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In this paper, we propose an age of incorrect information (AoII)-aware data dissemination scheme for distributed multi-agent systems (MASs). In the proposed scheme, AoII is utilized to measure the importance of data in terms of timeliness and content. We formulate the joint optimization of time slot allocation and agent selection as a decentralized partially observable Markov decision process (Dec-POMDP), with the objective of minimizing the AoII. To solve the Dec-POMDP, a novel multi-agent reinforcement learning algorithm (DV-MAPPO) is proposed. In particular, to tackle challenges posed by the partial observability of global system information, each agent estimates the global system state using variational inference. Moreover, to improve the accuracy of global system state estimation, each agent is given an intrinsic reward that is dominated by the accuracy of estimates. The proposed data dissemination scheme is implemented and evaluated in various missions. Simulation results show that the proposed data dissemination scheme outperforms traditional data dissemination schemes in terms of AoII. Furthermore, in typical multi-agent collaborative tasks, the proposed scheme facilitates more efficient cooperation among multiple agents compared to the data distribution mechanisms that ignore the importance of data.

The stability of multi-robot flocking is closely related to the control model and the quality of wireless communications. In this study, we delve into the discrete-time flocking control problem for multi-robot systems (MRS) operating under an ad hoc network with random link failures. Specifically, we initially propose a discrete-time control model for multi-robot flocking that describes the inherent instability in the transmission of robot state information as a Bernoulli variable. Compared to existing controllers, this model requires less information exchange and is more practical for implementation in multi-robot systems. Subsequently, stability analysis is conducted for the controller, revealing that the MRS cannot achieve asymptotic flocking but can attain flocking in expectation when relevant stability conditions are satisfied. The stability conditions are deduced using the Lyapunov method, imposing constraints on the controller gains, the interaction period, and the successful transmission probability of communication links. Notably, the upper bound for the interaction period is significantly improved, thereby alleviating the communication network's burden. Simulation results verify the efficacy of the proposed control model and the reliability of the derived stability conditions.

Filter bank multicarrier (FBMC) is emerging as a promising approach to combat the orthogonal frequency division multiplexing (OFDM) sensitivity to synchronization errors for machine-type communication (MTC). However, related designs in FBMC-MTC fail to meet the requirements of transmitting diverse data between machines as well as eliminating the effects of FBMC’s inherent imaginary interference. To address this issue, in this paper, we propose an FBMC-MTC system with deep learning (DL) assistance. Specifically, we first construct a hybrid packet transmission architecture to meet the delay and throughput requirements of different packets. Second, we further present a DL-based receiver consists two modules driven by communication domain knowledge to enhance data reliability. The dense and convolutional layers are used in two different modules since the two types of packets have different pilot structures and propagation properties. Finally, we deploy the proposed FBMC-MTC system via universal software radio peripheral (USRP) and mobile robots and test the DL-based receiver over the air (OTA). Both simulation and OTA test results show that the proposed FBMC-MTC system can operate in various channel environments, and its receiver is better than the previously advanced OFDM and FBMC receivers.

Tactile Internet (TI) is envisioned as a key use case for 6G with the ability to support a plethora of remote control-based applications. To achieve precise remote control, TI needs to utilize real-world testbeds for executing practical tests instead of simulation. However, the existing TI prototypes cannot be used as versatile experimental platforms due to their single communication mode and limited application scenarios. Moreover, the implementation of versatile experimental platforms is challenging, as the integration of diverse hardware modules requires multi-disciplinary knowledge. To this end, we propose and implement a versatile TI experimental platform, which can support multiple communication modes and various application scenarios. Specifically, we first analyze two types of TI platforms based on inter-active objects, present the general architecture of TI with three domains, and highlight several typical challenges of TI platform implementation. Then, we detailed describe the guidelines and key steps for designing the practical TI experimental platform. Finally, we present how to effectively implement the versatile experimental platform. The performance evaluation results highlight that the proposed platform is advantageous in terms of modularity, transparency, and scalability. The proposed platform can lay a solid foundation for developing novel TI algorithms and facilitating researchers from different disciplines to conduct practical experiments on a unified framework.