Jitter‐sensitive data communication in emerging wireless networks

Abstract

Jitter and time-sensitive communications (TSC) will play an important role as part of the emerging wireless networks such as fifth-generation (5G) and sixth-generation (6G) cellular networks to enable applications including vehicle-to-everything (V2X), real-time audio/video, real-time industrial automation and control, and synchronized collaboration. TSC is characterized by stringent latency, jitter, and reliability requirements. This paper considers the problem of scheduling time-sensitive data packets with stringent playout deadlines over a downlink multiple-input multiple-output (MIMO) channel, as part of the emerging wireless networks, to minimize the average jitter of a data session. Even though extensive research has been performed on MIMO technology, jitter and time-sensitive data communication over a MIMO channel remains an under-studied problem. In the proposed model, early or late arrival of the packets with respect to their playout deadlines incurs an early-or late-arrival penalty, respectively. The proposed scheduling problem is proven to be NP-hard and three computationally-efficient scheduling heuristics are developed with varying performance and computational tradeoffs. These heuristics make packet scheduling decisions over the MIMO subchannels with the objective of minimizing the average jitter value of the data session. While making scheduling decisions, these heuristics intelligently take into consideration the playout deadlines and size of the packets, the time-varying channel conditions, and the cost of the early or late arrival of the packets. The performance of these heuristics is evaluated from a design perspective through extensive simulations. The simulation results provide key insights into the relative performance tradeoffs among the three heuristics as a function of the various system parameters. This information could be utilized in intelligently allocating system resources to obtain the desired optimal system performance.

Sensitive data identification for multi‐category and multi‐scenario data

“…In addition, current sensitive data identification methods do not take into account the application scenarios of data-efficient transmission technology. [1][2][3][4][5][6] This paper proposes a sensitive data identification method based on batch data and streaming data for the problem of identifying sensitive data in multiple categories and scenarios within Beike Inc. To mitigate the problems posed by the size and complexity of the data, this method further divides data into incremental data, stock data, structured data, semi-structured and unstructured data, then uses different sensitive data identification government schemes respectively. In addition, the method constructs a classification & annotation platform and a dataset management platform to provide a reference for the definition and classification of sensitive data, therefore further supporting the training and evaluation of machine learning models for sensitive data identification.…”

“…It can be seen that the existing methods cannot meet the needs of enterprise‐level sensitive data identification. In addition, current sensitive data identification methods do not take into account the application scenarios of data‐efficient transmission technology 1–6 …”