Ali Shahed hagh ghadam

Spectrum Sensing Under RF Non-Linearities: Performance Analysis and DSP-Enhanced Receivers

Intermodulation products arise as a result of low noise amplifier (LNA) and mixer non-linearities in wideband receivers. In the presence of strong blockers, the intermodulation distortion can deteriorate the spectrum sensing performance by causing false alarms and degrading the detection probability. We theoretically analyze the impact of third-order non-linearities on the detection and false alarm probabilities for both energy detectors and cyclostationary detectors under front-end LNA non-linearities. We show that degradation of the detection performance due to nonlinearities of both energy and cyclostationary detection is strongly dependent on the modulation type of the blockers. We then propose two DSP-enhanced receiver architectures to compensate the impact of nonlinearities. The first approach is a post-processing technique which compensates for nonlinearities effect on the test statistic by adapting the sensing time and detection threshold. The second approach is a pre-processing method that compensates by correcting received samples prior to computing the test statistic. This approach is based on adaptively estimating the intermodulation distortion, weighting it by a scalar constant and subtracting it from the subband of interest. We propose a method to adaptively compute the optimal weighting coefficient and show that it depends on the power and modulation of the blockers. Our results show that the pre-processing sample-based compensation method is more effective and that clear dynamic range extension can be obtained by using intermodulation compensation without resorting to increasing the sensing time. We also study the impact of uncertainties about the knowledge or estimates for nonlinearity parameters.

Implementation and Performance of DSP-Oriented Feedforward Power Amplifier Linearizer

In this paper, a digital signal processing-oriented implementation of feedforward power amplifier linearizer (DSP-FF) is introduced. In DSP-FF, the signal and error cancellation circuits are implemented, partially, in the DSP regime. By doing so, the number of bulky radio frequency (RF) components is reduced and their functionality is replaced by more flexible DSP circuitry and also various implementation nonidealities can be efficiently controlled. A two-stage estimation approach stemming from least-squares model fitting is proposed to identify proper DSP-FF coefficients. This improves the linearization performance by decoupling the effects of estimation inaccuracies between the two DSP-FF circuits. Furthermore, a comprehensive performance analysis of DSP-FF is carried out, taking also the memory of the core power amplifier into account. In particular, a closed-form expression for the intermodulation distortion reduction is derived in terms of the errors in the circuit coefficients. Also the measurement noise effects and large sample properties of the estimators are analyzed. The outcomes of computer simulated experiments verify the analytical results which are presented in this paper. Moreover, laboratory measurement setup utilizing a highly nonlinear RF power amplifier and contemporary telecommunication waveform demonstrates the linearization capability of the DSP-FF in terms of improvement in the measured adjacent channel leakage ratio.

Blind Diversity Reception and Interference Cancellation using ICA

In this paper, we consider blind diversity reception and interference rejection in multi-antenna communications context, in terms of maximizing the output signal-to-interference-and-noise ratio (SINR). More specifically, we demonstrate that independent component analysis (ICA), although originally designed for noise-free linear models, is able to provide essentially the best possible output SINR among all linear transformations of received data in noisy linear models. In particular, our experiments indicate that one of the most widely applied ICA algorithms, equivariant adaptive source identification (EASI) algorithm, is, in practice, identical with SINR maximizing generalized eigenfilter in terms of SENR, even though it does not use explicit knowledge of the channel states and noise statistics. We also show that, in a special case of interference-free (that is, noise only) system, the EASI algorithm attains the greatest diversity gain blindly, i.e., performs as a blind maximal ratio combiner (MRC).

Reduced-complexity FFT-based method for Doppler estimation in GNSS receivers

In this article, we develop a novel algorithm for Doppler acquisition in fast Fourier transform (FFT)-based Global Navigation Satellite System (GNSS) receivers. The Doppler estimation is carried out in FFT domain by finding the frequency shift which maximizes the energy of the correlation vector. Subsequently, energy detection is used for preliminary decision about the presence of the target code. Then, the final decision and code phase estimation are done in the time domain after taking the inverse fast Fourier transform (IFFT). It is shown that the proposed algorithm has the potential for reducing the average number of required IFFTs in the acquisition process. For improving the sensitivity of the proposed approach, time-domain block averaging and FFT-domain non-coherent integration are investigated as alternative methods. They exhibit rather similar performance improvement, but the non-coherent integration approach is found to be computationally more effective.

Steady-State Performance Analysis and Step-Size Selection for LMS-Adaptive Wideband Feedforward Power Amplifier Linearizer

Balancing between power amplifier (PA) linearity and power efficiency is one of the biggest implementation challenges in radio communication transmitters. Among various linearization methods, the feedforward linearization technique is a fairly established principle offering a good tradeoff between linearity and power-efficiency even under wideband operation. Moreover, adaptive techniques for such linearizer have been proposed in literature to track parameter changes in the main PA and other circuitry. Among those, least mean squares (LMS) method for adapting signal cancellation loop (SCL) and error cancellation loop (ECL) coefficients is an attractive low-complexity alternative. In this paper, we carry out extensive closed-form performance analysis of the achievable intermodulation distortion (IMD) reduction of the overall LMS-adaptive feedforward linearizer, as a function of the used step-sizes and essential waveform statistics. Such analysis is currently missing from the state-of-the-art literature. Both memoryless nonlinearities and Wiener-Hammerstein type PA memory models are studied for which IMD suppression expressions are derived. Comprehensive computer simulations are also provided to illustrate the accuracy of the analysis when practical OFDM waveforms are used. Design examples are given as well where the analysis results are used to choose proper linearizer step-sizes to meet given transmitter spectral mask specifications.

DSP oriented implementation of a feedforward power amplifier linearizer

In this contribution we introduce a new digital signal processing (DSP)-oriented implementation of a feedforward linearizer. The intermodulation distortion (IMD) signal is regenerated entirely in the digital baseband which gives better control over the linearization process and also improves the efficiency of the overall amplifier. A new scheme to estimate the parameters of the proposed feedforward linearizer is presented which decouples the parameters of the signal and error cancellation loop. We have verified the new DSP oriented feedforward linearizer by means of simulations in the presence of component impairments like, e.g., IQ mismatch. Initial measurements results in a laboratory setup show a reduction of the adjacent channel power of approximately 15 to 20 dB.

Effects of power amplifier memory on adaptive feedforward linearizers

Feedforward linearization is one of the most well-known and widely-applied methods for linearizing power amplifiers (PA). In order to prevent performance degradation due to implementation inaccuracies, adaptive or self-designing structures utilizing e.g. gradient-descent type methods have been developed. Although the basic feedforward structure as such is insensitive to PA memory, the effects of memory on the adaptation behavior can be significant. In this paper, we present an analysis on the convergence of feedforward linearizer coefficients and the resulting reduction of inter-modulation distortion (IMD) when gradient-descent type adaptations are used with a PA that exhibits memory. A Hammerstein model is used for PA modeling, and computer simulations are used to demonstrate the validity and accuracy of the analysis.

Implementation of Farrow structure based interpolators with subfilters of odd length

Interpolation filters are used to interpolate new sample values at arbitrary points between existing discrete-time samples. An interesting class of such filters is polynomial-based interpolation filter. These filters can be efficiently implemented using the Farrow structure and its modifications. Traditionally, the polynomial based interpolation filters have been implemented by using Farrow structure with finite impulse response (FIR) subfilters of even length. This paper presents the modification of the Farrow structure, which can have FIR subfilters of odd length. Applying the proposed modification of this paper will result in a natural implementation form for even order Lagrange and spline based interpolators. The obtained results provides more freedom in designing Farrow structure based filters, as structures with odd and even length FIR subfilters may be equally applied. These results are extended to a modified Farrow structure case as well, in which the number of multipliers is nearly halved.

Utilization of multi-rate signal processing for GNSS-SDR receivers

In this article, we propose a low-complexity solution for the decimation chain in the digital front-end (DFE) of global navigation satellite system (GNSS) receivers. The received signals are typically highly oversampled in the DFE of GNSS receivers to reduce the ranging error and therefore to improve the positioning accuracy in the tracking stage of the GNSS receivers. However, this oversampling imposes unnecessary complexity on the acquisition stage of the GNSS receivers where an approximate estimate of the code phase and Doppler frequency shift is produced. Therefore, reducing the sampling frequency for the acquisition stage reduces the overall receiver complexity without any significant effect on the performance of such receivers. The proposed solution for the decimation chain involves the use of infinite impulse response (IIR) filters as the decimation filter as they can be implemented more efficiently in comparison to finite impulse response (FIR). In addition, a hybrid time-frequency domain filtering scheme is proposed here to alleviate the effects of non-linear phase in the decimation IIR filter as well as the analog front-end receivers. The advantages of this proposed method is explored and stated, both from performance and complexity perspectives, through rigorous comparison with alternative available solutions.