Abstract
There exist few blind solutions for chaotic MIMO channel equalization. In this work, a chaotic MIMO channel equalization framework is proposed. The objective function to be minimized in the proposed solution is obtained by adopting the objective function developed for chaotic SISO channel equalization. Furthermore, an optimum filter that minimizes the proposed cost function is designed to recover chaotic input signals assuming that the channel is known. The stationary point of the adaptive solution is equal to the optimal filter if the adaptive filter coefficients change sufficiently slowly. The adaptive solution is contrasted with the optimum filter in terms of mean-square error and bit error rate performances. In addition, the proposed solution reconstructs chaotic input signals at the same time. Consequently, it can be applied to multiple signal separation problems as well.
Highlights
Chaos has attracted attention from researchers including physicists and engineers over the last two decades
In the first and second experiments, convergence behavior of the proposed framework is analyzed and it is contrasted with the classical blind multiple-input multiple-output (MIMO) equalization algorithm given previously [24]
An adaptive chaotic blind equalization approach to combat intersymbol interference (ISI) and multiuser interference (MUI) for MIMO systems was proposed in this work
Summary
Chaos has attracted attention from researchers including physicists and engineers over the last two decades. In classical digital communication systems, proposed statistics-based SISO and MIMO channel equalization algorithms may not yield desired results for chaotic systems since a discrete-time chaotic signal is deterministic. 2. MIMO chaotic blind channel equalization problem Chaos-based communication systems take into account the inherent properties of chaotic signals to achieve optimum transmission accuracy. MIMO chaotic blind channel equalization problem Chaos-based communication systems take into account the inherent properties of chaotic signals to achieve optimum transmission accuracy In these systems chaotic input signals can be generated by differential equations or iterative maps depending on the working principle. At time n let C, s[n] , and z[n] denote the channel coefficient matrix, input signal vector, and noise vector, respectively In the remainder of the paper, (17) will be the basis to describe the optimum filter in the case of a known channel and to derive the adaptive filter in the case of an unknown channel
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