Blind coding classification in the presence of interference in MIMO systems using ML algorithm

Abstract

This paper presents a framework for coding classification in multiple-input multiple-output (MIMO) systems in the presence of inter-user interference (IUI). This framework is performed at the receiver beginning with a signal separation step. The signal separation is implemented with a multi-user kurtosis (MUK) algorithm. The classification step estimates the code parameter (CP) using the maximum-likelihood (ML) method applied to the covariance matrix of the received signal without a priori knowledge about the transmitted signal.Experimental results show that the proposed coding classifier is easy to implement and efficient for the classification of the CP over Rayleigh fading channels in the presence of time and frequency offsets. Furthermore, the success rate of code classification is high at low signal-to-noise ratios (SNRs). The signal separation increases the probability of true classification. KEYWORDS blind coding classification, code parameter (CP), maximum likelihood (ML), MIMO, multi-user kurtosis (MUK) | INTRODUCTIONWireless communication has become a rapid growing field of electrical engineering. Its growth is due to the rapidly increasing demand for high data rates over wireless systems. In multiple-input multiple-output (MIMO) systems, without allocating a specific subchannel to each signal, we can transmit several signals on the same bandwidth. Signal processing techniques are used to equalize the signals, when multiple signals are transmitted over a MIMO channel and to separate the transmitted sources at the receiver. Recovering a number of independent and identically distributed (i.i.d) source signals that are transmitted through a linear instantaneous mixing channel, simultaneously, in a blind manner is a problem in wireless communication systems.Generally, inter-user interference (IUI) causes damage of the received signals. This paper adopts the multi-user kurtosis (MUK) algorithm for signal separation. The essence of the blind signal separation (BSS) problem is to recover the source signal from a group of sensor observations that are mixtures of the sources. Blind recognition of wireless communication parameters is an essential step that needs to be inserted between signal detection and signal decoding and demodulation to recover the user data. Blind recognition processes are used in civilian applications such as software-defined radio (SDR) to deal with a large section of communication systems. In electronic warfare, these processes are a pre-requisite for signal capturing and processing.