Showing papers on "Orthogonal frequency-division multiplexing published in 2021"
TL;DR: In this article, a two-dimensional-al modulation scheme referred to as orthogonal time-frequency space (OTFS) modulation is proposed to accommodate the channel dynamics via modulating information in the delay-Doppler domain.
Abstract: Sixth-generation (6G) wireless networks are envisioned to provide global coverage for the intelligent digital society of the near future, ranging from traditional terrestrial to non-terrestri-al networks, where reliable communications in high-mobility scenarios at high carrier frequencies would play a vital role. In such scenarios, the conventional orthogonal frequency division multiplexing (OFDM) modulation, that has been widely used in both the fourth-generation (4G) and the emerging fifth-generation (5G) cellular systems as well as in WiFi networks, is vulnerable to severe Doppler spread. In this context, this article aims to introduce a recently proposed two-dimension-al modulation scheme referred to as orthogonal time-frequency space (OTFS) modulation, which conveniently accommodates the channel dynamics via modulating information in the delay-Doppler domain. This article provides an easy-reading overview of OTFS, highlighting its underlying motivation and specific features. The critical challenges of OTFS and our preliminary results are presented. We also discuss a range of promising research opportunities and potential applications of OTFS in 6G wireless networks.
103 citations
TL;DR: Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.
Abstract: Intelligent reflecting surface (IRS) is envisioned as a revolutionary technology for future wireless communication systems since it can intelligently change radio environment and integrate it into wireless communication optimization However, most existing works adopted an ideal IRS reflection model, which is impractical and can cause significant performance degradation in realistic wideband systems To address this issue, we first study the dual phase- and amplitude-squint effect of reflected signals and present a simplified practical IRS reflection model for wideband signals Then, an IRS enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated We aim to jointly design the transmit beamformer and IRS reflection for the case of using both continuous and discrete phase shifters to maximize the average sum-rate over all subcarriers By exploiting the relationship between sum-rate maximization and mean square error (MSE) minimization, the original problem is equivalently transformed into a multi-block/variable problem, which can be efficiently solved by the block coordinate descent (BCD) method Complexity and convergence for both cases are analyzed or illustrated Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems
97 citations
TL;DR: This is the first paper to rigorously derive OTFS modulation from first principles and shows the degree of localization of the DD domain basis signals is inversely related to the time duration of the transmit signal, which explains the trade-off between robustness to Doppler shift and latency.
Abstract: Orthogonal Time Frequency Space (OTFS) modulation has been recently proposed to be robust to channel induced Doppler shift in high mobility wireless communication systems. However, to the best of our knowledge, none of the prior works on OTFS have derived it from first principles. In this paper, using the ZAK representation of time-domain (TD) signals, we rigorously derive an orthonormal basis of approximately time and bandwidth limited signals which are also localized in the delay-Doppler (DD) domain. We then consider DD domain modulation based on this orthonormal basis, and derive OTFS modulation. We show that irrespective of the amount of Doppler shift, the received DD domain basis signals are localized in a small interval of size roughly equal to the inverse time duration along the Doppler domain and of size roughly equal to the inverse bandwidth along the delay domain (time duration refers to the length of the time-interval where the TD transmit signal has been limited). With sufficiently large time duration and bandwidth, there is little interference between information symbols modulated on different basis signals, which allows for joint DD domain equalization of all information symbols. This explains the inherent robustness of DD domain modulation to channel induced Doppler shift when compared with Orthogonal Frequency Division Multiplexing (OFDM).
88 citations
TL;DR: This work introduces a low-complexity beam squint mitigation scheme based on true-time-delay and proposes a novel variant of the popular orthogonal matching pursuit (OMP) algorithm to accurately estimate the channel with low training overhead.
Abstract: Terahertz (THz) communication is widely considered as a key enabler for future 6G wireless systems. However, THz links are subject to high propagation losses and inter-symbol interference due to the frequency selectivity of the channel. Massive multiple-input multiple-output (MIMO) along with orthogonal frequency division multiplexing (OFDM) can be used to deal with these problems. Nevertheless, when the propagation delay across the base station (BS) antenna array exceeds the symbol period, the spatial response of the BS array varies over the OFDM subcarriers. This phenomenon, known as beam squint, renders narrowband combining approaches ineffective. Additionally, channel estimation becomes challenging in the absence of combining gain during the training stage. In this work, we address the channel estimation and hybrid combining problems in wideband THz massive MIMO with uniform planar arrays. Specifically, we first introduce a low-complexity beam squint mitigation scheme based on true-time-delay. Next, we propose a novel variant of the popular orthogonal matching pursuit (OMP) algorithm to accurately estimate the channel with low training overhead. Our channel estimation and hybrid combining schemes are analyzed both theoretically and numerically. Moreover, the proposed schemes are extended to the multi-antenna user case. Simulation results are provided showcasing the performance gains offered by our design compared to standard narrowband combining and OMP-based channel estimation.
88 citations
TL;DR: Two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal.
Abstract: Internet of Things (IoT) is able to provide various physical objects to exchange their information through the 6G wireless communication network. However, with the large increasing number of the IoT devices (IoDs), the deployment of IoDs faces two basic challenges, i.e., spectrum scarcity and energy limitation. Cooperative spectrum sharing and simultaneous wireless information and power transfer (SWIPT) provide effective ways to improve the spectrum and energy efficiency. In this article, two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal. Specifically, in phase1, the IoDs transmitter (DT) in the cognitive IoT network performs information decoding and energy harvesting with the received RF signal. In phase2, DT transmits the signals of the primary system and itself to the corresponding receiver by utilizing orthogonal subcarriers with the harvested energy to avoid the interference. Achievable rates of the cognitive IoT system with amplify-and-forward (AF) and decode-and-forward (DF) relaying mode are maximized through joint power and subcarrier optimization, while ensuring the target rate of the primary system. Simulation results are performed to illustrate the improvement of the spectrum and energy efficiency.
77 citations
TL;DR: In this paper, the authors investigated a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage rate by optimizing the three-dimensional (3D) positions of UAV, user clustering, and frequency band allocation.
Abstract: Recently, unmanned aerial vehicles (UAVs) have attracted lots of attention because of their high mobility and low cost. This article investigates a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage rate by optimizing the three-dimensional (3D) positions of UAVs, user clustering, and frequency band allocation. Compared with the existing works, the constraints of the required quality of service (QoS) and the service ability of each UAV are considered, which makes the problem more challenging. A three-step method is developed to solve the formulated mixed-integer programming problem. First, to ensure that each UAV can serve more number of users, the maximum service radius of UAVs is derived according to the required minimum power of the received signals for the users. Second, an algorithm based on artificial bee colony (ABC) algorithm is proposed to minimize the number of required UAVs. Third, the 3D position and the frequency band of each UAV are designed to increase the power of the target signals and to reduce the interference. Finally, simulation results are presented to demonstrate the superiority of the proposed solution for UAV-assisted communication systems.
73 citations
TL;DR: A data-aided channel estimation algorithm for a superimposed pilot and data transmission scheme, which can improve the spectral efficiency and coarsely estimate the channel based on the pilot symbol, followed by an iterative process which detects the data symbols and refines the channel estimates.
Abstract: The recently developed orthogonal time frequency space (OTFS) modulation has shown its capability of coping with the fast time-varying channels in high-mobility environments. In particular, OTFS modulation gives rise to the sparse representation of the delay-Doppler (DD) domain channel model. Hence, one can an enjoy accurate channel estimation by adopting only one pilot symbol. However, conventional OTFS channel estimation schemes require the deployment of guard space to avoid data-pilot interference, which inevitably sacrifices the spectral efficiency. In this letter, we develop a data-aided channel estimation algorithm for a superimposed pilot and data transmission scheme, which can improve the spectral efficiency. To accurately estimate the channel and detect the data symbols, we coarsely estimate the channel based on the pilot symbol, followed by an iterative process which detects the data symbols and refines the channel estimates. Simulation results show that the bit error rate (BER) performance based on the proposed method can approach the baseline scheme with perfect channel estimation.
71 citations
TL;DR: In this article, a joint waveform and control signaling optimization (JWCSO) strategy was proposed to optimize the subcarrier powers in a time-frequency region of interest.
Abstract: We consider the problem of time-frequency waveform design for an OFDM dual-functional radar-communications (DFRC) system that communicates with an OFDM receiver while simultaneously estimating target parameters using the backscattered signals. In particular, the goal is to achieve a favorable performance trade-off between radar and communications by optimizing subcarrier powers in a time-frequency region of interest. First, we focus on radar-optimal waveform design to minimize the Cramer-Rao bound (CRB) on delay-Doppler estimation subject to an integrated side-lobe level (ISL) constraint in the delay-Doppler ambiguity domain. Next, we investigate the problem of DFRC trade-off waveform design to optimize the communications rate under radar similarity constraint. Unlike the traditional DFRC systems which ignore feedforward overhead for conveying transmit waveform control information, we assume the existence of a low-rate feedforward channel between the DFRC transceiver and the communications receiver. Relying on the covariance matrix of linear minimum mean-squared-error (LMMSE) estimates of input symbols, we derive a novel communications metric as a function of both subcarrier powers and forwarded control information, and propose a joint waveform and control signaling optimization (JWCSO) strategy that leverages the sparsity and rank-one structure of DFRC waveforms within an alternating maximization framework. Simulation results show that the proposed JWCSO approach provides significant performance gains over the conventional feedforward-agnostic waveforms and achieves near-optimal radar-communications trade-off performance, reaching the boundary of the CRB-capacity region with only a limited feedforward information.
67 citations
TL;DR: This article considers an IRS-aided multiuser THz MIMO system with orthogonal frequency-division multiple (OFDM) access, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption.
Abstract: Terahertz (THz) communication has been regarded as one promising technology to enhance the transmission capacity of future Internet-of-Things (IoT) users due to its ultrawide bandwidth. Nonetheless, one major obstacle that prevents the actual deployment of THz lies in its inherent huge attenuation. Intelligent reflecting surface (IRS) and multiple-input–multiple-output (MIMO) represent two effective solutions for compensating the large path loss in THz systems. In this article, we consider an IRS-aided multiuser THz MIMO system with orthogonal frequency-division multiple (OFDM) access, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption. The objective is to maximize the weighted sum rate via jointly optimizing the hybrid analog/digital beamforming at the base station (BS) and reflection matrix at the IRS. Since the analog beamforming and reflection matrix need to cater all users and subcarriers, it is difficult to directly solve the formulated problem, and thus, an alternatively iterative optimization algorithm is proposed. Specifically, the analog beamforming is designed by solving a MIMO capacity maximization problem, while the digital beamforming and reflection matrix optimization are both tackled using semidefinite relaxation (SDR) technique. Considering that obtaining perfect channel state information (CSI) is a challenging task in IRS-based systems, we further explore the case with the imperfect CSI for the channels from the IRS to users. Under this setup, we propose a robust beamforming and reflection matrix design scheme for the originally formulated nonconvex optimization problem. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithms.
66 citations
TL;DR: It is pointed out that a widely-used assumption, that wireless channels can be considered to be sparse, has pitfalls and a sparse channel estimator cannot guarantee stable estimation accuracy even in channels with a high degree of sparsity, and considerable performance degradation will occur if a channel changes to non-sparse.
Abstract: Sparse channel arises in a number of applications in wireless communications such as channel estimation and signal processing. There is growing evidence that physical wireless channels exhibit a sparse structure, and channel sparsity has been even considered as a nature of channels in many recent research works. However, there still lacks a good measure of channel sparsity, and mostly the assumptions that channel is sparse or non-sparse are based on intuitive analysis without measurement validation, which leads to some contradictions. In this article, based on channel measurement data, it is pointed out that a widely-used assumption, that wireless channels can be considered to be sparse, has pitfalls. Without loss of generality, the measurements are conducted in an urban scenario with different degrees of channel multipath richness. The channel degrees of freedom, diversity measure, and the Ricean K factor are used to evaluate channel sparsity, and they are found to have fairly high accuracy of measuring degrees of channel sparsity. It is also observed from measurements that the degree of channel sparsity is not steady and a sparse channel may change to non-sparse within a short time/distance observation window. Moreover, sparse and non-sparse based channel estimators are evaluated based on the measurements and the performances are analyzed. The results show that a sparse channel estimator cannot guarantee stable estimation accuracy even in channels with a high degree of sparsity, and considerable performance degradation will occur if a channel changes to non-sparse, which actually often happens in realistic communication scenarios and should be carefully considered in performance analysis. Some sparse channel related technical issues are also discussed in the article.
66 citations
TL;DR: This letter proposes a fast channel estimation scheme with reduced OFDM symbol duration for arbitrary frequency-selective fading channels and proposes a new scheme based on the novel concept of sampling-wise IRS reflection variation.
Abstract: In this letter, we study efficient channel estimation for an intelligent reflecting surface (IRS)-assisted orthogonal frequency division multiplexing (OFDM) system to achieve minimum training time. First, a fast channel estimation scheme with reduced OFDM symbol duration is proposed for arbitrary frequency-selective fading channels. Next, under the typical condition that the IRS-user channel is line-of-sight (LoS) dominant, another fast channel estimation scheme based on the novel concept of sampling-wise IRS reflection variation is proposed. Moreover, the pilot signal and IRS training reflection pattern are jointly optimized for both proposed schemes. Finally, the proposed schemes are compared in terms of training time and channel estimation performance via simulations, as well as against benchmark schemes.
TL;DR: The trade-off between communication and sensing is investigated, which indicates that the lower bounds can be improved at the cost of the communication capacity.
Abstract: Joint communication and sensing (JCAS) is an emerging technology for managing efficiently the scarce radio frequency (RF) spectrum, and is expected to be a key ingredient in beyond fifth-generation (5G) networks. We consider a JCAS system, where the full-duplex radar transceiver and the communication transmitter are the same device, and pursue orthogonal frequency-division multiplexing (OFDM) waveform optimization by jointly minimizing the lower bounds of delay and Doppler estimation. This is attained by filling the empty subcarriers within the OFDM frame with optimized samples while reallocating a proportion of the communication subcarriers’ power, which essentially controls the fairness between the two functionalities. Both communication and filled radar subcarriers are used for radar processing. The optimized sample values are found analytically, and a computationally feasible algorithm is presented for this task. We also address how the peak-to-average power ratio of the waveform can be controlled and minimized along the optimization process. The results are then numerically evaluated in 5G New Radio (NR) network context, which indicate a trade-off between the minimization of the lower bounds. The main-lobe width and the peak side-lobe level (PSL) of the range and velocity profiles of the radar image are also analyzed. An inverse relation between the lower bounds and the PSLs is observed, while the main-lobe width can be minimized simultaneously. The trade-off between communication and sensing is investigated, which indicates that the lower bounds can be improved at the cost of the communication capacity. Moreover, over-the-air RF measurements are carried out with unoptimized and optimized 5G NR waveforms at the 28GHz mm-wave band, to validate the range profile’s PSL improvement in an outdoor mapping scenario, depicting considerable performance gain.
TL;DR: A deep fully convolutional neural network, DeepRx is proposed, which executes the whole receiver pipeline from frequency domain signal stream to uncoded bits in a 5G-compliant fashion and outperforms traditional methods.
Abstract: Deep learning has solved many problems that are out of reach of heuristic algorithms. It has also been successfully applied in wireless communications, even though the current radio systems are well-understood and optimal algorithms exist for many tasks. While some gains have been obtained by learning individual parts of a receiver, a better approach is to jointly learn the whole receiver. This, however, often results in a challenging nonlinear problem, for which the optimal solution is infeasible to implement. To this end, we propose a deep fully convolutional neural network, DeepRx, which executes the whole receiver pipeline from frequency domain signal stream to uncoded bits in a 5G-compliant fashion. We facilitate accurate channel estimation by constructing the input of the convolutional neural network in a very specific manner using both the data and pilot symbols. Also, DeepRx outputs soft bits that are compatible with the channel coding used in 5G systems. Using 3GPP-defined channel models, we demonstrate that DeepRx outperforms traditional methods. We also show that the high performance can likely be attributed to DeepRx learning to utilize the known constellation points of the unknown data symbols, together with the local symbol distribution, for improved detection accuracy.
TL;DR: Simulation results demonstrate a significant performance improvement for OTFS modulation over the conventional orthogonal frequency division multiplexing (OFDM) modulation over high-mobility channels.
Abstract: Orthogonal time frequency space (OTFS) modulation is a recently developed multi-carrier multi-slot transmission scheme for wireless communications in high-mobility environments. In this paper, the error performance of coded OTFS modulation over high-mobility channels is investigated. We start from the study of conditional pairwise-error probability (PEP) of the OTFS scheme, based on which its performance upper bound of the coded OTFS system is derived. Then, we show that the coding improvement for OTFS systems depends on the squared Euclidean distance among codeword pairs and the number of independent resolvable paths of the channel. More importantly, we show that there exists a fundamental trade-off between the coding gain and the diversity gain for OTFS systems, i.e., the diversity gain of OTFS systems improves with the number of resolvable paths, while the coding gain declines. Furthermore, based on our analysis, the impact of channel coding parameters on the performance of the coded OTFS systems is unveiled. The error performance of various coded OTFS systems over high-mobility channels is then evaluated. Simulation results demonstrate a significant performance improvement for OTFS modulation over the conventional orthogonal frequency division multiplexing (OFDM) modulation over high-mobility channels. Analytical results and the effectiveness of the proposed code design are also verified by simulations with the application of both classical and modern codes for OTFS systems.
01 Feb 2021
TL;DR: In this article, the authors examined the flexible utilization of existing IM techniques in a comprehensive manner to satisfy the challenging and diverse requirements of 5G and beyond services, and developed a framework that investigates the efficient utilization of these techniques and establishes a link between the IM schemes and 5G services, namely, enhanced mobile broadband (eMBB), massive machine type communications (mMTCs), and ultrareliable low-latency communication (URLLC).
Abstract: Index modulation (IM) provides a novel way for the transmission of additional data bits via the indices of the available transmit entities compared with classical communication schemes. This study examines the flexible utilization of existing IM techniques in a comprehensive manner to satisfy the challenging and diverse requirements of 5G and beyond services. After spatial modulation (SM), which transmits information bits through antenna indices, application of IM to orthogonal frequency-division multiplexing (OFDM) subcarriers has opened the door for the extension of IM into different dimensions, such as radio frequency (RF) mirrors, time slots, codes, and dispersion matrices. Recent studies have introduced the concept of multidimensional IM by various combinations of 1-D IM techniques to provide higher spectral efficiency (SE) and better bit error rate (BER) performance at the expense of higher transmitter (Tx) and receiver (Rx) complexity. Despite the ongoing research on the design of new IM techniques and their implementation challenges, proper use of the available IM techniques to address different requirements of 5G and beyond networks is an open research area in the literature. For this reason, we first provide the dimensional-based categorization of available IM domains and review the existing IM types regarding this categorization. Then, we develop a framework that investigates the efficient utilization of these techniques and establishes a link between the IM schemes and 5G services, namely, enhanced mobile broadband (eMBB), massive machine-type communications (mMTCs), and ultrareliable low-latency communication (URLLC). In addition, this work defines key performance indicators (KPIs) to quantify the advantages and disadvantages of IM techniques in time, frequency, space, and code dimensions. Finally, future recommendations are given regarding the design of flexible IM-based communication systems for 5G and beyond wireless networks.
TL;DR: This study indicates that the widespread IDI incurs a computational burden for the element-wise detector like the message passing in the state-of-the-art works, and proposes a block-wise OTFS receiver by exploiting the structure and characteristics of the O TFS transmission matrix.
Abstract: Orthogonal time frequency space (OTFS) is a two-dimensional modulation scheme realized in the delay-Doppler domain, which targets the robust wireless transmissions in high-mobility environments. In such scenarios, OTFS signal suffers from multipath channel with continuous Doppler spread, which results in significant inter-symbol interference and inter-Doppler interference (IDI). In this article, we analyze the interference generation mechanism, and compare statistical distributions of the IDI in two typical cases, i.e., limited-Doppler-shift channel and continuous-Doppler-spread channel (CoDSC). Focusing on the OTFS signal transmission over the CoDSC, our study firstly indicates that the widespread IDI incurs a computational burden for the element-wise detector like the message passing in the state-of-the-art works. Addressing this challenge, we propose a block-wise OTFS receiver by exploiting the structure and characteristics of the OTFS transmission matrix. In the receiver, we deliberately design an iteration strategy among the least squares minimum residual based channel equalizer, reliability-based symbol detector and interference eliminator, which can realize fast convergence by leveraging the sparsity of channel matrix. The simulations demonstrate that, in the CoDSC, the proposed scheme achieves much less detection error, and meanwhile reduces the computational complexity by an order of magnitude, compared with the state-of-the-art OTFS receivers.
TL;DR: This paper proposes a novel OTFS based multi-user precoder at the base station and a corresponding low complexity detector (LCD) at the user terminals (UTs), which allows for separate demodulation of each DD domain information symbol at the UT.
Abstract: We consider the problem of degradation in performance of multi-carrier multi-user massive MIMO systems when channel induced Doppler spread is high. Recently, Orthogonal Time Frequency Space (OTFS) modulation has been shown to be robust to channel induced Doppler spread. In OTFS based systems, information symbols are embedded in the delay-Doppler (DD) domain where they are jointly modulated to generate the time-domain transmit signal. Due to the multi-path delay and Doppler shifts, the DD domain information symbols need to be jointly demodulated at the receiver. For multi-carrier based communication (e.g., Orthogonal Frequency Division Multiplexing (OFDM)), massive MIMO systems have been shown to achieve high spectral and energy efficiency with low complexity multi-user precoding in the downlink. Extending the same to OTFS based downlink multi-user massive MIMO systems is challenging due to the requirement for joint demodulation of all information symbols at the user terminal (UT). In this paper, we solve this problem by proposing a novel OTFS based multi-user precoder at the base station (BS) and a corresponding low complexity detector (LCD) at the user terminals (UTs), which allows for separate demodulation of each DD domain information symbol at the UT. The complexity of the proposed precoder increases only linearly with increasing number of BS antennas $Q$ and the number of UTs. We show, through analysis, that as $Q$ increases (with total transmitted power decreased linearly with $Q$ ), the proposed low complexity detector achieves a sum spectral efficiency close to that achieved with optimal joint demodulation at each UT. Numerical simulations confirm our analysis and also show that the spectral efficiency and error rate performance of the proposed OTFS based massive MIMO precoder (with the proposed LCD detector at each UT) is significantly more robust to channel induced Doppler spread when compared to OFDM based multi-user massive MIMO systems.
TL;DR: In this article, the authors present RF-impairment signal models and discuss their impacts, including phase noise (PN), carrier frequency offset (CFO), and in-phase (I) and quadrature-phase imbalance.
Abstract: 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.
TL;DR: A generalized QSM (GQSM) scheme to further increase the SE of QSM by activating more than one transmit antenna in in-phase or quadrature domain is proposed and a low-complexity detection scheme for GQSM is provided to mitigate the detection burden of the optimal maximum-likelihood (ML) detection method.
Abstract: Quadrature spatial modulation (QSM) is recently proposed to increase the spectral efficiency (SE) of SM, which extends the transmitted symbols into in-phase and quadrature domains. In this paper, we propose a generalized QSM (GQSM) scheme to further increase the SE of QSM by activating more than one transmit antenna in in-phase or quadrature domain. A low-complexity detection scheme for GQSM is provided to mitigate the detection burden of the optimal maximum-likelihood (ML) detection method. An upper bounded bit error rate is analyzed to discover the system performance of GQSM. Moreover, by collaborating with the non-orthogonal multiple access (NOMA) technique, we investigate the practical application of GQSM to cooperative vehicular networks and propose the cooperative GQSM with OMA (C-OMA-GQSM) and cooperative GQSM with NOMA (C-NOMA-GQSM) schemes. Computer simulation results verify the reliability of the proposed low-complexity detection as well as the theoretical analysis, and show that GQSM outperforms QSM in the entire SNR region. The superior BER performance of the proposed C-NOMA-GQSM scheme make it a promising modulation candidate for next generation vehicular networks.
TL;DR: It is demonstrated that it is eminently suitable for intensity-modulation and direct-detection aided optical communication systems and characterize its design flexibility, and highlighted a suite of promising techniques capable of further improving the system performance, but require further research.
Abstract: Optical Orthogonal Frequency-Division Multiplexing (O-OFDM) is eminently suitable for mitigating the multi-path and chromatic dispersion in both Visible Light Communications (VLC) and Optical Fiber Communications. We commence our discourse by surveying the conception and historic evolution of O-OFDM designed for both VLC and optical fiber, culminating in the birth of its most flexible design alternative, namely Layered Asymmetrically Clipped Optical OFDM (LACO-OFDM). We demonstrate that it is eminently suitable for intensity-modulation and direct-detection aided optical communication systems and characterize its design flexibility. It is also shown that given its flexibility, it subsumes a wide range of optical OFDM schemes conceived over the past two decades or so. The LACO-OFDM transmitter and receiver designs strike a compelling compromise between the features of the popular Asymmetrically Clipped Optical OFDM (ACO-OFDM) and Direct-Current-biased Optical OFDM (DCO-OFDM). The pivotal role of forward error correction designs is also surveyed with the objective of striking a coding gain versus complexity trade-off. We conclude by highlighting a suite of promising techniques capable of further improving the system performance, but require further research. The take-away message of the paper crystallized in the associated design guidelines.
TL;DR: Simulation results indicate that the proposed schemes outperform the traditional counterparts in terms of accuracy, robustness and complexity, especially for the case of low-complexity IRSs with limited number of active sensing elements.
Abstract: We consider the channel estimation problem in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems assisted by intelligent reconfigurable surfaces (IRSs). To avoid the inherent estimation ambiguities of the two-hop channels from mobile stations (MS) to the base station (BS), we adopt a hybrid IRS architecture composed of passive reflectors and active sensors, and establish two independent subproblems of estimating the MS-to-IRS and BS-to-IRS channels. By leveraging the sparse characteristics of high-frequency propagation, we model the training signals as multi-dimensional canonical polyadic decomposition (CPD) tensors with missing fibers or slices. We develop algebraic algorithms to solve the tensor completion problems and recover channel multipath parameters, i.e., angles of arrival, time delays and path gains. Our methods require neither random initialization nor iterative operations, and for these reasons they can perform robustly with a low computational complexity. Moreover, we investigate the uniqueness condition of CPD tensor completion, which can be utilized to inform both the physical design of hybrid IRSs and the time-frequency resource allocation of training strategies. Simulation results indicate that the proposed schemes outperform the traditional counterparts in terms of accuracy, robustness and complexity, especially for the case of low-complexity IRSs with limited number of active sensing elements.
TL;DR: It is shown that the CD-OFDM J CS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in a low signal-to-interference-and-noise ratio regime.
Abstract: The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar-sensing signals to improve the communication reliability and radar-sensing accuracy, we propose a novel code-division orthogonal frequency-division multiplex (CD-OFDM) JCS MTC system, where MTC users can simultaneously and continuously conduct communication and sensing with each other. We propose a novel CD-OFDM JCS signal and corresponding successive-interference-cancelation-based signal processing technique that obtains code-division multiplex gain, which is compatible with the prevalent orthogonal frequency-division multiplex (OFDM) communication system. To model the unified JCS signal transmission and reception process, we propose a novel unified JCS channel model. Finally, the simulation and numerical results are shown to verify the feasibility of the CD-OFDM JCS MTC system and the error propagation performance. We show that the CD-OFDM JCS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in a low signal-to-interference-and-noise ratio regime.
TL;DR: Radio-over-fiber (RoF) transmission over single mode fiber (SMF) is experimentally implemented and tested for link lengths ranging from 100 m up to 10 km with injected PoF signals up to 2 W.
Abstract: We propose using power-over-fiber (PoF) in some part of future 5G cellular solutions based on radio access networks considering currently installed front-haul solutions with single mode fiber to optically power communication systems for 5G new radio (NR) data transmission. Simulations addressing design parameters are presented. Radio-over-fiber (RoF) transmission over single mode fiber (SMF) is experimentally implemented and tested for link lengths ranging from 100 m up to 10 km with injected PoF signals up to 2 W. 64QAM, 16QAM and QPSK data traffic of 100 MHz bandwidth are transmitted simultaneously with the PoF signal showing an EVM compliant with 5G NR standard, and up to 0.5 W for 256QAM. EVM of 4.3% is achieved with RF signal of 20 GHz and QPSK modulation format in coexistence with delivering 870 mW of optical power to a photovoltaic cell (PV) after 10 km-long SMF link. Using PoF technology to optically powering remote units and Internet-of-Things (IoT) solutions based on RoF links is also discussed.
TL;DR: A single-phase detector based on a message-passing algorithm (MPA) to detect multiple users’ symbols for the uplink of the OTFS system is proposed and an algorithm is proposed to devise an optimal codeword allocation scheme.
Abstract: Orthogonal time frequency space (OTFS) modulation is a two-dimensional (2-D) modulation technique that has the potential to overcome the challenges faced by orthogonal frequency division multiplexing (OFDM) in high Doppler environments. The performance of OTFS in a multi-user scenario with orthogonal multiple access (OMA) techniques has been impressive. Due to the requirement of massive connectivity in 5G and beyond, it is essential to devise and examine the OTFS system with the existing non-orthogonal multiple access (NOMA) techniques. This paper proposes a multi-user OTFS system based on a code-domain NOMA technique called sparse code multiple access (SCMA). This system is referred to as the OTFS-SCMA model. The framework for OTFS-SCMA is designed for both downlink and uplink. First, the sparse SCMA codewords are strategically placed on the delay-Doppler plane. The overall overloading factor of the OTFS-SCMA system is equal to that of the underlying basic SCMA system. The receiver in downlink performs the detection in two sequential phases: first, the conventional OTFS detection using the method of linear minimum mean square error (LMMSE) estimation, and then the SCMA detection. We propose a single-phase detector based on a message-passing algorithm (MPA) to detect multiple users’ symbols for the uplink. The expressions for the asymptotic diversity orders of the proposed OTFS-SCMA system are derived for downlink and uplink. OTFS-SCMA provides a significant diversity gain over other multiple access systems for OTFS. Based on the diversity analysis, an algorithm is proposed to devise an optimal codeword allocation scheme. The performance of the proposed OTFS-SCMA system is validated through extensive simulations both in downlink and uplink. We consider delay-Doppler planes of different parameters and various SCMA systems of overloading factor up to 200%. The performance of OTFS-SCMA is compared with those of the existing OTFS-OMA, OFDM-SCMA and OTFS-power-domain (PD)-NOMA techniques. The analysis of OTFS-SCMA with channel estimation is also presented along with the BER performance.
TL;DR: In this article, the authors proposed a finite-time chaos synchronization approach for secure communication of satellite imaging in orthogonal frequency-division multiplexing wireless networks, where chaotic oscillators are considered in both the transmitter and receiver ends to generate the chaotic encryption/decryption keys.
Abstract: This paper proposes a finite time chaos synchronization approach for the secure communication of satellite imaging. To this end, chaotic oscillators are considered in both the transmitter and receiver ends to generate the chaotic encryption/decryption keys. To mitigate the non-negligible channel time-delay between the receiver and transmitter, we propose a robust controller design. The proposed approach is designed based on the Lyapunov stability theory and the finite-time synchronization concept to attain finite time synchronization in a time-delayed channel. By using synchronized chaotic keys, a physical-layer chaotic encryption scheme for transmitting the satellite images is designed in orthogonal frequency-division multiplexing wireless network. The proposed chaotic-based satellite image encryption/decryption system is validated using a numerical simulation study. Additionally, to analyse the robustness and demonstrate the efficiency of the proposed chaotic encryption structure, a set of security analysis tools such as histogram analysis, key space analysis, correlation test, information entropy and other statistical analysis were performed.
TL;DR: A low-complexity sparse adaptive CE scheme is proposed that is based on a dynamic threshold that reduces the number of inner product calculations by considering only the columns of the measurement matrix greater than the threshold.
Abstract: Industrial applications can produce significant amounts of data that require low delay and high data rate communications. Multiple-input–multiple-output filter bank multicarrier (MIMO-FBMC) communications employing offset quadrature amplitude modulation has been proposed for industrial big data due to its reliability and high spectrum efficiency. One of the difficulties in implementing a MIMO-FBMC system is accurate channel estimation (CE). The main factor affecting the CE performance is intrinsic imaginary interference, and the conventional preamble-based CE is not effective in this case. Thus, in this article, a low-complexity sparse adaptive CE scheme is proposed that is based on a dynamic threshold. This reduces the number of inner product calculations by considering only the columns of the measurement matrix greater than the threshold. Simulation results are presented that show that the proposed scheme is better than other well-known methods in terms of computational complexity and CE accuracy.
TL;DR: A channel estimation scheme based on least square (LS) estimation with partial on-off and super-resolution (SR) network and the advantages of this proposed SR channel estimation compared with benchmark schemes are demonstrated.
Abstract: To solve the blockage effect in millimeter wave (mmWave) communication, intelligent reflecting surface (IRS) is introduced to create additional links and enhance the system performance, by properly optimizing the IRS phase shifts based on the channel state information (CSI). However, channel estimation in IRS-enhanced mmWave system is challenging, since IRS is unable to perform signal processing and the large number of reflecting elements of IRS leads to high complexity. To reduce the overhead and obtain accurate CSI, we propose a channel estimation scheme based on least square (LS) estimation with partial on-off and super-resolution (SR) network. Specifically, we switch on part of the reflecting elements and estimate the cascaded channel matrix, which can be considered as a low-resolution (LR) image with low-precision. Then it is expanded to a high-resolution (HR) image with low-precision by linear interpolation. Furthermore, we feed this HR image into an SR network to improve the estimation accuracy. Numerical results demonstrate the advantages of our proposed SR channel estimation compared with benchmark schemes.
TL;DR: In this article, the authors proposed a channel state information (CSI) acquisition scheme for downlink massive MIMO-OTFS in presence of the fractional Doppler, including deterministic pilot design and channel estimation algorithm.
Abstract: Although the combination of the orthogonal time frequency space (OTFS) modulation and the massive multiple-input multiple-output (MIMO) technology can make communication systems perform better in high-mobility scenarios, there are still many challenges in downlink channel estimation owing to inaccurate modeling and high pilot overhead in practical systems. In this paper, we propose a channel state information (CSI) acquisition scheme for downlink massive MIMO-OTFS in presence of the fractional Doppler, including deterministic pilot design and channel estimation algorithm. First, we analyze the input-output relationship of the single-input single-output (SISO) OTFS based on the orthogonal frequency division multiplexing (OFDM) modem and extend it to massive MIMO-OTFS. Moreover, we formulate an accurate model for the practical system in which the fractional Doppler is considered and the influence of subpaths is revealed. A deterministic pilot design is then proposed based on the model and the structure of the pilot matrix to reduce pilot overhead and save memory consumption. Since channel geometry changes very slowly relative to the communication timescale, we put forward a modified sensing matrix based channel estimation (MSMCE) algorithm to acquire the downlink CSI. Simulation results demonstrate that the proposed downlink CSI acquisition scheme has significant advantages over traditional algorithms.
TL;DR: The proposed NN architecture exploits fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers.
Abstract: With the large number of antennas and subcarriers the overhead due to pilot transmission for channel estimation can be prohibitive in wideband massive multiple-input multiple-output (MIMO) systems. This can degrade the overall spectral efficiency significantly, and as a result, curtail the potential benefits of massive MIMO. In this paper, we propose a neural network (NN)-based joint pilot design and downlink channel estimation scheme for frequency division duplex (FDD) MIMO orthogonal frequency division multiplex (OFDM) systems. The proposed NN architecture uses fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers. Our proposed NN architecture uses a non-local attention module to learn longer range correlations in the channel matrix to further improve the channel estimation performance.We also propose an effective pilot reduction technique by gradually pruning less significant neurons from the dense NN layers during training. This constitutes a novel application of NN pruning to reduce the pilot transmission overhead. Our pruning-based pilot reduction technique reduces the overhead by allocating pilots across subcarriers non-uniformly and exploiting the inter-frequency and inter-antenna correlations in the channel matrix efficiently through convolutional layers and attention module.
TL;DR: In this paper, a Support Vector Machine (SVM) based joint channel estimation and data detection (CE-DD) method is proposed for massive MIMO systems with one-bit ADCs, which makes use of both to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance.
Abstract: The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine ( SVM ), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones.