Ahmet Hasim Gokceoglu

Energy Detection under IQ Imbalance with Single- and Multi-Channel Direct-Conversion Receiver: Analysis and Mitigation

Direct-conversion radio receivers can offer highly integrated low-cost hardware solutions for cognitive radio (CR) devices. Such receivers are, however, also very sensitive to various radio frequency (RF) impairments such as IQ imbalance, which can considerably limit the spectrum sensing capabilities. Most of the existing spectrum sensing studies in literature assume an ideal RF receiver and hence neglect the impacts of such practical RF hardware limitations. In this article, we study energy detection (ED) based spectrum sensing in both single-channel and multi-channel direct-conversion receiver scenarios impaired by IQ imbalance. With complex Gaussian primary user (PU) signal models, we first derive the detection and false alarm probabilities in closed-form for both receiver scenarios. The analytical results, confirmed through extensive simulations, show that while the single-channel receiver scenario is fairly robust to IQ imbalance, the wideband multi-channel sensing receiver is very sensitive to the image channel crosstalk induced by IQ imbalance. More specifically, it is shown that the false alarm probability of multi-channel energy detection increases significantly, compared to ideal RF receiver case, and the exact performance depends on the image channel power level and IQ imbalance values. In order to prevent such degradation in the ability to identify free spectrum, a waveform level interference cancellation method is then proposed to mitigate the image channel crosstalk. The optimum cancellation coefficient yielding interference-free signal is first derived, being also complemented with a practical coefficient sample estimator. An explicit condition is also derived for which the proposed cancellation scheme deploying the practical coefficient sample estimator provides performance gain in the sensing decisions compared to uncompensated energy detection. Extensive computer simulations with various signal and imbalance conditions are provided which demonstrate that the proposed enhanced energy detection can suppress the image channel crosstalk efficiently, yielding detection and false alarm probabilities essentially identical to those of an ideal RF receiver.

Mutual Information Analysis of OFDM Radio Link Under Phase Noise, IQ Imbalance and Frequency-Selective Fading Channel

OFDM and other multicarrier waveforms are in general very sensitive to RF non-idealities, such as phase noise and IQ imbalance, of transmitting and receiving devices. Extensive work has been carried out in the open literature in analyzing the performance of OFDM radio link under such RF impairments in terms of detection error rate and mostly concentrating on one impairment at a time. However, there is only very limited work on analytical investigations of mutual information and rate loss expressions, the heart of communication theory, as functions of RF impairment levels. In this article, we derive two closed-form mutual information expressions, in the form of infinite series representation, for an arbitrary subcarrier of a general OFDM radio link impaired with transceiver phase noise and IQ imbalance in frequency-selective Rayleigh distributed block-fading radio channel, covering both uncorrelated as well as fully correlated mirror subcarrier scenarios. We also show that the mutual information saturates to a finite value due to the inherent RF impairments even in the case that the symbol-to-noise ratio approaches infinity. Extensive comparisons with results obtained from full OFDM radio link simulations are also provided to illustrate and verify the accurate match between analytical and simulated mutual information behavior.

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.

Multi-channel energy detection under phase noise: analysis and mitigation

For the development of highly integrated, flexible and low-cost cognitive radio (CR) devices, simple transceiver architectures, like direct-conversion receiver, are expected to be deployed and provide viable radio frequency (RF) spectrum sensing solutions for practical implementation. Yet, this can be very challenging task especially if spectrum sensing and down-conversion are conducted over multiple RF channels simultaneously for improved efficiency in channel scans. Then, the so-called dirty RF problem that degrades link performance of traditional transmission systems starts to be influential from spectrum sensing perspective as well. The unavoidable RF impairments, e.g., oscillator phase noise in direct-conversion receiver, could generate crosstalk between multiple channels that are down-converted simultaneously, and thus considerably limit the spectrum sensing capabilities. Most of the existing spectrum sensing studies in literature assume an ideal RF receiver and have not considered such practical RF hardware problem. In this article, we study the impact of oscillator phase noise on energy detection (ED) based spectrum sensing in multi-channel direct-conversion receiver scenario. With complex Gaussian primary user (PU) signal models, we first derive the detection and false alarm probabilities in closed-form expression. The analytical results, verified through extensive simulations, show that the wideband multi-channel sensing receiver is very sensitive to the neighboring channel crosstalk induced by oscillator phase noise. More specifically, it is shown that the false alarm probability of multi-channel energy detection increases significantly, compared to the ideal RF receiver case. The exact performance degradation depends on the power of neighboring channels as well as statistical characteristics of the phase noise in the deployed receiver. In order to prevent such performance degradation in spectrum identification, an enhanced energy detection technique is proposed. The proposed technique calculates the leakage power from neighboring channels for each channel and improves the sample energy statistics by subtracting this leakage power from the raw values. An analytical expression is derived for the leakage power which is shown to be a function of power spectral levels of neighboring channels and 3-dB bandwidth of phase noise process. Practical schemes for estimating these two quantities are discussed. Extensive computer simulations show that the proposed enhanced detection yields false alarm rates that are very close to those of an ideal RF receiver and hence clearly outperforms classical energy detection.

Augmented Volterra predistortion for the joint mitigation of power amplifier and I/Q modulator impairments in wideband flexible radio

Dynamic and flexible RF spectrum access through software-defined radio technologies is known to be limited by transmitter RF impairments, most notably spurious emissions due to mixer I/Q imbalance and power amplifier nonlinearity. In this article, a novel digital predistortion structure is developed for joint mitigation of frequency-dependent I/Q imbalance and dynamic nonlinear effects of amplifiers in wideband direct-conversion radio transmitters. The developed structure consists of two Volterra series in parallel, with built-in sparsity in the kernels, and is shown to be linear in parameters thus requiring only one feedback path for joint parameter identification. Extensive simulation results demonstrate significant improvements in transmitter linearization performance, compared to state-of-the-art memory polynomial based linearizers. Thus, the developed predistortion solution can be seen as one key enabling technique towards practical deployment of SDR technology with digitally-enhanced wideband RF front-ends.

Analysis and mitigation of RF IQ imbalance in eigenvalue based multichannel spectrum sensing

Direct-conversion radio receivers can provide highly-integrated and low-cost hardware solutions for cognitive radio (CR) devices. However, such receiver structures are also known to suffer from various RF circuit imperfections, especially IQ imbalance. When studying the performance of various spectrum sensing methods, most existing work in literature neglect the effects of such RF imperfections. In this paper, we analyze the performance of a well-known eigenvalue based sensing method, namely maximum-to-minimum eigenvalue test, in the challenging multichannel spectrum sensing context under sensing receiver RF IQ imbalance. It is analytically shown that under RF IQ imbalance, the false alarm probability of the spectrum sensor is always higher compared to ideal RF front-end case. Then, in order to compensate for such degradation in the ability to identify free spectrum, a waveform level interference cancellation solution is deployed to enhance the performance, and optimum cancellation coefficient together with a practical sample estimator are formulated. Extensive computer simulations are provided to illustrate the degradation of false alarm probability in the presence of practical IQ imbalance values. Furthermore, the simulations also show that the proposed interference cancellation method can efficiently suppress the IQ imbalance effects, achieving identical false alarm probability with the ideal RF front-end case.

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.

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.

Waveform design for massive MISO downlink with energy-efficient receivers adopting 1-bit ADCs

In high-density low-bitrate Internet-of-Things (IoT) use case of 5G networks, the terminals and sensors are to be of extremely low-cost and low energy-consuming. Typically, the analog-to-digital converters (ADCs) dominate the power-budget of receiver chains, in particular if the quantization resolution is high. Hence, receiver architectures deploying 1-bit ADCs are of high interest towards realizing low-cost, high energy-efficiency device solutions. In this paper, we study the waveform design and optimization for a narrowband low-bitrate massive MISO downlink targeting to achieve rates higher than 1 bits/sec (per real-dimension) where the terminal receivers adopt only simple 1-bit quantization (per real-dimension) with oversampling. In this respect, first we show that for a particular precoder structure, the overall link is equivalent to that of an AWGN SISO with controlled intersymbol interference (ISI). The filter design problem for generating the desired ISI in such SISO links has been studied in previous works, however, the only known method in literature is a computationally demanding brute force search method. As a novel contribution, we develop models and tools that elaborate on the conditions to be satisfied for unique detection and existence of solution for the filter coefficients. Then, as a concrete example, the developed models and tools are utilized to show that in the absence of noise, five-times oversampling is required for unique detection of 16-QAM input alphabet. Building on these findings, we then develop novel algorithms that can efficiently design the filter coefficients. Examples and simulations are provided to elaborate on filter coefficient design and optimization, and to illustrate good SER performance of the MISO link with 1-bit receiver even at SNRs down to 5 dB.