WIP: Demand-Driven Power Allocation in Wireless Networks with Deep Q-Learning

Giannopoulos, Anastasios E.; Spantideas, Sotirios T.; Capsalis, N.; Gkonis, Panagiotis K.; Karkazis, Panagiotis; Sarakis, Lambros; Trakadas, Panagiotis; Capsalis, Christos N.

doi:10.1109/wowmom51794.2021.00045

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…The evaluation criteria in Table 3 show that the number of users are monitored frequently in throughput maximization solutions, bringing the integration of user demanddriven ML solutions, such as the deep reinforcement learning throughput maximization algorithm used to achieve a minimum throughput target of 1 Mbit/s for 50 users [62]. The throughput scales well above 1 Mbit/s with the available bandwidth in mmWave frequencies, dense deployments and the use of MIMO; making eMBB applications such as V2X and mobile augmented reality the use case targets of mmWave radio and optical networks.…”

Section: Discussionmentioning

confidence: 99%

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

2021

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Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.

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Section: Discussionmentioning

confidence: 99%

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

2021

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“…Increasing the power allocation with respect to the training sequence phase can lead to an improvement in the DASR, as we will see later in the results section in Section 5. Note that machine learning (ML) algorithms can be integrated into the modeling framework of power allocation [64,[71][72][73]. However, a recognizable challenge could be the potential lack of interpretability in the decision-making process, thus making it challenging to understand and validate the underlying reasoning behind power allocation choices.…”

Section: Power Allocation Formulationmentioning

confidence: 99%

Maximizing the Downlink Data Rates in Massive Multiple Input Multiple Output with Frequency Division Duplex Transmission Mode Using Power Allocation Optimization Method with Limited Coherence Time

Naser,

Salman,

Alsabah

2024

Telecom

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The expected development of the future generation of wireless communications systems such as 6G aims to achieve an ultrareliable and low-latency communications (URLLCs) while maximizing the data rates. These requirements push research into developing new advanced technologies. To this end, massive multiple input multiple output (MMIMO) is introduced as a promising transmission approach to fulfill these requirements. However, maximizing the downlink-achievable sum rate (DASR) in MMIMO with a frequency division duplex (FDD) transmission mode and limited coherence time (LCT) is very challenging. To address this challenge, this paper proposes a DASR maximization approach using a feasible power allocation optimization method. The proposed approach is based on smartly allocating the total transmit power between the data transmission and training sequence transmission for channel estimation. This can be achieved by allocating more energy to the training signal than the data transmission during the channel estimation process to improve the quality of channel estimation without compromising more training sequence length, thus maximizing the DASR. Additionally, the theory of random matrix approach is exploited to derive an asymptotic closed-form expression for the DASR with a regularized zero-forcing precoder (RZFP), which allows the power optimization process to be achieved without the need for computationally complex Monte Carlo simulations. The results provided in this paper indicate that a considerable enhancement in the DASR performance is achieved using the proposed power allocation method in comparison with the conventional uniform power allocation method.

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“…Typical parameters to be fine-tuned include the learning rate, the discount factor and the hidden layers of the neural network [16], [17]. Noteworthy, the discount factor (γ) is used in the Bellman equation for updating the Q-values and is associated with the extent to which the agent prefers immediate (γ=0) or future (γ=1) rewards [18].…”

Section: B Xapp2: Training and Evaluating The Drl Agentsmentioning

confidence: 99%

Towards Closed-loop Automation in 5G Open RAN: Coupling an Open-Source Simulator with xApps

Karamplias

Spantideas

Giannopoulos

et al. 2022

2022 Joint European Conference on Networks and Communications &Amp; 6G Summit (EuCNC/6G Summit)

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The goal of this paper is to demonstrate the implementation of technological solutions that will enable the optimization of 5G network resources and services in an automated and self-configured manner. At first, the practical implementation of intelligence loops in the 5G network architecture is presented, according to the O-RAN specifications. Then, the development of an open source, general-purpose simulator, compliant with 3GPP specifications for generating physical-layer measurement reports from the radio access network is presented, while its functional logic and configuration capabilities are fully highlighted. Moreover, this paper illustrates how effectively trained machine learning (ML) models can be incorporated into the architecture for network configuration and optimization. In this context, an indicative use case is presented and evaluated, focusing on closed-loop power adjustment of the transmitters in a 5G cellular orientation, via the appropriate deployment of a deep reinforcement learning agent. The simulation results outline the interaction loop between the developed 5G simulator and the deployed ML model, targeting at increasing the network-wide throughput of user equipment.

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WIP: Demand-Driven Power Allocation in Wireless Networks with Deep Q-Learning

Cited by 15 publications

References 14 publications

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Maximizing the Downlink Data Rates in Massive Multiple Input Multiple Output with Frequency Division Duplex Transmission Mode Using Power Allocation Optimization Method with Limited Coherence Time

Towards Closed-loop Automation in 5G Open RAN: Coupling an Open-Source Simulator with xApps

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