Browsing by Subject "Gated recurrent units"

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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 Mete
We 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.
Open Access
Deep learning based decoders for concatenated codes over insertion and deletion channels
(2025-01) Kargı, Eksal Uras
Channels with synchronization errors, including insertion/deletion channels, are of significant importance, as they are encountered in various systems, such as communication networks and various storage technologies, including DNA data storage. Serially concatenated codes where the outer code is a powerful channel code, such as a low-density parity-check (LDPC) or convolutional code, and the inner code is a watermark or marker code, are shown to be effective solutions over such channels. In particular, the use of marker codes, referring to insertion of preselected sequences in the transmitted data stream periodically, are shown to work well in regaining synchronization at the receiver and achieving improved error rate performance compared to other alternatives. In the current literature, maximum a posteriori (MAP) detector realized by the well-known forward-backward algorithm is commonly employed to decode the inner marker code and estimate the log-likelihood ratios (LLRs) of the bits encoded by the outer code, and the resulting log-likelihood estimates are fed to the outer decoder to estimate the transmitted data. Alternative to the MAP detector, this thesis proposes deep learning-based solutions to estimate the LLRs of the coded bits in the paradigm of concatenated codes, exploiting the marker information and addressing some limitations of conventional methods. Bit-level deep learning-based detectors offer good alternatives when the channel statistics are not perfectly available at the decoder, degrading of the performance of the MAP detector. They can also be employed for one-shot decoding when the outer code is a convolutional code. Also developed are symbol-level deep learning-based detectors to exploit the correlations among adjacent bits at the detector output. Contrary to the existing symbol-level decoders for insertion/deletion channels, the newly proposed approaches can go beyond the case of combining three bits, offering further enhancements in performance while keeping the complexity tolerable. As a final contribution, deep learning-based detectors are developed for insertion and deletion channels that are further exacerbated by inter-symbol interference, e.g., modeling bit-patterned media recording channels, and their performance is studied via numerical examples.
Open Access
Deep-learning for communication systems: new channel estimation, equalization, and secure transmission solutions
(2023-08) Gümüş, Mücahit
Traditional 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.