Multi-type Fair Resource Allocation for Distributed Multi-Robot Systems

Fair resource allocation is essential to ensure that all resource requesters acquire adequate resources and accomplish tasks. We propose solutions to the fairness problem in multi-type resource allocation for multi-robot systems that have multiple resource requesters. We apply the dominant resource fairness (DRF) principle in our solutions to two different systems: single-tasking robots with multi-robot tasks (STR-MRT) and multi-tasking robots with single-robot tasks (MTR-SRT). In STR-MRT, each robot can perform only one task at a time, tasks are divisible, and accomplishing each task requires one or more robots. In MTR-SRT, each robot can perform multiple tasks at a time, tasks are not divisible, and accomplishing each task requires only one robot. We present centralized solutions to the fairness problem in STR-MRT. Meanwhile, we model the decentralized resource allocation in STR-MRT as a coordination game between the robots. Each robot subgroup is formed by robots that strategically select the same resource requester. For a requester associated with a specific subgroup, a consensus-based team formation algorithm further chooses the minimal set of robots to accomplish the task. We leverage the Deep Q-learning Network (DQN) to support requester selection. The results suggest that the DQN outperforms the commonly used Q-learning. Finally, we propose two decentralized solutions to promote fair resource allocation in MTR-SRT, as a centralized solution already exists. We first propose a task-forwarding solution in which the robots need to negotiate the placement of each task. In our second solution, each robot first selects resource requesters and then independently allocates resources to tasks that arrive from the selected requesters. The resource-requester selection phase of the latter solution models a coordination game that is solved by reinforcement learning. The experimental results suggest that both approaches outperform their baselines

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure