Hybrid Overlay-Underlay Cognitive Radio Networks With Energy Harvesting

Zheng, Kechen; Liu, Xiaoyang; Li, Xiaoying; Zhu, Yi-hua

doi:10.1109/tcomm.2019.2912605

Cited by 64 publications

(81 citation statements)

References 29 publications

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“…In the hybrid underlay/overlay mode, the SUs adaptively switches between underlay and overlay modes based on spectrum occupancy based on the PUs' activity. In the other words, the SU initiate a transmission in overlay mode at when there is no active PU and upon arrival of any PU switches to underlay mode [302]. The hybrid mode of underlay and overlay modes in CRNS has been considered by many authors.…”

Section: C1) Solutionsmentioning

confidence: 99%

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Piran¹

2020

Preprint

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The stringent requirements of wireless multimedia transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the issue with spectrum availability is not scarcity, but the inefficient utilization. Unique characteristics of cognitive radio (CR) such as flexibility, adaptability, and interoperability, particularly have contributed to it being the optimum technological candidate to alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and loss-tolerant service, exposes several severe challenges specially to guarantee quality of service (QoS) and quality of experience (QoE). As a result, to date, different schemes based on source and channel coding, multicast, and distributed streaming, have been examined to improve the QoS/QoE in MCRNs. In this paper, we survey QoS/QoE provisioning schemes in MCRNs. We first discuss the basic concepts of multimedia communication, CRNs, QoS and QoE. Then, we present the advantages of utilizing CR for multimedia services and outline the stringent QoS and QoE requirements in MCRNs. Next, we classify the critical challenges for QoS/QoE provisioning in MCRNs including spectrum sensing, resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for each challenge highlighting performance issues, strengths, and weaknesses. Furthermore, we discuss several important open research problems and provide some avenues for future research.

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Section: C1) Solutionsmentioning

confidence: 99%

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Piran¹

2020

Preprint

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“…In the third paradigm known as overlay, SU cooperates with PU and in exchange, PU allows SU to utilize the channel [13], [14]. In the literature, hybrid access schemes are also suggested to achieve a higher spectrum efficiency level [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Fair Resource Allocation in Cooperative Cognitive Radio Iot Networks

2020

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Due to the growing demand for spectral resources with the emergence of IoT networks and applications, cooperative cognitive radio IoT networks (CCR-IoTN) are expected to play an essential role in the world of smart technology. In this paradigm, the benefits of cooperative communication and cognitive radio networks (CRN) are merged to meet IoT networks' needs. In this work, we employ the hybrid overlayunderlay CRN to guarantee both the secondary user (SU) stability and provide acceptable total throughput. In our CCR-IoTN, SUs may act as a relay to help the primary transmission. If the primary user (PU) is active, the secondary non-relay nodes transmit concurrently with the PU in underlay mode. In return for their cooperation, SUs are allowed to access the spectrum to transmit their data in overlay mode. In this context, we propose several fair resource allocation frameworks and compare well-known problems such as spectrum efficiency, energy efficiency as an objective function for CCR-IoTN. This kind of problem is inherently nonconvex. To solve the problems, therefore, we reformulate them as a convex problem using quadratic transform. Finally, we use simulation to evaluate the performance of the proposed hybrid resource allocation method. The resource allocation framework based on proportional fairness policy is shown to have acceptable performance in terms of throughput, fairness, and energy efficiency.

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“…This study focused on optimal m-channel allocation under both the energy and collision constraints using the hybrid overlay/underlay mechanism, where spectrum reuse was increased by enabling energy harvesting without affecting the primary transmission. The energy constraint guarantees that the harvested energy should exceed the consumed energy, and ST should decrease spectrum usage attempts in cases where there are energy shortage situations [33]. For the collision constraint to be satisfied, the interference caused by STs on primary transmitters (PTs) should be over a certain signal to interference plus noise ratio.…”

Section: Introductionmentioning

confidence: 99%

“…However, in this journal, the ST is assumed to have two transmission modes: overlay and underlay. The transmission mode is determined according to its distance to the primary transmitter, remaining energy in the battery, and result of spectrum sensing activity [33]. Accordingly, the circular transmission area of a primary transmitter is divided into overlay mode area, underlay mode area, guard zone, and harvesting zone (HZ).…”

Section: Introductionmentioning

confidence: 99%

“…In this journal, a hybrid m-channel allocation mechanism is proposed that maximizes the m-channel allocation efficiency in EH-based CRNs, where a ST is capable of harvesting energy from the RF signals transmitted by a PT. Different from a previous work in [33], a multi-channel allocation mechanism was investigated for hybrid CRNs by dividing coverage area into four areas, including a guard zone to protect the primary transmitter. In addition, when allocating channels, STs are prioritized by proposing a new metric considering energy levels, number of ST transmission channels, and harvesting capability, which improves and speeds up the allocation of channels.…”

Section: Introductionmentioning

confidence: 99%

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Throughput Optimization of Multichannel Allocation Mechanism under Interference Constraint for Hybrid Overlay/underlay Cognitive Radio Networks with Energy Harvesting

Karaca

2020

Electronics

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By harvesting energy from ambient radio frequency (RF) signals, significant progress has been achieved in wireless networks self-maintaining their life cycles. Motivated by this and improved spectrum reuse by combined use of overlay/underlay modes of cognitive radio networks (CRNs), this paper proposes a novel multi-channel (m-channel) allocation performance maximization algorithm for low-power mobiles. CRNs, called secondary transmitters (STs), can harvest energy from RF signals by nearby active primary transmitters (PTs). In the proposed scheme, PTs and STs are distributed as independent homogeneous Poisson point processes and contact their receivers at fixed distances. Each PT contains a guard zone to protect its intended receiver from ST interference, and provides RF energy to STs located in its harvesting zone. Prioritization of STs during opportunistic allocation of channels is critical as properties like energy level and harvesting capability improve channel distribution performance. A novel metric is proposed that prioritizes STs based on initial energy levels, harvesting capability, and number of channels through which they can transmit. For comparison, three algorithms were considered: a greedy mechanism for m-channel allocation of hybrid CRNs without harvesting, the proposed m-channel allocation schemes based on maximum independent sets (MIS), and the proposed metric of hybrid CRNs with harvesting capability. The simulations show that the proposed m-channel allocation method based on MIS outperforms the greedy algorithm. The proposed m-channel allocation using the proposed metric on hybrid CRNs with energy harvesting ability produced the best performance of the three methods, proving the superiority of the proposed algorithm.

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Hybrid Overlay-Underlay Cognitive Radio Networks With Energy Harvesting

Cited by 64 publications

References 29 publications

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Fair Resource Allocation in Cooperative Cognitive Radio Iot Networks

Throughput Optimization of Multichannel Allocation Mechanism under Interference Constraint for Hybrid Overlay/underlay Cognitive Radio Networks with Energy Harvesting

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