Computational Method Using Quantum Annealing for TDMA Scheduling Problem in Wireless Sensor Networks

Ishizaki, Fumio

doi:10.1109/icspcs47537.2019.9008543

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Finally, Ishizaki 30 uses a quantum annealing scheme to find near-optimal scheduling solutions in time division multiple access (TDMA)-based WSNs. TDMA is a collision-free access protocol that allows nodes to transmit in an orderly fashion in a shared channel.…”

Section: Optimizationmentioning

confidence: 99%

Quantum-based wireless sensor networks: A review and open questions

Rivero-Ángeles

2021

International Journal of Distributed Sensor Networks

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Applications of quantum computing are growing at a very fast pace, for example, from quantum computers to quantum algorithms and even to the development of the quantum Internet. However, the use of quantum technology in wireless sensor networks has not been thoroughly investigated just yet. This is in part due to the complexity of using big, costly, and highly energy-consuming machines that are quantum computers to this date, compared to the nodes used in wireless sensor networks which are small, inexpensive, and operate with very low energy consumption requirements. However, we can expect that in the future (possibly in the next decade) quantum computers will be commercial and reduced in size, and hence, they can be used for sensor network applications which are the basis of the Internet of Things. In this review, we study the road from quantum computing to quantum wireless sensor networks and how the analysis and design of these systems have to change to accommodate quantum capabilities in sensors, processors, communication links, and overall performance of these monitoring networks.

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Section: Optimizationmentioning

confidence: 99%

Quantum-based wireless sensor networks: A review and open questions

Rivero-Ángeles

2021

International Journal of Distributed Sensor Networks

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“…One of the computational complex problems in wireless networks is to obtain an optimal scheduling in multi-user (MU) systems, which can help one effectively employ network resources. The authors in [6] introduced the QA approach to find an optimal solution for the time-division multiple access (TDMA) transmission scheduling problem in a wireless sensor network using a tree topology. The results revealed that QA outperforms the other scheduling solutions in terms of the runtime and proximity to the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Towards Quantum Annealing for Multi-user NOMA-based Networks

Gabdulsattarov

Rabie

et al. 2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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Quantum Annealing (QA) uses quantum fluctuations to search for a global minimum of an optimization-type problem faster than classical computers. To meet the demand for future internet traffic and mitigate the spectrum scarcity, this work presents the QA-aided maximum likelihood (ML) decoder for multi-user non-orthogonal multiple access (NOMA) networks as an alternative to the successive interference cancellation (SIC) method. The practical system parameters such as channel randomness and possible transmit power levels are taken into account for all individual signals of all involved users. The brute force (BF) and SIC signal detection methods are taken as benchmarks in the analysis. The QA-assisted ML decoder results in the same BER performance as the BF method outperforming the SIC technique, but the execution of QA takes more time than BF and SIC. The parallelization technique can be a potential aid to fasten the execution process. This will pave the way to fully realize the potential of QA decoders in NOMA systems.

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“…This paper presents the first extensive analysis on power consumption and quantum annealing (QA) architecture to make the case for the future feasibility of quantum processing based RANs. While recent successful point-solutions that apply QA to variety of wireless network applications [3,7,8,13,31,35,36,39,42,59,60] serve as our motivation, previous work stops short of a macroscopic power and cost comparison between QA and silicon. Despite QA's benefits demonstrated by these prior works in their respective point settings, a reasoning of how these results will factor into the overall computational performance and power requirements of the base station and C-RAN remains missing.…”

Section: Introductionmentioning

confidence: 99%

A Cost and Power Feasibility Analysis of Quantum Annealing for NextG Cellular Wireless Networks

Kasi¹,

Warburton²,

Kaewell³

et al. 2021

Preprint

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In order to meet mobile cellular users' ever-increasing network usage, today's 4G and 5G networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling the carbon footprint and operational costs of such networks remains a long-standing problem among network designers. This Challenge paper takes a long view on this problem, envisioning a NextG scenario where the network leverages quantum annealing computation for cellular baseband processing. We gather and synthesize insights on power consumption, computational throughput and latency, spectral efficiency, and operational cost, and deployment timelines surrounding quantum technology. Armed with these data, we analyze and project the quantitative performance targets future quantum hardware must meet in order to provide a computational and power advantage over silicon hardware, while matching its wholenetwork spectral efficiency. Our quantitative analysis predicts that with quantum hardware operating at a 140 𝜇s problem latency and 4.3M qubits, quantum computation will achieve a spectral efficiency equal to silicon while reducing power consumption by 40.8 kW (45% lower) in a representative 5G base station scenario with 400 MHz bandwidth and 64 antennas, and an 8 kW power reduction (16% lower) using 2.2M qubits in a 200 MHz-bandwidth 5G scenario.

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Computational Method Using Quantum Annealing for TDMA Scheduling Problem in Wireless Sensor Networks

Cited by 6 publications

References 10 publications

Quantum-based wireless sensor networks: A review and open questions

Quantum-based wireless sensor networks: A review and open questions

Towards Quantum Annealing for Multi-user NOMA-based Networks

A Cost and Power Feasibility Analysis of Quantum Annealing for NextG Cellular Wireless Networks

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