RF-Powered Cognitive Radio Networks Research Articles

DDPG-Based Throughput Optimization with AoI Constraint in Ambient Backscatter-Assisted Overlay CRN.

The combination of ambient backscatter (AB) communications (ABCs) and RF-powered cognitive radio networks (CRNs) deals with challenges of both energy supply and spectrum shortage, and improves the network performances. With the expansion of wireless networks, many applications raise requirements for both high-throughput and timely data. Driven by these facts, we study the long-term throughput optimization of the secondary network in the AB-assisted overlay CRN (ABO-CRN), ABCs, and CRNs with the age of information (AoI) constraint, which is a novel metric for measuring the freshness of data received by receivers. Due to the dynamic environment, complete knowledge of the environment could not be obtained. Then, the deep deterministic policy gradient (DDPG), a deep reinforcement learning (DRL) method that addresses decision issues in both continuous and discrete spaces, is deployed to address the throughput optimization. We consider the impacts of time and energy allocation on the reward when the AoI constraint can not be satisfied, and develop the corresponding reward functions. Furthermore, we analyze the impacts of the minimum throughput requirement and maximum allowable AoI on the throughput and AoI of the secondary networks in the ABO-CRN, ABCs, and CRNs. We compare the throughput optimization scheme under the AoI constraint with two baseline schemes (i.e., throughput-optimal (T-O) and AoI-optimal (A-O) baseline schemes), and the simulation results show that the throughput of the ABO-CRN is close to the optimal throughput of the T-O baseline scheme, and the AoI of the ABO-CRN is close to the optimal AoI of the A-O baseline scheme.

RF 충전 후방산란 인지 무선 네트워크에서 심층 강화학습 기반 모드 최적화 기법

RF 충전 후방산란 인지 무선 (Cognitive Radio) 네트워크에서 2차 송신단말 (Secondary Transmitter; ST)은 점유된 1차 채널에 접근하여 에너지를 수집하거나 주변 후방산란을 수행하여 정보를 전송할 수 있으며, 주사용자가 사용하지 않고 비어 있는 1차 채널에 접근하면 수집한 에너지를 사용하여 Active 모드로 데이터를 전송하게 된다. 랜덤하게 변화하는 1차 채널의 점유 상태에 적합한 동작 모드를 수행하면서 전송 성능을 최대화하기 위해, 본 논문에서는 ST가 임의 상태에서 최적의 동작 모드를 학습하는 방안을 제안한다. 또한 에너지 수집 모드를 통해 1차 채널의 점유 상태를 관찰하는 방안을 제안하여 부가적인 에너지 소모를 최소화하며, ST가 순차적으로 동작 모드를 결정하는 문제에 접근할 수 있도록 마르코프 결정 과정을 사용하여 제안하는 기법을 수학적으로 정의한다. ST가 1차 채널과 상호작용 하면서 스스로 최적의 정책을 학습할 수 있는 강화학습을 적용하며, 안정적인 학습을 위해 Deep Q-network 알고리즘을 사용한다. 본 논문에서는 Energy Outage 패널티 (Penalty)를 할당하는 방안을 고려하여 ST가 효율적으로 최적의 정책을 학습하도록 하였다. 학습된 인공신경망을 통해 탐욕 정책으로 동작 모드를 선택하여 수행하는 모의실험을 진행하여 ST가 제안 기법을 통해 달성할 수 있는 전송 성능을 비교 및 분석하였다.

Optimal decision making in multi-channel RF-powered cognitive radio networks with ambient backscatter capability

In RF-powered backscatter cognitive radio networks, the secondary user transmits his data either by harvesting energy then actively transmitting the data or by backscattering. However, it is challenging for the secondary user to determine the optimal licensed channel to select and the optimal action to do under the uncertainty of the state of the licensed channels and the dynamic behavior of the primary users. In this paper, we formulate the decision problem as a Markov decision process to obtain the optimal channel-action pair for the secondary user that maximizes his average throughput over the long run. In the proposed model, an incomplete information case is assumed where the secondary user does not know the actual state of the licensed channels. Moreover, sensing errors of missed detection and false alarm are considered and a general batch arrival stream is assumed. Numerical results show the superiority of the proposed optimal hybrid mode compared to the harvest-then-transmit mode in terms of both the average throughput and blocking probability, especially in the case of heavy primary user loads.

Time Allocation in Ambient Backscatter Assisted RF-Powered Cognitive Radio Network with Friendly Jamming against Eavesdropping

Time Allocation in Ambient Backscatter Assisted RF-Powered Cognitive Radio Network with Friendly Jamming against Eavesdropping

RF 충전 후방산란 CR 네트워크에서 2차 시스템의 전송 성능 향상 방안

RF 충전 후방산란 CR 네트워크에서 2차 시스템의 전송 성능 향상 방안

Opportunistic Ambient Backscatter Communication in RF-Powered Cognitive Radio Networks

In the present contribution, we propose a novel opportunistic ambient backscatter communication (ABC) framework for radio frequency (RF)-powered cognitive radio (CR) networks. This framework considers opportunistic spectrum sensing (SS) integrated with ABC and harvest-then-transmit (HTT) operation strategies. Novel analytic expressions are derived for the average throughput, the average energy consumption and the energy efficiency (EE) in the considered set up. These expressions are represented in closed-form and have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. In addition, we formulate an optimization problem to maximize the EE of the CR system operating in mixed ABC—and HTT—modes, for a given set of constraints, including primary interference and imperfect SS constraints. Capitalizing on this, we determine the optimal set of parameters which in turn comprise the optimal detection threshold, the optimal degree of trade-off between the CR system operating in the ABC—and HTT—modes and the optimal data transmission time. Extensive results from respective computer simulations are also presented for corroborating the corresponding analytic results and to demonstrate the performance gain of the proposed model in terms of EE.

Auction-Based Time Scheduling for Backscatter-Aided RF-Powered Cognitive Radio Networks

This paper investigates the time scheduling for a backscatter-aided radio-frequency-powered cognitive radio network, where multiple secondary transmitters transmit data to the same secondary gateway in the backscatter mode and the harvest-then-transmit mode. With many secondary transmitters connected to the network, the total transmission demand of the secondary transmitters may frequently exceed the transmission capacity of the secondary network. As such, the secondary gateway is more likely to assign the time resource, i.e., the backscattering time in the backscatter mode and the transmission time in the harvest-then-transmit mode, to the secondary transmitters with higher transmission valuations. Therefore, according to a variety of demand requirements from secondary transmitters, we design two auction-based time scheduling mechanisms for the time resource assignment. In the auctions, the secondary gateway acts as the seller as well as the auctioneer, and the secondary transmitters act as the buyers to bid for the time resource. We design the winner determination, the time scheduling, and the pricing schemes for both the proposed auction-based mechanisms. Furthermore, the economic properties, such as individual rationality and truthfulness, and the computational efficiency of our proposed mechanisms are analytically evaluated. The simulation results demonstrate the effectiveness of our proposed mechanisms.

무선 충전 후방산란 인지무선네트워크를 위한 전송시간 최적 할당 기법

무선 충전 후방산란 인지무선네트워크를 위한 전송시간 최적 할당 기법

Cooperative Energy Harvesting Cognitive Radio Networks With Spectrum Sharing and Security Constraints

Physical layer security is an important and timely topic in the research of future wireless systems and it constitutes a part of the Internet of Things (IoT) notion. IoT oriented systems are largely characterized by a stringent quality of service and enhanced security requirements, which comes at a cost of increased computational complexity that needs to be maintained within sustainable levels. In the present contribution, we investigate the physical-layer security of a dual-hop energy RF-Powered cognitive radio network over realistic multipath fading conditions. Assuming a spectrum sharing scenario, our analysis assumes that a source S communicates with a destination D with the aid of a multi-antenna relay R and in the presence of an eavesdropper E who is attempting to overhear the communication of both S-R and R-D links. The involved relay is powered by the renewable energy harvested from the signal sent by the source based on the power-splitting energy harvesting strategy. Furthermore, the relay uses a maximum ratio combining technique to process effectively the received signals. In addition, owing to the underlying strategy, both S and R adjust their respective transmit powers in order to avoid causing interference to the primary network. By considering both the independent identically distributed and the independent but not necessarily identically distributed flat Rayleigh fading channels, closed-form expressions for the secrecy outage probability are derived, based on which an asymptotic analysis is carried out. Our results quantify the impact of the main key system parameters and point out the optimal values ensuring a high-security performance of such a communication system. The validity of the derived results is verified extensively through comparisons with respective Monte Carlo simulation results and useful theoretical and technical insights are developed which are expected to be useful in the design of future cooperative CRNs.

Optimal Spectrum Sensing Policy in RF-Powered Cognitive Radio Networks

An orthogonal frequency division multiple access-based primary user (PU) network is considered, which provides different spectral access/energy harvesting opportunities in RF-powered cognitive radio networks. In this scenario, we propose an optimal spectrum sensing policy for opportunistic spectrum access/energy harvesting under both the PU collision and energy causality constraints. PU subchannels can have different traffic patterns and exhibit distinct idle/busy frequencies, due to which the spectral access/energy harvesting opportunities are application specific. Secondary user (SU) collects traffic pattern information through observation of the PU subchannels and classifies the idle/busy period statistics for each subchannel. Based on the statistics, we invoke stochastic models for evaluating SU capacity by which the energy detection threshold for spectrum sensing can be adjusted with higher sensing accuracy. To this end, we employ the Markov decision process model obtained by quantizing the amount of SU battery and the duty cycle model obtained by the ratio of average harvested energy and energy consumption rates. We demonstrate the effectiveness of the proposed stochastic models through comparison with the optimal one obtained from an exhaustive method.

Stackelberg Game for Distributed Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks

In this paper, we study the transmission strategy adaptation problem in an RF-powered cognitive radio network, in which hybrid secondary users are able to switch between the harvest-then-transmit mode and the ambient backscatter mode for their communication with the secondary gateway. In the network, a monetary incentive is introduced for managing the interference caused by the secondary transmission with imperfect channel sensing. The sensing-pricing-transmitting process of the secondary gateway and the transmitters is modeled as a single-leader-multi-follower Stackelberg game. Furthermore, the follower sub-game among the secondary transmitters is modeled as a generalized Nash equilibrium problem with shared constraints. Based on our theoretical discoveries regarding the properties of equilibria in the follower sub-game and the Stackelberg game, we propose a distributed, iterative strategy searching scheme that guarantees the convergence to the Stackelberg equilibrium. The numerical simulations show that the proposed hybrid transmission scheme always outperforms the schemes with fixed transmission modes. Furthermore, the simulations reveal that the adopted hybrid scheme is able to achieve a higher throughput than the sum of the throughput obtained from the schemes with fixed transmission modes.

Dynamic threshold-setting for RF-powered cognitive radio networks in non-Gaussian noise

The increasing demand for spectral and energy efficient communication networks has drawn an upsurge of interest in frequency (RF) energy harvesting (EH) cognitive radio networks (CRNs), and the spectrum analyzer is the brain to provide different spectral access and energy harvesting opportunities in wireless-powered cognitive radio networks (WP-CRNs). The performance of detecting the existence of primary signals will be more crucial for the EH node. However, to the best of our knowledge, no dynamic threshold-setting method to achieve target detection performance, particularly in the practical non-Gaussian noise, is reported in the literature. Traditional assumption of additive white Gaussian noise (AWGN) fails to model the behavior of certain types of noise found in practice.In this paper we give a dynamic threshold-setting method to detect the existence of primary signals for the energy harvesting node in non-Gaussian noise background. Combined with the fractional lower order statistics (FLOS), we propose a weighting factor for dynamic threshold-setting to exploit the existence of the PU to harvest RF energy, namely weighting factor based fractional lower order moment (WF-FLOM) detector. Numerical simulation results verify that the proposed method is effective for the EH node, when the signal-to-noise ratio is low or the back ground noise varies.

Performance Analysis of RF-Powered Cognitive Radio Networks with Integrated Ambient Backscatter Communications

Integrating ambient backscatter communications into RF-powered cognitive radio networks has been shown to be a promising method for achieving energy and spectrum efficient communications, which is very attractive for low-power or no-power communications. In such scenarios, a secondary user (SU) can operate in either transmission mode or backscatter mode. Specifically, an SU can directly transmit data if sufficient energy has been harvested (i.e., transmission mode). Or an SU can backscatter ambient signals to transmit data (i.e., backscatter mode). In this paper, we investigate the performance of such systems. Specifically, channel inversion power control and an energy store-and-reuse mechanism for secondary users are adopted for efficient use of harvested energy. We apply stochastic geometry to analyze coverage probability and achievable rates for both primary and secondary users considering both communication modes. Analytical tractable expressions are obtained. Extensive simulations are performed and the numerical results show the validity of our analysis. Furthermore, the results indicate that the performance of secondary systems can be improved with the integration of both communication modes with only limited impact on the performance of primary systems.

Ambient Backscatter: A New Approach to Improve Network Performance for RF-Powered Cognitive Radio Networks

This paper introduces a new solution to improve the performance for secondary systems in radio frequency (RF) powered cognitive radio networks (CRNs). In a conventional RF-powered CRN, the secondary system works based on the harvest-then-transmit protocol. That is, the secondary transmitter (ST) harvests energy from primary signals and then uses the harvested energy to transmit data to its secondary receiver (SR). However, with this protocol, the performance of the secondary system is much dependent on the amount of harvested energy as well as the primary channel activity, e.g., idle and busy periods. Recently, ambient backscatter communication has been introduced, which enables the ST to transmit data to the SR by backscattering ambient signals. Therefore, it is potential to be adopted in the RF-powered CRN. We investigate the performance of RF-powered CRNs with ambient backscatter communication over two scenarios, i.e., overlay and underlay CRNs. For each scenario, we formulate and solve the optimization problem to maximize the overall transmission rate of the secondary system. Numerical results show that by incorporating such two techniques, the performance of the secondary system can be improved significantly compared with the case when the ST performs either harvest-then-transmit or ambient backscatter technique.

RF-powered cognitive radio networks: technical challenges and limitations

The increasing demand for spectral and energy efficient communication networks has spurred a great interest in energy harvesting (EH) cognitive radio networks (CRNs). Such a revolutionary technology represents a paradigm shift in the development of wireless networks, as it can simultaneously enable the efficient use of the available spectrum and the exploitation of radio frequency (RF) energy in order to reduce the reliance on traditional energy sources. This is mainly triggered by the recent advancements in microelectronics that puts forward RF energy harvesting as a plausible technique in the near future. On the other hand, it is suggested that the operation of a network relying on harvested energy needs to be redesigned to allow the network to reliably function in the long term. To this end, the aim of this survey paper is to provide a comprehensive overview of the recent development and the challenges regarding the operation of CRNs powered by RF energy. In addition, the potential open issues that might be considered for the future research are also discussed in this paper.

Dynamic spectrum access in cognitive radio networks with RF energy harvesting

Spectrum efficiency and energy efficiency are two critical issues in designing wireless networks. Through dynamic spectrum access, cognitive radios can improve the spectrum efficiency and capacity of wireless networks. On the other hand radio frequency (RF) energy harvesting has emerged as a promising technique to supply energy to wireless networks and thereby increase their energy efficiency. Therefore, to achieve both spectrum and energy efficiencies, the secondary users in a cognitive radio network (CRN) can be equipped with the RF energy harvesting capability, and such a network can be referred to as an RF-powered cognitive radio network. In this article we provide an overview of the RF-powered CRNs and discuss the challenges that arise for dynamic spectrum access in these networks. Focusing on the trade-off among spectrum sensing, data transmission, and RF energy harvesting, we then discuss the dynamic channel selection problem in a multi-channel RF-powered CRN. In the RF-powered CRN a secondary user can adaptively select a channel to transmit data when the channel is not occupied by any primary user. Alternatively, the secondary user can harvest RF energy for data transmission if the channel is occupied. The optimal channel selection policy of the secondary user can be obtained by formulating a Markov decision process (MDP) problem. We present some numerical results obtained by solving this MDP problem.