Showing papers on "Orthogonal frequency-division multiplexing published in 2022"

Channel Estimation With Reconfigurable Intelligent Surfaces—A General Framework

Abstract: Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds.

Constellation Shaping Chaotic Encryption Scheme With Controllable Statistical Distribution for OFDM-PON

Abstract: In order to improve the signal transmission performance and physical layer security in orthogonal frequency division multiplexing-based passive optical network (OFDM-PON), we propose a constellation shaping chaotic encryption (CSCEn) scheme with controllable statistical distribution. In this scheme, a q uadrature amplitude modulation (QAM) symbol sequence is divided into several sub-sequences, and a further probabilistic shaping (PS) is performed by constellation region replacement in terms of the corresponding statistical information ( SI ). Then the sequence SI is encoded and encrypted into chaotic signal phases by employing our key distribution algorithm. At the receiver, the SI is extracted first to restore the original signal . To verify the effectiveness of this scheme, we successfully demonstrate an encrypted PS-16-QAM signal transmission over a 25-km standard single mode fiber (SSMF). The results show that the proposed scheme can flexibly improve bit error rate (BER) performance with low deployment complexity and provide sufficient security to resist attacks from illegal optical network units ( ONUs), thus it displays a promising application prospect for future secure PON.

Achievable Rate Upper-Bounds of Uplink Multiuser OTFS Transmissions

Abstract: In this letter, we study the achievable rates adopting two delay-Doppler (DD) domain multiples access (MA) schemes for uplink orthogonal time frequency space (OTFS) transmissions, namely delay division multiple access (DDMA) and Doppler division multiple access (DoDMA). To shed light on the system performance, we establish the achievable rate upper-bounds for both DDMA and DoDMA in comparisons to that of the conventional orthogonal frequency division multiple access (OFDMA). In particular, we show that both DDMA and DoDMA have more robust signal-to-interference-plus-noise ratio (SINR) performances against channel fluctuations compared to OFDMA and this robustness leads to higher achievable rates for both DDMA and DoDMA. To be more specific, we find that the achievable rate upper-bounds for OTFS depend only on the channel fading coefficients of different paths and the multi-user interference (MUI), while the delay and Doppler shifts also have significant impacts on that of the conventional OFDMA. Our simulation results are consistent with our derivations and demonstrate a noticeable improvement of the achievable rates for both DDMA and DoDMA over OFDMA.

An Overview of OTFS for Internet of Things: Concepts, Benefits, and Challenges

Abstract: The Internet of Things (IoT) is envisioned to connect everything, spanning from terrestrial to nonterrestrial terminals, where reliable communication is expected to be allowed in both time-invariant and time-variant wireless channels. Since classic orthogonal frequency-division multiplexing (OFDM) modulation, which has been widely used in both the fourth-generation (4G) and the fifth-generation (5G) cellular systems, is sensitive to high Doppler effect, it is challenging to satisfy the ever-growing demands of future IoT. To circumvent this issue, the orthogonal time–frequency space (OTFS) scheme is proposed, which modulates the information bits in both the delay and the Doppler domains, and exhibits beneficial advantages in both static and high-mobility wireless channel scenarios. In this article, we present a comprehensive overview of OTFS for IoT, including the current transceiver design, the potential benefits, the challenge issues, as well as future design guidelines.

High-Speed Terahertz Band Radio-Over-Fiber System Using Hybrid Time-Frequency Domain Equalization

Abstract: The radio-over-fiber (RoF) orthogonal frequency division multiplexing (OFDM) system at Terahertz (THz) band has been a promising solution to meet the demand for high capacity and flexibility. The signals transmitted in the RoF system will suffer severe impairments caused by the wireless multi-path effect, optical fiber transmission, and photoelectric device response. The time-frequency domain equalization (TFDE) based on the two-dimension convolution (Conv2D) neural network (NN) is proposed to resist the linear and nonlinear impairments of the RoF-OFDM system. Furthermore, complex convolution is applied to compensate for the complex channel response. Compared with several baseline models, the detailed performance improvement and model size discussion of the complex-valued Conv2D NN TFDE are investigated. With the aid of the complex-valued Conv2D NN TFDE, the 16 Gbaud 16-ary quadrature amplitude modulation (16QAM) signals are delivered over 54.6 m wireless distance and 20 km standard single mode fiber. The line rate of this RoF system achieves 53.5 Gbit/s.

Secure key distribution and synchronization method in an OFDM-PON based on chaos.

Abstract: A physical layer key distribution scheme based on chaotic encryption and signal synchronization is proposed in this paper, which can achieve secure key distribution and enhance the security of an orthogonal frequency division multiplexing based passive optical network (OFDM-PON). The key is embedded into the synchronization header and then encrypted by using chaos. The receiver needs to utilize the correct chaotic parameters to successfully decrypt the synchronization information and extract the key. An experiment is conducted to verify the availability of this method by setting key sequences of various length over different transmission distances. The signals of 35.29 Gb/s are successfully transmitted over 5 km, 15 km and 25 km standard single-mode fiber (SSMF), respectively. It is proved that the proposed scheme is feasible and compatible with the traditional encryption algorithms, and it has almost no effect on the synchronization performance, which can then distribute keys with the sending signals without occupying additional channel resources and enhance the security performance of OFDM-PON simultaneously.

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

Abstract: 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 Schur 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.

MIMO-OFDM Modulation Classification Using Three-Dimensional Convolutional Network

Abstract: Automatic modulation classification (AMC) plays a vital role in cognitive radio to improve spectrum utilization efficiency, however, most of the existing works have focused on single-carrier communications in single-input single-output systems. In this paper, we propose an efficient AMC method for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) communication systems with the assumption of unknown frequency-selective fading channels and signal-to-noise ratio. At the receiver, the complex envelope samples of a burst signal acquired by multiple antennas are decomposed into in-phase and quadrature samples, which are then structured into a high-dimensional data array. To learn the modulation patterns from received signals, we develop a deep network, namely three-dimensional MIMO-OFDM convolutional neural network (MONet). With cuboidal convolution filters, the proposed MONet allows the network to capture underlying features as intra- and inter-antenna correlations at multi-scale signal representations. Relying on simulations, MONet achieves the classification accuracy of over 95% at 0 dB SNR under various channel impairments and shows the robustness with different MIMO antenna configurations.

End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication

Abstract: The benefits of end-to-end learning has been demonstrated over AWGN channels but has not yet been quantified over realistic wireless channel models. This work aims to fill this gap by exploring the gains of end-to-end learning over a frequency- and time-selective fading channel using OFDM. With imperfect channel knowledge at the receiver, the shaping gains observed on AWGN channels vanish. Nonetheless, we identify two other sources of performance improvements. The first comes from a neural network-based receiver operating over a large number of subcarriers and OFDM symbols which allows to reduce the number of orthogonal pilots without loss of BER. The second comes from entirely eliminating orthogonal pilots by jointly learning a neural receiver together with either superimposed pilots (SIPs), combined with conventional QAM, or an optimized constellation. The learned constellation works for a wide range of signal-to-noise ratios, Doppler and delay spreads, has zero mean and does hence not contain any form of SIP. Both schemes achieve the same BER as the pilot-based baseline with 7% higher throughput. Thus, we believe that a jointly learned transmitter and receiver are a very interesting component for beyond-5G communication systems which could remove the need and associated overhead for demodulation reference signals.

Outage Probability for OTFS Based Downlink LEO Satellite Communication

Abstract: As an important technique of enabling global access for 6G, low earth orbit satellite (LEO-Sat) communication is still facing the challenges of large path-loss, and severe Doppler effect. This paper studies the reliability performance of a downlink LEO-Sat communication system, where the orthogonal time frequency space (OTFS) scheme is employed to combat severe Doppler effect. Further, the unmanned aerial vehicle (UAV) based cooperative transmission is developed, in order to compensate for the large margin of path-loss caused by the long transmission distance. In particular, the closed-form expression for the outage probability of the OTFS based LEO-Sat transmission is derived, where the novel moment matching approach is used to closely approximate the PDF of a sum of shadowing Rician (SR) variables. Further, the condition of using UAV cooperation is obtained so that the positive reliability gain is guaranteed. Finally, both the theoretical analysis and simulation results show that, the OTFS scheme can significantly outperform the traditional OFDM scheme, while demanding to carefully address the trade-off between reliability improvement and implementation complexity determined by modulation size.

Joint Energy and Correlation Detection Assisted Non-Coherent OFDM-DCSK System for Underwater Acoustic Communications

Abstract: A joint energy and correlation detection aided orthogonal frequency division multiplexing differential chaos shift keying (JECD-OFDM-DCSK) system is presented in this paper, where the reference and information-bearing signals of JECD-OFDM-DCSK are superimposed in the same time slot so that the phase offset between the reference and information-bearing signals is addressed. In JECD-OFDM-DCSK, only some subcarriers are activated and additional information bits are transmitted by the indices of these activated subcarriers, thereby improving the data rate while reducing the energy consumption. The energy efficiency, peak to average power ratio (PAPR) performance and system complexity of JECD-OFDM-DCSK are analyzed and then compared to other systems. The comparison results show JECD-OFDM-DCSK can obtain higher energy efficiency and better PAPR performance compared to its competitors at the cost of higher system complexity. Furthermore, the bit error rate (BER) expressions of JECD-OFDM-DCSK are derived in additive white Gaussian noise (AWGN) and multipath fading channels. Simulation results show that the BER performance of JECD-OFDM-DCSK outperforms that of other OFDM-based DCSK systems. Finally, JECD-OFDM-DCSK performs much better than other systems over the underwater acoustic (UWA) channel, which validates the robustness of JECD-OFDM-DCSK.

OFDM Radar and Communication Joint System Using Opto-Electronic Oscillator With Phase Noise Degradation Analysis and Mitigation

Abstract: Orthogonal frequency division multiplexing (OFDM) signal is a superior dual-functional waveform for the integration of radar sensing and communication in intelligent transportation. But the sensitivity to phase noise is a serious issue introducing interference and causing performance degradation during demodulation. In this paper, we explore the essential mechanism of the action and generation of phase noise through theoretical analysis, where the OFDM demodulation process and power spectrum density (PSD) of phase noise is discussed in the frequency domain, and draw the conclusion that high-speed phase jitter will cause unrecoverable deterioration of OFDM demodulation. Therefore, a photonics-aided radar and communication integrated system based on Optoelectronic oscillator (OEO) is proposed. The positive feedback oscillation with long energy storage time make the phase noise pattern of OEO just suitable to against the phase noise sensitivity of OFDM. A proof-of-concept experiment is demonstrated at 24 GHz with 2 GHz bandwidth to verify the radar sensing and communication function. A two-dimensional radar imaging with a range resolution of 0.075 m and velocity resolution of 4.4 km/h, a communication capacity of 6.4 Gbps is obtained. A quantitative performance comparison is also carried out. By using an ordinary microwave source and OEO separately, the demodulation constellation and error vector magnitude (EVM) under different subcarrier spacing is measured and compared. The result is corresponding to our analysis with the EVM decreasing from 12.5% to 4.7% under subcarrier spacing of 125 kHz.

Channel Estimation and User Localization for IRS-Assisted MIMO-OFDM Systems

Abstract: We consider the channel estimation problem and the channel-based wireless applications in multiple-input multiple-output orthogonal frequency division multiplexing systems assisted by intelligent reconfigurable surfaces (IRSs). To obtain the necessary channel parameters, i.e., angles, delays and gains, for environment mapping and user localization, we propose a novel twin-IRS structure consisting of two IRS planes with a relative spatial rotation. We model the training signal from the user equipment to the base station via IRSs as a third-order canonical polyadic tensor with a maximal tensor rank equal to the number of IRS unit cells. We present four designs of IRS training coefficients, i.e., random, structured, grouping and sparse patterns, and analyze the corresponding uniqueness conditions of channel estimation. We extract the cascaded channel parameters by leveraging array signal processing and atomic norm denoising techniques. Based on the characteristics of the twin-IRS structures, we formulate a nonlinear equation system to exactly recover the multipath parameters by two efficient decoupling modes. We realize environment mapping and user localization based on the estimated channel parameters. Simulation results indicate that the proposed twin-IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of user positioning.

Hybrid Beamforming for Intelligent Reflecting Surface Aided Millimeter Wave MIMO Systems

Abstract: As communication systems that employ millimeter wave (mmWave) frequency bands must use large antenna arrays to overcome the severe propagation loss of mmWave signals, hybrid beamforming has been considered as an integral component of mmWave communications. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative technology that can significantly improve the performance of mmWave communication systems through the use of low-cost passive reflecting elements. In this paper, we study IRS-aided mmWave multiple-input multiple-output (MIMO) systems with hybrid beamforming architectures. We first exploit the sparse-scattering structure and large dimension of mmWave channels to develop the joint design of IRS reflection matrix and hybrid beamformer for narrowband MIMO systems. Then, we generalize the proposed joint design to broadband MIMO systems with orthogonal frequency division multiplexing (OFDM) modulation by leveraging the angular sparsity of frequency-selective mmWave channels. Simulation results demonstrate that the proposed joint designs can significantly enhance the spectral efficiency of the systems of interest and achieve superior performance over the existing designs.

Joint Communications and Sensing Employing Multi- or Single-Carrier OFDM Communication Signals: A Tutorial on Sensing Methods, Recent Progress and a Novel Design

Abstract: Joint communications and sensing (JCAS) has recently attracted extensive attention due to its potential in substantially improving the cost, energy and spectral efficiency of Internet of Things (IoT) systems that need both radio frequency functions. Given the wide applicability of orthogonal frequency division multiplexing (OFDM) in modern communications, OFDM sensing has become one of the major research topics of JCAS. To raise the awareness of some critical yet long-overlooked issues that restrict the OFDM sensing capability, a comprehensive overview of OFDM sensing is provided first in this paper, and then a tutorial on the issues is presented. Moreover, some recent research efforts for addressing the issues are reviewed, with interesting designs and results highlighted. In addition, the redundancy in OFDM sensing signals is unveiled, on which, a novel method is based and developed in order to remove the redundancy by introducing efficient signal decimation. Corroborated by analysis and simulation results, the new method further reduces the sensing complexity over one of the most efficient methods to date, with a minimal impact on the sensing performance.

MIMO OFDM Dual-Function Radar-Communication Under Error Rate and Beampattern Constraints

Abstract: In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, which senses multiple spatial directions and serves multiple users. Upon resorting to an orthogonal frequency division multiplexing (OFDM) transmission format and a differential phase shift keying (DPSK) modulation, we study the design of the radiated waveforms and of the receive filters employed by the radar and the users. The approach is communication-centric, in the sense that a radar-oriented objective is optimized under constraints on the average transmit power, the power leakage towards specific directions, and the error rate of each user, thus safeguarding the communication quality of service (QoS). We adopt a unified design approach allowing a broad family of radar objectives, including both estimation- and detection-oriented merit functions. We devise a suboptimal solution based on alternating optimization of the involved variables, a convex restriction of the feasible search set, and minorization-maximization, offering a single algorithm for all of the radar merit functions in the considered family. Finally, the performance is inspected through numerical examples.

Message Passing-Based Impulsive Noise Mitigation and Channel Estimation for Underwater Acoustic OFDM Communications

Abstract: The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts.

OTFS vs. OFDM in the Presence of Sparsity: A Fair Comparison

Abstract: Many recent works in the literature declare that Orthogonal Time-Frequency-Space (OTFS) modulation is a promising candidate technology for high mobility communication scenarios. However, a truly fair comparison with its direct concurrent and widely used Orthogonal Frequency-Division Multiplexing (OFDM) modulation has not yet been provided. In this paper, we present such a fair comparison between the two digital modulation formats in terms of achievable communication rate. In this context, we explicitly address the problem of channel estimation by considering, for each modulation, a pilot scheme and the associated channel estimation algorithm specifically adapted to sparse channels in the Doppler-delay domain, targeting the optimization of the pilot overhead to maximize the overall achievable rate. In our achievable rate analysis we consider also the presence of a guard interval or cyclic prefix. The results are supported by numerical simulations, for different time-frequency selective channels including multiple scattering components and under non-perfect channel state information resulting from the considered pilot schemes. This work does not claim to establish in a fully definitive way which is the best modulation format, since such choice depends on many other features which are outside the scope of this work (e.g., legacy, intellectual property, ease and know-how for implementation, and many other criteria). Nevertheless, we provide the foundations to properly compare multi-carrier communication systems in terms of their information theoretic achievable rate potential, within meaningful and sensible assumptions on the channel models and on the receiver complexity (both in terms of channel estimation and in terms of soft-output symbol detection).

WDM Based 10.8 Gbps Visible Light Communication With Probabilistic Shaping

Abstract: In this work, we study probabilistic shaping (PS) for optical wireless communications and apply the technique to wavelength division multiplexing (WDM) based visible light communication (VLC). The performance of the proposed scheme is validated with an experimental demonstration. The experimental set up uses lenses to collimate the light beam for triple LEDs. The system parameters of the WDM based VLC system are then optimised, the channel response measured, and PS based symbols are allocated to the individual orthogonal frequency division multiplexing (OFDM) subcarriers. For the channel conditions under consideration, PS resulted in a near Shannon capacity transmission rate of 10.81 Gb/s. Comparatively, the PS resulted in 25% higher transmission rate than the widely used adaptive bit-power loading algorithm under the same channel conditions.

Performance Analysis of Massive MIMO-OFDM System Incorporated with Various Transforms for Image Communication in 5G Systems

Abstract: Modern-day applications of fifth-generation (5G) and sixth-generation (6G) systems require fast, efficient, and robust transmission of multimedia information over wireless communication medium for both mobile and fixed users. The hybrid amalgamation of massive multiple input multiple output (mMIMO) and orthogonal frequency division multiplexing (OFDM) proves to be an impressive methodology for fulfilling the needs of 5G and 6G users. In this paper, the performance of the hybrid combination of massive MIMO and OFDM schemes augmented with fast Fourier transform (FFT), fractional Fourier transform (FrFT) or discrete wavelet transform (DWT) is evaluated to study their potential for reliable image communication. The analysis is carried over the Rayleigh fading channels and M-ary phase-shift keying (M-PSK) modulation schemes. The parameters used in our analysis to assess the outcome of proposed versions of OFDM-mMIMO include signal-to-noise ratio (SNR) vs. peak signal-to-noise ratio (PSNR) and SNR vs. structural similarity index measure (SSIM) at the receiver. Our results indicate that massive MIMO systems incorporating FrFT and DWT can lead to higher PSNR and SSIM values for a given SNR and number of users, when compared with in contrast to FFT-based massive MIMO-OFDM systems under the same conditions.

Reservoir Computing Meets Extreme Learning Machine in Real-Time MIMO-OFDM Receive Processing

Abstract: In this paper, we consider a real-time deep learning-based symbol detection approach for MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the time-frequency structure of OFDM signals, a recurrent neural network (RNN) with deep feedforward output layers is introduced, where the recurrent layers and feedforward output layers are designed to process time-domain and frequency-domain information respectively. Reservoir computing (RC), a special type of RNN, and extreme learning machine (ELM), a special type of feedforward neural network, are chosen as the corresponding building blocks to facilitate over-the-air training. An online training loss objective is introduced to recursively update the neural weights in real-time. We believe this is the first work in the literature to realize real-time machine learning for MIMO-OFDM symbol detection, i.e., conducting NN-based symbol detection on an OFDM symbol basis. We demonstrate that (1) the IEEE standardized WiFi training sequence can be directly applied as the real-time training sequence (2) the symbol detection performance can be further improved by using our theoretically derived pilot pattern. Evaluation results show that our RC-ELM-based symbol detection method outperforms traditional model-based techniques as well as state-of-the-art learning-based approaches in highly dynamic channel environments for real-time symbol detection.

Learning-Based Channel Estimation and Phase Noise Compensation in Doubly-Selective Channels

Abstract: In this letter, we propose a learning based channel estimation scheme for orthogonal frequency division multiplexing (OFDM) systems in the presence of phase noise in doubly-selective fading channels. Two-dimensional (2D) convolutional neural networks (CNNs) are employed for effective training and tracking of channel variation in both frequency as well as time domain. The proposed network learns and estimates the channel coefficients in the entire time-frequency (TF) grid based on pilots sparsely populated in the TF grid. In order to make the network robust to phase noise (PN) impairment, a novel training scheme where the training data is rotated by random phases before being fed to the network is employed. Further, using the estimated channel coefficients, a simple and effective PN estimation and compensation scheme is devised. Numerical results demonstrate that the proposed network and PN compensation scheme achieve robust OFDM performance in the presence of phase noise.

A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO

Abstract: Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.

Opportunistic Navigation Using Sub-6 GHz 5G Downlink Signals: A Case Study on A Ground Vehicle

Abstract: A user equipment (UE)-based navigation framework that opportunistically exploits 5G signals is developed. The proposed framework exploits the “always on” 5G downlink signals in a time-domain-based receiver. To this end, a so-called ultimate synchronization signal (USS) is proposed to utilize the time-domain orthogonality of the orthogonal frequency division multiplexing (OFDM)-based 5G signals. This approach simplifies the receiver's complexity and enhances the performance of the 5G opportunistic navigation framework. Experimental results are presented to evaluate the efficacy of the proposed framework on a ground vehicle navigating in a suburban environment, while utilizing sub-6 GHz 5G signals from two gNBs. It is shown that while a state-of-the-art frequency-domain-based 5G opportunistic navigation receiver can only reliably track the gNBs' signals over a trajectory of 1.02 km traversed in 100 seconds, producing a position root mean-squared error (RMSE) of 14.93 m; the proposed time-domain-based receiver was able to track over a trajectory of 2.17 km traversed in 230 seconds, achieving a position RMSE of 9.71 m.

Design and Performance Analysis of Spatial-Index Modulation Based Orthogonal Time Frequency Space System

Abstract: In order to enhance the effectiveness and the reliability of high mobility communication, we propose a spatial-index modulation (SIM) based orthogonal time frequency space (OTFS) system, named SIM-OTFS, which is a three dimensional index modulation (IM) adopting the transmit antenna, delay, and Doppler indexes in the space and delay-Doppler domains, respectively, to achieve higher transmission rate. The system model and the detailed signal processing of the SIM-OTFS system are provided. Then, we also analyze the average bit error rate (ABER) performance of the proposed SIM-OTFS system based on the union bound theory. Numerical results verify the correctness of the theoretical ABER analysis and demonstrate the ABER superiority of the SIM-OTFS system over the traditional multiple-input multiple-output OTFS (MIMO-OTFS) and spatial modulation (SM) and IM based orthogonal frequency division multiplexing (SM-OFDM-IM) systems under high mobility. Furthermore, the influence of the multipath channel on the ABER performance of the SIM-OTFS system is also illustrated.

LOS/NLOS Near-Field Localization With a Large Reconfigurable Intelligent Surface

Abstract: This paper considers a scenario where a reconfigurable intelligent surface (RIS) is deployed to allow the localization of mobile users adopting a single anchor node, even under non-line-of-sight (NLOS) channel conditions. When the RIS is large and the operating frequency is high, as in the millimeter-wave band, the system is likely to operate in the near-field propagation regime, which can be exploited to obtain robust localization. To this purpose, two practical signaling and positioning algorithms, based on an orthogonal frequency division multiplexing (OFDM) downlink system, are proposed along with methods to design the RIS time-varying reflection coefficients. In the numerical results, the two algorithms are compared in terms of performance in the presence of a synchronization mismatch and considering trade-offs between bandwidth, overhead, operating frequency, and latency. Finally, we provide an analysis of the soft-coverage capability, i.e., on the possibility of maintaining a high level of localization accuracy when in the presence of increasing levels of obstruction of the RIS.

Designing Low-PAPR Waveform for OFDM-Based RadCom Systems

Abstract: This paper is focused on the fusion of radar and wireless communication, called RadCom, which has been extensively studied in recent years for future intelligent transportation systems. We propose a new waveform design algorithm for reducing peak-to-average power ratio (PAPR) in OFDM-based RadCom systems. We consider a flexible and generic RadCom structure in which a number of non-contiguous sub-bands for data transmission are located within a large contiguous spectrum band for radar detection/sensing. New RadCom waveforms with low PAPR are obtained by carrying out optimization over those subcarriers which are complementary to the communication bands. As an application of the majorization-minimization (MM) optimization method, our major contribution is an $l$ -norm cyclic algorithm which is capable of efficiently reducing the maximum PAPR of RadCom waveforms. We show by numerical simulation results that significant performance enhancements can be achieved compared to OFDM RadCom waveforms from legacy approaches.

Composite Multiple-Mode Orthogonal Frequency Division Multiplexing with Index Modulation

Abstract: In this paper, we propose a composite multiple-mode orthogonal frequency division multiplexing with index modulation (C-MM-OFDM-IM) scheme to increase the spectral efficiency (SE) of OFDM-IM systems by extending the indexing to the energy and constellation domains. In C-MM-OFDM-IM, the information bits are mapped to not only the subcarrier activation patterns (SAPs) and modulation symbols, but also the energy allocation patterns (EAPs) and constellation activation patterns (CAPs). To cope with the practical situations, we propose a variant IM scheme named C-MM-OFDM-IM-II to build a new mapping rule between information bits and the increased CAPs, capable of further increasing the SE of C-MM-OFDM-IM. Upper-bounded bit error rate (BER) and lower-bounded achievable rate are both derived in closed-form to evaluate the performance of C-MM-OFDM-IM(-II). Moreover, we further propose two enhanced schemes, named generalized C-MM-OFDM-IM(-II) and C-MM-OFDM with in-phase/quadrature IM(-II), where the former jointly considers all SAPs, EAPs, CAPs and modulated symbols, while the latter expands the index implementation to the in-phase and quadrature constellation domains. Simulation results show that C-MM-OFDM-IM(-II) outperforms the conventional OFDM-IM related schemes, especially in the high signal-to-noise ratio (SNR) region, and verify the accuracy of the theoretical analysis for the upper-bounded BER and achievable rate.

Series-Constellation Feature Based Blind Modulation Recognition for Beyond 5G MIMO-OFDM Systems With Channel Fading

Abstract: Due to the shortage of radio spectrum in the current 5G and upcoming 6G systems, the cognitive radio (CR) technique is indispensable for spectrum management and can put the unutilized spectrum to good use. As the core technology of CR, blind modulation recognition (BMR) plays a pivotal role in improving spectral efficiency. However, the BMR research on MIMO-OFDM systems still lacks enough attention. Given the prosperity of deep learning, we propose a series-constellation multi-modal feature network (SC-MFNet) to recognize the modulation types of MIMO-OFDM subcarriers. Without any prior information, a blind signal separation algorithm is employed to reconstruct the impaired transmitted signal. Considering the insufficient features of signal series, we propose a segment accumulated constellation diagram (SACD) strategy to produce the striking constellation features. Moreover, the proposed multi-modal feature fusion network is employed to collect the advantages of series and SACD features, which are extracted by one-dimensional convolution (Conv1DNet) branch and improved EfficientNet branch, respectively. Experimental results demonstrate that in a realistic non-cooperative cognitive communication scenario where prior information is exempted, the proposed SC-MFNet outperforms the traditional feature-based methods and the state-of-the-art neural networks which are based on either constellation features or series features.