Browsing by Subject "Channel estimation"

Now showing 1 - 20 of 21

Open Access
5G PDSCH: performance analysis of DMRS and PTRS designs for channel and phase noise estimation in MM-WAVE
(2021-08) Pekcan, Doğan Kutay
The mm-Wave is one of the main enablers for the performance requirements of 5G. Although it provides communication systems with huge bandwidth and data rates, it also has some disadvantages as the carrier frequencies can significantly exceed 6 GHz and go up to 300 GHz. For example, there are significant challenges such as propagation loss and severe phase noise (PN). The PN can be observed in two parts: common phase error (CPE) and inter-carrier interference (ICI). In the literature, there are algorithms for the estimation and compensation of PN for OFDM-based systems. We apply both CPE and ICI compensation algorithms for 5G PDSCH at the carrier frequency of 70 GHz. Detailed performance analysis is performed for demodulation reference signal (DMRS) based channel estimation and phase-tracking reference signal (PTRS) based PN estimation. We observe the effects of different reference signal parameters in 5G for each PN compensation algorithm. For this purpose, we use up-to-date power spectral density (PSD) models for PN modeling and show uncoded bit error rate (BER) graphs obtained via extensive simulations for MATLAB's tapped delay line (TDL) channels. We also analyze the system performance under very high Doppler, where PTRS based channel estimation is compared with DMRS based channel estimation.
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
Coded-reference ultra-wideband systems
(2008-09) Gezici, Sinan
Transmitted-reference (TR) and frequency-shifted reference (FSR) ultra-wideband (UWB) systems employ pairs of reference and data signals, which are shifted in the time and frequency domains, respectively, to facilitate low-to-medium data rate communications without the need for complex channel estimation and template signal generation. On the other hand, the recently proposed coded-reference (CR) UWB systems provide orthogonalization of the reference and data signals in the code domain, which has advantages in terms of performance and/or implementation complexity. In this paper, CR UWB systems are investigated. First, it is shown that a CR UWB system can be considered as a generalized non-coherent pulse-position modulated system. Then, an optimal receiver according to the Bayes decision rule is derived for CR UWB systems. In addition, the asymptotic optimality properties of the conventional CR UWB receivers are investigated. Finally, simulation results are presented to compare the performance of the optimal and conventional CR UWB receivers. ©2008 IEEE.
Open Access
Çokyollu kanal parametre kestirimi için yeni bir dizilim sinyal işleme tekniği
(IEEE, 2007-06) Güldoǧan, Mehmet Burak; Arıkan, Orhan
Bu bildiride, çarpraz belirsizlik işlevinin kullanıldığı yeni bir dizilim sinyal işleme tekniği önerilmektedir. Geliştirilen teknik bir algılayıcı dizilimine gelen sinyallerden herbirinin geliş yönünü (GY), zaman gecikmesini Doppler kaymasını ve genliğini dürümlü bir sekilde kestirir. Önerilen Çarpraz Belirsizlik İşlevi - Yön Bulma (ÇBI-YB) tekniği ile Çoklu Sinyal Sınıflandırması (MUSIC) algoritmasının performansları sentetik sinyaller kullanılarak kök Ortalama Karesel Hata (kOKH) cinsinden değişik işaret Gürültü Oranı (İGO) değerleri için karşılaştırılmıştır. Önerilen tekniğin başarımı kayıt edilmiş çokyollu yüksek-enlem iyonosfer verileri üzerinde irdelenmiştir. Elde edilen sonuçlar, düşük İGO değerlerinde dahi çokyollu sinyal kaynaklarını ayırmada önerilen ÇBİ-YB tekniğinin ciddi başarım artışı sağladığını göstermektedir.
Open Access
Communication efficient channel estimation over distributed networks
(IEEE, 2014) Sayın, Muhammed O.; Vanlı, N. Denizcan; Göze, T.; Kozat, Süleyman Serdar
We study diffusion based channel estimation in distributed architectures suitable for various communication applications such as cognitive radios. Although the demand for distributed processing is steadily growing, these architectures require a substantial amount of communication among their nodes (or processing elements) causing significant energy consumption and increase in carbon footprint. Due to growing awareness of telecommunication industry's impact on the environment, the need to mitigate this problem is indisputable. To this end, we introduce algorithms significantly reducing the communication load between distributed nodes, which is the main cause in energy consumption, while providing outstanding performance. In this framework, after each node produces its local estimate of the communication channel, a single bit or a couple of bits of information is generated using certain random projections. This newly generated data is diffused and then used in neighboring nodes to recover the original full information, i.e., the channel estimate of the desired communication channel. We provide the complete state-space description of these algorithms and demonstrate the substantial gains through our experiments.
Open Access
Comparison of the CAF-DF and sage algorithms in multipath channel parameter estimation
(IEEE, 2008-07) Güldoğan, M. Burak; Arıkan, Orhan
In this paper, performance of the recently proposed Cross Ambiguity Function - Direction Finding (CAF-DF) technique is compared with the Space Alternating Generalized Expectation Maximization (SAGE) technique. The CAF-DF, iteratively estimates direction of arrival (DOA), time-delay, Doppler shift and amplitude corresponding to each impinging signal onto an antenna array by utilizing the cross ambiguity function. On synthetic signals, based on Monte Carlo trials, performances of the algoritms are tested in terms of root Mean Squared Error (rMSE) at different Signal-to-Noise Ratios (SNR). Cramer-Rao lower bound is included for statistical comparisons. Simulation results indicate the superior performance of the CAF-DF technique over SAGE technique for low and medium SNR values. © 2008 IEEE.
Open Access
Cross-ambiquity function domain multipath channel parameter estimation
(Elsevier, 2011-11-23) Guldogan, M. B.; Arıkan, Orhan
A new array signal processing technique is proposed to estimate the direction-of-arrivals (DOAs), time delays, Doppler shifts and amplitudes of a known waveform impinging on an array of antennas from several distinct paths. The proposed technique detects the presence of multipath components by integrating cross-ambiguity functions (CAF) of array outputs, hence, it is called as the cross-ambiguity function direction finding (CAF-DF). The performance of the CAF-DF technique is compared with the space-alternating generalized expectation-maximization (SAGE) and the multiple signal classification (MUSIC) techniques as well as the Cramer-Rao lower bound. The CAF-DF technique is found to be superior in terms of root-mean-squared-error (rMSE) to the SAGE and MUSIC techniques. (
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.
Unknown
Efficient channel estimation for reconfigurable MIMO antennas: training techniques and performance analysis
(Institute of Electrical and Electronics Engineers Inc., 2017) Bahceci, I.; Hasan, M.; Duman, T. M.; Cetiner, B. A.
Multifunctional and reconfigurable multiple-input multiple-output (MR-MIMO) antennas are capable of dynamically changing the operation frequencies, polarizations, and radiation patterns, and can remarkably enhance system capabilities. However, in coherent communication systems, using MR-MIMO antennas with a large number of operational modes may incur prohibitive complexity due to the need for channel state estimation for each mode. To address this issue, we derive an explicit relation among the radiation patterns for the antenna modes and the resulting channel gains. We propose a joint channel estimation/prediction scheme where only a subset of all the antenna modes is trained for estimation, and then, the channels associated with the modes that are not trained are predicted using the correlations among the different antenna modes. We propose various training mechanisms with reduced overhead and improved estimation performance, and study the impact of channel estimation error and training overhead on the MR-MIMO system performance. We demonstrate that one can achieve significantly improved data rates and lower error probabilities utilizing the proposed approaches. For instance, under practical settings, we observe about 25% throughput increase or about 3-dB signal-to-noise ratio improvement under the same training overhead with respect to non-reconfigurable antenna systems.
Open Access
Expectation maximization based matching pursuit
(IEEE, 2012) Gurbuz, A.C.; Pilanci, M.; Arıkan, Orhan
A novel expectation maximization based matching pursuit (EMMP) algorithm is presented. The method uses the measurements as the incomplete data and obtain the complete data which corresponds to the sparse solution using an iterative EM based framework. In standard greedy methods such as matching pursuit or orthogonal matching pursuit a selected atom can not be changed during the course of the algorithm even if the signal doesn't have a support on that atom. The proposed EMMP algorithm is also flexible in that sense. The results show that the proposed method has lower reconstruction errors compared to other greedy algorithms using the same conditions. © 2012 IEEE.
Open Access
Multiple-resampling receiver design for OFDM over Doppler-distorted underwater acoustic channels
(2013) Tu, K.; Duman, T. M.; Stojanovic, M.; Proakis J. G.
In this paper, we focus on orthogonal frequency-division multiplexing (OFDM) receiver designs for underwater acoustic (UWA) channels with user-and/or path-specific Doppler scaling distortions. The scenario is motivated by the cooperative communications framework, where distributed transmitter/receiver pairs may experience significantly different Doppler distortions, as well as by the single-user scenarios, where distinct Doppler scaling factors may exist among different propagation paths. The conventional approach of front-end resampling that corrects for common Doppler scaling may not be appropriate in such scenarios, rendering a post-fast-Fourier-transform (FFT) signal that is contaminated by user-and/or path-specific intercarrier interference. To counteract this problem, we propose a family of front-end receiver structures that utilize multiple-resampling (MR) branches, each matched to the Doppler scaling factor of a particular user and/or path. Following resampling, FFT modules transform the Doppler-compensated signals into the frequency domain for further processing through linear or nonlinear detection schemes. As part of the overall receiver structure, a gradient-descent approach is also proposed to refine the channel estimates obtained by standard sparse channel estimators. The effectiveness and robustness of the proposed receivers are demonstrated via simulations, as well as emulations based on real data collected during the 2010 Mobile Acoustic Communications Experiment (MACE10, Martha's Vineyard, MA) and the 2008 Kauai Acomms MURI (KAM08, Kauai, HI) experiment.
Open Access
A new OMP technique for sparse recovery
(IEEE, 2012) Teke, Oğuzhan; Gürbüz, A.C.; Arıkan, Orhan
Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. However in reality there is a mismatch between the assumed and the actual bases due to several reasons like discritization of the parameter space or model errors. Due to this mismatch, a sparse signal in the actual basis is definitely not sparse in the assumed basis and current sparse reconstruction algorithms suffer performance degradation. This paper presents a novel orthogonal matching pursuit algorithm that has a controlled perturbation mechanism on the basis vectors, decreasing the residual norm at each iteration. Superior performance of the proposed technique is shown in detailed simulations. © 2012 IEEE.
Open Access
Novel unsourced random access algorithms over Gaussian and fading channels
(2024-01) Ahmadi, Mohammad Javad
Random access techniques play a crucial role in machine-type communications (MTC), especially in the context of massive and sporadic device connectivity. Unlike traditional communication systems with scheduled access, random access allows devices to independently access the network without prior coordination. This flexibility is particularly beneficial for MTC scenarios where a large number of devices may transmit data sporadically. Unsourced random access (URA) is a form of grant-free random access in which devices remain entirely unidentified. As a result, there is no need for a codebook to store device identity preambles, whose dimension is squared to the number of connected users. This elimination of the codebook requirement empowers URA to efficiently accommodate an unbounded number of devices, reaching hundreds of millions of devices. This thesis proposes three unsourced random access algorithms suitable for Gaussian and wireless fading channels. First, we introduce a URA algorithm for use over Gaussian multiple access channels. In the proposed solution, the users are randomly separated by assigning varying levels of transmit power to each of them. This introduces power diversity, enhancing the system performance. In the second part, we offer a solution for URA over Rayleigh block-fading channels with a receiver equipped with multiple antennas. We employ a slotted structure with multiple stages of orthogonal pilots; each randomly picked from a codebook. In the proposed signaling structure, each user encodes its message using a polar code and appends it to the selected pilot sequences to construct its transmitted signal. The receiver employs an iterative algorithm to detect messages transmitted by different users. This algorithm comprises several components, including pi-lot detection, channel estimation, soft data detection, single-user polar decoder, and successive interference cancellation. Additionally, we improve this scheme by incorporating an efficient strategy that separates users by random grouping. Our extensive analytical and simulation results demonstrate the effectiveness of the proposed algorithm in terms of both energy efficiency and computational complexity. In the last part of the thesis, we study URA employing a passive reconfigurable intelligent surface, facilitating connections between the users and the base station when the direct link is blocked or significantly attenuated. We demonstrate through extensive simulations and analytical results that the pro-posed approach notably enhances system performance, particularly in channels with significant attenuation.
Open Access
On the interplay between channel sensing and estimation in cognitive radio systems
(IEEE, 2011) Gursoy, M.C.; Gezici, Sinan
Cognitive radio transmissions in the presence of channel uncertainty are considered. In practical scenarios, cognitive secondary users need to perform both channel sensing in order to identify whether the channel is being occupied by the primary users or not, and also channel estimation in order to learn the channel fading coefficients. Generally, errors occur in both channel sensing and estimation, and this leads to a coupling between the two. More specifically, imperfect sensing affects both the structure and the performance of channel estimation schemes. With this motivation, the interactions between channel sensing and estimation are studied in this paper. In particular, different channel estimation schemes including minimum mean-square error (MMSE), linear MMSE, and mismatched MMSE estimations are analyzed, and their dependence on sensing decisions and their performances are investigated. © 2011 IEEE.
Open Access
Online nonlinear modeling for big data applications
(2017-12) Khan, Farhan
We investigate online nonlinear learning for several real life, adaptive signal processing and machine learning applications involving big data, and introduce algorithms that are both e cient and e ective. We present novel solutions for learning from the data that is generated at high speed and/or have big dimensions in a non-stationary environment, and needs to be processed on the y. We speci cally focus on investigating the problems arising from adverse real life conditions in a big data perspective. We propose online algorithms that are robust against the non-stationarities and corruptions in the data. We emphasize that our proposed algorithms are universally applicable to several real life applications regardless of the complexities involving high dimensionality, time varying statistics, data structures and abrupt changes. To this end, we introduce a highly robust hierarchical trees algorithm for online nonlinear learning in a high dimensional setting where the data lies on a time varying manifold. We escape the curse of dimensionality by tracking the subspace of the underlying manifold and use the projections of the original high dimensional regressor space onto the underlying manifold as the modi ed regressor vectors for modeling of the nonlinear system. By using the proposed algorithm, we reduce the computational complexity to the order of the depth of the tree and the memory requirement to only linear in the intrinsic dimension of the manifold. We demonstrate the signi cant performance gains in terms of mean square error over the other state of the art techniques through simulated as well as real data. We then consider real life applications of online nonlinear learning modeling, such as network intrusions detection, customers' churn analysis and channel estimation for underwater acoustic communication. We propose sequential and online learning methods that achieve signi cant performance in terms of detection accuracy, compared to the state-of-the-art techniques. We speci cally introduce structured and deep learning methods to develop robust learning algorithms. Furthermore, we improve the performance of our proposed online nonlinear learning models by introducing mixture-of-experts methods and the concept of boosting. The proposed algorithms achieve signi cant performance gain over the state-ofthe- art methods with signi cantly reduced computational complexity and storage requirements in real life conditions.
Open Access
Particle swarm optimization for SAGE maximization step in channel parameter estimation
(IET, 2007-11) Bodur, Harun; Tunç, Celal Alp; Aktaş, Defne; Ertürk, Vakur .B.; Altıntaş, Ayhan
This paper presents an application of particle swarm optimization (PSO) in space alternating generalized expectation maximization (SAGE) algorithm. SAGE algorithm is a powerful tool for estimating channel parameters like delay, angles (azimuth and elevation) of arrival and departure, Doppler frequency and polarization. To demonstrate the improvement in processing time by utilizing PSO in SAGE algorithm, the channel parameters are estimated from a synthetic data and the computational expense of SAGE algorithm with PSO is discussed. (4 pages).
Open Access
A recursive way for sparse reconstruction of parametric spaces
(IEEE, 2015-11) Teke, Oğuzhan; Gürbüz, A. C.; Arıkan, Orhan
A novel recursive framework for sparse reconstruction of continuous parameter spaces is proposed by adaptive partitioning and discretization of the parameter space together with expectation maximization type iterations. Any sparse solver or reconstruction technique can be used within the proposed recursive framework. Experimental results show that proposed technique improves the parameter estimation performance of classical sparse solvers while achieving Cramér-Rao lower bound on the tested frequency estimation problem. © 2014 IEEE.
Open Access
Sabit genişbantlı telsiz uygulamalarında ÇGÇÇ-DFBÇ için kanal kestirimi
(IEEE, 2006-04) Karakaya, B.; Çırpan, H. A.; Panayırcı, Erdal
Systems employing multiple transmit and receive antennas, known as multiple input multiple output (MIMO) systems can be used with OFDM to improve the resistance to channel impairments. Thus the technologies of OFDM and MIMO are equipped in fixed wireless applications with attractive features, including high data rates and robust performance. However, since different signals are transmitted from different antennas simultaneously, the received signal is the superposition of these signals, which implies new challenges for channel estimation. In this paper we propose a time domain MMSE based channel estimation approach for MIMO-OFDM systems. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion, no matrix inversion is required in the proposed MMSE estimator. Also the performance of the proposed approach is studied through the evaluation of minimum Bayesian MSE. © 2006 IEEE.
Open Access
SAR image reconstruction by expectation maximization based matching pursuit
(Academic Press, 2015) Ugur, S.; Arıkan, Orhan; Gürbüz, A. C.
Synthetic Aperture Radar (SAR) provides high resolution images of terrain and target reflectivity. SAR systems are indispensable in many remote sensing applications. Phase errors due to uncompensated platform motion degrade resolution in reconstructed images. A multitude of autofocusing techniques has been proposed to estimate and correct phase errors in SAR images. Some autofocus techniques work as a post-processor on reconstructed images and some are integrated into the image reconstruction algorithms. Compressed Sensing (CS), as a relatively new theory, can be applied to sparse SAR image reconstruction especially in detection of strong targets. Autofocus can also be integrated into CS based SAR image reconstruction techniques. However, due to their high computational complexity, CS based techniques are not commonly used in practice. To improve efficiency of image reconstruction we propose a novel CS based SAR imaging technique which utilizes recently proposed Expectation Maximization based Matching Pursuit (EMMP) algorithm. EMMP algorithm is greedy and computationally less complex enabling fast SAR image reconstructions. The proposed EMMP based SAR image reconstruction technique also performs autofocus and image reconstruction simultaneously. Based on a variety of metrics, performance of the proposed EMMP based SAR image reconstruction technique is investigated. The obtained results show that the proposed technique provides high resolution images of sparse target scenes while performing highly accurate motion compensation.
Open Access
Secure space shift keying transmission using dynamic antenna index assignment
(IEEE, 2017-12) Aghdam, Sina Rezaei; Duman, Tolga M.
We propose a secure transmission scheme based on space shift keying (SSK) in which the indices associated with the transmit antennas are assigned dynamically according to the channel towards the legitimate receiver. We first derive an asymptotic secrecy rate under the perfect channel reciprocity assumption. Then, we study the impacts of imperfect reciprocity and presence of a nearby eavesdropper on the reliability and eavesdropping resilience of the proposed scheme. Finally, we introduce an enhanced antenna index assignment algorithm which is more robust to imperfect reciprocity, and is capable of preventing a nearby eavesdropper from acquiring the transmitted bits.