Optimal Energy-Efficient Power Allocation for OFDM-Based Cognitive Radio Networks

Wang, Yan; Xu, Wenjun; Yang, K. H.; Lin, Jiaru

doi:10.1109/lcomm.2012.070512.120662

Cited by 101 publications

(82 citation statements)

References 10 publications

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“…This is defined as the instantaneous transmission rate (or throughput) divided by the instantaneous transmit and consumed circuitry power (or energy), and is expressed in bits/joule (bits/J) [10,12,25,34,35,44,45,49].…”

Section: Energy Efficiency (Ee) Metricsmentioning

confidence: 99%

“…For instance, for multicarrier communication systems, if a single SU employs an orthogonal frequency division multiplexing (OFDM) in order to access the available spectrum, the instantaneous EE is defined as [35,45] …”

Section: Energy Efficiency (Ee) Metricsmentioning

confidence: 99%

“…In such a case, transmit power [1,27,34,35,40,45], signal-to-interference-plus-noise ratio (SINR) threshold [22], frequency bands/subcarriers [9,25,44,49], and/or packet length [38] are optimized separately or jointly in order to improve the CR system energy efficiency.…”

Section: Sensing-less Energy-aware Resource Allocationmentioning

confidence: 99%

“…The available spectrum can be accessed by a single SU employing OFDM modulation [27,34,45] or by multiple SUs employing OFDMA [25,44,49]. In case of a single SU accessing the spectrum, the power allocated to each OFDM subcarrier should be optimally allocated in order to improve EE.…”

Section: Power Allocation Of Multicarrier Systemsmentioning

confidence: 99%

See 3 more Smart Citations

Energy-Aware Cognitive Radio Systems

Bedeer

Amin

Dobre

et al. 2016

Energy Management in Wireless Cellular and Ad-Hoc Networks

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The concept of energy-aware communications has spurred the interest of the research community in the most recent years due to various environmental and economical reasons. It becomes indispensable for wireless communication systems to shift their resource allocation problems from optimizing traditional metrics, such as throughput and latency, to an environmental-friendly energy metric. Although cognitive radio systems introduce spectrum efficient usage techniques, they employ new complex technologies for spectrum sensing and sharing that consume extra energy to compensate for overhead and feedback costs. Considering an adequate energy efficiency metric-that takes into account the transmit power consumption, circuitry power, and signaling overhead-is of momentous importance such that optimal resource allocations in cognitive radio systems reduce the energy consumption. A literature survey of recent energy-efficient based resource allocations schemes is presented for cognitive radio systems. The energy efficiency performances of these schemes are analyzed and evaluated under power budget, co-channel and adjacentchannel interferences, channel estimation errors, quality-of-service, and/or fairness constraints. Finally, the opportunities and challenges of energy-aware design for cognitive radio systems are discussed.

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Section: Energy Efficiency (Ee) Metricsmentioning

confidence: 99%

Section: Energy Efficiency (Ee) Metricsmentioning

confidence: 99%

Section: Sensing-less Energy-aware Resource Allocationmentioning

confidence: 99%

Section: Power Allocation Of Multicarrier Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Aware Cognitive Radio Systems

Bedeer

Amin

Dobre

et al. 2016

Energy Management in Wireless Cellular and Ad-Hoc Networks

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show abstract

“…For efficient data collection, mobile sinks have been widely used for data collection. (8,9) Unlike the existing static sink, a mobile sink moves toward the deployed sensor nodes along a given path for data collection. This approach uses only one mobile sink and one-hop communication.…”

Section: Introductionmentioning

confidence: 99%

Regional Density-aware Data Collection Using Unmanned Aerial Vehicle in Large-scale Wireless Sensor Networks

Kim¹,

Choi²,

Kwon³

2018

Sensors and Materials

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This paper presents a regional density-aware data collection (RDDC) using unmanned aerial vehicles (UAVs) in large-scale wireless sensor networks (WSNs). The RDDC is designed to support fast data collection by adaptively adjusting the value of the minimum contention window (CW min ) of the sensor nodes depending on the regional node density of sensing fields. The operation of RDDC consists of the region-partitioning, node-scanning, and data-collecting phases. In the region-partitioning phase, the RDDC partitions the sensing field into multiple regions and assigns a region ID to each sensor node according to the region to which it belongs. In the node-scanning phase, the RDDC calculates the node density of each region. Finally, in the data-collecting phase, the RDDC assigns different CW min values to each sensor node considering the regional node density of each region. The experimental simulation results show that the RDDC obtains higher aggregate throughput and shorter delay than the existing data collection method.

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Joint sensing time and fusion rule threshold optimization for energy efficient CSS in cognitive radio sensor networks

Sharma

2018

Int J Communication

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Energy resources become precious when the secondary users (SUs) in cognitive radio (CR) network are battery operated, especially in cooperative spectrum sensing (CSS). So it becomes important to use their energy efficiently. In CSS, the number of cooperative users, fusion rule threshold, transmission power, and sensing time affects the energy efficiency (EE) of the CSS. In this paper, an iterative algorithm is proposed to determine joint optimal fusion rule threshold and sensing time that maximizes the EE of CSS. Results show that EE is maximum at optimum sensing duration τ s0 = 0.841 ms and fusion rule threshold k 0 = 4 at SNR = −18 dB. Maximum EE at optimal point is ξ max = 3.15 bits/Hz/J. KEYWORDS cognitive radio (CR), cooperative spectrum sensing (CSS), energy efficiency (EE), fusion rule (FR)

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Optimal Energy-Efficient Power Allocation for OFDM-Based Cognitive Radio Networks

Cited by 101 publications

References 10 publications

Energy-Aware Cognitive Radio Systems

Energy-Aware Cognitive Radio Systems

Regional Density-aware Data Collection Using Unmanned Aerial Vehicle in Large-scale Wireless Sensor Networks

Joint sensing time and fusion rule threshold optimization for energy efficient CSS in cognitive radio sensor networks

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