IEEE 1900.5.2: Standard Method for Modeling Spectrum Consumption: Introduction and Use Cases

Bastidas, Carlos E. Caicedo; Stine, John A.; Rennier, Anthony; Sherman, Matthew; Lackpour, Alex; Kokar, Mieczyslaw M.; Schrage, Reinhard

doi:10.1109/mcomstd.2018.1700054

Cited by 6 publications

(7 citation statements)

References 5 publications

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“…are generally considered in the deployment of antennas [30] and transmitters. In several cases, these parameters are simplified to obtain circular coverage areas [18,23], although recently, the use of spectrum consumption models has been generalized to share this information among different operators and regulatory entities [31].…”

Section: Inputsmentioning

confidence: 99%

“…Overview-Perspectives 2019 [1] shows important figures of market, industry, data traffic, and behavior of users of mobile services. 2018 [79] introduces technical challenges and enabling technologies for 5G 2016 [80] shows spectrum regulation initiatives and open problems in dynamic spectrum sharing 2016 [70] overviews different schemes used for LTE deployments in 3.5 GHz band 2016 [81] analyzes the sources of value creation regarding the CBRS spectrum sharing Markets 2018 [82] presents an economic analysis of the spectrum market in a CBRS scenario 2017 [5] shows new business models for CBRS Regulation & Standards 2018 [31] proposes the standard IEEE P1900.5.2 to share the information among the CBRS elements 2016 [83] studies the effectiveness of census tracts as units of area to license channels 2015 [78] provides technical and deployment parameters of each CBSD and the methodology used to compute exclusion zones 2015 [84] presents in a brief the framework defined by NTIA & FCC for CBRS Experimental deployments 2018 [85] proposes LTE (RP-ABS) mechanism to mitigate interference to pulse radar 2018 [86] evaluates the time for each SAS state in the evacuation and frequency change process 2018 [87] deploys an outdoor scenario based on LBT mechanism for GAA coexistence 2018 [88] evaluates the total network capacity within an existing LTE network and its effect on new technologies as CBRS Spectrum allocation 2018 [19] proposes a channel allocation algorithm and models it mathematically 2018 [89] allocates channels using graphs, and finds solutions by classical heuristic methods 2017 [90] allocates channels through an optimization problem, and solve it using its algorithm 2017 [91] proposes a generic graph representation to model the interference Security 2018 [92] addresses the problem for military users when their transmitters are discovered 2018 [93] develops strategies to keep incumbent users hidden Interference management 2018 [94] addresses the power control and channel allocation problem to predict the throughput 2017 [95] proposes power control algorithms to reduce the protection distances base on co-channel and adjacent interference. 2017 [96] provides expressions for the GAA coverage probability and spectral efficiency 2016 ...…”

Section: Topic Year Ref Contributionmentioning

confidence: 99%

“…Hence, [83] explored which implications this choice has on efficient spectrum usage. And finally, [31] proposed the spectrum consumption model being standardized under the IEEE 1900.5.2 working group to unify the information among spectrum sharing schemes, as CBRS. This standard is a means to inform and establish how each CBSD or SAS use spectrum resources in a particular scenario and at a specific location.…”

Section: Cbrs Regulationmentioning

confidence: 99%

See 2 more Smart Citations

Literature review of deploying small transmitters on a spectrum sharing scheme

Hurtado¹,

Ramírez

Caicedo

2021

Rev.Fac.Ing.Univ.Antioquia

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Nowadays, there are a lot of misinformation and myths related to both the deployment of antennas for 5G and the frequency bands used in such technology. Many of these myths arise from the lack of serious research documents released in common language to be understood by different knowledge areas. This work makes a small contribution in this regard and shows a bibliographic review of the most relevant characteristics related to the problem of deployment of transmitters (a.k.a. base stations), and also takes into account a spectrum sharing scheme. As a case study, we will analyze the CBRS regulation used in the United States for the deployment of 5G, it will provide an overview of engineering problems and also, the areas of research that still need to be explored.

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Section: Inputsmentioning

confidence: 99%

Section: Topic Year Ref Contributionmentioning

confidence: 99%

Section: Cbrs Regulationmentioning

confidence: 99%

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Literature review of deploying small transmitters on a spectrum sharing scheme

Hurtado¹,

Ramírez

Caicedo

2021

Rev.Fac.Ing.Univ.Antioquia

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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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“…Besides, additional interference can be caused by electrical equipment [6] or deliberate RF jamming [7]. Apart from a few proprietary solutions [8], there is a lack of mechanisms for intra-technology spectrum management and medium-access coordination [9]. Moreover, today's IoT wireless devices do not decode, or analyze the properties of signals from other technologies but pursue interference mitigation via threshold-based energy-sensing [10], and a posteriori channel-quality evaluation [11] only.…”

Section: Introductionmentioning

confidence: 99%

Onboard Spectral Analysis for Low-Complexity IoT Devices

et al. 2020

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The lack of coordinated spectrum access for IoT wireless technologies in unlicensed bands creates inefficient spectrum usage and poses growing concerns in several IoT applications. Spectrum awareness becomes then crucial, especially in the presence of strict quality-of-service (QoS) requirements and mission-critical communication. In this work, we propose a lightweight spectral analysis framework designed for strongly resource-constrained devices, which are the norm in IoT deployments. The proposed solution enables model-based reconstruction of the spectrum of single radio-bursts entirely onboard without DFT processing. The spectrum sampling exploits pattern-based frequency sweeping, which enables the spectral analysis of short radio-bursts while minimizing the sampling error induced by non-ideal sensing hardware. We carry out an analysis of the properties of such sweeping patterns, derive useful theoretical error bounds, and explain how to design optimal patterns for radio front-ends with different characteristics. The experimental campaign shows that the proposed solution enables the estimation of central frequency, bandwidth, and spectral shape of signals at runtime by using a strongly hardware-limited radio platform. Finally, we test the potential of the proposed solution in combination with a proactive blacklisting scheme, allowing a substantial improvement in real-time QoS of a radio link under interference.

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IEEE 1900.5.2: Standard Method for Modeling Spectrum Consumption: Introduction and Use Cases

Cited by 6 publications

References 5 publications

Literature review of deploying small transmitters on a spectrum sharing scheme

Literature review of deploying small transmitters on a spectrum sharing scheme

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

Onboard Spectral Analysis for Low-Complexity IoT Devices

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