Efficient Wideband Spectrum Sensing Using Mems Acoustic Resonators

2022 ◽  
Vol 25 (3) ◽  
pp. 23-27
Author(s):  
Junfeng Junfeng Guan ◽  
Jitian Zhang ◽  
Ruochen Lu ◽  
Hyungjoo Seo ◽  
Jin Zhou ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Dynamic Spectrum ◽  
Frequency Bands ◽  
Acoustic Resonators ◽  
Wireless Applications ◽  
Dynamic Spectrum Sharing ◽  
Wideband Spectrum Sensing ◽  
Broadband Radio ◽  
Increasing Demand

The ever-increasing demand for wireless applications has resulted in an unprecedented radio frequency (RF) spectrum shortage. Ironically, at the same time, actual utilization of the spectrum is sparse in practice [1]. To exploit previously underutilized frequency bands to accommodate new unlicensed applications and achieve highly efficient usage of the spectrum, the Federal Communications Committee (FCC) has repurposed many frequency bands for dynamic spectrum sharing. This includes the 6 GHz band to be shared between Wi-Fi 6 and the incumbent users [2] as well as the 3.5 GHz Citizens Broadband Radio Service (CBRS) band [3].

scholarly journals ESTUDO PRÉ-PROSPECTIVO PARA ROADMAP TECNOLÓGICO SOBRE RÁDIO COGNITIVO: EVOLUÇÃO CIENTÍFICO-PATENTÁRIA ENTRE 1970-2017

2018 ◽  
Vol 11 (3) ◽  
pp. 861
Author(s):  
Andrade José Luiz Andrade ◽  
David Fernandes Cruz Moura Moura ◽  
Suzana Borschiver Borschiver Borschiver
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Smart Antenna ◽  
Dynamic Spectrum ◽  
White Space ◽  
Dynamic Spectrum Sharing

<p>Avanços tecnológicos como digitalização das camadas físicas nos meios de comunicação tem promovido grande impacto na sociedade mundial em curto período de tempo. Nos últimos anos, complexos desafios propostos pela Era do Conhecimento incluem equilibrar o aumento exponencial de novos usuários a um espectro eletromagnético limitado se utilizado de forma convencional, como ocorre na atualidade. O Rádio Cognitivo emerge como uma tecnologia habilitadora para o uso eficiente e dinâmico do espectro, capaz de elevar os parâmetros de qualidade do serviço, ampliar o grau de confiabilidade e de plena utilização espectral. Este artigo explora tendências apontadas pelo estudo finlandês, <em>Trial Cognitive Radio Innovation Landscape, </em>no período 1970-2011 e monitora a evolução tecnológica entre 2012 e 2017. A análise bibliométrica permite antecipar crescente maturidade em domínios como <em>spectrum sensing, cognitive &amp; Spectrum sensing</em> e <em>dynamic spectrum sharing/management</em> e intenso esforço inovativo em <em>white space &amp; LTE, cognitive &amp; LTE </em>e<em> </em><em>cognitive &amp; smart antenna.</em></p>

scholarly journals Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing

2020 ◽  
Vol 34 (02) ◽  
pp. 1292-1299
Author(s):  
Kian Hamedani ◽  
Lingjia Liu ◽  
Shiya Liu ◽  
Haibo He ◽  
Yang Yi
Keyword(s):  
Time Series ◽  
Energy Efficient ◽  
Spectrum Sharing ◽  
Temporal Correlation ◽  
Delayed Feedback ◽  
Dynamic Spectrum ◽  
Frequency Bands ◽  
Dynamic Spectrum Sharing ◽  
Spectrum Occupancy ◽  
Mimo Ofdm

In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR with spiking neuros: DFRs are a new type of recurrent neural networks (RNNs) that are able to capture the temporal correlations in time series while spiking neurons are energy-efficient and biologically plausible neurons models. The introduced deep spiking DFR model is energy-efficient and has the capability of analyzing time series signals. The corresponding field programmable gate arrays (FPGA)-based hardware implementation of such deep spiking DFR model is introduced and the underlying energy-efficiency and recourse utilization are evaluated. Various spike encoding schemes are explored and the optimal spike encoding scheme to analyze the time series has been identified. To be specific, we evaluate the performance of the introduced model using the spectrum occupancy time series data in MIMO-OFDM based cognitive radio (CR) in dynamic spectrum sharing (DSS) networks. In a MIMO-OFDM DSS system, available spectrum is very scarce and efficient utilization of spectrum is very essential. To improve the spectrum efficiency, the first step is to identify the frequency bands that are not utilized by the existing users so that a secondary user (SU) can use them for transmission. Due to the channel correlation as well as users' activities, there is a significant temporal correlation in the spectrum occupancy behavior of the frequency bands in different time slots. The introduced deep spiking DFR model is used to capture the temporal correlation of the spectrum occupancy time series and predict the idle/busy subcarriers in future time slots for potential spectrum access. Evaluation results suggest that our introduced model achieves higher area under curve (AUC) in the receiver operating characteristic (ROC) curve compared with the traditional energy detection-based strategies and the learning-based support vector machines (SVMs).

scholarly journals Heuristic Greedy Method for Spectrum Sensing in Cognitive Radio Network

2019 ◽  
Vol 9 (2) ◽  
pp. 1454-1459
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Dynamic Spectrum Access ◽  
Cognitive Radio Network ◽  
Dynamic Spectrum ◽  
Radio Network ◽  
Secondary Users ◽  
Dynamic Spectrum Sharing ◽  
Radio Frequency Spectrum

In recent years, radio frequency spectrum in wireless communication is not effectively utilized. To utilize the spectrum effectively, an optimistic technology called “Cognitive Radio network” used. It is the best preferable next generation wireless networks. Using DSA (Dynamic Spectrum Access) approaches, it shares the spectrum effectively between the primary and secondary users. It allows the secondary users to use the spectrum by dynamic spectrum sharing algorithms. When the primary users and secondary users are using same frequency band and transmitting simultaneously, there is a spectrum underlay problem in the network. A novel heuristic greedy algorithm proposed for improving the performance parameters of cognitive radio network using co-operative spectrum sensing.

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