Simultaneous Power Harvesting and Cyclostationary Spectrum Sensing in Cognitive Radios

Jang, Won Mee

doi:10.1109/access.2020.2981878

Cited by 19 publications

(6 citation statements)

References 37 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples are illustrated, which show the method's efficiency in identifying the unknown CFs. The same approach is employed in [93] to provide spectrum sensing-based energy harvesting (EH) which enables the SU to utilize the identified PU signal as a battery charge. Specific expressions for G y (α c ) in the cases of several modern wireless communication signals (such as MSK/PSK/QAM) are derived.…”

Section: ) Spectral Correlation-based Detectionmentioning

confidence: 99%

“…Works in this field have diverged into two areas -rigid detection in low SNR, and lower number of computations for the TS. The former direction has yielded notable advancements in detecting the PU signal via TS based on the ACF, CAF or SCD, even in the presence of noise uncertainty and fading [77], [78], [89], [93]. On the other hand, if the pairs (α c , τ ) for which the received signal is tested, are known, lowest complexity is usually achieved by ACF approaches because, in order to compute CAF and SCD, more Fourier transformations are necessary.…”

Section: ) Lessons Learnedmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

et al. 2021

View full text Add to dashboard Cite

With the advent of Sixth Generation (6G) telecommunication systems already envisioned, increased effort is made to further develop current communication technologies, so they can be incorporated together with the novel ones, to deliver uninterrupted and satisfactory service for any application in every location on the ground, underwater, in the air, or in space. One such technology is Cognitive Radio (CR) which has received much attention due to its potential for increase of utilization, especially in the bands below 6 GHz. The main enabler for CR is spectrum sensing because it provides the opportunity for dynamic assessment of the radio environment to identify unused channels. This functionality has been the object of many research works for that very reason. In spite of this, the provision of accurate and fast spectrum characterization in time, frequency and space has proven to be a non-trivial task. This paper presents a detailed review of probabilistic spectrum sensing methods classified by the feature they extract from the received signal samples, to provide accurate detection of the primary user (PU) signal. The main design characteristics (such as probability of detection, robustness to noise and fading, signal/noise model assumptions, and computational complexity), strengths and weaknesses for each type are also summarized. Based on current concepts for 6G networks and applications, a framework for human-centric cognition-based wireless access is presented, which specifies the role of spectrum sensing-based CR in future networks.INDEX TERMS 6G, cognitive radio, feature detection, human-centric wireless access, Internet of Things, probabilistic spectrum sensing, wireless communications.

show abstract

Section: ) Spectral Correlation-based Detectionmentioning

confidence: 99%

Section: ) Lessons Learnedmentioning

confidence: 99%

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the next simulation, we concentrate on analyzing spectral efficiency and power control in scalable cell-free massive MIMO networks. Moreover, the NFOEnsemble method is compared with the NFOPar2 method and the method of [9]. As shown in Fig.…”

Section: B Simulation Of Cellular Networkmentioning

confidence: 99%

“…In [8], the authors developed a novel transmission scheme by superposing the training symbols for active device detection and channel estimation on the data symbols in the uplink transmission to mitigate the cross interference among the superposed signals and improve the efficiency. In [9], the authors showed that cognitive spectrum sensing and power harvesting can be accomplished simultaneously. They focused on cyclostationary spectrum sensing.…”

Section: Introductionmentioning

confidence: 99%

Spectral Efficiency Improvement and Power Control Optimization of Massive MIMO Networks

2021

View full text Add to dashboard Cite

Higher requirements must be put forward about wireless signal transmission in harsh environments. Energy loss mitigation, channel estimation, noise interference reduction demands high quality of service in wireless communication. In the fifth generation wireless communication, massive multiple-input multiple-output (MIMO) networks have been used and play a significant role to fulfill the requirements. Cell-Free massive MIMO networks are recognized as possible solution in the future wireless communication. Spectral efficiency (SE) is a very important index in assessing massive MIMO networks. This article tries to optimize both SE and power control of cell-free massive MIMO networks. Both uplink and downlink transmission are included in the study. The optimization model contains both SE and power control of massive MIMO networks. To tackle the model, a novel ensemble method is developed inspired by ensemble learning. The ensemble method is built up on the neighborhood field optimization method. The goodness of the developed ensemble method is verified by comparing with genetic algorithm, gradient descent method and Newton method. Extensive simulations are performed to study the optimization of SE and power control. Different wireless sensors are taken into consideration to simulate different requirements of massive MIMO networks. The results demonstrate the effectiveness of the proposed method for applying in massive MIMO networks.INDEX TERMS Massive multiple-input multiple-output, neighborhood field optimization, optimization, power control, spectral efficiency.

show abstract

“…The benefits of proposed system can sense both kinds effectively, but the proposed system cannot sense for SNR>(-7) dB. In 2020, Jang [8] have used the CFD for the massive multiple-input and multiple-output (MIMO)-based mmWave-5G network. Though the proposed system is effective for different mappers and low SNR, the CFD requires coherency rather than higher complexity.…”

Section: Introductionmentioning

confidence: 99%

The impact of M-ary rates on various quadrature amplitude modulation detection

Algriree

Sulaiman²,

Isa³

et al. 2023

IJECE

View full text Add to dashboard Cite

The 5G system-based cognitive radio network is promised to meet the requirements of huge data applications with spectrum. However, the M-ary effect on the detection has not been thoroughly investigated. In this paper, an M-ary of quadrature amplitude modulation detection system is studied. Many rates are used in this study 4, 16, 64, and 256 constellation points. The detection system is applied to cooperative spectrum sensing to enhance the performance of detection for various rates of M-ary with low signal-to-noise ratio (SNR). Further, three kinds of signals based 5G system are sensed: filtered-orthogonal frequency division multiplexing (F-OFDM), filter bank multi-carrier (FBMC), and universal filtered multi-carrier (UFMC). The best detection performance is obtained when the M-ary=4 and number of SUs=50 user, whereas the worst detection performance is obtained when the M-ary=256 and number of SUs=10 user, as revealed in the simulation results. In addition, the detection performance for the F-OFDM signal is better than that of UFMC and FBMC signals for SNR <0 dB.

show abstract

Simultaneous Power Harvesting and Cyclostationary Spectrum Sensing in Cognitive Radios

Cited by 19 publications

References 37 publications

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

Spectral Efficiency Improvement and Power Control Optimization of Massive MIMO Networks

The impact of M-ary rates on various quadrature amplitude modulation detection

Contact Info

Product

Resources

About