Blind coding classification in the presence of interference in MIMO systems using ML algorithm. (26th April 2019)

• Record Type: Journal Article
• Authors: Al‐Makhlasawy, Rasha M.; Hefnawy, Alaa A.; Abd Elnaby, Mustafa M.; Abd El‐Samie, Fathi E.
• Is Part Of: International journal of communication systems. Volume 32:Number 10(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-26
• Subjects: blind coding classification -- code parameter (CP) -- maximum likelihood (ML) -- MIMO -- multi‐user kurtosis (MUK); Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.3901
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: 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. Abstract : In this paper, an efficient method for blind classification of the space‐time block code (STBC) in multiple‐input multiple‐output (MIMO) systems considering inter‐user interference (IUI). This method depends on the multi‐user kurtosis (MUK) for signal separation and maximum likelihood (ML) for code classification. The code parameter (CP) estimation algorithm that uses the ML does not require threshold value estimation, prior knowledge about the received signal, or channel estimation. The proposed scenario achieves high success rates with different configurations of the system. It is recommended for usage in future 5G systems.