4.7 Article

Resource Allocation for Secure URLLC in Mission-Critical IoT Scenarios

期刊

IEEE TRANSACTIONS ON COMMUNICATIONS
卷 68, 期 9, 页码 5793-5807

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2020.2999628

关键词

Ultra reliable low latency communication; Security; Resource management; Mission critical systems; Decoding; Error probability; Reliability; URLLC; secure communications; short packet transmission; mission-critical applications; industrial 40

资金

  1. U.K. Engineering and the Physical Sciences Research Council [EP/N029666/1]
  2. EPSRC [EP/R006466/1, EP/N029666/1] Funding Source: UKRI

向作者/读者索取更多资源

Ultra-reliable low latency communication (URLLC) is one of three primary use cases in the fifth-generation (5G) networks, and its research is still in its infancy due to its stringent and conflicting requirements in terms of extremely high reliability and low latency. To reduce latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and higher decoding error probability. In this case, conventional resource allocation based on Shannon capacity achieved with infinite blocklength codes is not optimal. Security is another critical issue in mission-critical internet of things (IoT) communications, and physical-layer security is a promising technique that can ensure the confidentiality for wireless communications as no additional channel uses are needed for the key exchange as in the conventional upper-layer cryptography method. This paper is the first work to study the resource allocation for a secure mission-critical IoT communication system with URLLC. Specifically, we adopt the security capacity formula under finite blocklength and consider two optimization problems: weighted throughput maximization problem and total transmit power minimization problem. Each optimization problem is non-convex and challenging to solve, and we develop efficient methods to solve each optimization problem. Simulation results confirm the fast convergence speed of our proposed algorithm and demonstrate the performance advantages over the existing benchmark algorithms.

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