Multiband Joint Research Articles

Research on high-precision gas concentration inversion based on adaptive infrared multi-band joint spectral analysis

Research on high-precision gas concentration inversion based on adaptive infrared multi-band joint spectral analysis

Multiband Joint Spectrum Sensing via Covariance Matrix-Aware Convolutional Neural Network

This work investigates multiband spectrum sensing in a cognitive radio network where a multi-antenna secondary user attempts to detect several consecutive frequency bands occupied by multiple primary users. Specifically, we propose a multiband joint spectrum sensing approach based on the covariance matrix-aware convolutional neural network (CNN), in which the multiband sample covariance matrices are concatenated as the input of CNN. The proposed approach is free of model assumptions and the hidden correlation features between subbands can be learned to improve the spectrum sensing performance. Simulation results show that the proposed algorithm outperforms the state-of-the-art spectrum sensing methods in cases with and without the noise uncertainty.

Selection strategies of narrowband channels in wideband licensed spectrum

ABSTRACTCognitive radio (CR) technology has been proposed to facilitate dynamic spectrum access in licensed frequency bands. For wide licensed frequency bands, a CR device can select a narrowband sub-channel for spectrum sensing. Multiband joint detection, wavelet-based detection and compressed sensing are possible options where all the sub-channels may be sensed simultaneously; however, the techniques may have some difficulties. Alternatively, a secondary user (SU) opportunistically searches for a vacant sub-channel one by one from a primary user communication system over a wideband channel. We propose three random selection strategies for searching a narrowband channel. An SU can sense the sub-channels randomly following a particular sampling technique (or selection strategy). For simple random sampling with replacement (SRSWR), once a narrowband channel is selected for sensing, it can be re-selected again in some other trials whereas in simple random sampling without replacement (SRSWOR), this re-selection is not possible. A restricted SRSWR sampling scheme can also be opted where re-selection of a narrowband channel is possible after a finite number of trials. In this paper, different aspects of these sampling schemes are explored in detail and the coefficient of variation has been taken as figure of merit to substantiate the findings.

Energy efficiency of compressed spectrum sensing in wideband cognitive radio networks

In cognitive radio networks, wideband spectrum sensing (WSS) has been advocated as an effective approach to increase the spectrum access opportunity of the secondary users. On the other hand, because of the increase of sampling rate and sensing time duration cost by the analog-to-digital converter (ADC), energy saving has been a significant problem for WSS. In this paper, taking advantage of the frequency-domain sparsity of the wideband spectrum, a WSS scheme combining compressed sensing and multi-band joint detection technique is proposed to reduce the energy consumption. Based on extensive analysis and simulation, we identify the sparsity order of the wideband spectrum, the received signal-to-noise ratio (SNR) of the primary signal, and the compression rate employed in sampling as three key factors that affect the sensing performance. In particular, we derive a closed-form analytical model of the scheme. Based on these observations, the energy efficiency, defined as the ratio of the spectrum access opportunity to the energy consumption, is maximized through the optimization process of the compression rate under the sensing performance constraints. We also indicate the uniqueness of the optimal compression rate for maximizing the energy efficiency. Numerical and simulation results show that our proposed scheme is more energy efficient if the wideband spectrum is sparser in the frequency domain.

PERFORMANCE OPTIMIZATION OF COGNITIVE RADIO WITH WIDEBAND SPECTRUM SENSING

Cognitive radio (CR) technology allows the unlicensed user to access the licensed spectrum bands. Spectrum sensing is an essential function in cognitive radio to detect the spectrum holes and opportunistically use the underutilized frequency bands without causing interference to primary user (PU). In this paper we are maximizing the throughput capacity of cognitive radio user and hence the performance of spectrum sensing and protection to licensed user improves over a wideband spectrum sensing band. The simulation of cognitive radio is done by analyzing the performance of energy detector spectrum sensing technique to detect primary user and to formulate the optimization using multiband joint detection method to achieve suitable trade-off between secondary user access and primary user network. The main aim of this paper is to maximize the probability of detection and to decrease the probabilities of miss detection and false alarm. To maximize the throughput it requires minimizing the throughput loss caused by miss detection and the significant reduction in probability of false alarm helps in achieving the spectral efficiency from the secondary users perspective. The simulation results show that the performance increases with the multiband joint detection method.

Study the effect of PAPR on wideband cognitive OFDM radio networks

Cognitive radio (CR) technology is viewed as a novel approach for maximizing the utilization of the radio electromagnetic spectrum. Spectrum sensing methods are often used for finding free channels to be used by CR. Recently, Orthogonal Frequency Division Multiplexing system (OFDM) has been suggested as a candidate technology for multicarrier-based CR systems. However, one problem that appears in OFDM systems is the high Peak to Average Power Ratio (PAPR). In this paper, the effect of PAPR reduction of the primary signal on the performance of the multiband joint detection for wideband spectrum sensing and the profit of the primary user will be investigated. Moreover, the optimal solutions for the multi-band joint detection for the non-cooperative and cooperative schemes will be analyzed by considering the primary user's PAPR reduction. Also, the wideband cooperative spectrum sensing to improve the signal detection with high reduction in the PAPR will be suggested. Simulation results show that the PAPR reduction decreases the total price of the primary user and the aggregate opportunistic throughput of the secondary user. The cooperative scheme is effective in improving the performance in terms of the aggregate opportunistic throughput with PAPR reduction.

Optimal multiband joint detection in cognitive radio networks with the Taguchi method

SUMMARYThis paper studies multiband joint detection in cognitive radio networks with the Taguchi method. At the fusion center, linear fusion rule is adopted to collect the observations from distributed secondary users. We aim to achieve maximum aggregate throughput with limited aggregate interference. The problem is challenging due to its nonconvexity and high computational complexity to find the global optimum. Existing works attempt to convert the problem into convex optimization problem. In this paper, the Taguchi method is employed to estimate the gradient of the aggregate throughput, determine the optimal thresholds of the energy detectors and combination weights of the linear fusion rule regardless of convexity. To optimize thresholds and linear weights simultaneously, we employ newly defined variables to represent the changing ranges of detector thresholds when we search for optimal linear weights. In addition, the sensing duration is another factor to be optimized in the Taguchi method. The simulation results show that the proposed method is efficient and applicable for all classes of cognitive radio without considering convexity. The optimization performance is considerably improved. Moreover, the Taguchi method is insensitive to parameter initialization, which provides a relatively robust output. Copyright © 2013 John Wiley & Sons, Ltd.

Cooperative Multiband Joint Detection With Correlated Spectral Occupancy in Cognitive Radio Networks

In this paper, a frequency-coupled optimum linear energy combiner (OLEC) structure, recently proposed for single user scenarios, is generalized to multiple users and integrated into the spatial-spectral joint detection framework introduced by Quan , to take further advantage of subband occupancy correlation in cooperative wideband spectrum sensing. In particular, the design of the detection thresholds and fusion weights used by a bank of subband multiuser OLECs is formulated as a joint optimization problem, i.e. maximization of aggregate opportunistic throughput under interference constraints (or vice versa). Through numerical experiments with a Markov model of subband occupancy, the proposed scheme is shown to significantly enhance CR network performance in terms of these global metrics.

Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to legacy (primary) networks. In this paper, a novel wideband spectrum sensing technique referred to as multiband joint detection is introduced, which jointly detects the primary signals over multiple frequency bands rather than over one band at a time. Specifically, the spectrum sensing problem is formulated as a class of optimization problems, which maximize the aggregated opportunistic throughput of a cognitive radio system under some constraints on the interference to the primary users. By exploiting the hidden convexity in the seemingly nonconvex problems, optimal solutions can be obtained for multiband joint detection under practical conditions. The situation in which individual cognitive radios might not be able to reliably detect weak primary signals due to channel fading/shadowing is also considered. To address this issue by exploiting the spatial diversity, a cooperative wideband spectrum sensing scheme refereed to as spatial-spectral joint detection is proposed, which is based on a linear combination of the local statistics from multiple spatially distributed cognitive radios. The cooperative sensing problem is also mapped into an optimization problem, for which suboptimal solutions can be obtained through mathematical transformation under conditions of practical interest. Simulation results show that the proposed spectrum sensing schemes can considerably improve system performance. This paper establishes useful principles for the design of distributed wideband spectrum sensing algorithms in cognitive radio networks.