Showing papers on "Orthogonal frequency-division multiplexing published in 2020"

Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization

Abstract: Intelligent reflecting surface (IRS) is a promising new technology for achieving both spectrum and energy efficient wireless communication systems in the future. However, existing works on IRS mainly consider frequency-flat channels and assume perfect knowledge of channel state information (CSI) at the transmitter. Motivated by the above, in this paper we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system under frequency-selective channels and propose a practical transmission protocol with channel estimation. First, to reduce the overhead in channel training as well as exploit the channel spatial correlation, we propose a novel IRS elements grouping method, where each group consists of a set of adjacent IRS elements that share a common reflection coefficient. Based on this method, we propose a practical transmission protocol where only the combined channel of each group needs to be estimated, thus substantially reducing the training overhead. Next, with any given grouping and estimated CSI, we formulate the problem to maximize the achievable rate by jointly optimizing the transmit power allocation and the IRS passive array reflection coefficients. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the power allocation and the passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the case without using IRS. Moreover, it is shown that there exists an optimal size for IRS elements grouping which achieves the maximum achievable rate due to the practical trade-off between the training overhead and IRS passive beamforming flexibility.

Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization

Abstract: In the intelligent reflecting surface (IRS)-enhanced wireless communication system, channel state information (CSI) is of paramount importance for achieving the passive beamforming gain of IRS, which, however, is a practically challenging task due to its massive number of passive elements without transmitting/receiving capabilities. In this letter, we propose a practical transmission protocol to execute channel estimation and reflection optimization successively for an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system. Under the unit-modulus constraint, a novel reflection pattern at the IRS is designed to aid the channel estimation at the access point (AP) based on the received pilot signals from the user, for which the channel estimation error is derived in closed-form. With the estimated CSI, the reflection coefficients are then optimized by a low-complexity algorithm based on the resolved strongest signal path in the time domain. Simulation results corroborate the effectiveness of the proposed channel estimation and reflection optimization methods.

Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication

Abstract: Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices are optimized for different subcarriers while only one common set of IRS reflection coefficients is designed to cater to all the subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.

Deep Convolutional Neural Networks for Indoor Localization with CSI Images

Abstract: With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. Leveraging a modified device driver, we extract phase data of channel state information (CSI), which is used to estimate the angle of arrival (AoA). We then create estimated AoA images as input to a DCNN, to train the weights in the offline phase. The location of mobile device is predicted based using the trained DCNN and new CSI AoA images. We implement the proposed CiFi system with commodity Wi-Fi devices in the 5GHz band and verify its performance with extensive experiments in two representative indoor environments.

A Simple Variational Bayes Detector for Orthogonal Time Frequency Space (OTFS) Modulation

Abstract: The emerging orthogonal time frequency space (OTFS) modulation technique has shown its superiority to the current orthogonal frequency division multiplexing (OFDM) scheme, in terms of its capabilities of exploiting full time-frequency diversity and coping with channel dynamics. The optimal maximum a posteriori (MAP) detection is capable of eliminating the negative impacts of the inter-symbol interference in the delay-Doppler (DD) domain at the expense of a prohibitively high complexity. To reduce the receiver complexity for OTFS scheme, this paper proposes a variational Bayes (VB) approach as an approximation of the optimal MAP detection. Compared to the widely used message passing algorithm, we prove that the proposed iterative algorithm is guaranteed to converge to the global optimum of the approximated MAP detector regardless the resulting factor graph is loopy or not. Simulation results validate the fast convergence of the proposed VB receiver and also show its promising performance gain compared to the conventional message passing algorithm.

Adaptive Transmission for Reconfigurable Intelligent Surface-Assisted OFDM Wireless Communications

Abstract: Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the coverage, throughput, and energy/spectrum efficiency of future wireless communications. In this paper, we propose a new transmission protocol for wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) communication systems, where each transmission frame is divided into multiple sub-frames to execute channel estimation simultaneously with passive beamforming. As the training symbols are discretely distributed over multiple sub-frames, the channel state information (CSI) associated with RIS cannot be estimated at once. As such, we propose a new channel estimation method to progressively estimate the associated CSI over consecutive sub-frames, based on which the passive beamforming at the RIS is fine-tuned to improve the achievable rate for data transmission. In particular, during the channel training, the RIS plays two roles of embedding training reflection states for progressive channel estimation and performing passive beamforming for data transmission on the data tones. Based on the estimated CSI in each sub-frame, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different sub-carriers and different receive antennas. As the formulated problem is non-convex and thus difficult to solve optimally, we propose two efficient algorithms to find high-quality solutions. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods. It is shown that the proposed joint channel estimation and passive beamforming scheme is able to drastically improve the average achievable rate and reduce the delay for data transmission as compared to existing schemes.

Low Complexity Iterative Rake Decision Feedback Equalizer for Zero-Padded OTFS systems

Abstract: This paper presents a linear complexity iterative rake detector for the recently proposed orthogonal time frequency space (OTFS) modulation scheme. The basic idea is to extract and coherently combine the received multipath components of the transmitted symbols in the delay-Doppler grid using maximal ratio combining (MRC) to improve the SNR of the combined signal. We reformulate the OTFS input-output relation in simple vector form by placing guard null symbols or zero padding (ZP) in the delay-Doppler grid and exploiting the resulting circulant property of the blocks of the channel matrix. Using this vector input-output relation we propose a low complexity iterative decision feedback equalizer (DFE) based on MRC. The performance and complexity of the proposed detector favorably compares with the state of the art message passing detector. An alternative time domain MRC based detector is also proposed for even faster detection. We further propose a Gauss-Seidel based over-relaxation parameter in the rake detector to improve the performance and the convergence speed of the iterative detection. We also show how the MRC detector can be combined with outer error-correcting codes to operate as a turbo DFE scheme to further improve the error performance. All results are compared with a baseline orthogonal frequency division multiplexing (OFDM) scheme employing a single tap minimum mean square error (MMSE) equalizer.

Effective Diversity of OTFS Modulation

Abstract: Orthogonal time frequency space (OTFS) modulation, which encodes information symbols in the delay–Doppler domain, offers a promising solution to the problem of high Doppler sensitivity of orthogonal frequency division multiplexing (OFDM) transmission. In this letter we study the diversity of OTFS assuming rectangular waveforms and a delay–Doppler channel with two paths. After introducing the concept of effective diversity (ED), which we argue to be more significant than “standard” diversity in the case of a large number of transmitted symbols, we examine the conditions under which OTFS achieves full ED for QAM symbols. We validate our analytical results through numerical simulations, which show that OTFS practically achieves full ED with sufficiently large signal constellations.

IRS-Enhanced OFDMA: Joint Resource Allocation and Passive Beamforming Optimization

Abstract: Intelligent reflecting surface (IRS) is an emerging technique to enhance the wireless communication spectral efficiency with low hardware and energy cost. In this letter, we consider the integration of IRS to an orthogonal frequency division multiple access (OFDMA) based multiuser downlink communication system, and study the pertinent joint optimization of the IRS reflection coefficients and OFDMA time-frequency resource block as well as power allocations to maximize the users’ common (minimum) rate. Specifically, due to the lack of frequency-selective passive beamforming capability at the IRS, only one set of reflection coefficients can be designed for adapting to a large number of channels of multiple users over different frequency sub-bands. To tackle this difficulty, we propose a novel dynamic passive beamforming scheme where the IRS reflection coefficients are dynamically adjusted over different time slots within each channel coherence block to create artificial time-varying channels and select only a subset of the users to be simultaneously served in each time slot, thus achieving a higher passive beamforming gain. Although the formulated optimization problem is non-convex, we propose an efficient algorithm to obtain a high-quality suboptimal solution to it. Numerical results show that the proposed scheme significantly improves the system common rate over the setup without IRS and that with random IRS reflection coefficients. Moreover, our proposed dynamic passive beamforming outperforms the fixed passive beamforming which employs a common set of reflection coefficients in each channel coherence block, by more flexibly balancing between passive beamforming and multiuser diversity gains.

Massive MIMO Channel Estimation With an Untrained Deep Neural Network

Abstract: This paper proposes a deep learning-based channel estimation method for multi-cell interference-limited massive MIMO systems, in which base stations equipped with a large number of antennas serve multiple single-antenna users. The proposed estimator employs a specially designed deep neural network (DNN) based on the deep image prior (DIP) network to first denoise the received signal, followed by conventional least-squares (LS) estimation. We analytically prove that our LS-type deep channel estimator can approach minimum mean square error (MMSE) estimator performance for high-dimensional signals, while avoiding complex channel inversions and knowledge of the channel covariance matrix. This analytical result, while asymptotic, is observed in simulations to be operational for just 64 antennas and 64 subcarriers per OFDM symbol. The proposed method also does not require any training and utilizes several orders of magnitude fewer parameters than conventional DNNs. The proposed deep channel estimator is also robust to pilot contamination and can even completely eliminate it under certain conditions.

Intelligent Reflecting Surface Assisted Multi-User OFDMA: Channel Estimation and Training Design

Abstract: To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient channel estimation schemes for different channel setups in an IRS-assisted multi-user broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first channel estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading channels. In contrast, the second channel estimation scheme, which exploits a key property that all users share the same (common) IRS-AP channel to enhance the training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS channels. For the two proposed channel estimation schemes, we further optimize their corresponding training designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the channel estimation error. Moreover, we derive and compare the fundamental limits on the minimum training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed channel estimation schemes and training designs, and show their significant performance improvement over various benchmark schemes.

Framework for a Perceptive Mobile Network Using Joint Communication and Radar Sensing

Abstract: In this paper, we develop a framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network. We propose a unified system platform that enables downlink and uplink sensing, sharing the same transmitted signals with communications. We aim to tackle the fundamental sensing parameter estimation problem in perceptive mobile networks, by addressing two key challenges associated with sophisticated mobile signals and rich multipath in mobile networks. To extract sensing parameters from orthogonal frequency division multiple access and spatial division multiple access communication signals, we propose two approaches to formulate it to problems that can be solved by compressive sensing techniques. Most sensing algorithms have limits on the number of multipath signals for their inputs. To reduce the multipath signals, as well as removing unwanted clutter signals, we propose a background subtraction method based on simple recursive computation, and provide a closed-form expression for performance characterization. The effectiveness of these methods is validated in simulations.

Practical Modeling and Beamforming for Intelligent Reflecting Surface Aided Wideband Systems

Abstract: Intelligent reflecting surface (IRS) has emerged as a revolutionizing solution to enhance wireless communications by intelligently changing the propagation environment. Prior studies on IRS are based on an ideal reflection model with a constant amplitude and a variable phase shift. However, it is difficult and unrealistic to implement an IRS satisfying such ideal reflection model in practical applications. In this letter, we aim to investigate the phase-amplitude-frequency relationship of the reflected signals and propose a practical model of reflection coefficient for an IRS-aided wideband system. Then, based on this practical model, joint transmit power allocation of each subcarrier and IRS beamforming optimization are investigated for an IRS-aided wideband orthogonal frequency-division multiplexing (OFDM) system. Simulation results illustrate the importance of the practical model on the IRS designs and validate the effectiveness of our proposed model.

NOMA-Assisted Machine-Type Communications in UDN: State-of-the-Art and Challenges

Abstract: The inevitable next era of smart living requires unprecedented advances in enabling technologies. The building blocks of this era are devices such as sensors, actuators, and Internet of Things (IoT) devices. Machine-Type Communication (MTC), also known as Machine-to-Machine (M2M) communication, constitutes the main enabling technology to support communications among such devices. The massive number of these devices and the immense amount of traffic generated by them require a paramount shift in cellular and non-cellular wireless technologies to achieve the required connectivity. Ultra-Dense Network (UDN) is intuitively one of the most convenient approaches to tackle such requirements of massive connectivity and high throughput found in MTC. In UDNs, small cells are deployed in large densities compared to Human-Type Communication Users (HTCUs) like smartphones and tablets. In a different scope, multiple access techniques also require a paradigm shift to cope with the increasing density of required connections while confined by limited resources. Recently, Non-Orthogonal Multiple Access (NOMA) has received a great attention as an efficacious candidate to enhance the network’s performance compared to traditional Orthogonal Multiple Access (OMA) techniques. For this sake, in this paper, we provide a comprehensive survey and illustrative simulation results on the application of NOMA to support MTC in a UDN environment. First, we give a brief discussion on MTC and its different applications and supporting technologies. Then, we focus our effort on investigating the main challenges facing MTC along with the existing opportunities found in both NOMA and UDN, respectively. Finally, we show via simulations the possible gains of both technologies and conclude by discussing the open problems for future research directions.

Deep Residual Learning Meets OFDM Channel Estimation

Abstract: In this letter we apply deep learning tools to conduct channel estimation for an orthogonal frequency division multiplexing (OFDM) system based on downlink pilots. To be specific, a residual learning based deep neural network specifically designed for channel estimation is introduced. Due to the compact network size as well as the underlying network architecture, the computation cost can be greatly reduced. Furthermore, this residual network architecture is compatible with any downlink pilot patterns making it compatible for modern wireless systems. The estimation error of the introduced residual learning approach is evaluated under 3rd Generation Partnership Project (3GPP) channel models. It outperforms other deep learning based estimation method with comparable to minimum mean square error (MMSE) estimation performance.

Uplink-Aided High Mobility Downlink Channel Estimation Over Massive MIMO-OTFS System

Abstract: Although it is often used in the orthogonal frequency division multiplexing (OFDM) systems, application of massive multiple-input multiple-output (MIMO) over the orthogonal time frequency space (OTFS) modulation could suffer from enormous training overhead in high mobility scenarios. In this paper, we propose one uplink-aided high mobility downlink channel estimation scheme for the massive MIMO-OTFS networks. Specifically, we firstly formulate the time domain massive MIMO-OTFS signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including the angle, the delay, the Doppler frequency, and the channel gain for each physical scattering path. Correspondingly, with the help of the fast Bayesian inference, one low complex approach is constructed to overcome the bottleneck of the EM-VB. Then, we fully exploit the angle, delay and Doppler reciprocity between the uplink and the downlink and reconstruct the angles, the delays, and the Doppler frequencies for the downlink massive channels at the base station. Furthermore, we examine the downlink massive MIMO channel estimation over the delay-Doppler-angle domain. The channel dispersion of the OTFS over the delay-Doppler domain is carefully analyzed and is utilized to associate one given path with one specific delay-Doppler grid if different paths of any user have distinguished delay-Doppler signatures. Moreover, when all the paths of any user could be perfectly separated over the angle domain, we design the effective path scheduling algorithm to map different users' data into the orthogonal delay-Doppler-angle domain resource and achieve the parallel and low complex downlink 3D channel estimation. For the general case, we adopt the least square estimator with reduced dimension to capture the downlink delay-Doppler-angle channels. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.

Hybrid Beamforming for Terahertz Multi-Carrier Systems Over Frequency Selective Fading

Abstract: We propose novel hybrid beamforming schemes for the terahertz (THz) wireless system where a multi-antenna base station (BS) communicates with a multi-antenna user over frequency selective fading. Here, we assume that the BS employs sub-connected hybrid beamforming and multi-carrier modulation to deliver ultra high data rate. We consider a three-dimensional wideband THz channel by incorporating the joint effect of molecular absorption, high sparsity, and multi-path fading, and consider the carrier frequency offset in multi-carrier systems. With this model, we first propose a two-stage wideband hybrid beamforming scheme which includes a beamsteering codebook searching algorithm for analog beamforming and a regularized channel inversion method for digital beamforming. We then propose a novel wideband hybrid beamforming scheme with two digital beamformers. In this scheme, an additional digital beamformer is developed to compensate for the performance loss caused by the constant-amplitude hardware constraints and the difference of channel matrices among subcarriers. Furthermore, we consider imperfect channel state information (CSI) and propose a probabilistic robust hybrid beamforming scheme to combat channel estimation errors. Numerical results demonstrate the benefits of our proposed schemes for the sake of practical implementation, especially considering its high spectral efficiency, low complexity, and robustness against imperfect CSI.

Sparse Bayesian Learning of Delay-Doppler Channel for OTFS System

Abstract: Orthogonal time frequency space (OTFS) modulation has superior performance than traditional orthogonal frequency division multiplexing (OFDM) in fast time-varying scenarios. However, due to the effect of Doppler shift, higher pilot overhead and pilot power are required to estimate the channel state information. According to the sparseness of the channel in the delay-Doppler domain, this letter proposes a new pilot pattern and a sparse Bayesian learning (SBL)-based channel estimation algorithm. There is no guard pilot in the pilot pattern, and the pilot has the same power as the data. Based on the new pilot pattern, we first convert the channel estimation problem to a sparse signal recovery problem. Then, we introduce a sparse Bayesian learning framework and construct a sparse signal prior model as a hierarchical Laplace prior. Finally, the expected maximum (EM) algorithm is used to update the parameters in the prior model. Numerical simulation highlights the superiority of the proposed algorithm in terms of pilot overhead, pilot power consumption, and anti-noise interference.

Analysis and Design of Highly Efficient Wideband RF-Input Sequential Load Modulated Balanced Power Amplifier

Abstract: The analysis and design of an RF-input sequential load modulated balanced power amplifier (SLMBA) are presented in this article. Unlike the existing LMBAs, in this new configuration, an over-driven class-B amplifier is used as the carrier amplifier while the balanced PA pair is biased in class-C mode to serve as the peaking amplifier. It is illustrated that the sequential operation greatly extends the high-efficiency power range and enables the proposed SLMBA to achieve high back-off efficiency across a wide bandwidth. An RF-input SLMBA at 3.05–3.55-GHz band using commercial GaN transistors is designed and implemented to validate the proposed architecture. The fabricated SLMBA attains a measured 9.5–10.3-dB gain and 42.3–43.7-dBm saturated power. Drain efficiency of 50.9–64.9/46.8–60.7/43.2–51.4% is achieved at 6-/8-/10-dB output power back-off within the designed bandwidth. By changing the bias condition of the carrier device, higher than 49.1% drain efficiency can be obtained within the 12.8-dB output power range at 3.3 GHz. When driven by a 40-MHz orthogonal frequency-division multiplexing (OFDM) signal with 8-dB peak-to-average power ratio (PAPR), the proposed SLMBA achieves adjacent channel leakage ratio (ACLR) better than −25 dBc with an average efficiency of 63.2% without digital predistortion (DPD). When excited by a ten-carrier 200-MHz OFDM signal with 10-dB PAPR, the average efficiency can reach 48.2% and −43.9-dBc ACLR can be obtained after DPD.

Multipath Estimating Delay Lock Loop for LTE Signal TOA Estimation in Indoor and Urban Environments

Abstract: Long-term evolution (LTE) signals are potential signals-of-opportunity for position and navigation, especially in challenging urban and indoor environments. A major challenge is that the LTE signal time-of-arrival (TOA) estimations are susceptible to the multipath propagation effects. In this paper, the multipath estimating delay lock loop (MEDLL), which is originally designed for global positioning system receivers, is applied to LTE signal TOA estimation in multipath environments. We derive the analytical expression of the correlation function for LTE signals and present the procedure for estimating parameters of the detected multipath components. Two initialization methods without and with super-resolution algorithm (SRA) are developed for the MEDLL. Our analyses show that the MEDLL with SRA-based initialization can achieve better multipath resolution, while the one without SRA has less complexity. Extensive simulations involving static multipath scenarios are conducted to examine the statistical TOA estimation performance of the proposed MEDLL with LTE cell-specific reference signal. The simulation results and computational complexity analysis indicate that the proposed MEDLL outperforms the conventional delay lock loop and SRA in term of multipath mitigation performance and computational complexity. Experimental results using real collected LTE signals in urban environments are also provided to demonstrate the effectiveness of the proposed technique for realistic scenarios.

Position Based Compressed Channel Estimation and Pilot Design for High Mobility OFDM Systems

Abstract: With the development of high speed trains (HST) in many countries, providing broadband wireless services in HSTs is becoming crucial. Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communications due to its high spectral efficiency. However, OFDM is sensitive to the time selectivity caused by high-mobility channels, which costs large spectrum or time resources to obtain the accurate channel state information (CSI). Therefore, the channel estimation in high-mobility OFDM systems has been a long-standing challenge. In this paper, we first propose a new position-based high-mobility channel model,in which the HST's position information and Doppler shift are utilized to determine the positions of the dominant channel coefficients. %In this way, we can reduce the estimation complexity and to design the transmitted pilot.Then, we propose a joint pilot placement and pilot symbol design algorithm for compressed channel estimation. It aims to reduce the coherence between the pilot signal and the proposed channel model, and hence can improve the channel estimation accuracy. Simulation results demonstrate that the proposed method achieves better performances than existing channel estimation methods over high-mobility channels. Furthermore, we give an example of the designed pilot codebook to show the practical applicability of the proposed scheme.

Dynamic Hybrid Beamforming With Low-Resolution PSs for Wideband mmWave MIMO-OFDM Systems

Abstract: Analog/digital hybrid beamforming is considered as a key enabling multiple antenna technology for implementing millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications since it can reduce the number of costly and power-hungry radio frequency (RF) chains while still providing for spatial multiplexing. In this paper, we introduce a novel hybrid beamforming architecture with dynamic antenna subarrays and hardware-efficient low-resolution phase shifters (PSs) for a wideband mmWave MIMO orthogonal frequency division multiplexing (MIMO-OFDM) system. By dynamically connecting each RF chain to a non-overlapping antenna subarray via a switch network and PSs, multiple-antenna diversity can be exploited to mitigate the performance loss due to the employment of practical low-resolution PSs. For this dynamic hybrid beamforming architecture, we jointly design the hybrid precoder and combiner to maximize the average spectral efficiency of the mmWave MIMO-OFDM system. In particular, the spectral efficiency maximization problem is first converted to a mean square error (MSE) minimization problem. Then, an efficient iterative hybrid beamformer algorithm is developed based on classical block coordination descent (BCD) methods. An analysis of the convergence and complexity of the proposed algorithm is also provided. Extensive simulation results demonstrate the superiority of the proposed hybrid beamforming algorithm with dynamic subarrays and low-resolution PSs.

Deep Learning Based Channel Estimation Algorithm for Fast Time-Varying MIMO-OFDM Systems

Abstract: Channel estimation is very challenging for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in high mobility environments with non-stationarity channel characteristics. In order to handle this problem, we propose a deep learning (DL)-based MIMO-OFDM channel estimation algorithm. By performing offline training to the learning network, the channel state information (CSI) generated by the training samples can be effectively utilized to adapt the characteristics of fast time-varying channels in the high mobility scenarios. The simulation results show that the proposed DL-based algorithm is more robust for the scenarios of high mobility in MIMO-OFDM systems, compared to the conventional algorithms.

A Hybrid Downlink NOMA With OFDM and OFDM-IM for Beyond 5G Wireless Networks

Abstract: In this paper, a hybrid power domain non-orthogonal multiple accessing (NOMA) scheme by the superposition of orthogonal frequency division multiple accessing (OFDM) and index modulated OFDM (OFDM-IM) technologies is presented and named IM-NOMA. It is shown via both computer-based simulations and mathematical analysis that IM-NOMA outperforms the classical OFDM-NOMA in terms of bit error rate (BER) under a total power constraint and achievable sum rate. The system performance of IM-NOMA not only depends on the power difference between the overlapping users but also on features of the OFDM-IM signal. Hence, this scheme is robust against possible catastrophic error performance in case similar power is assigned to the users.

Low-Complexity Detection for Index Modulation Multiple Access

Abstract: Index modulation multiple access (IM-MA) is recently proposed to exploit the IM concept to the uplink multiple access system, where multiple users transmit their own signals via the selected time slots. However, the computational complexity of the optimal maximum-likelihood (ML) detection in IM-MA is tremendously high when the number of users or time slots is large. In this letter, we propose a low-complexity detection method for IM-MA, which is inspired by the log likelihood ratio (LLR) algorithm. In addition, because of the heavy search burden for all LLR values, we further propose a suboptimal method to determine the permutation set, which records the number of users allocated to each time slot. Simulation results and the complexity analysis verify that the proposed detection performs closely to the optimal ML detection with reduced computational complexity.

Positioning in a Multipath Channel Using OFDM Signals With Carrier Phase Tracking

Abstract: In developing a high accuracy terrestrial radio navigation system, as a complement to a global navigation satellite system (GNSS), it is recognized that the performance of time delay estimation is proportional to, and thereby limited by, the signal bandwidth. Given a possibly narrow signal bandwidth, the central carrier phase can, alternatively, provide a better distance accuracy, though the central carrier phase cycle ambiguity should be resolved. In practice, the carrier phase may be perturbed by multipath. In this paper, considering an orthogonal frequency division multiplexing (OFDM) signal, we propose a two-step carrier phase estimation method to reduce the error introduced by multipath. First, the propagation delay of the LoS path is coarsely determined, then the carrier phase is estimated using the earlier determined coarse time delays. Furthermore, a positioning model only based on carrier phase estimates is presented in this paper. The proposed technique is evaluated by statistical analyses and a simulated OFDM-based terrestrial positioning system in different roadway multipath environments. The results show that the impact of multipath on carrier phase estimation can be largely mitigated, so that the carrier phase can be used for precise positioning. In addition, fixing the integer carrier phase cycle ambiguities can significantly reduce the time for the position solution to converge to high precision.

Performance Analysis of Coded OTFS Systems 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.

26.8-m THz wireless transmission of probabilistic shaping 16-QAM-OFDM signals

Abstract: Recently, remarkable efforts have been made in developing wireless communication systems at ultrahigh data rates, with radio frequency (RF) carriers in the millimeter wave (30–300 GHz) and/or in the terahertz (THz, >300 GHz) bands. Converged technologies combining both the electronics and the photonics show great potential to provide feasible solutions with superior performance compared to conventional RF technologies. However, technical challenges remain to be overcome in order to support high data rates with considerably feasible wireless distances for practical applications, particularly in the THz region. In this work, we present an experimental demonstration of a single-channel THz radio-over-fiber (RoF) system operating at 350 GHz, achieving beyond 100 Gbit/s data rate over a 10-km fiber plus a >20-m wireless link, without using any THz amplifiers. This achievement is enabled by using an orthogonal frequency division multiplexing signal with a probabilistic-shaped 16-ary quadrature amplitude modulation format, a pair of highly directive Cassegrain antennas, and advanced digital signal processing techniques. This work pushes the THz RoF technology one step closer to ultrahigh-speed indoor wireless applications and serves as an essential segment of the converged fiber-wireless access networks in the beyond 5G era.

Realistic Indoor Hybrid WiFi and OFDMA-Based LiFi Networks

Abstract: The increasing number of mobile devices challenges the current radio frequency (RF) networks, e.g. wireless fidelity (WiFi) networks. Light Fidelity (LiFi) is considered as a promising complementary technology, which operates within the visible light spectrum and infrared spectrum. In an indoor scenario, a hybrid LiFi/WiFi network (HLWN) provides a potential solution to future wireless communications where LiFi augments WiFi in providing ultra-high speed and low latency wireless connectivity. In this paper, dynamic load balancing (LB) with handover in HLWNs is studied. The orientation-based random waypoint (ORWP) mobility model is considered to provide a more realistic framework to evaluate the performance of HLWNs. Based on the low-pass filtering effect of the LiFi channel, we firstly propose an orthogonal frequency division multiplexing access (OFDMA)-based resource allocation (RA) method in LiFi systems. Also, an enhanced evolutionary game theory (EGT)-based LB scheme with handover in HLWNs is proposed. In the EGT scheme, each user adapts their strategy to improve the payoff until LB is achieved across LiFi and WiFi. Then, the LiFi system uses the proposed OFDMA-based RA method while the WiFi system applies the carrier sense multiple access with collision detection (CSMA/CA). Simulation results show that in the LiFi system the OFDMA-based RA scheme outperforms the time division multiple access (TDMA) scheme in terms of both user data rate and fairness. Regarding LB in HLWNs, the proposed EGT scheme can achieve a remarkable enhancement in throughput compared to benchmark schemes, such as hard threshold (HT) scheme and random access point assignment (RAA) scheme.

2 × 300 Gbit/s Line Rate PS-64QAM-OFDM THz Photonic-Wireless Transmission

Abstract: The proliferation of wireless broadband services have significantly raised the demand for high data rates. Due to the limited bandwidth of radio frequency (RF) bands that are currently in use for communication purposes, the choice of the ‘Terahertz (THz) frequency region’ (0.3–10 THz) is getting favored thanks to its merits of bringing together wireless and optical communications with photonics technologies. We report on an experimental demonstration of a hybrid THz photonic-wireless transmission based on a THz orthogonal polarization dual-antenna scheme. Probabilistic shaped 64-ary quadrature amplitude modulation based orthogonal frequency division multiplexing (64QAM-OFDM) modulation format is used to realize high transmission rate. A potential total system throughput of 612.65 Gbit/s (around 2 × 300 Gbit/s line rate) with an average net spectral efficiency of 4.445 bit/s/Hz per antenna is successfully achieved.