期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 70, 期 1, 页码 754-767出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2020.3042551
关键词
OFDM; Radar; Resource management; Radar detection; Heuristic algorithms; Dynamic scheduling; Wireless networks; Joint communication-radar; resource allocation; network stability; OFDMA; Lyapunov optimization
资金
- National Natural Science Foundation of China [61631003, 61790553, 61941102]
- Natural Science Foundation of Beijing Municipality [L192031]
The research introduces the OFDMA technique to optimize the JCRS system, designs a queue scheduling model for user data packets, solves the problem of user data packet backlog, improves network stability, and achieves a power-backlog tradeoff.
The joint communication-radar system (JCRS) is promising in the future wireless network which can provide great advantages, such as structure simplification, interference mitigation and less resource occupation, compared with the traditional separate deployment of communication and radar systems. However, with the large number of users accessing the network both for communication and radar purposes, massive user data packets may arrive at the base station equipped with the JCRS in a short period of time, which can easily cause the backlog of user data packets and affect network stability. To solve this problem, we introduce orthogonal frequency division multiple access (OFDMA) technique into the JCRS to support communication function for massive users. Then, we design a queue scheduling model of user data packets to perceive the status of current queue backlog and schedule the queue dynamically. Different from the existing works, we formulate a transmit power minimization problem, while taking network stability and radar detection performance into account. Since the problem is a non-convex problem, we transform it into the upper-bound minimization of drift-plus-function by using the Lyapunov optimization technique. Further, the successive convex approximation method and parameter transformation method are exploited to convert this problem into a convex problem. Finally, we derive an optimal closed-form power allocation expression by using the Lagrangian dual decomposition method and propose a dynamic subcarrier and power allocation algorithm. Simulation results show that the proposed algorithm can achieve an [O(1/eta), O(eta)] power-backlog tradeoff and demonstrate the efficacy of the proposed algorithm in reducing transmit power while significantly enhancing network stability.
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