Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks

Anh, Tran The; Luong, Nguyen Cong; Niyato, Dusit; Liang, Ying-Chang; Kim, Dong In

doi:10.1109/wcnc.2019.8885426

Cited by 36 publications

(21 citation statements)

References 13 publications

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“…In particular, the authors in [212] leverage reinforcement learning algorithms to investigate optimal sensing, channel probing, i.e., CSI estimation process, and transmission power control for an ST operating in a fading channel with harvested energy from ambient sources. Learning algorithms have been further investigated for backscatter-assisted wireless-powered CRNs for time scheduling and optimal policies [135], [213], [214] (Section IV-A).…”

Section: Artificial Intelligencementioning

confidence: 99%

Large-Scale Wireless-Powered Networks With Backscatter Communications—A Comprehensive Survey

Rezaei

Tellambura

Herath

2020

IEEE Open J. Commun. Soc.

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Massive and ubiquitous deployment of devices in networks of fifth generation (5G) and beyond wireless has necessitated the development of ultra-low-power wireless communication paradigms. Recently, wireless-powered networks with backscatter communications (WPN-BCs) has been emerged as a most prominent technology for enabling large-scale selfsustainable wireless networks with the capabilities of RF energy harvesting (EH) and of extreme low power consumption. Therefore, we provide a comprehensive literature review on the fundamentals, challenges and the on-going research efforts in the domain of WPN-BCs. Our emphasis is on large-scale networks. In particular, we discuss signal processing aspects, network design issues and efficient communication techniques. Moreover, we review emerging technologies for WPN-BCs to bring about the best use of resources. Some applications of this innovative technology are also highlighted. Finally, we address some open research problems and future research directions.

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Section: Artificial Intelligencementioning

confidence: 99%

Large-Scale Wireless-Powered Networks With Backscatter Communications—A Comprehensive Survey

Rezaei

Tellambura

Herath

2020

IEEE Open J. Commun. Soc.

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“…In [15], the problem of vehicle-to-vehicle (V2V) transmission of the message was considered. However, there exist a few studies on backscatter communication that employ machine learning techniques [16], [17]. For instance, the authors of [18] used a supervised machine learning technique (support vector machine) to detect the signal from a backscatter tag by transforming the tag detection into a classification task.…”

Section: A Related Workmentioning

confidence: 99%

Reinforcement Learning for Scalable and Reliable Power Allocation in SDN-based Backscatter Heterogeneous Network

Jameel

Khan

Jamshed

et al. 2020

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Backscatter heterogeneous networks are expected to usher a new era of massive connectivity of low-powered devices. With the integration of software-defined networking (SDN), such networks hold the promise to be a key enabling technology for massive Internet-of-things (IoT) due to myriad applications in industrial automation, healthcare, and logistics management. However, there are many aspects of SDN-based backscatter heterogeneous networks that need further development before practical realization. One of the challenging aspects is the high level of interference due to the reuse of spectral resources for backscatter communications. To partly address this issue, this article provides a reinforcement learning-based solution for effective interference management when backscatter tags coexist with other legacy devices in a heterogeneous network. Specifically, using reinforcement learning, the agents are trained to minimize the interference for macro-cell (legacy users) and small-cell (backscatter tags). Novel reward functions for both macro-and small-cells have been designed that help in controlling the transmission power levels of users. The results show that the proposed framework not only improves the performance of macro-cell users but also fulfills the quality of service requirements of backscatter tags by optimizing the long-term rewards.

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“…The multi-user scenario model is more complicated, for the resource allocation must consider the interaction between users. The time scheduling strategy of the multi-user AmBC system was studied in [10,19,24,26]. RL and DRL have certain advantages in dealing with resource allocation problems in time-varying communication networks because of their characteristics of learning in environment interaction.…”

Section: Performance Evaluationmentioning

confidence: 99%

Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning

et al. 2020

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With the rapid increase in the number of wireless sensor terminals in smart grids, backscattering has become a very promising green technology. By means of backscattering, wireless sensors can either reflect energy signals in the environment to exchange information with each other or capture the energy signals to recharge their batteries. However, the changing environment around wireless sensors, limited radio frequency and various service priorities in uplink communications bring great challenges in allocation resources. In this paper, we put forward a backscatter communication model based on business priority and cognitive network. In order to achieve optimal throughput of system, an asynchronous advantage actor-critic (A3C) algorithm is designed to tackle the problem of uplink resource allocation. The experimental results indicate that the presented scheme can significantly enhance overall system performance and ensure the business requirements of high-priority users.Electronics 2020, 9, 622 2 of 16 compared to the active WPCN. The authors of [4] maximized the throughput of backscatter and radio frequency (RF) WSNs by formulating the optimization objectives.Harvest-the-transmit (HTT) protocol is employed in a traditional RF-powered cognitive network (CRN) where the secondary transmitter (ST) harvests the energy in primary signals and then transmits information to the receiver using the energy stored in the battery actively. Recently, ambient energy signals have been used to transmit ST information by backscattering. The overhead of transmitting data is further reduced. Therefore, it is advisable to combine ambient backscatter communication with RF-powered CRN. The authors of [5] analyzed and compared the performance and throughput of RF-powered CRN in overlay and underlay scenarios. Internet of Things (IoT) is regarded as an ultimate solution to connect everything and has aroused extensive interest in academia and industry. However, energy limitation and implementation cost are the challenges in the construction of IoT. In [6], ambient backscatter communication (AmBC) was introduced as a green communication paradigm that could exploit the ambient wireless signals to power sensors and backscatter the information.There have been many optimization problems for RF-powered backscatter networks. The authors of [7] studied a full-duplex communication mode in the AmBC network where a full-duplex communication device transmitted the energy signal while the receiving signal was reflected back from the backscatter devices (BDs). An iterative algorithm was proposed to optimize the minimum throughput for all BDs by jointly scheduling time, power resources and adjustment reflection coefficients. The authors of [8] introduced backscatter communication into a multi-user device-to-device (D2D) network where the device could harvest energy from RF signals. A game-theoretic approach was proposed to balance relaying performance and energy harvesting.The above algorithms need to know the complete environment information. However, ...

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Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks

Cited by 36 publications

References 13 publications

Large-Scale Wireless-Powered Networks With Backscatter Communications—A Comprehensive Survey

Large-Scale Wireless-Powered Networks With Backscatter Communications—A Comprehensive Survey

Reinforcement Learning for Scalable and Reliable Power Allocation in SDN-based Backscatter Heterogeneous Network

Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning

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