In this letter, a novel polarized spatial scattering modulation (PSSM), which exploits the available degree of freedom in the polarization domain in addition to the spatial scattering and signal domains to increase the spectral efficiency, is proposed. At the receiver, the maximum likelihood algorithm is applied to detect received signals. A tight union upper bound on the average bit error probability (ABEP) is derived in a closed-form. For the sake of fast system evaluation and comparison, an asymptotic bound on the ABEP is derived. Numerical results show that the proposed PSSM outperforms the conventional spatial scattering modulation and maximum beamforming systems in terms of the ABEP.

In this paper, two novel adaptive spatial scattering modulation (ASSM) algorithms, namely unequal transmission probability-based ASSM (UTP-ASSM) and equal transmission probability-based ASSM (ETP-ASSM), are proposed to pursue a better tradeoff between the computational complexity and spectral efficiency. According to available channel state information, the proposed ASSM algorithms are conducted to compute the optimal numbers of scatters and the optimal modulation orders constrained by the maximal tolerable symbol error probability (SEP). In addition, the minimal tolerate value of the minimal tolerant signal-to-noise ratio and SEP threshold are computed with a given data rate requirement by the bisection algorithm. At the receiver, a new type of optimal maximum likelihood detector is proposed to enhance the SEP performance. Furthermore, the union upper bound on the SEP is derived and analyzed. In addition, the computational complexity and system performance of the ASSM schemes are analyzed. Simulation results demonstrate that the proposed ASSM algorithms yield a lower SEP than the conventional spatial scattering modulation at the same value of average data rate.

This paper addresses the blind signal recovery for convolutive multiple-input multiple-output systems with high-order quadrature amplitude modulation (QAM) signals. First, a family of batch blind recovery algorithms is proposed. Concretely, they introduce the error function of multimodulus algorithm and the cross-correlation among different equalizer output vectors into the penalty term of the support vector regression (SVR) framework to recover all sources simultaneously. Then, the corresponding dual-mode blind recovery schemes are constructed to further decrease the interference. The new blind formulation through iterative re-weighted least square achieves low complexity optimization. The SVR framework, in essence, determines that the proposals perform better than the conventional methods in terms of data block size, total interference, and symbol error rate. Moreover, the excellent initialization provided by the first mode and the accurate error expression in the second mode ensure that the SVR-based dual-mode schemes work well with the high-order QAM signals. Finally, the efficiency of the proposals over the classical approaches is evaluated by simulations.

Given the nonlinear channel, the online blind equalization algorithm for Quadrature Amplitude Modulation (QAM) signals based on the prediction principle via the Complex Echo State Network(C-ESN) is proposed. The traditional linear prediction error filter is replaced by the C-ESN with good nonlinear mapping ability, and the minimum prediction error is attained by the Kalman filter(KF). After then, the amplitude and phase are adjusted in sequence. Simulation results show that the proposed algorithm can effectively reduce the distortion of the transmitted QAM signals caused by nonlinear channels. Compared with other prediction principle-based blind equalization algorithms and the traditional cost function based ESN blind equalization algorithm, it has lower mean square error and faster convergence speed.

For nonlinear channels, we propose a new blind equalization method for chaotic signals based on echo state network( ESN) and Kalman filter(KF) in this letter. Using the shortterm predictability of chaotic signals, ESN, a new feedback neural network, is used to approximate unknown chaotic maps and predict the output of the equalizer. Furthermore, KF combined with the finite impulse response(FIR) is used as the equalizer in this letter, and its parameters are adjusted by minimizing the prediction error of ESN. Simulation results show that compared with other blind equalization methods for chaotic signals, the proposed method has the advantages of smaller mean square error and faster convergence rate, and is suitable for strong nonlinear channels.