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Flexible Bit and Semantic On-Demand Transmission Framework in Hyper-Reliable and Low Latency Communications Scenarios
Journal article   Peer reviewed

Flexible Bit and Semantic On-Demand Transmission Framework in Hyper-Reliable and Low Latency Communications Scenarios

Jingxuan Zhang, Xiqi Cheng, Haijun Zhang, Peng Cui, Suyu Lv, Xiaodong Xu, Ping Zhang and Tony Q. S. Quek
IEEE transactions on wireless communications, Vol.25, pp.2668-2681
2026

Abstract

6G mobile communication Bandwidth Decoding Delays flexibility HRLLC intelligent Quality of service Reliability Resource management Semantic communication stochastic network calculus Ultra reliable low latency communication Wireless communication
As a typical scenario for the 6^{th} Generation mobile communication systems (6G), Hyper Reliable Low Latency Communication (HRLLC) is expected to ensure extremely low delay and high reliability, while supporting wireless transmission of large-scale massive data. However, existing communication networks face the dual challenges of inadequate performance metrics and limited network resources. Therefore, this paper proposes the Flexible Bit and Semantic on-demand Transmission (FBST) framework, including three key technologies: adaptive transmission mode decision, flexible transmission time interval scheduling, adjustable semantic compression ratio. The FBST framework could satisfy the strict QoS requirements of users and provide on-demand services for users. Based on the Stochastic Network Calculus (SNC) modeling method, we conduct precise delay analysis and provided a general expression for the delay violation probability of the \alpha -\kappa - \mu channel, which could be extended to various complex channels. In addition, the Knowledge-base Parameterized Deep Q-Network (KP-DQN) algorithm is proposed to solve the resource allocation issue, which is a mixed action space problem with complex calculations caused by SNC. Finally, the simulation results show that FBST framework could satisfy extremely strict delay and reliability requirements of users, and the KP-DQN algorithm improving operational efficiency by over 76.8%.

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