Full-Duplex Backscatter Communications in Symbiotic Radio Systems

Long, Ruizhe; Guo, Huayan; Zhang, Lin; Liang, Ying-Chang

doi:10.1109/access.2019.2898474

Cited by 99 publications

(55 citation statements)

References 25 publications

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“…It is noted that to solve the optimization problem P1, the complete real-time channel information is required to calculate the real-time SINR in (5). However, according to the frame structure in Fig.…”

Section: B the Optimal Policymentioning

confidence: 99%

“…As described in Section III-B, the optimal policy of the user association problem requires the full real-time SINR in (5), which means that the full real-time channel information is needed. However, it is impractical for the BS to obtain the full real-time channel estimation since in one frame, the BS can only obtain the channel information between the IoT device and its associated cellular user instead of all channel information between all IoT devices and all cellular users.…”

Section: A Basic Principlementioning

confidence: 99%

“…• Local Information: According to (5), the agent needs to feed the channel information into the DQN to provide useful knowledge for learning the optimal policy. Since it is difficult to obtain the channel information between IoT Device n and all cellular users, the agent uses the historical information to explore and infer the current channel information, which is similar to Section IV-C2.…”

Section: Distributed Drl-based User Association Algorithmmentioning

confidence: 99%

“…Meanwhile, the state at frame t includes the action taking by IoT Device n at frame (t − 1) to suggest the effect of historical action. In addition, since the agent trains the DQN using all environment experiences, in order to identify all IoT devices, the state is designed to include the identity number, i.e., n. • Interferer Information: The state is designed to include the interferer information to observe the interference from other IoT devices when decoding the message from IoT Device n. In particular, according to (5), if IoT Device n is associated with User m in frame (t − 1), User m will feedback the interferer information when decoding the IoT Device n message, I n (t − 1), to the BS, where I n (t − 1) = l∈Ĩn(t−1) pa d m,l (t − 1)h m,l (t − 1) and a d m,l (t − 1) ∈ {0, 1} indicates whether the IoT Device l is associated with User m in frame (t − 1). • Interfered Information: Finally, the agent uses the feedback from User m to sense the interference O n (t − 1) from IoT Device n to other IoT devices in frame 1), l = 1, · · · , N } is the identify number set of the IoT device that may be interfered by IoT Device n at frame (t − 1).…”

Section: Distributed Drl-based User Association Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent User Association for Symbiotic Radio Networks Using Deep Reinforcement Learning

Zhang

Liang

Poor

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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In this paper, we are interested in symbiotic radio networks, in which an Internet-of-Things (IoT) network parasitizes in a primary network to achieve spectrum-, energy-, and infrastructure-efficient communications. Specifically, the BS serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. We focus on the user association problem, whose objective is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem. To overcome this issue, we propose two deep reinforcement learning (DRL) algorithms, both use the historical information to infer the current information in order to make appropriate decisions. One algorithm, centralized DRL, makes decisions for all IoT devices at one time with global information. The other algorithm, distributed DRL, makes a decision only for one IoT device at one time using local information. Finally, simulation results show that the two DRL algorithms achieve comparable performance as the optimal user association policy which requires perfect real-time information, and the distributed DRL algorithm has the advantage of scalability.Index Terms-Symbiotic radio networks (SRN), ambient backscatter communication (AmBC), user association, deep reinforcement learning.

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Section: B the Optimal Policymentioning

confidence: 99%

Section: A Basic Principlementioning

confidence: 99%

Section: Distributed Drl-based User Association Algorithmmentioning

confidence: 99%

Section: Distributed Drl-based User Association Algorithmmentioning

confidence: 99%

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Intelligent User Association for Symbiotic Radio Networks Using Deep Reinforcement Learning

Zhang

Liang

Poor

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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“…Secondly, IRS is a green and energyefficient technique which reflects the incident signal passively without additional energy consumption while the AF relay and the active intelligent surface require active radio frequency (RF) components. Thirdly, although both the IRS and the backscatter communication make use of passive communications, IRS can be equipped with a large number of reflecting elements while backscatter devices are usually equipped with a single/few antenna(s) due to the limitations of complexity and cost [15]. Besides, IRS only attempts to assist the transmission of the signals between the intended transmitter and receiver pair with no intention for its own information transmission while backscatter communication needs to support the information transmission of the backscatter device [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reflecting Surface: A Programmable Wireless Environment for Physical Layer Security

et al. 2019

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In this paper, we introduce an intelligent reflecting surface (IRS) to provide a programmable wireless environment for physical layer security. By adjusting the reflecting coefficients, the IRS can change the attenuation and scattering of the incident electromagnetic wave so that it can propagate in a desired way toward the intended receiver. Specifically, we consider a downlink multiple-input single-output (MISO) broadcast system where the base station (BS) transmits independent data streams to multiple legitimate receivers and keeps them secret from multiple eavesdroppers. By jointly optimizing the beamformers at the BS and reflecting coefficients at the IRS, we formulate a minimum-secrecy-rate maximization problem under various practical constraints on the reflecting coefficients. The constraints capture the scenarios of both continuous and discrete reflecting coefficients of the reflecting elements. Due to the non-convexity of the formulated problem, we propose an efficient algorithm based on the alternating optimization and the path-following algorithm to solve it in an iterative manner. Besides, we show that the proposed algorithm can converge to a local (global) optimum. Furthermore, we develop two suboptimal algorithms with some forms of closed-form solutions to reduce the computational complexity. Finally, the simulation results validate the advantages of the introduced IRS and the effectiveness of the proposed algorithms.Index Terms-Intelligent reflecting surface, programmable wireless environment, physical layer security, beamforming.

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Non‐orthogonal multiple access in backscatter communication system under the scene of the moving reader

Liu

et al. 2022

Trans Emerging Tel Tech

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This article studies non-orthogonal multiple access (NOMA) backscatter communication (BackCom) system under the scene of the moving reader. As the reader moves, the communication link between backscatter nodes (BNs) and the reader is dynamic, and the computational complexity of regrouping is high.To address these problems, a dynamic NOMA-BackCom scheme is proposed, which consists of a dynamic multiplexing BNs scheme and a dynamic scheme of choosing the reflection coefficient. Moreover, a performance evaluation is conducted to compare the decoding rate of the proposed scheme and the traditional scheme. Simulation results show that the proposed scheme has higher decoding rate and better throughput than the traditional scheme.

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Full-Duplex Backscatter Communications in Symbiotic Radio Systems

Cited by 99 publications

References 25 publications

Intelligent User Association for Symbiotic Radio Networks Using Deep Reinforcement Learning

Intelligent User Association for Symbiotic Radio Networks Using Deep Reinforcement Learning

Intelligent Reflecting Surface: A Programmable Wireless Environment for Physical Layer Security

Non‐orthogonal multiple access in backscatter communication system under the scene of the moving reader

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