Age of Information-Oriented Probabilistic Link Scheduling for Device-to-Device Networks
Abstract: This paper focuses on optimizing the long-term average age of information (AoI) in device-to-device (D2D) networks through age-aware link scheduling. The problem is naturally formulated as a Markov decision process (MDP). However, finding the optimal policy for the formulated MDP in its original form is challenging due to the intertwined AoI dynamics of all D2D links. To address this, we propose an age-aware stationary randomized policy that determines the probability of scheduling each link in each time slot based on the AoI of all links and the statistical channel state information among all transceivers. By employing the Lyapunov optimization framework, our policy aims to minimize the Lyapunov drift in every time slot. Nonetheless, this per-slot minimization problem is nonconvex due to cross-link interference in D2D networks, posing significant challenges for real-time decision-making. After analyzing the permutation equivariance property of the optimal solutions to the per-slot problem, we apply a message passing neural network (MPNN), a type of graph neural network that also exhibits permutation equivariance, to optimize the per-slot problem in an unsupervised learning manner. Simulation results demonstrate the superior performance of the proposed age-aware stationary randomized policy over baselines and validate the scalability of our method.
- S. Kaul, R. Yates, and M. Gruteser, “Real-time status: How often should one update?” in Proc. IEEE INFOCOM, Mar. 2012, pp. 2731–2735.
- M. N. Tehrani, M. Uysal, and H. Yanikomeroglu, “Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions,” IEEE Commun. Mag., vol. 52, no. 5, pp. 86–92, May 2014.
- K. Shen and W. Yu, “FPLinQ: A cooperative spectrum sharing strategy for device-to-device communications,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2017, pp. 2323–2327.
- F. Baccelli and C. Singh, “Adaptive spatial aloha, fairness and stochastic geometry,” in Proc. 11th Int. Symp. Model. Optim. Mobile Ad Hoc Wireless Netw. (WiOpt), May 2013, pp. 7–14.
- F. Baccelli, B. Błaszczyszyn, and C. Singh, “Analysis of a proportionally fair and locally adaptive spatial aloha in Poisson networks,” in Proc. IEEE INFOCOM, Jul. 2014, pp. 2544–2552.
- I. Kadota, A. Sinha, E. Uysal-Biyikoglu, R. Singh, and E. Modiano, “Scheduling policies for minimizing age of information in broadcast wireless networks,” IEEE/ACM Trans. Netw., vol. 26, no. 6, pp. 2637–2650, Dec. 2018.
- I. Kadota and E. Modiano, “Minimizing the age of information in wireless networks with stochastic arrivals,” IEEE Trans. Mobile Comput., vol. 20, no. 3, pp. 1173–1185, Mar. 2021.
- Q. He, D. Yuan, and A. Ephremides, “Optimizing freshness of information: On minimum age link scheduling in wireless systems,” in Proc. 14th Int. Symp. Model. Optim. Mobile Ad Hoc Wireless Netw. (WiOpt), Jun. 2016, pp. 1–8.
- R. Talak, S. Karaman, and E. Modiano, “Optimizing information freshness in wireless networks under general interference constraints,” IEEE/ACM Trans. Netw., vol. 28, no. 1, pp. 15–28, Dec. 2019.
- S. Leng and A. Yener, “Age of information minimization for wireless ad hoc networks: A deep reinforcement learning approach,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2019, pp. 1–6.
- Y. Liu, C. She, W. Hardjawana, and B. Vucetic, “Graph neural networks for timely updates of short packets in interference-limited networks,” in Proc. Asilomar Conf. Signals Syst. Comput. (ACSSC), Oct. 2022, pp. 1050–1054.
- Z. Liu, Z. Chen, L. Luo, M. Hua, W. Li, and B. Xia, “Age of information-based scheduling for wireless device-to-device communications using deep learning,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Mar. 2021, pp. 1–6.
- N. Jones and E. Modiano, “Minimizing age of information in spatially distributed random access wireless networks,” in Proc. IEEE INFOCOM, May 2023, pp. 1–10.
- J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proc. 34th Int. Conf. Mach. Learn. (ICML), Aug. 2017, pp. 1263–1272.
- C.-H. Yu, K. Doppler, C. B. Ribeiro, and O. Tirkkonen, “Resource sharing optimization for device-to-device communication underlaying cellular networks,” IEEE Trans. Commun., vol. 10, no. 8, pp. 2752–2763, Jun. 2011.
- P. Phunchongharn, E. Hossain, and D. I. Kim, “Resource allocation for device-to-device communications underlaying LTE-advanced networks,” IEEE Wireless Commun. Mag., vol. 20, no. 4, pp. 91–100, Aug. 2013.
- Y. Sun, E. Uysal-Biyikoglu, R. D. Yates, C. E. Koksal, and N. B. Shroff, “Update or wait: How to keep your data fresh,” IEEE Trans. Inf. Theory, vol. 63, no. 11, pp. 7492–7508, Nov. 2017.
- M. Eisen and A. Ribeiro, “Optimal wireless resource allocation with random edge graph neural networks,” IEEE Trans. Signal Process., vol. 68, pp. 2977–2991, April. 2020.
- Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,” IEEE J. Sel. Areas Commun., vol. 39, no. 1, pp. 101–115, Jan. 2021.
- J. Guo and C. Yang, “Learning power allocation for multi-cell-multi-user systems with heterogeneous graph neural networks,” IEEE Trans. Wireless Commun., vol. 21, no. 2, pp. 884–897, Aug. 2021.
- R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas, “PointNet: Deep learning on point sets for 3d classification and segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 77–85.
- Propagation Data and Prediction Methods for the Planning of ShortRange Outdoor Radiocommunication Systems and Radio Local Area Networks in the Frequency Range 300 MHz to 100 GHz, document I. R. P.1411-11 2021.
- D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learn. Represent. (ICLR), Dec. 2014, pp. 1–15.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.