基于非局部相似性和交替迭代优化算法的图像压缩感知

压缩感知理论突破了信号带宽对奈奎斯特采样定理的限制,并且实现了在数据采样的同时进行压缩。目前压缩感知系统通常利用图像在某个变换域具有稀疏性的先验知识,从少量观测值中重构原始图像。本文利用图像像素的邻域结构信息及图像子块的相似性,将图像的非局部相似性作为先验知识运用到压缩感知图像重构中。结合图像的非局部相似性及其在变换域的稀疏性先验知识,提出了基于非局部相似性和交替迭代优化算法的图像压缩感知重构算法,该算法利用迭代阈值法和非局部全变差来交替迭代求解变换域的稀疏性优化问题和非局部相似性的优化问题。实验结果表明,本文算法可以有效提高图像重构的视觉效果和峰值信噪比。      

A novel compressed sensing ultra‐wideband channel estimation method based on non‐convex optimization

Due to the low power spectral density and complicated transfer propagation of ultra‐wideband (UWB) signal, it is important to estimate UWB channel accurately. But it is difficult to sample UWB signals directly due to their wider band width. However, compressed sensing (CS) theory provides a feasible way through lower sampling speed. Common CS‐UWB channel estimation methods adopt convex optimization, non‐sparse or non‐restricted form. In order to strengthen the restriction on sparsity of the reconstructed channel vector, a non‐convex optimization method is proposed in this paper to estimate UWB channel. Proposed method sets the objective function as a non‐convex optimization model using l_p–norm. This model is combined as a convex function to approximate the objective function and reconstruct the UWB channel vector iteratively. Because l_p–norm is closer to l₀–norm than l₁ and l₂–norm, its restriction on sparsity of objective vector is stricter. The simulation results show that this method can enhance reconstruction performance compared with existing CS‐UWB channel estimation methods. Copyright © 2013 John Wiley & Sons, Ltd.     

Three dimensional compressed sensing for wireless networks‐based multiple node localization in multi‐floor buildings

In wireless network‐based node localization, the received signals are hampered by complex phenomena, such as shadowing, noise, and multi‐path fading. In this work, the localization is stated as an ill‐posed problem that can be solved by compressed sensing (CS) technique. A three dimensional (3D)‐CS approach using the ratio of received signal strength (R2S2) and the time difference of arrival metrics was proposed to improve the localization accuracy of multiple target nodes in 3D wireless networks, and to reduce deployment complexity and processing time. Simulation and experimental tests were conducted in a large multi‐floors building using the strength of the received signals and the radio map of the localization area. The results indicated that the 3D‐CS approach is reliable for identifying the floor number and estimating the horizontal position. The localization precision is less affected by the propagation medium variation than the conventional 2D‐CS method. The localization mean error is lower when the number of access points increases, and the radio map spacing decreases. In addition, the accuracy of the 3D‐CS approach was assured as well as the building material characteristics, position of access points, and wireless‐terminal real transmission power are unknown. Copyright © 2015 John Wiley & Sons, Ltd.     

Joint optimization of spectrum sensing and accessing in multiuser multiple‐input single‐output cognitive networks

In this paper, a joint spectrum sensing and accessing optimization framework for a multiuser cognitive network is proposed to significantly improve spectrum efficiency. For such a cognitive network, there are two important and limited resources that should be distributed in a comprehensive manner, namely feedback bits and time duration. First, regarding the feedback bits, there are two components: sensing component (used to convey various users' sensing results) and accessing component (used to feedback channel state information). A large sensing component can support more users to perform cooperative sensing, which results in high sensing precision. However, a large accessing component is preferred as well, as it has a direct impact on the performance in the multiuser cognitive network when multi‐antenna technique, such as zero‐forcing beamforming, is utilized. Second, the tradeoff of sensing and accessing duration in a transmission interval needs to be determined, so that the sum transmission rate is optimized while satisfying the interference constraint. In addition, the aforementioned two resources are interrelated and inversive under some conditions. Specifically, sensing time can be saved by utilizing more sensing feedback bits for a given performance objective. Hence, the resources should be allocation in a jointly manner. Based on the joint optimization framework and the intrinsic relationship between the two resources, we propose two joint resource allocation schemes by maximizing the average sum transmission rate in a multiuser multi‐antenna cognitive network. Simulation results show that, by adopting the joint resource allocation schemes, obvious performance gain can be obtained over the traditional fixed strategies.Copyright © 2014 John Wiley & Sons, Ltd.     

Joint sensing and transmission for cognitive amplify‐and‐forward two‐way relay networks

In this paper, we propose a cognitive transmission scheme for Amplify‐and‐Forward (AF) two‐way relay networks (TWRNs) and investigate its joint sensing and transmission performance. Specifically, we derive the overall false alarm probability, the overall detection probability, the outage probability of the cognitive TWRN over Rayleigh fading channels. Furthermore, based on these probabilities, the spectrum hole utilization efficiency of the cognitive TWRN is defined and evaluated. It is shown that smaller individual or overall false alarm probability can result in less outage probability and thus larger spectrum hole utilization efficiency for cognitive TWRN, and however produce more interference to the primary users. Interestingly, it is found that given data rate, more transmission power for the cognitive TWRN does not necessarily obtain higher spectrum hole utilization efficiency. Moreover, our results show that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time slots between the spectrum sensing and data transmission phases. Finally, simulation results are provided to corroborate our proposed studies. Copyright © 2016 John Wiley & Sons, Ltd.     

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

In cognitive radio (CR) networks, secondary users should effectively use unused licensed spectrums, unless they cause any harmful interference to the primary users. Therefore, spectrum sensing and channel resource allocation are the 2 main functionalities of CR networks, which play important roles in the performance of a CR system. To maximize the CR system utility, we propose a joint out‐of‐band spectrum sensing and operating channel allocation scheme based on genetic algorithm for frequency hopping–based CR networks. In this paper, to effectively sense the primary signal on hopping channels at each hopping slot time, a set of member nodes sense the next hopping channel, which is called out‐of‐band sensing. To achieve collision‐free cooperative sensing reporting, the next channel detection notification mechanism is presented. Using genetic algorithm, the optimum sensing and data transmission schedules are derived. It selects a sensing node set that participate the spectrum sensing for the next expected hopping channel during the current channel hopping time and another set of nodes that take opportunity for transmitting data on the current hopping channel. The optimum channel allocation is performed in accordance with each node's individual traffic demand. Simulation results show that the proposed scheme can achieve reliable spectrum sensing and efficient channel allocation.     

A joint resource optimization and adaptive modulation framework for uplink single‐carrier frequency‐division multiple access systems

In this paper, we study joint resource allocation and adaptive modulation in single‐carrier frequency‐division multiple access systems, which is adopted as the multiple access scheme for the uplink in the 3GPP Long Term Evolution standard. We formulate an adaptive modulation and sum‐cost minimization (JAMSCmin) problem. Unlike orthogonal frequency‐division multiple access, in addition to the restriction of allocating a subchannel to one user at most, the multiple subchannels allocated to a user in single‐carrier frequency‐division multiple access systems should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for orthogonal frequency‐division multiple access, etc.) cannot help towards its optimal solution. We propose a novel optimization framework for the solution of this problem that is inspired from the recently developed canonical duality theory. We first formulate the optimization problem as binary‐integer programming (BIP) problem and then transform this BIP problem into continuous space canonical dual problem that is the concave maximization problem. Based on the solution of the canonical dual problem, we derive joint resource allocation and adaptive modulation algorithm, which has polynomial time complexity. We provide conditions under which the proposed algorithm is optimal. We compare the proposed algorithm with the existing algorithms in the literature. The results show a tremendous performance gain. Copyright © 2013 John Wiley & Sons, Ltd.