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1. 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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2. RIS-Aided Interference Cancellation for Joint Device-to-Device and Cellular Communications

   https://doi.org/10.1109/ICCWorkshops59551.2024.10615548  

   Nguyen, Ly V; Swindlehurst, A Lee (June 2024, IEEE)

   Joint device-to-device (D2D) and cellular communication is a promising technology for enhancing the spectral efficiency of future wireless networks. However, the interference management problem is challenging since the operating devices and the cellular users share the same spectrum. The emerging reconfigurable intelligent surfaces (RIS) technology is a potentially ideal solution for this interference problem since RISs can shape the wireless channel in desired ways. This paper considers an RIS-aided joint D2D and cellular communication system where the RIS is exploited to cancel interference to the D2D links and maximize the minimum signal-to-interference plus noise (SINR) of the device pairs and cellular users. First, we adopt a popular alternating optimization (AO) approach to solve the minimum SINR maximization problem. Then, we propose an interference cancellation (IC)-based approach whose complexity is much lower than that of the AO algorithm. We derive a representation for the RIS phase shift vector which cancels the interference to the D2D links. Based on this representation, the RIS phase shift optimization problem is transformed into an effective D2D channel optimization. We show that the AO approach can converge faster and can even give better performance when it is initialized by the proposed IC solution. We also show that for the case of a single D2D pair, the proposed IC approach can be implemented with limited feedback from the single receive device. 

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3. A Distributed Real-time Scheduling System for Industrial Wireless Networks

   https://doi.org/10.1145/3464429  

   Modekurthy, Venkata P.; Saifullah, Abusayeed; Madria, Sanjay (July 2021, ACM Transactions on Embedded Computing Systems)

   null (Ed.)

   The concept of Industry 4.0 introduces the unification of industrial Internet-of-Things (IoT), cyber physical systems, and data-driven business modeling to improve production efficiency of the factories. To ensure high production efficiency, Industry 4.0 requires industrial IoT to be adaptable, scalable, real-time, and reliable. Recent successful industrial wireless standards such as WirelessHART appeared as a feasible approach for such industrial IoT. For reliable and real-time communication in highly unreliable environments, they adopt a high degree of redundancy. While a high degree of redundancy is crucial to real-time control, it causes a huge waste of energy, bandwidth, and time under a centralized approach and are therefore less suitable for scalability and handling network dynamics. To address these challenges, we propose DistributedHART—a distributed real-time scheduling system for WirelessHART networks. The essence of our approach is to adopt local (node-level) scheduling through a time window allocation among the nodes that allows each node to schedule its transmissions using a real-time scheduling policy locally and online. DistributedHART obviates the need of creating and disseminating a central global schedule in our approach, thereby significantly reducing resource usage and enhancing the scalability. To our knowledge, it is the first distributed real-time multi-channel scheduler for WirelessHART. We have implemented DistributedHART and experimented on a 130-node testbed. Our testbed experiments as well as simulations show at least 85% less energy consumption in DistributedHART compared to existing centralized approach while ensuring similar schedulability. 

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4. Self-Interference Cancellation and Beamforming in Repeater-assisted Full-duplex Vehicular Communication

   https://doi.org/10.1109/VTC2020-Spring48590.2020.9128558  

   Ibrahim, Ahmed S. (May 2020, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring))

   Self-driving vehicles will need low-latency and high-capacity vehicular communication for acquiring wider view of their surroundings. Such vehicle-to-vehicle communication can be indirectly supported in some circumstances (e.g., if blocked) through adjacent road side units (RSUs). RSUs will be acting as full-duplex repeaters among the vehicles to ensure low latency and high data rate. However, full-duplex repeaters result in self-interference phenomenon which can degrade the reliability of the communication links. In this work, we aim to enhance the reliability of full-duplex repeaters by canceling out the self-interference impact, and applying a beamforming scheme that is matched to the source-destination composite channel. We show that the proposed self-interference cancellation and beamforming (SICAB) algorithm significantly reduces the error rate for low-isolated repeaters. Finally, we illustrate the impact of the repeater isolation capability on the performance of the proposed SICAB algorithm. 

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5. Deep Reinforcement Learning for Interference Management in UAV-Based 3D Networks: Potentials and Challenges

   https://doi.org/10.1109/MCOM.001.2200973  

   Vaezi, Mojtaba; Lin, Xingqin; Zhang, Hongliang; Saad, Walid; Poor, H Vincent (February 2024, IEEE Communications Magazine)

   Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This challenge is growing as cellular networks become three-dimensional with the adoption of unmanned aerial vehicles (UAVs). This is because the strength and number of interference links rapidly increase due to the line-of-sight channels in UAV communications. Existing interference management solutions require each transmitter to know the channel information of interfering signals, rendering them impractical due to excessive signaling overhead. In this article, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming. In particular, we show that interference can still be effectively mitigated even without knowing its channel information. We then discuss novel approaches to scale the algorithms with linear/sublinear complexity and decentralize them using multi-agent reinforcement learning. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs. 

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