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Adoption of Orthogonal Frequency Division Multiplexing (OFDM) in 5th Generation New Radio (5G NR) as a multicarrier modulation technique allows high data rate transmission with lower complexity. However, problem such as peak to average power ratio (PAPR) has decreased the transmitter efficiency. Recently, several attempts have been made to reduce the high PAPR in OFDM utilizing deep learning (DL) based on autoencoder architecture. However, the proposed autoencoder using symmetrical autoencoder (SAE) is followed by high computational complexity at both transmitter and receiver as well as BER performance degradation. Since 5G NR focuses on massive 5G internet of things eco-system, a flexible carrier spacing is used to support diverse spectrum bands which is afterward named as Cyclic Prefix OFDM (CP-OFDM). In this study, we aimed to contribute to this growing area of research by exploring the potential of our proposed asymmetrical autoencoder (AAE) to reduce high PAPR in the CP-OFDM system. Four AAE models have been developed in this study and the performance of the models were evaluated based on comprehensive conditions such as data training at different corruption levels, cyclic prefix length, upsampling factors and loss function levels. The investigation of AAE in 5G CP-OFDM system has shown superior performance using a 5×1 AAE model that can reduce a substantial amount of PAPR, BER degradation and computational complexity compared to conventional SAE. This study lays the groundwork for future research into the asymmetrical approach of autoencoder especially in 5G and beyond networks.

Least squares (LS) is a commonly used pilot-based channel estimation algorithm in orthogonal frequency division multiplexing (OFDM) systems. This algorithm is simple and easy to implement because of its low computation complexity. However, it has poor performance, especially at low signal-to-noise ratio (SNR). To solve this problem, an improved LS channel estimation method based on convolutional neural network (CNN) is proposed on the basis of analyzing the traditional LS channel estimation methods. A channel estimation compensated network is designed based on CNN, which can solve the problem of performance degradation of the mean square error (MSE) through the online and offline modules. By designing the input-output relations, training data set, and testing data set, a CNN network is iteratively trained to learn the relevant features of the channels, so that the traditional LS estimation value can be corrected to improve the accuracy. Simulation results show that the proposed method can achieve better performance in bit error rate (BER) and MSE, compared with the traditional channel estimation methods.

Least squares (LS) is a commonly used pilot-based channel estimation algorithm in orthogonal frequency division multiplexing (OFDM) systems. This algorithm is simple and easy to implement because of its low computation complexity. However, it has poor performance, especially at low signal-to-noise ratio (SNR). To solve this problem, an improved LS channel estimation method based on convolutional neural network (CNN) is proposed on the basis of analyzing the traditional LS channel estimation methods. A channel estimation compensated network is designed based on CNN, which can solve the problem of performance degradation of the mean square error (MSE) through the online and offline modules. By designing the input-output relations, training data set, and testing data set, a CNN network is iteratively trained to learn the relevant features of the channels, so that the traditional LS estimation value can be corrected to improve the accuracy. Simulation results show that the proposed method can achieve better performance in bit error rate (BER) and MSE, compared with the traditional channel estimation methods. [ABSTRACT FROM AUTHOR]

This paper examines the performance of orthogonal frequency division multiplexing (OFDM) systems for vehicle-to-vehicle (V2V) communication channels. More specifically, a doubly selective channel under high intercarrier interference (ICI) is considered. Current solutions involve complex detection and/or reduced spectral efficiency receivers. This paper proposes the use of virtual carriers (VC) in an OFDM system with a low-complexity maximal ratio combining (MRC) detector to improve the bit error rate (BER) performance. The results show that VC provides diversity in received data, resulting in a ≥5 dB gain compared to previous OFDM systems with conventional linear/nonlinear detectors used as a reference. The detector presented in this paper has linear complexity, making it a suitable solution for real-time V2V communication systems. [ABSTRACT FROM AUTHOR]

In order to improve the signal detection capability of orthogonal frequency-division multiplexing systems, a signal detection method based on an improved LSTM network for OFDM systems is proposed. The LSTM network is optimized by the Chameleon Swarm Algorithm (CLCSA) with the coupling variance and lens-imaging learning. The signal detection method based on the traditional LSTM network has the problem of a complex manual tuning process and insufficient stability. To solve the above problem, the improved Chameleon Swarm Algorithm is used to optimize the initial hyperparameters of the LSTM network and obtain the optimal hyperparameters. The optimal hyperparameters initialize the CLCSA-LSTM network model and the CLCSA-LSTM network model is trained. Finally, the trained CLCSA-LSTM network model is used for signal detection in the OFDM system. The simulation results show that the signal detection performance of the OFDM receiver has been significantly improved, and the dependence on CP and pilot overhead can be reduced. Under the same channel environment, the proposed method in this paper has better performance than other signal detection methods, and is close to the performance of the MMSE method, but it does not need prior statistical characteristics of the channel, so it is easy to implement. [ABSTRACT FROM AUTHOR]

The IEEE 802.11ah standard is designed to overcome the limited transmission range in conventional wireless local area network (WLAN) technologies and connect hundreds and thousands of sensors in the Internet of Things (IoT). Therefore, as a promising communication technology, it will play an important role in the IoT in the future. In this paper, to support the adaptive transmissions in the IoT, we focus on the channel prediction issue based on the IEEE 802.11ah standard. In particular, we introduce an adaptive echo state network (AESN) to predict the channel state information (CSI) in the orthogonal frequency division multiplexing (OFDM) systems of the IEEE 802.11ah standard. The AESN includes a basic echo state network (ESN) model and an adaptive elastic network. Thereinto, the adaptive elastic network is utilized to estimate the output weight matrix in the ESN. Therefore, the AESN produces the well sparse output weight matrix, enjoys the oracle property, and offers the excellent prediction performance. In the simulation section, an outdoor scenario with the Rayleigh channel and an indoor scenario with the Nakagami-m channel are considered based on the OFDM systems of the IEEE 802.11ah technology. In the simulations, we firstly use the recurrence quantification analysis (RQA) to explore the local predictability of the CSI samples. Then, the AESN is further evaluated in the one-step prediction, the multistep prediction and the robustness test. The results indicate that by using the AESN, we can effectively predict the CSI to support the adaptive transmissions in the IoT. [ABSTRACT FROM AUTHOR]

The high-speed of train (HST) in combination with the high carrier frequency of HST systems leads to the severe inter carrier interference (ICI) in the HST orthogonal frequency division multiplexing (HST-OFDM) systems. To avoid the complexity in OFDM receiver design for ICI eliminations, the OFDM system parameters such as symbol duration, signal bandwidth, and the number of subcarriers should be chosen appropriately. This paper aims to propose a process of HSTOFDM system performance investigation to determine these parameters in order to enhance spectral efficiency and meet a given quality-of-service (QoS) level. The signal-to-interferenceplus- noise ratio (SINR) has been used as a figure of merit to analyze the system performance instead of signal-to-noise ratio (SNR) as most of recent research studies. Firstly, using the nonstationary geometry-based stochastic HST channel model, the SINR of each subcarrier has been derived for different speeds of the train, signal bandwidths, and number of subcarriers. Consequently, the system capacity has been formulated as the sum of all the single channel capacity from each sub-carrier. The constraints on designing HST-OFDM system parameters have been thoughtfully analyzed using the obtained expressions of SINR and capacity. Finally, by analyzing the numerical results, the system parameters can be found for the design of HSTOFDM systems under different speeds of train. The proposed process can be used to provide hints to predict performance of HST communication systems before doing further high cost implementations as hardware designs.

This paper examines the performance of orthogonal frequency division multiplexing (OFDM) systems for vehicle-to-vehicle (V2V) communication channels. More specifically, a doubly selective channel under high intercarrier interference (ICI) is considered. Current solutions involve complex detection and/or reduced spectral efficiency receivers. This paper proposes the use of virtual carriers (VC) in an OFDM system with a low-complexity maximal ratio combining (MRC) detector to improve the bit error rate (BER) performance. The results show that VC provides diversity in received data, resulting in a ≥5 dB gain compared to previous OFDM systems with conventional linear/nonlinear detectors used as a reference. The detector presented in this paper has linear complexity, making it a suitable solution for real-time V2V communication systems.

Abstract Nonlinear behavior and power efficiency of the Power Amplifier (PA) contradictorily depend on the input signal amplitude distribution. The transmitted signal in multi-carrier modulation exhibits high Peak-to-Average Power Ratio (PAPR) and large bandwidths, leading to the degradation of the radio link and additional generation out-of-band interferences, which degrade the quality of the transmission. Practical solutions exist, like a power back-off, but with unacceptable efficiency performances of the transmitter. This paper deals with efficiency and linearity improvement using a new PAPR reduction method based on the combination of Discrete Cosine Transform and shaping technique. The main principle is to determine an optimal coding scheme according to a trade-off between coding complexity and performance benefits in the presence of PA nonlinearities. Simulation and experimental results in the context of OFDM signal and using a 20 W–3.7 GHz Radio-Frequency Power Amplifier show an improvement on PAPR reduction of about 3.25 dB. Also, the communication criteria like Bit Error Rate and Error Vector Magnitude are improved by about one decade and a half and $$8\%$$ 8 % , respectively.

In order to improve the signal detection capability of orthogonal frequency-division multiplexing systems, a signal detection method based on an improved LSTM network for OFDM systems is proposed. The LSTM network is optimized by the Chameleon Swarm Algorithm (CLCSA) with the coupling variance and lens-imaging learning. The signal detection method based on the traditional LSTM network has the problem of a complex manual tuning process and insufficient stability. To solve the above problem, the improved Chameleon Swarm Algorithm is used to optimize the initial hyperparameters of the LSTM network and obtain the optimal hyperparameters. The optimal hyperparameters initialize the CLCSA-LSTM network model and the CLCSA-LSTM network model is trained. Finally, the trained CLCSA-LSTM network model is used for signal detection in the OFDM system. The simulation results show that the signal detection performance of the OFDM receiver has been significantly improved, and the dependence on CP and pilot overhead can be reduced. Under the same channel environment, the proposed method in this paper has better performance than other signal detection methods, and is close to the performance of the MMSE method, but it does not need prior statistical characteristics of the channel, so it is easy to implement.

The peak-to-average power ratio (PAPR) reduction and linearization techniques are both effective methods to improve the efficiency of the transmitter in digital video broadcasting (DVB) systems. Traditional methods deploy the PAPR reduction model and the linearization model, respectively, without considering their mutual influence. Therefore, the joint optimizations of PAPR reduction and linearization techniques are proposed. However, these methods train the PAPR reduction model and the linearization model based on the time-division training method. It is difficult to meet the requirements of multiple objectives. To address this issue, this paper proposes a joint PAPR reduction and digital predistortion (DPD) method using the real-valued neural network (RVNN) for Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed method jointly trains the PAPR reduction function and the DPD function with multi-objective optimization, to achieve PAPR reduction and linearization simultaneously. Especially, this method unifies the PAPR reduction function and the DPD function into one model based on RVNN, and no extra processing is required at the receiver. Compared with the traditional methods, the experimental results show that the proposed method has superior performance in PAPR, adjacent channel power ratio (ACPR) and bit error rate (BER), while having lower computational complexity. [ABSTRACT FROM AUTHOR]

We study the problem of downlink beamforming training and channel estimation for millimeter wave (mmWave) OFDM systems, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station) and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). To efficiently probe the channel, we form multiple directional beams simultaneously at the transmitter and steer them towards different directions. The objective is to devise the beam training sequence and develop an efficient algorithm to estimate the channel. By exploiting the sparse scattering nature of mmWave channels, the above problem is formulated as one of sparse encoding and signal recovery, which involves finding a sparse sensing matrix to compress the sparse channel and an efficient channel estimation algorithm to recover the sparse channel from compressive measurements. In this article, we propose a sparse bipartite graph code-based algorithm, where a set of bipartite graphs are employed to encode the sparse channel and a simple decoding procedure that relies on the presence of a No-Multiton-graph (NM-graph) is used to reconstruct the sparse channel. Theoretical analysis shows that our proposed method can help achieve a substantial training overhead reduction. Simulations are provided to show the effectiveness of the proposed algorithm and its performance advantage over compressed sensing-based methods. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications.

In this work, an estimation algorithm based on the compressive sensing (CS) technique is proposed for an orthogonal frequency division multiplexing (OFDM) system applied to multi-path time-varying channels. A fitting polynomial is used to approximate each channel path, and full-band training symbols are adopted. The variation of the channel response can then be evaluated according to the observation on the inter-carrier interference (ICI) from the determinate signals. The CS-based technique is introduced to explore the sparsity of the double-selective channel and the usage of high-order fitting polynomials is allowed. The orthogonal matching pursuit (OMP) algorithm is designed to cooperate with the fitting polynomial model, and more accurate channel estimation results can be provided compared to those provided by conventional least-square (LS) algorithms. When multiple transmission antennas are required, the advantage of the proposed polynomial-fitting OMP (PF-OMP) algorithm is more obvious as more transmission antennas are used.

In this paper, we develop the family of Golay–Rudin–Shapiro (GRS) m-complementary many-parameter sequences and many-parameter Golay transforms. The approach is based on a new generalized iteration generating construction, associated with n unitary many-parameter transforms and n arbitrary groups of given fixed order. We are going to use multi-parameter Golay transform in Intelligent-OFDM-TCS instead of discrete Fourier transform in order to find out optimal values of parameters optimized PARP, BER, SER, anti-eavesdropping and anti-jamming effects.

Nonlinear behavior and power efficiency of the Power Amplifier (PA) contradictorily depend on the input signal amplitude distribution. The transmitted signal in multi-carrier modulation exhibits high Peak-to-Average Power Ratio (PAPR) and large bandwidths, leading to the degradation of the radio link and additional generation out-of-band interferences, which degrade the quality of the transmission. Practical solutions exist, like a power back-off, but with unacceptable efficiency performances of the transmitter. This paper deals with efficiency and linearity improvement using a new PAPR reduction method based on the combination of Discrete Cosine Transform and shaping technique. The main principle is to determine an optimal coding scheme according to a trade-off between coding complexity and performance benefits in the presence of PA nonlinearities. Simulation and experimental results in the context of OFDM signal and using a 20 W–3.7 GHz Radio-Frequency Power Amplifier show an improvement on PAPR reduction of about 3.25 dB. Also, the communication criteria like Bit Error Rate and Error Vector Magnitude are improved by about one decade and a half and 8 % , respectively. [ABSTRACT FROM AUTHOR]

Summary: One of the effective peak‐to‐average power ratio (PAPR) reduction methods in orthogonal frequency division multiplexing (OFDM) systems is the partial transmit sequence (PTS) method in which the input data stream is divided into disjoint subblocks and the subcarriers in each subblock multiplied by predefined phase factors. Consecutive data symbols corresponding to a source/user are often more correlated, so when the corresponding subcarriers are weighted by the same phase factor, a sufficient reduction in PAPR cannot be attained by the PTS algorithm in common subblocks. To solve this problem, in this paper, we propose a new systematic PAPR reduction method based on a two‐dimensional subcarrier mapping. Specifically, we scramble/interleave each OFDM symbol based on T‐transform to obtain a new set of subblocks which will be multiplied by predefined phase factors. In the proposed method, each one‐dimensional symbol sequence is converted to a two‐dimensional matrix, and the partitions are formed starting from the main diagonal diameters in the matrix. We present the permutation matrix and reformulate the T‐transform technique based on the permutation matrix. Through numerical experimentations, it is shown that the proposed PAPR reduction technique outperforms the previous counterparts. Also, we analyzed our proposed results by aperiodic autocorrelation function (ACF); reducing of ACF side lobes shows that our proposed method cause considerable PAPR parameter reduction respected to other methods. [ABSTRACT FROM AUTHOR]

In this study, the authors have proposed a low complexity companding function for peak-to-average-power ratio (PAPR) reduction in orthogonal frequency division multiplexing systems. This function transforms the signals into a probability density function with a cumulative distribution function an anti-trigonometric function. By carefully choosing the transform parameters, the bit error rate (BER) performance can be improved significantly during the reduction of PAPR. In addition, the proposed companding function offers design flexibility. Simulation results show that for the same PAPR performance, the proposed companding scheme can offer better BER performance. For example, upon assigning BER = 10−4 in an additive white Gaussian noise channel without a solid-state power amplifier, the minimum required bit energy to noise power spectral ratio (Eb/No) for the proposed companding scheme with c = 0 and A = 1.414 is 11 dB. It is 5, 4.2, and 3 dB lower than exponential companding function, root-based μ-law companding function, and amplitude limiting companding schemes, respectively. Also, their PAPR performance is the same. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive polarization-QAM modulation (APQAM) scheme is proposed to improve the power amplifier (PA) energy efficiency (EE) in orthogonal frequency division multiplexing (OFDM) systems. Since we found that polarization modulation (PM) has better EE performance than QAM in the nonlinear region of PA while QAM outperforms PM in the linear region, we can combine these two modulation schemes together and adjust their modulation orders adaptively to obtain higher EE performance. First, a truncated Taylor series model is used to represent PA nonlinearity, so the effects of both the in-band distortions and the out-of-band emissions on the OFDM signals can be analyzed. Second, two APQAM algorithms are investigated in both AWGN channel and depolarization channel with power-dependent loss. Furthermore, symbol error rate performance of the APQAM scheme is obtained in different channel conditions, and the influence of channel estimation error is analyzed to testify the robustness of the proposed APQAM scheme. Simulation results show that, if perfect channel state information is obtained, the EE of the proposed scheme is nearly 50% more than that of the traditional QAM scheme in OFDM systems.

In this paper, a diamond shape pilot arrangement for OFDM channel estimation is investigated. Such arrangement will decrease the number of pilots transmitted over the communication channel which in turn will increase data throughput while maintaining acceptable accuracy of the channel estimation, the adaptive antenna array (AAA) is combined with orthogonal frequency division multiplexing (OFDM) to combat the intersymbol interference (ISI) and the directional interferences. In this paper, The optimum beamformer weight set is obtained based on minimum bit error rate (MBER) criteria in diamond-type pilot-assisted in 3GPP long term evolution (LTE) OFDM systems under multipath fading channel. The simulation results show that the quadrature phase shift keying signaling based on MBER technique utilizes the antenna array elements more intelligently than the standard minimum mean square error (MMSE) technique.

Wireless communications play a key role in intelligent transportation systems for bearing multiple application services with different quality of service (QoS) requirements. Hierarchical modulation (HM) is an effective technique to increase the spectral efficiency (SE) of differentiated services multiplexing transmissions. In high mobility scenario, due to the impact of the feedback delay and channel estimation error, only the imperfect channel state information is available at the transmitter. Thus, the performance of the existing HM schemes degrades severely. In this paper, we propose a robust adaptive HM scheme to increase the overall SE in the mobile orthogonal frequency division multiplexing system. The bit error rate of the HM symbol is derived for the reliable QoS-aware multi-service transmission, and the strategies of bit assignment and modulation parameters selection for different services are designed. Finally, comparing with the traditional uniform modulation scheme, the proposed robust adaptive HM scheme increases the overall SE of multiplexing transmissions by 15% at 300km/h. [ABSTRACT FROM AUTHOR]

In this paper, we consider an orthogonal frequency division multiplexing communication system that adopts frame-by-frame transmission in high-speed railway (HSR) scenario. Due to the increase in demand for the QoS sensitive services, an efficient QoS-aware transmission strategy that improves the system performance is required urgently. Many efforts have been devoted to addressing this problem with the assumption of block fading channel in a frame duration. However, due to the frequent channel quality variation in a frame duration and serious inter channel interference in HSR scenario, the throughput of the QoS sensitive services degrades severely. Hence, a robust cross-layer transmission strategy that combines adaptive modulation (AM) scheme with truncated automatic repeat request protocol is proposed. In this cross-layer formulation, the normalized average throughput is optimized subject to the average power and the packet loss rate (PLR) requirements. First, we derive the closed form average bit error rate that represents the PLR requirement at the physical layer. Second, we obtain the solution of robust AM scheme and power allocation policy in the case of continuous rate. Third, we present the adaptive bits and power allocation scheme in the case of discrete rate, which can be implemented in practice. Finally, the performance of the proposed transmission strategy is evaluated by extensive simulations. Comparing with the constant transmit power AM scheme, the throughput increases by 20%, which demonstrates that the proposed robust cross-layer design is suitable for the HSR communication systems.

In OFDM systems, peak-to-average power ratio (PAPR) reduction of the signal is one of the main challenges that need to be overcome in order to use the transmitter in an efficient manner. As one of attractive techniques, interleaving can be used in PAPR reduction for multicarrier signals without spectrum distortion. In this paper, the authors propose to extend the possibilities of interleaving to improve PAPR reduction, to use a new coding of interleaver keys at the transmitter and a robust decoding procedure at the receiver. In order not to degrade the data rate, the use of null subcarriers to transmit side information to the receiver is proposed and evaluated. Simulation results in the context of the WLAN 802.11a standard in the presence of a nonlinear power amplifier model with memory, show a reduction of PAPR of approximately 5.2 dB, and an improvement of bit error rate and error vector magnitude of about 2 decades and 4% respectively, while respecting the spectral mask specification.

In this Letter, the authors derive the moment generating function‐based BER expressions for binary phase shift keying and M‐QAM signal for OFDM systems over α–κ–μ fading. This fading model is known as the generalised fading model because it can accurately characterise all the well known small‐scale fading models given in the literature. In order to validate the derived BER expressions, analytical and simulated BER curves for a variety of modulation formats at different values of fading parameters are also shown in the letter. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In OFDM systems, peak-to-average power ratio (PAPR) reduction of the signal is one of the main challenges that need to be overcome in order to use the transmitter in an efficient manner. As one of attractive techniques, interleaving can be used in PAPR reduction for multicarrier signals without spectrum distortion. In this paper, the authors propose to extend the possibilities of interleaving to improve PAPR reduction, to use a new coding of interleaver keys at the transmitter and a robust decoding procedure at the receiver. In order not to degrade the data rate, the use of null subcarriers to transmit side information to the receiver is proposed and evaluated. Simulation results in the context of the WLAN 802.11a standard in the presence of a nonlinear power amplifier model with memory, show a reduction of PAPR of approximately 5.2 dB, and an improvement of bit error rate and error vector magnitude of about 2 decades and 4% respectively, while respecting the spectral mask specification. [ABSTRACT FROM AUTHOR]

Multicarrier wireless communication systems, which usually operate over frequency-selective fading channels, are practically also impaired by environmental impulsive noise. In order to boost the signal-to-noise power ratio (SNR) at the receiver, this paper proposes and analyzes nonlinear estimators based on the multiple thresholding, with associated piecewise attenuation, or clipping, of the received signal amplitude. The proposed approach exploits a new heuristic criterion to set the thresholds. Although the obtained thresholds are slightly suboptimal in output SNR, the proposed approach has the nice and useful property to allow closed-form analytical derivations for both the threshold(s) and the associated attenuating/clipping parameters, when a Gaussian source is impaired by an impulsive noise. Such a quite general class of estimators, which could be applied also in other scenarios, is particularly attractive for orthogonal frequency division multiplexing communication systems in the presence of additive multicomponent impulsive noise. We are also letting to derive closed-form expressions for the output SNR in frequency-selective multipath fading channels. The SNR performance and the associated symbol error rate have been compared with those of more traditional blanking, clipping, and clipping-blanking processors. Specifically, analytical and simulation results, carried out for Class-A and symmetrical alpha-stable (SαS) impulsive noise, show that the proposed threshold-based suppressors are superior to the traditional ones. Furthermore, as the number of thresholds increases, the proposed estimators closely approach the performance of the optimal minimum mean square error Bayesian estimator. However, as it is shown, in practical conditions only few thresholds are necessary. [ABSTRACT FROM AUTHOR]

In this article, we propose a new selected mapping (SLM) technique using interleavers for reducing high peak-to-average-power ratio (PAPR) in orthogonal frequency division multiplexing systems. The proposed technique can be considered as a modified version of the conventional SLM technique where a set of candidate symbol blocks can be generated from the original symbol block by inserting some pre-known block interleavers between the mapper and the phase rotation multipliers. One of the advantages of this approach is that with very low complex block interleavers the PAPR performance of the SLM scheme can be improved without increasing the number of IFFTs. Simulation results show that the proposed algorithm has better PAPR performance than the conventional SLM for a given number of IFFTs with no additional side information. [ABSTRACT FROM AUTHOR]

Nowadays, Power Line Communication (PLC) technology is receiving a renewed attention for a wide class of innovative applications and services, mainly thanks to the advantage of using the existing electrical infrastructures, thus reducing the deployment costs. However, such electrical infrastructures have been originally conceived for power distribution and not for data. Hence, signal propagation is affected by more severe impairments than traditional media, such as impulsive noise and frequency selectivity. Furthermore, new emerging PLC applications need more efficient information delivery schemes that encompass both unicast and multicast mode. Towards this end, this paper deals with an efficient Network Coding (NC) based multicast reliable PLC scheme that makes use of (i) a decision direct impulsive noise mitigation approach and of (ii) a scheme based on multiple transmissions of a same symbols in the frequency domain. A suitable optimization procedure is also proposed here with the aim of improving the performance of the proposed scheme, under the constraint of a specified maximum data flow delivery failure probability. Finally, comparisons with alternative solutions are provided in order to highlight the significant advantages of the proposed scheme in terms of data flow delivery delay and overall transmission energy cost, under specific service constraints. [ABSTRACT FROM AUTHOR]

Joint estimation of channel and sampling frequency offset (SFO) in orthogonal frequency division multiplexing (OFDM) systems, using Bayesian framework, is shown in this study. Hybrid Cramér–Rao lower bounds (HCRLBs) for the estimation of SFO together with channel are obtained. The significance of Bayesian approach in the formulation of joint estimator is shown by comparing HCRLB with the corresponding standard CRLB. The authors propose a joint maximum a posteriori (JMAP) algorithm for the estimation of channel and SFO in OFDM, utilising the prior statistical knowledge of channel. To reduce the complexity of JMAP estimator, a modified MAP algorithm, which has no grid searches, is also proposed. Also, they analyse the effect of inaccurate knowledge of channel statistics and signal‐to‐noise ratio on the estimation accuracy. The estimation methods are analysed by numerical simulations and resultant conclusions validate the better performance of the proposed algorithms when compared with previous algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The vast majority of existing secret key generation protocols exploit the inherent randomness of the wireless channel as a common source of randomness. However, independent noise added at the receivers of the legitimate nodes affects the reciprocity of the channel. In this paper, we propose a new simple technique to generate the secret key that mitigates the effect of noise. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We study the properties of our generated SRP and derive a closed form expression for the probability mass function of the realizations of our SRP. We simulate an orthogonal frequency division multiplexing system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate between the legitimate nodes when compared with the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques. Moreover, we compute the conditional probabilities used to estimate the secret key capacity. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Power line communication technology receives today a renewed attention for a wide class of advanced applications and services, thanks to the advantage of using the existing electrical infrastructure, which requires only limited deployment costs. However, wires have been originally designed for power and not for data, and signal propagation is affected by several impairments such as impulsive noise and frequency selectivity. On the other end, new emerging application perspectives—mainly related to the smart grid or Internet of Things paradigms—need reliable unicast and/or multicast information delivery methodologies. This paper deals with an efficient multicast power line communication system based on the use of a decision direct impulsive noise mitigation scheme and of multiple transmissions of symbols in the frequency domain, allowing an efficient exploitation of the communication channel frequency diversity. The performance of the reliable multicast power line communication system is derived by resorting to the application of the absorbing Markov chain theory in terms of mean overall delivery delay and throughput, in relation to different application scenarios. Moreover, a suitable optimization procedure is also proposed here in order to achieve the best performance. The advantages of the proposed optimized reliable multicast power line communication system are finally validated by comparisons with a classical system. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate antenna selection strategies for multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems from an energy efficiency (EE) perspective. We first derive closed-form expressions of the EE and the energy efficiency–spectral efficiency (EE–SE) tradeoff in conventional antenna selection MIMO-OFDM systems. The obtained results show that these systems suffer from a significant loss in EE. To achieve a better EE performance, we propose an adaptive antenna selection method where both the number of active radio-frequency chains and the antenna indexes are selected depending on the channel condition. This selection scheme could be implemented by an exhaustive search technique for a small number of antennas. Moreover, we develop a greedy algorithm that achieves a near-optimal performance with much lower complexity compared with the (optimal) exhaustive search method when the number of antennas is large. In addition, the efficacy of power loading across subcarriers for improved EE in the conventional and proposed antenna selection MIMO-OFDM systems is considered. Monte Carlo simulation results are provided to validate our analyses. [ABSTRACT FROM AUTHOR]

Adaptive feed-forward (FF) linearization schemes of power amplifiers provide high distortion cancelation and adjacent channel power ratio (ACPR) reduction. However, FF schemes are very sensitive to imbalances in the circuit parameters used to achieve signal cancelation. In this paper, an FF linearization circuit is analyzed using the orthogonalization of the nonlinear model and considering the effects of complex gain errors in the signal and distortion cancelation loops on the overall performance of the FF linearizer. The analysis enables the effective signal-to-distortion ratio (SDR) and ACPR to be estimated from FF circuit model. It is shown that complex gain errors in the distortion cancelation loop have a more significant effect on in-band distortion than out-of-band distortion, which means that the design of FF linearizers based on ACPR improvement is not optimal. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Spatial domain processing using adaptive antenna arrays has been widely used to suppress co-channel interferences and mitigate the channel effect in single-input multiple-output (SIMO) OFDM systems. Two main mechanisms are pre-FFT scheme based on time-domain beamforming and post-FFT scheme based on frequency-domain beamforming. Both schemes have been recently extended to multiple-input multiple-output (MIMO) OFDM systems. However, there is no study, neither in SIMO-OFDM systems nor in MIMO-OFDM systems, to compare these two methods in details and provide specific guidelines to the designers on which scheme to deploy. Since performance improvement due to beamforming in both SIMO- and MIMO-OFDM systems are based on the same principles and noting the complexity of analysis in MIMO-OFDM beamforming systems, in this article we compare these two schemes in the simpler structure of a pilot assisted SIMO-OFDM system. This comparison is performed both from analytical perspective and by simulation results. It is shown under what conditions the more complex post-FFT scheme can exhibit better results and under what conditions the less costly pre-FFT scheme can demonstrate better performance. This greatly helps the designers to choose the right scheme based on their system characteristics. [ABSTRACT FROM AUTHOR]

This paper first proposes a computationally efficient, pilot-aided linear minimum mean square error (MMSE) batch channel estimation algorithm for OFDM systems in unknown wireless fading channels. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion, no matrix inversion is required in the proposed MMSE estimator. Moreover, optimal rank reduction is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We then consider the stochastic Cramér-Rao bound and derive the closed-form expression for the random KL coefficients and consequently exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also analyze the effect of a modelling mismatch on the estimator performance. To further reduce the complexity, we extend the batch linear MMSE to the sequential linear MMSE estimator. With the fast convergence property and the simple structure, the sequential linear MMSE estimator provides an attractive alternative to the implementation of channel estimator.

A novel scheme with the method of subcarrier modulation is proposed. Comparing with the other existing self‐cancellation methods, the proposed scheme not only has the advantages of good inter‐carrier interference (ICI) suppression capability and high bandwidth efficiency, but also shows a better bit error rate performance over multipath fading channels. [ABSTRACT FROM AUTHOR]

In this paper, we consider a novel optimal design for per-subcarrier antenna selection multi-input multi-output orthogonal frequency division multiplexing systems from an energy-efficiency perspective. The optimal number of antennas, i.e., the number of radio frequency chains, that needs to be built-in on a transmitter to achieve the maximum energy-efficiency is determined. Specifically, an optimization problem for maximizing the average energy-efficiency with respect to the number of antennas is formulated. To solve this optimization problem, we first derive a closed-form formula expressing the cost function (i.e., the average energy-efficiency) as a function of the number of antennas. Search algorithms are then designed to obtain the optimal number of antennas. It is shown that the optimal number of equipped antennas depends on particular values of the circuit power consumption, the actual transmitted power, and the channel characteristics. Simulation results are provided to validate the analysis. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the tradeoff between increasing the secondary users' (SUs) transmission rate and reducing the interference levels at the primary users (PUs) for orthogonal-frequency-division-multiplexing-based cognitive radio systems. To achieve this target, we formulate a generalized multiobjective optimization (MOOP) problem that jointly maximizes the transmission rate of the SU and minimizes the cochannel interference (CCI) and adjacent channel interference (ACI) to existing PUs. We additionally constrain the allowed CCI and ACI to the PUs to guarantee the PUs' protection from harmful interference. The MOOP problem is solved by linearly combining the normalized competing objective functions—through weighting coefficients—into a single objective function. Prior work in the literature that maximizes the SU transmission rate can be considered as a special case of the generalized MOOP problem by setting the weighting coefficients associated with interference minimization to zero. Since estimating the full channel state information (CSI) of the links between the SU transmitter and the PU receivers is practically challenging, we assume only partial CSI knowledge of these links. Simulation results illustrate the performance of the proposed algorithm and quantify the SU performance loss due to incomplete CSI knowledge. Furthermore, the proposed algorithm is compared with state-of-the-art techniques, and our performance results show that the proposed algorithm is more energy aware, yet with reduced complexity. [ABSTRACT FROM PUBLISHER]

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In this letter, we propose new frequency-domain pilot-multiplexing techniques (FDPMTs) for the channel estimation and equalization in orthogonal frequency-division multiplexing (OFDM) systems. A robust and effective pilot insertion and detection scheme is devised thereby. These pilot positions are optimally selected to minimize the distortion of the transmitted time-domain signal caused by the subcarrier-removal at the corresponding pilot positions. Besides, three different blind pilot-detection techniques are designed at the receiver without any a priori knowledge of the pilot positions, and the distorted data symbols can thus be iteratively reconstructed. Rigorous theoretical analysis and Monte Carlo simulation results both demonstrate that our proposed new OFDM system using dynamical pilot positions is more robust than the conventional OFDM system using the fixed pilot positions over multipath fading channels. [ABSTRACT FROM AUTHOR]