In this paper, the problem of joint communication and sensing is studied in the context of terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles (SPVs) provide either communication service or sensing service to target vehicles, where it is essential to determine 1) the service mode (i.e., providing either communication or sensing service) for each SPV and 2) the subset of target vehicles that each SPV will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of the communication target vehicles, while satisfying the sensing service requirements of the sensing target vehicles, by determining the service mode and the target vehicle association for each SPV. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle’s graph information related to its location, connection, and communication interference. Using this extracted graph information, a joint service mode selection and target vehicle association strategy is then determined to adapt to the dynamic vehicle topology with various vehicle types (e.g., target vehicles and service provider vehicles). Simulation results show that the proposed GNN-based scheme can achieve 93.66% of the sum rate achieved by the optimal solution, and yield up to 3.16% and 31.86% improvements in sum rate, respectively, over a homogeneous GNN-based algorithm and a conventional optimization algorithm without using GNNs.

Hybrid analog and digital structure with reduced radio frequency (RF) chains is a key enabler to reach a compromise between system complexity and performance in future fifth generation high frequency communications. A fully-connected architecture that each antenna connects to all RF chains has been proven to achieve similar performance compared to a traditional full-digital system where the number of RF chains is equal to the number of antennas. However, such a structure is difficult for practical implementation owing to the problem of circuit layout for high frequency antenna array structures. Therefore, a sub-connected alternative where each RF chain only connects to a disjoint subset of antennas becomes more attractive. In this paper, we study the system with the sub-connected subarray architecture employed by both the transmitter and the receiver, and aim to maximize the sum-rate by taking joint precoding and combining into consideration. We propose two schemes, named joint-SIC and MU-SIC, for single-user and multi-user communications, respectively. Moreover, we provide an upper bound of the sum-rate achieved by such a sub-connected subarray structure. The simulation results show the fast convergence and effectiveness of the proposed schemes.

The key challenges in live video multicasting include how to properly form multicast groups, select video versions and allocate wireless resources, in order to guarantee the quality of experience (QoE) while ensuring low latency delivery. To address these challenges, in this paper, a novel multicast framework that leverages the advantages of network-assisted dynamic adaptive streaming over HTTP and cloud radio access networks is proposed, where a multicast assistant server is deployed at the edge of a mobile network. Under this architecture, a joint user grouping, version selection, and bandwidth allocation method is designed to optimize the sum of users’ utilities. In particular, a two-step scheme is proposed to solve this complex problem. The number of multicast groups is first automatically determined and a user clustering method is presented. Then, group-level version selection and spectrum assignment algorithms are performed at different time scales. Simulation results demonstrate that our proposed scheme can improve at least 7% QoE compared to baseline methods.

The existing research on multi-modal semantic communication ignores the exploration of reasoning correlation among multi-modal data. Motivated by this, a multi-modal semantic representation and fusion model based on knowledge graph (KG-MSF) is proposed in this paper. In KG-MSF, the direct and reasoning correlation semantic information is extracted and mapped into a two-layer semantic architecture to represent the semantics of each modal fully. After that, the knowledge graph with structural advantage is utilized to fuse multi-modal semantic information, which is transmitted under different channel conditions. To assess the efficacy of semantic representation and fusion of the proposed KG-MSF in the multi-modal semantic communication system, we conduct comprehensive experiments on the task of visual question answer (VQA) with a metric of answer accuracy. The results demonstrate the superiority compared with existing models for multi-modal semantic representation, fusion, transmission efficiency and channel robustness.

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based method can improve the number of successfully served vehicles by up to 17% and 28% compared to a GNN based algorithm with a fixed neural network model and a conventional optimization algorithm without using GNNs.