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epsilon-greedy-Based MQTT QoS Mode Selection and Power Control algorithm for Power Distribution IoT

INTERNATIONAL JOURNAL OF MOBILE COMPUTING AND MULTIMEDIA COMMUNICATIONS 2023年第1期14卷 306976-306976页

作者： You, Xinhong Zhang, Pengping Liu, Minglin Lin, Lingqi Li, Shuai State Grid Corp China Shandong Elect Power Res Inst Beijing Peoples R China

Employing message queuing telemetry transport (MQTT) in the power distribution internet of things (PD-IoT) can meet the demands of reliable data transmission while significantly reducing energy consumption through the dynamic and flexible selection of three different quality of service (QoS) modes and power control. However, there are still some challenges, including incomplete information, coupling of optimization variables, and dynamic tradeoff between packet-loss ratio and energy consumption. In this paper, the authors propose a joint optimization algorithm named EMMA for MQTT QoS mode selection and power control based on the epsilon-greedy algorithm. Firstly, the joint optimization problem of MQTT QoS mode selection and power control is modeled as a multiarmed bandit (MAB) problem. Secondly, the authors leverage the online learning capability of the epsilon-greedy algorithm to achieve joint optimization of MQTT QoS mode selection and power control. Finally, they verify the superior performance of the proposed algorithm through simulations.

关键词： Energy Consumption epsilon-greedy algorithm Message Queuing Telemetry Transport Multi-Armed Bandit Packet-Loss Ratio Power Control Power Distribution Internet of Things QoS Mode Selection

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Adaptive dynamics of Ising spins in one dimension leveraging reinforcement learning

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PHYSICA SCRIPTA 2025年第5期100卷 056009-056009页

作者： Kumar, Anish Mishra, Pawan Kumar Singh, Riya Mishra, Shradha Giri, Debaprasad Indian Inst Technol BHU Dept Phys Varanasi 221005 India

A one-dimensional flocking model using active Ising spins is studied, where the system evolves through the reinforcement learning approach via defining state, action, and cost function for each spin. The orientation of spin with respect to its neighbouring spins defines its state. The state of spin is updated by altering its spin orientation in accordance with the epsilon-greedy algorithm (action) and selecting a finite step from a uniform distribution to update position. The epsilon parameter is analogous to the thermal noise in the system. The cost function addresses cohesion among the spins. By exploring the system in the plane of the self-propulsion speed and epsilon parameter, four distinct states are found: disorder, flocking, flipping, and oscillatory. In the flipping state, a condensed flock reverses its direction of motion stochastically. The mean reversal time < T > exponentially decays with epsilon. A new state, an oscillatory state, is also found, a stochastic chaos state with a positive Lyapunov exponent. The findings obtained from the reinforcement learning approach for the active Ising model system exhibit similarities with the outcomes of other conventional techniques, even without defining any explicit interaction among the spins.

关键词： active Ising spins reinforcement learning epsilon-greedy algorithm active matter non-linear dynamics

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A greedy energy efficient clustering scheme based reinforcement learning for WSNs

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第6期15卷 2572-2588页

作者： Bourebia, Nour El Houda Li, Chunlin Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Peoples R China

Enhancing energy efficiency and extending the network lifetime are major concerns in the field of wireless sensor networks (WSNs). Clustering helps improving the energy efficiency and extends the network lifetime. In each cluster, a cluster head (CH) is chosen to collect data from its cluster members. However, the cluster head selection process is a vital task as it influences the network lifetime and the data delivery in the network. In this paper, the greedy energy efficient clustering scheme (GEECS) is proposed as a routing protocol based clustering. The proposed scheme treats the CH selection process as a multi-armed bandit problem. Cluster heads are selected using an improved epsilon-greedy algorithm that balances the CH selection rate and increases the network's efficiency. The epsilon-greedy algorithm is combined with the scalable K-means++ algorithm to perform the clustering process. The performance of GEECS is evaluated in terms of packet delivery ratio, number of dead nodes, network lifetime and average cluster head energy per round with respect to the network densities. The simulation results reveal that the GEECS protocol boosts the data delivery rate to the base station, reduces the CH energy dissipation and prolongs the network lifetime compared to other routing protocols. It is notable that for a network of 100 nodes, GEECS increases the network lifetime to 3155 seconds whereas the LEACH and ICFP protocols exhibit an increase of 2250 and 2738 seconds respectively.

关键词： WSNs Clustering epsilon-greedy algorithm Scalable K-means plus Cluster head selection Multi-armed bandit

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Pilot Contamination Mitigation in Massive MIMO Cloud Radio Access Networks

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IEEE ACCESS 2022年 10卷 58212-58224页

作者： Taleb, Hussein Khawam, Kinda Lahoud, Samer El Helou, Melhem Martin, Steven St Joseph Univ Beirut Ecole Super Ingn Beyrouth Beirut 11042020 Lebanon Univ Versailles UVSQ F-78035 Versailles France Univ Paris Saclay Lab Interdisciplinaire Sci Numer CNRS F-91405 Orsay France

Massive multiple-input multiple-output (MIMO) technology is expected to achieve significant gains in both signal-to-noise-plus-interference ratio (SINR) and throughput for 5G cellular wireless networks. Efficient and highly accurate channel state information (CSI) acquisition at the base stations (BS) is essential to achieve the potential benefits of massive MIMO systems. However, the inadequate number of orthogonal pilot sequences used for CSI estimation leads to erroneous channel estimation as it causes interference between pilot sequences. This phenomenon is coined pilot contamination and severely limits the system performance. Therefore, we address in this paper the pilot contamination problem in massive MIMO Cloud-Radio Access Networks systems. Leveraging on a real telecom operator data delivered by Call Detail Records (CDR), our objective is to maximize the average uplink achievable rate by reducing the effect of pilot contamination in massive MIMO Cloud-Radio Access Networks (C-RAN). As such a problem is a non-linear integer problem that has no solution in polynomial time, we develop a two-stage solution to solve it. First, a coalition game is proposed where Remote Radio Heads (RRHs) gather into clusters with random user-pilot allocation. Then, two greedy heuristics are applied to match each user in the cluster with a given pilot. The goal is to further improve the average uplink rate achieved in the first stage. Simulation results show that our heuristic solutions for pilot contamination mitigation outperform the traditional pilot allocation solution and a state-of-the-art pilot allocation scheme based on large-scale fading in terms of average uplink achievable rate and SINR.

关键词： Call detail records coalition game cloud-radio access networks epsilon-greedy algorithm greedy algorithm massive MIMO pilot contamination

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Reinforcement Learning Made Affordable for Hardware Verification Engineers

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MICROMACHINES 2022年第11期13卷 1887-1887页

作者： Dinu, Alexandru Ogrutan, Petre Lucian Transilvania Univ Brasov Dept Elect & Comp Brasov 500036 Romania

Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today's hardware devices must be supported by powerful development and simulation environments, powerful computational mechanisms, and appropriate software to exploit them. Reinforcement learning, a powerful technique belonging to the field of artificial intelligence, provides the means to efficiently exploit computational resources to find even the least obvious correlations between configuration parameters, stimuli applied to digital design inputs, and their functional states. This paper, in which a novel software system is used to simplify the analysis of simulation outputs and the generation of input stimuli through reinforcement learning methods, provides important details about the setup of the proposed method to automate the verification process. By understanding how to properly configure a reinforcement algorithm to fit the specifics of a digital design, verification engineers can more quickly adopt this automated and efficient stimulus generation method (compared with classical verification) to bring the digital design to a desired functional state. The results obtained are most promising, with even 52 times fewer steps needed to reach a target state using reinforcement learning than when constrained random stimulus generation was used.

关键词： digital design functional verification reinforcement learning automation software system stimuli generation epsilon-greedy algorithm

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Machine Intelligence Technique for Blockage Effects in Next-Generation Heterogeneous Networks

Machine Intelligence Technique for Blockage Effects in Next-...

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作者： Amalorpava Mary Rajee, S. Brno University of Technology

Millimeter wave (mmWave) links such as 28 GHz and 60 GHz propose high data rates and capacity needed in 5G Heterogeneous network (Hetnet) real-time system. The key factors in network planning of Hetnet are the locations and links of base stations, and their coverage, transmitted power, antenna angle, orientation etc. How-ever, large-scale blockages like static buildings, human etc. affect the performance of urban Hetnets especially at mmWave frequencies. A mathematical framework to model dynamic blockages is adapted and their impact on cellular network performance is analyzed. A machine learning approach based on Q-learning with epsilon-greedy algo¬rithm is proposed to solve the blockage problem in such complex networks. The proposed results are evident and show the positive effect of increasing the base station den¬sity linearly with the blockage density to maintain the net¬work connectivity. The performance of the proposed Epsi¬lon-greedy algorithm is compared with epsilon-Soft algo-rithm. The performances of above said mmWave links are compared in terms of their coverage probability and throughput. The results show that an epsilon-greedy algo¬rithm outperforms an epsilon-Soft algorithm.

关键词： Heterogeneous network millimeter wave dynamic blockage Q-Learning epsilon-greedy algorithm

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Open quantum system control based on reinforcement learning 38

Open quantum system control based on reinforcement learning

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38th Chinese Control Conference (CCC)

作者： Wei, Peng Li, Na Xi, Zairong Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9789881563972

The fast preparation of a quantum state with high fidelity is a key problem for a quantum system. We propose two modified algorithms based on the cutting-edge reinforcement learning methods for the flip from an initial state to a target one in a quantum spin system. The balance between exploration and exploitation and the size of a state space are key factors for reinforcement learning methods. In the first algorithm, we propose a modified c-greedy strategy instead of the c-greedy strategy to balance exploration and exploitation. Furthermore, we use the fidelity of the final state as the reward and leverage piecewise-constant driving protocols in every duration. The results show that the rate of correction of this algorithm is greater than that of Q-learning with c-greedy strategy. However, it will still cost much time resource if the rotation angle from a state to the next state is too small. Thus, we initialize the state-action value table to reduce the state space in the second modified Q-learning algorithm (MQL), which can be found that the efficiency of learning is greatly improved.

关键词： Quantum spin epsilon-greedy algorithm Q-learning piecewise constant

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ADAPTIVE RANDOMIZATION TO IMPROVE UTILITY-BASED DOSE-FINDING WITH BIVARIATE ORDINAL OUTCOMES

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2012年第4期22卷 785-801页

作者： Thall, Peter F. Nguyen, Hoang Q. Univ Texas MD Anderson Canc Ctr Dept Biostat Houston TX 77230 USA

A sequentially outcome-adaptive Bayesian design is proposed for choosing the dose of an experimental therapy based on elicited utilities of a bivariate ordinal (toxicity, efficacy) outcome. Subject to posterior acceptability criteria to control the risk of severe toxicity and exclude unpromising doses, patients are randomized adaptively among the doses having posterior mean utilities near the maximum. The utility increment used to define near-optimality is nonincreasing with sample size. The adaptive randomization uses each dose's posterior probability of a set of good outcomes, defined by a lower utility cutoff. Saturated parametric models are assumed for the marginal dose-toxicity and dose-efficacy distributions, allowing the possible requirement of monotonicity in dose, and a copula is used to obtain a joint distribution. Prior means are computed by simulation using elicited outcome probabilities, and prior variances are calibrated to control prior effective sample size and obtain a design with good operating characteristics. The method is illustrated by a Phase I/II trial of radiation therapy for children with brainstem gliomas.

关键词： Adaptive design Bayesian design Clinical trial Dose-finding epsilon-greedy algorithm Phase I/II clinical trial Utility

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