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1. Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency

   Manfredi, Victoria; Wolfe, Alicia P.; Zhang, Xiaolan; Wang, Bing (December 2022, Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th Conference on Neural Information Processing Systems (NeurIPS))

   Mobile wireless networks present several challenges for any learning system, due to uncertain and variable device movement, a decentralized network architecture, and constraints on network resources. In this work, we use deep reinforcement learning (DRL) to learn a scalable and generalizable forwarding strategy for such networks. We make the following contributions: i) we use hierarchical RL to design DRL packet agents rather than device agents, to capture the packet forwarding decisions that are made over time and improve training efficiency; ii) we use relational features to ensure generalizability of the learned forwarding strategy to a wide range of network dynamics and enable offline training; and iii) we incorporate both forwarding goals and network resource considerations into packet decision-making by designing a weighted DRL reward function. Our results show that our DRL agent often achieves a similar delay per packet delivered as the optimal forwarding strategy and outperforms all other strategies including state-of-the-art strategies, even on scenarios on which the DRL agent was not trained. 

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2. Distributed Scheduling Algorithms for Optimizing Information Freshness in Wireless Networks

   Talak, Rajat; Karaman, Sertac; Modiano, Eytan (July 2018, SPAWC)

   Age of Information (AoI), measures the time elapsed since the last received information packet was generated at the source. We consider the problem of AoI minimization for singlehop flows in a wireless network, under pairwise interference constraints and time varying channel. We consider simple, yet broad, class of distributed scheduling policies, in which a transmission is attempted over each link with a certain attempt probability. We obtain an interesting relation between the optimal attempt probability and the optimal AoI of the link, and its neighboring links. We then show that the optimal attempt probabilities can be computed by solving a convex optimization problem, which can be done distributively. 

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3. Analysis of Joint Scheduling and Power Control for Predictable URLLC in Industrial Wireless Networks

   Wang, Ling; Zhang, Hongwei (January 2019, IEEE International Conference on Industrial Internet (ICII))

   Wireless networks are being applied in various industrial sectors, and they are posed to support mission-critical industrial IoT applications which require ultra-reliable, low-latency communications (URLLC). Ensuring predictable per-packet communication reliability is a basis of predictable URLLC, and scheduling and power control are two basic enablers. Scheduling and power control, however, are subject to challenges such as harsh environments, dynamic channels, and distributed network settings in industrial IoT. Existing solutions are mostly based on heuristic algorithms or asymptotic analysis of network performance, and there lack field-deployable algorithms for ensuring predictable per-packet reliability. Towards addressing the gap, we examine the cross-layer design of joint scheduling and power control and analyze the associated challenges. We introduce the Perron–Frobenius theorem to demonstrate that scheduling is a must for ensuring predictable communication reliability, and by investigating characteristics of interference matrices, we show that scheduling with close-by links silent effectively constructs a set of links whose required reliability is feasible with proper transmission power control. Given that scheduling alone is unable to ensure predictable communication reliability while ensuring high throughput and addressing fast-varying channel dynamics, we demonstrate how power control can help improve both the reliability at each time instant and throughput in the long-term. Based on the analysis, we propose a candidate framework of joint scheduling and power control, and we demonstrate how this framework behaves in guaranteeing per-packet communication reliability in the presence of wireless channel dynamics of different time scales. Collectively, these findings provide insight into the cross-layer design of joint scheduling and power control for ensuring predictable per-packet reliability in the presence of wireless network dynamics and uncertainties. 

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4. Optimal Joint Offloading and Wireless Scheduling for Parallel Computing with Deadlines

   Qin, Xudong; Xu, Weijian; Li, Bin (June 2019, International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt))

   In this paper, we consider the problem of joint offloading and wireless scheduling design for parallel computing applications with hard deadlines. This is motivated by the rapid growth of compute-intensive mobile parallel computing applications (e.g., real-time video analysis, language translation) that require to be processed within a hard deadline. While there are many works on joint computing and communication algorithm design, most of them focused on the minimization of average computing time and may not be applicable for mobile applications with hard deadlines. In this work, we explicitly take hard deadlines for computing tasks into account and develop a joint offloading and scheduling algorithm based on the stochastic network optimization framework. The proposed algorithm is shown to achieve average energy consumption arbitrarily close to the optimal one. However, this algorithm involves a strong coupling between offloading and scheduling decisions, which yields significant challenges on its implementation. Towards this end, we first successfully decouple the offloading and scheduling decisions in the case with one time slot deadline by exploring the intrinsic structure of the proposed algorithm. Based on this, we further implement the proposed algorithm in the general setups. Simulations are provided to corroborate our findings. 

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5. Study on Characteristics of Metric-aware Multipath Algorithms in Real Heterogeneous Networks

   https://doi.org/10.1109/GLOBECOM46510.2021.9685343  

   Zhang, Xiaoya; Dong, Ping; Du, Xiaojiang; Zhang, Yuyang; Zhang, Hongke; Guizan, Mohsen (July 2021, 2021 IEEE Global Communications Conference (GLOBECOM))

   Multipath transmission is considered one of the promising solutions to improve wireless resource utilization where there are many kinds of heterogeneous networks around. Most scheduling algorithms rely on real-time network metrics, including delay, packet loss, and arrival rates, and achieve satisfying results in simulation or wired environments. However, the implicit premise of a scheduling algorithm may conflict with the characteristics of real heterogeneous wireless networks, which has been ignored before. This paper analyzes the real network metrics of three Chinese heterogeneous wireless networks under different transmission rates. To make the results more convincing, we conduct experiments in various scenarios, including different locations, different times of the day, different numbers of users, and different motion speeds. Further, we verify the suitability of a typical delay-aware multipath scheduling algorithm, Lowest Round Trip Time, in heterogeneous networks based on the actual data measured above. Finally, we conclude the characteristics of heterogeneous wireless networks, which need to be considered in a well-designed multipath scheduling algorithm. 

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