Resource Allocation for Joint Interference Management and Security Enhancement in Cellular-Connected Internet-of-Drones Networks

被引:6
作者
Hassan, Md. Zoheb [1 ,2 ]
Kaddoum, Georges [1 ]
Akhrif, Ouassima [1 ]
机构
[1] Univ Quebec, Ecole Technol Super, Quebec City, PQ H3C 1K3, Canada
[2] Virgina Polytech Inst & State Univ, Wireless VT, Blacksburg, VA 24061 USA
关键词
Internet-of-drones; interference management; physical layer security; resource allocation; POWER-CONTROL; UAV COMMUNICATIONS; OPTIMIZATION; COMMUNICATION; NOMA; DESIGN;
D O I
10.1109/TVT.2022.3196500
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
Internet-of-drones (IoD) systems require enhanced data transmission security and efficient interference management to accommodate the rapidly growing drone-based and rate-intensive applications. This paper develops a novel resource allocation scheme to jointly manage interference and enhance the physical layer security of cellular-connected IoD networks in the presence of a multi-band eavesdropping drone. Our envisioned cellular-connected IoD network has multiple full-duplex cellular base stations (CBSs), where each CBS reserves an orthogonal cellular radio resource block (RRB) for the aerial communication links. To efficiently utilize the cellular RRBs, each CBS is connected to a cluster of data transmitting drones using uplink non-orthogonal multiple access (NOMA) scheme. In addition, all the CBSs simultaneously transmit artificial noise signals to weaken the eavesdropper links. A joint optimization problem, considering the transmit power allocation and clustering of the legitimate drones, and the jamming power allocation of the CBSs, is formulated to maximize the worst-case average sum-secrecy-rate of the network. The joint optimization problem is decomposed into drone-clustering and power allocation sub-problems to obtain an efficient solution. A multi-agent reinforcement-learning framework is devised to solve the drone-clustering sub-problem. Meanwhile, the transmit and jamming power allocation sub-problem is solved by employing fractional programming, successive convex approximation, and alternating optimization techniques. By iteratively solving these two sub-problems, a convergent resource allocation algorithm, namely, $\underline{\text{s}}$ecurity and $\underline{\text{i}}$nterference management with $\underline{\text{re}}$inforcement-learning and $\underline{\text{N}}$OMA (SIREN), is proposed. The superiority of SIREN over several benchmark schemes is verified via extensive simulations.
引用
收藏
页码:12869 / 12884
页数:16
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