Chenglin Zhao

Energy Detection Based Spectrum Sensing for Cognitive Radios Over Time-Frequency Doubly Selective Fading Channels

Cognitive radios may operate in practice under various adverse environments. For typical mobile and short-range scenarios, wireless links may tend to be time and frequency selective, i.e., the multipath propagations with time-varying fading coefficients will be inevitable. To cope with the encountered doubly-selective channels, in this paper we present a new spectrum sensing algorithm for distributed applications. First, a dynamic discrete state-space model is established to characterize sensing process, where the occupancy state of primary band and the time-varying multipath channel are treated as two hidden states, while the summed energy is adopted as the observed output. With this new paradigm, spectrum sensing is realized by acquiring primary states and time-dependent multipath channel jointly. For the formulated problem, unfortunately, Bayesian statistical inference may be impractical due to the absence of likelihoods and involved non-stationary distributions. To remedy this problem, an iterative algorithm is further designed by resorting to sequential importance sampling techniques; thus, the dynamic non-Gaussian multipath channel and primary states are estimated recursively. Another critical challenge, e.g., the noise uncertainty, is also considered, which may be incorporated conveniently into this sensing diagram and, furthermore, addressed effectively by the designed algorithm. Simulations validate the proposed algorithm. While classical schemes fail to deal with doubly selective channels, the new sensing scheme can exploit the underlying channel memory and operate well, which provides a great promise to realistic applications.

Deep Sensing for Future Spectrum and Location Awareness 5G Communications

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 PUs emission state is identified by estimating its moving positions jointly. Taking both PUs 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.

Spectrum Sensing for Cognitive Radios in Time-Variant Flat-Fading Channels: A Joint Estimation Approach

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.

TDoA for Passive Localization: Underwater versus Terrestrial Environment

The measurement of an emitters 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.

Efficient and Robust Cluster Identification for Ultra-Wideband Propagations Inspired by Biological Ant Colony Clustering

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.

A security authentication scheme in machine-to-machine home network service

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

A multicontroller load balancing approach in software-defined wireless networks

Software-defined networking (SDN) is currently seen as one of the most promising future network technologies, which can realize the separation between control and data planes. Furthermore, the increasing complexity in future wireless networks (i.e., 5G, wireless sensor networks) renders the control and coordination of networks a challenging task. Future wireless networks need good separation of control and data planes and call for SDN method to handle the explosive increase of mobile data traffic. Relying on a single controller in future wireless networks imposes a potential scalability problem. To tackle this problem, the thought of using multiple controllers to manage the large wide-area wireless network has been proposed, where the load balance problem of multicontroller needs to be resolved. In this paper, we propose a multicontroller load balancing approach called HybridFlow in software-defined wireless networks, which adopts the method of distribution and centralization and designs a double threshold approach to evenly allocate the load. Simulation results reveal that the proposed approach can significantly relieve the working load on the super controller and reduces the load jitter of multicontroller load in a single cluster compared with the BalanceFlow method.

Characterization on Clustered Propagations of UWB Sensors in Vehicle Cabin: Measurement, Modeling and Evaluation

In this paper, we study the clustered propagation characteristics of ultrawideband sensors in vehicle cabin. Inspired by the amplitude discontinuity of channel impulse responses, we first develop an automatic cluster identification algorithm. A moving averaging ratio is extracted from measured responses, which thoroughly reflects prevailing amplitude ruptures induced by different clusters. Based on a novel wavelet scales multiplication, an efficient cluster extraction scheme is presented. Our scheme can automatically discover multiple clusters, which shows great promise in realistic measurement analysis. Subsequently, from a ray-optical and statistical point of view, a parametric model of inter-cluster shape is suggested, which interprets the observed inter-cluster power as a kind of large-scale frequency selectivity. Parameter fitting to real data validates this power delay profile (PDP) model. Finally, we apply the identified clusters and fitted PDP to the practical design of low-complexity ultra-wideband sensors, which is of particular interest to the emerging wireless sensor networks. By utilizing the cluster PDP as a roughly matched template for a received signal, detection performance can be noticeably reinforced. This method simultaneously provides a practical criterion to evaluate the clustered propagation modeling, and the remarkable receiving gain verifies the effectiveness of our suggested cluster identification scheme and PDP model.

A Bayesian Approach for Nonlinear Equalization and Signal Detection in Millimeter-Wave Communications

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 Taylors 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.

Deep Sensing for Space-Time Doubly Selective Channels: When a Primary User Is Mobile and the Channel Is Flat Rayleigh Fading

The unrestrained mobility and dynamic spectrum sharing are considered as two key features of next-generation communications. In this paper, spectrum sensing in mobile scenarios is investigated, which faces still great challenges as both the mobile location of primary-user and fading channel will become time-variant. Such two uncertainties would arouse remarkable fluctuations in the strength of received signals, making most existing sensing schemes invalid. To cope with this exceptional difficulty, a novel paradigm, i.e., deep sensing, is designed, which estimates the time-dependent flat-fading gains and primary-users mobile positions jointly, at the same time of detecting its emission status. All three hidden states involved by the space-time doubly selective scenario are taken into accounts. A unified dynamic state-space model is established to characterize the dynamic behaviors of unknown states, in which the time-dependent flat fading is modeled as a stochastic discrete-state Markov chain. A Bayesian approach, premised on a formulation of random finite set, is suggested to recursively estimate primary users unknown states accompanying two others link uncertainties. In order to avoid the mis-tracking of the mobile positions, which is caused either by the incessant disappearance of primary-user or time-variant channels, an adaptive horizon expanding mechanism is also integrated. Numerical simulations validate the proposed scheme.