This paper investigates the application of artificial intelligence (AI) to wireless technology, specifically in the context of beam management (BM) in the advanced 5th-generation (5G) communication system. Our focus lies in aligning our study with the ongoing discussions within the Third Generation Partnership Project (3GPP) as of December 2022. Instead of evaluating the performance of specific AI models, we take user equipment (UE) receiver (Rx) beam prediction as an illustrative example of AI-based BM. We explore various aspects of AI model management, including model selection, monitoring, and activation/deactivation operations, from a 3GPP perspective. For model selection, we propose deploying distinct AI models for different propagation environments, categorized based on base station (BS) transmitter (Tx) beam measurement results. Reference Signal Received Power (RSRP) serves as a pivotal key performance index (KPI) for model performance monitoring. Our simulation results indicate that, instead of training one all-encompassing AI model with numerous layers for universal application, transitioning between domain-specific AI models with fewer layers yields superior performance. Model activation/deactivation procedures determine whether AI-based BM or traditional BM should be employed in a given scenario. We also introduce the use of AI for predicting the performance of both AI-based BM and traditional BM. By comparing the performance of these strategies, we can ascertain whether link performance degradation results from AI output errors or UE movement into challenging propagation environments. This approach enables the effective management of model switching between AI-based BM and traditional BM. The simulation shows that we can reduce the number of unnecessary switches by 10%.

Multi-agent multi-sensor fusion between connected vehicles for cooperative perception has recently been recognized as the best technique for minimizing the blind zone of individual vehicular perception system and further enhancing the overall safety of autonomous driving system. This technique relies heavily on the reliability and availability of vehicle-to-everything (V2X) communication. In practical cooperative perception application scenarios, the non-line-of-sight (NLOS) issue causes blind zones for not only the perception system but also V2X direct communication, especially for busy traffic scenarios. Cooperative perception can address the NLOS issue for vehicular perception systems once. However, to ensure effective real-world implementation, we must also solve the NLOS challenge a second time for the communication systems that support cooperative perception, NLOS “dies" twice. To counteract underlying communication issues, we introduce an abstract perception matrix matching method for quick sensor fusion matching procedures and mobility-height hybrid relay determination procedures, proactively improving the efficiency and performance of V2X communication to serve the upper layer application fusion requirements. To demonstrate the effectiveness of our solution, a new simulation framework is designed to consider autonomous driving, cooperative perception and V2X communication in general, paving the way for end-to-end performance evaluation and further solution derivation.

Cellular-based V2X (C-V2X) technology promoted by Third Generation Partnership Project (3GPP) is gaining increasing attention globally, after many year-long competition with dedicated short-range communications (DSRC) supported by the Institute of Electrical and Electronics Engineers (IEEE) for vehicular to everything communication. As a rising star, China continuously and actively focuses on and contributes to the C-V2X technology development in this technology marathon. Starting from the standardization progress, a quite few Chinese-specific use cases and communication messages are defined for the complicated urban traffics. Based on these dedicatedly designed higher layer protocols, the annual field tests are progressively conducted to testify the interoperability among chipsets, modules, security certificates, and original equipment manufacturer (OEM). Putting enough efforts on industry standards and tests, China is fast commercializing the C-V2X-based road services, for example, robotaxi and robot-bus in representative cities. Thus, in this chapter, we propose to provide sufficient technology views and a summary to show such advanced Chinese C-V2X philosophy.

This paper considers information transmission issue observed in a large scale test of cellular-based vehicular to everything (C-V2X) technology in China, which is the information blockage for the vehicle-to-vehicle (V2V) wireless communication. With such findings, we propose utilizing relaying-based information routing solutions by the support of K-means-type algorithm and an on-request relay selection method in a vehicular adhoc network (VANET). From the algorithm perspective, we refine the K-means-based VANET seeding principle with considerations on the global vehicle layout. On the other hand, base station and/or core network aided relay selection associated with vehicle maneuver is introduced for waking up relays when necessary in order to address the typical non-line-of-sight (NLOS) signal attenuation by taking the power consumption into consideration. To evaluate the system performance, we conduct Matlab and network simulator (NS)-3 simualtions to justify the algorithms, and adopt Veins-based simulations to show the data packets loss gain over non-relaying scenarios of realistic V2V scenario with NLOS signal dissemination.

Beam alignment (BA), a core component of beamforming, is essential for achieving optimal directional gain in high-frequency communications. However, in single base station (BS) scenarios, BA may fail when the line-of-sight (LoS) path is obstructed. Multi-BS cooperation can effectively address this issue by leveraging the LoS paths from other BSs. However, applying traditional single-BS BA schemes to multi-BS cooperation scenarios leads to a substantial training overhead. To address this, we propose a sensing-aided simultaneous multi-BS BA (SSMBA) strategy in this paper. In the first phase, compressed sensing (CS) is utilized to enable simultaneous multi-beam training across multiple BSs, significantly reducing training overhead. In the second phase, leveraging integrated sensing and communication (ISAC) capabilities, a sensing-aided beam refinement technique is introduced to further increase BA accuracy and beamforming gain. The simulation results demonstrate that the proposed SSMBA strategy has advantages in both reducing the training overhead and increasing beamforming gain.