LiNKS

Welcome to LiNKS!


Recent News

  • May 2022: Visiting PhD student Seok-Hyun Yoon is joining us from Prof. Young-Chai Ko’s research group in Korea University. Welcome Seok-Hyun.
  • April 2022: Dr. Byungju Lim is our postdoctoral scholar joining LiNKS from Korea University. Welcome Dr. Lim!
  • Sep. 2021: Prof. Vu received a grant jointly with Prof. Afsar from the National Science Foundation to study the coexistence of mobile transceivers with passive satellite receivers in millimeter wave spectra.
  • Spring 2021: Prof. Vu organizes a Tripods Spring seminar series on machine learning and data science in wireless communications, with potential applications in 5G and beyond-5G systems, IoT networks, and personal wireless devices.
  • Jan. 2021: Congratulations to Alireza for successfully defending his PhD thesis. Alireza will be joining Apple soon. Well done, Alireza!
  • Sep. 2020: Our group is a member of the new Tufts Tripods Institute focusing on interdisciplinary efforts at Tufts to advance the understanding of foundations of data science.


Recent Publications

  • May 2022: Our paper “Reinforcement Learning for User Association and Handover in mmWave-enabled Networks” has been accepted for publication in the IEEE Transactions on Wireless Communications. In the paper we designed new, efficient multi-arm bandit reinforcement learning algorithms for association and handover in a mobile wireless network, achieving higher network transmission sum rate than conventional method at a fraction of the handover rate, all the while maintaining the load balancing at each BS.
  • Our work on Energy-efficient Joint Wireless Charging and Computation Offloading In MEC Systems to minimize the total energy consumption while performing computation and charging the largest feasible amount of energy to the wireless end user will appear in an upcoming special issue in the IEEE Journal of Selected Topics in Signal Processing.
  • Our paper on Maximum Wireless Charging and Partial Offloading in Massive-MIMO Enabled Multi-Access Edge Computing Systems will be published in an upcoming issue of the IEEE Transactions on Wireless Communications.
  • Our novel and fully distributed early-acceptance matching game for user association in beyond-5G networks will be published in an upcoming issue of the IEEE Transactions on Wireless Communications.
  • Our latest results on Energy-efficient Computation Offloading to MEC edge networks using Data partitioning are published in the October 2020 issue of the IEEE Transactions on Wireless Communications.
  • Our initial results on using reinforcement learning for load-balancing user association in millimeter-wave networks for 5G and beyond systems are published at the IEEE GLOBECOM 2020 in a paper entitled “Multi-Armed Bandit Load Balancing User Association in 5G Cellular HetNets”.


Current Research

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Wireless communication networks have evolved towards denser deployments with an increasingly large number of connected devices. Future generation networks including 5G and beyond are therefore expected to offer services at high data rate and ultra-low latency. To address this challenge, Multi-access Edge Computing (MEC) is a promising technology which can provide distributed and decentralized services in close proximity to mobile subscribers at low latency, and high rate access. In this project we work on a resource allocation problem for system level energy minimization in a network where multiple Access Points (APs) with integrated edge servers are equipped with massive MIMO antenna arrays. The MEC-APs simultaneously accommodate multiple co-channel users and provide computation offloading and wireless charging to ground users.

Relay-aided cooperative communication techniques represent a promising technology that improves performance in poor coverage areas by enabling ubiquitous coverage even for users in the most unfavorable channel conditions. In this project, we exploit stochastic geometry to study the potential of using user-assisted relaying in future cellular networks, propose geometric based cooperation policies, and study the effect of the additional transmission of the relaying nodes on interference and the performance of user-assisted relaying when deployed system-wide in a cellular network.

The current trend of increasing heterogeneity in communication networks brings a variety of communication needs and requirements, which often lead to different priorities. This is especially true in IoT applications which can generate messages with different degrees of importance. In this Project we introduce a priority-based coding scheme in the finite blocklength regime and apply the scheme to two different channels: the general discrete memoryless channel (DMC) and the AWGN channel. The scheme simultaneously encodes two messages, one with high and one with low priority, both requiring finite delay. The code structure allows the transmission of the high priority message with higher reliability and shorter decoding delay. We further derive tight and computationally efficient analytical upper bounds on the error probability in both the DMC and AWGN channel.

With the rapid evolution of wireless networks, energy efficiency is now deemed as a figure of merit for the design of next generation communication systems. We can envision future communication networks to employ relays capable of providing cooperation in terms of both information and energy. Far-field, radio frequency (RF) energy harvesting has recently garnered significant interest for communication systems with the prospect of simultaneous information and power transfer. In this work, we consider a MIMO communication system assisted by a full-duplex relay, where the relay is capable of harvesting energy. To focus on the benefits of MIMO and full duplex features, we consider the scenario in which the relay is self-sustained, that is, it has no power source of its own and hence relies solely on energy harvesting for its operations.

Fifth generation (5G) and beyond wireless systems aim to provide a minimum of 1 Gb/s data rate anywhere with up to 5 Gb/s for high mobility users and 50 Gb/s data rates for pedestrians by employing dense network of base stations and mobile users at mmWave spectrum. Even under beamforming, the high BS and user densities can drive cellular networks to be more interference rather than noise limited. While large adaptive arrays with narrow beams can boost the received signal power and hence reduce the impact of out-of-cell interference, this interference remains an important performance-limiting factor in dense mmWave networks. Modeling and characterization of wireless interference under these scenarios is essential for cellular system analysis and design.

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