However, in reality, model training under less frequent weather faces the problem of insufficient sampling. Also, in the real world, weather, sunlight, etc., can only change with the speed of the real-time clock, so the traditional sampling process is very slow. Moreover, currently, collective learning, which can make up the limited experience and computing power of a single vehicle, is always introduced to cases where the data from participants have the same structure, wasting massive heterogeneous data from vehicles of different brands. Therefore, in this paper, we propose a quantum collective learning and many-to-many matching game-based scheme in the metaverse for CAVs. The environment is simulated in the metaverse, which has its own time clock system, thereby expanding sample size and speeding up the sampling process. And we quantify the quality of intelligence in collective learning from the perspective of feature diversity. It is the cornerstone of collective learning between heterogeneous vehicles, facilitating maximum utilization of data with different structures. Then, we formulate the distributed vehicles selection problem as a many-to-many matching game and use Gale–Shapely algorithm to solve it. Also, we formulate the spectrum resource allocation problem as a discrete Markov decision process (MDP) and adopt a quantum-inspired reinforcement learning (QRL) algorithm to find the optimal policy to achieve the high revenue of the system. In simulations, the performance of the proposed scheme is compared with existing methods. [ABSTRACT FROM AUTHOR]

Communication-based train control (CBTC) is being rapidly adopted in urban rail transit systems, as it can significantly enhance railway network efficiency, safety, and capacity. Since CBTC systems are mostly deployed in underground tunnels and trains move at high speeds, building a train–ground wireless communication system for CBTC is a challenging task. Modeling the tunnel channels is very important in designing the wireless networks and evaluating the performance of CBTC systems. Most existing works on channel modeling do not consider the unique characteristics of CBTC systems, such as high mobility speed, deterministic moving direction, and accurate train-location information. In this paper, we develop a finite-state Markov channel (FSMC) model for tunnel channels in CBTC systems. The proposed FSMC model is based on real field CBTC channel measurements obtained from a business-operating subway line. Unlike most existing channel models, which are not related to specific locations, the proposed FSMC channel model takes train locations into account to have a more accurate channel model. The distance between the transmitter and the receiver is divided into intervals and an FSMC model is applied in each interval. The accuracy of the proposed FSMC model is illustrated by the simulation results generated from the model and the real field measurement results. [ABSTRACT FROM PUBLISHER]

A dynamic power allocation scheme is proposed to maximise the energy efficiency (EE) for multi‐antenna transmissions when both the imperfect channel estimation and feedback are considered. The lower bounded ergodic EE is given by the temporal correlation analysis from the Kalman estimation and the differential codebook‐based feedback of the Gauss–Markov channel, based on which both the powers of data and training are adaptively optimised. Simulations show that the proposed dynamic power allocation scheme outperforms the traditional power allocation schemes. [ABSTRACT FROM AUTHOR]