Guosheng Yang

Myriad MSK signal detection for VLF/LF communication

In VLF/LF communication systems, atmospheric noise is the dominant interference, which is usually highly impulsive and can be effectively modeled as symmetric α-stable (SαS) distribution. It has been known that communication techniques designed under Gaussian noise assumption always have poor performance under impulsive noise. In this work, we focus on the detection problem of minimum shift keying (MSK) signals under SαS noise, because this modulation is widely used in VLF/LF communication systems. As SαS distribution usually has no closed form probability density function (PDF), the optimal maximum likelihood (ML) based detection of MSK signals is hard to implement. To address this problem, we proposed a closed form approximation of the branch metric used in the Viterbi algorithm (VA) for MSK coherent detection, and provided a closed form approximation of the local optimal MSK non-coherent detection. Compared with ML based algorithms, the proposed algorithms have closed form and are simple to implement. Simulation results show that the bit error ratio (BER) performance of the proposed MSK coherent detector can closely approach that of the ML based coherent detector. Meanwhile, the proposed MSK non-coherent detector can attain the similar BER performance as that of the ML based non-coherent detector in low signal-to-noise ratio region.

Coherent sequence detection of MSK signals under impulsive noise

Impulsive noise modeled by symmetric α-stable (SαS) distribution can be found in many communication scenarios, such as atmospheric noises in very low frequency and low frequency (VLF/LF) communication systems and network interferences in wireless communication networks, etc. Under these cases, as Gaussian noise based signal processing methods always have poor performance, it is necessary to design robust signal processing algorithms to combat with the impulsive noise. In this work, we focus on the coherent signal detection of minimum shift keying (MSK) under SαS noise, because MSK is widely used in VLF/LF communication systems. Based on the received phase-coherent pass-band MSK signal model, a sequence detection algorithm is proposed by using the Viterbi algorithm. Under SαS noise, as the maximum likelihood (ML) based branch metric in the Viterbi algorithm has no closed form and is hard to implement, a robust branch metric is first proposed based on a closed form approximation to the ML based branch metric. Furthermore, the symbol error rate (SER) performance of the proposed sequence detection algorithm is analyzed. Our analytical results match the simulation results well, and both of them validate the robustness of our proposed algorithm.

Nonlinear Processing for Correlation Detection in Symmetric Alpha-Stable Noise

In this letter, the optimal and suboptimal nonlinear processing for correlation-based signal detection is addressed in symmetric alpha-stable noise. By maximizing the correlator output signal-to-noise ratio, a constrained functional optimization problem is established. As this optimization problem is hard to get analytical solution, we apply finite discretization to it and prove that the resulting approximation problem is a convex quadratic programming problem. This optimal nonlinear processing provides performance bound and design criteria for correlator detection. Based on the noise parameter

Energy efficiency maximization oriented resource allocation in 5G ultra-dense network: Centralized and distributed algorithms

\alpha

Complex Baseband Myriad Filtering and Maximum Likelihood MSK Demodulation under Symmetric Alpha-Stable Noise

and the order statistic of received data, we further propose an adaptive method to determine the optimal threshold of the commonly used soft limiter. Simulation results show that the proposed method achieves near-optimal performance.

Non-coherent symbol-by-symbol detection of MSK signals under impulsive noise

Abstract Spurred by both economic and environmental concerns, energy efficiency (EE) has now become one of the key pillars for the fifth generation (5G) mobile communication networks. To maximize the downlink EE of the 5G ultra dense network (UDN), we formulate a constrained EE maximization problem and translate it into a convex representation based on the fractional programming theory. To solve this problem, we first adopt a centralized algorithm to reach the optimum based on Dinkelbach’s procedure. To improve the efficiency and reduce the computational complexity, we further propose a distributed iteration resource allocation algorithm based on alternating direction method of multipliers (ADMM). For the proposed distributed algorithm, the local and dual variables are updated by each base station (BS) in parallel and independently, and the global variables are updated through the coordination and information exchange among BSs. Moreover, as the noise may lead to imperfect information exchange among BSs, the global variables update may be subject to failure. To cope with this problem, we propose a robust distributed algorithm, for which the global variable only updates as the information exchange is successful. We prove that this modified robust distributed algorithm converges to the optimal solution of the primal problem almost surely. Simulation results validate our proposed centralized and distributed algorithms. Especially, the proposed robust distributed algorithm can effectively eliminate the impact of noise and converge to the optimal value at the cost of a little increase of computational complexity.

A Spectrum Adaptive NC-CI/OFDM System

Symmetric α-stable (SαS) distribution noise is widely used to model co-channel and network interference in wireless communication systems. As robust and adaptive techniques, myriad filtering (MyF) and spherically symmetric vector MyF have been applied to suppress univariate and spherically symmetric multivariate SαS distribution noise, respectively. At a communication receiver, the received band-pass noisy signals are usually down-converted to complex baseband, and the resulted complex baseband SαS noise has been demonstrated not to be circularly symmetric. In this paper, we proposed a complex baseband MyF (CBMyF) to suppress the non- circularly symmetric complex baseband SαS noise. Besides, there are few researches with respect to the demodulation of memory modulation signals, e.g., minimum shift keying (MSK) signal, under SαS noise. Thus, based on CBMyF, we proposed coherent and non-coherent MSK demodulation algorithms under SαS noise in this paper. Furthermore, maximum likelihood (ML) MSK demodulation under SαS noise also been proposed. Simulation results show that the bit error rate (BER) performance of the proposed CBMyF based MSK demodulation can closely approach that of ML demodulation. Meanwhile, the proposed CBMyF is compared with the common used clipper, and the results validate its advantage of robustness and adaptivity.

Cooperative Spectrum Sensing in Heterogeneous Cognitive Radio Networks Based on Normalized Energy Detection

The symmetric α-stable (SaS) distribution has been widely used to model the impulsive noise, which typically exists in many communication scenarios, such as atmospheric noises in very low frequency and low frequency (VLF/LF) communication systems and network interferences in wireless communication networks, etc. Since Gaussian noise model based signal processing algorithms always perform poorly under impulsive noise, designing robust signal processing methods for impulsive noise is therefore well-motivated. In this work, we focus on the non-coherent detection problem of minimum shift keying (MSK) signals under the impulsive noise modeled as S α S distribution, because this modulation scheme is widely used in VLF/LF communication systems. The local optimal symbol-by-symbol non-coherent detection algorithm of MSK signals is first given by the principle of maximum likelihood (ML) detection. As the ML based algorithm has no closed form and is hard to implement, a robust symbol-by-symbol non-coherent detection algorithm of MSK signals is further proposed based on a closed form approximation to the ML based algorithm. Simulation results illustrate the robustness of the proposed algorithm for SaS noise. Moreover, an analytical symbol error rate (SER) upper bound of our proposed algorithm is derived, and simulation results verify the effectiveness of the analytical SER upper bound.

Joint Estimation of Timing and Carrier Phase Offsets for MSK Signals in Alpha-Stable Noise

Carrier Interferometry Orthogonal Frequency Division Multiplexing (CI/OFDM) can mitigate the problem of high peak-to-average power ratio (PAPR) of OFDM signal. In practice, some subcarriers have to be deactivated in order to avoid harmful interference to licensed system in cognitive radio (CR) context. For these cases, Non-Continuous CI/OFDM (NC-CI/OFDM) has recently been proposed, for which some subcarriers are unused intentionally. Unfortunately, the PAPR performance of current NC-CI/OFDM schemes is sensitive to the distribution of unused subcarriers. In this paper, we propose a new spectrum adaptive NC-CI/OFDM system with improved CI spreading scheme to achieve desired PAPR performance. A novel iterative method is proposed herein to design pilot symbols so that desirable balance between the channel estimation performance and the PAPR can be obtained. We further propose a minimum mean square error (MMSE) based soft signal detection and its complexity-reduced implementation. Simulation results show that our proposed new NC-CI/OFDM scheme can achieve significantly better PAPR performance than that of existing schemes, while its bit-error-rate (BER) performance close approaches that of the existing schemes with negligible performance gap.