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The authentication of wireless devices through physical layer attributes has attracted a fair amount of attention recently. Recent work in this area has examined various features extracted from the wireless signal to either identify a uniqueness in the channel between the transmitter–receiver pair or more robustly identify certain transmitter behaviors unique to certain devices originating from imperfect hardware manufacturing processes. In particular, the carrier frequency offset (CFO), induced due to the local oscillator mismatch between the transmitter and receiver pair, has exhibited good detection capabilities in stationary and low-mobility transmission scenarios. It is still unclear, however, how the CFO detection capability would hold up in more dynamic time-varying channels where there is a higher mobility. This paper experimentally demonstrates the identification accuracy of CFO for wireless devices in time-varying channels. To this end, a software-defined radio (SDR) testbed is deployed to collect CFO values in real environments, where real transmission and reception are conducted in a vehicular setup. The collected CFO values are used to train machine-learning (ML) classifiers to be used for device identification. While CFO exhibits good detection performance (97% accuracy) for low-mobility scenarios, it is found that higher mobility (35 miles/h) degrades (72% accuracy) the effectiveness of CFO in distinguishing between legitimate and non-legitimate transmitters. This is due to the impact of the time-varying channel on the quality of the exchanged pilot signals used for CFO detection at the receivers. [ABSTRACT FROM AUTHOR]

Orthogonal Frequency-Division Multiplexing (OFDM) is a form of digital systems and a way of encoding digital data across multiple frequency components that is used in telecommunication services. Carrier Frequency Offset (CFO) inaccuracy is a serious disadvantage of OFDM. In this research, Fuzzy based Slime Mould optimization for CFO (FSM-CFO) has been proposed. The proposed FSM-CFO not only estimates the CFO with increased precision, but also allocates resources effectively, achieving maximum utilization of dynamic resources. Initially, Slime Mould Algorithm is utilized to extract the precise CFO. Additionally, the base station (BS) manages the Resource Units (RU), which could be used to distribute resources in such a manner that the user-requests are met. The resources are assigned using fuzzy rules, with the type of resource that is most appropriate for the nodes that have requested the resources being chosen. To assign resources to a certain job, fuzzy rules are devised. Once the residence time for the duration has passed, the highest-priority tasks receive the specific resources. Finally, experiments are carried out to demonstrate the effectiveness of the proposed method. The performance of the proposed method is evaluated in terms of Resource utilization, failure rate, life span, and energy consumption. According to the simulated results, the suggested configuration can perform better compared to the existing techniques. [ABSTRACT FROM AUTHOR]

With the advancement of information technology construction, the integration of radar and communication represents a crucial technological evolution. Driven by the research boom of integrated sensing and communications (ISACs), some scholars have proposed utilizing orthogonal frequency-division multiplexing (OFDM) to separately modulate radar and communication signals. However, the OFDM symbols in this paper incorporate a cyclic prefix (CP) and a virtual carrier (VC) instead of zero padding (ZP). This approach mitigates out-of-band power caused by ZP, in addition to reducing adjacent channel interference (ACI). In addition, we introduce low-density parity-check (LDPC) and use an improved normalized min-sum algorithm (NMSA) in decoding. The enhanced decoding efficiency and minimized system errors render the proposed waveform more suitable for complex environments. In terms of signal processing methods, this paper continues to use radar signals as a priori information to participate in channel estimation. Further, we consider the symbol timing offset (STO) and carrier frequency offset (CFO) issues. In order to obtain more reliable data, we use the minimum mean-square error (MMSE) estimation based on the discrete Fourier transform (DFT) to evaluate the channel. Simulation experiments verify that the system we propose not only realizes the transmission and detection functions but also improves the performance index of the integrated signal, such as the bit error rate (BER) of 7 × 10−5, the peak side lobe ratio (PSLR) of −13.81 dB, and the integrated side lobe ratio (ISLR) of −8.98 dB at a signal-to-noise ratio (SNR) of 10 dB. [ABSTRACT FROM AUTHOR]

The multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has developed rapidly since its discovery. At the same time, the low-disturbance and high-gain requirements of the MIMO system are continuing to increase. Through the application of digital beamforming (DBF) techniques, the multidimensional waveform encoding (MWE) technique can play a key role in MIMO systems, which can greatly improve the system's performance, especially the multi-mission capability of radar. Intrapulse beamsteering in elevation is a typical form of multi-dimensional waveform encoding which can greatly improve the transmitting efficiency and multi-mission performance of radar. However, because of the high sensitivity of the DBF technique to height, there is significant deterioration in performance in the presence of terrain undulations. The OFDM (Orthogonal Frequency Division Multiplexing) technique is widely used in communication. Due to the similarity of radar and communication systems and the great waveform diversity of OFDM signals, the OFDM radar has recently begun to emerge as a new radar system, simultaneously, the orthogonality of OFDM signals is in the time and frequency domains, and is not affected by terrain undulation. So, this paper proposes a novel radar mode combining intrapulse beamsteering in elevation and OFDM-Chirp signals, that is, the combination of "beam orthogonality" and "waveform orthogonality", which can greatly improve the performance and fault tolerance to interference signals. In this manuscript, the system working mode and signal processing flow are introduced in detail, and simulations for both point targets and distributed targets are carried out to verify the feasibility of the proposed mode. Simultaneously, a comparison experiment is carried out, which shows the high level of fault tolerance to terrain undulation and the high potential of the proposed radar mode in Earth observation. [ABSTRACT FROM AUTHOR]

The authentication of wireless devices through physical layer attributes has attracted a fair amount of attention recently. Recent work in this area has examined various features extracted from the wireless signal to either identify a uniqueness in the channel between the transmitter–receiver pair or more robustly identify certain transmitter behaviors unique to certain devices originating from imperfect hardware manufacturing processes. In particular, the carrier frequency offset (CFO), induced due to the local oscillator mismatch between the transmitter and receiver pair, has exhibited good detection capabilities in stationary and low-mobility transmission scenarios. It is still unclear, however, how the CFO detection capability would hold up in more dynamic time-varying channels where there is a higher mobility. This paper experimentally demonstrates the identification accuracy of CFO for wireless devices in time-varying channels. To this end, a software-defined radio (SDR) testbed is deployed to collect CFO values in real environments, where real transmission and reception are conducted in a vehicular setup. The collected CFO values are used to train machine-learning (ML) classifiers to be used for device identification. While CFO exhibits good detection performance (97% accuracy) for low-mobility scenarios, it is found that higher mobility (35 miles/h) degrades (72% accuracy) the effectiveness of CFO in distinguishing between legitimate and non-legitimate transmitters. This is due to the impact of the time-varying channel on the quality of the exchanged pilot signals used for CFO detection at the receivers.

In this paper, we propose a robust direction-of-arrival (DOA) estimation and tracking scheme for a massive multiple input multiple output (MIMO) orthogonal frequency-division multiplexing (OFDM) system using integrated sensing and communication (ISAC). First, a frame structure is designed to enable integrated DOA estimation and tracking, as well as data transmission. Then, coherent combining and multipath combining methods are proposed as pilot signal preprocessing methods for robust DOA estimation and tracking. Finally, for ISAC massive MIMO-OFDM systems, a fast Fourier transform (FFT) algorithm-based robust DOA estimator design is proposed, including a preamble-based initial DOA estimation scheme and an embedded pilot-based DOA tracking scheme. The simulation results demonstrate that the proposed robust DOA estimation and tracking method can obtain accurate DOA estimation even when the signal-to-noise ratio is low. Furthermore, the estimation performance outperforms the conventional multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance technique (ESPRIT) methods. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

Silicon photonics (SiPh) are considered a promising technology for increasing interconnect speed and capacity while decreasing power consumption. Mode division multiplexing (MDM) enables signals to be transmitted in different orthogonal modes in a single waveguide core. Wideband MDM components simultaneously supporting wavelength division multiplexing (WDM) and orthogonal frequency-division multiplexing (OFDM) can significantly increase the transmission capacity for optical interconnects. In this work, we propose, fabricate and demonstrate a wideband and channel switchable MDM optical power divider on an SOI platform, supporting single, dual and triple modes. The switchable MDM power divider consists of two parts. The first part is a cascaded Mach–Zehnder interferometer (MZI) for switching the data from their original TE0, TE1 and TE2 modes to different modes among themselves. After the target modes are identified, mode up-conversion and Y-branch are utilized in the second part for the MDM power division. Here, 48 WDM wavelength channels carrying OFDM data are successfully switched and power divided. An aggregated capacity of 7.682 Tbit/s is achieved, satisfying the pre-forward error correction (pre-FEC) threshold (bit-error-rate, BER = 3.8 × 10−3). Although up to three MDM modes are presented in the proof-of-concept demonstration here, the proposed scheme can be scaled to higher order modes operation. [ABSTRACT FROM AUTHOR]

The multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has developed rapidly since its discovery. At the same time, the low-disturbance and high-gain requirements of the MIMO system are continuing to increase. Through the application of digital beamforming (DBF) techniques, the multidimensional waveform encoding (MWE) technique can play a key role in MIMO systems, which can greatly improve the system’s performance, especially the multi-mission capability of radar. Intrapulse beamsteering in elevation is a typical form of multi-dimensional waveform encoding which can greatly improve the transmitting efficiency and multi-mission performance of radar. However, because of the high sensitivity of the DBF technique to height, there is significant deterioration in performance in the presence of terrain undulations. The OFDM (Orthogonal Frequency Division Multiplexing) technique is widely used in communication. Due to the similarity of radar and communication systems and the great waveform diversity of OFDM signals, the OFDM radar has recently begun to emerge as a new radar system, simultaneously, the orthogonality of OFDM signals is in the time and frequency domains, and is not affected by terrain undulation. So, this paper proposes a novel radar mode combining intrapulse beamsteering in elevation and OFDM-Chirp signals, that is, the combination of “beam orthogonality” and “waveform orthogonality”, which can greatly improve the performance and fault tolerance to interference signals. In this manuscript, the system working mode and signal processing flow are introduced in detail, and simulations for both point targets and distributed targets are carried out to verify the feasibility of the proposed mode. Simultaneously, a comparison experiment is carried out, which shows the high level of fault tolerance to terrain undulation and the high potential of the proposed radar mode in Earth observation.

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

The growing demand for wireless devices capable of performing complex communication processes has imposed an urgent need for high-speed communication systems and advanced network processors. This paper proposes a hardware workflow developed for the Long-Term Evolution (LTE) communication system. It studies the multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) LTE system. Specifically, this work focuses on the implementation of the OFDM block that dominates the execution time in high-speed communication systems. To achieve this goal, we have proposed an NoC-based low-latency OFDM LTE multicore system that leverages Inverse Fast Fourier Transform (IFFT) parallel computation on a variable number of processing cores. The proposed multicore system is implemented on an FPGA platform using the ProNoC tool, an automated rapid prototyping platform. Our obtained results show that LTE OFDM execution time is drastically reduced by increasing the number of processing cores. Nevertheless, the NoC's parameters, such as routing algorithm and topology, have a negligible influence on the overall execution time. The implementation results show up to 24% and 76% execution time reduction for a system having 2 and 16 processing cores compared to conventional LTE OFDM implemented in a single-core, respectively. We have found that a 4×4 Mesh NoC with XY deterministic routing connected to 16 processing tiles computing IFFT task is the most efficient configuration for computing LTE OFDM. This configuration is 4.12 times faster than a conventional system running on a single-core processor. [ABSTRACT FROM AUTHOR]

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

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

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

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

This paper is focused on the fusion of radar and wireless communication, called RadCom, which has been extensively studied in recent years for future intelligent transportation systems. We propose a new waveform design algorithm for reducing peak-to-average power ratio (PAPR) in OFDM-based RadCom systems. We consider a flexible and generic RadCom structure in which a number of non-contiguous sub-bands for data transmission are located within a large contiguous spectrum band for radar detection/sensing. New RadCom waveforms with low PAPR are obtained by carrying out optimization over those subcarriers which are complementary to the communication bands. As an application of the majorization-minimization (MM) optimization method, our major contribution is an $l$ -norm cyclic algorithm which is capable of efficiently reducing the maximum PAPR of RadCom waveforms. We show by numerical simulation results that significant performance enhancements can be achieved compared to OFDM RadCom waveforms from legacy approaches. [ABSTRACT FROM AUTHOR]

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

Adaptive modulation received significant attention for underwater acoustic (UA) communication systems with the aim of increasing the system efficiency. It is challenging to attain a high data rate in UA communication, as UA channels vary fast, along with the environmental factors. For a time-varying UA channel, a self-adaptive system is an attractive option, which can choose the best method according to the channel condition to guarantee the continuous connectivity and high performance constantly. A real-time orthogonal frequency-division multiplexing (OFDM)-based adaptive UA communication system is presented in this paper, employing the National Instruments (NI) LabVIEW software and NI CompactDAQ device. In this paper, the received SNR is considered as a performance metric to select the transmission parameters, which are sent back to the transmitter for data transmission. In this research, a UA OFDM communication system is developed, employing adaptive modulation schemes for a nonstationary UA environment which allows to select subcarriers, modulation size, and allocate power adaptively to enhance the reliability of communication, guarantee continuous connectivity, and boost data rate. The recent UA communication experiments carried out in the Canning River, Western Australia, verify the performance of the proposed adaptive UA OFDM system, and the experimental results confirm the superiority of the proposed adaptive scheme. [ABSTRACT FROM AUTHOR]

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

Abstract In this paper, we present a joint time-variant carrier frequency offset (CFO) and frequency-selective channel response estimation scheme for multiple input-multiple output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems for mobile users. The signal model of the MIMO-OFDM system is introduced, and the joint estimator is derived according to the maximum likelihood criterion. The proposed algorithm can be separated into three major parts. In the first part of the proposed algorithm, an initial CFO is estimated using derotation, and the result is used to apply a frequency-domain equalizer. In the second part, an iterative method is employed to locate the fine frequency peak for better CFO estimation. An adaptive process is used in the third part of the proposed algorithm to obtain the updated CFO estimation and track parameter time variations, including the time-varying CFOs and time-varying channels. The computational complexity of the proposed algorithm is considerably lower than that of the maximum likelihood-based grid search method. In a simulation, the mean squared error performance of the proposed algorithm was close to the Cramer-Rao lower bound. The simulation results indicate that the proposed novel joint estimation algorithm provides a bit error rate performance close to that in the perfect channel estimation condition. The results also suggest that the proposed method has reliable tracking performance in Jakes’ channel models.

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

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

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

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

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

Silicon photonics (SiPh) are considered a promising technology for increasing interconnect speed and capacity while decreasing power consumption. Mode division multiplexing (MDM) enables signals to be transmitted in different orthogonal modes in a single waveguide core. Wideband MDM components simultaneously supporting wavelength division multiplexing (WDM) and orthogonal frequency-division multiplexing (OFDM) can significantly increase the transmission capacity for optical interconnects. In this work, we propose, fabricate and demonstrate a wideband and channel switchable MDM optical power divider on an SOI platform, supporting single, dual and triple modes. The switchable MDM power divider consists of two parts. The first part is a cascaded Mach–Zehnder interferometer (MZI) for switching the data from their original TE0, TE1 and TE2 modes to different modes among themselves. After the target modes are identified, mode up-conversion and Y-branch are utilized in the second part for the MDM power division. Here, 48 WDM wavelength channels carrying OFDM data are successfully switched and power divided. An aggregated capacity of 7.682 Tbit/s is achieved, satisfying the pre-forward error correction (pre-FEC) threshold (bit-error-rate, BER = 3.8 × 10−3). Although up to three MDM modes are presented in the proof-of-concept demonstration here, the proposed scheme can be scaled to higher order modes operation.

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

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

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

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

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

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

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

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

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

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

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

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