Compressed spectrum sensing (CSS) plays a pivotal role in dynamic spectrum access within mobile cognitive radio networks by offering reduced power consumption and lower hardware costs. The multicoset sampler, a well-known implementation for periodic nonuniform sampling, has been widely studied and is considered a promising architecture for realizing CSS. This paper focuses on the design of the multicoset sampling pattern, aiming at enhancing the isometry property of the sensing matrix. Unlike previous studies which assume a noise-free setup, our work considers the problem in a real-world environment with noise. First, we propose a deterministic algorithm for sampling pattern generation, particularly for specific hardware setup parameters. This algorithm offers strict mutual-coherence control in the multicoset sensing matrix. To address more general hardware configurations, we propose two optimization algorithms. One of them searches for nearly optimal sampling patterns through a random search strategy, while the other employs a greedy pursuit strategy to find a local optimizer. Furthermore, we propose an algorithm to iteratively optimize the sampling pattern between consecutive spectrum sensing windows by minimizing a restricted version of mutual coherence. The excellent performance of our proposed algorithms has been demonstrated through numerical experiments and has been verified on a self-developed hardware platform.

Compressive sensing (CS) is a technique frequently adopted in wireless communications. By utilizing CS, a receiver could sense the state of channels with sub-Nyquist analog to digital converters when signals are sparse. Traditional CS methods struggle with non-sparse signals due to their intrinsic sparsity assumption. Therefore, we propose using deep learning (DL) to solve the vector support recovery problem with channels’ high occupancy. The simulation results show that the proposed CS framework powered by DL can perform better than a traditional CS analytical benchmark, both in high and low channel occupation regions. We also observe that the ML can work under a lower sampling rate than traditional CS methods. To process data sampled with high channel numbers, a divide and conquer tactic is implemented.

Adaptive compressed spectrum sensing (ACSS) can effectively save sampling resources in wideband spectrum sensing. Almost all of the existing ACSS algorithms are based on the discrete multitone signal model. However, real-world spectra are always multiband signals. In this paper, we derive mathematical models and algorithms enabling the ACSS suitable for multiband signals, which can save sampling resources and has lower computational complexity. Firstly, we introduce the multicoset sampling system into ACSS to sample multiband signals. Besides, we propose a leave-one-out cross-validation (LOOCV) based ACSS scheme with low sampling costs. To save sampling resources, we choose only one sampling channel as a testing subset to validate reconstructed signal and repeat this several times with different sampling channels. Then, we use the mean of the multiple validation results to determine the accuracy of the reconstructed signal. To reduce computational complexity, we propose a LOOCV-ACSS algorithm, in which we only perform the least square method several times in the LOOCV procedure, rather than the complicated compressed sensing reconstruction algorithms. Numerical simulations and real-world signal test results demonstrate that our derivation and algorithms are effective to reduce the sampling cost while keeping the same performance as conventional algorithms.

Compared with traditional compressed spectrum sensing, adaptive compressed spectrum sensing can greatly reduce the sampling rate. However, it requires complex optimization algorithms for signal reconstruction and extra samples to validate the accuracy of the reconstructed signal, which makes real-time signal processing impossible. In order to further reduce the number of samples and enable low complexity greedy algorithms, e.g., orthogonal matching pursuit (OMP), in the adaptive compressed spectrum sensing, we propose a cross validation (CV) OMP algorithm (CV-OMP) for the adaptive compressed spectrum sensing. The proposed CV-OMP algorithm embeds the CV process into an OMP algorithm, so it does not require extra samples as testing set to validate the accuracy of the reconstructed signal. Furthermore, the accuracy of the reconstructed signal is verified by the CV, which is used as the criterion to stop the iteration, so that the proposed CV-OMP algorithm does not require the prior knowledge of sparsity order for signal reconstruction. Simulation results indicate that the proposed CVOMP algorithm achieves better reconstruction performance than that of conventional OMP algorithms, and also effectively reduce the number of samples in the adaptive compressed spectrum sensing.

Modulated wideband converter (MWC) is a blind undersampling system for sparse multiband signal. By the available hardware and compressed sensing method, it can realize energy-efficient wideband spectrum sensing under sub-Nyquist sampling rate. In this paper, we propose an optimized circulant measurement matrix constructing method which is suitable for the MWC system, aiming at the problem that the random measurement matrix is hard to implement in hardware. Simulation results show that the MWC system can reconstruct the original signal with high probability using the proposed method. And its signal to noise ratio (SNR) and sampling channel number performance are improved 8dB and 4 channels respectively compared with traditional Toeplitz measurement matrix and circulant measurement matrix.

Compressed sensing (CS) is often utilized to lower the complexity of cyclostationary detector (CD) in wideband sensing. In this paper, a methodology is proposed to further simplify the complexity for the combination of CS and CD. Firstly, the relationship between spectral coherence function (SCF) and the compressed samples is deduced in matrix form, thus the reconstruction of original signal can be skipped. Secondly, we notice complexity is highly dependent on compression ratio, which is determined by signal sparsity. Through careful analysis, a strong dependence of sparsity on modulation mode and symbol rate is discovered. Therefore we propose to adjust compression ratio according to the modulation mode and symbol rate of the target signal. To facilitate the adjustment, a modulation classification algorithm based on correlation of SCF is formulated. Moreover, the relationship between compression ratio and performance loss is also explored. Simulation proves our method can reduce complexity significantly with marginal loss in accuracy.

This paper proposes an efficient bandwidth allocation scheme to fully utilize the multi-access ability of multimode terminals in heterogeneous networks. To model the fact that the suitability of different networks to different traffics varies, which is seldom considered in previous literature, the term “Match-Degree” is introduced and calculated using grey relational analysis. Afterwards, two methods, namely weighted bandwidth factor and cumulative utility function are proposed to integrate the influence of “Match-Degree” into the conventional utility-functions. Moreover, energy consumption and network price are also considered to make our model more comprehensive. The bandwidth allocation problem is formulated as maximizing the overall utility by adjusting the bandwidth allocated to each user. Based on optimization theory, an iterative algorithm is constructed to solve this problem. And numerical results validate the performance enhancement of our scheme compared with the existing ones.

Under current popularity of full duplex radio, this paper explores the adaption of energy detector (ED) to full duplex scenario. To improve sensing performance,firstly a correlation based channel estimation algorithm is formulated to cancel out self-interference. We then notice traditional ED has a long delay in finding sensing error. To tackle this problem, sliding window ED is proposed, in which samples utilized for different sensing instances are allowed to overlap. Moreover, it is also observed that sensing performance degrades seriously when primary user sate changes in the sensing period, which happens frequently in full duplex sensing. Therefore, weighted ED is proposed, in which samples at the end of the sensing period are assigned bigger weights. Simulation results show that our strategies can better adapt energy detector to full duplex sensing with marginal loss in accuracy.

With recent breakthroughs in full-duplex radio, there is a growing trend to combine it with cognitive radio. Thus the topic of spectrum sensing in full-duplex scenario is addressed in this paper. Firstly, the concept of full-duplex spectrum sensing and its advantage over traditional half-duplex spectrum sensing are analyzed. Then a correlation based least square algorithm is formulated to cancel out self-interference, in which no preamble is required. We notice that the phase distribution of noise differs greatly from that of noise-perturbed signals. Therefore, a novel spectrum sensing scheme using phase difference as test statistics is introduced. Its theoretical performance is also analyzed by approximating the test statistics as Gaussian distribution. The proposed detector is simple and immune to noise uncertainty due to the independence of its threshold on noise power. Simulation results show that robust sensing performance can be achieved in full-duplex scenario using correlation based least square and the phase based sensing scheme.

The scaling laws for the throughputs of cognitive network with transmission power of secondary users being constrained are studied. The primary nodes and secondary nodes are randomly and uniformly distributed over the same unit square, with density of n and m (n > m). In this paper, we propose a new hybrid access scheme of multi-hop cognitive network, with primary users and secondary users using TDMA scheme and CDMA scheme respectively. It is shown that primary network achieves the same throughput scaling as that in the case when both primary and secondary network adopts TDMA scheme. Meanwhile, we demonstrate that the upper bound of secondary per- node throughput increases with node density m, in contrast to the decreasing pernode throughput in the existing research, which shows that our scheme not only guarantees primary network but also improves the performance of secondary network.

Spectrum sensing is one of the most challenging tasks in cognitive radio. Unfortunately traditional schemes fail to balance between accuracy and complexity, which are the key indicators for the performance of spectrum sensing. In this paper, a new spectrum sensing scheme based on phase difference is proposed. Through analyzing the distributions of phase difference between adjacent samples of noise and noise-perturbed primary signal, we notice that the mean of phase difference varies from noise when primary signal is present. On this basis, a novel sensing scheme using the accumulation of phase difference as test statistics is formulated. Then the analytical performance of our scheme is derived and its complexity is analyzed. Our proposed scheme is simple, accurate and immune to noise uncertainty. Simulation results show that our scheme outperforms conventional energy detection and can achieve a detection probability of 99% at −7dB signal-to-noise ratio using 500 data samples.

The fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge for the radio environment cognition. Traditional spectrum sensing technique is neither efficient nor necessary, wasting the spectrum access opportunities on the vacant spectrum holes of primary users (PUs). Considering the sparse signal feature, a novel compressed sensing technique is proposed by using the minimal sampling rate to detect spectrum holes, which is more efficient than the Nyquist sampling rate and traditional compressed sampling rate that is required to reconstruct the original signal. The proposed compressed sensing process is divided into two stages called approaching stage and monitoring stage. The first stage is to gradually approach the minimal sampling rate required to achieve the spectrum detection performance by using the feedback mechanism. And the second stage is to monitor the status of PU according to the threshold using the sampling rate from the first stage. Therefore, the overall sampling rate can be dramatically reduced without spectrum detection performance deterioration compared to the conventional static sampling algorithm. Numerous results show that the proposed compressed sensing technique can reduce the sampling rate to 35%, with acceptable detection probability over 0.9.