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1. Optimal Load-Balancing for High-Density Wireless Networks with Flow-Level Dynamics

   https://doi.org/10.1145/3209582.3225205  

   Li, Bin; Kong, Xiangqi; Wang, Lei (June 2018, Mobihoc '18 Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing)

   We consider the load-balancing design for forwarding incoming flows to access points (APs) in high-density wireless networks with both channel fading and flow-level dynamics, where each incoming flow has a certain amount of service demand and leaves the system once its service request is complete. The efficient load-balancing design is strongly needed for supporting high-quality wireless connections in high-density areas. In this work, we propose a Joint Load-Balancing and Scheduling (JLBS) Algorithm that always forwards the incoming flows to the AP with the smallest workload in the presence of flow-level dynamics and each AP always serves the flow with the best channel quality. Our analysis reveals that our proposed JLBS Algorithm not only achieves maximum system throughput, but also minimizes the total system workload in the heavy-traffic regime. Moreover, we observe from both our theoretical and simulation results that the mean total workload performance under the proposed JLBS Algorithm does not degrade as the number of APs increases, which is strongly desirable in high-density wireless networks. 

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2. Optimal Offloading for Dynamic Compute-Intensive Applications in Wireless Networks

   https://doi.org/10.1109/GLOBECOM38437.2019.9013327  

   Li, Bin (December 2019, 2019 IEEE Global Communications Conference (GLOBECOM))

   With the rapid growth of wireless compute-intensive services (such as image recognition, real-time language translation, or other artificial intelligence applications), efficient wireless algorithm design should not only address when and which users should transmit at each time instance (referred to as wireless scheduling) but also determine where the computation should be executed (referred to as offloading decision) with the goal of minimizing both computing latency and energy consumption. Despite the presence of a variety of earlier works on the efficient offloading design in wireless networks, to the best of our knowledge, there does not exist a work on the realistic user- level dynamic model, where each incoming user demands a heavy computation and leaves the system once its computing request is completed. To this end, we formulate a problem of an optimal offloading design in the presence of dynamic compute-intensive applications in wireless networks. Then, we show that there exists a fundamental logarithmic energy- workload tradeoff for any feasible offloading algorithm, and develop an optimal threshold-based offloading algorithm that achieves this fundamental logarithmic bound. 

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3. Optimal Offloading for Dynamic Compute-Intensive Applications in Wireless Networks

   Li, Bin (December 2019, IEEE Conference on Global Communications Conference (GLOBECOM))

   With the rapid growth of wireless compute-intensive services (such as image recognition, real-time language translation, or other artificial intelligence applications), efficient wireless algorithm design should not only address when and which users should transmit at each time instance (referred to as wireless scheduling) but also determine where the computation should be executed (referred to as offloading decision) with the goal of minimizing both computing latency and energy consumption. Despite the presence of a variety of earlier works on the efficient offloading design in wireless networks, to the best of our knowledge, there does not exist a work on the realistic user-level dynamic model, where each incoming user demands a heavy computation and leaves the system once its computing request is completed. To this end, we formulate a problem of an optimal offloading design in the presence of dynamic compute-intensive applications in wireless networks. Then, we show that there exists a fundamental logarithmic energy-workload tradeoff for any feasible offloading algorithm, and develop an optimal threshold-based offloading algorithm that achieves this fundamental logarithmic bound. 

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4. On the Credibility of Information Flows in Real-Time Wireless Networks

   Guo, Daojing; Hou, I-Hong (January 2019, 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WIOPT 2019)

   This paper considers a wireless network where multiple flows are delivering status updates about their respective information sources. An end user aims to make accurate real-time estimations about the status of each information source using its received packets. As the accuracy of estimation is most impacted by events in the recent past, we propose to measure the credibility of an information flow by the number of timely deliveries in a window of the recent past, and say that a flow suffers from a loss-of-credibility (LoC) if this number is insufficient for the end user to make an accurate estimation. We then study the problem of minimizing the system-wide LoC in wireless networks where each flow has different requirement and link quality. We show that the problem of minimizing the system-wide LoC requires the control of temporal variance of timely deliveries for each flow. This feature makes our problem significantly different from other optimization problems that only involves the average of control variables. Surprisingly, we show that there exists a simple online scheduling algorithm that is near-optimal. Simulation results show that our proposed algorithm is significantly better than other state-of-the-art policies. 

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5. Power Control in Internet of Drones by Deep Reinforcement Learning

   https://doi.org/10.1109/ICC40277.2020.9148749  

   Yao, Jingjing; Ansari, Nirwan (June 2020, 2020 IEEE International Conference on Communications (ICC 2020))

   Internet of Drones (IoD) employs drones as the internet of things (IoT) devices to provision applications such as traffic surveillance and object tracking. Data collection service is a typical application where multiple drones are deployed to collect information from the ground and send them to the IoT gateway for further processing. The performance of IoD networks is constrained by drones’ battery capacities, and hence we utilize both energy harvesting technologies and power control to address this limitation. Specifically, we optimize drones’ wireless transmission power at each time epoch in energy harvesting aided time-varying IoD networks for the data collection service with the objective to minimize the average system energy cost. We then formulate a Markov Decision Process (MDP) model to characterize the power control process in dynamic IoD networks, which is then solved by our proposed model-free deep actor-critic reinforcement learning algorithm. The performance of our algorithm is demonstrated via extensive simulations. 

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