Deep Stacked CNN-LSTM (DS-CNN-LSTM) based Spectrum Sensing in Cognitive Radio

The multidimensionality of spectrum sensing, the intrinsic complexity of its dependence, and the unpredictability associated with spectrum data all contribute to the difficulty of the task. The network of cognitive radio (CR) is comprised of both primary and secondary users inside its network. The SUs that are part of the CR network are able to identify the spectrum band and access white space in an opportunistic manner. Enhancing spectrum efficiency may be accomplished by using white spaces. This study presents a Deep Stacked CNN-LSTM (DS-CNN-LSTM)-based spectrum sensing strategy that learns implicit features from spectrum data, such as temporal correlation. This approach is based on the research that we have conducted. The effectiveness of the recommended method is shown by a sufficient number of simulations, and the results of the simulations demonstrate that it outperforms the current state of the art in terms of detection probability and classification accuracy. A comparison is made between the most cutting-edge spectrum sensing approaches and the DS-CNN-LSTM method that has been recommended. The results of the experiments indicate that the proposed methods improve detection performance and classification accuracy even when the signal-to-noise ratio is low. As we can see, the improvement that was achieved comes at the price of a longer amount of time spent on training and a little increase in the amount of time spent on execution

Cooperative Spectrum Sensing based on 1-bit Quantization in Cognitive Radio Networks

The wireless frequency spectrum is a very valuable resource in the field of communications. Over the years, different bands of the spectrum were licensed to various communications systems and standards. As a result, most of the easily accessible parts of it ended up being theoretically occupied. This made it somewhat difficult to accommodate new wireless technologies, especially with the rise of communications concepts such as the Machine to Machine (M2M) communications and the Internet of Things (IoT). It was necessary to find ways to make better use of wireless spectrum. Cognitive Radio is one concept that came into the light to tackle the problem of spectrum utilization. Various technical reports stated that the spectrum is in fact under-utilized. Many frequency bands are not heavily used over time, and some bands have low activity. Cognitive Radio (CR) Networks aim to exploit and opportunistically share the already licensed spectrum. The objective is to enable various kinds of communications while preserving the licensed parties' right to access the spectrum without interference. Cognitive radio networks have more than one approach to spectrum sharing. In interweave spectrum sharing scheme, cognitive radio devices look for opportunities in the spectrum, in frequency and over time. Therefore, and to find these opportunities, they employ what is known as spectrum sensing. In a spectrum sensing phase, the CR device scans certain parts of the spectrum to find the voids or white spaces in it. After that it exploits these voids to perform its data transmission, thus avoiding any interference with the licensed users. Spectrum sensing has various classifications and approaches. In this thesis, we will present a general review of the main spectrum sensing categories. Furthermore, we will discuss some of the techniques employed in each category including their respective advantages and disadvantages, in addition to some of the research work associated with them. Our focus will be on cooperative spectrum sensing, which is a popular research topic. In cooperative spectrum sensing, multiple CR devices collaborate in the spectrum sensing operation to enhance the performance in terms of detection accuracy. We will investigate the soft-information decision fusion approach in cooperative sensing. In this approach, the CR devices forward their spectrum sensing data to a central node, commonly known as a Fusion Center. At the fusion center, this data is combined to achieve a higher level of accuracy in determining the occupied parts and the empty parts of the spectrum while considering Rayleigh fading channels. Furthermore, we will address the issue of high power consumption due to the sampling process of a wide-band of frequencies at the Nyquist rate. We will apply the 1-bit Quantization technique in our work to tackle this issue. The simulation results show that the detection accuracy of a 1-bit quantized system is equivalent to a non-quantized system with only 2 dB less in Signal-to-Noise Ratio (SNR). Finally, we will shed some light on multiple antenna spectrum sensing, and compare its performance to the cooperative sensing

Far-infrared polarimetry from the Stratospheric Observatory for Infrared Astronomy

Multi-wavelength imaging polarimetry at far-infrared wavelengths has proven to be an excellent tool for studying the physical properties of dust, molecular clouds, and magnetic fields in the interstellar medium. Although these wavelengths are only observable from airborne or space-based platforms, no first-generation instrument for the Stratospheric Observatory for Infrared Astronomy (SOFIA) is presently designed with polarimetric capabilities. We study several options for upgrading the High-resolution Airborne Wideband Camera (HAWC) to a sensitive FIR polarimeter. HAWC is a 12 x 32 pixel bolometer camera designed to cover the 53 - 215 micron spectral range in 4 colors, all at diffraction-limited resolution (5 - 21 arcsec). Upgrade options include: (1) an external set of optics which modulates the polarization state of the incoming radiation before entering the cryostat window; (2) internal polarizing optics; and (3) a replacement of the current detector array with two state-of-the-art superconducting bolometer arrays, an upgrade of the HAWC camera as well as polarimeter. We discuss a range of science studies which will be possible with these upgrades including magnetic fields in star-forming regions and galaxies and the wavelength-dependence of polarization.Comment: 12 pages, 5 figure

Wideband Autonomous Cognitive Radios: Spectrum Awareness and PHY/MAC Decision Making

The cognitive radios (CRs) have opened up new ways of better utilizing the scarce wireless spectrum resources. The CRs have been made feasible by recent advances in software-defined radios (SDRs), smart antennas, reconfigurable radio frequency (RF) front-ends, and full-duplex RF front-end architectures, to name a few. Generally, a CR is considered as a dynamically reconfigurable radio capable of adapting its operating parameters to the surrounding environment. Recent developments in spectrum policy and regulatory domains also allow more flexible and efficient utilization of wider RF spectrum range in the future. In line with the future directions of CRs, a new vision of a future autonomous CR device, called Radiobots, was previously proposed. The goals of the proposed Radiobot surpass the dynamic spectrum access (DSA) to achieve wideband operability and the main features of cognition. In order to ensure the practicality and robust operation of the Radiobot structure, the research focus of this dissertation includes the following aspects: 1) robust spectrum sensing and operability in a centralized CR network setup; 2) robust multivariate non-parametric quickest detection for dynamic spectrum usage tracking in an alien RF environment; 3) joint physical layer and medium access control layer (PHY/MAC) decision-making for wideband bandwidth aggregation (simultaneous operation over multiple modes/networks); and 4) autonomous spectrum sensing scheduling solutions in an alien ultra wideband RF environment. The major contribution of this dissertation is to investigate the feasibility of the autonomous CR operation in heterogeneous RF environments, and to provide novel solutions to the fundamental and crucial problems/challenges, including spectrum sensing, spectrum awareness, wideband operability, and autonomous PHY/MAC protocols, thus bringing the autonomous Radiobot one step closer to reality