SATORI Project Publications
S. Chen, Z. Jiang, S. Zhou, Z. Niu, A. Marinescu, and L. A. DaSilva, “Learning-based Remote Channel Inference: Feasibility Analysis and Case Study,” IEEE Transactions on Wireless Communications, vol. 18, no. 7, pp. 3554-3568, Jul. 2019.
R. Xu, Y. Li, and S. Chen, “On the Opportunistic Topology of Vehicular Networks in Urban Mobility Environment,” IEEE Transactions on Big Data, to appear.
H. Cao, F. Xu, J. Sankaranarayanan, Y. Li, and H. Samet, “Habit2vec: Trajectory Semantic Embedding for Living Pattern Recognition in Population,” IEEE Transactions on Mobile Computing, to appear.
H. Wang, Y. Li, S. Zeng, G. Wang, P. Zhang, P. Hui, and D. Jin, “Modeling Spatio-Temporal App Usage for a Large User Population,” ACM UbiComp’19 2019 (IMWUT), London, UK, Sep. 2019.
Z. Jiang, S. Zhou, and Z. Niu, ‘‘Distributed Policy Learning Based Random Access for Diversified QoS Requirements,” IEEE ICC’19, Shanghai, China, 20-24 May 2019
J. Hribar, A. Marinescu, G. Ropokis, and L. A. DaSilva, “Using Deep Q-Learning to Prolong the Lifetime of Correlated Internet of Things Devices,” IEEE ICC Workshops, Shanghai, China, 20-24 May 2019
W. Shi, Y. Hou, S. Zhou, Z. Niu, Y. Zhang, and L. Geng, ‘‘Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning,” IEEE INFOCOM’19 Workshop, Paris, May 2019
J. Sun, Z. Jiang, S. Zhou, and Z. Niu, ‘‘Optimizing Information Freshness in Broadcast Network with Unreliable Links and Random Arrivals: An Approximate Index Policy,” IEEE INFOCOM’19 Workshop, Paris, May 2019
Z. Jiang, S. Zhou, Z. Niu, and Y. Cheng, ‘‘A Unified Sampling and Scheduling Approach for Status Update in Multiaccess Wireless Networks,” IEEE INFOCOM’19, Paris, May 2019
J. Hribar and L. A. DaSilva, “Utilising Correlated Information to Improve the Sustainability of Internet of Things Devices,” IEEE World Forum on Internet of Things, Limerick, Ireland, 15-18 April 2019
S. Chen, Z. Jiang, S. Zhou, and Z. Niu, ‘‘Time-sequence channel inference for beam alignment in vehicular networks,” IEEE GlobalSIP’18, Anaheim, CA, Nov. 2018.
J. Feng, Y. Li, C. Zhang, F. Sun, F. Meng, A. Guo, and D. Jin, “DeepMove: Predicting Human Mobility with Attentional Recurrent Networks,” WWW’18, Lyon, France, Oct. 2018.
Other Machine Learning Publications
A. Marinescu, I. Macaluso, and L. A. DaSilva, “A Multi-Agent Neural Network for Dynamic Frequency Reuse in LTE Networks,” IEEE ICC Workshops, Kansas City, MO, 20-24 May 2018
A. Marinescu, I. Macaluso, and L. A. DaSilva, “System Level Evaluation and Validation of the ns-3 LTE Module in 3GPP Reference Scenarios,” 20th ACM Intl. Conf. on Modelling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), Miami Beach, FL, 21-25 November 2017
A. Selim, F. Paisana, J. A. Arokkiam, Y. Zhang, L. E. Doyle, and L. A. DaSilva, “Spectrum Monitoring for Radar Bands Using Deep Convolutional Neural Networks,” IEEE Globecom, Singapore, 4-8 December 2017
F. Paisana, A. Selim, M. Kist, J. Tallon, C. Bluemm, A. Puschmann, and L. A. DaSilva, “Context-aware Cognitive Radio Using Deep Learning,” IEEE Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD, 6-9 March 2017
J. Liu, B. Krishnamachari, S. Zhou, and Z. Niu, ‘‘DeepNap: Data-driven base station sleeping operations through deep reinforcement learning,” IEEE Internet Things J., 2018, 5(6):4273-4282.
I. Macaluso, D. Finn, B. Ozgul, and L. A. DaSilva, “Complexity of Spectrum Activity and Benefits of Learning for Dynamic Channel Selection,” IEEE Journal on Selected Areas in Communications (JSAC), Cognitive Radio Series, vol. 31, no. 11, November 2013, pp. 2237-2248. (DOI: 10.1109/JSAC.2013.131115)