ECE Researchers win Best Paper Award at the 2021 IEEE/IFIP Wireless On-demand Network systems and Services Conference (WONS)

Associate Professor Dario Pompili and ECE graduate students Ayman Younis and Brian Qi have won the Best Paper Award at the 2021IEEE/IFIP Wireless On-demand Network systems and Services Conference (WONS), which was held remotely on 9-11th March 2021, for their paper titled “QLRan: Latency-Quality Tradeoffs and Task Offloading in Multi-node Next Generation RANs”.
Wireless on-demand network systems and services have become pivotal in shaping our future networked world. Starting as a niche application over Wi-Fi, they can now be found in mainstream technologies like Bluetooth LE, LTE Direct and Wireless LANs, and have become the cornerstone of upcoming networking paradigms including mesh and sensor networks, cloud networks, vehicular networks, disruption tolerant and opportunistic networks, and in-body networks. The challenges of this exciting research field are numerous. Examples include how to make smart use of these novel technologies when multiple technologies or a mix of permanent services and on-demand networking opportunities are available to a network node, how to provide robust services in highly dynamic environments, how to efficiently employ and operate heavily resource-constrained devices, and how to develop robust and lightweight algorithms for self-organization and adaptation. WONS, now in its 16th edition, is a high-quality forum to address these challenges. 
The winners were presented with an award certificate and a USD 600 prize. The abstract of the award winning paper is below.
Abstract: Next-Generation Radio Access Network (NG-RAN) is an emerging paradigm that provides flexible distribution of cloud computing and radio capabilities at the edge of the wireless Radio Access Points (RAPs). Computation at the edge bridges the gap for roaming end users, enabling access to rich services and applications. In this paper, we propose a multi-edge node task offloading system, i.e., QLRan, a novel optimization solution for latency and quality tradeoff task allocation in NG-RANs. Considering constraints on service latency, quality loss, and edge capacity, the problem of joint task offloading, latency, and Quality Loss of Result (QLR) is formulated in order to minimize the User Equipment (UEs) task offloading utility, which is measured by a weighted sum of reductions in task completion time and QLR cost. The QLRan optimization problem is proved as a Mixed Integer Nonlinear Program (MINLP) problem, which is a NP-hard problem. To efficiently solve the QLRan optimization problem, we utilize Linear Programming (LP)-based approach that can be later solved by using convex optimization techniques. Additionally, a programmable NG-RAN testbed is presented where the Central Unit (CU), Distributed Unit (DU), and UE are virtualized using the OpenAirInterface (OAI) software platform to characterize the performance in terms of data input, memory usage, and average processing time with respect to QLR levels. Simulation results show that our algorithm performs significantly improves the network latency over different configurations.
Congratulations to Dario, Ayman, and Brian on this recognition!