With the advent of Sixth Generation (6G) telecommunication systems already envisioned, increased effort is made to further develop current communication technologies, so they can be incorporated together with the novel ones, to deliver uninterrupted and satisfactory service for any application in every location on the ground, underwater, in the air, or in space. One such technology is Cognitive Radio (CR) which has received much attention due to its potential for increase of utilization, especially in the bands below 6 GHz. The main enabler for CR is spectrum sensing because it provides the opportunity for dynamic assessment of the radio environment to identify unused channels. This functionality has been the object of many research works for that very reason. In spite of this, the provision of accurate and fast spectrum characterization in time, frequency and space has proven to be a non-trivial task. This paper presents a detailed review of probabilistic spectrum sensing methods classified by the feature they extract from the received signal samples, to provide accurate detection of the primary user (PU) signal. The main design characteristics (such as probability of detection, robustness to noise and fading, signal/noise model assumptions, and computational complexity), strengths and weaknesses for each type are also summarized. Based on current concepts for 6G networks and applications, a framework for human-centric cognition-based wireless access is presented, which specifies the role of spectrum sensing-based CR in future networks.

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Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

Modulation of Galactic Cosmic Rays from Helium to Nickel in the Inner Heliosphere

Spatial gradients of galactic cosmic-ray (GCR) fluxes are important for studying charged particle transport in the heliosphere. Little is known so far about how these gradients evolve with time. Here we present how the radial gradient (G r ) evolves between 2006 January and 2017 September using Cassini measurements of >300 MeV protons, which we combine with proton spectra obtained by advanced observatories at Earth (BESS, BESS-Polar, PAMELA, and AMS-02). All gradient calculations were performed for a nearly constant heliocentric distance of Cassini from Earth’s orbit and near the ecliptic, thus revealing only how G r changes with time. The variability patterns of G r are well established as they rely on a single data set for ∼9.5 au and accurately cross-calibrated GCR spectra for Earth at 1 au. We show that over solar-cycle timescales, G r is regulated by both the polarity of the solar magnetic field and the solar-cycle phase. During the negative-polarity phase (A   0 phase (2014–2017). Regular G r enhancements over yearly or biennial timescales are also observed, in phase with quasi-biennial oscillations in GCR intensity. While all aforementioned results are based on Cassini measurements that are integral in energy, they are most representative for GCR protons in the low gigaelectronvolt range.

Long- and Short-term Variability of Galactic Cosmic-Ray Radial Intensity Gradients between 1 and 9.5 au: Observations by Cassini, BESS, BESS-Polar, PAMELA, and AMS-02

Galactic cosmic rays (CRs) inside the heliosphere are affected by solar modulation. To investigate this phenomenon and its underlying physical mechanisms, we have performed a data-driven analysis of the temporal dependence of the CR proton flux over the solar cycle. The modulation effect was modeled by means of stochastic simulations of cosmic particles in the heliosphere. The model was constrained using measurements of CR protons made by AMS-02 and PAMELA experiments on a monthly basis from 2006 to 2017. With a global statistical analysis of these data, we have determined the key model parameters governing CR diffusion, its dependence on the particle rigidity, and its evolution over the solar cycle. Our results span over epochs of solar minimum and solar maximum, as well as epochs with magnetic reversal and opposite polarities. Along with the evolution of the CR transport parameters, we study their relationship with solar activity proxies and interplanetary parameters. We find that the rigidity dependence of the parallel mean free path of CR diffusion shows a remarkable time dependence, indicating a long-term variability in the interplanetary turbulence that interchanges across different regimes over the solar cycle. The evolution of the diffusion parameters shows a delayed correlation with solar activity proxies, reflecting the dynamics of the heliospheric plasma, and distinct dependencies for opposite states of magnetic polarity, reflecting the influence of charge-sign-dependent drift in the CR modulation.

Numerical modeling of cosmic rays in the heliosphere: Analysis of proton data from AMS-02 and PAMELA

Simultaneous and continuous observations of galactic cosmic-ray electrons and positrons from the PAMELA and AMS02 space experiments are most suitable for numerical modeling studies of the heliospheric modulation of these particles below 50 GeV. A well-established comprehensive three-dimensional modulation model is applied to compute full spectra for electrons and positrons with the purpose of reproducing the observed ratio positrons/electrons for a period which covers the previous long and unusual deep solar minimum activity and the recent maximum activity phase including the polarity reversal of the solar magnetic field. For this purpose the very local interstellar spectra for these particles were established first. Our study is focused on how the main modulation processes, including particle drifts, and other parameters such as the three major diffusion coefficients, had evolved, and how the corresponding charge-sign dependent modulation had occurred subsequently. The end result of our effort is the detailed reproduction of positron/electrons from 2006 to 2015, displaying both qualitative and quantitative agreement with the main observed features. Particularly, we determine how much particle drifts is needed to explain the time dependence exhibited by the observed positron/electron during each solar activity phase, especially during the polarity reversal phase when no well-defined magnetic polarity was found.

Time and Charge-Sign Dependence of the Heliospheric Modulation of Cosmic Rays

A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design of hand-crafted expert features. With the intuition of convolutional layers with pooling serving as the role of front-end feature distillation and dimensionality reduction, sequential convolutional recurrent neural networks are developed to take complementary advantage of parallel computing capability of convolutional neural networks and temporal sensitivity of recurrent neural networks. Experimental results demonstrate that the proposed architecture delivers overall superior performance in signal to noise ratio range above −10 dB, and achieves significantly improved classification accuracy from 80% to 92.1% at high signal to noise ratio range, while drastically reduces the average training and prediction time by approximately 74% and 67%, respectively. Response patterns learned by the proposed architecture are visualized to better understand the physics of the model. Furthermore, a comparative study is performed to investigate the impacts of various sequential convolutional recurrent neural network structure settings on classification performance. A representative sequential convolutional recurrent neural network architecture with the two-layer convolutional neural network and subsequent two-layer long short-term memory neural network is developed to suggest the option for fast automatic modulation classification.

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

As a recent swarm-based intelligent optimization algorithm, Pigeon Inspired Optimization (PIO), is motivated by the natural bio-mechanism of pigeons for their superior skills in destination finding and navigating. The standard PIO has been successfully implemented to solve complex optimization problems. Similar to other swarm intelligent techniques, PIO is suitable to solve global optimization problems for its robustness in adapting to dynamic environments, where the convergence rate is generally limited, since the algorithm does not employ much local information to establish a most promising direction for optima searching. In this paper, we propose a novel hybridized optimization algorithm, Nelder-Mead Pigeon Inspired Optimization (NMPIO), incorporating the global optimization ability of PIO and the capability of fast local convergent of the Nelder-Mead Simplex method. With the implementation of synthesizing a bio-inspired optimization algorithm and a direct local search method, feasible global optimal solution can be found with a faster convergence rate, compared with the original PIO algorithm. Numerical experiments for several well-known benchmarks are conducted to study the performance of this algorithm. The results reveal that our hybridization strategy is effective and efficient for solving global optimization problems.

A hybrid pigeon inspired optimization algorithm based on Nelder-Mead simplex operations

Searching and locating the position of the signal source is of great significance in wireless sensor network, mobile communication, public safety, and so on. A traditional signal source search and localization method requires either the signal source to be in the line-of-sight or a large number of operations to compute, which is difficult to satisfy in a complex environment for an unknown signal source with limited computational resources. In this paper, we propose a hybrid gradient-free optimization method combining the advantages of the Nelder-Mead Simplex algorithm and the Particle Swarm Optimization to study the search and localization of a signal source by using received signal strengths with a multi-agent system. Integrating a direct search method with a bio-inspired evolutionary method enables a feasible optimal solution to be found with a rapid convergence rate. To validate our proposal, numerical experiments are conducted to investigate the localization performance; three cases are studied, including two standard objective test functions and a complex 2.4GHz mobile signal strength distribution. The findings demonstrate the approving achievements of the proposed method in terms of global optimization, accuracy, and rate of convergence.

A Multi-Agent Based Hybrid Optimization Method for Signal Source Search and Localization

Finding the source of a hidden hazardous substance in an unknown environment is of great importance both in military and civil use. This paper presents a coordinated search algorithm that enables robots to perceive the environment and plan the route autonomously. Gaussian Process Regression is applied to estimate the intensity distribution of the hazardous source within the search area. A bi-level nested optimization algorithm, in which the first level optimizes a temporary target and the second level finds the optimal path for each robot to the temporary target, is implemented to search for the hazardous source location via a multi-robot system. A contrast simulation between multi-robot search and single-robot search is carried out for an indoor heat source search scenario, proving the feasibility and effectiveness of the proposed algorithm.

Yaping Zhang

Papers

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

A hybrid pigeon inspired optimization algorithm based on Nelder-Mead simplex operations

A Multi-Agent Based Hybrid Optimization Method for Signal Source Search and Localization

A Coordinated Hazardous Source Search algorithm Based on Gaussian Process Regression