Spectrum Cartography using Adaptive Multi-kernels – Experimental validation

Master's thesis Information- and communication technology IKT590 - University of Agder 201

Experimental Validation for Spectrum Cartography using Adaptive Multi-kernels

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A Joint Tensor Completion and Prediction Scheme for Multi-Dimensional Spectrum Map Construction

Spectrum data, which are usually characterized by many dimensions, such as location, frequency, time, and signal strength, present formidable challenges in terms of acquisition, processing, and visualization. In practice, a portion of spectrum data entries may be unavailable due to the interference during the acquisition process or compression during the sensing process. Nevertheless, the completion work in multi-dimensional spectrum data has drawn few attention to the researchers working in the eld. In this paper, we rst put forward the concept of spectrum tensor to depict the multi-dimensional spectrum data. Then, we develop a joint tensor completion and prediction scheme, which combines an improved tensor completion algorithm with prediction models to retrieve the incomplete measurements. Moreover, we build an experimental platform using Universal Software Radio Peripheral to collect real-world spectrum tensor data. Experimental results demonstrate that the effectiveness of the proposed joint tensor processing scheme is superior than relying on the completion or prediction scheme only

Spectrum cartography techniques, challenges, opportunities, and applications: A survey

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.publishedVersio

Spectrum Map And Its Application In Resource Management In Cognitive Radio Networks

Measurements on radio spectrum usage have revealed an abundance of under-utilized bands of spectrum that belong to primary (licensed) networks. Prior knowledge about the occupancy of such bands and the expected achievable performance on those bands can help secondary (unlicensed) networks to devise effective strategies to improve utilization. Such prior spatio-Temporal spectrum usage statistics can either be obtained from a database that is maintained by the primary networks or could be measured by customized sensors deployed by the secondary networks. In this paper, we use Shepard\u27s interpolation technique to estimate a spatial distribution of spectrum usage over a region of interest, which we call the spectrum map. The interpolation is achieved by intelligently fusing the data shared by the the secondary nodes considering their mutual distances and spatial orientation with each other. The obtained map is a two-dimensional (2-D) interpolation function that is continuously differentiable and passes through all the spectrum usage values recorded at arbitrary locations; thus providing a reference for primary occupancy in that region. For determining the optimal locations for sensing primary activity, we use an iterative clustering technique that uses tree structured vector quantization. We use the spectrum map to estimate different radio and network performance metrics like channel capacity, network throughput, and spectral efficiency. As a comprehensive case study, we demonstrate how the spectrum map can be used for efficient resource allocation in TV white space. In particular, we consider an IEEE 802.22-based WRAN and show how the rendezvous probability can be improved for better radio resource allocation, thereby increasing the secondary spectrum utilization