期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 21, 期 12, 页码 11125-11138出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2022.3190315
关键词
Age of information; MDP; Lyapunov optimiza-tion; adaptive NOMA/OMA/cooperative-SWIPT-NOMA transmission scheme
资金
- National Natural Science Foundation of China [61871147, 62071141]
- Shenzhen Municipal Science and Technology Plan [GXWD20201230155427003-20200730122528002]
In this paper, the information freshness of short-packet communication in wireless networks is studied, and an adaptive multiple access technology with corresponding power allocation is proposed to optimize the Age of Information (AoI) performance. The advantages and feasibility of the proposed scheme are demonstrated through analysis and simulation experiments, providing valuable insights for practical system designs.
In this paper, we study the information freshness of short-packet communication in wireless networks, where a base station (BS) sends time-sensitive status updates to users via adaptive multiple access technology. To improve the Age of Information (AoI) performance of the network, the BS adapively switches among non-orthogonal multiple access (NOMA), orthogonal multiple access (OMA) and cooperative NOMA with simultaneous wireless information and power transfer (SWIPT). Specifically, the BS carefully decides the appropriate multiple access technology and the corresponding power allocation according to the state of the network to optimize the expected weighted sum of AoI (EWSAoI) of the system. To this end, we first analyze the EWSAoI of these three multiple access technologies and propose an adaptive NOMA/OMA/cooperative-SWIPT-NOMA transmission scheme. In specific, we formulate a Markov Decision Process (MDP) problem and develop an optimal policy for the BS to decide whether to use NOMA, OMA or cooperative-SWIPT-NOMA for downlink transmission based on the current state of the network. We further prove the existence of optimal stationary and deterministic policy. Furthermore, to reduce the computation complexity, a suboptimal adaptive policy based on Lyapunov Optimization is also devised, which can achieve near optimal performance according to our simulation results. The extensive simulation results demonstrate the advantages of the proposed policies, which provide useful insights for practical system designs.
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