Adaptive Modulation Using Neuro-Fuzzy (N-F) Controller for OFDM System

Abstract

As demand for high quality transmission increases, improving spectrum efficiency and error performance in wireless communication systems are important. OFDM is a multi-carrier modulation technique with densely spaced sub-carriers that has gained a lot of popularity among the broadband community in the last few years. One of the promising approaches to next generation communication systems are adaptive OFDM (AOFDM). Fixed modulation systems uses only one type of modulation scheme (or order), so that either performance or capacity should be compromised but in adaptive modulated systems change modulation scheme (or order) depending on instantaneous Signal to Noise Ratio (SNR) to attain superior performance and capacity compared to fixed modulated systems. Neuro-fuzzy controller combines advantages of fuzzy logic and neural networks. Neuro-fuzzy controller provides automatic adaption procedure to fuzzy logic controller. Neural networks requires sufficient prior knowledge to be initialized but neuro-fuzzy systems doesn't requires any prior knowledge to be initialized and is efficient compared to fuzzy logic and neural networks. In this paper we propose an adaptive modulated OFDM system using neuro-fuzzy controller. The proposed system is simulated in MATLAB and compared with existing systems, simulation results shown significant improvement in systems performance.

Adaptive Multi User Detection for FD-MC-CDMA in Presence of CFO

“…In this paper we proposed adaptive population/Generation sizing genetic algorithm assisted MUD scheme where adaptive population/Generation size is adjusted based on SNR. In the proposed scheme at the receiver non data aided SNR detector proposed in [9] is utilized to estimate SNR in block fading channel. Based on present SNR and present size of population/generation a fuzzy controller is used to estimate the size of population/generations to be employed as shown in fig…”

“…Various multi-user detectors (MUD) have been developed and analyzed for this proposed MC-CDMA systems [2,3,4,6,7,8,9]. Multi user detectors may be optimal or sub-optimal.…”

“…In contrast to traditional computation systems which may be good at accurate and exact computation but have brittle operations, evolutionary computation provides a more robust and efficient approach for solving complex real world problem. Many evolutionary algorithms, such as Genetic algorithm (GA) [5,9,10,11,22,23,25,26], ant colony optimization (ACO) [27], simulated annealing (SA) [28]. This paper is organized as follows in section 2 introduces FD-MC-CDMA model and ML estimation of Multi User Detector.…”