Rydberg atomic receiver has recently emerged as a breakthrough technology for sensing and communications in next-generation Internet-of-Things (IoT) owing to its potential to surpass sensitivity limits of classical radio frequency (RF) receivers. In this paper, we consider a multiple-input multiple-output (MIMO) system with an RF transmitter and a Rydberg atomic receiver. Unlike prior works, a key technical innovation of our approach lies in the joint optimization of both transmit and receive processing techniques along with the design of reference signal injection according to a mean square error (MSE) criterion for signal recovery. However, the design problem is nonconvex on account of phase information loss in the received signal and nonlinearity of the objective function. To overcome this tricky challenge in an effective and intelligent manner, we propose a novel and high-performing deep learning (DL) framework calledDeepQ-MIMObased on the construction of an advanced DL network with innovative customization mechanisms. Numerical results confirm the supremacy and efficacy of the proposed DeepQ-MIMO system, and further provide useful design insights.

Anomaly detection in run-to-failure (RTF) data is one of the pivotal tasks in a variety of real-world signal processing applications. Vast majority of the existing anomaly detection methods in the literature are based on generative adversarial networks (GANs), but they are ineffective and problematic when applied to the RTF data. To break through such a limitation, in this paper, we proposeRTF-Skip-GANomaly, a novel and high-performing deep learning (DL) framework for anomaly detection in the RTF data, by leveraging the off-the-shelf Skip-GANomaly as backbone. Key innovations in our approach are two-fold: (i) we introduce an extra dual-stream DL network calledWeibull-Netand integrate it into the Skip-GANomaly backbone such that a pseudo Weibull function is predicted from the latent representation of the reconstructed RTF data with time embedding, and (ii) we also devise an effective strategy to train the Weibull-Net jointly with the Skip-GANomaly backbone through a customized loss function design. Experimental results validate the superior performance and enhanced efficacy of our framework over the existing approaches.

In real-world AI applications, training datasets are often contaminated, containing a mix of in-distribution (ID) and out-of-distribution (OOD) samples without labels. This contamination poses a significant challenge for developing and training OOD detection models, as nearly all existing methods assume access to a clean training dataset of only ID samples—a condition rarely met in real-world scenarios. Customizing each existing OOD detection method to handle such contamination is impractical, given the vast number of diverse methods designed for clean data. To address this issue, we propose a universal, model-agnostic framework that integrates with nearly all existing OOD detection methods, enabling training on contaminated datasets while achieving high OOD detection accuracy on test datasets. Additionally, our framework provides an accurate estimation of the unknown proportion of OOD samples within the training dataset—an important and distinct challenge in its own right. Our approach introduces a novel dynamic weighting function and transition mechanism within an iterative training structure, enabling both reliable estimation of the OOD sample proportion of the training data and precise OOD detection on test data. Extensive evaluations across diverse datasets, including ImageNet-1k, demonstrate that our framework accurately estimates OOD sample proportions of training data and substantially enhances OOD detection accuracy on test data.

Vision–Language Models (VLMs) have recently attracted considerable attention in Federated Learning (FL) due to their strong and robust performance. In particular, few-shot adaptation with pre-trained VLMs like CLIP enhances the performance of downstream tasks. However, existing methods still suffer from substantial communication overhead, high local computational demands, and suboptimal performance under non-IID user data. To simultaneously address all those limitations, we propose NormFit, a lightweight solution that selectively fine-tunes only a very small portion of the model parameters, specifically only the Pre-LayerNorm parameters of the vision encoder within a VLM. Overcoming the existing tradeoff between performance and communication/computation efficiency in few-shot FL, NormFit sets a new benchmark by simultaneously achieving superior accuracy and substantially reduced communication and computational demands. Theoretically, we show that NormFit yields a considerably smaller generalization gap compared to tuning all LayerNorm parameters. Importantly, NormFit can function effectively as a standalone solution or integrate seamlessly with existing few-shot fine-tuning methods to further enhance their performance. Notably, NormFit offers implementation simplicity, achieving these improvements without any algorithmic modifications, changes to the underlying model architecture, or the addition of external parameters.

In this letter, we propose a novel and high-performing deep learning framework for remaining useful life (RUL) prediction, called RUL-Diff, by leveraging a generative diffusion model. It is composed of two modules that are connected in tandem: 1) a feature extractor corresponding to the encoder part of our customized U-Net and 2) a RUL predictor constructed by a multilayer perceptron. We further devise an effective two-stage training methodology for the proposed RUL-Diff, in which the feature extractor is initially pretrained for high-quality feature learning, and then, is retrained jointly with the RUL predictor for accurate RUL prediction. Extensive experimental results on NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets demonstrate the superiority and effectiveness of the proposed scheme.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a candidate technology for 6G wireless networks and massive Internet-of-Things (IoT) communications. In this paper, we consider an XL-MIMO system operating in the near-field communication range, where a base station equipped with multiple movable (i.e., adjustable-position) antennas serves multiple desired users in the presence of multiple undesired users. In this system, we investigate a new joint problem of multi-beamforming design and antenna position optimization to maximize the minimum beamforming gain for the desired users with a constraint on the maximum interference leakage to the undesired users. To effectively and intelligently solve this challenging nonconvex problem, we propose a novel DL model, called NMAP-Net, which is composed of three main learnable modules, namely, DL blocks I–III, for feature extraction, antenna position optimization, and multi-beamforming design, respectively. A novel training strategy for the proposed NMAP-Net is also devised in an elegant manner using a customized loss function, called adaptive loss function, to maximize the minimum beamforming gain while adaptively suppressing the maximum interference leakage. Furthermore, an effective inference mechanism for the proposed NMAP-Net is developed based on a Gaussian randomization technique to ensure the feasibility of the predicted solution. Extensive simulation results substantiate that the proposed NMAP-Net performs markedly better and more effective than the existing techniques while achieving almost the same performance as its upper limit.

pinching antenna system (PASS) has been demonstrated as a feasible flexible-antenna technology for upcoming 6G wireless networks and Internet of Things (IoT). In this article, we investigate a new design problem on capacity maximization for a point-to-point multiple-input–multiple-output (MIMO) PASS in a realistic IoT environment by jointly optimizing precoding matrix and antenna positioning. Unfortunately, this problem is not mathematically tractable. To break through this challenge in an effective and intelligent manner, we propose a novel and high-performing deep learning framework, named CaMPASS-Net, based on an advanced dual-stream network architecture with a residual connection, inspired by our insight into the problem. Furthermore, we present an effective unsupervised training strategy for the proposed CaMPASS-Net based on an innovative loss function design. Simulation results confirm that the proposed CaMPASS-Net exhibits remarkable performance improvements over baseline and existing schemes.

Super-resolution (SR) of remote sensing imagery based on generative AI models is vital for practical applications such as urban planning and disaster assessment. However, current approaches suffer from poor performance trade-offs among the pivotal, yet competing, objectives: perceptual quality, factual accuracy, and inference speed. To break through this limitation, we propose a novel and high-performing two-stage SR framework for the remote sensing imagery based on a generative diffusion model. First, in Stage 1, factually grounded base images are generated by employing a guidance-free diffusion process relying solely on the original low-resolution images, such that the risk of semantic hallucination can be effectively mitigated. The generated images are refined subsequently in Stage 2 such that high-frequency details for SR quality can be restored via our customized and innovative guidance mechanism with a vision–language model (VLM) and a ControlNet, and a dynamic inference acceleration technique is applied to ensure efficiency. Extensive experimental results confirm that our proposed framework excels in perceptual quality—achieving top CLIP-IQA scores—and in structural integrity while achieving robust performance. In particular, it enables reliable, high-fidelity SR for large-scale, real-world remote sensing pipelines by surpassing the conventional fidelity–hallucination trade-off at practical inference speed. Source code is available at https://github.com/Bluear7878/Remote-Sensing-Vision-Language-Diffusion-Model.

The precise and efficient extraction of buildings and road networks from aerial imagery is crucial for remote sensing applications, such as map building, urban development, and autonomous driving guidance systems. However, accurately extracting buildings and roads from remote sensing imagery is challenging due to varying resolutions, object scale variation, and diverse appearances of buildings and roads. While sophisticated models achieve high accuracy, their computational demands limit practical use on resource-constrained devices. Conversely, mainstream lightweight models often fail to generate high-quality segmentation maps for remote sensing data. To address these challenges, we introduce a dual attention selective kernel network (DASK-Net), a novel lightweight architecture for efficient pixelwise dense prediction. DASK-Net’s core features a dual attention selective kernel (DASK) module that integrates multiscale feature extraction with adaptive receptive fields and dual attention mechanisms. This design captures diverse scales and orientations of features while focusing on salient input aspects. We conducted experiments on the Massachusetts roads, DeepGlobe and WHU building datasets, comparing DASK-Net with numerous methods. The results demonstrate that DASK-Net outperforms these networks while significantly reducing computational complexity. With only 0.48M parameters, DASK-Net achieves an 89.54% IoU and a 94.48% F1 score on the WHU building dataset, setting new performance standards for lightweight methods.

Presents corrections to the paper, (Corrections to “DASK-Net: A Lightweight Dual-Attention Selective Kernel Network for Efficient Dense Prediction in Remote Sensing Imagery”).

We have found a few typos in[1, eqs. (10) and (13)]. The corrections for these equations are given as follows:

We have found a typo in[1, eq. (4)]. The range of the summation in[1, eq. (4)]needs to be corrected as follows:

When the second-order statistics of channel and noise, such as their covariance matrices, are not exactly known, the acquisition of accurate channel state information (CSI) for a wireless propagation environment becomes quite challenging. In this paper, we tackle the problem of robust channel estimation for multiple-input multiple-output (MIMO)-aided Internet-of-Things (IoT) systems in the presence of uncertainties in the channel and noise covariance matrices. Our goal is to minimize the mean square error (MSE) of the channel estimation under the channel and noise covariance uncertainties by jointly optimizing the channel estimator and pilot signal, which is however highly nonconvex and mathematically intractable. To effectively and intelligently cope with this issue, we exploit a deep learning (DL) technique and propose a novel network architecture with two modules, namely, the pilot optimizer and channel predictor, both of which are designed by neural networks with their own local connections and weight sharings. Moreover, a novel and effective training strategy for the proposed DL model is devised in a self-supervised manner, in which samples obtained by properly compensated channel and noise covariance matrices are utilized to overcome any adverse impacts of the underlying uncertainties on the channel estimation. Through extensive numerical results simulated in realistic propagation environments, we substantiate the superior performance and effectiveness of the proposed scheme.

This letter proposes a new methodology for training sequence design in Internet-of-Things (IoT) systems based on deep learning, called RobuT-Net. The proposed RobuT-Net is constructed via a dual convolutional neural network (CNN) architecture composed of two CNN modules to effectively and intelligently design a statistically robust training sequence for the minimum mean square error (MMSE) channel estimator, against uncertainties in both channel and noise covariance matrices. Furthermore, we develop an effective learning strategy for the proposed RobuT-Net in an unsupervised manner, which leverages artificially distorted samples for the channel and noise covariance matrices to mitigate the adverse impacts of the uncertainties. Simulation results substantiate the superiority and efficacy of the proposed scheme.

Long Range (LoRa), a low power and wide area modulation scheme based on a chirp spread spectrum, is the most popular and widely adopted Internet-of-Things (IoT) technique. In LoRa, acquiring channel state information (CSI) is imperative for improving the system performance, but is a challenging task. In this paper, we tackle the problem of preamble design for the CSI acquisition with LoRa modulation in a multiple access scenario with multiple transmitters and a receiver. The main contributions of this paper are three-fold. Firstly, we propose two novel and effective preamble transmission strategies: (i) single-chirp preamble transmission and (ii) multi-chirp preamble transmission, both employing modulated up chirps. Secondly, we present how to optimize such preamble signals jointly with transmission power (for the former strategy) and combining coefficients (for the latter strategy) in the sense of minimizing total mean square error (MSE) in estimating the CSI of the whole links between the transmitters and the receiver under practical power constraints, adopting the linear minimum mean square error (LMMSE) criterion. In addition, to gain more insights and further lower the design complexity, we investigate several special, yet important, scenarios. Lastly, we present extensive numerical results simulated in realistic propagation environments to substantiate the superiority and effectiveness of the proposed schemes. Also, we reveal new tradeoffs between the proposed single-and multi-chirp preamble transmission strategies in terms of performance and complexity.

The Purpose of this correspondence is to provide corrections to typos appeared in[1]and to report that[1]contains a missing citation of the reference[2].

We have found a few typos in[1, eqs. (20), (44), and (55)]. In these equations, the term$\lambda _{\min }$must be corrected to$\lambda _{\min }^{-1}$as follows:

Optical character recognition (OCR) is one of the key enabling technologies in industrial internet-of-things (IIoT) for extracting and utilizing useful textual information, but it is technically challenging due to poor environmental conditions. To deal with such challenges, in this letter, we propose a novel two-stage deep learning framework for OCR using a generative diffusion model, namely, OCR-Diff. In the first stage, our customized conditional U-Net is pre-trained jointly with a feature extractor with the aid of the forward diffusion process such that the quality of a low-resolution text image is improved via the reverse diffusion process. In the next stage, the pre-trained conditional U-Net and feature extractor are jointly fine-tuned for an off-the-shelf text recognizer to precisely recognize the texts in the image. Experimental results on TextZoom datasets substantiate the superiority and effectiveness of the proposed scheme.

Long Range (LoRa) is one of the widely adopted techniques for Internet of Things (IoT). Preamble detection is a key initial task for LoRa systems. Meanwhile, the so-called threshold-based preamble detection is a common technique for compatibility with LoRa. However, the existing methods on the threshold-based LoRa preamble detection suffer from low performance because the detection thresholds are heuristically chosen. To tackle this issue, in this letter, we aim to optimize those thresholds by maximizing the preamble detection probability while satisfying a constraint on false alarm rate. For this purpose, coherent and noncoherent procedures for the preamble detection are presented in a universal manner, followed by conducting their performance analysis. Simulation results demonstrate the superiority and effectiveness of the proposed scheme.

LoRa (Long Range) is a widely adopted Internet-of-Things (IoT) technique, but its fatal limit is a low data rate. In this letter, we propose a new index modulation for LoRa to increase the data rate, in which multiple quasi-orthogonal chirps modulated under different spreading factors (SFs) are concurrently transmitted, thereby leveraging the SF domain as a means to carry more information. Both coherent and non-coherent detection algorithms for the proposed index modulation are also developed in efficient forms. Numerical results demonstrate that the proposed scheme notably outperforms the state-of-the-art techniques.

Multiple-input–multiple-output (MIMO) and long range (LoRa) have been synergistically combined to better support a variety of Internet of Things (IoT) applications. This letter investigates the problem of demodulation for a MIMO-LoRa system. The existing demodulation strategy for MIMO-LoRa is based on the maximum-likelihood criterion, which, however, suffers from its high computational complexity. To cope with this issue, in this letter, we develop efficient coherent and noncoherent demodulation algorithms for the MIMO-LoRa system by leveraging useful properties of modulated chirps through multiple antennas. We also analyze the computational complexities of the proposed algorithms and demonstrate their validity through numerical simulations.

LoRa (Long Range), a low power and wide area modulation scheme based on chirp spread spectrum, is the most popular and widely adopted Internet-of-Things (IoT) technique in industry. A notable and interesting property of LoRa modulation is the quasi-orthogonality of signals modulated under different spreading factors (SFs). Unfortunately, in the literature, there has been no analytical effort to establish the theoretical validity of such quasi-orthogonality. This paper, for the first time, theoretically tackles the quasi-orthogonality of the LoRa modulation. First, we derive in both continuous-and discrete-time domains the cross-correlation between two non-synchronized LoRa signals with different SFs, based on which we analyze the quasi-orthogonality of the LoRa modulation and draw some useful engineering insights. Particularly, we analytically show that in the continuous-time domain, the quasi-orthogonality is guaranteed if one of the SFs of the two LoRa signals is large enough; while, in the discrete-time domain, the quasi-orthogonality is ensured if the maximum of the SFs is large enough. Furthermore, for practical values of the SF, the maximum squared magnitudes of the cross-correlation in the continuous-and discrete-time domains are shown to be 1.14% and 1.08%, respectively, compared to their peak values. We demonstrate the validity and accuracy of our analysis through extensive numerical simulations.

Device-to-device (D2D) communications and simultaneous wireless information and power transfer (SWIPT) technologies are key to Internet of Things (IoT) to achieve massive connectivity and to prolong battery lifetime, respectively. In this article, we propose novel schemes for transmit power adaptation to maximize the average data rate and to minimize the outage probability of D2D communications over fast and slow-fading channels, respectively, where the D2D network coexists with a SWIPT-based IoT cellular network operating with the time-switching protocol. Also, different from the existing works, circuit power consumption for information decoding is taken into account in this article. To solve the formulated nonconvex power adaptation problems, we first derive tractable lower bound and upper bound on the average data rate and outage probability of cellular user (CU), respectively. Then, based on the conservative approximation, power adaptation solutions are determined. Intriguingly, we show that in the proposed schemes, the performance tradeoff between information decoding and energy harvesting at the CU is not degraded despite the reception of the D2D signals (i.e., interfering signals). Furthermore, deriving and analyzing closed-form bounds on the D2D performance, we obtain useful insights into the D2D performance improvement by the proposed power adaptation schemes. Numerical results demonstrate that the proposed schemes outperform the baseline scheme, and verify the obtained insights.

One of the most critical challenges of federated learning (FL) is to send data efficiently and reliably over the noisy wireless channels between the clients and server to achieve target learning accuracy as fast as possible. To achieve this goal, we design effective error correction coded FL with managed retransmissions. Rather than using Shannon capacity as the performance measure to design the communication mechanisms for FL, our approach relies critically on learning accuracy. Our fundamental idea is based on the observation that Stochastic Gradient Decent (SGD) and its family can tolerate some errors in the course of training. Inspired by this, to reduce the communication burden without degrading the learning accuracy, our FL framework with Managed Redundancy (FL-MR) has two phases: (i) the No-Retransmission phase, where retransmissions are never performed even in case of erroneous decoding of data and (ii) the Select Retransmission phase, where only some carefully selected data packets are retransmitted. Our extensive simulation results demonstrate that the proposed coded FL system achieves target accuracies much faster than the baseline coded approach.

Differential evolution (DE) has emerged as an effective technique for single-objective optimization problems (SOPs). Due to its efficient and straightforward framework, it has been extended further to address multiobjective optimization problems (MOPs). However, the existing multiobjective DE (MODE) algorithms focus on developing control strategies of mutation operators and parameters for a given population at every iteration, regardless of whether the population has insufficient distributions in objective space. Furthermore, several technical challenges exist when extending MODE approaches to deal with many-objective optimization problems (MaOPs). To break through such limitations, in this article, we propose a preference-inspired DE for multi and many-objective optimization (Pre-DEMO), which effectively and efficiently deals with a wide range of MOPs and MaOPs. First, a preference-inspired mutation operator is developed to generate individuals with good convergence and distribution properties. The local knee points are obtained among the nondominated individuals to articulate preferences in the mutation operator. Also, an adaptive strategy based on a clustering method is proposed to determine the local knee points. Second, a two-stage environmental selection is suggested in Pre-DEMO to preserve promising individuals for the next generations. Experimental results demonstrate that the Pre-DEMO approach outperforms the eight state-of-the-art algorithms on 35 benchmark problems.

In[1], in Section III-B (Algorithm 3) and Section III-E (Algorithm 5), there are mistakes regarding the equation numbers that are referred to in these algorithms.

Long range (LoRa) is a widely adopted modulation scheme for Internet of Things (IoT), but its data rate is low. To tackle this problem, in this letter, we introduce a novel concept of MIMO-LoRa: an integration of multiple-input multiple-output (MIMO) and LoRa for achieving high data rates. We also design a precoding for the proposed MIMO-LoRa system to further enhance the link reliability. The validity and effectiveness of the proposed MIMO-LoRa system are demonstrated through numerical simulations.

This article investigates the problem of preamble detection for the long-range (LoRa) modulation and analyzes its performance. For analysis, the inevitable multiuser interference is reasonably modeled as correlated noise. First, the optimal preamble detector is derived and the best preamble is designed to maximize the detection probability while achieving a target value of the false alarm rate. To reduce the required computational complexity and to gain more insights, the preamble detection problem is further investigated for two important special scenarios with: 1) a large spreading factor (SF) and 2) uncorrelated noise, respectively. The analysis is then extended to the case in the presence of phase offset. Our work reveals that the existing preamble detection methods are strictly suboptimal. In addition, various useful and interesting engineering insights into preamble detection with LoRa modulation are provided from the derived results, and the theoretical performance limit of the preamble detection with LoRa modulation is quantified. Extensive numerical results demonstrate the effectiveness and superiority of the proposed scheme. Particularly, the proposed scheme outperforms the existing schemes by more than 10 dB in terms of signal-to-noise ratio (SNR) at the detection probability of 80% and the target false alarm rate of 10%.

This letter studies deep learning-based efficient ground vibration monitoring systems. In this work, artificial intelligence (AI) techniques are adopted to effectively deal with practical issues of data collection and classification. Specifically, we develop a novel energy-efficient data collection scheme by adopting deep Q-network-based reinforcement learning. Also, we propose an enhanced joint recurrent neural network (RNN) and convolutional neural network (CNN) approach for ground vibration classification. The performance of the proposed scheme is evaluated using real-world ground vibration data. The experimental results show that the proposed classification scheme outperforms the best existing scheme with CNN by more than 13% in terms of classification accuracy. It is also shown that the proposed energy management scheme can improve the accuracy of the proposed ground vibration monitoring system by 7.6% over the comparable scheme using equal power allocation.

Simultaneous wireless information and power transfer (SWIPT) is very promising for various applications with the Internet of Things (IoT). In this article, we study dynamic power splitting for the SWIPT in an ergodic fading channel. Considering nonlinearity of practical energy harvesting (EH) circuits, we adopt the realistic nonlinear EH model rather than the idealistic linear EH model. To characterize the optimal rate-energy (R-E) tradeoff, we consider the problem of maximizing the R-E region, which is nonconvex. We solve this challenging problem for two different cases of the channel state information (CSI): 1) when the CSI is known only at the receiver (the CSIR case) and 2) when the CSI is known at both the transmitter and the receiver (the CSI case). For these two cases, we develop the corresponding optimal dynamic power-splitting schemes. To address the complexity issue, we also propose the suboptimal schemes with low complexities. Comparing the proposed schemes to the existing schemes, we provide various useful insights into the dynamic power splitting with nonlinear EH. Furthermore, we extend the analysis to the scenarios of the partial CSI at the transmitter and the harvested energy maximization. The numerical results demonstrate that the proposed schemes significantly outperform the existing schemes and the proposed suboptimal scheme works very close to the optimal scheme at a much lower complexity.

Ground vibration monitoring with microelectromechanical systems (MEMS) sensors is very effective and promising for alerting geological disasters. In this letter, explicitly considering and effectively addressing several specific issues related to practical MEMS sensors, we develop a novel ground vibration monitoring scheme for MEMS sensed data based on a deep convolutional neural network (CNN). Experiments are then conducted on the synthetic and real data sets. Experimental results on both data sets demonstrate that the proposed scheme significantly outperforms the other comparable schemes. For the synthetic data set, the proposed scheme achieves a very high overall accuracy of 98.82%. Also, for the real data set, the proposed scheme achieves a high overall accuracy of 81.64%, which is about 7% higher than that reported in the literature.

In this paper, the problem of joint mode switching and power adaptation is studied for simultaneous wireless information and power transfer (SWIPT) over a fading channel. The receiver dynamically switches between information decoding (ID) and energy harvesting (EH) modes while the transmitter dynamically adapts the transmit power. Considering the nonlinearity of practical EH circuits, a realistic nonlinear EH model is adopted rather than the idealistic linear EH model. To characterize the ultimate performance tradeoff between ID and EH, an optimization problem is formulated to maximize the average harvested energy under the constraints on the average achievable rate and the average transmit power, which is a nonconvex and combinatorial problem. To solve this problem, first, the optimal power adaptation scheme for the nonlinear EH receiver that operates only in the EH mode is proposed. Using this scheme, the jointly optimal solution for the mode switching and power adaptation is then derived. By comparing the obtained results to the existing results, various useful and interesting insights into the optimized SWIPT system with nonlinear EH are presented. An important insight into the impact of nonlinear EH is that, to exploit the high energy conversion efficiency of the nonlinear circuit, the EH mode has to be selected only in the moderate range of channel gains. Also, in the EH mode, the power has to be adapted to the short-term power threshold only for the moderate channel gains.

In this paper, we study rate-energy (R-E) tradeoffs for simultaneous wireless information and power transfer (SWIPT). In the existing literature, by invoking a simplistic and ideal assumption of linear energy harvesting, the R-E tradeoff performance was analyzed only for the four SWIPT schemes: the dynamic power splitting, type-I on-off power splitting (OPS), static power splitting, and time switching. Different from such works, in this work, we consider the realistic and practical scenario of nonlinear energy harvesting. Furthermore, to characterize the R-E tradeoff with nonlinear energy harvesting, we propose a new SWIPT scheme, the generalized OPS (GOPS). As a special case of the proposed GOPS, we also investigate an additional SWIPT scheme, the type-II OPS. Through the analysis based on the realistic nonlinear models reported in the literature, we derive new theoretical results on the R-E tradeoff, which are in sharp contrast to those in the existing literature obtained with linear energy harvesting. Furthermore, we provide various useful insights into the SWIPT system with nonlinear energy harvesting.

In this paper, we study time-varying channel estimation and optimal pilot design for multiple-input multiple-output two-way multi-relay systems in the sense of minimizing the total estimation mean square error (MSE) under the power constraints at two source nodes and multiple relays. A particularly challenging issue in the optimal pilot design is to completely eliminate the inter-link interference while most efficiently using the channel resource. To address this challenging issue, we consider a pilot design approach based on the phase rotation technique at multiple relays. First, we establish various optimality conditions for the pilot design in the sense of the minimum total MSE. Then, we propose the optimal pilot scheme by designing the pilot signals, phase rotations, and pilot symbol positions to satisfy the established optimality conditions. Finally, we propose the asymptotically optimal pilot scheme in the high signal-to-noise ratio (SNR) regime with low complexity. Simulation results show that the proposed schemes significantly outperform the existing schemes in terms of the channel estimation and the bit error rate, and the proposed asymptotically optimal scheme provides the near-optimal performance even in the low to moderate SNR range.

In this paper, we study a training design problem for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) two-way relay networks. Unlike the existing studies, we assume the spatially correlated fading and consider the nonreciprocal channel condition, which is a more practical assumption but makes the training problem more challenging. The equivalent channels of bidirectional relaying links, which consist of self-interfering channels and information-bearing channels, are estimated at each source node based on a linear minimum mean square error (LMMSE) approach. The total mean square error (MSE) of the channel estimation is minimized under the transmit power constraints at the source nodes and at the relay. To solve this problem, we first derive an optimal structure of the training signals, and then, convert the optimization problem into a tractable convex form, from which the optimal training scheme is designed efficiently. Furthermore, for a practical special case, the optimal training design is derived in semi-closed form, which provides useful insights. To reduce the required complexity, a low-complexity training scheme is also derived in closed-form. This scheme is shown to be asymptotically optimal in the high signal-to-noise ratio (SNR) regime and gives further insights into the optimal training. The performance of the proposed schemes is demonstrated through numerical simulations.

In this paper, the fundamental performance of the simultaneous wireless information and power transfer (SWIPT) system is studied. Unlike any existing works where the codelength was assumed to be infinity, we explicitly consider the case of the finite codelength, which is much more realistic especially for the practical SWIPT system due to its limited power and complexity. For the four well-known SWIPT schemes, we analyze the tradeoff between the rate and energy; then we study the optimality of those SWIPT schemes. Furthermore, to fully characterize the fundamental performance of the SWIPT system in the regime of finite codelength, we propose to additionally use the new tradeoff between the decoding error probability and the harvested energy. In the sense of this new tradeoff, we study the optimality of the four SWIPT schemes. For the analysis of the two types of tradeoffs, we consider two different cases: when the transmit power of symbols is adapted or not. For various scenarios, we provide useful insights into the performance of the SWIPT system in the finite codelength.

We propose the source and relay precoder designs to maximize sum rate under individual power constraints in two-way relay system with one relay and multiple sources. However, it is difficult to directly solve the sum rate maximization problem. For this reason, we establish the relationship between sum rate and weighted mean square error(WMSE), and show that the solution of the sum rate maximization problem can be also the solution of the WMSE minimization problem if the weight matrices are properly chosen. Then, based on the relationship between sum rate and WMSE, the sum rate maximization problem is equivalently formulated as the WMSE minimization problem. We finally propose an iterative algorithm for WMSE minimization problem. Simulation results show that the proposed scheme has better sum rate performance than conventional scheme at all SNR regimes.

This paper considers the problem of detecting the pilot contamination attack for multi-antenna secrecy systems. The detection problem is formulated as a binary hypothesis problem and the likelihood ratio or the generalized likelihood ratio is employed as a decision statistic for the detection of the pilot contamination. Using the Neyman-Pearson criterion, we then develop several detection methods under the different assumptions of the channel and noise statistics. To be specific, the cases of interest are as follows: (i) exact knowledge of all covariance matrices, (ii) exact knowledge of the legitimate channel and noise covariance matrices, but no knowledge of the jamming channel covariance matrix and (iii) no knowledge of all covariance matrices. The performance comparison of the proposed schemes with the conventional scheme is performed by simulations.

This letter proposes channel training designs for two-hop multi-relay networks, where a source, a destination and multiple amplify-and-forward (AF) relays are all equipped with multi-antenna, based on mean square error (MSE) and signal-to-noise ratio (SNR) criteria with taking into account spatial fading correlation between multiple-input multiple-output (MIMO) channel elements. The minimum MSE and maximum SNR channel estimators are initially derived and, then, optimal structures on source training signal and relay matrices are determined. An iterative and a closed-form power allocation solutions are proposed for both channel estimators. Simulation results show that the proposed schemes outperform the conventional schemes.