Orthogonal Frequency Division Multiplexing Channel Research Articles

Channel Estimation for LEO Satellite Massive MIMO OFDM Communications

In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency division multiplexing channel estimation for low-earth-orbit satellite communication systems. First, we use the angle-delay domain channel to characterize the space-frequency domain channel. Then, we show that the asymptotic minimum mean square error (MMSE) of the channel estimation can be minimized if the array response vectors of the user terminals (UTs) that use the same pilot are orthogonal. Inspired by this, we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance. In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing. Moreover, the space domain processing of each UT is shown to be identical for all the subcarriers, and an asymptotically optimal vector for the per-subcarrier space domain linear processing is derived. The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver. Simulation results show that the proposed TSCE approach can achieve a near performance to the MMSE estimation with much lower complexity.

Channel Estimation for Underwater Acoustic OFDM Communications: Recent Advances

Background: To resist the time-variant underwater acoustic (UWA) channel, large amounts of channel estimation algorithms for the UWA orthogonal frequency division multiplexing (OFDM) are presented. An updated review of the recent UWA OFDM channel estimators is suggested in this article. Objective: The goal of this work is to review and conclude the development of different types of channel estimators. The possible perspectives about the future UWA channel estimator design are also discussed. Methodology: The principles and performances of the linear channel estimators, the compressed sensing (CS)-based channel estimators, and the neural network (NN)-based channel estimators are reviewed and discussed. Simulations are conducted to compare the typical implementations of the different methods. Conclusion: To take more channel state characteristics into account, the data-driven methods have been applied in the channel estimator design. Compared with the linear and CS-based methods, the NN-based channel estimator shows the higher performance, robustness and lower complexity, which is promising to be applied with the proper structure and training sets.

Deep Learning-Assisted OFDM Channel Estimation and Signal Detection Technology

The orthogonal frequency division multiplexing (OFDM) technique has received wide attention because of its high spectrum utilization. However, the drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. In this paper, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet). The simulation results show that the SCBiGNet model outperforms the existing techniques for the different numbers of pilots and lengths of CPs. The BER performance is improved by 0.2~9 dB.

Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems

Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability.

Effect of DNN Approximation for Channel Estimation and Signal Detection on OFDM Applications

This paper offers a deep learning approximation to realize channel estimation and signal detection that creates the main communication structure skeleton for the orthogonal frequency-division multiplexing (OFDM) system known as an efficient modulation type on 5G. This letter offers an application of deep learning to handle the wireless OFDM channels' end-to-end conduct. First, channel state information (CSI) is predicted explicitly that differs from existing OFDM receivers, then detected the transmitted symbols utilizing the predicted CSI. In the end, CSI is predicted by the suggested deep learning approximation indirectly and transmitted symbols are directly recovered. The structure of the designed receiver occurs of a layer of DNN and soft decisions, which resolves the issues channel estimation error, time delay, and limitation of decoding between users in classic detection techniques. In the simulation results, it is observed that the receiver has powerful stability on the power distribution of user, not only convenient for the linear channel, but also for nonlinear channel when enhancement the number of users, also detection can be well on the receiver. Generally, the efficiency of the modulation system decreases with the features of the multipath channel utilized for transmission. Channel estimation and detection of symbols utilize to reduce the impacts of the channel, which needs high computation and bandwidth conventionally. This paper is used deep neural networks (DNN) for detecting the signal, in this way much effort in detecting the channel is prevented. The proposed method saves priceless bandwidth via used CP in OFDM with a big increase in SNR.

Device-Free Sensing in OFDM Cellular Network

This paper considers device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular network to enable integrated sensing and communication (ISAC). A novel two-phase sensing framework is proposed to localize the passive targets that cannot transmit/receive reference signals to/from the base stations (BSs), where the ranges of the targets are estimated based on their reflected OFDM signals to the BSs in Phase I, and the location of each target is estimated based on its ranges to different BSs in Phase II. Specifically, in Phase I, we design a model-free range estimation approach by leveraging the OFDM channel estimation technique for determining the delay values of all the two-way BS-target-BS paths, which does not rely on any BS-target channel model. In Phase II, we reveal that ghost targets may be falsely detected in some cases as all the targets reflect the same signals to the BSs, which thus do not know how to match each estimated range with the right target. Interestingly, we show that the above data association issue is not a fundamental limitation for device-free sensing: under the ideal case of perfect range estimation in Phase I, the probability for ghost targets to exist is proved to be negligible when the targets are randomly located. Moreover, under the practical case of imperfect range estimation in Phase I, we propose an efficient algorithm for joint data association and target localization in Phase II. Numerical results show that our proposed two-phase framework can achieve very high accuracy in the localization of passive targets, which increases with the system bandwidth.

Provable Chaotically Authenticated Encrypted Biomedical Image Using OFDM Transmission

In this research, a unique multiband random chaotic key generator based provable authenticated encrypted technique for biomedical picture for the healthcare biomedical system, which can be used in 5G communication system is presented. In addition, the encryption method employed in this research is based on Multiband Random Chaotic Key Generator, and the proposed provable authenticated methodology is based on symmetric authenticated encryption data (MBRCKG). In the proposed proven Orthogonal Frequency Division Multiplexing (OFDM) communication system, the Authenticated Chaotic Encrypted Biomedical Image (ACE-BI) is utilized. This study uses discrete wavelet transformation (DWT) and discrete cosine transformation (DCT) to mask patient data and hospital watermarks in biological images. With various statistical and OFDM settings, channel analysis and statistical analysis have been examined for their effects on the collected hospital logo and patient data. The simulation studies demonstrate how resistant to communication signal processing the proposed ACE-BI method is. Additionally, the proposed algorithm is able to reduce encryption time to one quarter because the partial encryption based in one level DWT scheme.

Performance Evaluation of WDM Optical Fiber Communication System in the presence of PMD

This paper investigates for rising optical fiber transmission strength, increasing bandwidth, and decreasing communication system weakness by using wavelength division multiplexing (WDM). WDM gives today's distention speed and communication traffic. Systems using WDM faces nonlinearities, which the most intensive nonlinear attack is, four wave mixing (FWM). FWM creates and increases crosstalk between WDM channels as a result slows down and impairs the performance of the communication system. This investigation uses orthogonal frequency division multiplexing (OFDM) for evaluating execution of WDM fiber system by repairing Polarization Mode Dispersion (PMD). We took results in the case of trying PMD-Emulator and without trying PMD-Emulator in the system design. We compared the results got in both cases. Furthermore, we compared the performance of the system with the investigations done using different ways, methods, and techniques for compensating PMD and FWM appears in WDM systems. As PMD-Emulator, helps enhancing the system design performance, and OFDM gives the feature of robustness and useful execution to the system. OFDM examined by appointing interfered orthogonal signal sets, for 16 channels; with equally spaced OFDM channels. Oure results showed that the optical fiber communication system using OFDM technique gives perfect removing FWM signal crosstalk, and accurate data transmission, comparing to other techniques used in other researches. We got a decreased FWM power to -77dBm, and the BER of -0.317. Furthermore, the system quality increased with applying PMD-Emulator and OFDM. In addition, using PMD-Emulator in the system design raised the results effectiveness. The program used in the present work is optisystem-15, and the results obtained in this study coincide with the theoretical and actual results obtained by the previous studies.

Game Theory for Anti-Jamming Strategy in Multichannel Slow Fading IoT Networks

The open nature of the wireless communication medium renders it vulnerable to jamming attacks by malicious users. To detect their presence and to avoid such attacks, several techniques are present in the literature. Most of these techniques aim to reduce the effect of the jamming signals by increasing the transmission power or by using complex coordination schemes. However, the implementation of such power consuming techniques might be challenging or not feasible in limited resources Internet-of-Things (IoT) devices. Therefore, a defending strategy against jamming attacks in health monitoring IoT networks is proposed in this article. This strategy operates in orthogonal frequency-division multiplexing channels and takes into consideration the effect of slow fading channels in the strategy design. Specifically, the jamming combating problem is formulated as a Colonel Blotto game where the equilibrium defines the minimization of the worst case jamming effect on the IoT sensors communications. Then, the optimal power allocation strategy for all the potential jammer power ranges is derived by investigating the Nash equilibrium of the game. This proposed strategy is shown to be efficient in combating jamming attacks while minimizing the IoT sensors power consumption.

GaRuDa: A Blockchain-Based Delivery Scheme Using Drones for Healthcare 5.0 Applications

Over the years, the healthcare industry has transformed from being hospital-centric to patient-centric. This shift is mainly due to the convergence of emergent technologies such as Internet of Things (IoT)-based healthcare, fifth generation (5G)-assisted networking, and data-driven analytics through artificial intelligence. The convergence, referred to as Healthcare 5.0, has improved healthcare services to support real-time analytics, user mobility, remote monitoring, and personalized user experience through decentralized applications. However, the medical supply chain systems and delivery operations among healthcare stakeholders suffer from limitations of harsh environmental conditions due to restricted zones, rough terrains, war-prone areas, poor road conditions, congested traffic, and remote locations. Thus, the Internet of Drones (IoD) is deployed in Healthcare 5.0 supply chains to streamline and expedite the medical delivery process through open channels (i.e. the Internet). However, the Internet is an open channel that is prone to malicious activities which can violate the privacy and confidentiality of patient data. Blockchain is a promising technology that can handle the security and reliability of drone delivery among untrusted open channels. Motivated by the aforementioned facts, we propose GaRuDa, a blockchain-based drone delivery scheme for Healthcare 5.0 applications. The proposed scheme integrates IoD and blockchain through 5G-enabled tactile Internet to facilitate low-latency responsive delivery of medical supplies that can be chronologically monitored and tracked among different stakeholders. We compared the proposed scheme with the traditional medical delivery scheme with payment gateways to demonstrate its effectiveness in terms of data storage, computation, and communication costs. The simulation results obtained show that the average transaction cost has been reduced by ≈ 44.56 percent, and 5G tactile Internet has a reduced latency of 0.051 ms compared to 18.3 ms in 4G LTE service. The average communication overheads have reduced by ≈ 67.34 percent compared to traditional 4G LTE and orthogonal frequency-division multiplexing channels, which indicates the scheme's viability.

Sparse, Group-Sparse, and Online Bayesian Learning Aided Channel Estimation for Doubly-Selective mmWave Hybrid MIMO OFDM Systems

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier's quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequency-domain (FD) correlation of the channel's frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.

Optimal Pilot-Based Channel Estimation in Cognitive Radio

Cognitive radio (CR) technology is to assure the better utilization of the spectrum, permitting the non-licensed users to utilize the unused bands provided to the Licensed or primary users, which is made effective through the application of the Orthogonal Frequency Division Multiplexing (OFDM). The effective utilization of the OFDM channels through temporarily allocating the bands to the unlicensed users avoids interference. In order to ensure the reliable spectrum sensing in CR with effective spectral efficiency, the channel estimation (CE) is enabled in the OFDM-based multi-input and multi-output systems. The channel estimation is facilitated through the pilot-based sequential approach for the least square CE in OFDM-based CR. The optimal insertion of the pilots in the input data symbols before transmitting the signal through the fading OFDM channels in CR is made through the proposed rider grey wolf optimization, which is the integration of the grey wolf optimization and rider optimization algorithm. The estimation is done through the least square of the original channel state and the estimated symbols. The experimental analysis is progressed both in Rician and Rayleigh environments with minimal mean square error of 1.28E−06 and bit error rate of 0.000626.

Incorporation of prior knowledge into sparse time dispersive OFDM channel estimation via weighted atomic norm minimisation

A new estimator for sparse time dispersive channels in pilot aided orthogonal frequency division multiplexing (OFDM) systems is developed by considering prior knowledge on channel time dispersions. The authors propose a weighted atomic norm minimisation (WANM) in order to incorporate the prior information into the estimator. The dual of the WANM is then converted to a tractable semidefinite programming using positive trigonometric polynomial theory. After solving the dual problem, the channel response is identified by solving a least squares approach. In this work, they assume that time dispersions associated delays can take any value with a mild minimum separation condition on the normalised interval [ 0 , 1 ) . The performance of the new estimator is compared with conventional approaches. With respect to the pilot number and signal to noise ratio (SNR), simulation results reveal that the proposed estimator performs superior to that of traditional methods. It is shown that both a lower SNR and number of pilots are required to achieve the same mean square error reported in previous works.

Video coding for OFDM systems with imperfect CSI: A hybrid digital–analog approach

Performance of an orthogonal frequency division multiplexing (OFDM) system is greatest when the exact channel state information (CSI) is used for transmitter rate control and power allocation. However, in real systems CSI can only be approximately known. Moreover, in video communication, it can be difficult to use any CSI for rate control of a video codec if the channel changes significantly during a group of pictures coded jointly, such as when the receiver is moving. We address this issue through a hybrid digital–analog (HDA) coding system where a standard video codec is used to generate a fixed-rate base layer upon which the analog quantization error is superimposed as a refinement layer. The system adapts to channel variations by proper transmit-power allocation between digital and analog components and across OFDM subcarriers, based on CSI. We present a power allocation scheme for this system which explicitly takes into account the imprecise nature of the available CSI. Experimental results obtained with simulated OFDM channel traces show that proposed scheme is able to achieve a much better quality-vs-reliability trade-off in video transmission, compared to the best known digital-only and analog-only alternatives.

Performance evaluation of high mobility OFDM channel estimation techniques

In wireless communication, Orthogonal Frequency Division Multiplexing (OFDM) has been adopted due to its robustness to multipath fading and high data rate transmissions. At the other hand, the performance of OFDM systems severely degraded due to multi-path fading and Doppler frequency shifts in mobile systems, which causes inter-carrier-interference (ICI). Thus, Estimation of channel parameters is required at the receiver using a pre designed estimator where pilot tones are inserted in each OFDM symbol. In this paper, a random pilot data are generated and inserted in each OFDM symbol at equally spaced locations. The performance test of Least Square (LS) and Linear Minimum Mean Square (LMMSE) estimation methods are proposed with Discrete Fourier Transform (DFT) based on both LS and LMMSE, where different ITU channel models are considered in order to compare their performance for data transmission in high mobile systems with different Doppler frequencies exceeds 200 Hz and minimal number of pilots.

Hybrid OFDM receiver assisted by a variable frequency comb

A physically assisted orthogonal frequency division multiplexing (OFDM) receiver is described and characterized. In contrast to recent reports that utilize two physically distinct frequency combs with Verniered frequency pitch, the new receiver topology relies on a single frequency-toggled frequency comb. Dual-comb photonic front end was replaced by a single comb split into two switched paths to achieve spectral decomposition. To demonstrate new receiver operation, hybrid RF-photonic architecture for discrete Fourier transform (DFT) was constructed and used to decompose a wideband RF signal. The receiver demodulated a 4-QAM OFDM channel using 50 carriers from a single frequency comb. OFDM channel, spanning 3-7.9GHz RF band, was encoded using 100MHz-separated subcarriers. OFDM receiver performance was quantified by measuring its error vector magnitude (EVM).

Eliminating Impulsive Noise in Pilot-Aided OFDM Channels via Dual of Penalized Atomic Norm

In this paper, we propose a novel estimator for pilot-aided orthogonal frequency division multiplexing (OFDM) channels in an additive Gaussian and impulsive perturbation environment. Due to sensor failure which might happen because of man-made noise, a number of measurements in high rate communication systems is often corrupted by impulsive noise. High power impulsive noise is generally an obstacle for OFDM systems as valuable information will be completely lost. To overcome this concern, an objective function based on a penalized atomic norm minimization (PANM) is provided in order to promote the sparsity of time dispersive channels and impulsive noise. The corresponding dual problem of the PANM is then converted to tractable semidefinite programming. It has shown that one can simultaneously estimate the time dispersive channels in a continuous dictionary and the location of impulsive noise using the dual problem. Several numerical experiments are carried out to evaluate the performance of the proposed estimator.

Joint Resource Allocation for Frequency-Domain Artificial Noise Assisted Multiuser Wiretap OFDM Channels with Finite-Alphabet Inputs

The security issue on the physical layer is of significant challenge yet of paramount importance for 5G communications. In some previous works, transmit power allocation has already been studied for orthogonal frequency division multiplexing (OFDM) secure communication with Gaussian channel inputs for both a single user and multiple users. Faced with peak transmission power constraints, we adopt discrete channel inputs (e.g., equiprobable Quadrature Phase Shift Keying (QPSK) with symmetry) in a practical communication system, instead of Gaussian channel inputs. Finite-alphabet inputs impose a more significant challenge as compared with conventional Gaussian random inputs for the multiuser wiretap OFDM systems. This paper considers the joint resource allocation in frequency-domain artificial noise (AN) assisted multiuser wiretap OFDM channels with discrete channel inputs. This security problem is formulated as nonconvex sum secrecy rate optimization by jointly optimizing the subcarrier allocation, information-bearing power, and AN-bearing power. To this end, with a suboptimal subcarrier allocation scheme, we propose an efficient iterative algorithm to allocate the power between the information and the AN via the Lagrange duality method. Finally, we carry out some numerical simulations to demonstrate the performance of the proposed algorithm.

Reliable OFDM Receiver With Ultra-Low Resolution ADC

The use of low-resolution analog-to-digital converters (ADCs) can significantly reduce power consumption and hardware cost. However, their resulting severe nonlinear distortion makes reliable data transmission challenging. For orthogonal frequency division multiplexing (OFDM) transmission, the orthogonality among subcarriers is destroyed. This invalidates conventional OFDM receivers relying heavily on this orthogonality. In this study, we move on to quantized OFDM (Q-OFDM) prototyping implementation based on our previous achievement in optimal Q-OFDM detection. First, we propose a novel Q-OFDM channel estimator by extending the generalized Turbo (GTurbo) framework formerly applied for optimal detection. Specifically, we integrate a type of robust linear OFDM channel estimator into the original GTurbo framework and derive its corresponding extrinsic information to guarantee its convergence. We also propose feasible schemes for automatic gain control, noise power estimation, and synchronization. Combined with the proposed inference algorithms, we develop an efficient Q-OFDM receiver architecture. Furthermore, we construct a proof-of-concept prototyping system and conduct over-the-air (OTA) experiments to examine its feasibility and reliability. This is the first work that focuses on both algorithm design and system implementation in the field of low-resolution quantization communication. The results of the numerical simulation and OTA experiment demonstrate that reliable data transmission can be achieved.

Flexible Multi-Group Single-Carrier Modulation: Subcarrier Mapping and Power Allocation Optimization

Orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization (SC-FDE) are the two commonly adopted modulation schemes for frequency-selective channels. Compared to SC-FDE, OFDM generally achieves higher data rates but at the cost of higher transmit signal peak-to-average power ratio (PAPR), which leads to lower power-amplifier efficiency. This paper studies a multi-group single carrier modulation scheme, termed flexible multi-group single carrier (FMG-SC), which encapsulates both OFDM and SC-FDE as special cases, thus achieving more flexible rate-PAPR tradeoffs between them. Specifically, in FMG-SC, the total bandwidth is divided into a set of orthogonal subcarriers that, based on the channel gains, are flexibly mapped to multiple non-overlapping groups, and SC-FDE is applied over each group independently to send multiple data streams in parallel. We investigate the joint subcarrier grouping and power allocation optimization problem to maximize the achievable rate of our proposed FMG-SC scheme for both the cases with Gaussian signaling and with practical modulation constellation (e.g., quadrature amplitude modulation), respectively. For both cases, the optimization problem is non-convex in general, for which we propose a two-step approach for finding a high-quality approximate solution efficiently. First, with any given subcarrier grouping, we show that the optimal power allocation can be obtained via convex optimization techniques. Second, with fixed power allocation, we propose low-complexity algorithms for the subcarrier grouping design catering to the SC-FDE receiver. Numerical results show that our proposed algorithms perform close to the optimal solution obtained via exhaustive search over all subcarrier groupings yet with substantially reduced complexity. Moreover, the achievable rate of our proposed FMG-SC scheme with Gaussian signaling approaches the OFDM channel capacity and significantly outperforms that of SC-FDE. Furthermore, with practical modulation constellation, numerical results show that our proposed FMG-SC scheme greatly outperforms the existing single carrier frequency-division multiple access in terms of achievable rate but with moderately increased PAPR.