Cellular network configuration via online learning and joint optimization

Guo, Xueying; Trimponias, George; Wang, Xiaoxiao; Chen, Zhitang; Geng, Yanhui; Liu, Xin

doi:10.1109/bigdata.2017.8258058

Cited by 5 publications

(3 citation statements)

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“…In a DTSC context, the length of the decision period varies among different applications. For example, Guo et al (2017) focus on the global network configuration problem and provide an online-learning approach of joint optimization to change a labor-intensive and error-prone configuration to one that is optimally designed. Ulmer (2019) reoptimizes vehicle routing as a reaction to new customer requests and in anticipation of future requests.…”

Section: Real-time Supply Chain Optimizationmentioning

confidence: 99%

Digital twin-driven smart supply chain

et al. 2022

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Today’s supply chain is becoming complex and fragile. Hence, supply chain managers need to create and unlock the value of the smart supply chain. A smart supply chain requires connectivity, visibility, and agility, and it needs be integrated and intelligent. The digital twin (DT) concept satisfies these requirements. Therefore, we propose creating a DT-driven supply chain (DTSC) as an innovative and integrated solution for the smart supply chain. We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept. We discuss three research opportunities in building a DTSC, including supply chain modeling, real-time supply chain optimization, and data usage in supply chain collaboration. Finally, we highlight a motivating case from JD.COM, China’s largest retailer by revenue, in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic.

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Section: Real-time Supply Chain Optimizationmentioning

confidence: 99%

Digital twin-driven smart supply chain

et al. 2022

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“…In all these papers, BS similarities are not considered, and thus require more exploration. In [12], the authors study the pilot power configuration problem and design a Gibbs-sampling-based online learning algorithm so as to maximize the throughput of users. In comparison, they make the assumption that all BSs are equal while we allow different BSs to learn different mappings.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, learning-based methods are proposed [7,12,18,19]. In [18], the authors propose a tailored form of reinforcement learning to adaptively select the optimal antenna configuration in a time-varying environment.…”

Section: Related Workmentioning

confidence: 99%

Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration

Wang

Guo

Chuai

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Cellular network configuration plays a critical role in network performance. In current practice, network configuration depends heavily on field experience of engineers and often remains static for a long period of time. This practice is far from optimal. To address this limitation, online-learning-based approaches have great potentials to automate and optimize network configuration. Learningbased approaches face the challenges of learning a highly complex function for each base station and balancing the fundamental exploration-exploitation tradeoff while minimizing the exploration cost. Fortunately, in cellular networks, base stations (BSs) often have similarities even though they are not identical. To leverage such similarities, we propose kernel-based multi-BS contextual bandit algorithm based on multi-task learning. In the algorithm, we leverage the similarity among different BSs defined by conditional kernel embedding. We present theoretical analysis of the proposed algorithm in terms of regret and multi-task-learning efficiency. We evaluate the effectiveness of our algorithm based on a simulator built by real traces. Figure 1: Cellular networkOnline-learning-based cellular BS configuration faces multiple challenges. First, the mapping between network configuration and performance is highly complex. Since different BSs have different deployment environments, they have different mappings between network configuration and performance, given a BS condition. Furthermore, for a given BS, its condition also changes over time due to network dynamics, leading to different optimal configurations at different points in time. In addition, for a given BS and given condition, the impact of network configuration on performance is too complicated to model using white-box analysis due to the complexity and dynamics of network environment, user diversity, traffic demand, mobility, etc. Second, to learn this mapping and to optimize the network performance over a period of time, operators face a fundamental exploitation-exploration tradeoff: in this (m) t ∈ R d . The state may include the number of users in a BS, user mobility, traffic demand, and neighboring BS

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