We study a certain random groeth model in two dimensions closely related to the one-dimensional totally asymmetric exclusion process. The results show that the shape fluctuations, appropriately scaled, converges in distribution to the Tracy-Widom largest eigenvalue distribution for the Gaussian Unitary Ensemble.

Shape Fluctuations and Random Matrices

Integrated satellite-terrestrial networks (ISTNs) toward beyond fifth-generation (B5G) wireless systems benefiting from both satellite and terrestrial systems can achieve all-time seamless and broad coverage. Considering the scarcity of frequency resources and intense satellite-terrestrial cochannel interference, intelligent resource allocation with high spectrum efficiency and low co-channel interference has received a substantial amount of attention. Focusing on the spectrum efficiency advantages achieved by spectrum sensing and prediction, a hierarchical satellite and terrestrial spectrum shared framework based on the spectrum management unit (SMU) is proposed. Moreover, an intelligent resource management scheme in the SMU composed of spectrum sensing, prediction and allocation is formulated to improve spectrum efficiency with different user densities. We present a support vector machine (SVM) based algorithm that improves the accuracy and robustness of the learned model for the detection of spectrum occupancy. Then, a convolutional neural network (CNN) based spectrum prediction (SP) is performed, where the CNN is trained with the historical detection results from spectrum sensing. In addition, an intelligent resource management scheme including spectrum sensing, prediction and allocation based on the priorities and requirements of users is proposed to improve spectrum utilization. The evaluation results demonstrate that the proposed intelligent resource management scheme can achieve lower error detection probability and better spectrum efficiency.

Intelligent Resource Management for Satellite and Terrestrial Spectrum Shared Networking toward B5G

Soil quality is vital in agriculture. People often use sensor networks to obtain the soil data of a piece of land. Sometimes, people detect soil data by using one-dimensional (1-D) ultrawideband (UWB) signals, which is too energy consuming. Hence, we want an energy-saving model for this condition. Compressed sensing (CS) is a feasible model to save energy. As soon as CS was put forward, it attracted wide attention. However, how to design a suitable sparse dictionary is still a problem until now. Aiming at different situations, designers should change their sparse dictionary. Unfortunately, soil data are always changing because of the variance of weather and environment. Therefore, if there is a computational intelligent CS algorithm which is suitable for variant signals, the problem is solved. In this paper, we proposed a deep learning model of CS, which can avoid designing a sparse dictionary. We combine deep learning and CS together and regard the output of networks as the recovery signals, because deep learning's end-to-end structure solves the above-mentioned problem. We use deep image prior, which is derived from deep convolutional generative adversarial networks to recover 1-D signals of soil data. Meanwhile, we used the mean square error between the recovery signals and the original signals of soil echoes to verify our algorithm. Finally, we classified PH values with recovery signals based on Xgboost because of its excellent classification ability. The experiment results show that our model's accuracy is much higher than those by traditional CS algorithms with a relatively small number of sensors. Also, our model has a better robustness.

Soil PH Measurement Based on Compressive Sensing and Deep Image Prior

The performances of data transmissions in vehicular network are investigated. In the vehicular network, the data transmissions are realized by the proposed transmission strategies. Under a cognitive way, the specific strategy is adopted in the corresponding transmission duration. In this paper, the vehicle-to-pedestrian, vehicle-to-vehicle, and vehicle-to-infrastructure/network transmission modes are adopted to implement the data transmission. In the data transmission durations, the transmission modes transit among one another based on the strategies decisions. A state transition model is proposed to describe the communication and evaluate the performances. The concept of effective capacity is adopted, and the mathematical expression of effective capacity is derived. Besides, the effective capacity under the parameter constraints is also demonstrated. From the specific simulations, it is shown that the effective capacity is the most sensitive to quality-of-service exponent. The pedestrian density and vehicle density also impose significant impact on the transmission performances. Besides, short transmission range of the infrastructures and vehicles is necessary to improve the effective capacity.

Cognitive Communication in Link-Layer Evaluation Based Cellular-Vehicular Networks

The problem of wideband spectrum sensing/sampling in the sub-Nyquist domain is solved in this paper using sparse (low-density) binary-valued measurement matrices. Key objectives are (i) to achieve an efficient compression ratio, and (ii) improve the signal reconstruction performance. We propose a novel RF front-end with parallel branches that we have called Low-Density Wideband Converter (LDWC). We show that the LDWC implements a binary Low-Density Parity Check (LDPC) matrix as the compressive sensing (CS) measurement matrix. We evaluate, using an Information-Theoretic approach, the asymptotic bound on the required number of LDWC parallel branches for sparsity detection. We develop two new belief propagation (BP) algorithms that operate on the Tanner graph of the CS measurements. We have derived the first algorithm by assuming independence among the variable nodes (VNs) of the Tanner graph. For the second method, we have accounted for the joint probability distribution of the VNs. Analytical and simulated performance results prove the concepts of the LDWC and the proposed BP algorithms and quantify the attainment of objectives (i) and (ii) stated above.

Sub-Nyquist Spectrum Sensing of Sparse Wideband Signals Using Low-Density Measurement Matrices

The large-scale deployment of wireless sensor networks is indispensable to the success of Internet of Things. Considering dynamic spectrum access and the limited spectrum resources in cognitive radio sensor networks, sub-Nyquist spectrum sensing based on the modulated wideband converter is introduced. Since the transmission signals are usually modulated by different carrier frequencies, the interested spectrum can be modeled as the multiband signal. Modulated wideband converter (MWC) is an attractive alternative among several sub-Nyquist sampling systems because it has been implemented in practice and the frequency support reconstruction algorithm is the most important part in MWC. However, most existing reconstruction methods require the sparse information, which is difficult to acquire in practical scenarios. In this paper, we propose a blind multiband signal reconstruction method, referred to as the statistics multiple measurement vectors (MMV) iterative algorithm to bypasses the above problem. By exploiting the jointly sparse property of MMV model, the supports can be obtained by statistical analysis for the reconstruction results. Simulation results show that, without the sparse prior, the statistics MMV iterative algorithm can accurately determine the support of the multiband signal in a wide range of signal-to-noise ratio by using various numbers of sampling channels.

Sub-Nyquist Spectrum Sensing Based on Modulated Wideband Converter in Cognitive Radio Sensor Networks

For the compressed sensing of multiband signals, modulated wideband converter (MWC) is used as the sampling system, and the signal is reconstructed by the simultaneous orthogonal matching pursuit algorithm (SOMP) and its derivative algorithms. In order to find matching atoms, we need to obtain the inner product between atoms in sensing matrix and columns in residual matrix. Next, several inner products corresponding to the same atom constitute an inner product vector. By calculating its 2-norm, we can find the maximum value, whose corresponding atom is the matched atom. However, the inner product actually cannot reflect the relevancy between atoms and residual matrix very accurately, which may eventually lead to wrong results for a certain probability. The main idea of this paper is to change the inner product into the correlation coefficient, so that we can measure the relevancy between atoms and the residual better. Simulation results show that the improved algorithms can get higher probability of the signal reconstruction compared with the original algorithms in the condition of high signal-noise ratio (SNR). It also means that less samples were needed to reconstruct signals than traditional algorithms when the number of bands is unchanged. Since calculating correlation coefficient at each iteration will cost a lot of time, we also proposed a simplified algorithm, which can also improve reconstruction probability and reconstruction time is about the same as corresponding traditional algorithms.

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Improved algorithm based on modulated wideband converter for multiband signal reconstruction

The millimeter wave (mm wave) communications have been proposed to be an important part of the 5G mobile communication networks, and it will bring more difficulties to signal processing, especially signal sampling, and also cause more pressures on hardware devices. In this paper, we present a simplified sampling and detection method based on MWC structure by using the idea of blind source separation for mm wave communications, which can avoid the challenges of signal sampling brought by high frequencies and wide bandwidth for mm wave systems. This proposed method takes full advantage of the beneficial spectrum aliasing to achieve signal sampling at sub-Nyquist rate. Compared with the traditional MWC system, it provides the exact quantity of sampling channels which is far lower than that of MWC. In the reconstruction stage, the proposed method simplifies the computational complexity by exploiting simple linear operations instead of CS recovery algorithms and provides more stable performance of signal recovery. Moreover, MWC structure has the ability to apply to different bands used in mm wave communications by mixed processing, which is similar to spread spectrum technology.

/pdf/a-simplified-multiband-sampling-and-detection-method-based-2mm7kvdgek.pdf

A Simplified Multiband Sampling and Detection Method Based on MWC Structure for Mm Wave Communications in 5G Wireless Networks

In cognitive radio networks, spectrum sensing is a necessary technique to detect the status of spectrum resources. Considering the actual usage of authorized spectrum, various spectrum sensing techniques make the authorized spectrum reused possible without disturbing the authorized user. Energy detection is widely used because of its simple and efficient, but it is easily affected by the uncertainty signal-to-noise ratio, known as the SNR wall. However, covariance detection just has an ability to overcome this weakness. For this reason, we expect to exploit the advantages of both energy and covariance detection to realize the fast and efficient spectrum sensing process. In this paper, an improved spectrum sensing based on energy detection and covariance detection is proposed. The algorithm consists of two parts, namely coarse detection and fine detection, to ensure the accuracy of detection results. Simulation results show that the detection performance will have more improvement. The complexity is also analyzed in this paper. And there is a tradeoff between the detection performance and the complexity.

An improved spectrum sensing algorithm based on energy detection and covariance detection

Spectrum sensing is a fundamental functionality for cognitive radio networks to detect spectrum resource status and to provide the opportunity for the cognitive user to use the under-utilized frequency resource without causing harmful interference to primary user. Energy detection, which is the most widely used in Cognitive Radio system, belongs to a kind of crude detection method and is not sensitive to noise uncertainty, especially under low SNR condition, detection performance will decline. On the other hand, covariance detection is more feasible to avoid the uncertainty of noise effect on detection performance. In this paper, the fractional-step detection based on energy and covariance detection is proposed to achieve spectrum sensing. This algorithm combines the simplicity of energy detection and the fine statistical function of covariance detection, and improves the accuracy by detecting step by step. But it will cause more hardware overhead. So to use fractional-step algorithm in low SNR has to compromise in the detection accuracy and the complexity of the algorithm.

Xue Wang

Papers

Sub-Nyquist Spectrum Sensing Based on Modulated Wideband Converter in Cognitive Radio Sensor Networks

Improved algorithm based on modulated wideband converter for multiband signal reconstruction

A Simplified Multiband Sampling and Detection Method Based on MWC Structure for Mm Wave Communications in 5G Wireless Networks

An improved spectrum sensing algorithm based on energy detection and covariance detection

The fractional-step spectrum sensing algorithm based on energy and covariance detection