Fig 2 - uploaded by Deepaknath Tandur
Content may be subject to copyright.
Source publication
Zero-intermediate frequency (IF)-based orthogonal frequency division multiplexing (OFDM) transmitters and receivers are gaining a lot of interest because of their potential to enable low-cost terminals. However, such systems suffer from front-end impairments such as in-phase/quadrature-phase (IQ) imbalance which may have a huge impact on the perfor...
Context in source publication
Context 1
... to reach this good signal quality. At each analog IF, the filters and the amplifier all add to the component cost. Not only are they quite expensive components, but as they are external, they also add to the assembling cost. An alternative to the superheterodyne architecture is the zero-IF architecture (or direct-conversion architecture) shown in Fig. 2. As the name suggests, the zero-IF architecture con- verts the RF signal directly to baseband or vice versa without any IFs. This clearly results in a lower component count and consequently a lower cost. The zero-IF architecture enables an easier integration and leads to a smaller form ...
Similar publications
In Liu and Toreli (1998), a blind carrier frequency offset (CFO) estimation method was proposed for orthogonal frequency multiplexing (OFDM) systems. This method makes use of the fact that in practical OFDM systems, there are some subcarriers that do not carry any data. However, the computation complexity of the method is rather high. In this paper...
MIMO OFDM is a very promising technique for highspeed
wireless transmission. Implementation of MIMO-OFDM
systems suffers from impairments such as carrier frequency
offset (CFO) due to the Doppler effect and, in-phase and
quadrature-phase (IQ) imbalances due to analog processing of
the radio frequency signal at both transmitter and receiver. CFO
cou...
Citations
... for a random phase ψ ∼ U[0, 2π), where the additive noise is v[i] ∼ CN (0, SNR −1 ) for signal-to-noise ratio level SNR. Furthermore, the I/Q imbalance function [83] is defined as In (28), the channel state c consists of the tuple c = (ψ, ϵ, δ) encompassing the complex phase ψ and the I/Q imbalance parameters (ϵ, δ). ...
When used in complex engineered systems, such as communication networks, artificial intelligence (AI) models should be not only as accurate as possible, but also well calibrated. A well-calibrated AI model is one that can reliably quantify the uncertainty of its decisions, assigning high confidence levels to decisions that are likely to be correct, and low confidence levels to decisions that are likely to be erroneous. This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees. Conformal prediction transforms probabilistic predictors into set predictors that are guaranteed to contain the correct answer with a probability chosen by the designer. Such formal calibration guarantees hold irrespective of the true, unknown, distribution underlying the generation of the variables of interest, and can be defined in terms of ensemble or time-averaged probabilities. In this paper, conformal prediction is applied for the first time to the design of AI for communication systems in conjunction with both frequentist and Bayesian learning, focusing on the key tasks of demodulation, modulation classification, and channel prediction. For demodulation and modulation classification, we apply both validation-based and cross-validation-based conformal prediction; while we investigate the use of online conformal prediction for channel prediction. For each task, we evaluate the probability that the set predictor contains the true output, validating the theoretical coverage guarantees of conformal prediction, as well as the informativeness of the predictor via the average predicted set size.
... for a random phase ψ ∼ U[0, 2π), where the additive noise is v[i] ∼ CN (0, SNR −1 ) for signal-to-noise ratio level SNR. Furthermore, the I/Q imbalance function [77] is defined as ...
When used in complex engineered systems, such as communication networks, artificial intelligence (AI) models should be not only as accurate as possible, but also well calibrated. A well-calibrated AI model is one that can reliably quantify the uncertainty of its decisions, assigning high confidence levels to decisions that are likely to be correct and low confidence levels to decisions that are likely to be erroneous. This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees. Conformal prediction transforms probabilistic predictors into set predictors that are guaranteed to contain the correct answer with a probability chosen by the designer. Such formal calibration guarantees hold irrespective of the true, unknown, distribution underlying the generation of the variables of interest, and can be defined in terms of ensemble or time-averaged probabilities. In this paper, conformal prediction is applied for the first time to the design of AI for communication systems in conjunction to both frequentist and Bayesian learning, focusing on demodulation, modulation classification, and channel prediction.
... In particular, several techniques have addressed the estimation of the IQ imbalance parameters and FIR channel coefficients in OFDM or Generalized Frequency Division Multiplexing (GFDM) systems [20][21][22][23]. Joint estimation of the carrier frequency, timing offsets and IQ parameters have also been considered in [19], [24][25][26]. In most of these studies, the estimation of the channel and system parameters is carried out using a Least Squares (LS) approach. ...
In this paper, we propose a new approach for the estimation of the channel parameters under In-phase/ Quadrature (IQ) impairments, where the term channel refers to any transformation occurring between the transmitter and receiver IQ impairments. While most of the available strategies for channel estimation under IQ impairments are based on the Least-Squares (LS) approach, we propose a new estimator based on the Maximum Likelihood (ML) technique. We show that a simple estimator obtained from the ML gives better statistical performance than any LS-based technique while having a significant lower computational complexity. Mathematically, the proposed ML-based estimator is obtained from the minimization of the smallest eigenvalue of a particular 2 × 2 matrix. Simulations show the benefit of the proposed approach for the estimation of the Carrier Frequency Offset (CFO) and for the estimation of Finite Impulse Response (FIR) channel coefficients under both transmitter and receiver IQ impairments.
... Due to IQ imbalance power leaks from the signal on the mirror carrier to the carrier under consideration and thus causes ICI. As OFDM is very sensitive to ICI, IQ imbalance results in severe performance degradation [24]. ...
This thesis is concerned with data-aided (DA) scheme and CFO tracking for OFDM system. OFDM system model is developed first without CFO and then with CFO. The system performance is evaluated via simulation. The bit error rate (BER), constellation diagram, the phase output and phase error were taken as performance measures. The performance is evaluated for different types of modulation over different channel conditions. In this thesis, a dual bandwidth for CFO tracking based on type-2 control loop to reduce the acquisition time of PLL is proposed. It is proved that the proposed scheme is significantly faster than, and improves the system BER. Also, further refinement to the dual bandwidth scheme is improved by using clustered pilot tones. The improved dual bandwidth scheme is better than the dual bandwidth scheme.
... for a random phase ψ ψ ψ ∼ U[0, 2π), where the additive noise is v[i] ∼ CN (0, SNR −1 ) for a given signal-to-noise ratio level SNR. Furthermore, the I/Q imbalance function [16] is ...
AI tools can be useful to address model deficits in the design of communication systems. However, conventional learning-based AI algorithms yield poorly calibrated decisions, unabling to quantify their outputs uncertainty. While Bayesian learning can enhance calibration by capturing epistemic uncertainty caused by limited data availability, formal calibration guarantees only hold under strong assumptions about the ground-truth, unknown, data generation mechanism. We propose to leverage the conformal prediction framework to obtain data-driven set predictions whose calibration properties hold irrespective of the data distribution. Specifically, we investigate the design of baseband demodulators in the presence of hard-to-model nonlinearities such as hardware imperfections, and propose set-based demodulators based on conformal prediction. Numerical results confirm the theoretical validity of the proposed demodulators, and bring insights into their average prediction set size efficiency.
... In some communication scenarios, especially Internet-of-Things (IoT) systems involving low-complexity devices, linear models may fail to fully describe the relationship between the transmitted symbols and the received signal. In particular, they do not account for non-linear effects such as transmitter's imperfections [108]. By addressing this "model deficit" [104], data-driven demodulation can outperform the outlined conventional model-based strategy. ...
Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are often difficult to translate into real-world engineering systems because deep learning models require a massive number of training samples, which are costly to obtain in practice. To address labeled data scarcity, few-shot meta-learning optimizes learning algorithms that can efficiently adapt to new tasks quickly. While meta-learning is gaining significant interest in the machine learning literature, its working principles and theoretic fundamentals are not as well understood in the engineering community. This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications. After introducing meta-learning in comparison with conventional and joint learning, we describe the main meta-learning algorithms, as well as a general bilevel optimization framework for the definition of meta-learning techniques. Then, we summarize known results on the generalization capabilities of meta-learning from a statistical learning viewpoint. Applications to communication systems, including decoding and power allocation, are discussed next, followed by an introduction to aspects related to the integration of meta-learning with emerging computing technologies, namely neuromorphic and quantum computing. The monograph is concluded with an overview of open research challenges.
... Phase imbalance ϑ refers to the phase variation from the ideal 90 • between the two IQ signals [54], [55]. The IQ imbalance can be modeled as in [56], [57]: ...
This paper proposes a parametric network for the joint compensation of multiple linear impairments in coherent optical communication systems. The considered linear impairments include both static and time-variant effects such as in-phase/quadrature (IQ) imbalance, laser phase noise (PN), chromatic dispersion (CD), polarization mode dispersion (PMD), and carrier frequency offset (CFO). To jointly compensate for these considered impairments, the proposed network is composed of parametric layers that exploit the particular signal model of each impairment. The layers' parameters are jointly learned during a training stage. This stage uses a supervised step that exploits the knowledge of some transmitted data (preamble and/or pilots) and a self-labeling step that uses the knowledge of the symbol constellation. In addition, a new validation technique that does not require a different dataset is developed to avoid overfitting. The parametric network performance is compared to classical digital signal processing (DSP), and Deep Learning (DL) approaches using simulated data. Simulation results show that the proposed network outperforms the competing approaches in terms of Bit Error Rate (BER) while maintaining a relatively reduced computational complexity. In the scenarios considered, compared to the parametric network, the DSP approach introduces an OSNR penalty between 0.2 dB and 1.7 dB at a BER of 4 × 10 −3. Furthermore, simulation results demonstrate that the proposed network is way more flexible than other approaches since it can easily be adapted to a different scenario and coupled with other techniques.
... Then, the compensation is usually obtained in two steps. First, the channel parameters are estimated globally using a trained, semi-blind, or blind technique [6][7][8][9][14][15][16][17][18][19][20][21][22][23]. Secondly, the transmitted signal is detected from the received signal by replacing the channel by its estimate. ...
In this paper, we focus on the joint impairments compensation and symbol detection problem in communication systems. First, we introduce a new multi-layer channel model that represents the underlying physics of multiple impairments in communication systems. This model is composed of widely linear parametric layers that describe the input-output relationship of the front-end impairments and channel effects. Using this particular model, we show that the joint compensation and zero-forcing detection problem can be solved by a particular feedforward network called PhyCOM. Because of the small number of network parameters, a PhyCOM network can be trained efficiently using sophisticated optimization algorithms and a limited number of pilot symbols. Numerical examples are provided to demonstrate the effectiveness of PhyCOM networks with communication systems corrupted by transmitter and receiver IQ imbalances, carrier frequency offset, finite impulse response channels, and transmitter and receiver phase noise distortions. Compared to conventional digital signal processing approaches, simulation results show that the proposed technique is much more flexible and offers better statistical performance both in terms of MSE and SER with a moderate increase of the computation complexity.
... The phase imbalance ϑ refers to the phase deviation from the ideal 90 • between the branches. Starting from these, the IQ distortion parameters can be designed in an analytical convenable manner as in [39], [40]: ...
This paper proposes a new estimation and compensation approach to mitigate several linear and widely linear effects in coherent optical systems using digital signal processing (DSP) algorithms. Compared to most of the available strategies that employ local estimation and/or compensation algorithms, this approach performs a global impairments estimation and compensation based on Nonlinear Least Squares. The proposed method estimates and compensates for the chromatic dispersion (CD), carrier frequency offset (CFO), in-phase/quadrature (IQ) imbalance, and laser phase noise (PN) in two steps. Firstly, it estimates the quasi-static parameters related to the CD, CFO, and both transmitter and receiver IQ imbalance. Secondly, it estimates both transmitter and receiver lasers’ phases and compensates for all the imperfections by using a Zero-Forcing (ZF) equalizer. Simulations show the effectiveness of the approach in terms of statistical performance and computational time. The estimation performance is assessed by computing the Cramér Rao Lower Bound (CRLB), while the detection performance is compared to a modified Clairvoyant equalizer.<br
... The phase imbalance ϑ refers to the phase deviation from the ideal 90 • between the branches. Starting from these, the IQ distortion parameters can be designed in an analytical convenable manner as in [39], [40]: ...
This paper proposes a new estimation and compensation approach to mitigate several linear and widely linear effects in coherent optical systems using digital signal processing (DSP) algorithms. Compared to most of the available strategies that employ local estimation and/or compensation algorithms, this approach performs a global impairments estimation and compensation based on Nonlinear Least Squares. The proposed method estimates and compensates for the chromatic dispersion (CD), carrier frequency offset (CFO), in-phase/quadrature (IQ) imbalance, and laser phase noise (PN) in two steps. Firstly, it estimates the quasi-static parameters related to the CD, CFO, and both transmitter and receiver IQ imbalance. Secondly, it estimates both transmitter and receiver lasers’ phases and compensates for all the imperfections by using a Zero-Forcing (ZF) equalizer. Simulations show the effectiveness of the approach in terms of statistical performance and computational time. The estimation performance is assessed by computing the Cramér Rao Lower Bound (CRLB), while the detection performance is compared to a modified Clairvoyant equalizer.<br
