Browsing by Subject "Channel equalization"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item Open Access Adaptive tracking of narrowband HF channel response(Wiley-Blackwell Publishing, 2003) Arikan, F.; Arıkan, OrhanEstimation of channel impulse response constitutes a first step in computation of scattering function, channel equalization, elimination of multipath, and optimum detection and identification of transmitted signals through the HF channel. Due to spatial and temporal variations, HF channel impulse response has to be estimated adaptively. Based on developed state-space and measurement models, an adaptive Kalman filter is proposed to track the HF channel variation in time. Robust methods of initialization and adaptively adjusting the noise covariance in the system dynamics are proposed. In simulated examples under good, moderate and poor ionospheric conditions, it is observed that the adaptive Kalman filter based channel estimator provides reliable channel estimates and can track the variation of the channel in time with high accuracy.Item Open Access Channel estimation and symbol demodulation for OFDM systems over rapidly varying multipath channels with hybrid deep neural networks(Institute of Electrical and Electronics Engineers, 2023-05-01) Gümüş, Mücahit; Duman, Tolga MeteWe consider orthogonal frequency division multiplexing over rapidly time-varying multipath channels, for which performance of standard channel estimation and equalization techniques degrades dramatically due to inter-carrier interference (ICI). We focus on improving the overall system performance by designing deep neural network (DNN) architectures for both channel estimation and data demodulation. To accomplish this, we employ the basis expansion model to track the channel tap variations, and exploit convolutional neural networks’ learning abilities of local correlations together with a coarse least square solution for a robust and accurate channel estimation procedure. For data demodulation, we use a recurrent neural network for improved performance and robustness as single tap frequency-domain equalizers perform poorly, and more sophisticated equalization techniques such as band-limited linear minimum mean squared error equalizers are vulnerable to model mismatch and channel estimation errors. Numerical examples illustrate that the proposed DNN architectures outperform the traditional algorithms. Specifically, the bit error rate results for a wide range of Doppler values reveal that the proposed DNN-based equalizer is robust, and it mitigates the ICI effectively, offering an excellent demodulation performance. We further note that the DNN-based channel estimator offers an improved performance with a reduced computational complexity.Item Open Access Deep-learning for communication systems: new channel estimation, equalization, and secure transmission solutions(2023-08) Gümüş, MücahitTraditional communication system design takes a model-based approach that aims to optimize relevant performance metrics using somewhat simple and tractable channel and signal models. For instance, channel codes are designed for simple additive white Gaussian or fading channel models, channel equalization algorithms are based on mathematical models for inter-symbol interference (ISI), and channel estimation techniques are developed with the underlying channel statistics and characterizations in mind. Through utilizing superior mathematical models and expert knowledge in signal processing and information theory, the model-based approach has been highly successful and has enabled development of many communication systems until now. On the other hand, beyond 5G wireless communication systems will further exploit the massive number of antennas, higher bandwidths, and more advanced multiple access technologies. As communication systems become more and more complicated, it is becoming increasingly important to go beyond the limits of the model-based approach. Noting that there have been tremendous advancements in learning from data over the past decades, a major research question is whether machine learning based approaches can be used to develop new communication technologies. With the above motivation, this thesis deals with the development of deep neural network (DNN) solutions to address various challenges in wireless communications. We first consider orthogonal frequency division multiplexing (OFDM) over rapidly time-varying multipath channels, for which the performance of standard channel estimation and equalization techniques degrades dramatically due to inter-carrier in-terference (ICI). We focus on improving the overall system performance by designing DNN architectures for both channel estimation and data demodulation. In addition, we study OFDM over frequency-selective channels without cyclic prefix insertion in an effort to improve the overall throughputs. Specifically, we design a recurrent neu-ral network to mitigate the effects of ISI and ICI for improved symbol detection. Furthermore, we explore secure transmission over multi-input multi-output multi-antenna eavesdropper wiretap channels with finite alphabet inputs. We use a linear precoder to maximize the secrecy rate, which benefits from the generalized singular value decomposition to obtain independent streams and exploits function approximation abilities of DNNs for solving the required power allocation problem. We also propose a DNN technique to jointly optimize the data precoder and the power allocation for artificial noise. We use extensive numerical examples and computational complexity analyses to demonstrate the effectiveness of the proposed solutions.Item Open Access A novel technique for a linear system of equations applied to channel equalization(IEEE, 2009) Pilancı, Mert; Arıkan, Orhan; Oǧuz, B.; Pınar, Mustafa Ç.In many inverse problems of signal processing the problem reduces to a linear system of equations. Accurate and robust estimation of the solution with errors in both measurement vector and coefficient matrix is a challenging task. In this paper a novel formulation is proposed which takes into account the structure (e.g. Toeplitz, Hankel) and uncertainties of the system. A numerical algorithm is provided to obtain the solution. The proposed technique and other methods are compared in a channel equalization example which is a fundamental necessity in communication.