Blind sequence detection using reservoir computing

The performance of M-ary quadrature amplitude modulation (QAM) can seriously be degraded by inter-symbol interference (ISI) as the number of levels increases. To mitigate ISI, blind sequence detection (BSD) has very important applications in data transmission systems, particularly where sending a training sequence is disruptive or costly. A new BSD approach of short data in QAM systems using reservoir computing (RC) is presented, together with the detailed theoretical derivation of the algorithm. Its convergence can be guaranteed within a short data packet and, therefore, it works in systems with a much shorter data record and faster time-varying channels. A RC network is constructed to solve the special issue of BSD, with reservoir weight matrix generated via the reduced QR decomposition from the view of receiving signal subspace instead of being selected randomly. The design methods of the activation function and readout function, the variation rule of initial vector which is changed by reservoir weight, and complexity of the proposed algorithm are described, respectively. The readout weight of the RC network is trained and updated by support vector regression (SVR) with a Gaussian insensitive loss function. The correctness and effectiveness of the new approach are verified by simulations, and some special simulation phenomena of the algorithm are discussed.