Radio Frequency Impairments Research Articles

High-speed 0.22 THz communication system with 84 Gbps for real-time uncompressed 8K video transmission of live events

Terahertz frequency band (0.1 ~ 10 THz) can provide ultra-high transmitting rates for massive emerging applications. However, due to the radio frequency impairments and limited signal processing property of baseband devices, it is difficult for the current terahertz communication systems to be used in real applications. In this work, we report a 0.22 THz communication system, which can provide 84 Gbps air interface rate over 1.26 kilometers distance. Such a system is underpinned by three breakthroughs, namely, integrating the reported terahertz wireless communication system, redesigning the signal processing in baseband devices, and increasing the cut-off frequency and transmitting power of radio frequency component by parameter optimizing. Being applied to realize the real-time transmission of the uncompressed 8K ultra high-definition video in the 31st International University Sports Federation in Chengdu, China, this work is a leaping advancement to enable terahertz systems from experiment to promising applications.

Nonlinear RF impairments modeling with OFDM symbol-based parallel Wiener–Hammerstein structure

In the paper, a novel orthogonal frequency division multiplexing (OFDM) symbol-based parallel Wiener–Hammerstein structure is proposed to deal with the problem of nonlinear radio frequency (RF) impairments modeling for a cyclic prefix OFDM (CP-OFDM) waveform in telecommunication systems. The aforementioned structure is an equivalent linear-in-parameters representation of the parallel Wiener–Hammerstein structure in the considered problem. The structure is formally derived enabling the usage of identification algorithms compared with the artificial neural network (ANN) approach. The solution is compliant with current telecommunication standards.

Digital self-interference cancellations addressing radio-frequency impairments for in-band full duplex

Digital self-interference cancellations addressing radio-frequency impairments for in-band full duplex

Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach

The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection.

Analysis and Optimization of Hardware Impaired FD Rician-Faded mMIMO Systems

We consider a full duplex (FD) multi-cell massive multi-input-multi-output (mMIMO) network, wherein FD mMIMO base-stations (BSs) serve multiple FD user equipments (UEs) on the same time-frequency resource. We derive closed-form uplink and downlink spectral efficiency (SE) expressions by considering i) radio-frequency impairments at the BSs and the UEs; ii) dynamic-resolution analog-to-digital converter/digital-to-analog converter (ADC/DAC) architecture at the BSs and low-resolution ADC/DAC at the UEs; and iii) spatially-correlated Rician channels. We optimize the non-convex global energy efficiency metric for this FD system by using the minorization-maximization (MM) framework, which transforms the optimization into a sequence of surrogate concave problems. We construct a novel surrogate function to exploit the MM framework. We numerically characterize the effect of FD interference on Rayleigh- and Rician-faded mMIMO networks, and show the higher immunity of the latter to the FD interference. We also show the SE gain provided by the dynamic ADC/DAC architecture which allows us to judiciously choose ADC/DAC resolution, while reducing the power consumption.

A Random Fourier Feature Based Receiver Detection for Enhanced BER Performance in Nonlinear PD-NOMA

Non-orthogonal multiple access (NOMA) has been proposed as a potential enabler for massive connectivity. Among the different NOMA schemes, power-domain (PD-NOMA) is particularly appealing as it improves user fairness while enhancing spectral efficiency. However, the practical implementation of NOMA faces several challenges, including radio frequency impairments, such as power-amplifier (PA) nonlinearity, which can limit its performance. In this paper, we study the impact of PA nonlinearity on the detection performance of PD-NOMA, and propose a random Fourier feature (RFF) based solution to mitigate the effects of such imperfections. Computer simulations carried out assuming different PA nonlinearity types demonstrate that the proposed RFF based algorithm considerably improves the BER performance and achieves results that are close to the ideal case. Lastly, the analytical proof of our proposed algorithms performance is provided to support our simulation results.

A Comprehensive Experimental Emulation for OTFS Waveform RF-Impairments.

The orthogonal time-frequency space (OTFS) waveform exceeds the challenges that face orthogonal frequency division multiplexing (OFDM) in a high-mobility environment with high time-frequency dispersive channels. Since radio frequency (RF) impairments have a direct impact on waveform behavior, this paper investigates the experimental implementation of RF-impairments that affect OTFS waveform performance and compares them to the OFDM waveform as a benchmark. Firstly, the doubly-dispersive channel effect is analyzed, and then an experimental framework is established for investigating the impact of RF-impairments, including non-linearity, carrier frequency offset (CFO), I/Q imbalances, DC-offset, and phase noise are considered. The experiments were conducted in a real indoor wireless environment using software-defined radio (SDR) at carrier frequencies of 2.4 GHz and 5 GHz based on the Keysight EXG X-Series devices. The comparison of the performances of OFDM and OTFS in the presence of RF-impairments reveals that OTFS significantly outperforms OFDM.

Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing

This article introduces a neural network-based signal processing framework for intelligent Reflecting surface-aided (IRS) wireless communications systems. By modeling radio frequency (RF) impairments inside the meta-atoms of IRS - including nonlinearity and memory effects) - we present an approach that generalizes the entire IRS-aided system as a reservoir computing (RC) system, an efficient recurrent neural network (RNN) operating in a state near the “edge of chaos.” This framework enables us to take advantage of the nonlinearity of this “fabricated” wireless environment to overcome link degradation due to model mismatch. Accordingly, the randomness of the wireless channel and RF imperfections are naturally embedded into the RC framework, enabling the internal RC dynamics lying on the edge of chaos. Furthermore, several practical issues, such as channel state information acquisition, passive beamforming design, and physical layer reference signal design, are discussed.

Joint Frequency Offset and Channel Estimation for NB-IoT Systems

The nonlinear distortions resulting from the radio frequency (RF) impairments such as frequency offset, I/Q-imbalance, phase noise, and quantization errors. The hardware impairments severely degrade signal reception performance in NB-IoT systems and hence estimation for hardware impairments is necessary and mitigated by compensation algorithms. To this end, this paper focuses on the problem of estimating channel gains and frequency offsets in an NB-IoT system. Specifically, we first separate channel gains and frequency offsets and derive the analytical expressions of interest, based on the maximum likelihood estimation; We derive the expectation-maximization algorithm and apply it to the NB-IoT system for solving joint estimation of frequency offsets and channel gains; We carry out extensive simulations to illustrate that how systems parameters (e.g., channel phase difference, frequency offset difference, SNR, the number of sampling) can affect estimation performance.

One-bit mMIMO with defective RF chain over Ricean fading in beyond 5G networks

In this paper, we investigate one-bit massive multiple-input multiple-output (mMIMO) systems with defective radio frequency (RF) chains over Ricean fading channels in beyond 5G networks. The approximate achievable rate expressions are derived in closed-form for the scenarios with ideal and non-ideal channel state information by utilizing the Bussgang decomposition. Furthermore, the scaling laws for transmit powers and RF hardware impairments are established when the number of antennas, M, at the base station (BS) grows indefinitely. Specially, the power scaling laws under the case of a large number of Internet of Things (IoT) devices (users) are also investigated. It is revealed that, as M increases, the systems can yield a non-vanishing rate while cutting down the transmit powers of the IoT devices, and/or scaling up the RF impairments of the BS. Moreover, it is also found that the relationship between the number of IoT devices N and M can influence the results of the power scaling laws. Numerical results are conducted to demonstrate our analysis. It is also revealed that the appropriate M can contribute to better energy efficiency.

Massive MIMO relaying with imperfect RF chains and coarse ADC/DAC in beyond 5G networks

AbstractMassive connectivity, low cost, and energy saving are key requirements in providing Internet of Things (IoT) services in the beyond 5G (B5G) communication networks. Motivated by these requirements, we investigate a massive multiple‐input multiple‐output (MIMO) relaying system with imperfect radio frequency (RF) chains and coarse analog‐to‐digital converters/digital‐to‐analog converters (ADCs/DACs), where IoT user pairs communicate through the assistance of a relay equipped with transceiver antennas in quantity. First, the accurate and the approximate achievable rate expressions are derived in closed form. Then, we evaluate the impacts of critical design parameters on the rate performance. Moreover, scaling laws for transmit powers and RF hardware impairments are established when the number of antennas, M, at the relay grows infinity. It is revealed that, as M increases, the system can yield a non‐vanishing rate while cutting down the transmit powers of the IoT devices and relay, and/or scaling up the RF impairments of the relay. The power allocation scheme for maximizing the sum rate is proposed. Numerical results are conducted to demonstrate the analysis and show that, in the large scale antennas regime, employing high‐quality RF hardware at the IoT users and high‐resolution DACs at the transmit end of the relay can significantly improve the system's sum rate.

Secure Transmission in Cell-Free Massive MIMO With RF Impairments and Low-Resolution ADCs/DACs

This paper considers the secure transmission in a cell-free massive MIMO system with imperfect radio frequency (RF) chains and low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at both access points (APs) and legitimate users, where an active eavesdropper attempts to wiretap the confidential data. The Gaussian RF impairment model (GRFIM) and additive quantization noise model (AQNM) are used to evaluate the impacts of the RF impairments and low resolution ADCs/DACs, respectively. The analytical results of the linear minimum mean square error (MMSE) channel estimation show that there is nonzero floor on the estimation error with respect to the RF impairments, ADC/DAC precision and the pilot power of the eavesdropper which is different from the conventional case with perfect transceiver. Then, a tractable closed-form expression for the ergodic secrecy rate is obtained with respect to key system parameters, such as the antenna number per AP, the AP number, user number, quality factors of the ADC/DAC and the RF chain, pilot signal power of the eavesdropper, etc. Moreover, a compensation algorithm between the imperfect RF components and the inexpensive coarse ADCs/DACs is also presented. Finally, numerical results are provided to illustrate the efficiency of the achieved expressions and the devised algorithm, and show the effects of RF impairments and low resolution ADC/DAC on the secrecy performance.

Hybrid Precoding for Millimeter Wave Systems under Phase Noise problem

Millimeter wave - mm Wave - communications has been considered as a key authorised technology for the fifth generation networks. Hybrid precoding, which refer to a blend of analog and digital precoding, can achieve high spectral and energy efficiencies. However a cost-effective alternative and power consumption are needed. So hybrid precoding strategies become promising solutions. In the higher carrier frequencies, especially the millimeter-wave range, there are significant degradations in the transmitted and received signals due to radio-frequency impairments such as phase noise introduced by the local oscillators (LO), which is the irregular difference among the phase of the local oscillator and the phase of the carrier signal. It’s becoming a limiting factor in high data rate digital communication systems. In this research, we study the impact of phase noise on the performance of hybrid precoding structure in millimeter wave Multiple-Input Multiple-Output (MIMO) (mmWave MIMO) systems. Manifold optimization based alternating minimization algorithm is proposed. An optimization problem is formulated for phase noise issue in millimeter wave MIMO system. Results show that the impact of phase noise can degrade the system performance at higher RF chains.

On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers

One of the major impediments in the design and operation of a full-duplex radio transceiver is the presence of self-interference (SI), that is, the transceiver’s transmitted signal, 60–100 dB stronger than the desired signal of interest. To reduce the SI signal below the receiver’s sensitivity before coupling it to the receiver, radio frequency (RF)/analog domain cancellation is carried out. Even after SI cancellation to the required level in the analog domain, the residual SI signal still exits and lowers the transceiver’s performance. For residual SI cancellation, a digital domain cancellation is carried out. RF impairments are the major obstacle in the residual SI cancellation path in the digital domain. Linearization of RF impairments such as IQ mixer imbalance in the transmitter and receiver chain, non-linear PA with memory, and non-linear LNA are also carried out. Performance evaluation of the proposed techniques is carried out based on SINR, the power of different SI signal components, PSD, output to input relationship, SNR vs. BER, spectrum analyzer, constellation diagram, and link budget analysis. The proposed techniques provide attractive RF/analog SI cancellation of up to 80–90 dB, digital residual SI cancellation of up to 35 to 40 dB, total SI cancellation of up to 110 to 130 dB, and an SINR improvement of up to 50 dB.

Hardware-Impaired Rician-Faded Massive MIMO FD Relay: Analysis and Optimization

We consider two-way multi-pair full-duplex (FD) massive multi-input-multi-output (mMIMO) relaying, where multiple FD user-pairs exchange information via a shared FD relay. We derive a closed-form spectral efficiency (SE) lower bound by considering i) dynamic analog-to-digital converter (ADC) and digital-to-analog converter (DAC) architecture at the relay, where each antenna is connected to a different resolution ADC/DAC; and ii) low cost radio frequency (RF) chains at the relay and users. The dynamic ADC/DAC architecture allows us to judiciously choose ADC/DAC resolution to achieve high SE with low power consumption. We unlike, the most existing mMIMO relaying works, consider a correlated Rician fading channel, which captures realistic line-of-sight (LoS) signal propagation and spatial correlation between closely-packed relay antennas. We maximize the derived SE lower bound by first proposing its valid surrogate function, and then by using minorization-maximization approach. We investigate the impact of ADC/DAC and RF impairments, and show that the proposed optimization allows reduction in ADC/DAC resolution without compromising the SE.

Channel Estimation and Equalization for Terahertz Receiver With RF Impairments

The radio frequency (RF) impairments of analog devices have been regarded as an important factor degrading the performance of Terahertz (THz) communications, where in-phase/quadrature (IQ) imbalance and phase noise (PN) are the two typical RF impairments at THz transceiver. In this paper, we investigate the channel estimation (CE) and equalization for THz receiver in the presence of wideband IQ imbalance and PN, where single-carrier frequency-domain equalization is used. Since PN is inserted between channel impulse response (CIR) and wideband IQ imbalance at the receiver, the CIR and IQ imbalance cannot be treated together as an effective channel. Therefore, a novel two-stage CE method is derived to separately estimate the CIR and wideband IQ imbalance parameters, and its corresponding channel equalization method is designed to compensate both wideband IQ imbalance and PN, and equalizes the CIR. Theoretical and simulation results validate the efficiency of our proposed CE, and the proposed equalization method is shown to outperform the state-of-the-art methods.

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

Wireless transceivers for mass-market applications must be cost effective. We may achieve this goal by deploying non-ideal low-cost radio frequency (RF) analog components. However, their imperfections may result in RF impairments, including phase noise (PN), carrier frequency offset (CFO), and in-phase (I) and quadrature-phase (Q) imbalance. These impairments introduce in-band and out-of-band interference terms and degrade the performance of wireless systems. In this survey, we present RF-impairment signal models and discuss their impacts. Moreover, we review RF-impairment estimation and compensation in single-carrier (SC) and multicarrier systems, especially orthogonal frequency division multiplexing (OFDM). Furthermore, we discuss the effects of the RF impairments in already-established wireless technologies, e.g., multiple-input multiple-output (MIMO), massive MIMO, full-duplex, and millimeter-wave communications and review existing estimation and compensation algorithms. Finally, future research directions investigate the RF impairments in emerging technologies, including cell-free massive MIMO communications, non-orthogonal multicarrier systems, non-orthogonal multiple access (NOMA), ambient backscatter communications, and intelligent reflecting surface (IRS)-assisted communications. Furthermore, we discuss artificial intelligence (AI) approaches for developing estimation and compensation algorithms for RF impairments.

DeepWiPHY: Deep Learning-Based Receiver Design and Dataset for IEEE 802.11ax Systems

In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal frequency division multiplexing (OFDM) receivers. We first train DeepWiPHY with a synthetic dataset, which is generated using representative indoor channel models and includes typical radio frequency (RF) impairments that are the source of nonlinearity in wireless systems. To further train and evaluate DeepWiPHY with real-world data, we develop a passive sniffing-based data collection testbed composed of Universal Software Radio Peripherals (USRPs) and commercially available IEEE 802.11ax products. The comprehensive evaluation of DeepWiPHY with synthetic and real-world datasets (110 million synthetic OFDM symbols and 14 million real-world OFDM symbols) confirms that, even without fine-tuning the neural network’s architecture parameters, DeepWiPHY achieves comparable performance to or outperforms the conventional WLAN receivers, in terms of both bit error rate (BER) and packet error rate (PER), under a wide range of channel models, signal-to-noise (SNR) levels, and modulation schemes.

Analog Self-Interference Cancellation Using Auxiliary Transmitter Considering IQ Imbalance and Amplifier Nonlinearity

In-band full-duplex communication, which transmits and receives simultaneously on the same frequency, causes self-interference (SI). In this paper, to cancel SI present in the radio frequency (RF) domain, we propose a novel nonlinear SI cancellation approach using an auxiliary transmitter which is effective in the presence of IQ imbalance and nonlinear distortion. The proposed approach estimates the local transceiver channel by using a time-domain least squares method and creates a signal for SI cancellation based on estimation results and a finite impulse response filter, whose coefficients are derived in this paper. Additionally, we theoretically calculate the SI cancellation limit of the proposed approach. Information about the SI cancellation limit due to phase noise is important for meeting SI cancellation requirements and being able to compare the effects of RF impairments such as IQ imbalance and nonlinear distortion. From simulation results, we show that the proposed approach outperforms the conventional approach and the case of using a general adaptive algorithm for the proposed approach. Furthermore, the SI cancellation limit is improved by adjusting the propagation delay of the SI signal and the canceling signal in addition to sharing one local oscillator in the local transceiver.

Performance enhancement of generalized frequency division multiplexing with RF impairments compensation for efficient 5G wireless access

Fifth generation (5G) is expected to support diverse applications with reduced latency, reliability, high spectral efficiency, huge data rate and improved energy efficiency. Among the several waveforms, Generalized Frequency Division Multiplexing (GFDM) gained more attention and considered as one of the candidate waveform for 5G. Radio frequency (RF) impairments such as carrier frequency offset (CFO), intercarrier interference (ICI), out-of-band (OoB) emission, peak to average power ratio (PAPR) and in-phase (I) and quadrature phase (Q) imbalances greatly degrades the GFDM system performance. An efficient spectral shaping with advanced signal processing techniques is highly essential to counteract the RF impairments and to realize the robust physical layer for 5G applications. Hence a spectrally efficient precoder which comprises differential encoding followed by (1 + D) correlative coding with precise, low complex blind IQ imbalance compensation is proposed to preserve GFDM from RF impairments. Simulation results show that the proposed GFDM outperforms the conventional GFDM and OFDM in terms of bit error rate (BER), ICI power, PAPR and OoB radiations which enables the proposed GFDM is one of the solutions to meet the key requirements of 5G wireless access.