In order to satisfy the increasingly massive connection in mMTC system, multiple access technology is a key enabler in the future 6G mMTC. Recently, an emerging multiple access scheme named resource hopping based multiple access (RHMA) has been proposed to achieve reliable user identification and data detection in grant-free random access for mMTC. However, the collision resolution of RHMA is still limited for the future 6G mMTC requirements. Therefore, an improved design is proposed in this paper to enhance the collision resolution capability of RHMA. Specifically, successive interference cancellation (SIC) is combined with user identification and segment decoding at the receiver of RHMA. Also, the user identification of RHMA is improved to eliminate the false alarm user caused by collision and blind channel estimation is considered to recover the signal on the colliding segments. The simulation results show that the improved design is able to achieve a higher collision resolution capability of RHMA.

With the emergence of various IoT applications, a large-scale IoT system is set to revolutionize the various industry. Massive machine type communication will play a crucial role in providing robust support to this system. However, in mMTC, implementing user access control to block malicious users remains a critical issue that needs to be addressed. To this end, this paper proposes a user access control scheme based on resource hopping multiple access (RHMA). This scheme utilizes the unique resource hopping patterns of different users to control user access. The controllability of these resource hopping patterns effectively prevents malicious users from intruding into the system. Moreover, the proposed user access control scheme is implemented in practice with USRP platform. The experimental results confirm the reliability and effectiveness of the proposed access control system.

In distributed systems and microservice applications, tracing is a crucial observability signal employed for comprehending their internal states. To mitigate the overhead associated with distributed tracing, most tracing frameworks utilize a uniform sampling strategy, which retains only a subset of traces. However, this approach is insufficient for preserving system observability. This is primarily attributed to the long-tail distribution of traces in practice, which results in the omission or rarity of minority yet critical traces after sampling. In this study, we introduce an observability-preserving trace sampling method, denoted as STEAM, which aims to retain as much information as possible in the sampled traces. We employ Graph Neural Networks (GNN) for trace representation, while incorporating domain knowledge of trace comparison through logical clauses. Subsequently, we employ a scalable approach to sample traces, emphasizing mutually dissimilar traces. STEAM has been implemented on top of OpenTelemetry, comprising approximately 1.6K lines of Golang code and 2K lines of Python code. Evaluation on four benchmark microservice applications and a production system demonstrates the superior performance of our approach compared to baseline methods. Furthermore, STEAM is capable of processing 15,000 traces in approximately 4 seconds.