Multihypothesis-based compressed video sensing scheme attracts wide attention in the research of resource-constrained video application scenarios. However, high-accuracy weight prediction of hypotheses is always challenging especially for the high-motion sequences. To solve this problem, this paper proposes a novel multihypothesis-based distributed compressed video sensing (NMH-DCVS) system. The new multihypothesis system contains two components: hypotheses acquisition, and weight prediction. First, to acquire more high-quality hypotheses, a new hypotheses acquisition scheme is proposed by constructing the search window based on the temporal, and spatial correlation of the image blocks, respectively. The optimal matching block can be quickly determined. Second, to improve the accuracy of the multihypothesis weight prediction, a new residual transforming preprocessing-based weight prediction algorithm is proposed by transforming the original hypothesis set to residual hypothesis set. The influence of the quality fluctuation of the hypotheses on prediction accuracy is effectively suppressed. Moreover, the improved hypotheses further improve the sparsity of the residual hypothesis set, leading to the additional improvement of the accuracy of the proposed residual-based weight prediction algorithm. Experiment results show that compared with the state-of-the-art methods reported in the literature, the proposed new multihypothesis system significantly improves the decoding performance both in objective, and subjective quality.

For multihypothesis-based compressed video sensing schemes, the low accuracy of weight prediction and degradation of recovery quality for high-motion videos are open challenges. To solve this problem, this paper proposes a new multihypothesis prediction scheme. To efficiently get high-quality hypotheses, a new hypotheses acquiring method is proposed by building the search window based on the temporal and spatial correlation. To improve the accuracy of weight prediction, a residual transforming preprocessing for weight prediction is proposed. By converting the original hypotheses to residual hypotheses, the influence of quality fluctuation of hypotheses on the recovery quality is suppressed effectively. The sparsity and accuracy of the prediction model are improved efficiently. Simulation results show that a significant improvement in recovery quality is obtained in the proposed scheme compared to the state-of-the-art systems.

It is challenging for resource-constrained wireless terminals to realize the high-efficiency video upload. This paper proposes a compressed sensing based high-efficiency video upload (CS-HEVU) system for the terminal-to-cloud network. The main contributions are as follows. First, to develop higher transmission efficiency, a new encoder sampling scheme is proposed by utilizing skip block based residual compressed sensing sampling. The redundant image blocks are effectively removed in encoding. Second, to improve the decoding quality, a local secondary reconstruction based multi-reference frames cross recovery algorithm is proposed in decoder. The user experience is improved by reducing the quality fluctuation of non-key frames. Compared with the state-of-the-art systems reported in literature, simulation results show that a significant improvement in encoding efficiency and decoding performance is obtained in the proposed scheme.

To promote future intelligent systems, a novel cyber-physical-social smart system (CPS3) powered by the Internet of Things is presented in this paper, where wireless network virtualization is adopted to enhance the diversity and the flexibility of the service operation and the system management. Based on the presented system, a robust energy-efficient resource allocation scheme is proposed to guarantee the outage probability requirements of controllers and actuators while realizing the maximization of the system energy efficiency. Different from the existing works, imperfect channel state information is studied for energy-efficient resource allocation in CPS3. To effectively handle the formulated optimization problem, the concept of virtual devices is introduced to equivalently reformulate the original problem. Afterward, the probabilistic mixed problem is approximately transformed into a nonprobabilistic problem though outage probability analyses. After the transformation, the optimization problem can be decomposed into power allocation and channel allocation, where an iterative algorithm for power allocation is adopted to maximize the system energy, and a heuristic greedy algorithm is presented to schedule sensors and actuators on different subchannels based on the obtained power allocation results. Simulation results demonstrate the convergency of the proposed algorithm and the advantages of the proposed scheme.

With the rapid popularization of mobile intelligent terminals, mobile video and cloud services applications are widely used in people's lives. However, the resource-constrained characteristic of the terminals and the enormous amount of video information make the efficient terminal-to-cloud data upload a challenge. To solve the problem, this paper proposes an efficient compressed sensing-based high-efficiency video upload system for the terminal-to-cloud upload network. The system contains two main new components. First, to effectively remove the inter-frame redundant information, an encoder sampling scheme with high efficiency is developed by applying the skip block-based residual compressed sensing sampling technology. For the time-varying channel state, the encoder can adaptively allocate the sampling rate for different video frames by the proposed adaptive sampling scheme. Second, a local secondary reconstruction-based multi-reference frames cross-recovery algorithm is developed at the decoder. It further improves the reconstruction quality and reduces the quality fluctuation of the recovered video frames to improve the user experience. Compared with the state-of-the-art reference systems reported in the literature, the proposed system achieves the high-efficiency and high-quality terminal-to-cloud transmission.

As a flexible network sharing mechanism, wireless network virtualization (WNV) can significantly improve the resource utilization and reduce the overall expense for the next generation wireless networks. Considering the users' common interests on popular contents, we introduce wireless multicast into wireless virtual networks. To serve the users who have requests on the same content via multicast transmission, both the virtual resources and content can be shared simultaneously. Thus, the virtual resources and backhaul bandwidth can be further released. In this paper, we propose a novel WNV based multicast system. Subsequently, the multicast group forming and sub-channel allocation strategies are jointly investigated in considered frameworks. Then we formulate the strategies as an integer nonlinear programming problem. Furthermore, we propose a heuristic algorithm with the aid of cutting-plane method to tackle the problem. Finally, Simulation results verify that our proposed scheme can significantly enhance the total utility of wireless virtual networks with lower backhaul usage and the proposed heuristic algorithm can achieve a perfect performance.

This paper proposes a new optimal time-switching relaying protocol for wireless-powered decode-and- forward relay networks. The relay has no embedded power supply, and it has to harvest energy from radio-frequency signals transmitted by the source. In specific, we focus on the time-switching mechanism where a fraction of block time is used for energy harvesting (EH) and the remaining is used for information transmission (IT). Equipped with a rechargeable battery, the relay can accumulate the harvested energy and choose a proper transmit power. For system throughput maximization, we propose an optimal time-switching relaying (TSR) protocol to optimally determine the EH time and the relay power, according to the channel state and the battery state. Interestingly, we prove that the optimal EH time is a discrete two-state variable, i.e., the whole block time is used for either IT or EH. With the proposed TSR relaying protocol, the analytical expression for the system throughput is derived. Simulations validate the analysis and demonstrate that a significant throughput improvement can be achieved.

Recently, there are increasing requirements of security in wireless communications, especially for the multiuser systems. In this study, the authors consider the security of two‐hop cooperative multicast networks with an untrusted relay. An energy harvesting destination aided cooperative jamming framework is proposed to protect information from being intercepted by the untrusted relay. For the multiple destinations, a maximum interference jammer selection (MIJS) scheme is proposed to further improve physical layer security. Then the ergodic secrecy rate (ESR) for MIJS scheme is investigated and both the lower bound of ESR and the approximation of ESR are obtained. Finally, the effect of both the energy arrival rate and the number of destination nodes on ESR has been shown via numerical simulations. It can be revealed that as the number of destinations increases, the ESR is determined with two common behaviours regarding the multicast nature and energy arrival rate: (i) multicast nature forces the secrecy rate to decrease with the increasing number of destinations and (ii) the probability that a jammer exists increases with the increasing number of destinations but limited to one.

The inherent broadcast characteristics of wireless medium make wireless data transmission difficult to be shielded from unintended recipients. As such, secure communication over wireless channels becomes a critical issue in the design of wireless networks. In this study, the authors study cooperative jamming and power optimisation in untrusted relay networks to improve physical layer security. By exploiting the direct link, a source‐based jamming (SBJ) scheme is proposed to hinder the untrusted relay from intercepting the confidential message. Considering the total power budget, a power allocation strategy is also proposed to optimally determine the power of source and untrusted relay as well as the power of information and jamming signals. Furthermore, to evaluate the secrecy performance of the proposed SBJ scheme with the power allocation strategy, tight lower bound of ergodic secrecy capacity and asymptotic secrecy outage probability are derived in closed form. Simulation verifies the advantages of the proposed SBJ scheme and the power allocation strategy.

In this study, the authors focus on the downlink resource allocation to optimise energy‐efficiency (EE) of orthogonal frequency division multiplexing‐based cognitive heterogeneous network (HetNet), where network service providers operate multi‐radio access technologies. A more practical case is considered, in which imperfect channel state information (CSI) is available for wireless channels between secondary base stations and secondary users (SUs), primary users (PUs). Since imperfect CSI may result in outage in secondary networks and make collision occur in primary networks, a joint subcarrier and power allocation scheme for the cognitive HetNet is proposed to guarantee quality of service requirements for both SUs and PUs in probabilistic manners. Then the probabilistic EE optimisation scheme is approximated into a deterministic convex form, and the optimal solutions for the scheme are derived by double‐loop iteration method. Afterwards, the authors develop a low‐complexity algorithm in the presence of imperfect CSI with a small reduction of system performance. Simulation results are provided to show the impact of channel estimation error on the EE optimisation problem and to highlight the performance improvement from considering channel estimation error in the joint subcarrier and power allocation scheme for the cognitive HetNet system.

Successive relaying has recently emerged as a spectral-efficient technique for cooperative wireless communications. However, in scenarios with uncertain accuracy of instantaneous channel state information (CSI), the scope of conventional two-path successive relaying protocol shrinks. This is due to the challenges of cancelling inter-relay interference (IRI) and detecting signals efficiently without specific CSI. The two challenges motivate us to propose a novel double listening 3-path successive relaying (DL3PSR) protocol to mitigate the successive relaying's dependence on accurate instantaneous CSI. We overcome the first challenge by proposing a blind IRI cancellation technique, and its effectiveness is proven. After blind IRI cancellation, the challenge of efficient signal detection is overcome by robust cooperative non-coherent detection, which consists of exclusive OR (XOR) demodulation and joint decoding. Based on constellation mapping, XOR demodulation is designed to demodulate M-ary phase-shift keying symbols without instantaneous CSI. Moreover, joint forward and backward decoding strategy is proposed to improve the robustness of data detection by retrieving two independent versions of the original data. Furthermore, the detection threshold, bit error probability, and achievable rate of DL3PSR are theoretically obtained. Simulations verify that in scenarios without full knowledge of CSI, DL3PSR protocol outperforms conventional two-path successive relaying in bit error performance and that the rate of DL3PSR is close to that of the full-duplex relaying. Copyright © 2016 John Wiley & Sons, Ltd.

Owing to environmental, financial and quality of service (QoS) considerations, energy efficient wireless communications have been paid increasing attention under the background of limited energy resource. This study considers a spectrum sharing cognitive radio network, where the primary system leases its radio spectrum to the secondary system for a fraction of time in exchange for secondary users served as relays to assist the transmission of the primary traffic. Based on the cooperative spectrum leasing protocol, the authors formulate a joint optimisation of relay selection and power allocation under QoS requirements to improve energy efficiency (EE). By employing a greedy spectrum sharing (GSS) algorithm, the optimal relay selection, power and sharing time allocation are readily obtained. Monte–Carlo simulations are performed to demonstrate that significant EE improvement is achieved by the proposed GSS algorithm, and that the network performance is enhanced.

The high‐speed mobility of modern transportation worsens the situation of unstable direct links and imprecise channel estimation, which poses challenges on fast and reliable data delivery in vehicular ad hoc networks (VANETs). The goal of the proposed scheme is to provide high‐speed full‐duplex relaying connectivity for unstable direct links, and reliable information detection without precise channel state information (CSI) in VANETs. In the proposed scheme, full‐duplex relaying connectivity is provided by differential successive relays (DSRs) at roadside or inside other vehicles. To improve robustness of DSR without specific CSI, efficient blind interference cancellation is presented to mitigate inter‐relay interference. Meanwhile, reliable information detection without precise CSI is enabled by superposition coding with differential modulation. Furthermore, the advantages, applications of DSR and complexities for real‐world implementation are discussed, and the decision rule, bit error probability and ergodic capacity are theoretically obtained. Numerical results verify significant improvements in the performance of the proposed scheme when compared with conventional schemes with half‐duplex relays and frequent channel estimation in VANETs.

Cognitive radio networks (CRNs) are applied to solve spectrum scarcity. In this study, the authors propose an efficient power control game to improve its performance based on outage probability of primary user in a spectrum‐underlay CRN. The interference threshold deduced from outage probability and normalised signal to interference plus noise ratio are used to develop a novel non‐linear pricing function, which is a key element of obtaining Pareto improvement in non‐cooperative power control game. In addition, an efficient swarm intelligent algorithm originated from eco‐group activities is designed in detail to accelerate the convergence speed and improve the energy‐efficiency. Theoretical analysis and simulation results are presented to prove the effectiveness and superiority of the proposed power control game.

This paper presents a modulation identification system based on software radio platform. The signals have been conveyed to baseband by frequency conversion. Only three features have been chosen, which are kurtosis of the envelope, variance of phase histogram differential coefficients and valid area of power spectrum density. A fuzzy classifier is used for identification. It is proved by the results that the system performs well with a high percentage of correct identification.

It is important for digital modulation identification based on neural network to determine a suitable size of a network. This paper proposes that the number of nodes in hidden layer which is the core for network optimizing can be confirmed by K-L transform. The validity and robustness are verified by simulation. The percentage of correct identification (PCI) is almost the same before and after the network optimizing.