Prediction and Modeling of Spectrum Occupancy for Dynamic Spectrum Access Systems

Mosavat-Jahromi, Hamed; Li, Yue; Cai, Lin; Pan, Jianping

doi:10.1109/tccn.2020.3048105

Cited by 17 publications

(6 citation statements)

References 35 publications

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“…The frequency bands below 1 GHz are heavily occupied whereas the frequency bands above 1 GHz with the distance between BSs ranging from 400 m to 1500 m are mostly vacant [13]. Therefore, assuming that the measured frequency band is above 1 GHz, and the distance between BSs is about 1000 m in this paper.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The results from the SOM around the world showed significant spectrum opportunities or low spectrum occupancy in frequencies above 1 GHz [13]. There are some literatures that attempt to deal with the challenges of 3D SOM in Section I-A.…”

Section: B Related Workmentioning

confidence: 99%

“…The expectation of S i and P e,i in (13) are shown in (23) and (24). Because the upper and lower limits of the integral with respect to θ and α are constants in ( 23) and ( 24), only the integral about x is calculated.…”

Section: Performance Analysis Of 3d Sommentioning

confidence: 99%

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Three-Dimensional Spectrum Occupancy Measurement using UAV: Performance Analysis and Algorithm Design

Wei¹,

Yao²,

Kang³

et al. 2022

Preprint

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Spectrum sharing, as an approach to significantly improve spectrum efficiency in the era of 6th generation mobile networks (6G), has attracted extensive attention. Radio Environment Map (REM) based low-complexity spectrum sharing is widely studied where the spectrum occupancy measurement (SOM) is vital to construct REM. The SOM in three-dimensional (3D) space is becoming increasingly essential to support the spectrum sharing with space-air-ground integrated network being a great momentum of 6G. In this paper, we analyze the performance of 3D SOM to further study the tradeoff between accuracy and efficiency in 3D SOM. We discover that the error of 3D SOM is related with the area of the boundary surfaces of licensed networks, the number of discretized cubes, and the length of the edge of 3D space. Moreover, we design a fast and accurate 3D SOM algorithm that utilizes unmanned aerial vehicle (UAV) to measure the spectrum occupancy considering the path planning of UAV, which improves the measurement efficiency by requiring less measurement time and flight time of the UAV for satisfactory performance. The theoretical results obtained in this paper reveal the essential dependencies that describe the 3D SOM methodology, and the proposed algorithm is beneficial to improve the efficiency of 3D SOM. It is noted that the theoretical results and algorithm in this paper may provide a guideline for more areas such as spectrum monitoring, spectrum measurement, network measurement, planning, etc.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

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Three-Dimensional Spectrum Occupancy Measurement using UAV: Performance Analysis and Algorithm Design

Wei¹,

Yao²,

Kang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The dynamic spectrum allocation model reported in Mosavat‐Jahromi et al 17 combines autoregressive moving average and low pass filter for complexity reduction in the prediction process. A non‐homogeneous Markov chain extracts occupancy details as a binary sequence and the combined approach is used to extract the low frequency periodic, aperiodic, and burst components from the time series data in the prediction process.…”

Section: Related Workmentioning

confidence: 99%

Optimized channel prediction and auction‐based channel allocation for personal cognitive networks

Jeyakanth¹,

Venkatakrishnan²,

Chitra³

2022

Int J Communication

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Cognitive networks are stands out as intelligent technology which evolved to enhance spectrum utilization. Secondary users are allowed to utilize the primary user's frequency bands on idling times. Identifying the idle licensed spectrum is achieved through spectrum sensing. The spectrum holes should be explored such that a suitable spectrum can be selected and allocated to the secondary users. Existing spectrum sensing and selection schemes have limitations due to interferences. Thus, an optimization algorithm based on bioinspired improved weed optimization was presented in this research work for enhanced channel utilization. The optimization model explores the channel characteristics and reduces the primary network interferences through its optimal solution. Further, Markov greedy-based auction scheme was presented for channel allocation. Considering the channel capacity, delay, and switching rates the allocation is performed to enhance the overall system performance. Simulation analysis demonstrates the superior performance of the proposed model over existing techniques like particle swarm optimization and genetic algorithm.

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“…With the advent of the Internet of Things (IoT), the electromagnetic spectrum scarcity has become an increasingly important problem [ 1 , 2 , 3 ]. Cognitive radio (CR) is an encouraging solution to resolve spectrum scarcity in wireless communications using dynamic spectrum access (DSA) [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation

Lin

Zhang

Tian

et al. 2022

Sensors

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Multi-signal detection is of great significance in civil and military fields, such as cognitive radio (CR), spectrum monitoring, and signal reconnaissance, which refers to jointly detecting the presence of multiple signals in the observed frequency band, as well as estimating their carrier frequencies and bandwidths. In this work, a deep learning-based framework named SigdetNet is proposed, which takes the power spectrum as the network’s input to localize the spectral locations of the signals. In the proposed framework, Welch’s periodogram is applied to reduce the variance in the power spectral density (PSD), followed by logarithmic transformation for signal enhancement. In particular, an encoder-decoder network with the embedding pyramid pooling module is constructed, aiming to extract multi-scale features relevant to signal detection. The influence of the frequency resolution, network architecture, and loss function on the detection performance is investigated. Extensive simulations are carried out to demonstrate that the proposed multi-signal detection method can achieve better performance than the other benchmark schemes.

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Prediction and Modeling of Spectrum Occupancy for Dynamic Spectrum Access Systems

Cited by 17 publications

References 35 publications

Three-Dimensional Spectrum Occupancy Measurement using UAV: Performance Analysis and Algorithm Design

Three-Dimensional Spectrum Occupancy Measurement using UAV: Performance Analysis and Algorithm Design

Optimized channel prediction and auction‐based channel allocation for personal cognitive networks

Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation

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