## Junting CHEN – Research AreasI work on ## Dynamic 3D Communication NetworksThe recent success of commercial UAV applications motivates a new dimensino of optimizing wireless networks, where the network dynamically optimizes the node positions for enhanced commmunication capability. This can be done by introducing a flying relay to connect two geographically separated ground users. The fundamental novelty and challenge is to adapt the flying relay position to the
Solutions to these problems require synergies between communication theory, machine learning, and optimizations. Some preliminary results can be found in: J. Chen and D. Gesbert, ”Local map-assisted positioning for flying wireless relays”, under review, 2018. [Preprint]
J. Chen and D. Gesbert, ”Optimal Positioning of Flying Relays for Wireless Networks: A LOS Map Approach”, in *Proc. IEEE Int. Conf. Commun.*, Paris, May 2017. [Slides][IEEE]
J. Chen, U. Yatnalli, and D. Gesbert, ”Learning Radio Maps for UAV-aided Wireless Networks: A Segmented Regression Approach”, in *Proc. IEEE Int. Conf. Commun.*, Paris, May 2017. [IEEE]
## Machine Learning for Network Localization and Communication## Source Localization without Energy-decay ModelsIn practice for many applications, the signal propagation characteristics are not fully known due to the complex environment, such as for an acoustic signal in water. We proposed a model to arrange a number of measurements taken at random locations into a sparse matrix, and proved that the matrix has a unimodal property that can be leveraged to localize the source. We developed a non-parametric localization method based on a smart integration of various modern techniques: sparse matrix completion, eigenspace analysis, singular vector perturbation analysis, and peak localization. The method demonstrates robustness as well as order-wise reduction in the number of sensors over the state-of-the-art baselines.
Our preliminary results can be found in [J13, C15, C16] [Slides] [Preprint]. ## Learning 3D Propagation MapIn the past, statistical predictions were made based on the UAV-to-user distance (as well as elevation angle, Preliminary results: J. Chen, O. Esrafilian, D. Gesbert, and U. Mitra, “Efficient algorithms for air-to-ground channel reconstruction in UAV-aided communications,” in *Proc. IEEE Global Telecomm. Conf.*, Dec. 2017, Wi-UAV workshop. [Preprint]
## Massive MIMO## Massive MIMO assisted by (device-to-device) D2D communications
Preliminary results can be found in: J. Chen, H. Yin, L. Cottatellucci, D. Gesbert, “Efficient feedback mechanisms for FDD massive MIMO under user-level cooperation,” *IEEE Trans. Wireless Commun.*, 2017, [Preprint] [Code].
J. Chen, H. Yin, L. Cottatellucci, D. Gesbert, “Dual-regularized Feedback and Precoding for D2D Assisted MIMO Systems,” *IEEE Trans. Wireless Commun. accepted*, 2017, to appear. [Preprint] [Code]
## Low complexity massive MIMO precoding algorithmsWe exploited both the J. Chen and V. K. N. Lau, “Multi-stream iterative SVD for massive MIMO communication systems under time varying channels,” in *Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing,*Florence, May 2014, pp. 3152–3156.
J. Chen and V. K. Lau, “Two-tier precoding for FDD multi-cell massive MIMO time-varying interference networks,” *IEEE J. Sel. Areas Commun.,*vol. 32, no. 6, pp. 1230–1238, Jun. 2014.
## Cooperative Localization from A Game Perspective
We found closed form conditions to determine whether a link should not be considered for cooperation; with that, a lot of coordination overheads among agents can be saved. In particular, we developed a novel link bargaining equilibrium concept, found sufficient conditions where the equilibrium is unique, and proposed iteration that can converge to the unique equilibrium. Such a novel game theoretic design and analysis framework may apply to other cooperation networks as well. [J9, J10, C8]. ## Algorithms Tracking Analysis and Enhancement via Compensation TechniquesMy thesis work focuses on the design and analysis of algorithms for network resource optimization under time-varying channels. The motivation is that network resource optimization usually requires online algorithms with explicit signaling among wireless nodes. Due to signaling latency, the algorithm cannot converge fast enough, and the time-varying nature of the wireless network yields moving optimal solutions. We developed a mathematical framework to analyze the algorithm tracking performance, and based on that, we developed a class of As one modern application, we studied multi-timescale stochastic algorithms. Motivationally, wireless communication systems are driven by multi-timescale time-varying parameters. Traditionally, people usually treated control variables individually in different layers separated by different timescales, instead of putting them all in the same optimization framework. By contrast, we tried to derive online multi-timescale algorithms from solving a unified optimization problem in terms of control variables in different timescales. The advantage is that the slow timescale control variable (and control) can make its update before the fast timescale variable converges. However, the challenge is to understand what the convergence error will be, and how much performance to gain. Our results have been published in [J7, J8, C3] With these general problems, we have attempted several challenging scenarios driven by various applications: *Parameter evolves as a Markov chain:*Network utility maximization (NUM) under finite state channel models [J1]
*Parameter evolves as a stable continuous process:*NUM problem [J2]
*Parameter (e.g., queue length) is coupled with control action:*Delay-aware resource allocation for ad hoc networks [J3]
*Parameter evolves in two-timescale:*Cross-layer resource optimization for heterogeneous networks [J7, J8, C3]
*Multiple local optima (non-convex problem):*Low complexity precoder tracking for massive MIMO systems [J5, C7]
The algorithm design and analysis framework is summarized in my thesis dissertation [Thesis]. |