Multi-Slot Spectrum Sensing Schedule and Transmitted Energy Allocation in Harvested Energy Powered Cognitive Radio Networks Under Secrecy Constraints

Hoan, Tran Nhut Khai; Koo, Insoo

doi:10.1109/jsen.2017.2658608

Cited by 10 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Long-established studies in [3], [5] give the foundations of rate maximization in EH systems, and more recent work in [15], [16] consider cognitive radio networks (CRNs) powered by energy harvesters, where [15] optimize spectral efficiency and [16] focus on secrecy performance. Finally, the paper in [17] investigates RF (Radio Frequency) EH for mobile devices in cloud-based networks.…”

Section: State Of the Art And Contributionsmentioning

confidence: 99%

Radio Resource Allocation with QoS Constraints in Energy Harvesting and Hybrid Power Systems

Carvalho

Lima

Maciel

et al. 2018

JCIS

View full text Add to dashboard Cite

In this paper we formulate the radio resource allocation problem of maximizing throughput in an OFDMA (Orthogonal Frequency Division Multiple Access) downlink where the BS (Base Station) adapts the power allocation according to non-causal (offline) knowledge of the harvested energy and channel state. The offline case is important from a theoretical point of view since it provides a bound on the performance of the online problem (causal). Differently from previous works that consider a continuous mapping between SNR (Signal-to-Noise Ratio) and transmit data rate, we employ a discrete mapping that depends on the required MCSs (Modulation and Coding Schemes). Also, we propose a heuristic algorithm that provides near-optimal results and achieves a good complexity/performance trade-off. In addition, we analyze the online version of the problem, and we propose two novel solutions to solve the problem only with causal information. Furthermore, we reformulate our problem to satisfy QoS (Quality of Service) constraints for each user in a hybrid power system where the BS is powered by a fixed power source from the electric grid and by a stochastic power source from renewable energy sources. Lastly, we present an offline solution to this new problem that also achieves a considerable complexity/performance gain.Index Terms-Resource allocation, rate maximization, energy harvesting, OFDMA, MCS, hybrid power system, QoS. the energy consumption causality constraints that limit the used energy to the quantity available at the moment, despite the knowledge of future energy arrivals in the offline case. Both restrictions are known as energy harvesting constraints, and are present in several works [2]-[5].

show abstract

Section: State Of the Art And Contributionsmentioning

confidence: 99%

Radio Resource Allocation with QoS Constraints in Energy Harvesting and Hybrid Power Systems

Carvalho

Lima

Maciel

et al. 2018

JCIS

View full text Add to dashboard Cite

show abstract

“…As a promising solution to the frequency scarcity, Cognitive Radio (CR) is presented to allow Secondary Users (SUs) to exploit licensed spectrum opportunistically, which breaks the fixed frequency allocation regulation. Great developments have been achieved in some crucial CR technologies such as spectrum sensing, spectrum access and power allocation [2][3][4]; however, there has been little study on cross-layer routing in the research community. Routing plays a very important role in the overall performance of any networks [5].…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

Xue

et al. 2019

Energies

View full text Add to dashboard Cite

Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in large energy consumption and transmission delay. In this paper, an apprenticeship learning scheme is proposed for the energy-efficient cross-layer routing design. Firstly, to guarantee energy efficiency and compress huge action space, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power efficiently with multi-level transition mechanism. On top of this, the Prioritized Memories Deep Q-learning from Demonstrations (PM-DQfD) is presented to speed up the convergence and reduce the memory occupation. Then the PM-DQfD is applied to the cross-layer routing design for power efficiency improvement and routing latency reduction. Simulation results confirm that the proposed method achieves higher energy efficiency, shorter routing latency and larger packet delivery ratio compared to traditional algorithms such as Cognitive Radio Q-routing (CRQ-routing), Prioritized Memories Deep Q-Network (PM-DQN), and Conjecture Based Multi-agent Q-learning Scheme (CBMQ).

show abstract

A POMDP‐based long‐term transmission rate maximization for cognitive radio networks with wireless‐powered ambient backscatter

Giang

Hoan

Thanh

et al. 2019

Int J Communication

View full text Add to dashboard Cite

SummaryWireless energy harvesting enables wireless‐powered communications to accommodate data services in a self‐sustainable manner over a long operational time. Along with energy harvesting, an ambient backscatter technique helps a secondary transmitter reflect existing radio frequency (RF) signal sources to communicate with a secondary receiver when the primary channel (PC) is utilized. However, secondary system performance is significantly affected by factors such as the availability of the primary channel, imperfect spectrum sensing, and energy‐constrained problems. Therefore, we propose a novel approach for wireless‐powered cognitive radio networks (CRNs) to improve the transmission performance of secondary systems. To reduce the dependence of the secondary system on RF sources, in the paper, we provide a new paradigm by integrating ambient backscattering with both RF and non‐RF wireless‐powered communications to facilitate secondary communications. On the basis of the sensing result in a time slot, the secondary transmitter can dynamically select the operational action: (a) backscattering, (b) harvesting, or (c) transmitting to maximize the long‐term achievable data transmission rate at the secondary receiver. In addition, the optimal action set for CRNs with wireless‐powered ambient backscatter is selected by the partially observable Markov decision process (POMDP), which maximizes an expected transmission rate calculated over a number of subsequent time slots. The proposed scheme aims to improve long‐term transmission rate of CRNs with wireless‐powered ambient backscatter in comparison with conventional schemes where an action is taken only to maximize the immediate reward in every single time slot.

show abstract

Multi-Slot Spectrum Sensing Schedule and Transmitted Energy Allocation in Harvested Energy Powered Cognitive Radio Networks Under Secrecy Constraints

Cited by 10 publications

References 26 publications

Radio Resource Allocation with QoS Constraints in Energy Harvesting and Hybrid Power Systems

Radio Resource Allocation with QoS Constraints in Energy Harvesting and Hybrid Power Systems

An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

A POMDP‐based long‐term transmission rate maximization for cognitive radio networks with wireless‐powered ambient backscatter

Contact Info

Product

Resources

About