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The use of an accurate cyclic prefix length is crucial in orthogonal frequency division multiplexing (OFDM) to avoid intercarrier and intersymbol interference. Although there have been many works that analyse the interference of windowed OFDM, this study remains open in the context of power line communications (PLCs) taking into account the physical-layer (PHY) specifications of the standards. This paper focuses on obtaining a closed-form expression of the input-output relationship in windowed OFDM power line communication (PLC) systems under the condition of insufficient cyclic prefix, while incorporating various blocks deployed in the PHY under IEEE 1901 standards. The derived analysis is important for quantifying the undesired signal component in each subcarrier at a specific time, which renders the detection of the corresponding symbol more difficult. Moreover, a novel procedure is proposed that allows the use of a smaller number of redundant samples to avoid interference. This novel procedure, performed in the receiver after the windowing stage, replaces the overlap-and-add operations with multiplications, offering the advantage of requiring fewer samples from the time-domain received signal to recover each transmitted data symbol. Numerical results demonstrate the feasibility of interference-free transmission on channels with a larger number of samples, thereby yielding better results across various PLC scenarios.

We propose a new physical layer security scheme for a wiretap channel in polar-coded OFDM-based wireless communication systems. Our approach is based on the adaptive bit channel selection, where the input bit channels of polar code are selected according to the frequency selectivity of the main channel. Specifically, the polar code is constructed by the legitimate receiver based on its observed channel state information (CSI), and the receiver informs the transmitter of the resulting code structure. Since the proposed scheme attempts to improve the block error rate (BLER) performance exclusively for the main channel, it provides a significant performance gain over the wiretap channel, as long as the channel of the eavesdropper is not highly correlated with that of the legitimate receiver. On the assumption that the wiretap channel is uncorrelated with the main channel, simulation results demonstrate that the main channel can achieve significant performance gains over the wiretap channel, even under the worst-case scenario where the selected polar code structure (i.e., a set of the bit channels selected by the legitimate receiver for information transmission) is completely known to the eavesdropper. We also consider the case where the main channel and wiretap channel are correlated and reveal that our approach is effective even in the presence of mild channel correlation. Finally, the effect of the channel estimation error on the resulting BLER is also examined, pointing out the importance of accurate CSI acquisition at the receiver side.

In this paper, we investigate hybrid beamforming and resource allocation design problems for uplink millimeter wave (mmWave) multi-user (MU) massive multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. We propose three hybrid beamforming designs with dynamic streams assignment (DSA) integrated into the solution for the formulated resource allocation problem to maximize spectral efficiency. In the analog beamforming stage, three different designs are developed to maximize the channel gains of the equivalent baseband channel at each subcarrier, while the number of data streams is also determined for each mobile station (MS). Specifically, for the first design, the analog beamformers are designed to match the corresponding channel across the entire band for each MS. In the second one, to significantly reduce the complexity of the first design, we can completely avoid the matrix decomposition by selecting the appropriate set of array response vectors. The last design only performs singular value decomposition (SVD) on the channel of the center subcarrier to find the analog beamformers. In the digital beamforming stage, for the interference cancellation, the digital precoder and the combiner are obtained at each subcarrier using the block diagonalization (BD) and the coordinated transmit-receive processing method. We also develop fully-digital beamforming and another three hybrid beamforming schemes based on the existing algorithms as performance benchmarks. By being verified through numerical simulations, the spectral efficiency of the proposed schemes can be significantly improved after dynamically assigning the number of data streams for each MS. Moreover, simulations indicate the first and the third proposed hybrid beamforming designs can achieve a very competitive performance and even outperform the fully-digital design in some situations.

Orthogonal frequency-division multiplexing (OFDM) is a key technology for cellular and Wi-Fi systems, but its performance may be degraded by hardware impairments. Existing works focus mostly on single hardware impairment in OFDM systems, without considering the joint effect of hardware impairments on the entire system. In this paper, hardware impairments including nonlinear power amplification, clipping, in-phase/quadrature-phase (IQ) imbalance, phase noise, carrier frequency offset, and sampling clock offset in OFDM systems are simultaneously considered. We propose end-to-end deep learning-based designs, which jointly optimize transmitter and receiver, to effectively mitigate the performance loss due to hardware impairments. For single-antenna systems and $2\times 2$ multiple-input and multiple-output (MIMO) systems, the proposed design featuring the dense layer neural network (DLNN) significantly outperforms traditional impairment-mitigating methods under both the additive white Gaussian noise (AWGN) channel and the Rayleigh fading channel. Meanwhile, the complexity of the proposed scheme is six times smaller. For $2\times 4$ MIMO systems, the proposed design featuring the residual dense convolution dense neural network (ResNet-DCDNN) outperforms the traditional methods by a large margin. Additionally, transfer learning is applied to effectively address the issue of time-varying impairment levels.

This paper demonstrates the implementation of a space–frequency line coded-orthogonal frequency-division multiplexing (SFLC-OFDM) system. The SFLC-OFDM system utilizes downlink channel state information at the transmitter, which can be obtained from uplink channel estimation under channel reciprocity assumption between uplink-and-downlink channels. However, in practical implementations, channel reciprocity is violated due to the imperfect synchronization and radio frequency chain mismatch between transceivers. To recover the channel reciprocity, frequency-domain signal processing methods are proposed. Furthermore, a low computational complexity single-instruction multiple-data method is implemented to meet the time constraints in real-time operating systems. The experimental results demonstrate that channel reciprocity can be effectively recovered in the implemented SFLC-OFDM system, resulting in practical communication performance regarding the symbol error rate.

Wireless smart utility network (Wi-SUN) is one of the wireless communication technologies that realize the Internet of things (IoT). The existing Wi-SUN adopts frequency shift keying compliant with IEEE 802.15.4 for smart utility networks, also known as SUN-FSK, as its physical layer. However, owing to the increased demand for Wi-SUN applications, higher-performance communication methods even in severe fading environments with mobility are required. Orthogonal frequency-division multiplexing compliant with IEEE 802.15.4 is expected to be the most promising candidate for this. Studies on receiving methods have been conducted to accommodate high environment mobility. However, the maximum moving speed of the device is 200 km/h if diversity reception is adopted. In this study, we proposed two channel estimation methods to obtain accurate channel state information (CSI), particularly in a frequency selective fading environment with high mobility using the coherent and linear method. Further, we also proposed a Viterbi decoder with a likelihood ratio adjusted for CSI. Using the proposed channel estimation method and decoder, the required packet error rate of 10% is achieved at a maximum moving speed of 216 km/h by computer simulation. If a two-branch maximum ratio combining antenna diversity is used, the PER is achieved at 720 km/h.

The applications and services envisioned for the sixth Generation (6G) Internet of Things (IoT) communication systems will place different requirements on the system design. The current 5G waveform, Orthogonal Frequency Division Multiplexing (OFDM), suffers from a list of drawbacks that questions its suitability and ability to fulfill these prerequisites. Filtered multi-carrier waveforms provide viable alternatives for future mobile networks. Based on the best recent advances including contributions by the authors, this work provides a comprehensive assessment of the current status and future challenges related to the design of a next-generation Filter Bank Multi-Carrier/Offset Quadrature Amplitude Modulation (FBMC/OQAM) transceiver. We start by showing how the next-generation FBMC/OQAM waveform can overcome the drawbacks of OFDM. Furthermore, we highlight the advantages and possibilities of extending the next-generation FBMC/OQAM waveform for massive Multiple-Input-Multiple-Output (mMIMO) in well-identified 6G-IoT scenarios. Results show that the next-generation FBMC/OQAM receivers associated with short prototype filters can unleash appealing properties for 6G-IoT applications. Considered a cornerstone for grant-free spectrum and massive access, asynchronous communications are shown to be achievable. Furthermore, a largely improved robustness is observed compared to OFDM under different channel impairments such as carrier frequency offsets, timing offsets, and multipath channels. Compared to the original FBMC/OQAM associated with long filters, this next-generation transceiver achieves a reduction in complexity down to a comparable level to OFDM. Finally, open research opportunities and challenges for the next-generation FBMC/OQAM waveform are discussed.

Orthogonal frequency-division multiplexing (OFDM) systems have been widely implemented in guided and non-guided communications. Even though they are becoming a standard for popular technologies such as WiFi, their implementation in newly emerging ones is still in evolution. As the number of connected devices increases, the radio frequency (RF) channel gets crowded. Here is where visible light communication (VLC) links gain interest. VLC offers a wireless alternative to RF with a trade-off between the necessity of line of sight with the offer of a free, highly secure, and immune to electromagnetic interference communication channel. This paper proposes a programmable software implementation of an OFDM system using a novel phase correction technique for VLC channels. The new equation considers two types of phase shifts, one that will be non-deterministic (has a random variation) and another that will be deterministic and, therefore, can be estimated and corrected. This second can be split into two: one that comes from the clock differences and has more impact on higher subcarriers, and the second that appears from the electronics of the link and will directly impact the symbols' phase. The main difference with current phase correction techniques is the identification and estimation of the deterministic noise specific to VLC systems. The deterministic noise impact all subcarriers equally and must be subtracted before the measure of the shift induced by the differences between the transmitter and receiver clocks. Another difference is that the analysis and tests have been done with data collected in an actual hardware implementation.

Visible light communication (VLC) has emerged as a viable supplement to existing radio frequency (RF) communication systems. The limited modulation bandwidth of sources is one of the significant challenges in the multiuser (MU) VLC systems, which prohibits transmission at high data rates for each user. Since an array of LEDs is used to illuminate a room, a MU VLC system utilizing massive multiple-input multiple-output (mMIMO) orthogonal frequency-division multiplexing (OFDM) is proposed for achieving high data rates in this paper. Since the distances of the multiple transmitter-receiver links are different, their temporal delays are also different, resulting in complex channel gain and phase differences when transformed to the frequency domain in the OFDM technique. Complex channel vectors associated with different users are mutually spatially orthogonal as the number of transmitters increases. Therefore, inter-user interference (IUI) can be eliminated with simple linear signal processing, and more users can simultaneously communicate in the same time-frequency resource. In this paper, three linear precoding methods, including the maximum ratio transmission (MRT), the minimum mean square error (MMSE), and the zero-forcing (ZF), are investigated for VLC systems based on proposed MU-mMIMO-OFDM in intensity-modulation and direct-detection link. We evaluate and compare the performance of the aforementioned precoding methods in terms of the achievable spectral efficiency and total downlink optical power for different scenarios. Moreover, we derive a closed-form expression for the lower bound on the average achievable sum-rate, which we confirm the accuracy of the derived expression with the numerical simulation. The results demonstrate that the performance of the proposed method in this paper for all precoder techniques, i.e., ZF, MMSE, and MRT, is better than the previous works since the complex channel in the frequency domain is used in this paper. Furthermore, the ZF and MMSE methods are better than MRT when the ratio of the number of LEDs to the number of users is large. At the same time, when this ratio is relatively small, the MRT precoding technique outperforms the ZF and MMSE techniques.

This paper is concerned with the problem of channel estimation in virtual angular domain in massive MIMO orthogonal frequency division multiplexing (OFDM) systems, in the presence of time varying channels. The proposed channel estimator is based on dichotomous coordinate decent joint sparse recovery (DCD-JSR) algorithm, which was well suited for massive MIMO systems with channels static over several OFDM symbols, but it is not accurate for time-varying channels. The estimator proposed in this paper exploits the basis expansion model (BEM) for approximation of the time-variation of the channel and the sparsity of the channel in the virtual angular domain with a common support over OFDM subcarriers and OFDM symbols for joint estimation of the BEM expansion coefficients. It is shown that, when using the Legendre polynomials as the BEM and depending on the normalized Doppler frequency, only a small number of expansion coefficients is required to provide accurate channel estimation.

This paper elucidates the relative benefits of bit loading and power loading for discrete multitone (DMT) modulation over wireline links. There is considerable variability in the use of bit and power loading in recent research on DMT for wireline applications. Here, we first compare different combinations of bit and power loading schemes in simulations. We propose a method for dynamically adjusting DMT bit and power profiles based on the bit error distribution across sub-channels, as determined by the receiver. We then present experimental results showing DMT operation at 75 Gb/s with a spectral efficiency of 2.5 b/sample over a channel with a smooth magnitude response exhibiting 26 dB attenuation at half the sampling rate and a loss of 33 dB at 1/4 the data rate (i.e., the Nyquist rate of a 4-level pulse-amplitude modulated link at the same data rate). The proposed dynamic bit and power allocation method reduces the bit error rate from $\textrm {3}\times \textrm {10}^{-\textrm {3}}$ to $\textrm {2} \times \textrm {10}^{-\textrm {5}}$ , measured with 23,265,420 bits.

This paper derives the closed-form bit error probability (BEP) of massive multiple-input, multiple-output (M-MIMO) systems using orthogonal frequency-division multiplexing (OFDM) and zero-forcing (ZF) detection. We improve the BEP accuracy by increasing the Neumann series expansion (NSE) to second order for the system that we previously analyzed by deriving the probability distribution function (PDF) of the effective noise. The proposed PDF is then utilized to evaluate the BEP, the PDF of output signal-to-noise ratio (SNR), and the outage probability as a function of the output SNR of the system. Furthermore, a simplified closed-form expression for the effective noise PDF, in terms of the Gaussian distribution, and the noise variance are firstly derived in this paper for simplifying the performance analysis. Monte-Carlo simulation results confirm that the outcome from the derived equation and the approximation closely matched those obtained by simulation. In addition, we employ the proposed noise variance to estimate the log-likelihood ratio (LLR) instead of the approximate noise variance for the low-complexity soft-output ZF detection. The computational complexity of the proposed detection is thus significantly reduced, whereas its bit error rate (BER) is lower than that of the classical detection. Focusing on a $10\times 200$ Coded-OFDM-M-MIMO system, 97.81% of multiplications, required for producing the LLR from the estimated symbol, were minimized by utilizing the proposed detection. Therefore, the derived equations can be efficiently used for analyzing the performance of OFDM-M-MIMO systems, and reducing the computational complexity of the soft-output ZF detection.

In this letter, we propose a Deep Unfolding Network called PR-DUN to reduce the peak-to-average power ratio (PAPR), which is a thorny problem in Orthogonal Frequency-Division Multiplexing (OFDM) systems. The proposed multi-layer model is constructed by unrolling an iterative algorithm resulting in layers with trainable parameters, which are optimized to minimize a loss function related to the PAPR value. The deep unfolding model uses the backpropagation algorithm to transfer gradients backwards to adjust parameters. Furthermore, the proposed scheme can accommodate any transmit power constraint, and therefore can control the power increase caused by the auxiliary signal. Simulation results show that the proposed PR-DUN model achieves a larger PAPR reduction and a smaller bit error rate while being less computationally intensive than related solutions. [ABSTRACT FROM AUTHOR]

Non-binary low-density parity-check (NB-LDPC) codes can exhibit more considerable coding gain than their binary counterparts in numerous cases. To further exploit the benefit of NB-LDPC codes, in this letter, a novel type of non-binary LDPC codes, alterable subset non-binary LDPC (AS-NB-LDPC) codes, is proposed for reducing the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) systems. Firstly, we design the alteration scheme of AS-NB-LDPC codes to make $q$ -ary LDPC codes generate $q$ valid codewords through one alterable subset, and the codeword with the minimum PAPR is selected for transmission. Then inspired by the invertible subset for binary LDPC codes, the necessary and sufficient conditions for alterable subset generation are derived. Furthermore, we propose an improved progressive edge growth (PEG) construction method to obtain AS-NB-LDPC codes with favorable frame error rate (FER) performance. Simulation results verify the effectiveness of the proposed NB-LDPC codes. [ABSTRACT FROM AUTHOR]

Intrinsically safe power converters have been highly demanded in many harsh industrial environments, such as gas stations, chemical plants, and mine tunnels, to realize accurate voltage or current regulations with minimum safety risks. Communication among different devices is the key to realizing important operations, such as coordinated control, fault detection, and state update, for intrinsically safe converters. In this article, a capacitor current pulse train (CC-PT) control strategy is designed to increase the dynamic response of dc–dc converter with intrinsic operational safety. An orthogonal frequency-division multiplexing (OFDM) based power and signal synchronous transmission (PSST) method is then proposed. The mathematical models of signal modulation are established, and the intrinsic safety region is drawn in the L–C parameters plane, considering the requirements of electric performance, the inductor disconnection discharging, and output short-circuit tests. Simulations and prototype experiments are conducted and presented to verify the feasibility of the OFDM-based PSST in the CC-PT-controlled buck converter with intrinsically safe parameters. [ABSTRACT FROM AUTHOR]

In this paper, simultaneous wireless information and power transfer based on time–frequency block allocation (TFBA-SWIPT) is proposed to save the energy of an orthogonal frequency-division multiplex (OFDM) decode-and-forward cooperative communication system, by allocating different resource blocks for information decoding and energy harvesting at the relay and destinations. The joint allocation of time–frequency blocks and power is optimized to maximize the transmission rate of the destination, while ensuring the transmission rate and energy supply of the relay. The time–frequency block pairing algorithm and the block power allocation algorithm are integrated to achieve the optimal solution. Simulation results illustrate the benefits of using TFBA-SWIPT in the OFDM cooperative communication system over time-varying channels. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Phase-noise (PN) estimation and compensation are crucial in millimeter-wave (mmWave) communication systems to achieve high reliability. The PN estimation, however, suffers from high computational complexity due to its fundamental characteristics, such as spectral spreading and fast-varying fluctuations. In this paper, we propose a new framework for low-complexity PN compensation in orthogonal frequency-division multiplexing systems. The proposed framework also includes a pilot allocation strategy to minimize its overhead. The key ideas are to exploit the coherence bandwidth of mmWave systems and to approximate the actual PN spectrum with its dominant components, resulting in a non-iterative solution by using linear minimum mean squared-error estimation. The proposed method obtains a reduction of more than $2.5 \times $ in total complexity, as compared to the existing methods. Furthermore, we derive closed-form expressions for normalized mean squared-errors (NMSEs) as a function of critical system parameters, which help in understanding the NMSE behavior in low and high signal-to-noise ratio regimes. Lastly, we study a trade-off between performance and pilot-overhead to provide insight into an appropriate approximation of the PN spectrum. [ABSTRACT FROM AUTHOR]

Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (large-scale) multiple-input and multiple-output (MIMO) systems have received increasing attention due to high angular resolution. This paper presents an indoor localization testbed based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system, which supports physical-layer channel measurements. Instead of exploiting channel state information (CSI) directly for localization, we focus on positioning from the perspective of multipath components (MPCs), which are extracted from the CSI through the space-alternating generalized expectation-maximization (SAGE) algorithm. On top of the available MPCs, we propose a generalized fingerprinting system based on different single-metric and hybrid-metric schemes. We evaluate the impact of the varying antenna topologies, the size of the training set, the number of antennas, and the effective signal-to-noise ratio (SNR). The experimental results show that the proposed fingerprinting method can achieve centimeter-level positioning accuracy with a relatively small training set. Specifically, the distributed uniform linear array obtains the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from the high spatial resolution. [ABSTRACT FROM AUTHOR]

High-performance millimeter-wave (mm-wave) multiple-in-multiple-out (MIMO) radars have been using efficient multiplexing with increasingly complicated radar waveforms, where conventional hardware architectures suffer from inevitably high design complexity and cost. This article presents a new software-defined mm-wave multistatic imaging system which provides great efficiency and flexibility to generate and process complicated radar waveforms. A general system block diagram and its signal model are proposed to use space-time-coded orthogonal frequency-division multiplexing (OFDM) for simultaneous MIMO transmission at the same time and frequency. The space-time coding (STC) is designed based on the Hadamard matrix which allows simple decoupling and decoding for imaging processing. A radar prototype is designed to validate the proposed system architecture, which operates at 83.5 GHz with a frequency bandwidth of 4.8 GHz. The mm-wave front-end consists of a metal-printed 8-by-8 waveguide array with a small element-separation of 9 mm to form 64 virtual channels for three-dimensional imaging. Experimental results show good imaging performance, giving great potential to design future cost-effective high-performance mm-wave imaging with efficient multiplexing. [ABSTRACT FROM AUTHOR]

The joint radar-communication (RadCom) concept has been continuously gaining interest due to the possibility of integrating radar sensing and communication functionalities in the same radio frequency hardware platform. Besides a number of challenges in terms of hardware design and signal processing, the choice of suitable modulation schemes plays a significant role in driving the performance of RadCom systems. In this sense, this article presents an overview of state-of-the-art modulation schemes for RadCom systems, namely, chirp sequence, phase-modulated continuous wave, orthogonal frequency-division multiplexing, and orthogonal chirp-division multiplexing. For each of them, a detailed system model is outlined, and parameters for quantifying both radar and communication performances are presented. Finally, a comparative analysis of the aforementioned RadCom modulation schemes is carried out to illustrate the presented discussion. [ABSTRACT FROM AUTHOR]

The physical-layer radio access of 5G New Radio (NR) and other modern wireless networks builds on the cyclic prefix (CP) orthogonal frequency-division multiplexing (OFDM), known to suffer from the high peak-to-average power ratio (PAPR) challenge. In this article, novel PAPR reduction methods are developed, referred to as the iterative clipping and weighted error filtering (ICWEF) approach. To this end, clipping noise is separated from the data signal in frequency domain and properly tailored frequency-selective clipping noise filtering is adopted to control the tradeoff between PAPR reduction and transmitted signal quality. Furthermore, as 5G NR networks support adopting different OFDM numerologies at different bandwidth parts within one channel bandwidth, the ICWEF approach is also extended to take into account and suppress the resulting inter-numerology interference-something that most existing state-of-the-art methods do not consider. To facilitate comprehensive performance evaluations, a software-defined radio based prototyping testbed including a high-power base station power amplifier is also developed and used for assessing the performance of PAPR reduction solutions. The proposed ICWEF-based PAPR reduction concept is thereon thoroughly validated with extensive numerical and experimental results and shown to outperform the existing state-of-the-art reference solutions.

This paper investigates a backscatter communications system that exploits ambient pilot symbols used in existing standards based on orthogonal frequency-division multiplexing (OFDM), such as IEEE802.11, in order to realize ultra-low power communications with longer transmission range and higher data rate than conventional ambient backscatter systems. Two modulation schemes, phase-shift keying (PSK) and a new approach named delay-shift keying (DSK), are investigated for the proposed system, and the optimal design of DSK is provided based on the theoretical upper bound of the symbol error rate (SER) over double frequency-selective channels. An optimal maximum-likelihood (ML) detector is also developed for the proposed system along with a feasible transmission protocol including channel estimation. Computer simulation results reveal that, at a bandwidth efficiency less than 3 bits per channel use (bpcu), PSK achieves the lowest SER while DSK achieves the lowest SER at efficiencies greater than 2 bpcu. The performance of the proposed detector was comparable to that of the conventional joint ML detectors with a lower degree of complexity, even when a limited number of subcarriers were available as pilot symbols.

Recently much research work has focused on employing deep learning (DL) algorithms to perform channel estimation in the upcoming 6G communication systems. However, these DL algorithms are usually computationally demanding and require a large number of training samples. Hence, this work investigates the feasibility of designing efficient machine learning (ML) algorithms that can effectively estimate and track time-varying, frequency-selective channels. The proposed algorithm is integrated with orthogonal frequency-division multiplexing (OFDM) to eliminate intersymbol interference (ISI) induced by the frequency-selective multipath channel and compared with the well-known least square (LS) and linear minimum mean square error (LMMSE) channel estimation algorithms. The obtained results have demonstrated that even when a small number of pilot samples, $N_{P}$ , is inserted before the $N$ subcarriers OFDM symbol, the introduced ML-based channel estimation is superior to the LS and LMMSE algorithms. This dominance is reflected in the bit-error-rate (BER) performance of the proposed algorithm, which attains a gain of 2.5 dB and 5.5 dB over the LMMSE and LS algorithms, respectively, when $N_{P}=\frac {N}{8}$ . Furthermore, the BER performance of the proposed algorithm is shown to degrade by only 0.2 dB when the maximum Doppler frequency is randomly varied. Finally, the number of iterations required by the proposed algorithm to converge to the smallest achievable mean-squared error (MSE) are thoroughly examined for various signal-to-noise ratio (SNR) levels.

Automatic modulation classification (AMC) is an essential factor in dynamic spectrum access to fulfill the spectrum demand of 5G wireless communications for achieving high data rate and low latency. Many deep learning (DL)-based AMC methods have achieved improved accuracy for classifying analog modulation schemes, single-carrier-based modulation schemes, and multi-carrier signals using several DL architectures such as the convolutional neural network (CNN) and long-short term memory (LSTM). However, most conventional DL-based AMC methods have confused the orthogonal frequency multiplexing division (OFDM)-based signals with different OFDM useful symbol lengths. To resolve the issue, we propose a CNN model operating on the fast Fourier transformation window bank (FWB) to extract the useful symbol length in OFDM, which represents the identification of each OFDM-based wireless communication technology. After extracting the OFDM useful symbol length, we propose a DL-based AMC system combined with FWB and in-phase and quadrature-phase signals to classify the OFDM symbol length and single-carrier modulation schemes simultaneously. Furthermore, we explore the constraints of the FWB parameters according to the length and the fast Fourier transformation (FFT) size of the OFDM signal to achieve good classification accuracy through the experiment. We constructed a dataset by generating OFDM signals of different lengths while changing the FFT size in a fixed bandwidth and selecting only quadrature amplitude modulation (QAM) schemes from RadioML2016.10a. Experimental results show the improved classification accuracy by about 30% over conventional classifiers in additive white Gaussian noise, synchronization impairments, and fading environments.

Channel estimation is a challenging and timely issue for the 3rd Generation Partnership Project (3GPP) standardized low power wide-area network technology named narrowband Internet of Things (NB-IoT). Channel estimation is crucial to achieve extended radio coverage, energy efficiency, coherent detection, and channel equalization for signal repetition dominated NB-IoT uplink transmission. The NB-IoT inherits simplified baseband radio frequency processing, physical channels, reference signal structure, and numerology from existing Long Term Evolution (LTE) systems to save power and costs. Thus, channel estimation methods extensively employed in LTE systems may not be applied to the NB-IoT uplink systems. In this paper, efficient discrete cosine transform type-I (DCT-I)-based transform-domain channel estimation approaches are proposed by modifying the original definition of DCT-I. The proposed methods can mitigate the problems experienced in the discrete Fourier transform (DFT)-based channel estimation, such as signal aliasing error, and border effect. The proposed approaches improve channel estimation precision by reducing signal distortion from the high-frequency region in the time-domain when non-sample-spaced path delays exist in multipath fading channels. Signal aliasing error experienced from the virtual subcarriers is also minimized with the anticipated schemes. The proposed methods are applied on simple least squares (LS) estimates in time-domain to eliminate estimation noise. The viability of the proposed estimators is verified as compared to the conventional LS, DFT-based de-noising LS, and standard DCT-I based methods through extensive numerical simulations. Based on the numerical simulations, the proposed estimators show better mean square error and bit error rate performances than their competitors in extremely low coverage conditions.

This paper proposes a novel bit loading algorithm to multicarrier non-orthogonal multiple access (NOMA) systems to maximize the system total throughput while satisfying the users’ individual quality of service (QoS) constraints. Although bit loading is generally a non-deterministic polynomial-time (NP)-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users, and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, in this paper, we propose an efficient bit loading algorithm for multicarrier NOMA systems and compare the complexity and throughput with a corresponding OMA system. The obtained results show that for the case of NOMA, bit loading provides an additional degree of freedom that allows non-uniform spectrum sharing among the users. In particular scenarios, the system may experience hybrid modes of operation, where the system switches between NOMA and OMA. The obtained numerical results show that NOMA can outperform OMA by 100% in terms of spectral efficiency, for a two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high complexity associated with the bit error rate (BER) computation in the case of NOMA. [ABSTRACT FROM AUTHOR]

Loading methods for wireline and wireless transmission systems are explained and reviewed to allow examination of fundamental equivalences between approaches to optimum transmission with linear cross-dimensional interference. Shannon’s famous water-filling method is then re-interpreted by examining the difference between mutual information and the constellation size used on each tone. Results reinforce that highest performance in all methods retains use of Shannon’s water-filling. However, results also find that a constant-size constellation on all energized water-filling dimensions, when used with a good code and an appropriate joint maximum-likelihood decoder, is sufficient to approximate this highest performance. This means that tone-dependent constellation variation is not necessary, which increases receiver complexity but reduces the need for channel-state feedback. This then leads to some adaptive MCS (modulation and coding scheme) equivalent loading methods that can be applied generally to wireline and wireless transmission.

The use of low-resolution data converters in the radio-frequency (RF) chains of all-digital massive multiple-input multiple-output (MIMO) basestations promises significant reductions in power consumption, hardware costs, and interconnect bandwidth. We propose a quantization-aware data-detection algorithm which mitigates the performance loss of 1-bit quantized massive MIMO orthogonal frequency-division multiplexing (OFDM) systems. Since the system performance heavily depends on the quality of channel estimates, we also develop a nonlinear 1-bit channel estimation algorithm that builds upon the proposed data detection algorithm. We show that the proposed algorithms significantly outperform linear data detectors and channel estimators in terms of bit error rate. For the proposed nonlinear data detection algorithm, we develop a very large scale integration (VLSI) architecture and present implementation results on a Xilinx Virtex-7 field programmable gate array (FPGA). Our implementation results are, to the best of our knowledge, the first for 1-bit massive MU-MIMO-OFDM systems and demonstrate comparable hardware efficiency with respect to state-of-the-art linear data detectors designed for systems with high-resolution data converters, while achieving lower bit error rate.

This paper presents an enhanced peak cancellation method with simplified in-band distortion compensation for massive multi-input multi-output (mMIMO) orthogonal frequency division multiplexing (OFDM). The method compensates an in-band distortion due to peak cancellation by utilizing extra transmit antennas, where a compensation signal is designed and transmitted using extra antenna elements so that in-band distortion is canceled at the receiver end. Consequently, deep peak cancellation is possible without degrading bit error rate performance. The proposed method is further extended to non-linear precoded mMIMO-OFDM systems, where the perturbation vector cancellation signal is superimposed on the compensation signal so that the received signal is demodulated without non-linear processing to remove the perturbation vector. Thus, the proposed method does not require the iterative calculation to compensate for an in-band distortion. Our results show the effectiveness of the proposed method in terms of peak-to-average power ratio (PAPR) characteristics, signal to noise and distortion power ratio (SDNR), bit error rate (BER), and throughput in comparison with the state-of-the-art.

We report on the realization of a multichannel imaging radar that achieves uniform 2-D cross-range resolution by means of a linear array of a special form of leaky-wave antennas. The presented aperture concept enables a tradeoff between the available range resolution and a reduction in the number of channels required for a given angular resolution. The antenna front end is integrated within a multichannel radar based on stepped-carrier orthogonal frequency-division modulation, and the advantages and challenges specific to this combination are analyzed with respect to signal processing and a newly developed calibration routine. The system concept is fully implemented and verified in the form of a mobile demonstrator capable of soft real-time 3-D processing. By combining radio frequency (RF) components operating in the $W$ -band (85–105 GHz) with the presented aperture, a 3-D resolution of less than ${1.5}^\circ \times {1.5}^\circ \times $ 15 cm is demonstrated using only eight transmitters and eight receivers. [ABSTRACT FROM AUTHOR]

A radar transceiver with two transmitters (TXs) and two receivers (RXs) is reported in 22 nm fully depleted silicon-on-insulator (FDSOI) CMOS. It includes a novel 200 MHz bandwidth 80 GHz phase-locked loop (PLL) based on a single-sideband (SSB) upconverter and an 11 GHz bandwidth phase-frequency detector to achieve >8 GHz locking range with record phase noise of −97, −103, and −113 dBc/Hz at 100 kHz, 1 MHz, and 10 MHz offset, respectively, and rms jitter < 40 fs. Stepped-frequency chirps with orthogonal frequency-division multiplexing (OFDM) modulation covering the 152–160 GHz range were demonstrated in a through-the-air loopback link along with a record settling time < 30 ns, limited by the test equipment. The RXs have an IIP3 of −8 dBm, SSB noise figure between 7.5 and 10 dB and a conversion gain of 15 dB, controllable from the LNA back gate over a range of 12 dB. The OP1 dB and $P_{\mathrm {SAT}}$ of the power amplifier (PA) in each TX are 5 and 9 dBm, respectively. The IQ amplitude mismatch and phase error of each RX are < 0.5 dB and 1°, respectively, while the $P_{\mathbf {out}}$ mismatch between the TXs is < 1 dB. The sensor consumes 1.13 W, with 300 mW by the PLL, 275 mW by the 160 GHz local oscillator (LO)-tree, 190 mW by each TX, and 87.5 mW by each RX. [ABSTRACT FROM AUTHOR]

With the prevalence of orthogonal frequency-division multiplexing (OFDM) in many standards, e.g., IEEE 802.11, IEEE 802.16, DVB-T, and DVB-T2, a number of variant modulation schemes based on OFDM have been proposed, which resort to signal sparsity to further enhance spectral efficiency and mitigate the high peak-to-average ratio (PAPR) problem. Among these variants, OFDM with subcarrier number modulation (OFDM-SNM) has been proven to be efficient for simple communication systems with low constellation modulation orders and limited decoding capability. To rigorously verify the performance advantages of OFDM-SNM, we present the study of OFDM-SNM in this paper from the information-theoretic perspective. In particular, we determine an upper bound on the mutual information of OFDM-SNM in closed form by using the log sum inequality. Also, we analyze the optimal pattern utilization probabilities (PUPs) for OFDM-SNM by channel-dependent coding and propose an easy-to-implement iterative algorithm to approach the optimal PUPs. Moreover, considering the practical achievability, we propose a Huffman coding based achievable PUP vector construction scheme to obtain the achievable PUPs and the corresponding achievable rate. We carry out numerical simulations to verify the effectiveness of this study and illustrate the efficiency of the obtained PUPs in comparison with several benchmarks. [ABSTRACT FROM AUTHOR]

A well-known drawback of orthogonal frequency-division multiplexing (OFDM) is its signal with high peak-to-average power ratio (PAPR). Among a number of PAPR reduction techniques, clipping and filtering (CAF) is the simplest approach, which effectively reduces the PAPR of band-limited OFDM signals at the cost of increasing in-band distortion. In order to mitigate the performance degradation caused by the in-band distortion, several iterative distortion recovery techniques have been proposed in the literature, and they are largely classified into time-domain (TD) and frequency-domain (FD) compensation approaches: The former and latter are represented by the decision-aided reconstruction (DAR) and clipping noise cancellation (CNC), respectively. To date, however, their theoretical performance limits have not been studied. In this work, we revisit the performance limits of CAF and derive a closed-form signal-to-distortion power ratio (SDR) expression. Furthermore, we introduce a time-domain distortion model for characterizing the OFDM signal with CAF, based on which we make performance comparison between the two compensation approaches. Theoretical analysis and simulations in terms of their achievable symbol error rate (SER) reveal that, unlike the FD counterpart, the TD compensation may suffer from unrecoverable distortion when filtering after clipping is applied at the transmitter. [ABSTRACT FROM AUTHOR]

Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternating optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements. [ABSTRACT FROM AUTHOR]

Inverse synthetic aperture sonar (ISAS) is the major instrument to observe moving underwater vehicles with high-resolution acoustic images. In this article, we consider the cyclic prefix (CP)-based orthogonal frequency-division multiplexing (OFDM) ISAS imaging for a noncooperative underwater vehicle. Compared to the ISAS using the conventional linear frequency-modulated (LFM) waveform, the OFDM signal is more sensitive to Doppler shifts/errors. The fluctuation of acoustic velocity in the propagating medium brings random phase errors and leads to heavily blurred images. Existing ISAS imaging methods are based on the translational motion of the target, and these methods suffer from defocusing in severe ocean conditions. In this article, we propose the motion compensation algorithm for the OFDM ISAS imaging to minimize the effect of velocity variations caused by ocean turbulence. The sparsity of the ISAS image is utilized as a global metric for the phase error estimation and image generation by constructing an $\ell _{1} $ -norm optimization. It is demonstrated that inter-range-cell interference (IRCI)-free ISAS images can be obtained no matter whether there are phase errors and/or strong noise. Simulation results indicate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The advancement of next-generation systems has led to a rapid increase in day-to-day applications by connecting billions of devices. Mobile networks require a considerable cost to support these billions of devices, and conventional IEEE 802.11 is meant for the usage of small-scale applications. To support long-range applications, IEEE task group-ah has proposed a new standard named IEEE 802.11ah. However, it has challenges like efficient device grouping based on variable sampling rates and power consumption. This standard employs orthogonal frequency-division multiplexing as a multicarrier scheme that suffers from high peak-to-average power ratio (PAPR). Hence, to support multiple devices of variable rates in the physical layer along with low PAPR and acceptable bit error rate, a new framework and an optimized architecture of carrier interferometry-group orthogonal–orthogonal frequency-division multiple access architecture is proposed. The proposed architecture reduces the 16-point inverse fast Fourier transform area by 57.1%, device padding area by 50% along with three extra adders functional area compared with the existing architecture. Furthermore, the proposed architecture is prototyped on Virtex5 field-programmable gate array for on-chip validation, and the hardware parameters are measured in real time. The area-timing complexity of the proposed architecture is estimated and further achieves the lowest area- and power-delay product than the existing architecture. Application-specific integrated circuit implementation is carried out using commercially available 32 - nm technology library, where the postlayout results reported less area utilization and low power consumption. [ABSTRACT FROM AUTHOR]

In this letter, a novel and low-cost echo separation technique for the multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is presented, based on the orthogonal frequency-division multiplexing (OFDM) chirp waveforms. The proposed scheme allows the generation of multiple OFDM chirp waveforms on common spectral support. In the new scheme, a series of simple time-domain operations including replica, T-shift, and superposition is applied to eliminate the interference waveform within a limited time. Then, a combination with a bandpass filter instead of a matched filter to focus signal power and digital beamforming (DBF) on receive in elevation enables the suppression of interference signals for a realistic spaceborne SAR scenario, where the swath width exceeds the spatial extension of the transmitted pulse. Furthermore, the distributed scene simulation results are presented to verify the practicability of the proposed scheme. [ABSTRACT FROM AUTHOR]

Impulsive noise (IN) and Doppler shift can significantly degrade the performance of orthogonal frequency-division multiplexing (OFDM)-based underwater acoustic (UWA) communication systems. In this paper, we propose a receiver structure that deals efficiently with both these channel impairments in a coded OFDM-based UWA system. First, an analog nonlinear preprocessor (ANP) is proposed to efficiently detect and mitigate IN in an analog domain. The proposed ANP exhibits intermittent nonlinearity when there is impulsivity. Next, the impact of IN on a two-step Doppler shift compensation approach is quantified. Specifically, the ability of the ANP to improve the robustness of Doppler shift compensation in the presence of IN is highlighted. The performance improvement of the proposed receiver is due to the fact that unlike the other nonlinear methods, the ANP is implemented in the analog domain where the outliers are still broadband and distinguishable. The simulation results also demonstrate the superior bit-error-rate (BER) performance of our approach relative to classic approaches that use blanking and/or clipping for IN mitigation.

In this paper, we establish a cryptographic primitive for wireless communications. An asymmetric physical layer encryption (PLE) scheme based on elliptic curve cryptography is proposed. Compared with the conventional symmetric PLE, asymmetric PLE avoids the need of key distribution on a private channel, and it has more tools available for processing complex-domain signals to confuse possible eavesdroppers when compared with upper-layer public key encryption. We use quantized information entropy to measure the constellation confusion degree. The numerical results show that the proposed scheme provides greater confusion to eavesdroppers and yet does not affect the bit error rate (BER) of the intended receiver (the information entropy of the constellation increases to 17.5 for 9-bit quantization length). The scheme also has low latency and complexity [O(N2.37), where N is a fixed block size], which is particularly attractive for implementation.

We investigate the peak-to-average power ratio (PAPR) reduction problem in orthogonal frequency-division multiplexing-based massive multi-user (MU) multiple-input multiple-output (MIMO) downlink systems. In this paper, we develop a downlink transmission scheme that performs jointly MU precoding and PAPR reduction (PP) by exploiting the excess degrees of freedom offered by equipping the BS by a large number of antennas. Specifically, the joint MU precoding and PAPR reduction scheme is formulated as a simple convex optimization problem solved via steepest gradient descent (GD) approach. Then, we develop a novel algorithm, referred to as MU-PP-GDm, to reduce the PAPR of the transmitted signals by exploiting the high-dimensional null space of the MIMO channel matrix while maintaining an excellent transmission quality. The simulation results show that the proposed MU-PP-GDm has low computational complexity and can achieve substantial PAPR performance with a fast convergence rate.

This paper devises a robust receiver for OFDM systems in the presence of residual timing offsets and unknown channel prior information. The proposed receiver constructs typical receiver models and resorts to the model selection technique to choose the best-matched receiver model to improve the channel estimation and signal detection. The typical receiver models are classified by considering the channel delay spread and the level of timing offset. Based on the receiver model selected by the Bayesian model selection algorithm, the channel length and timing offset parameters in the receiver model can provide the effective channel statistical information to make the channel estimator adapt to the altered circumstances and thus more accurate. Furthermore, the effective interference variance parameters in the selected receiver model are used to enhance the channel estimation and refine the soft symbol detection. The simulation results show that the proposed receiver achieves a significant performance gain compared to the existing methods. It is also shown that the proposed scheme makes the receiver robust to the diverse channel conditions and the timing offset states at a cost of the only a moderate increase in complexity.

A novel spectrally efficient optical transmission in a passive optical network (PON) with hexagonal quadrature amplitude modulation (QAM)-based adaptive modulated optical (AMO) orthogonal frequency division multiplexing (OFDM) is proposed and experimentally demonstrated. With the help of hexagonal QAM and 4-dimensional (4D) modulation, adaptive bit loading is effectively processed to enhance the total data rate. In this approach, some subcarriers are aggregated to transmit integer bits. To employ hexagonal QAM-based 4D AMO-OFDM, the signal to noise (SNR) ratio is calculated using the measured error vector magnitude of the training signal. In our experiment, hexagonal QAM-based 4D AMO-OFDM transmission with a total data rate of 21 Gbps is demonstrated over a 20 km standard single mode fiber for two optical network units (ONUs). The experimental results show that the total data rate obtained using the proposed method is increased by 14% compared to the conventional square QAM-based AMO-OFDM.

Signal modulation identification (SMI) plays a very important role in orthogonal frequency-division multiplexing (OFDM) systems. Currently, SMI methods are often implemented via feature extraction based on machine learning. However, the traditional methods encounter a bottleneck where the probability of correct classification (PCC) is very limited and hence it is hard to implement in practical OFDM systems due to the fact that traditional methods are difficult to extract feature of the OFDM signals. In order solve these problems, we propose a deep learning (DL) based SMI method for identifying OFDM signals. Specifically, convolutional neural network (CNN) is adopted to train in-phase and quadrature (IQ) samples for OFDM signals. Then we choose dropout layer to prevent overfitting and improve its identification accuracy. In addition, datasets with different modulation modes are adopted to verify our trained CNN. Experiments are conducted to show that our proposed method achieves higher accuracy and better consistency than traditional methods. Moreover, extensive results confirm that the proposed method performs robustly in different datasets.

Direction of arrival (DOA) in MIMO radar is estimated from the phase differences between its channels. Hence, the phase difference vector, called a virtual array response vector, needs to be accurately known. We propose a new model of the virtual array response with a wide-band interleaved orthogonal frequency-division multiplexing (OFDM) signal. The angle is estimated by maximum likelihood estimation (MLE), and we show that the estimation error is lower than the one when using the systematic phase correction method which compensates for the range-dependent phase term. Also, the root mean square error (RMSE) of angle estimation with the proposed method is closer to the Cramér-Rao bound (CRB). [ABSTRACT FROM AUTHOR]

Next-generation radar sensors require imaging capabilities with high angular resolution. As for a single sensor, the aperture, and thus the achievable resolution, is limited due to the constraints of the front end, radar networks consisting of multiple sensors are a possible solution. However, their incoherency usually makes joint angle estimation impossible. This article presents a network concept consisting of an orthogonal frequency-division multiplexing (OFDM) radar and repeater elements, which receive the reflections from targets and retransmit them back to the radar. Thereby, any frequency conversion from radio frequency to baseband and vice versa is omitted such that the signal remains coherent to the initial transmit signal. To distinguish the bistatic signal transmitted by the repeater from the monostatic one of the OFDM radar, the orthogonal subcarrier structure of OFDM waveforms is exploited by combining a sparse radar transmit signal with a low-frequency modulation in the repeater. This allows to evaluate the bistatic signals at the radar with standard multiple-input–multiple-output (MIMO)-OFDM signal processing, leading to separate range–Doppler images for each virtual channel. Finally, it is shown that this method offers a coherent angular estimation based on the extended aperture of the network. For this purpose, a method to establish phase coherency by a reconstruction of the modulation phase is presented. The network concept is proved with measurements at 77 GHz. [ABSTRACT FROM AUTHOR]

It is well-known that time-varying channels can provide time diversity and improve error rate performance compared to time-invariant fading channels. However, exploiting time diversity requires very accurate channel estimates at the receiver. In order to reduce the number of unknown channel coefficients while estimating the time-varying channel, basis expansion models can be used along with long transmission frames that contain multiple orthogonal frequency division multiplexing (OFDM) symbols that experience the channel variation. The design of these OFDM frames need to judiciously incorporate training and data insertions in the transmitted signal while maintaining orthogonality. In this work, we propose an inter channel interference (ICI)-free training model depending on pilot symbols only and provide a corresponding time-varying channel estimation method. This scheme relies on an algorithm to determine the number of OFDM symbols per frame and the number of basis functions per path with minimal information about the Doppler bandwidth. As a performance benchmark, Bayesian Cramér Rao lower bound (CRLB) and the corresponding MSE bound are derived analytically for the proposed training model. Theoretical MSE expressions of the proposed estimation scheme are also derived as well as the MSE expressions in the presence of Doppler frequency mismatch. Simulations exhibit substantial MSE improvement and the corresponding Symbol Error Rate (SER) performances of the low complexity estimation scheme. They also corroborate that, unlike the common results in the literature, an OFDM system can perform better as the Doppler frequency increases with judicious design of training and channel estimation schemes. [ABSTRACT FROM AUTHOR]

Various correlation-based receivers have been proposed in passive bistatic and active monostatic radar exploiting orthogonal frequency-division multiplexing communications signals, but too little has been dedicated to establishing their relations and advantages over each other. Accordingly, this article provides an analytical comparison of the common filters in such waveform sharing scenarios, along with a performance analysis regarding three criteria: computational complexity, signal-to-interference-plus-noise-ratio, and resilience to ground clutter. The last two especially assess the possible detrimental effects of the random sidelobes (or pedestal) induced by the data symbols in the range-Doppler map. Although simulations show that none of the filters performs unanimously better, the ones employing circular correlations globally evidence attractive results. [ABSTRACT FROM AUTHOR]

To overcome the necessity of high sampling rates for common orthogonal frequency-division multiplexing (OFDM)-based radar waveforms with a good range resolution, a scheme called frequency comb OFDM radar has recently been proposed. It keeps the sampling rates at the transmitter and receiver constant while simultaneously increasing the signal bandwidth in the radar channel. This letter describes the extension of the scheme for multiple-input multiple-output (MIMO) applications where a nonequidistant subcarrier interleaving can be used to prevent a reduction of the unambiguous range that occurs in case of regular allocation to the transmit channels. The functionality is demonstrated for the first time with a $4 \times 4$ MIMO system in a measurement scenario with static and emulated moving targets. [ABSTRACT FROM AUTHOR]

Multiuser visible light communication (MU-VLC) systems utilizing multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) are gaining increased attentions recently. Visible light communication (VLC) links are expected to work under different illumination conditions and, thus, the need for dimming control mechanisms. However, the traditional analog- and digital-based dimming schemes have adverse effects on the data communications performance, such as clipping distortion and the variation of the duty cycle. In this paper, spatial dimming schemes based on the zero-forcing and the minimum mean-squared error precoding schemes are proposed for direct-current biased optical OFDM based indoor MU-MIMO VLC system, and the bipolar optical OFDM signal is biased by a fixed dc level. Transmit antenna selection algorithms are designed for the optimum working light emitting diodes (LEDs) subset at each dimming level. Owing to the simultaneously exploration of the selection diversity of LEDs-based lights and the channel state information, the proposed spatial dimming schemes outperform the traditional dimming schemes, which is also verified by simulation results. Thus, the proposed schemes are shown to have a great potential to be applied in practical MU-MIMO-OFDM VLC systems.