Spectrum Sharing Optimization in Cellular Networks under Target Performance and Budget Restriction

Asauzzaman, Md.; Abozariba, Raouf; Patwary, Mohammad

doi:10.1109/vtcspring.2017.8108226

Cited by 3 publications

(3 citation statements)

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“…A deep reinforcement learning based power control method for spectrum sharing in cognitive radios has been proposed in [110]. Such dynamic sharing of spectrum aims at enhancing the spectrum utilization efficiency through the provision of access of under-utilized spectrum to the secondary network users [111]. Furthermore, the scope of ML for anomaly and fault diagnosis, intrusion detection and prevention, and network configuration and optimization has been reviewed in [112].…”

Section: ) Deep Learning For Cognitive Communicationsmentioning

confidence: 99%

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

et al. 2019

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The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users' service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum-and QML-assisted B5G networks are identified and discussed.

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Section: ) Deep Learning For Cognitive Communicationsmentioning

confidence: 99%

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

et al. 2019

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“…Consequently, the operators would need to make dynamic, on-demand and correct choices of borrowing additional bandwidths from other operators [13].…”

Section: A Background and Motivationmentioning

confidence: 99%

Dynamic Spectrum Sharing Optimization and Post-Optimization Analysis With Multiple Operators in Cellular Networks

Asaduzzaman

Abozariba

Patwary

2018

IEEE Trans. Wireless Commun.

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Dynamic spectrum sharing aims to provide secondary access to under-utilised spectrum in cellular networks. The main aim of the paper is twofold. Firstly, secondary operator aims to borrow spectrum bandwidths under the assumption that more spectrum resources exist considering a merchant mode. Two optimization models are proposed using stochastic and optimization models in which the secondary operator (i) spends the minimal cost to achieve the target grade of service assuming unrestricted budget or (ii) gains the maximal profit to achieve the target grade of service assuming restricted budget.Results obtained from each model are then compared with results derived from algorithms in which spectrum borrowings are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than heuristic counterparts. Secondly, post-optimization performance analysis of the operators in the form of blocking probability in various scenarios is investigated to determine the probable performance gain and degradation of the secondary and primary operators respectively. We mathematically model the sharing agreement scenario and derive the closed form solution of blocking probabilities for each operator. Results show how the secondary operator perform in terms of blocking probability under various offered loads and sharing capacity.

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“…Firstly, several LPP with available resources that are sufficient to handle the resource request are considered along with their respective costs. Merchant mode [21] is considered to find the LPP that can offer suitable amount of resource with minimum possible cost. Let P(·) be a function for purchasing a resource using the merchant mode.…”

Section: Symbolmentioning

confidence: 99%

Cognitive Edge Computing based resource allocation framework for Internet of Things

Amjad

Rabby

Sadia

et al. 2017

2017 Second International Conference on Fog and Mobile Edge Computing (FMEC)

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Due to the inherent property of the processing resource request from mobile active or passive devices as part of internet of things (IoT), processing capacity as well as latency become major optimization criteria. To achieve overall optimized uses of cloud resources -having dynamic tracking, monitoring as well as orchestration framework is one of the key challenges to overcome. In the same context, enhanced uses of computing devices at distributed location is predicted to facilitate the success of IoT; subsequently the success of fifth generation (5G) of Wireless technologies. This opens enormous potential to integrate the unused resources of such distributed computed devices within the conventional cloudlet or cloud federation. However, this requires an efficient micro-level distributed computing resource tracking, monitoring and orchestration; where resources are distributed in geo-location as well as the availability of unused resources are time variant in nature. In this paper, we have proposed a cognitive edge-computing based framework solution for these requirements in order to achieve an efficient use of these distributed resources. This provides the end-user with a dynamic soft extension of computing facilities of cloudlet and cloud federation, as well as a revenue generation avenue to enduser. The simulation results show that such extension can be an exponential function of the number of local processing platforms agreed to participate in the proposed cognitive resource sharing.

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Spectrum Sharing Optimization in Cellular Networks under Target Performance and Budget Restriction

Cited by 3 publications

References 28 publications

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

Dynamic Spectrum Sharing Optimization and Post-Optimization Analysis With Multiple Operators in Cellular Networks

Cognitive Edge Computing based resource allocation framework for Internet of Things

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