A Computationally Efficient Method for QoE-Driven Self-Planning of Antenna Tilts in a LTE Network

Ordonez, Pablo A. Sanchez; Luna-Ramírez, Salvador; Toril, M.

doi:10.1109/access.2020.3033325

Cited by 13 publications

(2 citation statements)

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“…Given the fact that the performance of these methods depends heavily on the accuracy of the approximation model, they need to be further validated in real scenarios. Another approach is to estimate the gradient by applying subtle changes to each network parameter [ 57,58 ] , which can be carried out on accurate network simulators. However, this kind of gradient estimator is also time‐consuming and inefficient in large scale network.…”

Section: Network Parameter Optimizationmentioning

confidence: 99%

Real‐World Wireless Network Modeling and Optimization: From Model/Data‐Driven Perspective

Yang

Zhang

Ren

et al. 2022

Chinese J of Electronics

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With the rapid development of the fifthgeneration wireless communication systems, a profound revolution in terms of transmission capacity, energy efficiency, reliability, latency, and connectivity is highly expected to support a new batch of industries and applications. To achieve this goal, wireless networks are becoming extremely dynamic, heterogeneous, and complex. The modeling and optimization for the performance of realworld wireless networks are extremely challenging due to the difficulty to predict the network performance as a function of network parameters, and the prohibitively huge number of parameters to optimize. The conventional network modeling and optimization approaches rely on drive test, trial‐and‐error, and engineering experience, which are labor intensive, error‐prone, and far from optimal. On the other hand, while the research community has spent significant efforts in understanding the fundamental limits of radio channels and developing physical layer techniques to operate close to it, very little is known about the performance limits of wireless networks, where millions of radio channels interact with one another in complex manners. This paper reviews the very recent mathematical and learning based techniques for modeling and optimizing the performance of real‐world wireless networks in five aspects, including channel modeling, user demand and traffic modeling, throughput modeling and prediction, network parameter optimization, and IRS empowered performance optimization, and also presents the corresponding notable performance gains.

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Section: Network Parameter Optimizationmentioning

confidence: 99%

Real‐World Wireless Network Modeling and Optimization: From Model/Data‐Driven Perspective

Yang

Zhang

Ren

et al. 2022

Chinese J of Electronics

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“…Several approaches relying on traditional control methods have attempted to mitigate the need for expert knowledge. In [2], [3], [4] optimization methods are used to find an optimal choice of tilt angles by changing the tilt angles electrically. This problem is commonly known as the remote electrical tilt (RET) problem.…”

Section: Introductionmentioning

confidence: 99%

Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement Learning

Forsberg,

Nikou,

Feljan

et al. 2023

2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)

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Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. Distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. The question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. The results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective.

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Dynamic Coverage Optimization for 5G Ultra-dense Cellular Networks Based on Their User Densities

Samal

Swain²,

Bandopadhaya

et al. 2022

Wireless Pers Commun

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This paper has proposed a user-density-based coverage optimization technique for ultradense cellular networks. Antenna tilting is a promising coverage optimization technique to be used in 5G networks that significantly improve the signal to interference plus noise ratio (SINR) by choosing the appropriate angle of tilt. In this paper, the cellular coverage has been optimized for scattered user densities/user-hotspots using an adaptive antenna tilting mechanism that steers the beams towards the temporal hot spot in the coverage area. The proposed method has the competence to improve the desired SINR level and coverage area for a group of the user rather than a single user. In this work, a reinforcement learning (RL) algorithm has been implemented to optimize the tilt angle. The performance of the proposed technique has been evaluated in the simulation platform considering a three-sectored multicellular mobile network where the groups of user clusters are distributed randomly. The result confirms the improvement in RSS and SINR value in the group of users having high density with maximum user satisfaction.

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A Computationally Efficient Method for QoE-Driven Self-Planning of Antenna Tilts in a LTE Network

Cited by 13 publications

References 50 publications

Real‐World Wireless Network Modeling and Optimization: From Model/Data‐Driven Perspective

Real‐World Wireless Network Modeling and Optimization: From Model/Data‐Driven Perspective

Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement Learning

Dynamic Coverage Optimization for 5G Ultra-dense Cellular Networks Based on Their User Densities

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