Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

Enabling Intelligence in Spectrum Sharing

We argue that the capital expenditures made by an individual mobile network operator is extremely high and risky. Also, radio spectrum sharing still lacks intelligence in the current architecture of mobile networks and needs to be rethought. We propose that the goal for a disruptive innovation, in the future mobile network architecture, that shall be able to free mobile network operators from having to hold spectrum licenses and natively enable intelligent radio spectrum sharing among multiple mobile network operators. On the basis of the design principles, the duty of a single mobile network operator is split into two roles, one focuses on infrastructure development, the other only contains authorizations on the radio spectrum usage. We introduce a new role to the mobile network architecture, named Spectrum Trader, is a primary broker for spectrum trading, and it is used to coordinate with the demand-side requests and the supply-side resources to drive demand in a \emph{real-time bidding} manner. We also introduce a spectrum embedding technique that shall enable efficient and intelligent spectrum allocation by recommending the right spectrum bands based on user scenario. Finally, several significant challenges that need to be addressed in practical deployment are investigated.

Enabling Intelligence in Spectrum Sharing

We argue that the capital expenditures made by an individual mobile network operator is extremely high and risky. Also, radio spectrum sharing still lacks intelligence in the current architecture of mobile networks and needs to be rethought. We propose that the goal for a disruptive innovation, in the future mobile network architecture, that shall be able to free mobile network operators from having to hold spectrum licenses and natively enable intelligent radio spectrum sharing among multiple mobile network operators. On the basis of the design principles, the duty of a single mobile network operator is split into two roles, one focuses on infrastructure development, the other only contains authorizations on the radio spectrum usage. We introduce a new role to the mobile network architecture, named Spectrum Trader, is a primary broker for spectrum trading, and it is used to coordinate with the demand-side requests and the supply-side resources to drive demand in a \emph{real-time bidding} manner. We also introduce a spectrum embedding technique that shall enable efficient and intelligent spectrum allocation by recommending the right spectrum bands based on user scenario. Finally, several significant challenges that need to be addressed in practical deployment are investigated.

COLDz: Probing Cosmic Star Formation With Radio Free–Free Emission

Radio free–free emission is considered to be one of the most reliable tracers of star formation in galaxies. However, as it constitutes the faintest part of the radio spectrum—being roughly an order of magnitude less luminous than radio synchrotron emission at the GHz frequencies typically targeted in radio surveys—the usage of free–free emission as a star formation rate tracer has mostly remained limited to the local universe. Here, we perform a multifrequency radio stacking analysis using deep Karl G. Jansky Very Large Array observations at 1.4, 3, 5, 10, and 34 GHz in the COSMOS and GOODS-North fields to probe free–free emission in typical galaxies at the peak of cosmic star formation. We find that z ∼ 0.5–3 star-forming galaxies exhibit radio emission at rest-frame frequencies of ∼65–90 GHz that is ∼1.5–2 times fainter than would be expected from a simple combination of free–free and synchrotron emission, as in the prototypical starburst galaxy M82. We interpret this as a deficit in high-frequency synchrotron emission, while the level of free–free emission is as expected from M82. We additionally provide the first constraints on the cosmic star formation history using free–free emission at 0.5 ≲ z ≲ 3, which are in good agreement with more established tracers at high redshift. In the future, deep multifrequency radio surveys will be crucial in order to accurately determine the shape of the radio spectrum of faint star-forming galaxies, and to further establish radio free–free emission as a tracer of high-redshift star formation.

Federated Learning for 5G Radio Spectrum Sensing

Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.

Sharing Radio Spectrum for Research and Innovation

In most countries academic researchers have access to advanced academic telecommunications networks and infrastructures to test and demonstrate the results of their research work. These networks are usually funded by national or regional public authorities. To provide access to the academic networks on a wider scale, European and international collaboration initiatives have been taken. For the fixed network environment this may suffice but the situation is different in the wireless context, partly because here, researchers must, in one way or another, obtain spectrum usage rights. Today spectrum usage rights can be quite easily obtained in the restricted territorial space of a testbed. Yet, small-scale testbeds are not sufficient anymore for realistic validation, and the scientific community today needs large-scale field deployments working with the same radio spectrum as the commercial networks and capable of supporting new technologies and services. The evolution from lab testbeds to field deployments is required to increase the validation capabilities for complex systems like connected cars, massive Internet of Things (IoT) or eHealth solutions. Appropriate frequency bands, needed by researchers to carry out, for example, large-scale 5G experiments, are generally allocated via auctions and on an exclusive basis to large mobile network operators. While it is perfectly feasible for these MNOs to keep dedicated slices for tests and demonstrations in their networks separate from their day-to-day operations without negative effects for the latter, there are few regulatory mechanisms for stimulating MNOs to make parts of their spectrum usage rights available for the academic research community. All EU Member States allow short-term licenses for the use of radio spectrum for research, testing, and experimental purposes, but procedures, requirements, and costs for obtaining such license vary significantly. These national differences do not allow for the creation of a persistent and pan-European network of wireless capacity for research, testing, and experimental purposes. On the secondary market, leasing or transferring radio spectrum usage rights is possible, and procedures seem more harmonized.

Modelling the Energy Spectra of Radio Relics

Radio relics are diffuse synchrotron sources that illuminate shock waves in the intracluster medium. In recent years, radio telescopes have provided detailed observations about relics. Consequently, cosmological simulations of radio relics need to provide a similar amount of detail. In this methodological work, we include information on adiabatic compression and expansion, which have been neglected in the past in the modelling of relics. In a cosmological simulation of a merging galaxy cluster, we follow the energy spectra of shock accelerated cosmic-ray electrons using Lagrangian tracer particles. On board of each tracer particle, we compute the temporal evolution of the energy spectrum under the influence of synchrotron radiation, inverse Compton scattering, and adiabatic compression and expansion. Exploratory tests show that the total radio power and, hence, the integrated radio spectrum are not sensitive to the adiabatic processes. This is attributed to small changes in the compression ratio over time.

Reducing Subcarriers Beam-Squinting of Ultra-Wideband Mobile Communication Systems using Phased Array Antennas

There has been increasing demand for accessible radio spectrum with the rapid development of mobile wireless devices and applications. For example, a GHz of spectrum is needed for fifth-generation (5G) cellular communication, but the avail- able spectrum below 6 GHz cannot meet such requirements. Fortunately, spectrum at higher frequencies, in particular, millimeter-wave bands, can be utilized through phased-array analog beamforming to provide access to large amounts of spectrum. However, the gain provided by a phased array is frequency dependent in the wideband system, an effect called beam squint. We examine the nature of beam squint and develop convenient models with a uniform linear array. To further simplify the evaluation of the system performance, an approximated closed-form expression for the array gain is derived. Furthermore, to evaluate the performance of the proposed design, rigorous numerical results concerning different system parameters are provided in this paper.