Resource Allocation for Secure URLLC in Mission-Critical IoT Scenarios

被引:100
|
作者
Ren, Hong [1 ]
Pan, Cunhua [1 ]
Deng, Yansha [2 ]
Elkashlan, Maged [1 ]
Nallanathan, Arumugam [1 ]
机构
[1] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England
[2] Kings Coll London, Dept Informat, London WC2R 2LS, England
基金
英国工程与自然科学研究理事会;
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; PHYSICAL-LAYER SECURITY; NETWORKS; DESIGN; PILOT; POWER;
D O I
10.1109/TCOMM.2020.2999628
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:5793 / 5807
页数:15
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