Frequency-selective Channels Research Articles

Waveform and Beamforming Design for Intelligent Reflecting Surface Aided Wireless Power Transfer: Single-User and Multi-User Solutions

In this paper, we study the waveform and passive beamforming design for intelligent reflecting surface (IRS)-aided wireless power transfer (WPT). Generalized multi-user and low complexity single-user algorithms are demonstrated based on alternating optimization (AO) framework to maximize the weighted sum output direct current (DC), subject to the transmit power constraints and passive beamforming modulus constraints. The input signal waveform and IRS passive beamforming phase shifts are jointly designed as a function of users’ individual frequency-selective channel state information (CSI). The energy harvester nonlinearity is explored and two IRS deployment schemes, namely frequency selective IRS (FS-IRS) and frequency flat IRS (FF-IRS), are modeled and analyzed. This paper highlights the fact that IRS can provide an extra passive beamforming gain on output DC power over conventional WPT designs and significantly influence the waveform design by leveraging the benefit of passive beamforming, frequency diversity and energy harvester nonlinearity. Even though FF-IRS exhibits lower output DC than the ideal FS-IRS, it still achieves substantially increased DC power over conventional WPT designs. Performance evaluations confirm the significant benefits of a joint waveform and passive beamforming design accounting for the energy harvester nonlinearity to boost the performance of single-user and multi-user WPT systems.

The effects of DC offset in direct-conversion receivers for WLAN systems

In this paper, the effects DC offset in direct-conversion receivers for WLAN systems are analyzed using both experimental and simulation data. DC offset estimation is performed by using data-aided methods which are based on the short training sequence of WLAN preamble. In the simulations, DC offset and frequency offset estimations are carried out on the signals affected by frequency selective Rayleigh fading channel and additive white Gaussian noise. Estimation performance of the methods is compared for different SNR levels and frequency offset values in terms of mean square error. Experimental data which is in the form of WLAN packets and transmitted through a wireless channel is captured by using a software defined radio. The experimental performance of the DC offset compensation methods is evaluated in terms of transmission success ratio.

Successive Interference Cancellation on Frequency-Selective Channels With Mode-Dependent Gain

In ultra-long-haul optical communication systems, polarization-dependent gain from single-mode semiconductor optical amplifiers or mode-dependent gain from multi-mode erbium-doped fiber amplifiers causes a channel’s achievable information rate (AIR) using linear minimum-mean-square error (MMSE) detection to become substantially lower than the capacity of optimal maximum likelihood detection owing to loss of orthogonality between modes. However, such capacity loss can be mitigated using techniques that retain reasonable complexity, such as successive interference cancellation (SIC). In wireless systems, multicarrier transmission with many subcarriers transforms the frequency-selective channel into a set of uncoupled memoryless subchannels, and it is possible to obtain good performance using SIC on each subchannel independently. Multicarrier transmission in optical fibers, however, is susceptible to performance loss caused by Kerr nonlinearity. Therefore, we study methods for applying SIC on frequency-selective optical fiber channels using single-carrier transmission. We compare the theoretical performance of MMSE and SIC equalizers on frequency-selective optical fiber channels operating at 64 Gbaud with transmission distances up to 15,000 km and evaluate the penalties of SIC implementation. We consider single-mode fiber links with random artificial polarization-mode dispersion inserted along the link to enhance frequency diversity, as well as multi-core fiber links supporting 14 spatial and polarization modes. We show that SIC can increase outage capacity by 19%, but the AIR increase is reduced by almost half if error correction coding is not performed across modes. We confirm that SIC can adapt to fast channel perturbations with only a 3% decrease in AIR.

Integration of Spatial Modulation Scheme With Code Division Multiple Access for VIVO Based Frequency Selective Nano Sensor Networks

Due to their suitability for human-body based communications, in-VIVO nano sensor networks are envisioned to use the promising Terahertz signals in order to ensure the forthcoming high-rate communication needs of the modern medicine. However, the propagation losses at these frequency bands are quite significant and dependent on the operation frequency as well as on the physiological characteristics, thus impeding the use of Terahertz rates to their utmost benefit. Using numerous emitting elements is likely to improve the quality of the received signal, but gives rise to multi-channel interference (MCI) emanating from multi-antenna signaling reception, which necessitates a relatively complex signal processing to mitigate it. When multiple physiological signals are of interest, detecting them necessitates to mitigate multiple-access interference (MAI). In this perspective, orthogonal frequency division multiplexing (OFDM) allows to combat the channel frequency selectivity, whereas code division multiple access (CDMA) scheme cancels MAI. In this paper, we propose to embed the novel spatial modulation technique with CDMA architecture in an OFDM framework to ensure a viable communication in in-VIVO frequency selective Nano channels. The immunity of our proposed solution to such an interference is confirmed, since less than −2 dB in SNR level is required to support 5 users simultaneously communicating with a BER which is inferior to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${10}^{-{3}}$ </tex-math></inline-formula> when the operating frequency is equal to 1THz. This hybrid scheme is shown to efficiently combat the MCI while enabling a safe retrieval of the useful signal at the very-high data rate communications.

Joint CFO and channel estimation using pilot aided interpolation for high performance MIMO-OFDM

ABSTRACT Multiple Input Multiple Output (MIMO) system with orthogonal frequency division multiplexing (OFDM) is a promising physical layer candidate for the current and future wireless standards. MIMO increases system throughput while OFDM maximizes spectrum efficiency. To reduce the impacts of frequency selective fading and increase the capacity, MIMO requires precise channel state information (CSI). The presence of carrier frequency offset (CFO) introduces intercarrier interference (ICI) among the subcarriers which degrades the system performance. MIMO channel estimation is done under the assumption of perfect frequency synchronisation between the transmitter and receiver, or zero CFO. However, such an assumption is incorrect for time-varying and frequency-selective wireless channels, and the presence of CFO has an impact on channel estimation accuracy. In the existing methods, CFO and channel estimations are carried out independently using separate training sequences which reduces the spectral efficiency. As a result, the pilot aided interpolation approach is proposed in this paper for the joint estimate and compensation of CFO and channel effects using the same set of pilot tones. Theresults show that the proposed method offers precise CFO and channel estimation which improves the bit error rate performance and carrier to interference power ratio (CIR) of MIMO-OFDM system.

B-Spline Neural Network Assisted Space-Time Equalization for Single-Carrier Multiuser Nonlinear Frequency-Selective MIMO Uplink

This paper designs a nonlinear space-time equalizer based on B-spline neural network (BSNN) for the singlecarrier high-throughput multiuser frequency-selective multipleinput multiple-output (MIMO) nonlinear uplink. Specifically, based on a BSNN parametrization of the nonlinear high power amplifiers (NHPAs) at mobile terminals’ transmitters, a novel nonlinear identification scheme is developed to estimate the nonlinear dispersive MIMO uplink channel, which includes the BSNN models for the NHPAs at transmitters as well as the frequency-selective MIMO channel impulse response (CIR) matrix. Furthermore, the BSNN inverse models of the NHPAs are also estimated in closed-form. This allows the base station to implement nonlinear multiuser detection effectively using the space-time equalization (STE) based on the estimated frequencyselective MIMO CIR matrix and followed by compensating for the nonlinear distortion of the transmitters’ NHPAs based on the estimated BSNN inverse models. Simulation results are utilized to demonstrate the superior bit error rate performance of our nonlinear STE approach for single-carrier highthroughput multiuser nonlinear frequency-selective MIMO uplink.

A Novel OTFS System Based on DFrFT-OFDM

Orthogonal Time Frequency Space modulation (OTFS) is a promising modulation technique expected to counter the severe Doppler effects encountered in a doubly dispersive channel. OTFS scheme is developed on the basis of Orthogonal Frequency Division Multiplexing (OFDM) systems to support communication between high-speed vehicles, whose superiority lies in the domain of the equivalent channel. OTFS converts the Time-Frequency (TF) domain channel in the OFDM system to Delay-Doppler (DD) domain channel that becomes a handy tool to overcome the difficulties faced in a frequency selective channel. This letter presents an OTFS system design that is developed on the Discrete Fractional Fourier Transform (DFrFT) based OFDM system, which is designed to perform better compared to the conventional OTFS system with the same design complexity. The simulation results evidentially show 1 dB gain in power at a Bit-Error-Rate (BER) of 10^&#x2212;5 and a significant 3 dB decrement in the PAPR when a high power pilot with a 25 dB is inserted in the OTFS data frame.

Deep Learning-Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation. The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance. Simulation results demonstrate that the proposed DL-based schemes significantly outperform conventional orthogonal matching pursuit (OMP) techniques in terms of the normalized mean-squared error (NMSE), computational complexity, and spectral efficiency, particularly in the low signal-to-noise ratio regime. When compared to OMP approaches that achieve an NMSE gap of <inline-formula> <tex-math notation="LaTeX">$\mathrm {\{4-10\}\,\,dB}$ </tex-math></inline-formula> with respect to the Cramer Rao Lower Bound (CRLB), the proposed algorithms reduce the CRLB gap to only <inline-formula> <tex-math notation="LaTeX">$\mathrm {\{1-1.5\}\,\,dB}$ </tex-math></inline-formula>, while reducing complexity by two orders of magnitude.

Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach

Multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) is a key technology component in the evolution towards cognitive radio (CR) in next-generation communication in which the accuracy of timing and frequency synchronization significantly impacts the overall system performance. In this paper, we propose a novel scheme leveraging extreme learning machine (ELM) to achieve high-precision synchronization. Specifically, exploiting the preamble signals with synchronization offsets, two ELMs are incorporated into a traditional MIMO-OFDM system to estimate both the residual symbol timing offset (RSTO) and the residual carrier frequency offset (RCFO). The simulation results show that the performance of the proposed ELM-based synchronization scheme is superior to the traditional method under both additive white Gaussian noise (AWGN) and frequency selective fading channels. Furthermore, comparing with the existing machine learning based techniques, the proposed method shows outstanding performance without the requirement of perfect channel state information (CSI) and prohibitive computational complexity. Finally, the proposed method is robust in terms of the choice of channel parameters (e.g., number of paths) and also in terms of “generalization ability” from a machine learning standpoint.

Adaptive Waveform Design for Communication-Enabled Automotive Radars

Large-scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support the high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76–81 GHz can be utilized for wideband communication as well. For this purpose, various joint automotive radar-communication (JARC) systems have been proposed in the literature to perform both functions using the same wideband waveform. However, the wideband joint waveforms encounter frequency-selectivity in both radar and communication channels due to multi-path propagation. In this paper, we address the optimal joint waveform design problem to exploit the frequency-selectivity for wideband JARC operations via orthogonal frequency-division multiplexing (OFDM) wherein subcarrier coefficients are designed for optimal power allocation and phase coding. We show that the problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is known to be NP-hard. Existing approaches to solve QCQP include semidefinite relaxation (SDR) which incurs high time complexity. Instead, we propose approximation methods to solve QCQP more efficiently by leveraging structured matrices and using convex approximations. Finally, we demonstrate the efficacy of the proposed approaches through numerical simulations.

Sparse Bayesian Learning Aided Estimation of Doubly-Selective MIMO Channels for Filter Bank Multicarrier Systems

Sparse Bayesian learning (SBL)-based channel state information (CSI) estimation schemes are developed for filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). Initially, an SBL-based channel estimation scheme is designed for a frequency-selective quasi-static single-input single-output (SISO)-FBMC system, relying on the interference approximation method (IAM). The IAM technique, although has low complexity, is only suitable for channels exhibiting mild frequency-selectivity. Hence, an alternative time-domain (TD) model based sparse channel estimation framework is developed for highly frequency-selective channels. Subsequently, the Kalman filtering (KF)-based IAM and its TD counterpart are developed for sparse doubly-selective CSI estimation in SISO-FBMC systems. These schemes are also extended to FBMC-based multiple-input multiple-output (MIMO) systems, for both quasi-static and doubly-selective channels, after demonstrating the special block and group-sparse structures of the IAM and TD-based models respectively, which are the characteristic features of such channels. The Bayesian Cramér-Rao lower bounds (BCRLBs) and the time-recursive BCRLBs are derived for the proposed quasi-static as well as doubly-selective sparse CSI estimation models, respectively. Our numerical results closely match the analytical findings, demonstrating the enhanced performance of the proposed schemes over the existing techniques.

Multiantenna-Cognitive-Radio-Based Blind Spectrum Sensing under Correlated Signals and Unequal Signal and Noise Powers

Adopting cognitive radios (CRs) having multiple antennas in blind non-cooperative and cooperative spectrum sensing (CSS) under fading channels has gained attention due to higher detection performances provided by the spatial diversity gain of multi-sensors in different geographical locations and lower complexity, respectively. However, most studies do not consider sensing scenarios of more practical significance: for example, sometimes adopting only uncalibrated antenna arrays and sometimes only correlated signals at antenna arrays of CRs, but almost always, none of these impairments. Therefore, this paper studies these combined impairments on the performances of two blind techniques in centralized CSS with decision fusion (DF) and data fusion for different numbers of CRs and antennas per CR, both under frequency selective fading channels. One is the circular folding cooperative power spectral density split cancellation (CFCPSC), and the other is the generalized likelihood ratio test (GLRT). Extensive numerical results show, for instance, that with sample fusion (SF) and calibrated antennas, GLRT outperforms CFCPSC independently of correlation, numbers of antennas, or CRs. However, uncalibrated antennas severely penalize GLRT while surprisingly benefiting CFCPSC. Correlation is detrimental to GLRT and CFCPSC with SF but may help CFCPSC in DF and GLRT in DF or eigenvalue fusion. Generally, CFCPSC outperforms GLRT.

Over‐the‐air equalization with reconfigurable intelligent surfaces

Reconfigurable intelligent surface (RIS)-empowered communications is on the rise and is a promising technology envisioned to aid in 6G and beyond wireless communication networks. RISs can manipulate impinging waves through their electromagnetic elements enabling some sort of control over the wireless channel. In this paper, the potential of RIS technology is explored to perform a sort of virtual equalization over-the-air for frequency-selective channels whereas, equalization is generally conducted at either the transmitter or receiver in conventional communication systems. Specifically, with the aid of an RIS, the frequency-selective channel from the transmitter to the RIS is transformed to a frequency-flat channel through elimination of inter-symbol interference (ISI) components at the receiver. ISI is eliminated by adjusting the phases of impinging signals particularly to maximize the incoming signal of the strongest tap. First, a general end-to-end system model is provided and a continuous to discrete-time signal model is presented. Subsequently, a probabilistic analysis for the elimination of ISI terms is conducted and reinforced with computer simulations. Furthermore, a theoretical error probability analysis is performed along with computer simulations. It is analyzed and demonstrated that conventional RIS phase alignment methods, can successfully eliminate ISI and the RIS-aided communication channel can be converted from frequency-selective to frequency-flat.

Efficient Message Passing Receivers for Downlink MIMO-SCMA Systems

Sparse code multiple access (SCMA) and multiple input multiple output (MIMO) are considered as two efficient techniques to provide both massive connectivity and high spectrum efficiency for future machine-type wireless networks. However, the computational complexity of the conventional message passing algorithm (MPA) for the downlink MIMO-SCMA detection increases exponentially with the degree of variable nodes (VNs). To address this issue, two novel fixed low complexity MPA detectors are proposed by utilizing the sparse feature of codewords, which are based on the serial schedule strategy in order to accelerate their convergence. First, a maximum distance MPA (MDMPA) detector is introduced to reduce the number of VNs involved in the message updating procedure in the first <inline-formula><tex-math notation="LaTeX">$T_0$</tex-math></inline-formula> iterations and fix <inline-formula><tex-math notation="LaTeX">$TJ_0$</tex-math></inline-formula> codewords at the <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math></inline-formula> iteration, which subsequently drops the redundant codeword combinations in the remaining iterations. To further improve the bit error ratio (BER) performance, an improved MDMPA (IMDMPA) is proposed, in which, the VNs associated with the target user are always allowed to participate in the iterative propagation of message. An efficient linear squares (LS) channel estimation scheme is also derived based on block pilots by stacking all the received signals at the same subcarrier together. Simulation results illustrate that the proposed MDMPA and IMDMPA detectors significantly reduce the computational complexity of the original MPA, but with comparable decoding capabilities, and the proposed LS channel estimator can closely approach the corresponding lower bound over frequency selective channels.

Efficient Fast-Convolution Based Hybrid Carrier System

The weighted-type fractional Fourier transform (WFRFT)-based hybrid carrier (HC) system has been proposed to bridge the gap between orthogonal frequency division multiplexing (OFDM) system and single-carrier system with frequency domain equalization (SC-FDE). In this paper, we propose a novel HC system based on efficient fast-convolution (FC) without a cyclic prefix to meet the requirements of complex scenarios towards future communications. The proposed system can achieve flexible optimization of system performance, such as minimizing out-of-band emission (OOBE), peak to average power ratio (PAPR) and bit error rate (BER). This efficient FC based hybrid carrier (FCHC) system is among the most competitive waveforms. The closed-form BER expressions are derived and analyzed over additive white Gaussian noise (AWGN) and frequency-selective fading channels. A modified equalizer over frequency-selective channels is also provided to resist the inter-block interference (IBI). Moreover, the banded minimum mean-square error (MMSE) equalization based on FC can be embedded in the proposed system over doubly-selective channels, and then the interference caused by fading channels can be further suppressed through the combination of FC and HC. The simulation results demonstrate the superiority of the proposed system over the conventional HC system in terms of OOBE, PAPR and BER. The significant flexibility and combined performance advantages make the proposed system a potential scheme for future communications.

Theoretical Performance of LoRa System in Multipath and Interference Channels

This paper presents a semi-analytical approximation of Symbol Error Rate (SER) for the well known LoRa Internet of Things (IoT) modulation scheme in the following two scenarios: 1) in multi-path frequency selective fading channel with Additive White Gaussian Noise (AW GN) and 2) in the presence of a second interfering LoRa user in flat-fading AW GN channel. Performances for both coherent and non-coherent cases are derived by considering the common Discrete Fourier transform (DF T) based detector on the received LoRa waveform. By considering these two scenarios, the detector exhibits parasitic peaks that severely degrade the performance of the LoRa receiver. We propose in that sense a theoretical expression for this result, from which a unified framework based on peak detection probabilities allows us to derive SER, which is validated by Monte Carlo simulations. Fast computation of the derived closedform SER allows to carry out deep performance analysis for these two scenarios.

A DFT-based Low Complexity LMMSE Channel Estimation Technique for OFDM Systems

The linear minimum mean square error (LMMSE) channel estimation technique is often employed in orthogonal frequency division multiplexing (OFDM) systems because of its optimal performance in the mean square error (MSE) performance. However, the LMMSE method requires cubic complexity of order O(N 3 p ), where Np is the number of pilot subcarriers. To reduce the computational complexity, a discrete Fourier transform (DFT) based LMMSE method is proposed in this paper for OFDM systems in the frequency selective channel. To validate the proposed method, the closed form mean square error (MSE) expression is also derived. Finally, a computer simulation is carried out to compare the performance of the proposed LMMSE method with the classical LS and LMMSE methods in terms of bit error rate (BER) and computational complexity. Results of the simulation show that the proposed LMMSE method achieves exactly the same performance as the conventional LMMSE method, with much lower computational complexity.

System-Level Assessment of a C-RAN based on Generalized Space–Frequency Index Modulation for 5G New Radio and Beyond

Index modulation (IM) has been attracting considerable research efforts in recent years as it is considered a promising technology that can enhance spectral and energy efficiency and help cope with the rising demand of mobile traffic in future wireless networks. In this paper, we propose a cloud radio access network (C-RAN) suitable for fifth-generation (5G) and beyond systems, where the base stations (BSs) and access points (APs) transmit multidimensional IM symbols, which we refer to as precoding-aided transmitter-side generalized space–frequency IM (PT-GSFIM). The adopted PT-GSFIM approach is an alternative multiuser multiple-input multiple-output (MU-MIMO) scheme that avoids multiuser interference (MUI) while exploiting the inherent diversity in frequency-selective channels. To validate the potential gains of the proposed PT-GSFIM-based C-RAN, a thorough system-level assessment is presented for three different three-dimensional scenarios taken from standardized 5G New Radio (5G NR), using two different numerologies and frequency ranges. Throughput performance results indicate that the 28 GHz band in spite of its higher bandwidth and higher achieved throughput presents lower spectral efficiency (SE). The 3.5 GHz band having lower bandwidth and lower achieved throughput attains higher SE. Overall, the results indicate that a C-RAN based on the proposed PT-GSFIM scheme clearly outperforms both generalized spatial modulation (GSM) and conventional MU-MIMO, exploiting its additional inherent frequency diversity.

Alignment of transmitters in indoor visible light communication for flat channel characteristics

Visible light communication (VLC) systems incorporate ambient lighting and wireless data transmission, and the experienced channel in indoor VLC is a major topic that should be examined for reliable communication. In this study, it is realized that multiple transmitters in classical alignment are the forceful factors for channel characteristics. In the frequency band, fluctuations with sudden drops are observed, where the fluctuation shape is related to the source layout and receiver location. These varying frequency-selective channels need solutions, especially for mobile users, because sustained channel estimation and equalization are necessary as the receiver changes its location. It is proven that using light-emitting diodes (LEDs) with highly directional beams as sources or using a detector with a narrow field of view (FOV) in the receiver may help partially alleviate the problem; the frequency selectivity of the channel reduces in some regions of the room. For flat fading channel characteristics all over the room, LEDs should be aligned in hexagonal cellular structure, and detector FOV should be arranged according to the cell dimension outcomes.

Low-Complexity ZF/MMSE MIMO-OTFS Receivers for High-Speed Vehicular Communication

Orthogonal time-frequency space (OTFS) scheme, which transforms a time and frequency selective channel into an almost non-selective channel in the delay-Doppler domain, establishes reliable wireless communication for high-speed moving devices. This work designs and analyzes low-complexity zero-forcing (LZ) and minimum mean square error (LM) receivers for multiple-input multiple-output (MIMO)-OTFS systems with perfect and imperfect receive channel state information (CSI). The proposed receivers provide exactly the same solution as that of their conventional counterparts, and reduce the complexity by exploiting the doubly-circulant nature of the MIMO-OTFS channel matrix, the block-wise inverse, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Schur</i> complement. We also derive, by exploiting the Taylor expansion and results from random matrix theory, a tight approximation of the post-processing signal-to-noise-plus-interference-ratio (SINR) expressions in closed-form for both LZ and LM receivers. We show that the derived SINR expressions, when averaged over multiple channel realizations, accurately characterize their respective bit error rate (BER) with both perfect and imperfect receive CSI. We numerically show the lower BER and lower complexity of the proposed designs over state-of-the-art exiting solutions.