With the rapid evolution of the Industrial Internet of Things (IIoT), industrial networks are required to support massive industrial devices with bounded low-latency transmission. To address these requirements, integrating the fifth-generation (5G) with time-sensitive networking (TSN) has been proposed. However, existing joint resource allocation methods struggle to achieve seamless low-latency deterministic scheduling between the 5G system (5GS) and TSN networks. Focusing on large-scale uplink transmission scenarios, a base station (gNB)-driven joint time-frequency resource allocation architecture is proposed. Within this architecture, the 5GS is modeled as a TSN bridge seamlessly integrated with the TSN cyclic queuing and forwarding (CQF) mechanism. Existing joint resource allocation algorithms suffer from local optima, low computational efficiency, and inability to capture global time-triggered (TT) flow interactions for globally optimal solutions. Accordingly, a gNB-driven multi-agent proximal policy optimization (MAPPO)-based joint time-frequency resource allocation algorithm, termed gNB-DMJRA, is further proposed. This algorithm adopts the centralized training and distributed execution (CTDE) framework, leveraging global information to better coordinate the scheduling of multiple TT flows and avoid convergence to local optimal solutions. In addition, the periodicity of TT flows is exploited to reduce computational complexity and the action space, thereby further improving learning efficiency and convergence. Simulation results demonstrate that under 1000 TT flows, the proposed algorithm reduces maximum latency by up to 74.27%, improves the scheduling success rate by 82.15%-331.75%, and achieves faster convergence than benchmarks, confirming its effectiveness and efficiency for large-scale TT flow scheduling.

The computing power network (CPN) serves as a key infrastructure for future networks, facilitating the connection of ubiquitous computing resources distributed across various locations. The continuous emergence of computation-intensive and delay-sensitive applications highlights the crucial need to fully utilize limited computing resources and the importance of building a resilient communication network. Primary-backup (PB) based transmission is a commonly used technique to enhance network reliability. However, implementing this approach in CPN with a consideration of load balancing introduces significant complexity and has received limited research attention. In this paper, we designed a deterministic computing power network (Det-CPN) architecture based on segment routing over IPv6 (SRv6). On top of the above architecture, we proposed a best computing node selection method based on a comprehensive index calculation and ranking (CICR) algorithm to determine the optimal computing node for task transmission. Subsequently, we developed a bandwidth sharing-based multi-path transmission (BSMT) algorithm to realize the maximization of the system efficiency. Simulation results demonstrate that in adverse network conditions (overloaded with a failure rate of 0.02), compared to the traditional dual-path redundant forwarding mechanism, the proposed solution achieves an average reduction of 22.3% in transmission latency, an average improvement of 39.94% in task success rate, a decrease of 19.4% in bandwidth occupation rate, and an increase of 31.05% in computing resource utilization rate.

Accurate estimation of channel state information (CSI) is crucial to realize the full potential of massive multiple-input multiple-output (MIMO) communication systems. Despite the high accuracy of existing CSI estimators, the high computational complexity makes them impractical for real-world massive MIMO systems. So, developing a highly accurate and low-complexity estimator has been a long-standing challenge in the field of massive MIMO. In this paper, we present a channel estimation scheme that significantly reduces computational complexity while achieving high accuracy. Firstly, we design a two-stage training protocol whereby only part of the transmit and receive antennas are activated for signal emission or reception. Secondly, we apply the low-complexity Least-Square (LS) estimator to acquire two sub-blocks of the channel matrix. Relying on the inherent low-rank property of channel matrix, the complete channel matrix is reconstructed via a randomized matrix approximation technique. Thirdly, we consider three different power allocation schemes to further optimize the pilot power in 2-stage training process. The theoretical bounds of estimation error for our CSI estimator are derived, and the optimal power allocation strategy is thus obtained by minimizing this error bound. Numerical simulations are provided to demonstrate our proposed method. As shown, the theoretical error bound is tight, and the optimal power allocation can achieve the substantial gain. Our CSI estimator incurs the same complexity as the popular LS estimator, whilst the estimation accuracy is improved by ~5 dB, which has the great promise to new-generation massive MIMO communications.

In recent years, vehicular communications have attached great interests in both academy and industry for its potential of promoting safety and autonomous driving. Unlike classical communication scenarios, in vehicular communications the optimal resource allocation must be accomplished in a real-time manner, in order to maximally reduce the response delay. This presents a substantial challenge for current machine learning based intelligent resource optimization methods which may be sample inefficient, especially when the problem space becomes extremely huge. In this study, we develop a fast reinforcement learning (RL) framework for the real-time resource optimization of vehicular communications, whereby the transmitting power and the accessing frequency channels need to be jointly allocated. The main concept of our new method is that it incorporates a sample efficient structured exploration mechanism in the action space, which firstly ignores the local exploitation but focuses on a randomized global exploration. Thus, our exploration-first method, in contrast to classical exploitation-first RL, can reconstruct the coarse-grained global landscape of a huge Q-table from only the few samples. This learned prior knowledge would remarkably accelerate the convergence of subsequent incremental learning process, by concentrating on the identified attentional subspace of the Q-table. As demonstrated by numerical results, our new method would reduce the time complexity or the response delay by around 10 folds. As such, our fast RL method would have the great potential to such challenging optimization problems whereby the acquisition of massive training samples is time demanding, which hence provides the great promise to the emerging vehicular networks.

This paper focuses on non-orthogonal multiple ac-cess (NOMA) scenarios with emerging sensing targets, where beamforming is utilized to integrate sensing and NOMA communications (NOMA-ISAC). This scheme does not require ded-icated sensing signals, avoids further degradation of system performance in the overloaded regime, and facilitates massive device connectivity in future wireless networks. We investigate the performance region of NOMA-ISAC from an information-theoretic perspective and propose a learning-based design to approach the given performance bound. Specifically, the upper bounds for the sensing estimation rate and communication information rate are derived to evaluate the achievable rate region of NOMA-ISAC. Guided by mutual information, the proposed end-to-end learning architecture for NOMA-ISAC enables joint transmitter-receiver optimization to achieve bound-approaching performance. Furthermore, we design a sensing mutual information neural estimation unit (SMEU) for the proposed architecture to evaluate the available sensing mutual information. Simulation results show that the proposed scheme can approach the theoretical bounds of NOMA-ISAC.

To glean the benefits offered by massive multi-input multi-output (MIMO) systems, channel state information must be accurately acquired. Despite the high accuracy, the computational complexity of classical linear minimum mean squared error (MMSE) estimator becomes prohibitively high in the context of massive MIMO, while the other low-complexity methods degrade the estimation accuracy seriously. In this paper, we develop a novel rank-1 subspace channel estimator to approximate the maximum likelihood (ML) estimator, which outperforms the linear MMSE estimator, but incurs a surprisingly low computational complexity. Our method first acquires the highly accurate angle-of-arrival (AoA) information via a constructed space-embedding matrix and the rank-1 subspace method. Then, it adopts thepost-receptionbeamforming to acquire the unbiased estimate of channel gains. Furthermore, a fast method is designed to implement our new estimator. Theoretical analysis shows that the extra gain achieved by our method over the linear MMSE estimator grows according to the rule of O(log10M), while its computational complexity islinearlyscalable to the number of antennasM. Numerical simulations also validate the theoretical results. Our new method substantially extends the accuracy-complexity region and constitutes a promising channel estimation solution to the emerging massive MIMO communications.

Abstract Accurate channel state information (CSI) is extremely crucial to realize high‐accurate hybrid precoding, in millimeter wave communication systems. In order to improve the CSI estimation performance, several traditional channel estimators have been developed by exploiting the sparse or low‐rank property, whilst they bring huge channel training overhead and computational complexity. In this work, based on jointly sparse and low‐rank property of massive multiple input multiple output (MIMO) channel, one non‐convex mmWave channel estimation problem is formulated and a novel scheme to acquire one accurate CSI estimation result with greatly reduced training overhead is proposed. Specifically, the non‐convex problem is reformulated as two simple sub‐problems, by exploiting the alternating direction method of multipliers (ADMM) technique. Based on the low‐rank characteristic, one fast gradient descent matrix completion algorithm is developed to accurately solve the first sub‐problem. On this basis, the compressed sensing (CS) technique to acquire the accurate CSI estimation matrix is further utilized. Numerical simulation validates that the method could achieve the much higher channel estimation accuracy, yet only incurs the lower overhead compared with the traditional scheme.

Molecular communication is attractive to the emerging nano-scale communication systems. Traditionally, a detector recovers the information from only the concentration of single messenger molecule, while ignoring the variation of multiple participants in biochemical reaction. In this paper, we propose a non-coherent signal detector, by fully exploiting this ubiquitous biochemical diversity property of multiple reacting molecules. After extracting the channel state information (CSI) independent non-coherent features of received signals, the dynamical transient characteristics of messenger, reactant and product molecules are all utilized to implement the diversity detection, thus formulating a functional single-input multiple-output (SIMO) system via reaction-diffusion communication that has been barely considered before. We design both hard and soft combination strategies to attain the potential diversity gain arise from the dynamical co-variation of participants. Theoretical analysis and numerical simulations are provided to demonstrate the advantages of our detector. Compared with conventional detectors that use only single messenger molecule, the bit error rate (BER) of is substantially reduced. Moreover, the BER performances of our non-coherent detector are even better than coherent maximum a posteriori (MAP) detector that requires accurate CSI estimation, which confirms the dramatic diversity gain provided by our detector. It would have great potentials in reliable nano-scale communications.

Federated learning (FL) enables multiple local clients to collaboratively train a global model, which can reduce privacy leakage by sharing model parameters instead of private datasets. However, recent works have revealed that gradient-based data reconstruction attacks, e.g., deep leakage from gradients (DLG), improved DLG (iDLG), and inverting gradients (IG), may still reveal private information by exploiting model parameters from a local client. Current privacy-preserving FL strategies, e.g., differential privacy or gradient compression, can handle such attacks to some extent, but seriously sacrifice their model accuracy. In this work, we propose a novel privacy-preserving FL method, named privacy-encoded FL (PEFL), to combat such data reconstruction attacks without degrading the model performance. The key concept of PEFL is that each large weight matrix of the neural network model is decomposed into multiple cascading sub-matrices, which thus establishes a novel privacy-encoded mechanism by introducing an entangling nonlinear mapping between model gradients and raw data. As such, multiple sub-matrices are directly trained in parallel at the local clients, but only partial sub-matrices are reported to a global server, which suffices to reconstruct the global model whilst increasing the complexity of the coupling between the model parameters and raw data. We provide a detailed analysis of the accuracy, security, and complexity of our method. As shown, it breaks the limit of classical defensive methods, by significantly reducing the risk of data reconstruction attacks yet not degrading the model performance. Compared to classical defenses, the proposed PEFL decreases the peak-signal-to-noise ratio (PSNR) between the reconstructed data and the raw data by ~20dB, without sacrificing the test accuracy. As a new paradigm for privacy-preserving FL, our proposed method has great potential in privacy-demanding learning applications.

Due to the public nature of wireless environments, legitimate communication systems are generally under the threat of Pilot Spoofing Attack (PSA). The malicious user may manipulate channel estimation by emitting the same pilot information, leading to the biased estimation of Channel State Information (CSI) and the degraded secrecy capacity. Although various methods have been proposed for combating PSA, the ubiquitous mobility of legitimate and malicious users introduces complex time-varying characteristics, making previous static PSA detection methods less attractive. In this paper, we present a novel location-awareness dynamical PSA detection mechanism. To model the complex dynamical behaviors, a Random Finite Set (RFS) is formulated to jointly describe the mobile positions and uncertain attack status. On this basis, we design a joint PSA detection and user localization algorithm relying on the sequential Bayesian inference. As such, the inherent correlations in mobile patterns can be exploited to effectively enhance PSA detection probability and localization accuracy. Numerical simulation results validate that the new method significantly improves PSA detection and CSI estimation accuracy compared with state-of-the-art counterparts, therefore the information leakage problem is greatly alleviated. Our new approach thus has the great potential in the emerging mobile scenarios by effectively enhancing the physical-layer secured transmissions.

With the increasing demand for resource scheduling efficiency in Industrial Internet of Things (IIoT), parallel reinforcement learning (PRL) based distributed edge-cloud collaborative resource scheduling scheme has attracted enormous attention. However, the computing and communication capacities, the security degree of massive distributed edge computing servers are different. It is difficult to make a large number of edge servers carry out security and efficiency PRL based edge-cloud collaboration resource scheduling scheme. Thus, in this paper, a large-scale distributed edge-cloud collaborative resource scheduling method based on picture delegated proof of state and suspicious practical byzantine fault tolerance (pDPoSt+sPBFT) consensus algorithm is proposed. To be specific, we first propose a collaborative edge-cloud industrial network architecture to support massive industrial intelligence tasks, then a distributed PRL based resource allocation scheme is utilized. Secondly, in order to improve the efficiency and security of distributed PRL training, we propose a server filtering strategy based on pDPoSt algorithm. Finally, a sPBFT algorithm is proposed to further realize security parameter aggregation of distributed PRL. Experimental results show that the proposed method has good efficiency and security performance compared with the traditional distributed edge-cloud collaborative resource scheduling algorithm. The proposed approach has great potential in complex IIoT scenarios.

With significantly dynamic characteristics of the new aerial users, the emerging cellular-enabled unmanned aerial vehicle (UAV) communication paradigm raises great challenges to current research of UAV applications. As far as the robust channel allocation is concerned, the high mobility of UAV nodes and the unexpected disturbance of external environment would render most existing methods which rely on definite information and are vulnerable to dynamic environment, become less attractive or even invalid. In this paper, we particularly investigate a cellular-enabled mesh UAV network exploiting partially overlapping channels (POCs), and propose a distributed fuzzy space based learning scheme for POCs allocation to combat the dynamic environment. Rather than the perfect channel state information (CSI) assumption, the dynamic and uncertain CSI of UAVs is characterized by fuzzy number. On this basis, the allocation process can be implemented in a mapped fuzzy space. Integrating fuzzy-logic and game based learning, we formulate the problem of POCs assignment as a fuzzy payoffs game (FPG), and demonstrate the existence of fuzzy Nash equilibrium for our designed FPG. Then, with the derived priority vector in the fuzzy space, the equilibrium solution can be achieved by the proposed algorithm. Numerical simulations demonstrate the advantages of our new scheme.

In recent robotic applications, a critical need is to simultaneously detect communication (emission state) and estimate its trajectory. Whilst wireless sensor observations are useful, they are often uncertain due to the stochastic communication bursts and robot mobility. Over-sampling the information environment can incur excessive radio interference and energy usage. Therefore, one challenge is how to improve the efficiency of sensing under sparse and dynamic information, and make accurate inference on the robot's location. Here, we design a novel mixed detection and estimation (MDE) scheme to enhance both the accuracy and the efficiency by exploiting the mobility pattern correlations. Relying on a Markov state-space model, dynamic behaviors of robot's communication state and movement are formulated. A two-stage sequential Bayesian scheme, premised on random finite set (RFS), is developed to detect and estimate the involved unknown states. Specifically, in order to counteract the probability likelihood disappearance (caused by no information emission) and improve robustness to ambient noise, a sequential pre-filtering technique is designed, which can refine local observations and thereby significantly improve the accuracy of the system. We validate the proposed MDE scheme via both theoretical analysis and numerical simulations, demonstrating it would improve both the detection and estimation accuracy and efficiency.

To support large-scale unmanned aerial vehicle (UAV) networks with both payload communication (PC) packets and control and non-payload communication (CNPC) packets, millimeter wave (mmWave) communication is convinced to be a promising solution. However, efficient user association and spectrum allocation are still challenging in mmWave-enabled UAV networks considering that the network state is dynamic and the status information of each UAV is incomplete. In this paper, we investigate a joint UAV association and spectrum allocation problem under a hybrid mmWave sharing paradigm, where PC packets are transmitted over both licensed and pooled bands in a shared manner to achieve high throughput and CNPC packets are transmitted over licensed band in an exclusive manner to guarantee high reliability. To this end, we introduce a strategic form game that can characterize network stochastic states and individual partial observations to reformulate the problem, and then propose a counterfactual regret minimization scheme to achieve its correlated equilibrium (CE). Benefited from the updating mechanism on randomobservation-actionpairs, the designed scheme can converge to the corresponding CE solutions for the two types of packets with partial observation. Finally, our simulation results demonstrate the superior performance of the proposed scheme over the baseline schemes.

Subspace methods, e.g., multiple signal classification algorithm (MUSIC), show great promise to high-resolution environment sensing in the 6G-enabled mobile Internet of Things (IoT), e.g., the emerging unmanned systems. Existing schemes, aiming to simplify the computational 1-D search of the MUSIC pseudospectrum, unfortunately have still an unaffordable complexity or the compromised accuracy, especially when the millimeter-wave massive multiple-input–multiple-output (MIMO) radar is considered. In this work, we address the fast and accurate estimation of the high-resolution pseudospectrum in massive MIMO radars. To enable real-time automotive sensing, we first formulate this computational procedure as one matrix product problem, which is then solved by leveraging randomized matrix sketching techniques. To be specific, we compute the large matrix productapproximatelyby the product of two small matrices abstracted via random sampling. To minimize the approximation error, we further design another sampling, pruning, and recomputing (SaPRe) algorithm, which refines the approximated results and thus attains the exact pseudospectrum. Finally, the theoretical analysis and numerical simulations are provided to validate the proposed methods. Our fast approaches dramatically reduce the time complexity and simultaneously attain the accurate Direction-of-Arrival (DoA) estimation, which have the great potential to real time and high-resolution automotive sensing with massive MIMO radars.

The advanced 6G Technology benefits the Internet of Things (IoT) in various applications. As one essential application scenario, smart grid (SG) incorporates communication and management techniques and promises an efficient and intelligent power system, whereby cognitive radio (CR) is believed to be an essential tool for better resource utilization in power generation and delivering processes. In the CR-assisted IoT in SG scenarios, channel detection (CD) will play an essential role to accurately sense the available channel resource. However, for SG scenarios, high-accuracy CD may become a challenging task in complex power supply environments with unexpected impulsive noise (IN) and channel fading, which will significantly affect the signal statistical property. To address this problem, we propose a novel CD mechanism in the context of the wireless environment with IN and random channel fading. To be specific, taking the wireless channel status, IN and time-variant fading into account, a novel quaternary hypothesis testing model (QHTM) is formulated to describe the detection task, and by which a new dynamic state-space model (DSM) is developed to capture the dynamical behavior of the CD system. On this basis, a joint detection and estimation algorithm based on Bayesian statistical inference is devised to accomplish the CD task. Benefiting from the jointposterioridistribution estimation procedure, our algorithm can not only accurately detect the unknown channel status, but also estimate the real-time channel state information (CSI), thereby eliminating their effects on the detection performance. Numerical simulation results validate the proposed CD mechanism.

In direct position determination (DPD), a large number of observation data need to be integrated and transmitted, which creates higher requirements for the transmission performance of the network. To alleviate the problem of performance degradation in a large number of data transmissions, this paper proposes a heterogeneous network architecture and transmission control algorithm for cross‐regional heterogeneous networks. Through the heterogeneous integration of satellite and multihop networks, a transmission control model suitable for the long delay and high bit error rate environments is established, the congestion window of each stage of network transmission is analyzed, and the efficiency and accuracy of the algorithm are verified by experiments. The results show that a large amount of data can be transmitted in a heterogeneous network. When dealing with direct location, the algorithm can effectively transmit a large number of observation data for cross‐regional heterogeneous networks. This simple and applicable transmission control algorithm can improve the satellite link throughput and reduce the download response time compared with the traditional transmission algorithm. These studies provide a reference for a large number of data transmissions in direct position determination.

Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades. In this work, we conduct a non-medical/clinical approach, generating graphs from five features concluded from the COVID-19 outbreak data and local community data in NSW (New South Wales), Australia, and offering insights from a visual analysis perspective. The results show that household income, population density and ethnicity affect the infection in different areas. Features such as human behaviours need to be imported for further COVID-19 research in the data science sector. This work is an initial step into this area and allows more insights to be brought into future COVID-19 research through a visual analysis perfective.

Emerging cellular‐enabled unmanned aerial vehicles (UAVs) communication poses both opportunities and challenges to the current research of UAV applications. On the one hand, the advanced cellular technologies and authentication mechanisms make significant performance improvements of reliability, security, coverage, and throughput of UAVs possible. On the other hand, the considerably dynamic characteristics of the new aerial users bring some complicated and open issues to the future heterogeneous network. To throw some light on this field, the authors study the joint power allocation and channel reuse problem of uplink transmission in a cellular‐enabled UAVs network with full consideration of the rapid channel variations resulting from high mobility of UAVs. Given the diverse requirements of terrestrial cellular users (CUs) and UAVs, i.e. large capacity for CU links and ultra‐reliability for UAV links, they formulate the problem as optimising the uplink capacities of CUs with reliable transmission constraint of UAVs. By decoupling the intricate problem into two sub‐problems, a joint resource allocation algorithm is proposed, which relies only on the statistical information of dynamic channels to implement, hence is robust. Finally, numerical results are provided to corroborate the anticipated performances of the proposed scheme.

Along with the development of 5G technology in mobile sensing and wireless communication, the internet of vehicles (IoV) has drawn much attention from the research community. The traditional centralised cloud‐based IoV network has become a bottleneck in providing computation‐intensive, high‐mobility and low‐latency services. As a promising computing paradigm, mobile edge computing (MEC) addresses such challenges. In this study, the authors propose a hierarchical IoV system, combined with MEC. They then focus on the problem of quality of service (QoS)‐enabled resource allocation for computing tasks in the system. However, the existing studies often fail to take into account different delay tolerances between different task types. In order to optimise the completion delay, they design an approach to classify tasks into different priorities according to their delay tolerances and then reorder tasks. After reordering, they use a reinforcement learning algorithm to allocate resources automatically and intelligently. Simulation results confirm that the proposed scheme is feasible and effective in the aspects of time efficiency and outage probability.

The geostationary (GEO) satellite networks have two important influencing factors: high latency and transmission errors. Similarly, they will happen in the large-scale multihop network of the Internet of things (IoT), which will affect the application of 5G- (5th-generation mobile networks-) IoT. In this paper, we propose an enhanced TCP mechanism that increases the amount of data transferred in the slow start phase of TCP Hybla to mitigate the effect of long RTT and incorporates a refined mechanism of TCP Veno, which can distinguish packet loss between random and congestion. This scheme is evaluated and compared with NewReno, Hybla, and Veno by simulation, and the performance improvement of the proposed TCP scheme for GEO satellite network in the presence of random packet losses is demonstrated. At the same time, the enhanced TCP scheme can improve the transmission performance in the future 5G-IoT heterogeneous network with high delay and transmission .

In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbol-interference (ISI), which deteriorates the detection performance. If the channel is unknown, existing coherent schemes (e.g., the state-of-the-art maximum a posteriori, MAP) have to pursue complex channel estimation and ISI mitigation techniques, which will result in either high computational complexity, or poor estimation accuracy that will hinder the detection performance. In this paper, we develop a novel high-dimensional non-coherent detection scheme for molecular signals. We achieve this in a higher-dimensional metric space by combining different non-coherent metrics that exploit the transient features of the signals. By deducing the theoretical bit error rate (BER) for any constructed high-dimensional non-coherent metric, we prove that, higher dimensionality always achieves a lower BER in the same sample space, at the expense of higher complexity on computing the multivariate posterior densities. The realization of this high-dimensional non-coherent scheme is resorting to the Parzen window technique based probabilistic neural network (Parzen-PNN), given its ability to approximate the multivariate posterior densities by taking the previous detection results into a channel-independent Gaussian Parzen window, thereby avoiding the complex channel estimations. The complexity of the posterior computation is shared by the parallel implementation of the Parzen-PNN. Numerical simulations demonstrate that our proposed scheme can gain 10dB in SNR given a fixed BER as 10-4, in comparison with other state-of-the-art methods.

With the developments of communication technologies and smart manufacturing, Industrial Internet of Things (IIoT) has emerged. Software-defined networking (SDN), a promising paradigm shift, has provided a viable way to manage IIoT dynamically, called software-defined IIoT (SDIIoT). In SDIIoT, lots of data and flows are generated by industrial devices, where a physically distributed but logically centralized control plane is necessary. However, one of the most intractable problems is how to reach consensus among multiple controllers under complex industrial environments. In this paper, we propose a blockchain (BC)-based consensus protocol in SDIIoT, along with detailed consensus steps and theoretical analysis, where BC works as a trusted third party to collect and synchronize network-wide views between different SDN controllers. Specially, it is a permissioned BC. In order to improve the throughput of this BC-based SDIIoT, we jointly consider the trust features of BC nodes and controllers, as well as the computational capability of the BC system. Accordingly, we formulate view change, access selection, and computational resources allocation as a joint optimization problem. We describe this problem as a Markov decision process by defining state space, action space, and reward function. Due to the fact that it is difficult to solve this joint problem by traditional methods, we propose a novel dueling deep Q-learning approach. Simulation results are presented to show the effectiveness of our proposed scheme.

The performances of cognitive radio system have been studied with primary user emulators existing. In this study, the authors use the theory of effective capacity to study the performances under quality‐of‐service (QoS) constraints in the link layer. New decision rules are proposed to decide the channel states. For a cognitive radio channel, the secondary users send data at two different rates regarding the channel states of being idle or occupied by primary users. When the channel is sensed as being occupied by a primary user emulator, which is performed by a secondary user, the secondary user stops the data transmission. To model the transmission channel, an 18‐state model of transition is constructed. The effective capacity expression of such cognitive system is also derived and analysed with different transmission rates. Besides, the impact on the effective capacity from the system parameters is investigated. It is shown from the numerical results that under the proposed decision rules, sensing durations and thresholds affect the detection probabilities greatly. For the effective capacity, it is shown that QoS imposes the most effect on the effective capacity, sensing durations and transmission rates can impact the effective capacity in certain regions.

To support the typical application scenarios defined in the fifth generation wireless network, such as the enhanced mobile broadband, massive machine type of communication, ultra-reliable and low-latency communication, virtual Mobility Management Entity (vMME) is a promising solution, which runs on universal servers and network functions virtualization instead of conventional hardware-dedicated mobility management entity. Among different vMME mapping solutions, the decomposing of MME into multiple components is a prospective approach to implementing distributed and virtualized mobility management. In this paper, the optimization of mobility management is addressed by using NFV and service function chain. A general signaling processing flow of vMME based on service function chain is analyzed. The performance of vMME is formulated considering the total signaling communication overhead cost, the total signaling communication overhead cost on backhauls as well as the migration overhead cost of state data under different function component placement of vMME. Since this optimization problem is NP-hard and the computation complexity is O(nk), a heuristic approach consisting of Min-TSCOC, MinTSCOCB, and Min-MOCSD are presented, which aims to obtain the optimal solutions to the optimization problems by using genetic algorithm. The simulation results show that it is beneficial to decompose the function of mobility management, and the performance gains from vMME for different network function composition strictly depend on four mobility events.

Dynamic spectrum sharing will facilitate the interference coordination in device-to-device (D2D) communications. In the absence of network level coordination, the timing synchronization among D2D users will be unavailable, leading to inaccurate channel state estimation and device detection, especially in time-varying fading environments. In this paper, we design an asynchronous device detection/discovery framework for cognitive-D2D applications, which acquires timing drifts and dynamical fading channels when directly detecting the existence of a proximity D2D device (e.g. or primary user). To model and analyze this, a new dynamical system model is established, where the unknown timing deviation follows a random process, while the fading channel is governed by a discrete state Markov chain. To cope with the mixed estimation and detection problem, a novel sequential estimation scheme is proposed, using the conceptions of statistic Bayesian inference and random finite set. By tracking the unknown states (i.e. varying time deviations and fading gains) and suppressing the link uncertainty, the proposed scheme can effectively enhance the detection performance. The general framework, as a complimentary to a network-aided case with the coordinated signaling, provides the foundation for development of flexible D2D communications along with proximity-based spectrum sharing.

Despite the great promises in next-generation wireless communications, dynamic spectrum access (DSA) remains still as a major challenge in uncertain environments, e.g., varying unknown channels. Existing popular schemes, i.e. potential game approaches, rely on the definite reward and a greedy strategy, which become unfortunately invalid in such varying and uncertain scenarios. In this paper, we propose a robust fuzzy-game approach to combat inherent channel uncertainties. Rather than the definite reward, we first project the decision space to another fuzzy-logical space and thereby characterize the varying uncertain information with the fuzzy numbers. Thus, the sensitiveness to random fluctuation in definite rewards would be alleviated. On this basis, we formulate DSA in uncertain channels as a centralized fuzzy payoffs game (FPG). We then develop a novel fuzzy-learning algorithm to achieve the optimal network throughput even in face of uncertain information, with which the network controller executes the decision making of sharing users by exploiting the fuzzy-logic method. Numerical results are finally provided to validate our new FPG scheme. Although uncertain environments render existing crisp-game approaches invalid, our new algorithm can converge after tens of iterations (even in fast-varying conditions), thereby permitting reliable shared accessing and improved throughput, which is of great significance to next- generation communications operated in dynamical and uncertain environments.

Molecular communications convey information via diffusion propagation. The inherent long-tail channel response causes severe inter-symbol interference, which may seriously degrade signal detection performances. Traditional linear signal detection techniques, unfortunately, require both high complexity and a high signal-to-noise (SNR) ratio to operate. In this paper, we proposed a new non-linear signal processing paradigm inspired by the biological systems that achieves low-complexity signal detection even in low SNR regimes. First, we introduce a stochastic resonance inspired non-linear filtering scheme for molecular communications, and show that it significantly improves the output SNR by transforming the noise energy into useful signals. Second, we design a novel non-coherent detector by exploiting the transient features of molecular signaling, which are independent of channel response and involves only lowcomplexity linear summation operations. Numerical simulations show that this new scheme can improve the detection performance remarkably (approx. 7dB gain), even when compared against linearly optimal coherent methods. This is one of the first attempts to demodulate molecular signals from an entirely biological point of view, and the designed non-linear noncoherent paradigm will provide significant potential to the design and future implementation of nano-systems in noisy biological environments.

We investigate the global throughput maximization of distributed spectrum reusing (DSR) in cognitive radio (CR) network, which reaches the two-dimensional spectrum multiplexing. Most previous works only consider the temporal-domain accessing, which greatly underutilize the spectrum resources. In this paper, we propose a new temporal-spatial spectrum reusing scheme by fully exploiting the location information of devices, where multiple users can access one channel simultaneously. In distributed applications, the global information will be unavailable, and therefore a non-cooperative game is formulated. It is proved as an exact potential game (EPG), which has at least one pure strategy Nash equilibrium (NE). Then, an improved decentralized reinforcement learning (RL) algorithm is developed to achieve the NE points. The network performance is evaluated by computer simulations.

This paper proposes a novel joint pilot assignment (JPA) approach aiming to eliminate pilot contamination in massive multiple-input multiple- output (MIMO) systems. The JPA approach jointly assigns pilots in both the time domain and spatial domain. With JPA, the multi-cell cellular massive MIMO network is divided into four cell groups. Pilot phases of different groups are designed to be successive in time domain, which means that users in different groups send pilots alternately in time domain. During the pilot phase of each group, a greedy spatial domain pilot assignment (SDPA) is introduced with the criterion of maximizing the minimal user signal-to-interference ratio (SIR). This paper shows that the JPA approach significantly eliminates pilot contamination from inter-groups when a finite but large number of base-station (BS) antennas is used, or even completely removed as the number of BS antennas becomes unbounded. Since the minimal uplink user SIR is increased, i.e., the intra-group pilot contamination is also alleviated. Numerical results verify the theoretical analysis.

Spatial correlation is a decisive factor for pragmatic multiple‐input multiple‐output (MIMO) system, simultaneously bringing about some problems in the received signal modulation identification respect. In this study, the authors focus on blind digital modulation identification in the spatially correlated MIMO system and deliver a robust signal recognition algorithm based on extreme learning machine (ELM) and higher order statistical features for MIMO signal identification without a priori knowledge of the channel and signal parameters. The superiority of ELM lies in random selections of hidden nodes and ascertains output weights analytically, which result in lower computational complexity. Theoretically, this algorithm has a tendency to supply excellent generalisation performance at staggering learning rate. Further, the simulation results indicate that the ELM could reap a perfectly acceptable recognition performance and thus provides a solid ground structure for tackling MIMO modulation challenges in low signal‐to‐noise ratio.

This study addresses the problem of interference mitigation in multi‐cell time division duplex cellular networks with large‐scale antennas. Network performance in such systems is hampered by pilot contamination effect, which results in the inter‐cell interference. To mitigate the interference, the authors propose a space‐time pilot design‐based approach to jointly eliminate interference from both time domain and space domain. The authors’ space‐time pilot design divides a cellular network into different groups which proceed channel estimation alternately in time. Moreover, a low‐rate coordination assisted pilot assignment scheme is added into each cell group during its pilot phase. This pilot assignment scheme relies on the minimum mean square of the updated Bayesian channel estimation. It can make use of the second‐order statistical information about the users' channel vectors within the same group to assign an identical pilot sequence to users that tend to interfere with each other at a lower level. On the basis of space‐time pilot design, the base station can proceed the maximal ratio transmitting and maximal ratio combining. With rigorous theoretic proof, their joint approach is able to significantly decrease interference. Simulations verified its excellent performance promotion.

In this study, the authors focus on the problem of interference alignment (IA) with random vector quantisation in device‐to‐device (D2D) uplink underlaying cellular networks. For a D2D underlaying system with one cellular network and one D2D local network, they first analyse the leakage interference introduced by limited feedback and, hence, imperfect IA. Then, they derive the exact closed‐form expressions of average sum rate in terms of transmit power for cellular communication and D2D communication. Under such condition, they investigate the adaptive feedback bits allocation schemes to achieve near‐optimal performance of systems with limited feedback including a greedy feedback bits allocation scheme and a waterfilling‐based feedback bits allocation scheme. Finally, simulation results validate their theoretical results and show that significant performance gain can be obtained by allocating feedback bits adaptively.

Molecular communications via diffusion (MCvD) represents a relatively new area of wireless data transfer with especially attractive characteristics for nanoscale applications. Due to the nature of diffusive propagation, one of the key challenges is to mitigate inter-symbol interference (ISI) that results from the long tail of channel response. Traditional coherent detectors rely on accurate channel estimations and incur a high computational complexity. Both of these constraints make coherent detection unrealistic for MCvD systems. In this paper, we propose a low-complexity and noncoherent signal detector, which exploits essentially the local convexity of the diffusive channel response. A threshold estimation mechanism is proposed to detect signals blindly, which can also adapt to channel variations. Compared to other noncoherent detectors, the proposed algorithm is capable of operating at high data rates and suppressing ISI from a large number of previous symbols. Numerical results demonstrate that not only is the ISI effectively suppressed, but the complexity is also reduced by only requiring summation operations. As a result, the proposed noncoherent scheme will provide the necessary potential to low-complexity molecular communications, especially for nanoscale applications with a limited computation and energy budget.

In this paper, a new detection framework, namely, deep sensing (DS), is proposed for 5G spectrum sharing, which is designed to proactively recover some informative states associated with realistic cognitive links (e.g., fading gains), except for detecting the occupancy of primary-band. Relying on a dynamic state-space approach, a unified mathematical model is formulated. The Bernoulli random finite set (BRFS) is exploited to theoretically characterize the complex DS procedures. A Bernoulli filter algorithm is suggested to recursively estimate unknown PU states accompanying related link information, which is further implemented by particle filtering. The proposed DS algorithm is applied to detect primary users over more challenging time-varying fading channels. Numerical simulations validate the new scheme. Spectrum sensing can be effectively implemented by estimating time-varying fading gains jointly.

Spectrum sensing, as one of the most important aspects, plays a crucial role on mitigating interference of secondary users (SU) in cognitive radio. However, moving primary user (PU) will sharply decrease the stability of observable information by considerably deteriorating the sensing performance. In this paper, a new joint estimating scheme is proposed for tracking PU proactively and detecting the occupation of primary band meanwhile. In view of both PU's state and its location, the united mathematical model based dynamic state-space model (DSM) is established in the new scheme. On this basis, a Bernoulli filter algorithm is suggested to jointly estimate the PU's state and its location recursively, which is further implemented by Particle filtering. Furthermore, an adaptive horizon expanding method is subtly designed to deal with loss of tracking resulting from the intermittent disappearance of PU's state. Experimental simulations demonstrate that, aiming at mobile PU, sensing performance of the new scheme is apparently better than other traditional methods, and the estimated PU's location may be further utilized by resource allocation of cognitive network.

Spectrum sensing based dynamic spectrum sharing is one of the key innovative techniques in future 5G communications. When realistic mobile scenarios are concerned, the location of primary user (PU) is of great significance to reliable spectrum detections and cognitive network enhancements. Given the dynamic disappearance of its emission signals, the passive locations tracking of PU, nevertheless, remains dramatically different from existing positioning problems. In this investigation, a new joint estimation paradigm, namely deep sensing, is proposed for such challenging spectrum and location awareness applications. A major advantage of this new sensing scheme is that the mutual interruption between the two unknown quantities is fully considered and, therefore, the PU's emission state is identified by estimating its moving positions jointly. Taking both PU's unknown states and its evolving positions into account, a unified mathematical model is formulated relying on a dynamic state-space approach. To implement the new sensing framework, a random finite set (RFS) based Bernoulli filtering algorithm is then suggested to recursively estimate unknown PU states accompanying its time-varying locations. Meanwhile, the sequential importance sampling is used to approximate intractable posterior densities numerically. Furthermore, an adaptive horizon expanding mechanism is specially designed to avoid the mis-tracking aroused by the intermittent disappearance of PU. Experimental simulations demonstrate that, even with mobile PUs, spectrum sensing can be realized effectively by tracking its locations incessantly. The location information, as an extra gift, may be utilized by cognitive performance optimizations.

Abstract Network coding has emerged some exciting future because of its smart technology in wireless sensor networks. At the same time, it is facing security attacks, especially conspiracy Attack. Most existing security strategies are concentrated on coding design, there has been almost no consideration from topological structure. In this background, a weakly‐secure scheme is proposed from the perspective of topology. Considering the performance of this scheme, an advanced scheme is put forward later. Simulations show that the two strategies can prevent cooperative eavesdroppers from acquiring any useful information transmitted from source node to sink node and the performance of advanced scheme is better. Copyright © 2013 John Wiley & Sons, Ltd.

The phrase ‘Big Data’ refers to large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future, as defined by U.S. National Science Foundation in its recent solicitation. The research of Big Data will accelerate the progress of scientific discovery and innovation; lead to new fields of inquiry that would not otherwise be possible, encourage the development of new data analytic tools and algorithms; facilitate scalable, accessible, and sustainable data infrastructure; increase understanding of human and social processes and interactions; and promote economic growth and improved health and quality of life. The new knowledge, tools, practices, and infrastructures produced will enable breakthrough discoveries and innovation in science, engineering, medicine, commerce, education, and national security. Big Data presents critical requirements for security in data collection and transmission of selected data through a communication network. This special issue contains 11 papers selected from submissions to the open call for papers on Security in Big Data. These papers highlight some of the current research interests and achievements in the area of security in Big Data. The wide use of high-performance image acquisition devices and powerful image-processing software has made it easy to tamper images for malicious purposes. The paper by Zhang et al. proposes an effective framework for revealing image-splicing forgery. The experiment results show that the proposed method can perform better than some state-of-the-art methods in terms of the detection performance over the Columbia image-splicing detection evaluation data set. Network coding has emerged some exciting future because of its smart technology in wireless sensor networks. At the same time, it is facing security attacks, especially conspiracy attack. The paper by Du et al. proposes a weakly secure scheme from the perspective of topology. Considering the performance of this scheme, an advanced scheme is put forward later. Simulations show that the two strategies can prevent cooperative eavesdroppers from acquiring any useful information transmitted from source node to sink node, and the performance of advanced scheme is better. Traditionally, jamming to the wireless system is a fatal threat to the security of home area networks (HANs), which impedes the two-way data transmission between electric devices and the smart meter and thus deteriorates the reliability of the in-home communication of Smart Grid. The paper by Li et al. incorporates the power line system into the HAN and proposes a hybrid architecture of orthogonal frequency-division multiplexing-based wireless communication and power line communication for the Smart Grid security application. With this new solution, the channel diversity of the HAN is realized, and the communication reliability is still guaranteed even when the wireless channel suffers from jamming. Information of multi-cells is big data because of the enormous quantities of various cells as well as their parameters and status. To securely and efficiently integrate all the cells' information and trace multi-cells are challenging because of varying number of the multi-cells, as well as the complicacy of the multi-cells' movement. The paper by Yin and Sun proposes an automatic big data integration algorithm based on the optical transfer function. The experimental results show that the algorithm can securely and efficiently integrate all the cell information and simultaneously track a large quantity of cells. Real-time digital video presents great challenges on processing and storage and is a typical example in Big Data. How to secure and efficiently transmit digital video is critical. The paper by Zhang et al. uses the distributed compressed sensing to deal with video coding. To reduce the orthogonal matching pursuit algorithm computational complexity, quantum-behaved particle swarm optimization algorithm is used to reconstruct video signal. Simulation results demonstrate that it can obtain the better reconstructed video with low sample value and it can guarantee safety performance. Wireless image sensor network generates a large number of images from the distributed camera sensors. The image data need to be delivered securely and efficiently to the sink in many circumstances. The current node-disjoint multipath and dispersive routings cannot provide enough security and efficiency for the image data collection and transportation. The paper by Su and Hu proposes an ellipse batch dispersive routing algorithm to address the secure and efficient data collection issue in wireless image sensor network. The smart grid system is composed of the power infrastructure and communication infrastructure and thus is characterized by the flow of electric power and information, respectively. The 24/7 information collection and transmission in smart grid is a good example of Big Data. The transmission of Big Data in smart grid needs wireless network, which introduces additional vulnerabilities, given the scale of potential threats. Therefore, the physical layer security issue is of first priority in the study of smart grid and has already attracted substantial attention in the industry and academia. The paper by Wang et al. aims to present a general overview of the physical layer security in wireless smart grid and covers the effective countermeasures proposed in the literature of smart grid to date. Security is a very broad topic; particular attention has been paid in communications, networking on security issues. However, in practical applications, providing security services increases the computation and the occupation of system resources. This problem is particularly important when energy is a limited resource for mobile communication devices operating on battery. Thus, energy-efficient security devices are very necessary for the communication. The paper by Yuan and Liang designed a new low voltage, low power consumption comparator for successive approximation register analog to digital converter to improve the energy efficiency in the problem of secure communication. Big data presents critical requirements for security in data collection and transmission of selected data through a communication network. The paper by Chen et al. presents a new secure transmission for big data based on nested sparse sampling and coprime sampling. With nested sampling and coprime sampling, besides the advantage of higher spectrum efficiency, big data could also achieve higher power spectral density for binary frequency shift keying signal. It proves that both nested sampling and coprime sampling could be used in big data transmission to resist interference, while guaranteeing the transmission performance. With the rapid adoption of cloud storage services, a great deal of data is being stored at remote servers, so a new technology, client-side deduplication, which stores only a single copy of repeating data, is proposed to identify the client's deduplication and save the bandwidth of uploading copies of existing files to the server. It was recently found, however, that this promising technology is vulnerable to a new kind of attack in which by learning just a small piece of information about the file, namely, its hash value, an attacker is able to obtain the entire file from the server. The paper by Yang et al. proposes a cryptographically secure and efficient scheme for a client to prove to the server his ownership on the basis of actual possession of the entire original file instead of only partial information about it. The paper by Wang et al. presents the definitions of big data and anomaly detection. The theory of ultra-wideband radar and the through-wall detection of a human model based on ultra-wideband radar are briefly introduced. The target criterion with wavelet packet transform is deduced, and the procedure for the through-wall human detection with statistical process control is constructed. The radar echo signals are collected at stationary and moving statuses of a human being for three types of walls. The experimental results demonstrate the effective of through-wall target detection based on the proposed algorithm. We would like to thank all authors for contributing papers to the special issue. We appreciate the staff of Security and Communication Networks for their support in editing this special issue. Qilian Liang is a University Distinguished Scholar Professor in the Department of Electrical Engineering, University of Texas at Arlington. He received the BS degree from Wuhan University in 1993, MS degree from Beijing Uni- versity of Posts and Telecommunica- tions in 1996, and PhD degree from University of Southern California (USC) in May 2000, all in Electrical Engineering. Prior to joining UTA in August 2002, he was a Member of Technical Staff in Hughes Network Systems Inc. at San Diego, California. His research interests include wireless sensor networks, wireless communications, signal processing, information theory, radar systems, and wireless networks. Dr. Liang has published more than 270 journal and conference papers. He received 2002 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, 2003 U.S. Office of Naval Research (ONR) Young Investigator Award, 2005 UTA College of Engineering Outstanding Young Faculty Award, 2007, 2009, 2010 U.S. Air Force Summer Faculty Fellowship Program Award, 2012 UTA College of Engineering Excellence in Research Award, 2013 UTA Outstanding Research Achievement or Creative Activity Award, and was inducted into UTA Academy of Distinguished Scholars in 2015. Jian Ren received the BS and MS degrees both in mathematics from Shaanxi Normal University and received the PhD degree in EE from Xidian University, China. He is an Associate Professor in the Department of ECE at Michigan State University. His current research interests include cryptography, network security, energy efficient sensor network security protocol design, privacy-preserving communications, secure and efficient cloud computing, and cognitive networks. He is a recipient of the US National Science Foundation Faculty Early Career Development (CAREER) award in 2009. Dr. Ren is a senior member of the IEEE. Jing Liang received the BS and MS degrees from Beijing University of Posts and Telecommunications, China in 2003 and 2006, respectively, and PhD degree from University of Texas at Arlington in August 2009, all in Electrical Engineering. She is currently a Professor in the Department of Electrical Engineering at University of Electronic Science and Technology of China. Her current research interests include radar sensor networks, collaborative and distributed signal processing, wireless communications, wireless networks, and fuzzy logic systems. Baoju Zhang is a Professor at the College of Physical and Electrical Information, Tianjin Normal Uni- versity. She received the BS degree from Tianjin Normal University in 1990, MS degree from Tianjin Nor- mal University in 1993, and PhD degree from Tianjin University in 2002. She was a Postdoctoral Fellow at Tianjin University from 2002 to 2004. Her research interests include radar sensor networks, digital audio and video technology, image compressing and coding, and compressive sensing. Yiming Pi was born in 1968 in China. He obtained PhD degree in Electronic Engineering from University of Electronic Science and Technology of China in 1993. Since 2002, he has been a Professor of Department of EE, University of Electronic Science and Technology of China. He is a councilor of Signal Processing Society in the Chinese Institute of Electronics and has served in organizing several international conferences in the field of Signal Processing and Radar Systems. He became IEEE Senior Member in 2011. He had been the leaders of some Natural Science Funding of China. He has more than 100 publications in the conferences and journals of IEEE/IET. His research interests are radar imaging, signal processing and terahertz technology, and so on. Chenglin Zhao received his BS degree in Tianjin University in 1986, MS degree and PhD degree in Beijing University of Posts and Telecommu- nications in 1993 and 1997, respec- tively. He is a Professor of the Key Lab of the ubiquitous wireless of Education Ministry, Information and Telecommunication engineering college, Beijing University of Post and Telecommunication. His main research areas include radar sensor networks, wireless broadband interconnection, wireless sensor network, and digital signal processing and its applications.

Abstract Machine‐to‐machine (M2M) techniques have significant application potential in the emerging internet of things, which may cover many fields from intelligence to ubiquitous environment. However, because of the data exposure when transmitted via cable, wireless mobile devices, and other technologies, its security vulnerability has become a great concern during its further extending development. This problem may even get worse if the user privacy and property are considered. Therefore, the authentication process of communicating entities has attracted wide investigation. Meanwhile, the data confidentiality also becomes an important issue in M2M, especially when the data are transmitted in a public and thereby insecure channel. In this paper, we propose a promising M2M application model that connects a mobile user with the home network using the existing popular Time Division‐Synchronous Code Division Multiple Access (TD‐SCDMA) network. Subsequently, a password‐based authentication and key establishment protocol is designed to identify the communicating parties and hence establish a secure channel for data transmissions. The final analysis shows the reliability of our proposed protocol. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract The wide use of high‐performance image acquisition devices and powerful image‐processing software has made it easy to tamper images for malicious purposes. Image splicing, which has constituted a menace to integrity and authenticity of images, is a very common and simple trick in image tampering. Therefore, image‐splicing detection is of great importance in digital forensics. In this paper, an effective framework for revealing image‐splicing forgery is proposed. First, the local binary pattern operator is used to model magnitude components of two‐dimensional arrays obtained by applying multisize block discrete cosine transform to test images. Then, all of bins of histograms computed from local binary pattern codes are served as discriminative features for image‐splicing detection. After that, kernel principal component analysis is utilized to reduce the dimensionality of the proposed features to avoid the high computational complexity, high mutual correlation among the constructed features and possible overfitting for support vector machine classifier. Finally, support vector machine classifier is employed to distinguish spliced images from authentic images by using the final dimensionality‐reduced feature set. The experiment results show that the proposed method can perform better than some state‐of‐the‐art methods in terms of the detection performance over the Columbia image‐splicing detection evaluation dataset. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract In this paper, we study the security assurance in application layer in wireless acoustic sensors via event forecasting and detection. In order to perform event forecasting and detection, we try to answer several challenging questions in acoustic signal research based on wireless acoustic sensors: (i) Are acoustic signals predictable? (ii) How are acoustic signals predicted? (iii) Are there any event‐forecasting applications for the security in wireless acoustic sensors? We study these questions based on Xbow acoustic sensors and demonstrate that real‐world acoustic signals are self‐similar, which means that they are predictable. We propose an acoustic signal prediction scheme using interval type‐2 fuzzy logic system (FLS). We show that a type‐2 fuzzy membership function (MF); that is, a Gaussian MF with uncertain mean is appropriate to model the acoustic signal strength. Two FLSs, a type‐1 FLS, and an interval type‐2 FLS are designed for signal strength forecasting. Furthermore, we propose a double sliding window scheme for event detection based on the forecasted signals. Simulation results show that the interval type‐2 FLS outperforms the type‐1 FLS in signal strength forecasting and the performance of event detection based on the forecasted signal from type‐2 FLS is much better than that based on type‐1 FLS. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, spectrum sensing is investigated and a new detection framework, namely, deep sensing (DS), is proposed for more challenging scenarios of future dynamic spectrum sharing. In contrast to existing methods, the DS scheme is designed to proactively recover and exploit some other informative states associated with realistic cognitive links (e.g., fading gains), except detecting the occupancy of primary-band. A unified mathematical model, relying on the dynamic state-space approach, is formulated, in which the Bernoulli random finite set (RFS) is further exploited to theoretically characterize complex DS procedures. A Bernoulli filter algorithm is suggested to recursively estimate unknown PU states accompanying related link information, which is implemented by particle filtering based on numerical approximations. The proposed DS algorithm is applied to detect primary users under time-varying fading channel, which may increase the observation uncertainty and, therefore, deteriorate the sensing performance. With this new framework, the time-varying fading gain, modeled as a stochastic discrete-state Markov chain (DSMC), is estimated along with unknown PU states. Simulations demonstrate that, by exploiting the underlying dynamic fading property, the sensing performance will surpass other traditional schemes. The DS scheme may be conveniently generalized to other applications, which will promote sensing performance and provides a new paradigm for next-generation spectrum sharing.

Cluster identification of ultra-wideband (UWB) propagations is of great significance to the parameter extraction and measurement analysis of channel modeling. In this paper, we address this challenging problem within a promising biological processing framework. Both the two large-scale characteristics of each multipath component, i.e., the decaying amplitude and the time of arrivals, are organically combined and fully explored in the suggested cluster identification algorithm. Each resolvable trajectory component is first projected onto a 2-D amplitude-time plane and further modeled as a virtual ant-agent, which can move around in this 2-D workspace with a preference to the high local-environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identifications can be realized by the biological ant colony clustering procedure. Owing to the population-based intelligence and the involved positive-feedback collaboration during the agents evolution, the suggested algorithm can efficiently identify the involved multiple clusters in a completely automatic manner. Experiments on UWB channels validate the proposed method. The practical parameter configuration is analyzed, and a group of numerical performance metrics is derived. As demonstrated by numerical investigations, multiple clusters involved in UWB channel impulse responses can be accurately extracted.

For the emerging 5G millimeter-wave communications, the nonlinearity is inevitable due to RF power amplifiers of the enormous bandwidth operating in extremely high frequency, which, in collusion with frequency-selective propagations, may pose great challenges to signal detections. In contrast to classical schemes, which calibrate nonlinear distortions in transmitters, we suggest a nonlinear equalization algorithm, with which the multipath channel and unknown symbols contaminated by nonlinear distortions and multipath interferences are estimated in receiver-ends. Attributed to the nonlinearity and marginal integration, the involved posterior density is analytically intractable and, unfortunately, most existing linear equalization schemes may become invalid. To solve this problem, the Monte-Carlo sequential importance sampling based particle filtering is suggested, and the non-analytical distribution is approximated numerically by a group of random measures with the evolving probability-mass. By applying the Taylor's series expansion technique, a local-linearization observation model is further constructed to facilitate the practical design of a sequential detector. Thus, the unknown symbols are detected recursively as new observations arrive. Simulation results validate the proposed joint detection scheme. By excluding transmitting pre-distortion of high complexity, the presented algorithm is specially designed for the receiver-end, which provides a promising framework to nonlinear equalization and signal detection in millimeter-wave communications.

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For the emerging 60GHz millimeter-wave communications, the nonlinearity is usually inevitable due to RF power amplifiers operating in the ultra-high frequency and enormous bandwidth, which, in collusion with frequency-selective propagations, poses great challenges to signal detections. In contrast to classical schemes calibrating nonlinear distortions in transmitters, a blind detection algorithm is presented in this investigation, with which both the multipath response and symbols contaminated by nonlinear distortions and multipath interferences are estimated in receiver-ends. The Monte-Carlo sequential importance sampling based particle filtering is used, and the non-analytical distribution is approximated numerically by a group of random measures with evolving weights. By applying the Taylor's series expansion techniques, a local linearization model is further constructed to facilitate the practical design of a sequential detector. Simulation results validate the proposed blind detection scheme. By excluding the transmitting predistorter with complex computations and implementations, the presented algorithm provides a promising signal detection framework in 60GHz systems.

As the application scope of cognitive radio grows continuously, time‐variant flat fading (TVFF) channels become common in practical spectrum sensing scenarios. Unfortunately, most existing spectrum sensing methods which are designed for time‐invariant propagation channels could hardly obtain good performance when they operate in realistic TVFF channels. To combat this difficulty, in this investigation the authors design a promising spectrum sensing method. Firstly, a novel dynamic state‐space model is proposed in which a two‐state Markov chain is employed to abstract the evolution of primary user states and a finite‐state Markov channel model is utilised to characterise the TVFF channel. Secondly, based on the maximum a posteriori probability criteria and the particle filtering mechanic, a joint estimation algorithm of the time‐dependent fading channel gain and the state of primary user is presented. Experimental simulations verify the performance superiority of the authors presented joint detection scheme, which could be properly applied to spectrum sensing in realistic TVFF channels.

Most of the existing spectrum sensing schemes utilize only the statistical property of fading channels, which unfortunately fails to cope with the time-varying fading channel that has disastrous effects on sensing performance. As a consequence, such sensing schemes may not be applicable to distributed cognitive radio networks. In this paper, we develop a promising spectrum sensing algorithm for time-variant flat-fading (TVFF) channels. We first formulate a dynamic state-space model (DSM) to characterize the evolution behaviors of two hidden states, i.e., the primary user (PU) state and the fading gain, by utilizing a two-state Markov process and another finite-state Markov chain, respectively. The summed energy, which serves as the observation of DSM, is employed for the ease of implementation. Relying on a Bayesian statistical inference framework, the sequential importance sampling based particle filtering is then exploited to numerically and recursively estimate the involved posterior probability, and thus, the PU state and the fading gain are jointly estimated in time. The estimations of two states are soft-outputs, which are successively refined with a designed iterative approach. Simulation results demonstrate that the new scheme can significantly improve the sensing performance in TVFF channels, which, in turn, provides particular promise to realistic applications.

In this paper, we investigate the cluster identification of ultra-wideband (UWB) multipath propagations from a promising biological processing perspective. In the presented biological cluster extraction method, both the amplitude decay and time of arrival of UWB channel impulse response (CIR) are fully taken into considerations. Each resolvable multipath component is projected onto a two dimensional amplitude-time workspace, and then modeled as a virtual ant-agent. Thus, these ant-agents can move around in this 2-D space with a preference to the high local environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identification can be elegantly realized by the biological ant colony clustering (ACC) procedure. As the experimental simulations shown, the suggested algorithm can accurately and efficiently identify the involved multiple clusters in a completely automatic manner, which is of great importance to UWB channel modeling and parameters extractions.

The measurement of an emitter's position using electronic support passive sensors is termed passive localization and plays an important part both in electronic support and electronic attack. The emitting target could be in terrestrial or underwater environment. In this paper, we propose a time difference of arrival (TDoA) algorithm for passive localization in underwater and terrestrial environment. In terrestrial environment, it is assumed that a Rician flat fading model should be used because there exists line of sight. In underwater environment, we apply a modified UWB Saleh-Valenzuela (S-V) model to characterize the underwater acoustic fading channel. We propose the TDoA finding algorithm via estimating the delay of two correlated channels, and compare it with the existing approach. Simulations were conducted for terrestrial and underwater environment, and simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and root-mean-square error (RMSE). Compared to the TDoA performance in terrestrial environment, the TDoA performance in underwater environment is much worse. This is because the underwater channel has clusters and rays, which introduces memory and uncertainties. For the two scenarios in underwater environment, the performance in rich scattering underwater environment is worse than that in less scattering underwater environment, because the latter has less clusters and rays, which would cause less uncertainties in TDoA.

The measurement of an emitter's position using electronic support passive sensors is termed passive geolocation. The emitting target could be in underwater environment. In this paper, we propose a Time Difference of Arrival (TDoA) algorithm for passive geolocation in underwater environment. In underwater environment, we apply a modified UWB Saleh-Valenzuela (S-V) model to characterize the underwater acoustic fading channel. To estimate the delay for TDoA of two correlated channels, we propose an TDoA finding algorithm, and compare with existing approach. Simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and Root-Mean-Square-Error (RMSE). In underwater environment, the performance in rich scattering underwater environment is worse than that in less scattering underwater environment, because the latter has less clusters and rays, which would cause less uncertainties in TDoA.

A compressed sensing radar detection scheme for closing vehicle detection is proposed in this paper. Because most of the man-made signals are cyclostationary, the cyclic autocorrelation domain of the target return signal is sparse. The compressed sensing radar detection scheme in this paper is designed based on the sparsity of the cyclic autocorrelation of the target return signal. From the measurements based on the compressed sensing of the cyclic autocorrelation, the actual cyclic autocorrelation of the signal can be reconstructed. From the simulation, the dissertation can be made that a very simple OMP algorithm can get a high probability of detection.