Artificial Intelligence-Aided Receiver for a CP-Free OFDM System: Design, Simulation, and Experimental Test

In recent years, an Ultra Wideband (UWB) system has attracted much attention in short range high speed wireless communications. Meanwhile, the single-carrier transmission with frequency domain equalization (SC-FDE) is also attracting much attention. It is reported that the SC-FDE system obtains an excellent performance as well as orthogonal frequency division multiplexing (OFDM), because the SC-FDE system has the advantage of frequency diversity gain even in strong frequency selective channels. However, in the SC-FDE system, cyclic extension reduces the bandwidth efficiency. To improve the bandwidth efficiency, we proposed UWB-IR and DS-UWB with Frequency-Domain Equalization (FDE) and Cyclic Prefix (CP) reconstruction, and showed that when the channel estimation is ideal, the proposed system achieves the good performance. Both FDE and CP reconstruction use channel gains and thus the performance is expected to depend largely on the channel estimation accuracy. In this paper, we evaluate the effect of imperfect channel estimation on the performance of UWB-IR with MMSE-FDE and CP Reconstruction. We show that CP reconstruction is effective for UWB-IR with MMSE-FDE in the IEEE UWB multipath channels, CM3, CM4.

Cyclic Prefix (CP) based block Recursive Least Squares (RLS) channel estimation algorithms have been proposed for Orthogonal Frequency Division Multiplexing (OFDM) systems. In this paper, we investigate the performance of CP based exponentially weighted block RLS channel estimator. Our analysis and extensive simulation results show that smaller values of exponential forgetting weightings cause increase in convergence time and steady state performance error of the algorithm. Furthermore, performance of the approach degrades with the increase in channel nulls and constellation size.

Orthogonal frequency division multiplexing (OFDM) is adopted in most wireless communication systems, and the performance of OFDM is affected by impulse noise (IN). Better channel estimation (CE) performance is required to detect the channel information on the receiving side. The OFDM system is considered with the number of subcarriers carrying the data and pilot symbols. In the OFDM modulator, the symbols of the frequency domain are transformed into the signals of the time domain using inverse discrete Fourier transformation (IDFT). Effective impulsive noise mitigation is crucial for improving the effectiveness of OFDM communication systems, and it improves the signal-to-noise ratio (SNR) at the receiver. A cyclic prefix is then appended before transmission through the channel. In the receiver noise, the effect of IN is mitigated jointly with CE using the Bayesian matching pursuit (BMP) and Moth-flame algorithm (MFA). In this approach, the IN and channel information is considered as a sparse vector. Here, the data symbols used are considered an unknown parameter. The approach incorporating all subcarriers has a lower mean square error (MSE) of IN estimate for impulsive noise reduction. The MFA algorithm is used to optimize the sensor matrix in BMP. The sparsity of the channel and the impulse response are observed in the time domain to represent the channel and IN together. It exactly recovers the sparse signal for finding the convex objectives of the sparse minimizer, and the sparse solution is obtained with fixed point updates. The proposed BMP algorithm improves the CE by explicitly considering the existence of IN. The BMP is a greedy algorithm that selects the most correlated residuals at each column. Under mutual incoherence, it recovers the signal with a higher probability. The simulated outcomes proved that the proposed CE and noise mitigation model achieved better performance based on the bit error rate (BER), throughput and MSE.

Orthogonal Frequency Division Multiplexing (OFDM) is a very attractive technique for high data rate transmission with good bandwidth efficiency. OFDM system utilize the cyclic prefix (CP) to mitigate the effect of inter symbol interference (ISI) produced by the multipath effect of wireless channel. The CP insertion only mitigates the effect of inter block interference of OFDM symbols, whereas the intra block interference remain present in the OFDM symbol. The intra block OFDM symbol interference degrades the error performance of the OFDM system. In order to improve the error performance of the OFDM system forward error correction scheme can be utilized. Many FEC schemes for single carrier and multicarrier systems have been proposed in the literature. Convolution and LDPC codes are two popular coding schemes for FEC. In this paper we have considered Convolution and LDPC codes for improving the error performance of OFDM system. The BER performance of convolution ally coded a LDPC coded OFDM is evaluated for various code rates in AWGN and fading channel. We have also compared the performance of coded OFDM system with an OFDM system without FEC.

Massive multi input multi output (MIMO) systems incorporate orthogonal frequency division multiplexing (OFDM) technology to render high data rate services for future wireless communication applications. The channel estimator (CE) employed by a reliable massive MIMO-OFDM system requires huge amount of overhead in the form of known and null data transmissions, hence limiting the system spectral efficiency (SE). Often, CE design is a tradeoff between SE and system reliability. In this paper, CE with three different overhead arrangements, namely time domain synchronous (TDS), comb type with cyclic prefix (CTCP), 2D grid type with cyclic prefix (GTCP) are investigated and a GTCP based CE is proposed which offers both high SE and improved system reliability. The proposed CE uses autocorrelation based denoising threshold for channel impulse response (CIR) estimation and does not require any knowledge of channel statistics (KCS). A 4 x 16 MIMO-OFDM system is simulated in a rayleigh fading channel environment with U-shaped doppler spectrum. From the bit error rate (BER) performance results inWiMax SUI-4, Advanced Television Technology Center (ATTC) and Brazil A channel environments, it is verified that the proposed CE with GTCP overhead and proposed denoising scheme, indeed improves both SE and system reliability. Hence it is suitable for application in all massive MIMO-OFDM systems.

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

<p>Orthogonal frequency division multiplexing (OFDM) is a transmission system that uses multiple orthogonal carriers that are sent out at the same time. OFDM is a technique for mobile and wireless communication that has high-efficient frequency utilization, high data-rate transmission, simple and efficient implementation using the fast Fourier transform (FFT) and the inverse fast Fourier transform (IFFT), and reduces inter symbol interference (ISI) by inserting cyclic prefix (CP). One of the most important approaches in an OFDM system is channel estimation. In this paper, the orthogonal frequency division multiplexing system with the Rayleigh channel module is analyzed for different areas. The proposed approach used large numbers of subcarriers to transmit the signals over 64-QAM modulation with pilot add channel estimation. The accuracy of the OFDM system is shown in the measuring of the relationships of peak power to the noise ratio and bit error rate.</p>

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.

In the recent fifth generation (5G) research, filter bank multi-carrier (FBMC) technique is one of the competitive substitutes to orthogonal frequency division multiplexing (OFDM). The most fascinating features of FBMC are theoretically higher spectral efficiency and extremely low out-of- band radiation by removing cyclic prefix (CP) and using well-localized pulse shape. However, conventional OFDM channel estimation (CE) methods cannot be directly applied to FBMC, because it exists the intrinsic inter-carrier/inter-symbol interference resulting in performance degradation. This paper presents a new preamble-based CE method using two different FBMC symbols to transmit the preamble sequences and their copy, respectively. Consequently, the intrinsic interference of the two received preambles can be eliminated by combining them. Simulation results indicate that the new method obtains the almost same CE performance as that of CP-OFDM in multi-path fading channel. In addition, the preamble power of the new method is significantly lower than that of other existing methods in FBMC.

The blend of Multiple-Input–Multiple-Output (MIMO) and Orthogonal Frequency-Division Multiplexing (OFDM) provides a formidable air interface for fourth-generation (4G) technology. In beyond 4G standards, the derivative technologies of OFDM along with MIMO are poised to function as the air interface. The task of optimizing the performance metrics for resource allocation in MIMO-OFDM is a crucial aspect in 4G systems like Long Term Evolution (LTE) or LTE Advanced (LTE-A). As the evolution of LTE-Advanced continues and opens the vistas to fifth-generation (5G) standard, several performance metrics need to be optimized. The prime metrics which characterize a wireless system are the ergodic capacity and Spectral Efficiency (SE) of the system. There exists a precarious trade-off between SE and Energy Efficiency (EE) which needs to be judiciously balanced. Other aspects of resource optimization include enhancement of the throughput at the user end, reduction of cost per bit. This paper explores the critical aspects of potential enhancement and discusses ways to optimize resource allocation. Specific attention is given to the provisioning of the Cyclic Prefix (CP) in OFDM and use of Multiuser MIMO (MU-MIMO). Although CP is an overhead, but it is required to overcome the effects of Inter-Symbol Interference (ISI). However, adaptive CP length adjustment can improve the spectral efficiency. It is demonstrated that MU-MIMO yields a higher energy efficiency as compared to point-to-point MIMO. Therefore, MU-MIMO is ideally suited to yield a higher throughput in 4G and beyond systems.

In this paper, we study the deep learning (DL) based channel extrapolation problem and conduct the over-the-air (OTA) antenna extrapolation and frequency channel interpolation test for the 3rd generation partnership project (3GPP) long-term evolution (LTE) time-division duplex (TDD)-like orthogonal frequency division multiplexing (OFDM) massive MIMO prototype. We first present measurement campaigns using universal software radio peripherals (USRP) at 3.5 GHz, where the base station (BS) is composed of a 64-element antenna array. A DL-based antenna extrapolation network is then designed to approximate the inner deterministic function among antennas from the attained channel data within the “training” pilots. We present an antenna selection network (ASN) that can select a limited number of antennas for the best extrapolation, which outperforms the uniform antenna selection in terms of channel reconstruction and signal detection. We also design a deep residual neural network for channel interpolation. The performance of the extrapolated channel is evaluated in terms of normalized mean squared error (NMSE) in comparison to the measured channels on all antenna ports or the full pilot-aided channels in all OFDM subcarriers. Experimental results show that ASN can reduce an average of 87.5% antenna ports and maintain channel estimation NMSE by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-2}$ </tex-math></inline-formula> when compared to 3GPP channel estimation protocols.

Orthogonal frequency-division multiplexing (OFDM), with the help of a cyclic prefix, enables low complexity frequency domain equalization, but suffers from a high crest factor. Single carrier with cyclic prefix (SC-CP) has the same advantage with similar performance, but with a lower crest factor and enhanced robustness to phase noise. The cyclic prefix is overhead, so we put more information in it by implementing this cyclic prefix as a training sequence (TS). This new training aided frequency domain equalized single carrier (TASC) scheme offers us additional known symbols and enables better synchronization and (potentially) channel estimation, with the same performance as SC-CP.

The performance of underwater acoustic (UWA) communication is heavily dependent on channel estimation, which is predominantly researched by simulating UWA channels modelled in complex and dynamic underwater environments. In UWA channels modelling, the measurement-based approach provides an accurate method. However, acquirement of environment data and simulation processes are scenario-specific and thus not cost-effective. To overcome such restraints, this article proposes a comprehensive simulation platform that combines UWA channel modelling with orthogonal frequency division multiplexing (OFDM) channel estimation, allowing users to model UWA channels for different ocean environments and simulate channel estimation with configurable input parameters. Based on the simulation platform, three independent simulations are conducted to determine the impacts of receiving depth, sea bottom boundary, and sea surface boundary on channel estimation. The simulations show that UWA channel estimation is greatly affected by underwater environments. The effect can be mainly attributed to changing acoustic rays tracing which result in fluctuating time delay and amplitude. With 10 m receiving depth and flat sea bottom, the channel estimation achieves optimal performance. Further study indicates that the sea surface has stochastic effects on channel estimation. As the significant wave height (SWH) increases, the average performance of channel estimation shows improvements.

Orthogonal Frequency Division Multiplexing (OFDM) underpins modern wireless communication due to its resilience against multi-path fading and computationally efficient implementations. However, on one hand, in scenarios with time-frequency doubly selective fading channels, OFDM systems face significant challenges, as channel variation induces inter-carrier interference (ICI) that disrupts subcarrier orthogonality. On the other hand, the limited length of the cyclic prefix (CP), often constrained to a fraction of the symbol duration, may be insufficient to fully mitigate inter-symbol interference (ISI) in scenarios with large time spreads. Extending CP length would reduce spectral efficiency and increase latency, making it incompatible with the demands of high-efficiency, low-latency systems. In this paper, we propose an autoencoder based OFDM architecture integrating adaptive pulse shaping with an equalization neural network (PS-EQNet) that jointly addresses ISI caused by insufficient CP and ICI due to channel dynamics through learnable time-frequency filters. Additionally, we introduce a detection network tailored for simplified channels, which reduces computational complexity compared to existing schemes while maintaining robust performance. Simulation results confirm that the proposed PS-EQNet OFDM system significantly relaxes CP requirements, enhances spectral efficiency (SE), and achieves reliable bit error rate (BER) performance in time-frequency selective channels, establishing a flexible trade-off among SE, BER, and computational complexity.

Multi carrier modulation is Orthogonal Frequency Division Multiplexing (OFDM), dividing the large band spectrum into small (narrow) band spectrum.OFDM offers flexibility in adaptation to time varying channel condition by adopting the parameters at each subcarrier accurately.Due to the multipath effect the OFDM system suffers Inter Symbol Interference (ISI).ISI can be reduced by introducing the guard band (cyclic prefix).Time-varying channel estimation is an important activity in next generation wireless communication.Channel estimation is required mainly for the significant signal processing operation in multiple input multiple output MIMO-OFDM systems.In this project DFT -based channel estimation based on block type arrangement is implemented in MAT Lab.The bit error rate (BER) of DFT (Discrete Fourier Transform)based channel estimation is less than the BER of LS (Least Square) and MMSE (Minimum Mean Square Error) based channel estimation with same signal to noise ratio (SNR).