Transmission Scheduling Research Articles

Link Scheduling Using Graph Neural Networks

Efficient scheduling of transmissions is a key problem in wireless networks. The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted independent set (MWIS) problem, which is known to be NP-hard. In practical schedulers, centralized and distributed greedy heuristics are commonly used to approximately solve the MWIS problem. However, most of these greedy heuristics ignore important topological information of the wireless network. To overcome this limitation, we propose fast heuristics based on graph convolutional networks (GCNs) that can be implemented in centralized and distributed manners. Our centralized heuristic is based on tree search guided by a GCN and 1-step rollout. In our distributed MWIS solver, a GCN generates topology-aware node embeddings that are combined with per-link utilities before invoking a distributed greedy solver. Moreover, a novel reinforcement learning scheme is developed to train the GCN in a non-differentiable pipeline. Test results on medium-sized wireless networks show that our centralized heuristic can reach a near-optimal solution quickly, and our distributed heuristic based on a shallow GCN can reduce by nearly half the suboptimality gap of the distributed greedy solver with minimal increase in complexity. The proposed schedulers also exhibit good generalizability across graph and weight distributions.

Data Placement and Transmission Scheduling for coded multicast in mobile edge networks

Edge caching is a promising strategy to reduce the traffic load in mobile core networks, by caching popular contents at the edge of mobile networks, e.g., base stations (BSs). For the BSs to transmit data to user equipment (UEs), coded multicast can be used to improve the transmission efficiency. Moreover, in future densely deployed networks, the coverage areas of adjacent BSs are overlapped. Therefore, these cache-aided BSs can collaboratively carry out coded multicast to improve the performance of data transmission. In this paper, we consider the Data Placement and Transmission Scheduling (DPTS) problem for cached-aided coded multicast in mobile edge networks. Our objective is to minimize the total cost of data download from the data source to BSs and data transmission from the BSs to UEs. The DPTS problem is proved to be NP-hard and we first propose an Iterative Relaxation Linear Programming (IRLP) algorithm to solve it. Since the complexity of IRLP algorithm is high, we also propose another two low-complexity algorithms to solve the DPTS problem. Performance evaluation by simulation shows that the proposed algorithms can achieve a substantial reduction in total cost of data download and transmission, compared with the existing methods.

Deep Reinforcement Learning for Joint Trajectory Planning, Transmission Scheduling, and Access Control in UAV-Assisted Wireless Sensor Networks.

Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance.

Cooperative Self-Scheduling Secure Routing Protocol for Efficient Communication in MANET

In wireless transmission, a Mobile Ad-hoc Network (MANET) contains many mobile nodes that can communicate without needing base stations. Due to the highly dynamic nature of wireless, MANETs face several issues, like malicious nodes making packet loss, high energy consumption, and security. Key challenges include efficient clustering and routing with optimal energy efficiency for Quality of Service (QoS) performance. To combat these issues, this novel presents Cooperative Self-Scheduling Secure Routing Protocol (CoS3RP) for efficient scheduling for proficient packet transmission in MANET. Initially, we used Elite Sparrow Search Algorithm (ESSA) for identifies the Cluster Head (CH) and form clusters. The Multipath Optimal Distance Selection (MODS) technique is used to find the multiple routes for data transmission. Afterward, the proposed CoS3RP transmits the packets based on each node authentication. The proposed method for evaluating and selecting efficient routing and data transfer paths is implemented using the Network simulator (NS2) tool, and the results are compared with other methods. Furthermore, the proposed well performs in routing performance, security, latency and throughput.

DR-ALOHA-Q: A Q-Learning-Based Adaptive MAC Protocol for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are challenged by the dynamic nature of the underwater environment, large propagation delays, and global positioning system (GPS) signal unavailability, which make traditional medium access control (MAC) protocols less effective. These factors limit the channel utilization and performance of UASNs, making it difficult to achieve high data rates and handle congestion. To address these challenges, we propose a reinforcement learning (RL) MAC protocol that supports asynchronous network operation and leverages large propagation delays to improve the network throughput.he protocol is based on framed ALOHA and enables nodes to learn an optimal transmission strategy in a fully distributed manner without requiring detailed information about the external environment. The transmission strategy of sensor nodes is defined as a combination of time-slot and transmission-offset selection. By relying on the concept of learning through interaction with the environment, the proposed protocol enhances network resilience and adaptability. In both static and mobile network scenarios, it has been compared with the state-of-the-art framed ALOHA for the underwater environment (UW-ALOHA-Q), carrier-sensing ALOHA (CS-ALOHA), and delay-aware opportunistic transmission scheduling (DOTS) protocols. The simulation results show that the proposed solution leads to significant channel utilization gains, ranging from 13% to 106% in static network scenarios and from 23% to 126% in mobile network scenarios.oreover, using a more efficient learning strategy, it significantly reduces convergence time compared to UW-ALOHA-Q in larger networks, despite the increased action space.

Joint Optimization of Relay Selection and Transmission Scheduling for UAV-Aided mmWave Vehicular Networks

To deal with the explosive growth of mobile traffic, millimeter-wave (mmWave) communications with abundant bandwidth resources have been applied to vehicular networks. As mmWave signal is sensitive to blockage, we introduce the unmanned aerial vehicle (UAV)-aided two-way relaying system for vehicular connection enhancement and coverage expansion. How to improve transmission efficiency and to reduce latency time in such a dynamic scenario is a challenging problem. In this paper, we formulate the joint optimization problem of relay selection and transmission scheduling, aiming to reduce transmission time while meeting the throughput requirements. To solve this problem, two schemes are proposed. The first one is the random relay selection with concurrent scheduling (RCS), a low-complexity algorithm implemented in two steps. The second one is the joint relay selection with dynamic scheduling (JRDS), which fully avoids relay contentions and exploits potential concurrent ability, to obtain further performance enhancement over RCS. Through extensive simulations under different environments with various flow numbers and vehicle speeds, we demonstrate that both RCS and JRDS schemes outperform the existing schemes significantly in terms of transmission time and network throughput. We also analyze the impact of threshold selection on achievable performance.

A Novel Deadline-/Interference-Aware Cooperative Data Transmission Scheduling Scheme for Optimizing AoI in Wireless Networks

A Novel Deadline-/Interference-Aware Cooperative Data Transmission Scheduling Scheme for Optimizing AoI in Wireless Networks

Cooperative Data Collection With Multiple UAVs for Information Freshness in the Internet of Things

Maintaining the freshness of information in the Internet of Things (IoT) is a critical yet challenging problem. In this paper, we study cooperative data collection using multiple Unmanned Aerial Vehicles (UAVs) with the objective of minimizing the total average Age of Information (AoI). We consider various constraints of the UAVs, including kinematic, energy, trajectory, and collision avoidance, in order to optimize the data collection process. Specifically, each UAV, which has limited on-board energy, takes off from its initial location and flies over sensor nodes to collect update packets in cooperation with the other UAVs. The UAVs must land at their final destinations with non-negative residual energy after the specified time duration to ensure they have enough energy to complete their missions. It is crucial to design the trajectories of the UAVs and the transmission scheduling of the sensor nodes to enhance information freshness. We model the multi-UAV data collection problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), as each UAV is unaware of the dynamics of the environment and can only observe a part of the sensors. To address the challenges of this problem, we propose a multi-agent Deep Reinforcement Learning (DRL)-based algorithm with centralized learning and decentralized execution. In addition to the reward shaping, we use action masks to filter out invalid actions and ensure that the constraints are met. Simulation results demonstrate that the proposed algorithms can significantly reduce the total average AoI compared to the baseline algorithms, and the use of the action mask method can improve the convergence speed of the proposed algorithm.

Integration of Heuristics-Based Energy Optimal Clustering Topology, Routing and Transmission Scheduling for Enhancing Lifetime in IoT Networks

Integration of Heuristics-Based Energy Optimal Clustering Topology, Routing and Transmission Scheduling for Enhancing Lifetime in IoT Networks

Application study of ant colony algorithm for network data transmission path scheduling optimization

Abstract With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within ±2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.

Adaptive Scheduling MAC Protocol in Underwater Acoustic Broadcast Communications for AUV Formation

With the rapid development of autonomous underwater vehicles (AUVs) and the continuous improvement of marine exploration requirements, AUV formation has emerged as a promising technique for performing underwater tasks with greater flexibility, adaptability, and scalability. To achieve AUV formation control, underwater acoustic communication networks (UACNs) are widely used to support information exchange among AUVs. Considering broadcast communications in fully connected mobile UACNs, existing medium access control (MAC) protocols face severe packet collision risk or low reuse efficiency. To improve the timeliness and reliability of the information exchange among AUVs, we develop an efficient scheduling-based adaptive broadcasting MAC (AB-MAC) protocol. The AB-MAC protocol is incompletely centralized, which can be reflected in two aspects. First, to ensure the consistency of the transmission schedule in the network, the AB-MAC protocol selects a control node for each frame to schedule the transmission of all nodes according to the updated network topology. The slot lengths and transmission sequences are changeable in each frame to avoid packet collisions and minimize frame lengths. Second, to further improve the efficiency and robustness of the protocol, all nodes adaptively adjust their transmission time by integrating the information contained in their previously received control packets and ordinary packets. The transmission scheduling problem is formulated as a combinatorial optimization problem, which can be solved by our proposed improved Genetic Algorithm. Numerical results demonstrate that the AB-MAC protocol not only performs well in terms of network throughput and average update interval but also has a high level of robustness even in low-quality underwater acoustic channels.

Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

SummaryFor the rising number of vehicles, and the advancement of communication and computation technologies, the perception of Internet of Vehicles (IoV) is introduced. It is utilized to capture vehicle‐based information that includes road description, road congestion, vehicle speed, and location. However, this information is important, and it showed more benefits in different ways, like route selection and message dissemination. IoV is the self‐structured network composed with vehicles lies in the road and the road side units (RSUs). It offers Infrastructure‐to‐Vehicle (I2V), as well as Vehicle‐to‐Vehicle (V2V) data transmission mechanism for transmitting service messages. To reliably broadcast the service information to the intended recipient in the IoV network still faced issues. Hence, an efficient service message transmission protocol is developed using the proposed fractional mayfly optimization algorithm (FMA) for selecting the relay vehicle and cooperative vehicle for transmitting service messages to the destination vehicle from RSU through the process of I2V and V2V scheduling. The RSU selects the relay vehicle for every service message using the proposed algorithm and allocates the cooperative vehicle by RSU for scheduling V2V transmission. The simulation results showed that the proposed scheduling method obtains the best channel quality indicator (CQI), delay, distance, packet delivery ratio (PDR), and throughput value of 0.92 for 150 vehicles and 0.005891, 6.060731, 83.45%, and 171.50 Mbps for 100 vehicles.

An open-source implementation and validation of 5G NR configured grant for URLLC in ns-3 5G LENA: A scheduling case study in industry 4.0 scenarios

Factories are undergoing a digital transformation towards more cost-efficient, zero-defect manufacturing. The digitalized factories require communication networks capable of satisfying their strict latency and reliability demands. 5G and beyond networks are being designed to efficiently support services demanding Ultra-Reliable and Low Latency Communications (URLLC). At the MAC level, the use of dynamic scheduling for uplink transmissions entails a non-negligible latency introduced by the signaling messages exchanged to request and inform about the radio resources allocated for each packet transmission. To reduce the transmission latency, 5G defines Configured Grant (CG) for UL transmissions that pre-allocates radio resources to the User Equipments (UEs) and eliminates the need for requesting resources for each transmission. In this context, the availability of 5G NR simulation tools that accurately implement all 5G NR functionalities, and in particular, the technological enablers introduced in 5G NR to support URLLC, is key to analyze the capability of 5G and beyond networks to support time-critical services and research on new solutions. The availability and access to such tools are limited, and to the best of the authors' knowledge, there are currently no open-source 5G NR simulators that implement configured grant in 5G NR. To overcome this issue, this work presents the first implementation of configured grant in an open-source 5G NR simulator. In particular, configured grant has been implemented in the ns-3 5G-LENA system-level simulator, and it is publicly available. To accurately model the flexibility of 5G NR, we have also improved the implementation of Orthogonal Frequency Division Multiple Access (OFDMA) access mode in 5G-LENA according to 5G NR. To validate the implementation of CG and analyze the capability of 5G NR to support time-critical services, we analyze the latency performance that can be achieved using CG with different scheduling policies in Industry 4.0 scenarios. The results show that the latency values achieved with CG in 5G-LENA match with those reported by previous analytical studies. In addition, this study shows the importance of efficiently using radio resources to reduce the latency experienced and meet the requirements of critical services.

ADAS: Adaptive Delay-Aligned Scheduling for Multipath Transmission in Heterogeneous Wireless Networks

ADAS: Adaptive Delay-Aligned Scheduling for Multipath Transmission in Heterogeneous Wireless Networks

Latency Aware Transmission Scheduling for Steerable Free Space Optics

Free space optics (FSO) has been demonstrated as a secure and high-speed solution for long distance and line-of-sight wireless communications. Applying FSO as fronthaul/backhaul communications between base stations (BSs) and the gateway can significantly increase the fronthaul/backhaul link capacity. Traditionally, the gateway has to be equipped with multiple FSO transceivers, each of which is used to communicate with a BS by establishing a dedicated FSO. In this paper, we propose to use a steerable FSO system, where the gateway is equipped with a steerable FSO transceiver to communicate with multiple FSO transceivers at different BSs in a time division multiplexing manner. Applying the steerable FSO system can reduce the number of FSO transceivers at the gateway, and thus reduce the capital cost of implementing an FSO based fronthaul/backhaul network. We formulate the transmission scheduling problem in the steerable FSO system to optimize the active time for each FSO link associated with the steerable FSO transceiver such that the overall delay of transmitting a packet from geo-distributed BSs to the steerable FSO transceiver at the gateway is minimized, while the latency requirements of the BSs are met. We propose the laTency aWare transmIssion Scheduling for sTeerable FSO (TWIST) algorithm to efficiently solve the proposed problem.

Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains

This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle (UAV), a typical Unmanned Aerial System (UAS). The ground sensors harvest renewable energy and are equipped with batteries and data buffers. The ground sensor model takes into account sensor data buffer and battery limitations. An asymptotically globally optimal method of joint UAV 3D trajectory optimization and data transmission schedule is developed. The developed method maximizes the amount of data transmitted to the UAV without losses and too long delays and minimizes the propulsion energy of the UAV. The developed algorithm of optimal trajectory optimization and transmission scheduling is based on dynamic programming. Computer simulations demonstrate the effectiveness of the proposed algorithm.

Query-Age-Optimal Scheduling Under Sampling and Transmission Constraints

This letter provides query-age-optimal joint sampling and transmission scheduling policies for a heterogeneous status update system, consisting of a stochastic arrival and a generate-at-will source, with an unreliable channel. Our main goal is to minimize the average query age of information (QAoI) subject to average sampling, average transmission, and per-slot transmission constraints. To this end, an optimization problem is formulated and solved by casting it into a linear program. We also provide a low-complexity near-optimal policy using the notion of weakly-coupled constrained Markov decision processes. The numerical results show up to 32% performance improvement by the proposed policies compared with a benchmark policy.

Multilayer Joint Optimization of Packet Size and Adaptive Transmission Scheduling of Wireless Sensor Networks for Mechanical Vibration Monitoring

Limitations pertaining to the storage capacity and bandwidth of wireless sensor networks (WSNs) for mechanical vibration monitoring (MVM) pose difficulties in the realization of reliable and energy-efficient communication of big data. To prolong the lifetime of battery-limited WSNs, this paper proposes a multi-layer joint optimization of packet size and adaptive transmission scheduling (ATS) to jointly optimize the energy consumption of acquisition nodes from the storage and transmission layers. First, owing to the frequent storage and transmission of massive mechanical vibration data in WSNs, the packet size significantly affects the energy consumption during storage and transmission. The proposed packet size optimization based on the lowest energy consumption (PSO-LEC) model optimizes the energy consumption of acquisition nodes from three aspects: writing, reading, and transmission of data. Second, when transmitting massive vibration data frequently, the direct beacon frame conflict and indirect beacon frame conflict markedly induce data conflict and delay problems. Therefore, adaptive transmission scheduling based on the time division multiple access (TDMA/ATS) method is proposed to optimize the errors in data collision and transmission delay. Thus, the packet size and ATS are considered jointly to improve the network lifetime for MVM. Comprehensive experimental results indicate that the transmission reliability and energy-efficiency of the proposed scheme are significantly improved, and the network life is effectively prolonged for MVM.

Optimal transmission scheduling for remote state estimation in CPSs with energy harvesting two-hop relay networks

This paper considers the optimal transmission power scheduling for accurate remote state estimation in cyber–physical systems (CPSs). The plant is modeled as a discrete-time stochastic linear system with sensor measurements transmitted to the remote estimator over an intelligent relay. A dynamic cooperative team is formulated in which the sensor and the relay sharing the same cost function are equipped with corresponding energy harvesters, and need to decide on the transmission power levels to minimize the average error covariance at the remote estimator. Since the nodes make simultaneous decisions to achieve a common goal, the sensor and the relay do not have access to the transmission power that will be selected by another node in advance, and hence they cannot build a value estimation of the cost function directly at each time point, which poses a challenge to determine the optimal transmission policies of both sides. To overcome this challenge, we provide a local-action value function and prove that both nodes select the transmission power with respect to the local-action value function without any loss of performance. In addition, a Markov decision process (MDP) and a multi-agent reinforcement learning algorithm are presented to obtain the optimal transmission power schedule over an infinite time horizon. Finally, simulation examples are provided to corroborate and illustrate the theoretical results.

Cooperative Transmission Scheduling and Computation Offloading With Collaboration of Fog and Cloud for Industrial IoT Applications

Energy consumption for large amounts of delay-sensitive applications brings serious challenges with the continuous development and diversity of Industrial Internet of Things (IIoT) applications in fog networks. In addition, conventional cloud technology cannot adhere to the delay requirement of sensitive IIoT applications due to long-distance data travel. To address this bottleneck, we design a novel energy–delay optimization framework called transmission scheduling and computation offloading ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSCO</monospace> ), while maintaining energy and delay constraints in the fog environment. To achieve this objective, we first present a heuristic-based transmission scheduling strategy to transfer IIoT-generated tasks based on their importance. Moreover, we also introduce a graph-based task-offloading strategy using constrained-restricted mixed linear programming to handle high traffic in rush-hour scenarios. Extensive simulation results illustrate that the proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSCO</monospace> approach significantly optimizes energy consumption and delay up to 12%–17% during computation and communication over the traditional baseline algorithms.