Enabling Wireless Closed Loop Communication: Optimal Scheduling Over IEEE 802.11ah Networks

Seferagic, Amina; Poorter, Eli De; Hoebeke, Jeroen

doi:10.1109/access.2021.3050426

Cited by 8 publications

(8 citation statements)

References 34 publications

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“…Moreover, it is worth noting that latency for technologies like BLE or UWB is conditioned by the selected beaconing intervals. Nonetheless, in the case of Wi-Fi HaLow, there is recent research that mitigates the mentioned dependency on beaconing intervals by adjusting the Restricted Access Window (RAW), a configurable medium access feature of IEEE 802.11ah [167].…”

Section: ) Latencymentioning

confidence: 99%

Next Generation Auto-Identification and Traceability Technologies for Industry 5.0: A Methodology and Practical Use Case for the Shipbuilding Industry

Fraga‐Lamas¹,

Varela-Barbeito²,

Fernández‐Caramés

2021

IEEE Access

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Section: ) Latencymentioning

confidence: 99%

Next Generation Auto-Identification and Traceability Technologies for Industry 5.0: A Methodology and Practical Use Case for the Shipbuilding Industry

Fraga‐Lamas¹,

Varela-Barbeito²,

Fernández‐Caramés

2021

IEEE Access

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“…Based on this observation, some studies have focused on finding the optimal RAW parameters to improve network performance. Researchers have conducted complicated mathematical models and have proposed heuristic methods to determine the optimal RAW parameters or grouping scheme [7][8][9]. However, most of the analytical models fail to consider the complexities and dynamic changes of network conditions, leading to discrepancies between the results derived by analytical models and those obtained from an NS-3 simulator.…”

Section: Energy Efficiency Scalabilitymentioning

confidence: 99%

“…Oriented towards delay-sensitive emergency alarm sensor networks and closed-loop communication scenarios, the authors in [23] proposed a RAW parameters selecting algorithm to minimize channel time-sharing consumption. Additionally, in [8], the authors formulated the optimal RAW scheduling problem as an integer nonlinear programming problem with the objective of minimizing channel time at key STAs and designed a heuristic algorithm to find the optimal RAW configurations.…”

Section: Optimization In Raw Mechanismmentioning

confidence: 99%

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

Jiang,

Gong,

Deng

et al. 2024

Sensors

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The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly.

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“…With the continued increase in demand for various types of wireless service, 5G and beyond networks are aiming at providing high throughput, high energy-efficiency, low latency, and ultra-reliable communications [1][2][3]. To this end, besides introducing the advanced physical layer technologies, such as massive multiple-input multiple-output (MIMO), non-orthogonal multiple access (NOMA), full-duplex etc., from the perspective of networking, ultra-dense network (UDN) has attracted a lot of attention, due to its high potential to meet the abovementioned demands [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…FIGURE3 An example to illustrate the selection of initial cluster centers based on the proposed modified distribution density, the distribution of LBSs is the same as that in Figure2.…”

mentioning

confidence: 99%

Bat algorithm based semi‐distributed resource allocation in ultra‐dense networks

Fan,

Ma,

Pan

et al. 2024

IET Communications

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This paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity.

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Enabling Wireless Closed Loop Communication: Optimal Scheduling Over IEEE 802.11ah Networks

Cited by 8 publications

References 34 publications

Next Generation Auto-Identification and Traceability Technologies for Industry 5.0: A Methodology and Practical Use Case for the Shipbuilding Industry

Next Generation Auto-Identification and Traceability Technologies for Industry 5.0: A Methodology and Practical Use Case for the Shipbuilding Industry

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

Bat algorithm based semi‐distributed resource allocation in ultra‐dense networks

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