Mengwei Sun

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.

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.

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.

Blind spectrum sensing for cognitive radio over time-variant multipath flat-fading channels

Cognitive radio has more extensive application in recent years, and it may operate in complex wireless environmental condition such as communication systems with time-variant multipath flat-fading channel. As an essential technology for cognitive radio, most existing spectrum sensing methods are designed for time-invariant propagation channel; thus, it could be extremely difficult to achieve acceptable sensing performance when we apply them to deal with time-variant multipath fading channel. In order to overcome this obstacle, we design a novel spectrum sensing method in this investigation. Firstly, a dynamic state-space model is proposed in which two different hidden Markov models are employed to abstract the evolution of primary user state and time-variant multipath flat-fading channel gain. Based on the dynamic state-space model, the spectrum sensing problem is formulated as blind estimation problem. Relying on maximum a posteriori probability criterion and particle filtering technology, a joint estimation algorithm of the time-variant channel gain and primary user state is presented. Experimental simulations demonstrate the superior performance of our presented sensing scheme, which could be used potentially in realistic cognitive radio systems.

Low-Complexity Noncoherent Signal Detection for Nanoscale Molecular Communications

Nanoscale molecular communication is a viable way of exchanging information between nanomachines. In this investigation, a low-complexity and noncoherent signal detection technique is proposed to mitigate the inter-symbol-interference (ISI) and additive noise. In contrast to existing coherent detection methods of high complexity, the proposed noncoherent signal detector is more practical when the channel conditions are hard to acquire accurately or hidden from the receiver. The proposed scheme employs the molecular concentration difference to detect the ISI corrupted signals and we demonstrate that it can suppress the ISI effectively. The difference in molecular concentration is a stable characteristic, irrespective of the diffusion channel conditions. In terms of complexity, by excluding matrix operations or likelihood calculations, the new detection scheme is particularly suitable for nanoscale molecular communication systems with a small energy budget or limited computation resource.

Joint detection scheme for spectrum sensing over time-variant flat fading channels

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.

Local Convexity Inspired Low-Complexity Noncoherent Signal Detector for Nanoscale Molecular Communications

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.

A Novel Spectrum Sensing for Cognitive Radio Networks With Noise Uncertainty

This correspondence investigates a joint spectrum sensing scheme in cognitive radio (CR) networks with unknown and dynamic noise variance. A novel Bayesian solution is proposed to recover the dynamic noise variance and detect the occupancy of primary frequency band simultaneously. The states of primary users are detected based on particle filtering technology, and then the noise parameters are tracked by using finite-dimensional statistics for each particle based on marginalized adaptive particle filtering. Simulation results are provided to validate that the proposed method can improve the sensing performance significantly and target the dynamic noise variance accurately.

Spectrum Sensing for Self-Organizing Network in the Presence of Time-Variant Multipath Flat Fading Channels and Unknown Noise Variance

Cognitive radio, as an important means of implementing spectrum-awareness and dynamic sharing, is of great significance to the future deployment of self-organizing networks. Given its practical application, cognitive radio technology may operate in various adverse self-organizing networks environments. For example, in wireless mobile communication, the multipath propagation with time-varying fading coefficients and unknown noise variance becomes inevitable. Unfortunately, most existing spectrum sensing methods could hardly acquire good performance in this adverse situation. To overcome this difficulty, in this paper we present a novel spectrum sensing algorithm in realistic cognitive radio applications. Firstly, we establish a dynamic state-space model which gives full consideration to the evolution characteristics of primary user’s state and time-variant multipath flat fading channel, while the received signal processed by matched filtering is viewed as the observation. On this basis, spectrum sensing is realized by estimating the primary user’s state, multipath channel impulse response and noise variance jointly and iteratively. This formulated problem is solved based on maximum a posteriori probability criterion and marginal particle filtering technology. Simulations demonstrate that the sensing performance achieved by proposed algorithm is satisfactory and at the same time, the channel response and noise variance are estimated accurately.

Energy detection based spectrum sensing in the presence of time-frequency double selective fading propagations

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