As generative audio models are rapidly evolving, AI-generated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on state-of-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

Federated learning (FL) offers significant advantages in preserving data privacy and enhancing communication efficiency, making it especially suitable for Internet of Things (IoT) networks supported by unmanned aerial vehicles (UAVs). However, most existing FL approaches rely on assumptions of uniformly distributed, well-labeled, and large-scale datasets-conditions that rarely hold in practical UAV-based IoT scenarios. These environments typically feature small-scale, non-independent and identically distributed (non-IID), and dynamically changing data. To address these challenges, we propose a novel self-supervised federated unsupervised learning (FUL) framework tailored for UAV-assisted IoT systems. The proposed framework comprises three key components: (1) a realistic UAV data collection model that considers limited onboard storage and mobility constraints; (2) a robust local training strategy that incorporates self-supervised regularization and a centered kernel alignment (CKA)-based similarity loss to mitigate the effects of data heterogeneity and rapid distribution shifts; and (3) an importance-aware hybrid normalized aggregation method at the global server, which leverages model divergence-based metrics to evaluate local model reliability and integrates both current and historical gradient information for stable model updates. Experimental results demonstrate that our framework achieves classification accuracies of 30.5%, 62.8%, and 70.5% under memory constraints of 500, 1000, and 2000 samples, respectively—outperforming the best baseline by 18.1%, 26.5%, and 9.1% under the same conditions. These results highlight the effectiveness of the proposed FUL framework in handling data heterogeneity and dynamic sample variations inherent in realistic UAV-enabled IoT applications.

Graph Neural Networks (GNN) have gained significant attention for their effectiveness across various domains. This study focuses on applying GNN to process 3D point cloud data for Human Pose Estimation (HPE) and Human Activity Recognition (HAR). We propose novel point cloud feature extraction techniques to capture meaningful information at the point, edge, and graph levels of the point cloud by considering point cloud as a graph. Moreover, we introduce a GNN architecture designed to efficiently process these features. Our approach is evaluated on four most popular publicly available millimeter-wave radar datasets—three for HPE and one for HAR. The results show substantial improvements, with significantly reduced errors in all three HPE benchmarks, and an overall accuracy of 98.8% in mmWave-based HAR, outperforming existing state-of-the-art models. This work demonstrates the great potential of feature extraction incorporated with GNN modeling approach to enhance the precision of point cloud processing.

Human activity recognition (HAR) with WiFi channel state information (CSI) is attractive for privacy-preserving, device-free sensing, yet real deployments still struggle with three coupled issues: robustness across rooms and bands, efficiency on edge hardware, and unified support for multiple tasks. We present UN-2DCNN, a lightweight 2D-CNN pipeline tailored to indoor, multi-user CSI sensing. The design reduces temporal redundancy via a simple temporal skipping augmentation, learns a compact 128-D representation with a small CNN+GAP backbone, and injects reliability feedback through uncertainty-aware feature scaling (UAFS): Stage-1 predictive entropy is mapped to a gating weight that rescales features before a second decision head. A channel-attention MLP further suppresses spurious subcarrier responses. Evaluated on a recent multi-user CSI benchmark across classrooms, meeting rooms, and empty environments at 2.4/5 GHz, UN-2DCNN consistently outperforms competitive RNN/Transformer baselines while using only ∼1M parameters and maintaining sub-2 s test-time latency. Beyond higher accuracy, the model exhibits faster, smoother convergence and improved calibration (fewer overconfident errors). Ablations confirm that removing attention, UAFS, or the second-stage head yields consistent drops, and simple temporal skipping on the data side complements model-side selectivity. These results indicate that reliability-aware, lightweight designs can deliver practical accuracy–efficiency trade-offs for CSI-based perception on edge/IoT platforms.

Wi-Fi Channel State Information (CSI)-based human activity recognition (HAR) leverages rich channel propagation characteristics to enable non-intrusive, privacy-preserving, and device-free sensing. In multi-user wireless environments, however, HAR faces significant challenges, including multi-path interference, signal overlap, label ambiguity, cross-domain channel variability, and constraints imposed by real-time deployment on resource-limited edge devices. This paper presents MuECNet (Multi-user Enhanced Convolutional Network), a lightweight and modular deep learning framework designed to operate under realistic multi-user, multi-activity CSI sensing conditions. MuECNet integrates three key components: (i) an Enhanced Convolutional Encoding Module (ECEM) for fine-grained temporal–spectral feature extraction that preserves channel propagation signatures; (ii) a Branch-wise Feature Normalization (BFN) module for user-specific channel representation learning; and (iii) an adaptive Decision Module for multi-label activity inference. To improve robustness under diverse and dynamic channel conditions, we introduce MixUp-based data augmentation to emulate activity overlap and reduce label ambiguity. Evaluations on the WiMANS dataset show that MuECNet achieves 64.46% (2.4 GHz) and 64.15% (5 GHz) accuracy with only 1.19M parameters, 1.74G FLOPs, and 0.28 s inference latency, outperforming baseline models such as ABLSTM and THAT while reducing model size by up to 75%. Ablation studies confirm the contribution of each module, with accuracy drops of up to 6.41% when removed. These results demonstrate that MuECNet provides a robust and communication-efficient solution for integrating CSI-based sensing into future wireless networks and IoT systems.

This work studies subject-independent continuous human activity recognition (HAR) using a modulation-independent multi-radar network. Our method is designed as a practical solution for real-world deployment, with a strong emphasis on robust generalization to unseen subjects, while also preventing abrupt performance drops and ensuring stable operation under varying radar availability. We introduce a Dynamic Local Range–Doppler Map (DL-RDM) that automatically tracks the line-of-sight (LoS) component range to generate subject-centered, short-time range–Doppler patches. These features are fused with micro-Doppler spectrograms via a learned gate, and multiple radar streams are combined using radar-wise attention. To reduce subject dependence, a triplet-loss pretraining stage precedes fine-tuning for frame-wise classification. Evaluations on public 5-node and 3-node HAR datasets show that gated fusion consistently outperforms single-stream models. On the 5-node dataset, fusion raises accuracy from the best single stream’s 85.3% to 87.2%, and to 87.5% with triplet pretraining, while the macro-F1 score increases from 77.2% to 81.2%. On the 3-node dataset, the MD spectrogram baseline reaches 71.9% accuracy; our proposed DL–RDM improves to 85.3%, gated feature fusion achieves 89.3%, and the pretrained gated fusion attains 89.4%. Our method achieves lower inter-subject performance variation, enhancing generalization ability on unseen users. Moreover, performance remains stable as active sensors are removed, preventing abrupt drops and demonstrating robustness under varying radar availability, making it well-suited for practical, real-world deployment.

Renewable energy has become a viable alternative to fossil fuels owing to its environmental benefits. However, its inherent uncertainty pose significant challenges. Demand response mechanisms have been developed to address these issues, facilitating renewable energy integration through consumer-side flexible resources. However, these mechanisms often affect consumer satisfaction, necessitating precise measurement and control of these impacts. In this paper, we propose a two-stage electricity trading and load dispatch optimization model aimed at reducing carbon emission by promoting renewable energy accommodation, and the proposed optimization model takes into account multi-category energy consumer satisfaction. We begin by classifying consumers into distinct categories and designing tailored satisfaction functions that reflect their unique power consumption preferences. The electricity trading and load dispatch processes are formulated as a two-stage optimization problem, which is then transformed into Markov decision processes. A model-free framework applying two state-of-the-art deep reinforcement learning algorithms is proposed to solve the optimization problem without requiring complex environmental modeling and prior knowledge. Numerical results demonstrate that the proposed framework outperforms benchmark algorithms regarding both consumer satisfaction preservation and carbon emission reduction.

In multi-intersection traffic signal timing (TST) the use of a uniform cycle time reduces efficiency at low-volume intersections and signal changes which can alter vehicle flows and degrade communication quality. This paper presents a solution to this problem introducing a Twin-Timescale model whereby a multi-intersection TST method is jointly considered with communication resource allocation (CRA) strategies. For each intersection, a Twin-Timescale Reinforcement Learning (RL) algorithm is proposed, by considering both CRA and TST as the Short- and the Long- Timescale models, respectively, and jointly formulating their rewards. Based on the characteristics of this Twin-Timescale model, the reward function is constructed as a singularly perturbed model (SPM), and a state feedback controller that can ensure asymptotic stability is designed. Furthermore, a novel asynchronous time Multi-Agent Coordinated RL algorithm is proposed, which allows each intersection to generate each own joint parameters for its decision strategy which are based upon information obtained from neighboring intersections at asynchronous time. In addition, the use of Federated Learning (FL) in conjunction with the proposed algorithms is investigated, and its advantages in enhancing the resistance to malicious attacks are discussed. Using real traffic data, various performance evaluation results obtained by means of extensive computer simulation experiments have confirmed that the proposed TST-CRA joint optimization approach significantly improves traffic indicators and maintains the quality of the overall communication performance. Through numerical calculations, the state feedback controller coefficients have been determined.

Due to the triple mobility of mobile users (MUs), unmanned aerial vehicle (UAV) relays, and low Earth orbit (LEO) satellites, handover becomes a critical and challenging issue for maintaining the continuity and quality of communication services in UAV-assisted LEO satellite networks. This paper proposes a distributed handover decision-making process aimed at improving scalability and reducing communication overhead. The handover problem is modeled as a decentralized Markov decision process (DEC-MDP) with the objective of maximizing the total end-to-end (E2E) throughput. We design an independent proximal policy optimization-based distributed intelligent handover (IPPO-DIH) algorithm within a centralized training with decentralized execution framework to solve the DEC-MDP. To analyze the theoretical optimal E2E throughput, we eliminate the correlation between handover decisions at different time steps. A three-sided matching algorithm with theoretical convergence guarantees is designed to obtain a stable matching among MUs, UAV relays, and LEO satellites at each time step. These stable matchings are combined to provide a theoretical performance benchmark for the handover algorithms. Simulation results validate the convergence of the proposed IPPO-DIH and three-sided matching algorithms. Additionally, the total E2E throughput achieved by the IPPO-DIH algorithm approaches the theoretical performance benchmark and outperforms typical handover algorithms.

Conventional multi-unmanned aerial vehicle (UAV) assisted non-orthogonal multiple access (NOMA) wireless caching networks (WCNs) usually operate in a distributed and non-collaborative manner, where each UAV serves users independently without coordination or relay support. When UAVs move beyond the communication range of ground base stations (BSs), backhaul disruption occurs, leading to high user latency and limited system scalability. To address these issues, we propose a multi-layer UAV-assisted NOMA WCN architecture, where a primary UAV (PUAV) communicates with the BS and cooperates with multiple secondary UAVs (SUAVs). The PUAV not only acts as a control and coordination hub but also serves as a relay for content transmission to SUAVs when necessary. To minimize user transmission latency, we propose a joint iterative algorithm that integrates user clustering, user pairing, power allocation, and UAV deployment. First, we develop an Advanced Balanced K-Means++ (ABKM) algorithm to ensure that each cluster contains a balanced number of users and to reduce the distance between users and their serving SUAVs. Next, we derive the NOMA power allocation factor that minimizes user transmission latency, ensuring efficient resource distribution among all paired users. Furthermore, we analyze the impact of PUAV and SUAV placement on user latency and propose a two-stage particle swarm optimization (PSO)-based algorithm to iteratively optimize the deployment of all UAVs. Finally, the user pairs and power allocation are jointly optimized based on the updated deployment of all UAVs to further reduce user latency. Simulation results show that, compared with a single-layer UAV architecture and benchmark schemes, the proposed multi-layer design with joint optimization achieves lower user latency. Additionally, comparisons with the optimal power allocation search method confirm the validity of the derived NOMA power allocation factor.

Millimeter-wave (mmWave) radar has emerged as a powerful sensing modality for human activity recognition (HAR) owing to its capability to capture 3D point cloud sequences without privacy concerns. However, effectively modeling the sparse, irregular, and non-uniform nature of radar data remains a major challenge. Existing approaches often rely on highly complex network architectures to improve accuracy, which leads to excessive computational overhead and poor scalability. To overcome these limitations, this paper proposes a compact hybrid feature extraction network for voxelized radar point cloud classification, which performs early-stage spatio-temporal fusion and is termed STFusionNet (Spatial-Temporal Fusion Network). The STFusionNet comprises (i) a lightweight 3D convolutional front-end, which treats consecutive temporal frames as input channels to encode motion dynamics into a compact volumetric representation and further aggregates features along the depth axis; (ii) a minimalist 2D convolutional backbone after a Depth-to-Channel Folding operation, which captures spatial features with minimal computational cost. Extensive experiments on the public MMActivity and MiliPoint datasets demonstrate that our model achieves competitive accuracy (e.g., 92.20% on MMActivity and 72.86% on MiliPoint) with only about 50K parameters and 46 MMac, outperforming or matching representative spatio-temporal baselines under a much lower computational budget. Extensive ablation experiments verify the contribution of key components, while statistical significance tests confirm the reliability of the performance improvements. These results confirm that STFusionNet offers a robust, efficient, and generalizable solution for mmWave radar-based HAR in Internet of Things (IoT) applications.

Accurate detection of surface defects on railway tracks is critical for safe railway operation. Most existing models rely solely on Red–Green–Blue (RGB) images, limiting their ability to capture structural information. Incorporating depth features provides richer spatial cues, significantly improving detection accuracy. However, current Red–Green–Blue and Depth (RGB-D) dual-stream models suffer from high computational complexity and hardware dependencies, making them impractical for real-world deployment. To address these limitations, we propose DAHNet, an asymmetric knowledge distillation model with a teacher–student architecture. DAHNet-T serves as the teacher network, taking RGB-D inputs and integrating a cross-modal attention feature enhancement (CAFE) module to capture contextual information, along with a depth feature interaction block (DFIB) for efficient cross-modal fusion. DAHNet-S is the student network, a lightweight single-stream RGB model employing depthwise separable convolutions to reduce computation. We introduce a multi-level distillation strategy with dynamic temperature scaling to balance coarse-grained and fine-grained knowledge transfer, while incorporating contrastive learning and structural loss to improve pixel-level accuracy. Extensive experiments on the NEU RSDDS-AUG dataset demonstrate that our distilled model DAHNet-KD outperforms state-of-the-art methods. Compared to DAHNet-T, the number of parameters is reduced from 87.72 MParams to 13.97 MParams, and the computational cost decreases from 19.79 GFLOPs to 5.41 GFLOPs. The proposed model achieves superior performance across various evaluation metrics and also generalizes well on other public datasets. Therefore, the model provides a lightweight and high-accuracy solution for deployment on mobile devices in real-world industrial scenarios.

Deep autoencoder (DAE) frameworks have demonstrated their effectiveness in reducing channel state information (CSI) feedback overhead in massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) systems. However, existing CSI feedback models struggle to adapt to dynamic environments caused by user mobility, requiring retraining when encountering new CSI distributions. Moreover, returning to previously encountered environments often leads to performance degradation due to catastrophic forgetting. Continual learning involves enabling models to incorporate new information while maintaining performance on previously learned tasks. To address these challenges, we propose a generative adversarial network (GAN)-based learning approach for CSI feedback. By using a GAN generator as a memory unit, our method preserves knowledge from past environments and ensures consistently high performance across diverse scenarios without forgetting. Simulation results show that the proposed approach enhances the generalization capability of the DAE framework while maintaining low memory overhead. Furthermore, it can be seamlessly integrated with other advanced CSI feedback models, highlighting its robustness and adaptability.

With the rapid proliferation of unmanned aerial vehicles (UAVs) in civilian and industrial applications, the risk of malicious or unauthorized UAV use has become a critical security concern. Existing machine learning (ML)-based UAV recognition methods offer a certain degree of interpretability, but their performance is often limited in complex environments and across diverse UAV types. In contrast, deep learning (DL)-based methods exhibit strong representation capability, yet they generally lack physical interpretability. To address this issue, we propose an interpretable UAV recognition framework, termed frequency-aware network for UAV recognition (FANet-UAV), which performs coarse-to-fine feature learning in the frequency domain. Specifically, a multiplication filter module (MFM) is first designed to capture coarse-grained spectral patterns by exploiting multi-mode and multi-scale frequency characteristics of UAV signals. Based on these coarse representations, a convolutional neural network (CNN) is further employed to extract fine-grained discriminative features for accurate classification. Experimental results on two public UAV datasets demonstrate the effectiveness of the proposed method. In particular, FANet-UAV improves the recognition accuracy from 90.45% to 96.82% on DroneRFa and from 94.15% to 98.83% on DroneRF. Moreover, visualization results and channel-wise SHAP analysis provide both pre-hoc and post-hoc interpretability, revealing that FANet-UAV mainly relies on flight control signal (FCS) features for decision-making, while video transmission signal (VTS) features contribute less to the final recognition results.

With the growing adoption of Internet of Things (IoT) devices, ensuring the security of wireless communications has become increasingly critical. Radio frequency fingerprint identification (RFFI) has shown promise in this regard due to its capability of uniquely identifying devices. Although deep learning (DL) approaches have significantly improved RFFI performance, they typically rely on large-scale centralized data. This poses challenges in terms of privacy preservation and heterogeneous data distributions. To address the performance degradation caused by non-independent and identically distributed (non-IID) data in cross-receiver scenarios, this paper proposes a feature alignment strategy based on federated learning (FL) for RFFI. In such scenarios, due to differences in receiver hardware characteristics, deployment locations, and channel conditions, the signals captured by different receivers often exhibit distribution shifts, resulting in misaligned feature spaces across clients. The proposed method guides each client to learn aligned intermediate feature representations during local training, effectively mitigating the resulting adverse impact on model generalization. Experiments conducted on a real-world RF dataset demonstrate that the proposed method achieves higher identification accuracy and improved stability compared with representative federated baselines, including FedAvg and FedProx. The highest identification accuracy reaches 90.83%, and the performance gains are accompanied by generally reduced variance across different client configurations, highlighting the robustness and generalization capability of the proposed approach in heterogeneous wireless environments.

This paper proposes a novel radar-based framework for non-contact biometric identification through heart signal extraction, targeting secure and privacy-conscious identification scenarios. Traditional biometric methods, such as fingerprint and facial recognition, face challenges including privacy concerns, vulnerability to spoofing, and the requirement for close proximity or direct line-of-sight. Our framework addresses these issues by reconstructing electrocardiogram (ECG) signals from radar-extracted cardiac motion data and implementing an open-set person identification system. Specifically, the framework integrates ECGReconNet, a specialized deep learning model for reconstructing ECG signals from human chest wall displacement, the InceptionTime model enhanced with fixed-Class Anchor Clustering (fixed-CAC) loss for robust feature anchoring, and a hypersphere-based delineation method to differentiate known from unknown individuals. Experimental results on a public dataset demonstrate state-of-the-art performance, achieving 99.61% accuracy in closed-set identification (27 subjects) and 93.97% accuracy under challenging open-set conditions (14 known and 13 unknown subjects). However, the proposed approach exhibits limitations, including sensitivity to abrupt body movements and environmental noise, potential performance degradation under severe cardiac irregularities, and reduced efficacy with increased numbers of unknown identities.

Millimeter-wave (mmWave) communication, which operates at high frequencies, has gained extensive research interest due to its significantly wide spectrum and short wavelengths. However, mmWave communication suffers from the notable drawbacks as follows: i) The mmWave signals are sensitive to the blockage, which is caused by the weak diffraction ability of mmWave propagation; ii) Even though the introduction of reconfigurable intelligent surfaces (RISs) can overcome the performance degradation caused by serve path loss, the location of users and RISs as well as their densities incur a significant impact on the coverage and rate performance; iii) When the RISs’ density is very high, i.e., the network becomes extremely dense, a user sees several line-of-sight RISs and thus experiences significant interference, which degrades the system performance. Motivated by the challenges above, we first analyze distributed multi-RIS-aided mmWave communication system over Nakagami-mfading from the stochastic geometry perspective. To be specific, we analyze the end-to-end (E2E) signal-to-interference-plus-noise-ratio (SINR) coverage and rate performance of the system. To improve the system performance in terms of the E2E SINR coverage probability and rate, we study the optimization of the phase-shifting control of the distributed RISs and optimize the E2E SINR coverage particularly when deploying a large number of reflecting elements in RISs. To facilitate the study, we optimize the dynamic association criterion between the RIS and destination. Furthermore, we optimize the multi-RIS-user association based on the physical distances between the RISs and destination by exploiting the maximum-ratio transmission. Numerical and simulation results indicate that the deployment of distributed RISs can significantly improve the E2E SINR coverage probability and achievable rate of the system compared to the selected benchmarks.

Massive multiple-input multiple-output (MIMO) array systems are a cornerstone technology for beyond fifth-generation (B5G) and sixth-generation (6G) wireless communications. This paper proposes a novel algorithm for the joint estimation of direction-of-departure (DOD) and direction-of-arrival (DOA) in massive MIMO systems under unknown gain-phase errors. The proposed method first exploits a normalized rotational invariance property to extract the relative amplitude-phase difference vectors between adjacent antenna elements. By incorporating the prior knowledge that the transmitter and receiver phase errors follow a zero-mean distribution, we formulate two decoupled cost functions to enable joint DOD and DOA estimation. We then obtain that the corresponding angular parameters efficiently through low-complexity spectral searches. Notably, the proposed method requires only one well-calibrated transmitter and one well-calibrated receiver, thereby substantially reducing the calibration effort compared with existing approaches. The gain errors are directly estimated from the amplitude-phase difference vectors, while the phase error vectors are reconstructed using the estimated DOD and DOA values. The proposed framework accommodates general conformal transceiver array geometries and effectively mitigates error accumulation in gain-phase calibration. Simulation results verify that the proposed algorithm achieves superior angular estimation accuracy and calibration precision compared with state-of-the-art techniques.

Human activity recognition (HAR) is important for assistive care, enhancing safety and timely assistance. Traditional HAR systems using wearables and non-wearables have limitations, such as intrusiveness, privacy concerns, dependence on environmental conditions, and reliance on a single modality where noise can drastically affect their performance. In this paper, we propose a novel HAR approach using infrared (IR) thermal sensors, which offer a non-intrusive and privacy-preserving solution. Our method addresses key challenges in HAR using IR sensors, including low resolution, sensor noise, and the limitations of single-modal approaches, by integrating multi-modal learning. The proposed deep learning framework combines depth estimation, pose detection, and thermal feature extraction, utilizing a hybrid CNN-LSTM model with an attention mechanism to improve activity classification accuracy. Through extensive experimentation, we demonstrate that our approach significantly outperforms traditional HAR methods which use raw IR sensor data and rely on a single modality, achieving high precision and robustness across diverse environmental conditions. By employing all modalities, our approach reaches an accuracy equal to 98.78%.

Edge artificial intelligence (AI) is expected to be a central part of 6G, where servers located at the edge of the network will support devices in performing inference using machine learning (ML) models. However, providing latency and accuracy guarantees needed by many 6G applications, such as automated driving and robotics, is challenging due to the black-box nature of ML models, the complexity of the tasks, and the random wireless channel. This paper proposes a novel framework leveraging conformal risk control to meet requirements on the expected loss under a strict deadline. To adapt to fluctuating channel conditions, our framework utilizes an ensemble of black-box encoder/decoder models and inference models of varying accuracy and complexity, and selects the model expected to yield the most informative prediction under the given requirements. We demonstrate the proposed framework on a deadline-constrained image classification task under a strict missed detection requirement. The results suggest that the proposed framework provides the required performance guarantees, making it a promising step toward achieving reliable real-time edge AI services in 6G.

Federated Learning (FL) enables collaborative training across distributed edge devices. However, it struggles with statistical heterogeneity in non-IID scenarios, which degrades overall model performance. Clustered FL addresses this issue by grouping clients with similar data distributions, allowing clients within each cluster to the similar data for more personalized training. However, existing clustered FL methods face challenges in accurately identifying data similarities and often introduce significant communication overhead and computational costs. In this paper, we propose a clustered FL framework (ProCFL) that exploits local prototypes during the initialization phase for one-shot identification of client data similarities, guiding subsequent federation. Each client computes a local prototype representing its data distribution using a common model and uploads it to the server. The server employs Singular Value Decomposition (SVD) to extract principal vectors from these prototypes, addressing label misalignment and enabling pairwise angular similarity computation. To achieve better cluster assignments, ProCFL incorporates a hierarchical soft clustering mechanism that forms overlapping cluster sets, promoting knowledge sharing across clusters. We evaluate our method under Label Skew and Feature Skew non-IID scenarios using multiple datasets, including Fashion-MNIST, CIFAR-10, and Digit-5. The results show that ProCFL achieves higher test accuracy than existing methods while reducing communication overhead from 11.71 MB to 0.27 MB and clustering time from 6.07 s to 0.44 s.

Traditional machine learning techniques have achieved great success in improving data-rate performance and reducing latency in millimeter wave (mmWave) communications. However, these methods still face two key challenges: (i) their reliance on large-scale paired data for model training and tuning, which limits performance gains and makes beam predictions outdated, especially in multi-user mmWave systems with large antenna arrays, and (ii) meta-learning (ML)-based beamforming solutions are prone to overfitting when trained on a limited number of tasks. To address these issues, we propose a memristorbased meta-learning (M-ML) framework for predicting mmWave beam in real time. The M-ML framework generates optimal initialization parameters during the training phase, providing a strong starting point for adapting to unknown environments during the testing phase. By leveraging memory to store key data, M-ML ensures the predicted beamforming vectors are wellsuited to episodically dynamic channel distributions, even when testing and training environments do not align. Simulation results show that our approach delivers high prediction accuracy in new environments, without relying on large datasets. Moreover, MML enhances the model's generalization ability and adaptability.

Deploying deep neural network (DNN) models to the Internet of Things (IoT) for distributed inference (DI) is becoming an increasingly common requirement for AI applications. When IoT transmissions occur over lossy networks, data loss can significantly affect the accuracy of inference tasks. Therefore, we propose a plug-and-play module called Loss-Adapter, which can improve the accuracy of DI on lossy networks. To simulate network packet loss, we design a Gaussian distribution sampling dropout. This idea is utilized during the training of Loss-Adapter to acquire fault-tolerant inference capability on unreliable communication links. Moreover, we use the Tree-structured Parzen Estimator algorithm to optimize the parameters of the Gaussian dropout layer. To avoiding catastrophic forgetting, we implement freeze training. In addition, as a plug-and-play module, it is very easy to train and use. Our experiments show that by applying the Loss-Adapter, the inference accuracy of DNN models on lossy IoT is significantly improved.

Stress has become an increasingly common issue, significantly impacting individuals' health and productivity. Traditional methods for assessing stress levels often rely on physiological signals, such as heart rate and skin conductance. However, these methods can be impractical for continuous, non-invasive monitoring. To address these limitations, we propose a machine learning approach for stress assessment using eye-tracking data. We collected eye-tracking and Electrocardiogram (ECG) data under different stress conditions. From this dataset, we extracted relevant features and conducted preliminary analysis. We further proposed a novel stress index, the Least Absolute Shrinkage and Selection Operator Stress Index (Lasso Stress Index), specifically tailored for stress detection in our dataset. In previous studies, researchers commonly used predefined subjective stress index or Baevsky's Stress Index (BSI) for stress classification. We compared these methods and found that our proposed Lasso Stress Index yielded the highest accuracy, achieving up to 90.6% in stress classification.

High Altitude Platform Station (HAPS) is a new airborne communications platform that provides wide-area communications services from the stratosphere and has the potential for coverage extension in 6G networks. In particular, it is a practical scenario where multiple HAPS work together to provide communications in densely populated urban areas and extensive regions. However, changes in ground coverage due to HAPS movement cause frequent handovers, which pose challenges to communication quality and stability. To solve this problem, “footprint fixation,” which maintains constant ground coverage even when the HAPS moves, is expected to reduce handovers and improve communication quality. How we should design beamforming and trajectory under fixed footprint conditions has not been clarified. In this paper, we propose a beamforming and trajectory planning method under fixed-footprint conditions for multiple HAPS systems. For footprint fixation, beamforming dynamically adapts to HAPS motion to eliminate ground coverage shifts, reduce handover frequency, and improve communication stability. Under fixed-footprint conditions, trajectory planning aims to improve the throughput of UEs with low (5th percentile) and medium (50th percentile) communication quality simultaneously through sequential multi-objective optimization. Simulation results in major Japanese cities with different UE distributions show that the proposed method improves both low and medium communication quality UEs, achieving a$\mathbf{2 0 - 3 6 \%}$improvement in 5th percentile throughput and an$8-20 {\%}$improvement in 50th percentile throughput compared to other methods. In addition, the footprint fixation increases coverage stability, reducing outage probability to less than 2 % and significantly reducing handover frequency.

This paper proposes a heart rate estimation method using Variational Mode Decomposition (VMD) employing Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. The proposed method first estimates the human position within the radar's coverage area, then reduces noise by focusing on the signal from these positions. The signal is decomposed into Intrinsic Mode Function (IMF) signals using VMD, and only the IMF signal related to the heartbeat is retained. Heart rate signal is reconstructed by weighting IMF signals based on their energy within the specific spatial area in which the human is located. The reconstructed signals are utilized for heart rate estimation through peak detection. The estimation is done over consecutive time windows. Until the fourth time window, among the estimated heart rates from the different cells, the selection is based on energy and periodicity. From the fifth time window onwards, the heart rate closest to the average of the previous four estimates is chosen. This approach considers the gradual transition of heart rate estimation over time to mitigate extraneous variations. Validation experiments involved 4 subjects being seated and stationary within the radar coverage area, with heart rate observed via MIMO FMCW radar. Results showed an average Mean Absolute Error (MAE) of 2.54 BPM, with an exclusion rate of 2.12%.

Semantic communication (SC) has emerged as a promising paradigm to address the bandwidth limitations of traditional wireless communication systems by transmitting only the essential meaning of data. This paper investigates an advanced SC framework for vehicular communication, employing diverse feature extraction techniques to encode multi-modal information, such as textual descriptions, object poses, semantic segmentation, and sketches, into compact semantic representations. These semantic encoders are evaluated based on their output size, faithfulness of reconstruction, and resilience to data loss. The proposed system model considers vehicular communication scenarios where vehicles transmit important information extracted from camera-collected data to other vehicles and road users, in bandwidth-constrained environments. Simulation results show the effectiveness of the proposed SC framework, with a reconstruction performance reaching 17.24 in Fréchet Inception distance (FID) and a an RMSE equal to 0.029 between the transmitted image and the reconstructed one. This performance is achieved while the data saving between the size of the original image and the transmitted semantics is equal to$\text{9 2. 2 5 \%}$.

High-Altitude Platform Stations (HAPSs) enable wide-area coverage in 6G networks but introduce interference when sharing spectrum with terrestrial networks (TNs). Null-forming is a technique to mitigate this interference by directing low-power beams (nulls) toward terrestrial users. Traditional nullforming methods, such as Null-Sweeping, rely on changing null directions across the resource blocks (RB) to improve the impact of nullforming. Yet these null directions are predefined for uniform user distributions and may not fully account for nonuniform deployments. We propose a user-aware nullforming approach that leverages K-means clustering to adapt null positions to dense user regions in the terrestrial cells, while time-frequency resources are allocated proportionally to user density. Simulations show that our method reduces HAPS interference for terrestrial users and improves fairness in interference distribution.

With the rapid advancement of wireless communication technologies, WiFi signals have become essential for modern connectivity across diverse applications. However, their widespread deployment introduces significant security vulnerabilities, including unauthorized access, data leakage, and interference. Accurate identification of WiFi transmitters is crucial for mitigating these threats. While existing methods perform well in ideal conditions, their effectiveness degrades in real-world scenarios, particularly in environments with low signal-to-noise ratios (SNRs). To address this limitation, we propose a novel transmitter identification framework that integrates blueprint separable convolution (BSC) and a squeeze-and-excitation (SE) module. The BSC extracts critical features efficiently, while the SE module dynamically enhances feature representations. Simulation results demonstrate that the proposed approach achieves competitive or superior accuracy compared to state-of-the-art models. Moreover, the framework exhibits strong robustness, maintaining high recognition performance even in challenging transmission conditions with low SNRs.

Wi-Fi-based Human Activity Recognition (HAR) using Channel State Information (CSI) has received significant attention due to its non-intrusive nature and wide applicability in smart cities, healthcare, and smart home systems. Despite its potential, real-world deployment faces a major challenge: ensuring the model’s ability to generalize across different users, who exhibit distinct behaviors and encounter diverse environmental factors. This issue, termed Cross-User Generalization (CUG), arises from distribution shifts caused by variations in users’ body shapes, postures, movements, and environmental conditions. To address this challenge, we propose a cross-domain generalization framework based on an Attention-based Bidirectional Long Short-Term Memory (ABLSTM) network. The proposed framework enhances the generalization capabilities of HAR models by leveraging multi-source training and incorporating attention mechanisms for effective temporal feature learning. Specifically, the model learns user-invariant features across diverse training users and adapts to new users without requiring direct exposure to their data. Extensive experiments on a multi-user CSI dataset demonstrate that the ABLSTM model improves average test accuracy by 2.0%–6.7% compared to BiLSTM and GRU models, and over 18.0% compared to MLP-based models, while achieving the lowest training and testing loss. This methodology offers a robust and scalable solution for Wi-Fi-based HAR, promoting reliable deployment in complex, multi-user environments encountered in smart cities, healthcare monitoring, and security surveillance.

In Low-Earth Orbit (LEO)-based Non-Terrestrial Networks (NTN), stable and continuous connectivity through efficient handover (HO) mechanisms should be ensured, especially given the high mobility and dynamic satellite coverage in these networks. Conventional HO optimization approaches, such as the Service Continuity Dynamic Programming (SCDP) strategy, optimize HO but may leave some User Equipments (UEs) experiencing low link rates, reduced service time, or frequent handovers. To address this, we propose the Service Continuity Max-Min (SCMM) strategy, which employs the MaxMin algorithm to reduce the number of UEs that have poor connectivity experiences, using a uniquely defined reward function that incorporates link rate, service availability time, and HO frequency. The simulation results demonstrate that SCMM improves the minimum reward across UEs while maintaining an overall performance comparable to that of the existing SCDP methods. This approach offers a robust solution to improve service continuity and user satisfaction in NTN, making it an effective strategy for next-generation satellite networks.

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multi-armed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and e-greedy-based methods in terms of both transmission success rate and energy efficiency.

The rapid increase in wireless devices has raised significant security and privacy concerns, positioning Specific Emitter Identification (SEI) as a crucial physical-layer security technology. While Deep Learning (DL) has been widely applied to SEI, it often requires large amounts of high-quality signal examples, which are laborious and expensive to obtain. Moreover, the DL-enabled SEI models have difficulties in extracting features from the signal examples in the testing process that are consistent with those from the signal examples in the training phase due to the wireless channel variations, further resulting in a significant reduction in identification performance. To address these challenges, we propose a channel-robust Few-Shot SEI (FS-SEI) method based on Meta-Feature Augmentation (MFA). Our approach utilizes datasets from base emitters to construct a meta-feature embedding function that can extract generalizable features from a few signal examples of target emitters. We then calculate and calibrate the statistics of these extracted features to describe the feature distribution of target emitters. A Multi-Layer Perceptron (MLP) is subsequently trained on both original and augmented features derived from this distribution, achieving a robust FS-SEI model. Experiments conducted on a Wi-Fi dataset comprising 16 emitter categories - 10 as base emitters and 6 as target emitters - demonstrate that our method achieves 93.75% identification accuracy with only 5 examples per target emitter, maintaining 92.56% accuracy even under varying wireless channel conditions. Code is available at https://github.com/lovelymimola/MFA-based-FS-SEI.

The deep autoencoder (DAE) framework has turned out to be efficient in reducing the channel state information (CSI) feedback overhead in massive Multiple-Input Multiple-Output (mMIMO) systems. However, these DAE approaches presented in prior works rely heavily on large-scale data collected through the base station (BS) for model training, thus rendering excessive bandwidth usage and data privacy issues, particularly for mMIMO systems. When considering users’ mobility and encountering new channel environments, the existing CSI feedback models may often need to be retrained. Returning back to previous environments, however, will make these models perform poorly and face the risk of catastrophic forgetting. To solve the above challenging problems, we propose a novel gossiping generative adversarial network (Gossip-GAN)-aided CSI feedback training framework. Notably, Gossip-GAN enables the CSI feedback training with low-overhead while preserving users’ privacy. Specially, each user collects a small amount of data to train a GAN model. Meanwhile, a fully distributed gossip learning strategy is exploited to avoid model overfitting, and to accelerate the model training as well. Simulation results demonstrate that Gossip-GAN can: 1) achieve a similar CSI feedback accuracy as centralized training with real-world datasets; 2) address catastrophic forgetting challenges in mobile scenarios, and 3) greatly reduce the uplink bandwidth usage. Besides, our results show that the proposed approach possesses an inherent robustness.

High altitude platform station (HAPS) systems have emerged as a key solution to address the increasing networking demands of the Internet of Things (IoT), providing wide-area coverage, low latency, enhanced network resilience, and cost-effective service delivery, particularly in remote regions. Given that the continuous movement of HAPS and the inherent mobility of user equipments (UEs) often lead to low and unevenly distributed UE throughput, it is crucial for HAPS systems to dynamically control the antenna using beamforming techniques. However, the current reactive approaches to dynamic control fail to effectively minimize the number of low throughput UEs and achieve low time complexity. To overcome these challenges, we propose a clustering-aided particle swarm optimization (PSO) algorithm to determine the antenna parameters, enabling HAPS to configure multiple cells and dynamically control beams based on UE distribution. This algorithm leverages UE clustering information to redefine the search space, reducing the complexity while enhancing the ability to find the global optimum. Specifically, we propose a novel regulated K-means algorithm that groups UEs into appropriately balanced clusters, precisely reducing the search space for global optimization. Simulations using real-world UE distributions demonstrate that our proposed method outperforms conventional approaches in reducing low throughput UEs and providing balanced throughput distribution, while maintaining low computational complexity.

WiFi-based passive noncontact sensing is widely regarded as a leading technology in wireless sensing, owing to its extensive application scope and favorable growth outlook. Nevertheless, although current WiFi-based sensing techniques attain remarkable accuracy in identifying activities within particular scenarios, they need stronger generalization capabilities across different targets and environments, hindering further commercial development. To address this issue, this article uses convolutional neural network (CNN), BLSTM, and attention layers to propose a cross-subject transfer learning method based on the CNN-ABLSTM algorithm model. This method combines widely used transfer learning methods with deep neural network algorithms in cross-domain sensing. Specifically, this method leverages the performance advantages of the CNN-ABLSTM algorithm model in processing time-series data like channel state information (CSI) and utilizes transfer learning to fine-tune the pretrained model from the source domain for application in the target domain with different subjects. This enables faster and more accurate achievement of cross-subject tasks. The simulated results show that the proposed new approach achieves higher recognition accuracy and shorter training times than traditional transfer learning methods for cross-subject tasks. In testing with the dataset used, it achieves up to around 85% performance of activity recognition accuracy in cross-subject tasks.

WiFi-based wireless sensing has emerged as a promising passive sensing technology that is precious for human activity recognition (HAR) across diverse applications. However, achieving robustness across varying scenarios presents a significant challenge, limiting its broader adoption. To address this issue, we propose a robust cross-scenario incremental learning (IL) method for WiFi-based wireless sensing that leverages WiFi channel state information (CSI) and elastic weight consolidation (EWC) loss. Our approach integrates a convolutional neural network and attention-based long short-term memory (CNN-ABLSTM) framework, which effectively captures the spatial and temporal features of CSI data. The IL strategy enhances model adaptability across dynamic environments, while EWC minimizes catastrophic forgetting by preserving critical weights from prior tasks. The method’s integration of a memory set and EWCLoss enables it to balance the retention of learned features with adaptation to new scenarios, effectively mitigating performance degradation across tasks. Experimental results on the MM-Fi dataset demonstrate robust cross-scenario performance: starting with initial training on scene E01, the model achieves incremental recognition in new scenes E02, E03, and E04 with cross-scenario accuracies of 88.01%, 80.16%, and 70.93%, respectively. The proposed approach substantially improves cross-scenario adaptability and test accuracy compared to traditional and cross-domain methods such as transfer learning. This work marks a significant advancement toward robust and scalable WiFi-based wireless sensing for diverse real-world applications.

The deployment of deep neural networks (DNNs) in the Internet of Things (IoT) is essential for various AI applications. This trend highlights the growing need for distributed inference (DI) capabilities to process data efficiently and effectively in IoT environments. In lossy networks, data loss can significantly affect inference accuracy. Prioritizing algorithmic optimizations over hardware modifications proves to be a more effective approach for enhancing DNN performance in the IoT. We propose a plug-and-play module called Loss-Adapter, which aims to improve the accuracy of DI on lossy networks. To simulate network packet loss, we design a Gaussian distribution sampling dropout. This method is utilized during the training of the Loss-Adapter to acquire fault-tolerant inference capabilities for operation over unreliable communication links. Moreover, we employ the tree-structured Parzen estimator algorithm to optimize the parameters of the Gaussian dropout layer. We design two IoT packet loss simulation systems to conduct experiments, exploring the impact of the DNN split position and packet size on inference accuracy. Our experiments demonstrate that applying the Loss-Adapter significantly enhances the inference accuracy of DNN models operating on lossy IoT networks.

As the global population continues to age and technologies such as the Internet of Things (IoT) and edge computing advance rapidly, indoor human activity recognition (HAR) based on Wi-Fi channel state information (CSI) has gained significant research attention. However, the high computational complexity of existing HAR methods limits their deployment on resource-constrained devices. To address this challenge, we propose a lightweight HAR method using branch decision lightweight two-stream convolution-augmented transformer (BLTHAT) model, which integrates depthwise separable convolutions (DSC) and an improved framework structure to enhance computational efficiency. Additionally, we introduce the branch fusion network (BFN), a decision-making module designed to optimize feature processing and improve model robustness. Further enhancements in attention mechanisms and regularization strategies contribute to reducing complexity while maintaining high recognition accuracy. Comprehensive experiments were conducted on a multi-label dataset. The results demonstrate that our proposed HAR method achieves high computational efficiency with minimal complexity, making it well-suited for IoT applications. Ablation studies further confirm that the multi-branch structure of the BFN module enhances feature extraction without significantly increasing computational overhead.

Activity recognition is essential in computer vision applications, such as smart homes and healthcare services. While RGB images have been widely used in this area, they pose challenges related to privacy invasion and environmental constraints. To address these issues, some research has explored using 3-D light detection and ranging (3-D LiDAR) to collect 3-D point cloud data for activity recognition. However, the high-computational cost and large model parameters required for processing 3-D point clouds remain major limitations. To overcome these challenges, we propose a novel multiclass activity recognition system based on skeleton extraction from depth images collected by 3-D LiDAR. First, we use 3-D LiDAR to collect depth images of ten distinct activities, such as walking, falling, and squatting. Next, we process these depth images using our proposed multiple dynamic nonlinear mapping (MDNLM) method. The MDNLM method enhances the clarity of human body details by adjusting the color distribution of depth values based on the human position, ensuring that more colors are allocated to specific regions of the human body. This enhancement allows a fine-tuned algorithm to extract skeleton joints accurately from the mapped images. Finally, the extracted skeletons are fed into a convolutional neural network combined with a long short-term memory network (CNN+LSTM) for multiclass activity recognition. Our proposed method achieved 100.0% accuracy for a 2-class classification task (fall detection), 99.0% accuracy for a 7-class classification task, and 94.7% accuracy for a 10-class classification task.

In dynamic and cluttered environments, traditional Simultaneous Localization and Mapping (SLAM) systems often suffer from degraded localization accuracy and unstable map construction due to the presence of moving objects and occlusions. To address these challenges, we propose DFusion-SLAM, a lightweight and robust SLAM framework that integrates an enhanced object detection module into ORB-SLAM3. The detection module is based on an improved D-Fine architecture, in which the original Transformer is replaced with a more efficient PolaLinear Attention mechanism. Furthermore, a MetaFormer-based semantic fusion structure is introduced to strengthen multi-scale feature representation. These architectural improvements jointly enhance detection accuracy while reducing model complexity, achieving a performance increase from 42.8% to 43.7% mean Average Precision (mAP). Experimental evaluations on dynamic RGB-D sequences from the TUM and Bonn datasets demonstrate that DFusion-SLAM significantly improves localization accuracy and mapping stability under dynamic conditions, while maintaining high computational efficiency. These results highlight the framework’s strong potential for real-time deployment in IoT-oriented mobile and robotic platforms operating in complex environments.

In simulating synthetic aperture radar (SAR) ship wakes, dynamic wake modeling often uses the linear superposition of sea waves and Kelvin wakes. This method, however, overlooks the alterations in sea surface roughness caused by the nonlinear interaction between waves and wakes, thus failing to accurately capture real sea surface variations. In this letter, we introduce a rapid SAR image simulation technique for ship wakes that incorporates sea waves using fluid velocity potential. Firstly, the computational domain and ship grid are constructed, with the grid scale tailored to the ship's surface structure to satisfy boundary conditions for efficient fluid velocity potential calculations. Next, to enhance boundary calculation accuracy, we employ the Taylor expansion boundary element method to swiftly resolve both steady and unsteady velocity potential components. Additionally, our approach not only depicts the interaction between sea waves and ship wakes but also facilitates the simulation analysis of various sea condition parameters. By treating the ship wake as noise and comparing images containing only background sea waves with the simulation images, the results show that the accuracy of the proposed approach is 0.2 SSIM higher than that of the linear superposition method, and the speed is 3 hours faster than that of CFD method.

Malware traffic classification (MTC) is a crucial step in network intrusion detection, which is significant for network security and management. With the continuous evolution of malware traffic, traditional MTC methods are difficult to adapt efficiently to new traffic categories, and manually designed neural network structures suffer from performance bottlenecks and low design efficiency. Hence, we propose an enhanced MTC method based on expandable class incremental learning (CIL) with architecture search. The architecture search can automatically design the optimal neural network structure tailored to different network traffic characteristics, avoiding the limitations of manually designing network structures and improving classification performance. Meanwhile, expandable CIL allows the MTC model to gradually learn new traffic categories without forgetting previous knowledge, avoiding the computational overhead and efficiency loss caused by frequent retraining of the model. The experimental results demonstrate that the proposed CIL-MTC approach surpasses advanced incremental learning methods on both the Edge-IIoTset and ISCX VPN-nonVPN datasets, achieving superior classification performance while maintaining lower average trainable parameters and training costs. Especially, it achieves an average incremental accuracy of 98.55% and 99.09% on the Edge-IIoTset dataset with incremental tasks of 5 and 2, respectively.

The development of a cost-effective digital biomarker for detecting dementia is highly needed. While numerous studies have explored dementia detection through speech and natural language analysis, only a few studies have focused on dementia detection using face video recordings, and more in-depth research is needed. In this paper, we propose a method for detecting dementia and mild cognitive impairment (MCI), a pre-dementia stage, by utilizing four types of facial expression features extracted from recorded videos of participants. These features include Action Units, emotion categories, Valence-Arousal, and face embeddings. From the above features obtained from each video frame, various statistical information was extracted and used as features, and predictions were performed using a decision tree-based model. Our method was evaluated using face video recordings during conversations. The method achieved an area under the receiver operating characteristic curve (AUC) of 0.933 for dementia detection and 0.889 for MCI detection. Statistical analysis of facial expression features revealed that participants with dementia had fewer positive emotions, more negative emotions, and lower valence and arousal than healthy participants. These results indicate that the proposed method could serve as an explainable screening tool for the early detection of dementia and MCI.

By extracting radio frequency (RF) fingerprints from received signals, specific emitter identification (SEI) becomes a promising technique for physical layer identification of wireless devices. Recently, channel-robust SEI has attracted increasing attention due to the weak robustness exhibited by deep learning (DL)-based SEI methods in cross-channel conditions. To address these limitations, we propose a novel channel-robust SEI framework based on single-source domain generalization (SDG). Initially, we analyze the weak robustness of existing SEI methods from the perspective of the “shortcut learning” phenomenon in DL. Shortcut learning may lead traditional SEI methods to prioritize easily-mined, yet transient, channel characteristics in signal samples, rather than focusing on the more stable RF fingerprints derived from hardware differences. Next, from the perspective of SDG, we outline the optimization goal to rectify the shortcut learning in SEI. Inspired by this optimization goal, we then propose a channel-robust SEI method. This method consists of feature embedding through a multi-scale convolutional attention network (MSCAN), domain expansion using random overlay augmentation (ROA) to generate multiple virtual domains, and dual alignment strategy based on contrastive learning. Specifically, supervised contrastive learning is implemented for category-wise alignment, while supervised contrastive adversarial learning is utilized for domain-wise alignment. This dual alignment strategy can optimize the MSCAN to learn discriminative and domain-invariant feature representations, thereby enhancing the robustness of SEI. Simulation experiments on the ORACLE dataset and the WiSig dataset have demonstrated the superiority of our method compared to state-of-the-art techniques. The codes can be downloaded from GitHub (https://github.com/BeechburgPieStar/SDG-for-Channel-Robust-SEI).

The muti-task Channel State Information (CSI) feedback is a downlink CSI feedback approach. It focuses on Clustered Delay Line (CDL) channel models as the channel environments. There are five different CDL channel models, from CDL-A to CDL-E. In this method, the autoencoder (source model) is pre-trained using the CDL-ALL dataset, which is an equal mixture of each CDL channel dataset (source data) rather than a single CDL channel dataset. The decoder at the BS is subsequently fine-tuned using a small amount of CSI data from the target channel environment (target data) to generate a target model. The source model has a significant influence on the reconstruction performance of the target model, the amount of data, and the number of training epochs required to obtain it. This paper investigates the mixing ratio of each CDL channel dataset used as source data to enhance the CSI reconstruction performance of the source model in the muti-task CSI feedback. Furthermore, we explore the possibility of enhancing the CSI reconstruction performance of the target model by employing the improved source model for fine-tuning the target model. To determine the mixing ratio, two distinct criteria for source model performance are employed. The simulation results identified several mixing ratios of source data in these criteria that improve the reconstruction performance of both the source and the target models compared to the use of CDL-ALL as a source dataset in the multi-task CSI feedback.

Currently, the field of wireless sensing is moving towards non-contact and easy-to-deploy passive sensing technologies. Among these, sensing techniques based on WiFi channel state information (CSI) have emerged as highly promising due to their excellent performance and suitability for current sensing needs. However, despite the potential of WiFi CSI-based sensing technologies, there are still pressing issues that need to be addressed. Traditional WiFi CSI-based methods face challenges such as weak generalization ability and poor robustness, especially when the sensing target and environment change, which hinders the further development. To address this problem, this paper introduces the transfer learning method and combines it with the deep neural network to propose a novel cross-subject WiFi sensing method. Specifically, this method utilizes the source domain-target domain partitioning approach in transfer learning. In the source domain, deep neural networks are trained to obtain the source domain feature model, which is then transferred to the target domain. Fine-tuning is performed using techniques such as network freezing, aiming to meet the faster and more accurate sensing task demands of cross-subject. From the final experimental results, the novel cross-subject transfer learning method proposed in this paper achieved higher recognition accuracy and shorter training time. Moreover, the implementation of network freezing further enhanced the performance and efficiency of cross-subject, achieving up to 86% performance on the dataset used in this paper.

MIMO FMCW Radar, a non-contact heart rate (HR) monitoring technology, has emerged as one of the superior alternatives to contact-based sensors, providing precise HR estimation without the drawbacks of discomfort or privacy concerns, and excelling in diverse environmental conditions. Recent HR estimation studies using conventional methods have achieved high accuracy but face challenges with lengthy processing times and sensitivity to experimental conditions, affecting real-time application and robustness. A previous Deep learning (DL)-based approach improves on these aspects but is limited by the range resolution of SISO FMCW Radar and the requirement for close positioning of subjects. Additionally, this method has inability to utilize contextual information from adjacent time windows restricts its effectiveness for time series analysis. Thus, our proposed method combines a Curve-Length (CL) approach with a DL rough-to-fine model, addressing the limitations of previous studies and improving HR estimation accuracy and robustness. This integrated process begins with human location detection through a CL-based approach and is followed by a two-phase rough-to-fine HR estimation. The experimental results show significant improvement over the conventional methods.

This paper presents a radar-based heart rate monitoring and Inter-beat interval (IBI) estimation. Traditional IBI estimation relies on peak detection combined with signal processing techniques. Our approach uses signal reconstruction with a neural network, which significantly improves the accuracy and robustness of IBI estimation compared to conventional methods. By utilizing multi-channel input data, our approach effectively mitigates various sources of noise and interference, resulting in highly accurate and reliable IBI predictions. Instead of relying solely on peak detection, we use the U-net architecture and cross-channel attention mechanism to reconstruct the triangular waveform generated from the ground truth ECG signals. The incorporation of multi-channel input and channel-wise attention mechanism improves our model's ability to discriminate and em-phasize critical features from different input channels, further improving IBI estimation accuracy. To evaluate the accuracy and robustness of our proposed method, we trained our model on the open-source dataset [1], and then performed leave-one-subject-out validation, which ensures our approach to be evaluated only on unseen independent subjects. We have achieved a Root Mean Square Error (RMSE) of 26.7 ms for the IBI of each heart-beat. Our results demonstrate the transformative potential of adopting signal reconstruction methods supported by state-of-the-art deep learning techniques. This shift in perspective promises more accurate and robust heart rate and IBI estimation, opening new avenues for improving the accuracy and reliability of cardiac monitoring systems.11This work was supported by JST ASPIRE Grant Number JPMJAP2326, Japan.

Non-contact heart rate measurement is anticipated to be used in various scenarios such as medical and disaster sites, driver monitoring, and smart homes. Millimeter-wave radar-based non-contact heart rate measurement leverages the chest wall displacement signal, which is a superposition of heartbeat and respiration. However, the chest wall displacement signals are susceptible to higher harmonics of respiration and other intermodulation harmonics, which can impede heart rate measurement. In this paper, we investigate a heart rate estimation method utilizing MIMO (Multiple-Input Multiple-Output) FMCW (Frequency Modulated Continuous Wave) radar. Conventional methods that utilize mode decomposition are not robust enough, as the accuracy of the results is highly sensitive to the SNR (Signal-to-Noise Ratio) of the selected IMFs (Intrinsic Mode Functions). Therefore, we propose performing chirp correction in addition to the conventional method that combines PA-LI (Phase Accumulation-Linear Interpolation) and ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to improve the accuracy of heart rate estimation. Our experimental results show that this approach enhances the SNR of the decomposed signal containing heart rate information and improves the accuracy of heart rate estimation.

Mental stress is something we experience on a daily basis. However, when it becomes chronic, it has various negative effects on our body. Although stress monitoring methods based on physiological signals are effective, they may not provide a comfortable experience for users, as they require sensors to be worn or attached to the body. As such, the need for non-invasive, non-contact and comfortable methods arises. In this paper, we propose a stress detection method using face-related and emotion-related features, all of which can be acquired by using a camera. Face-related features include action units and face embedding. Emotion related features include valence, arousal and emotion-related labels. Through these features and their fusions we achieved accuracy of 1.000 for stress detection, 0.891 for task recognition and 0.883 for stress level classification, respectively, with the sample sizes of 111, 316, and 319, respectively. These results demonstrate that our proposed method using face-related and emotion-related features and their fusion is effective in stress monitoring and detection.

We develop an accurate real-time scheduling framework for federated learning (FL) in wireless caching networks to guarantee the successful delivery of files at a low cost and with a short delay. The following persisting challenges motivated our work: i) Enforcing an excessive number of FL model update per communication round is infeasible due to the limited backhaul spectrum; ii) Naive scheduling policy accounting for FL model update renders service backlogs, thus leading to network parameter staleness and in-network caching utility (ICU) deterioration. Optimal scheduling in FL is challenging, as the mobile users' preferences for content, request patterns, and network traffic are dynamic and unknown. To tackle that challenge, we first formulate an instantaneous ICU optimization problem against the stale FL models. Afterward, based on the concept of age-of-update (AoU), we propose a federated learning with an unsatisfactory set selection (FedUSS) approach capable of executing the multiple-tasks of short-term predictions and making cache replacement decisions at low cost. Theoretical and numerical analyses manifest the effectiveness of our approach.

Electrocardiogram (ECG) measurement is a fundamental diagnostic and monitoring tool in cardiology and healthcare. It plays a crucial role in identifying and managing cardiac conditions, assessing heart function, and improving patient outcomes through timely interventions and treatment adjustments. Conventional approaches for ECG measurement are often unsuitable for everyday use due to their invasiveness, inaccessibility, or limited accuracy. In this context, we introduce ECGDiff, a novel approach based on conditional denoising diffusion models for ECG signals reconstruction from Doppler signals. ECGDiff provides a non-intrusive and accessible method for non-contact ECG measurement. Our method represents a pioneering use of denoising diffusion models for time series trans-lation. In the proposed framework, ECG signals are iteratively reconstructed from random noise using preprocessed signals derived from a Doppler sensor as input conditions. Extensive experiments have been conducted on a dataset comprising 19 healthy individuals, consisting of pairs of synchronized Doppler and ECG signals. Our experimental results demonstrate that the proposed ECGDiff outperforms other state-of-the-art methods by a large margin for the task of ECG signals reconstruction. Specifically, we achieved a DTW score of 8.32, a Frechet distance score of 3.10, and a Pearson correlation coefficient of 0.93. These results highlight the effectiveness of our approach for reconstructing ECG signals from Doppler signals.

Pose estimation and human action recognition (HAR) are pivotal technologies spanning various domains. While the image-based pose estimation and HAR are widely admired for their superior performance, they lack in privacy protection and suboptimal performance in low-light and dark environments. This paper exploits the capabilities of millimeter-wave (mmWave) radar technology for human pose estimation by processing radar data with Graph Neural Network (GNN) architecture, coupled with the attention mechanism. Our goal is to capture the finer details of the radar point cloud to improve the pose estimation performance. To this end, we present a unique feature extraction technique that exploits the full potential of the GNN processing method for pose estimation. Our model mmGAT demonstrates remarkable performance on two publicly available benchmark mmWave datasets and establishes new state of the art results in most scenarios in terms of human pose estimation. Our approach achieves a noteworthy reduction of pose estimation mean per joint position error (MPJPE) by 35.6% and PA-MPJPE by 14.1% from the current state of the art benchmark within this domain.

With the aging of the population, the health of the elderly people has become a concern for many families. Monitoring of elderly people and the detection of their activities have become hot research topics. Several approaches have been proposed in the literature to detect such activity (e.g., falls), but their reliance on RGB cameras poses a serious threat to the privacy of the elderly. Therefore, Light Detection and Ranging (LiDAR) has attracted the attention of researchers. In this paper, we propose a novel method for fall detection using transfer learning and dynamic non-linear mapping to extract the human skeletons from depth images and implement a fall detection method using a Long Short-Term Memory (LSTM) neural network. We collected RGB images and depth images using 3D-LiDAR. The RGB images are used as a ground-truth for annotation and evaluation, whereas depth images are used as input to our proposed method. For fall detection, we achieved 98.5% accuracy, 97.0% precision, 100% recall, and 98.5% F1 score.

Semantic communication plays a crucial role in human interactions, allowing for the exchange of complex ideas and concepts. In this paper, we introduce a novel approach to semantic communication leveraging image generative Artificial Intelligence (AI) models, specifically stable diffusion models. Unlike conventional works, our system enables the transmission of images through a physical channel by transforming them into multi-dimensional semantic representations consisting of text descriptions, low-resolution sketches, and pose information. At the receiver’s end, these semantic representations are used to reconstruct the original image using a trained stable diffusion model. The benefits of our approach include reduced transmission bandwidth requirements, flexibility in reconstruction styles, adaptability to multiple receivers’ preferences, and the ability to omit unwanted image elements. We present preliminary results demonstrating the feasibility and effectiveness of our method. The similarity score between the transmitted images and reconstructed ones reach values ranging between 0.015 and 0.029 in Root Mean Square Error (RMSE) and between 0.993 and 0.998 using a Siamese network.

This paper aims to address the issue of low through-put for users, caused by the random movement of High-Altitude Platform Stations (HAPS) due to winds. The proposed solution involves developing an Equal Clustering (EC) approach that groups users into high-density clusters, ensuring an equal num-ber of users in each cluster while maintaining low complexity. To further enhance the system's throughput performance, we fine-tune the antenna parameters using a Deep Q-Network (DQN) and the results of the EC clustering. To evaluate the effectiveness of the proposed method, we compare it with three Reinforcement Learning (RL)-based approaches and a K-Means clustering-based method. Simulation results indicate that both the EC method and the EC-aided DQN method successfully enhance the Cumulative Distribution Function (CDF) performance of throughput distribution when compared to the RL-based method for both rotation and shift scenarios. Furthermore, the EC-aided DQN method outperforms the K-Means clustering-based method in terms of the CDF of throughput performance.

Millimeter-wave (mmWave) communication, which operates at high frequencies, has gained extensive research interest due to its significantly wide spectrum and short wavelengths. However, mmWave communication suffers from the notable drawbacks as follows: i) The mmWave signals are sensitive to the blockage, which is caused by the weak diffraction ability of mmWave propagation. ii) The mmWave communication incorporating with reconfigurable intelligent surfaces (RISs) is efficient in overcoming the performance degradation caused by severe path loss. Nevertheless, the location of users and RISs as well as their densities impose a significant impact on the coverage and rate performance. Motivated by the challenges above and based on the stochastic geometry concept, we first construct distributed multi-RIS-aided mmWave communication system over Nakagami-m fading channel. Afterward, we analyze the end-to-end (E2E) signal-to-interference-plus-noise-ratio (SINR) coverage and rate performance of the system. To improve the system performance in term of the E2E SINR coverage probability and achievable rate, we study the optimization of the phase-shifting control of the distributed RISs and optimize the E2E SINR coverage particularly when installing a large number of reflecting elements in RISs. To facilitate the study, we optimize the dynamic association criterion between the source and destination. Our simulation results demonstrate that the deployment of distributed RISs can significantly improve the E2E SINR coverage probability and achievable rate of the system compared to the selected benchmarks.

In this paper, we introduce an innovative Radio Frequency Fingerprinting (RFF)-based device authentication scheme for the Internet of Things (IoT), a network marked by extensive interconnections and interactions among various entities. Our approach, designed for an open and dynamic communication environment, not only identifies devices encountered during training but also effectively rejects those not previously seen. The scheme employs a hypersphere projection for feature embedding, strategically avoiding the need to optimize intra-device variations in the radial direction. It uses a K-Means-based binary classifier for initial device assessment based on cosine similarity scores, followed by a SoftMax classifier for precise identification of known devices. Our extensive numerical analysis confirms that this method delivers superior performance, setting a new benchmark in RFF authentication for IoT security.

High Altitude Platform Stations (HAPS) are instrumental in wireless communications, providing enhanced connectivity and extensive coverage by complementing ground-based infrastructure where its expansion is limited. HAPS enhance wireless communications by employing advanced beamforming technology, traditionally based on 2D with free space path loss models. Addressing the inadequacy of these models in different areas, our research introduces a novel beamforming strategy that incorporates detailed 3D geographic and architectural information. This approach models line-of-sight (LOS) and non-line-of-sight (NLOS) conditions with 3D information and optimizes beamforming patterns using Deep Reinforcement Learning (DRL). Our results indicate that by incorporating 3D information, the beamforming performance in terms of average throughput and SINR is markedly enhanced across all user equipments (UEs), compared to traditional 2D approaches. By taking 3D information into account, beamforming in HAPS systems becomes more equitable.

To leverage antenna diversity effectively in Massive Multiple-Input Multiple-Output (MIMO) systems, Channel State Information (CSI) utilization at the base station (BS) is crucial. In Frequency Division Duplexing (FDD) MIMO setups, downlink CSI is acquired by user equipment (UE) and transmitted back to the BS. However, this feedback overhead diminishes communication throughput, necessitating CSI compression technologies. Methods such as traditional compression or more advanced ones such as Transformer-based neural networks which aim to address this issue suffer from low compression rates or high computational cost. In this context, Reformers emerge as a potential solution as they employ Locality-Sensitive Hashing (LSH) attention, offering a comparable accuracy to Transformers but with reduced computational demands. In this paper, we propose a CSI feedback method using Reformer. Specifically, by replacing the multi-head attention of the CSI feedback method using Transformer with the LSH attention of Reformer, we aimed to achieve similar CSI reconstruction accuracy while reducing computational complexity. Computer simulations confirm that the CSI feedback method using Reformer reduces computational complexity by about 12% while achieving comparable CSI reconstruction accuracy compared to the CSI feedback method using Transformer.

This paper studies user fairness of an integrated sensing and communication (ISAC) system adopting both simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NO-MA). Note that optimizing user fairness of an ISAC system is critical, since the performance trade-off in terms of spectrum resource utilization between communication users and sensing users is hard to balance and improve, particularly at the low signal-to-noise ratio (SNR) regimes. However, such optimization problem is a coupled, non-convex, and is NP-hard in general. To solve this challenging problem, a low-complexity algorithm based on both the successive convex approximation and the semi-deterministic programming techniques is proposed. Notably, the proposed approach can maximize the signal-to-interference-plus-noise ratio (SINR) for the weakest communication user, while guaranteeing the superior sensing performance characterized by the cramér-rao lower bound (CRLB) for the sensing user. Simulation results demonstrate that our approach is capable of enabling superior sum-rate performance than the STAR-RIS with orthogonal multiple access (OMA), the conventional-RIS with NOMA, and the conventional-RIS with OMA schemes.

The increasing demand for spectral efficiency and system capacity in communication networks has driven the integration of Non-Orthogonal Multiple Access (NOMA) technology with Unmanned Aerial Vehicle (UAV) networks. In this paper, we propose an enhanced user clustering and pairing scheme for NOMA-aided UAV networks. Our study aims to maximize the minimum user throughput by proposing an Advanced Balanced K-Means (ABKM) algorithm, based on the traditional K-Means (KM) and Balanced K-Means (BKM) algorithms. The ABKM algorithm addresses the issue in the BKM algorithm where some users are assigned to sub-optimal clusters, resulting in increased distances to the cluster centroids, while simultaneously ensuring balanced clustering. Through extensive numerical simulations, we demonstrate that the ABKM algorithm significantly outperforms KM and BKM algorithms in terms of the minimum user throughput. The results highlight the potential of the proposed ABKM algorithm to enhance system performance and ensure fair resource allocation in NOMA-aided UAV networks, making it a promising solution for future 6G wireless communication systems.

In this paper, we propose an unmanned aerial vehicles (UAVs) assisted emergency caching framework based on non-orthogonal multiple access (NOMA), involving a primary UAV and several secondary UAVs. The primary UAV obtains rescue information from a remote base station (BS) and sends it to secondary UAVs hovering at designated locations to serve ground users. Specifically, a multi-objective optimization problem is formulated to maximize the number of files that can be successfully decoded in the primary UAV and minimize the number of user clusters for secondary UAVs. The multi-objective optimization problem is decomposed into three sub-problems: primary UAV deployment, power allocation, and secondary UAV deployment. First, a primary UAV deployment scheme is proposed to maximize the number of received files subject to the constraint of the demand for the line-of-sight (LoS) link probability between the primary UAV and the ground users. Subsequently, to achieve the same goal, a closed-form solution for the NOMA power allocation factor of the files is derived. Finally, due to the limited range of services of the secondary UAVs, we propose a secondary UAV deployment scheme to minimize the number of their flights, which can save energy and reduce user latency. Extensive numerical results demonstrate that the proposed algorithms outperform the random scheme and the worst scheme.

The rapid changes in high-mobility vehicle environments make it challenging for base stations (BS) to obtain comprehensive channel state information. Furthermore, road and traffic safety require communication with low latency and high reliability, posing significant challenges to spectrum resource allocation in vehicular networks. To address these challenges, this paper proposes a method combining dueling double deep-Q network (D3QN) reinforcement learning (RL) with long short term memory (LSTM) network. By using a Manhattan Grid Layout City Model as the foundational environment, a multi-agent model is constructed, with each vehicle-to-vehicle (V2V) link acting as an individual agent. These agents collaborate and interact with the environment, receiving feedback, and then determining the optimal resource allocation to ensure both superior mobile service and a safe driving environment. The experimental results indicate that our proposed method outperforms the conventional D3QN network in both the vehicle-to-infrastructure (V2I) links and the V2V links.

Belief propagation (BP) detection is a technique for separating and detecting incoming signals with minimal complexity in massive multiple-input multiple-output (MIMO) systems. However, because of the interference and noise that remain in the received signals even after attempting to remove them through conventional techniques, errors manifest in the transmitted messages. Due to the MIMO channel's numerous loops, a message containing mistakes spreads across the factor graph leading to a degradation in the BP's convergence properties and detection performance. In this paper, we propose a BP detection using deep image prior (DIP) with deep neural network (DNN)-trained scaling factor. By applying DIP to the BP detection algorithm, we achieve a reduction in residual interference and noise. Post DIP application, there is a modification in the variance of both interference and noise components. To align it more accurately with its true value and enhance message reliability, we adjust the variance using scaling factors trained through DNN-based damped BP (DNN-dBP). Using computer simulations, we demonstrate that applying DIP helps decrease the power of the residual interference and noise after removing the interference at each iteration in the BP detection. It is also shown that the proposed method improves the detection performance compared to the normal BP detection, BP detection without DIP when training the scaling factors of the variance, and BP detection to which DIP is applied without training the scaling factors of the variance.

Radiofrequency signal identification (RSI) provides a critical security solution for device authentication in the Internet of Things (IoT), characterized by extensive interconnections and interactions among numerous entities. By analyzing received radiofrequency signals, device-specific features are extracted at the receiver and used for identification. In a dynamic and ever-changing communication environment, where some devices not visible during the training process may appear during testing, a robust RSI method must not only identify devices encountered during training but also reject those that were not. In this article, we propose an open-set RSI method based on hypersphere manifold embedding. This approach leverages hypersphere projection for radiofrequency signal feature extraction on a hypersphere manifold, thereby avoiding the need to optimize intradevice variation in the radial direction. Additionally, we introduce an open-set identification approach based on generalized Pareto distribution, which does not rely on any radiofrequency signals from unknown devices. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art identification performance.

In this paper, a deep-Q network (DQN)-based approach is proposed to improve the harvested energy and the secrecy capacity by optimizing the beamforming vectors in multiple-input multiple-output (MIMO) networks with simultaneous wireless information and power transmission (SWIPT). Here, in the presence of multi-antenna eavesdroppers, a multi-antenna hybrid access point (HAP) serves energy harvesting Internet of Things (EH-IoT) nodes and information decoding (ID) receiver. The EH-IoT nodes and the ID receiver are separated from each other and experience different channels from the HAP. Two cases of signal models are studied to maximize the harvested energy. The first case is the consolidated signal case, where the HAP transmits an information-bearing signal to both the EH-IoT nodes and the ID receiver. The second case is the split signal case, where the HAP sends both information-carrying signal and energy signals to the EH-IoT nodes and the ID receiver. For both these cases, the optimization strategies for maximizing the harvested energy under Quality of Service (QoS) constraints are formulated with the secrecy capacity and the harvested energy requirements. The harvested energy maximization problems are complex to handle due to the presence of high-dimensional variables. An efficient, robust, and secure DQN-based algorithm is proposed, where Q-learning and deep neural network (DNN) methods are extended to solve these problems. Simulation results show that the proposed DQN approach can achieve a balance between harvested energy and security. By incorporating action and reward spaces that are adaptable to the formulated problem, significant performance gains are achieved.

Specific emitter identification (SEI) plays an important role in secure Industrial Internet of Things (IIoT). In recent years, many SEI methods based on machine learning (ML) and deep learning (DL) have been proposed due to their great performance. However, DL-based SEI methods are accompanied by huge computation overhead, which is not suitable for IIoT applications. In addition, the existing ML-based SEI methods rely on feature extraction and a heavy and redundant classifier, which do not ensure optimal feature combination and efficient computation. To solve the above problem, we propose an improved DL-based SEI method using a signal feature embedded knowledge graph (KG) composed of universal features. To the best of our knowledge, this is the first attempt to apply KG for SEI technology. Specifically, we explore an adaptive feature combination (AFC) strategy through the attention mechanism to realize an efficient SEI classifier. The simulation results show that the proposed KG-AFC algorithm outperforms existing SEI methods in identification performance and computation overhead. At the same time, under the optimal compression rate, the average accuracy of the proposed SEI algorithm is higher than 99.2% and can effectively reduce complexity. The code and the data set can be downloaded fromhttps://github.com/Lollipophua/KG-AFC.

Malware traffic classification (MTC) is one of the important techniques to ensure the security of cyberspace, which aims to detect anomalies and classify different types of network traffic. Recently, MTC methods based on deep learning (DL) have shown their excellent performance. However, these DL-based methods rely on datasets with manually labeled samples for training, which are costly and hard to obtain. To address this problem, this paper proposes a novel self-supervised MTC method based on the framework of masked auto-encoder (MAE). Specifically, MAE first constructs a reasonable unsupervised pretext task with a random masking strategy, which reduces the redundant information in samples and speeds up the pre-training process. The transformer-based backbone network then efficiently extracts features from the non-redundant traffic data efficiently. The proposed MTC-MAE method employs self-supervised learning on a large-scale unlabeled dataset to acquire unbiased features, and fine-tunes on specific datasets to adapt to diverse traffic classification scenarios. Simulation experiments show that our proposed MTC-MAE method is able to learn universal features with high quality and has excellent classification performance on various downstream datasets. The datasets we used, code implementation, and pre-trained models are available on GitHub.

Ensuring network security, effective malware detection is of paramount importance. Traditional methods often struggle to accurately learn and process the characteristics of network traffic data, and must balance rapid processing with retaining memory for previously encountered malware categories as new ones emerge. To tackle these challenges, we propose a cutting-edge approach using self-paced class incremental learning (SPCIL). This method harnesses network traffic data for enhanced class incremental learning (CIL). A pivotal technique in deep learning, CIL facilitates the integration of new malware classes while preserving recognition of prior categories. The unique loss function in our SPCIL-driven malware detection combines sparse pairwise loss with sparse loss, striking an optimal balance between model simplicity and accuracy. Experimental results reveal that SPCIL proficiently identifies both existing and emerging malware classes, adeptly addressing catastrophic forgetting. In comparison to other incremental learning approaches, SPCIL stands out in performance and efficiency. It operates with a minimal model parameter count (8.35 million) and in increments of 2, 4, and 5, achieves impressive accuracy rates of 89.61%, 94.74%, and 97.21% respectively, underscoring its effectiveness and operational efficiency.

The wake target has garnered increasing attention due to its length, which can be up to ten times that of the ship, and its inclusion of critical navigation information such as heading and speed. However, deep learning methods used in synthetic aperture radar (SAR) image wake detection tasks are limited to analyzing the features of the image itself, overlooking the characteristics of ship wakes in the frequency domain. This letter proposes a network called pseudo-siamese and multidomain feature fusion network (PSMDNet) that is composed of two parallel feature extraction branches. The feature extraction in the frequency domain uses the frequency channel attention network (FcaNet) as the backbone, incorporating an adjacent scale space attention module (ASSAM) to fuse high-level features into low-level features. The time domain uses the residual network (ResNet) as the backbone, incorporating a bidirectional feature channel module (BFCM) to enhance the representation of low-level spatial information. These two parallel branches extract the time- and frequency-domain features from the image to better capture the wake feature information. The proposed ASSAM module calculates weighted coding with context information, thereby selectively aggregating the unique linear spatial features of the wake into the low-level feature map. Verification experiments were conducted on the SAR-WAKE dataset, and the results demonstrate that the proposed method excels in detection accuracy compared with other algorithms, achieving excellent results of 92.71%. Particularly noteworthy is that the positioning and visualization of wake vertex and Kelvin arms are realized by the loss function designed for the wake.

We consider a service cost minimization problem for resource-constrained small-cell networks with caching, where the challenge mainly stems from (i) the insufficient backhaul capacity and limited network bandwidth and (ii) the limited storing capacity of small-cell base stations (SBSs). Besides, the optimization problem is NP-hard since both the users’ mobility patterns and content preferences are unknown. In this paper, we develop a novel mobility-aware joint routing and caching strategy to address the challenges. The designed framework divides the entire geographical area into small sections containing one SBS and several mobile users (MUs). Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs and make a caching decision. The FRPL method completes multiple tasks in one shot, thus reducing the average processing time per global aggregation of learning. By exploiting the outcomes of FRPL together with the estimated service edge of SBSs, the proposed cache placement solution greedily approximates the minimizer of the challenging service cost optimization problem. Theoretical and numerical analyses show the effectiveness of our proposed approaches.

Malware traffic classification (MTC) is one of the important research topics in the field of cyber security. Existing MTC methods based on deep learning have been developed based on the assumption of enough high-quality samples and powerful computing resources. However, both are hard to obtain in real applications especially in availability of IoT. In this paper, we propose a few-shot MTC (FS-MTC) method combining knowledge transfer and neural architecture search (i.e. NAS-based FS-MTC) with limited training samples as well as acceptable computational resources, in order to mitigate the identified challenges. Specifically, our proposed method first converts the raw network traffic into traffic images through data pre-processing to serve as input data for the neural network. Second, we use neural architecture search to adaptively search for the effective feature extraction model on the source domain (including Edge-IIoTset, Bot-IoT, and benign USTC-TFC2016). Third, the searched model is pre-trained on source task to achieve the generic feature representation of malware traffic. Finally, we only use few-shot malware traffic samples to fine-tune the pre-trained model to quickly adapt to new types of MTC tasks in realistic network environments. The experimental results show that the proposed NAS-based FS-MTC method has great scalability and classification performance in different FS-MTC tasks, including 5-wayK-shot USTC-TFC2016 dataset and 10-wayK-shot CIC-IoT dataset. Compared with state-of-the-art methods in the field of malware classification, the proposed NAS-based FS-MTC has higher classification accuracy. Especially in the 1-shot case of the USTC-TFC2016 dataset, its average accuracy is as high as 86.91%.

Contactless Wi-Fi sensing, using channel state information (CSI) fingerprints, plays a pivotal role in communication, smart healthcare, and industrial automation. Deep learning has revolutionized the efficiency of non-contact sensing technology. Owing to its robust feature extraction capabilities and the interconnectedness of diverse sensing tasks, methods that address multiple tasks at once, like joint activity recognition and indoor localization (JARIL), have gained prominence. The primary goal of JARIL is to improve performance while reducing computational demands. Nevertheless, there remains substantial potential for enhancing its effectiveness through additional refinement and optimization measures. To address this, we introduce a regularized multi-label learning (RML) framework specifically designed for JARIL. This framework combines a parameter-efficient backbone network based on multi-scale separable convolution with residual connections, and a regularization training strategy. The latter strategy boosts performance by linearly combining two distinct CSI samples with their labels, creating new training instances in the training process. Simulation results show that the proposed method boasts a recognition accuracy of 91.73% and a localization precision of 99.64%. This marks an improvement of 4.32% and 3.60% respectively, in comparison to the prior ResNet1D+-based JARIL method. The codes can be downloaded fromhttps://github.com/BeechburgPieStar/JARIL.

Over the past few years, there has been a significant rise in the number of fall accidents occurring among elderly individuals, a problem that has been accentuated with to the aging population. Researchers and developers have focused their efforts on investigating and creating various fall detection methods that utilize an accelerometer. However, conventional fall detection methods typically target specific positions where accelerometers are placed. In addition, they suffer from low accuracy which can be attributed to the fact that the classification algorithms commonly employed, such as the support vector machine (SVM) and the random forest (RF), are not specialized in making predictions based on time series data. In this paper, we propose the fall detection method based on a long short-term memory (LSTM) neural network, using an accelerometer. In the proposed method, four kinds of possession positions are set: (i) in hand, (ii) inside a chest pocket, (iii) inside a waist pocket, and (iv) in a bag. The acceleration data collected are classified using the LSTM classifies into one of four classes: (i) standing, (ii) walking, (iii) falling, and (iv) lying down. The results of the multi-class classification are further reclassified into two classes, i.e., fall and non-fall. The experimental results demonstrate that our approach outperforms the conventional methods in terms of fall detection accuracy.

In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO), the downlink channel state information (CSI) feedback method based on deep transfer learning (DTL) has been proposed to obtain the downlink CSI at the Base Station (BS). In the CSI feedback method based on DTL, a target model for one channel environment is obtained by fine-tuning the parameters of a source model trained on a large number of the CSI dataset (source data) of another channel environment. The fine-tuning is done with a small number of the CSI dataset (target data) of the target channel environment. Thus, a target model can be obtained at a low learning cost. However, the performance of the target model could highly depend on the source data. In this paper, we investigate two metrics as criteria for selecting source data to obtain a target model with a high CSI reconstruction performance: (i) Jensen-Shannon Divergence (JSD), which represents the similarity between target and source data, and (ii) entropy, which represents the diversity of source data. The simulation results showed when the target channel model is non line-of-sight (NLOS), the source data with high entropy and low JSD tend to provide higher CSI reconstruction performance of the target model. These results indicate that the JSD and the entropy could be a source data selection metric.

A key concept in health monitoring systems for elderly people is the continuous and non-intrusive detection of their activities to identify when hazardous events such as sudden falling occur/are about to occur. The existence of obstacles in the environment largely limits the detection performance of existing approaches of activity detection relying on non-contact sensors. A simple, yet effective, approach to address this issue is the use of multiple sensors which collaborate with one another. In this paper, we propose an approach that relies on 2D Light Detection and Ranging (LiDAR) technology for activity detection. We employ multiple 2D LiDARs placed at different locations in a single room with difference obstacles (e.g., furniture) and working in coordination to construct a fuller representation of the activities being performed. Our approach transforms the concatenation of the different LiDAR data into a more comprehensible data format (i.e., images). The generated images are then processed using a Convolutional LSTM Neural Network to perform the classification. For 3 different tasks, namely activity detection, fall detection, and unsteady gate detection, our proposed approach reaches an accuracy equal to 96.10%, 99.13% and 93.13%, respectively.

An Electrocardiograms (ECG) is a recording of the heart's electrical activity. It can help detect problems with one's heart rate or heart rhythm. Traditional methods to measure and collect ECG are not practical for daily use due to their invasive nature and inaccessibility. Thus, a less intrusive and more accessible method is needed. In this paper, we propose a method that uses attention-based Wasserstein Generative Adversarial Networks-Gradient Penalty (aWGAN-GP) to reconstruct ECG signals from ones collected using a Doppler sensor. GANs can capture pertinent features in the Doppler signals to enable such reconstruction. Our approach uses the integrated spectrum of the Doppler signal to identify R-peaks, which are then employed to train the aWGAN-GP to reconstruct the ECG signal. We evaluated this method on 19 healthy subjects and obtained a correlation coefficient of 0.88 between the reconstructed and actual ECG signals, outperforming the conventional CNN-based method which reached only 0.35. Our results demonstrate that it is possible to reconstruct an ECG signal from a heartbeat signal collected via a Doppler sensor using aWGAN-GP.

High Altitude Platform Station (HAPS), functioning as a flying base station (BS) positioned in the stratosphere, has gradually captured more interest in the field of mobile communications. HAPS holds the potential to deliver extensive coverage areas and resilient networks in the face of disasters. Using beamforming techniques, a HAPS can configure multiple cells within its coverage area to serve a large user population. However, the position of HAPS and the distribution of user equipment (UE) changes with time, resulting in changes in the relative position between HAPS and UEs. Consequently, the maximum throughput of UE decreases and some UEs can suffer network outages. A previously proposed method treats this as an optimization problem and tries to maximize throughput based on an objective function. However, the computational complexity is still too high for real-time control. To help beams respond faster to position changes, some AI-based methods are proposed but they cannot guarantee that low throughput UEs are well minimized. In this paper, we propose a K-means clustering-aided particle swarm optimization (PSO) algorithm that can adjust the cell configuration to minimize the number of low throughput UEs. Taking advantage of K-means clustering, this method redefines the search range for each parameter to help PSO quickly converge to the optimum. By running simulations using realistic UE distributions in some cities, we demonstrate the superiority of our proposed method in terms of computational complexity and ability to reduce low throughput UEs.

Depression is a common mental illness and the second leading cause of disability worldwide. Traditional depression diagnosis requires communication with patients and subjective cooperation of patients, which consumes a lot of manpower, material resources, and time costs. With the accumulation of user data in social media and the development of natural language processing, computer-aided diagnosis is realized, better and objective analysis is provided, and a new idea for the diagnosis of depression is provided. We propose a multimodal model based on EmoBERTa and Transformer and a text preprocessing method for a specific pre-trained model and application context. We use user behavior information and past tweets to detect whether a user has depression. Our model achieves state-of-the-art performance on a publicly available multimodal Twitter dataset.

High altitude platform station (HAPS) is a platform that provides direct communication to wireless devices such as smart phones by equipping unmanned aerial vehicles (UAVs) flying in the stratosphere with cellular base station functions. In this paper, we propose a HAPS trajectory optimization method based on a throughput heat map, where the HAPS trajectory is designed considering the trajectory constraints of HAPS as an aircraft. The HAPS trajectory is optimized according to the throughput characteristics to be improved, such as the average throughput of each UE (user equipment). Computer simulations using actual UE distributions for Tokyo, Osaka, and Nagoya showed that the sub-optimized figure-8 trajectory improved the 50th percentile throughput by 15-16% and the 5th percentile throughput by 3–20% for each city, respectively, compared to the non-optimized circular trajectory. Trajectories aimed at improving the 50th, 75th, and 95th percentiles improved throughput characteristics around the 50th percentile, around the 75th-95th percentile, and above the 96th percentile, respectively.

In this paper, we propose a novel data augmentation framework where an attention-based Wasserstein Generative Adversarial Networks-Gradient penalty (aWGAN-GP) is used to augment Doppler radar signals from electrocardiogram (ECG) signals. Besides, we present a new heart rate (HR) range classi-fication algorithm by applying support vector machine (SVM) to classify Doppler radar signals depending on the HR ranges (i.e., into low, normal, and high). Finally, since the class distribution of ECG samples is also imbalanced, a simple augmentation method for ECG signals is proposed to prepare new ECG inputs for the trained generator, which can be used to synthesize Doppler radar signals to balance the training set used by SVM. Our experiments show that the Doppler radar signals generated by aWGAN-GP provide more accurate morphology in terms of root mean squared error (RMSE) and Pearson's correlation coefficient (PCC). In addition, the proposed method achieves the lowest relative Fréchet inception distance (rFID), which shows a better diversity in the generated Doppler radar signals. In regard to HR range classification, the proposed method relieves the data imbalance problem and obtains better classification performance in terms of accuracy, F1-score, and recall.

This paper presents a radar-based, non-contact Blood Pressure (BP) estimation model based on accurate detection of cardiac activities, which enables BP monitoring to be performed in a touch-free and continuous manner. Cardiac movements have been regarded as essential factors for BP estimation. However, accurately obtaining these movements by a radar system remains a challenging problem because these movements are too inconspicuous and could be easily hindered by respiration and random noise. In this paper, we propose a method that mainly focuses on cardiac feature extraction in radar-based BP monitoring. First, we employ an integrated-spectrum waveform. It is derived from short-time Fourier transform (STFT) and is capable of recording and preserving minor cardiac activities. Compared with the pulse-wave signals used in previous works, the integrated-spectrum focuses on energy changes introduced by short and high-frequency vibrations. It can eliminate the interference of respiration and random noise, and cardiac contractile movement can be reserved accurately. Second, we propose a cardiac features estimation method in which a hidden semi-Markov model (HSMM) is applied to the integrated-spectrum for feature extraction. Compared to the pulse wave signal, the Root-Mean-Square Error (RMSE) of the estimated interbeat intervals (IBI), Systolic time, and Diastolic time is reduced by 44.2%, 73.3%, and 76.7% respectively. The estimated accurate cardiac features are further used as inputs for a Random Forest model for BP prediction. Though previous work required the subject to hold one's breath, we achieved a comparable prediction accuracy even when our subject is breathing normally. The Diastolic BP (DBP) error of our model is$4.27\pm 5.84$mmHg (Mean Absolute Difference ± Standard Deviation), and the Systolic BP (SBP) error is$6.63\pm 8.95$mmHg.

In this paper, we propose a Mean Field reinforcement learning (MFRL) method for dynamic antenna control in High-Altitude-Platform-Station (HAPS) communication system with Multi-Cell Configuration. HAPS works at stratospheric altitudes of about 20 km to provide an ultra-wide coverage area. However, the wind pressure caused HAPS movement leads to the degradation of users' throughput. Considering the multi-antenna arrays in the HAPS, to find the optimal antenna parameters of all antenna arrays for reducing the number of low-throughput users, we formulate the antenna control problem into stochastic game equilibrium. Usually, solving the stochastic to find the equilibrium needs very high computation complexity to calculate the transition probability for getting the$\mathcal{Q}$-value under a certain state and action. Therefore, we use the reinforcement learning (RL) named Deep$\mathcal{Q}$-Network (DQN) to learn the transition probability and predict the Q-value according to the reward fed backed from the environment. Besides, we employ the Mean field Game theory in conjunction with RL during the training phase of DQN to reduce the complexity of the interactions among agents. To evaluate the proposed method, we compare the proposed method with a genetic algorithm (GA) named Particle Swarm Optimization (PSO),$\mathcal{Q}$-learning, Fuzzy$\mathcal{Q}$-learning, and conventional DQN under four realistic user distribution scenarios. The simulation results show that the proposed method achieves comparable throughput performance with a high convergence rate.

Blood pressure (BP) estimation methods using photoplethysmogram (PPG) based on deep learning models have been actively studied. These methods are also the basis of non-contact BP estimation methods using a camera or a Doppler radar. However, most previous studies are under data leakage, where subjects are not separated between training and test data. In this paper, we propose a method for BP estimation from PPG under the condition that subjects are separated between training and test data (inter-subject paradigm) using a subject-distinguishable large public dataset. Our BP estimation method estimates an 8-second BP waveform called arterial BP (ABP) from an 8-second PPG segment using U-Net. From the estimated ABP, systolic BP (SBP), diastolic BP (DBP), and mean BP (MBP) are calculated, which are the discrete single values for the 8-second ABP. In addition, we apply domain adversarial training, which facilitates the BP estimation model to extract subject-invariant features to improve the BP estimation accuracy under the inter-subject paradigm. Moreover, we use a considerably larger amount of training data than the previous studies and evaluate our model with smaller model sizes for better generalizability. Our experimental results showed that our method can estimate BP with moderate accuracy under the inter-subject paradigm, particularly MBP. The mean absolute errors for the estimated SBP, DBP, MBP, and ABP were 15.21, 7.12, 8.20, and 10.14 mmHg, respectively. The Pearson's correlation coefficients between the true and estimated values were 0.49, 0.39, 0.54, and 0.84, respectively.

Change detection based on synthetic aperture radar (SAR) images is a challenging task in the field of remote sensing image analysis due to the influence of noise and the lack of labeled data. In this paper, we propose a new unsupervised change detection algorithm based on deep learning, which explores the spatial and frequency domain features of SAR images in parallel to improve detection performance. Our proposed method first obtains pseudo-labels by clustering and then combines them with neural networks for unsupervised detection. To reduce the impact of noise and improve sensitivity to changes, we integrate an attention mechanism (AM) into the network. We also use complementary features to integrate the spatial and frequency domain features. These complementary features include a multi-regional feature weighted by channel-spatial AM and a deep feature filtered out by a gated linear unit (GLU). Experimental results demonstrate that the proposed method improves the detection accuracy.

Remote sensing change detection (CD) has been widely studied, and the CD of heterogeneous images based on cross-sensor acquisition has significant research significance. However, the scarcity of heterogeneous data and the difficulty in obtaining high-quality change maps remain significant challenges. To address these issues, we propose a deep image translation-based feature refinement-aggregation change detection network (FRAN) designed for heterogeneous images, such as optics and SAR images. First, we use data augmentation to increase the number of available images and a no-independent-component-for-encoding GAN (NICE-GAN) to translate the features from the optical domain to the SAR image domain, enabling direct comparison of images from different domains. Finally, we introduce feature refinement module and feature aggregation module to extract more accurate change information and obtain an accurate change region. Our experiments on two public datasets demonstrate that the proposed FRAN’s re-detection accuracy is superior to that of four other heterogeneous detection methods.

To reduce the outage of the connection between unmanned aerial vehicles (UAVs) and cellular networks in complex real-time channel state, and reduce the energy consumption of UAV during flight mission, an online trajectory optimization scheme of UAV based on outage probability knowledge map reconstruction is proposed. The outage probability knowledge map is a database that simulates the connection between UAV and the cellular network during real hovers. The UAV first samples sparsely from the target area and calculates the outage probability of the sampling point, and then uses the Kriging algorithm to reconstruct the outage probability knowledge map. Based on the reconstructed outage probability knowledge map, with the goal of minimizing the energy consumption of UAV task execution, the UAV trajectory optimization problem is established, and a trajectory optimization algorithm based on deep reinforcement learning (DRL) is proposed to solve it. Numerical results show that the proposed online trajectory optimization scheme based on outage probability knowledge map can obtain great returns in terms of maintaining connectivity, reducing task completion time and energy consumption.

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, these methods highly rely on expert experience to design network structures. These hand-designed fixed network structures lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) can be seen as a subdomain of automatic machine learning (AutoML), which can automatically adjust network structure and parameters according to a specific task. In this paper, we propose a neural architecture search-based SEI method, which can achieve an efficient search of the architecture with the use of a gradient descent algorithm. Experimental results show that the proposed NASEI method both improves the accuracy and reduces the parameter quantity when compared with state-of-the-art methods. Code available at https://github.com/huangyuxuan11/NASEI.git.

High-altitude platform stations (HAPSs), which directly deliver communication services to smartphones on the ground, are attracting much attention as novel mobile communication platforms for ultrawide coverage areas. A multi-cell configuration is needed to enhance communication capacity in wide areas. Assuming the use of a phased-array antenna, we proposed an optimization method for controlling antenna parameters, such as beamwidth and beam direction, to improve spectral efficiency. HAPSs are also promising disaster-resilient networks, as they can recover coverage immediately when coverage holes appear due to the breaking down of terrestrial base stations (BSs). Users can recover connections immediately during disasters through HAPSs that use the same frequency band as terrestrial networks. However, in this scenario, HAPSs become a source of interference in terrestrial networks, so network operators should avoid affecting existing terrestrial networks through HAPSs. However, cell optimization in this coexistence scenario between HAPSs and terrestrial networks has not been investigated thoroughly. In this paper, we propose a design scheme that optimizes antenna parameters for each cell to maximize HAPS coverage in this coexistence scenario using a genetic algorithm (GA). The proposed method can effectively protect terrestrial networks through optimization by setting the initial direction of each beam to avoid terrestrial BSs. Using this scheme, the GA can start optimization with candidate combinations that satisfy the interference constraints of terrestrial networks, which can expedite optimization for better coverage. Simulation results show that the proposed scheme can achieve better coverage than the conventional scheme, which only controls beam direction. Furthermore, they show that the initial beam direction setting effectively reduces the number of performed combinations required for GA to converge.

Federated Learning is now widely used to train neural networks under distributed datasets. One of the main challenges in Federated Learning is to address network training under local data heterogeneity. Existing work proposes that taking similarity into account as an influence factor in federated learning can improve the speed of model aggregation. We propose a novel approach that introduces Centered Kernel Alignment (CKA) into loss function to compute the similarity of feature maps in the output layer. Compared to existing methods, our method enables fast model aggregation and improves global model accuracy in non-IID scenario by using Resnet50.

With the rapid development of information technology, the multisource heterogeneous data containing meaningful information have been significantly generated by various edge devices in Internet of Energy, which is one of essential foundations of many knowledge discovery tasks based on edge computing. For some complicated tasks, essential features are owned by different data sellers offering data by blockchains. With limited budgets, buying features are crucial steps in knowledge discovery tasks in electricity spot markets, especially for learning-based algorithms. However, there are lack of proper data pricing mechanisms tailored to dynamic learning processes. Besides, existing methods cannot efficiently employ edge computing servers to obtain optimal policies for selecting features according to dynamic pricing with limited budgets. To overcome such drawbacks, a data pricing mechanism is proposed in this article, which consists of static and dynamic pricing parts. Based on this mechanism, given limited budgets, a feature selection (FS) algorithm considering multiple new factors is proposed, which offers near-optimal solutions for FS at different scenarios. Numeric results show the effectiveness of the proposed algorithms.

Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using wireless sensor and actor networks (WSANs). Temperature sensors are utilized to detect fires, and actors (robots) are employed to extinguish them. Sensors and robots are distributed at random throughout the forest, forming clusters. Clustering, sleep/active scheduling for the sensors, and energy harvesting (EH)/moving modes for the robots, are used to extend and maximize the sensors/robots lifetime in the WSAN. In such a network, robots should move to the fire site as quickly as possible. To do this, we further propose a robot routing mechanism that focuses on determining the shortest path for each firefighting robot. In particular, each firefighting robot equipped with on-board processing uses a fuzzy$Q$-learning (FQL)-based trajectory mechanism to learn the shortest path to the fire zone in the least amount of time. Simulations are conducted to demonstrate the benefits of employing the proposed framework for rapid and effective fire response. When compared to the traditional$Q$-learning, the total approaching rate (a measure of how quickly the firefighting robots can reach the fire) to the fire spot is greater when utilizing the proposed FQL-based strategy.

This article discusses a cellular network assisted by an energy- and spectral-efficient unmanned aerial vehicle (UAV), in which the UAV is deployed to serve mobile users in the cellular network and enable mobile data offloading from a ground base station (GBS) by taking a circular flight route. We explore a visible light communication (VLC)-enabled UAV, in which a light-emitting diode (LED) is mounted on a rotary-wing UAV to offer communications to the users. Our aim is to simultaneously optimize both energy efficiency (EE) and spectral efficiency (SE) of the VLC-enabled UAV by jointly optimizing the common throughput of all users as well as the UAV’s trajectory and flying speed. We employ a unified metric, called resource efficiency (RE), and explore the RE optimization to obtain an adaptive EE–SE tradeoff. The problem posed is seen in a complex and nonconvex shape, making it hard to solve. Motivated by the enormous achievement of deep reinforcement learning (DRL) in solving complex control problems, we propose a DRL-based approach to handle this nonconvex and complicated optimization. The findings of the simulation reveal that the developed framework achieves a substantial performance in terms of the solution convergence as well as the promising quality of the solutions.

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based intrusion detection methods, however, suffer from three limitations: 1) model parameters transmitted in each round may be used to recover private data, which leads to security risks; 2) not independent and identically distributed (non-IID) private data seriously adversely affect the training of FL (especially distillation-based FL); and 3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this article develops an intrusion detection method based on a semisupervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a model based on convolutional neural networks (CNNs) for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, the discriminator is designed to improve the quality of each client’s predicted labels, and to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of the hard-label strategy and voting mechanism further reduces communication overhead. The experiments on the real-world traffic data set with three non-IID scenarios show that our proposed method can achieve better detection performance as well as lower communication overhead than state-of-the-art methods.

Change detection based on synthetic aperture radar (SAR) images is an important application in the remote-sensing technology field. However, the lack of labeled data has been a difficult problem in SAR image detection, especially for pixel-level change detection. In this letter, we propose a novel unsupervised change detection algorithm, which improves the detection accuracy by exploring features from both spatial and frequency domains of SAR images. In particular, first clustering is used as preclassification to obtain pseudo-labels and then by incorporating classifiers and pseudo-labels in terms of feature learning, a novel unsupervised detection algorithm is proposed. To improve the sensitivity of the algorithm to changed details and enhance the antinoise ability of the change detection network, the attention mechanism (AM) is integrated into the network to fully extract important spatial structure information. Moreover, a multidomain fusion module is proposed to integrate spatial and frequency domain features into complementary feature representations. This module contains multiregion features weighted by the channel-spatial AM and deep features filtered out by the gated linear units (GLUs) in the frequency domain. To verify the effectiveness of the proposed algorithm, it is compared against the other four SAR image change detection algorithms using three real datasets. The experimental results show that the proposed method outperforms the other four algorithms in terms of percent correct classification (PCC) and Kappa coefficient (KC).

Internet of energy (IoE) is one of the most complex industrial systems, and its stable operation is very important. Situation awareness (SA) has been proposed to ensure the stable operation for IoE and making full use of the relationships between components has become the key point for designing an efficient SA model. In this article, graph digital twin (GDT) is proposed by combining digital twin technology with graph theory, to describe the logical relationships between physical entities more accurately in digital space, and then a novel SA model for IoE based on GDT is proposed. In order to make full use of the relationship between nodes, two classifiers based on graph convolution network are designed for fault location and stability prediction. The experimental results show that the proposed SA model can localize the multiple fault components with high accuracy, and can accurately predict the stability of the system.

This paper presents a new three-dimensional (3D) trajectory design approach for a solar-powered, fixed-wing unmanned aerial vehicle (UAV) to harvest solar energy and collect data from multiple smart devices (SDs). The trajectory is optimized based on max-min fairness to balance the total amount of uploaded data and the fairness among the SDs. The key idea is that we develop non-trivial variable substitution and successive convex approximation (SCA) techniques to convexify data transmission, UAV energy consumption and mobility, and energy harvesting constraints under a persistent round-robin transmission schedule of the SDs. The resulting algorithm guarantees a locally optimal trajectory satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that we further jointly optimize the transmission schedule along with the trajectory, and prove that absolute fairness in terms of uploaded data can be achieved among the SDs under the max-min fairness. The new algorithms apply to both line-of-sight (LoS)-dominant and probabilistic SD-UAV channels. Numerical results show that a 3D trajectory increases the uploaded data by 111%, compared to a two-dimensional (2D) trajectory. The proposed algorithms can balance the energy harvesting and data collection, and achieve fairness in both LoS-dominant and probabilistic SD-UAV channels.

In the pilot-based channel estimations, a large number of pilot signals enable an improvement in the channel estimation accuracy but force a decrease in the data transmission efficiency. Therefore, the pilot pattern design schemes on the resource grid have been researched to achieve high channel estimation accuracy with a small number of pilot signals. In the conventional scheme for pilot pattern design, the autoencoder which enables discrete feature selection is utilized to design pilot patterns for Vehicular-A channels. However, the pilot pattern for other channels is not reported, and it is not clear whether this scheme has good performance compared to uniform pilot patterns. In this paper, we derive the pilot patterns for Clustered Delay Line (CDL) channels specified in the 5th Generation Mobile Communication System (5G) standard using the autoencoder-based pilot pattern design scheme. We also derive the pilot patterns for CDL-A and CDL-D, which model respectively NLOS (Non-Line-of-Sight) and LOS (Line-of-Sight) environments, with different three speeds of User Equipment (UE). Through computer simulation, we show that the autoencoder-based pilot patterns improve the channel estimation accuracy compared to the uniform pilot pattern.

Twitter has skyrocketed over the past few years and has become a major social media platform. At the same time, the number of social robots on Twitter has also increased significantly. These bot accounts imitate the speeches of normal users to manipulate public opinions, affect the normal communication of users. Therefore, bot account detection came into being. Despite extensive research efforts, bots on Twitter are still evolving to evade detection. Most of the current bot detection methods have a single structure and cannot detect and identify different types of bot accounts well. In this paper, we propose a new system for social robot detection that uses a RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach) classifier and a random forest regressor with similarity analysis. In particular, the system considers the similarity of tweets and uses a voting system in addition to a set of features extracted from the user profile information and the tweets themselves. We conduct experiments using the largest dataset of bots available and show that the accuracy of our system is up to 0.8588, which is higher than that of all the other baseline methods.

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmia. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results showed that the proposed method could outperform the conventional methods with heartbeat detection and feature detection in terms of accuracy, specificity, and recall.

The Doppler ultrasound (DUS) transducer has been widely used for fetal heart rate (FHR) monitoring. However, the fetal DUS signals from the transducers can be corrupted by several interference sources such as maternal and fetal movements, which makes FHR estimation using fetal DUS signals challenging. Fetal DUS signal quality assessment (SQA) can help to remove or interpolate unreliable FHRs estimated from noisy signals to improve the accuracy of FHR estimation. There are some existing approaches for fetal DUS SQA, and most of these approaches with high accuracy are based on supervised learning-based algorithms and human-defined properties. Nonetheless, the fetal DUS datasets with quality-level annotations are limited, and human-defined properties place a limitation on mining more deep information related to signal quality in fetal DUS signals. In this paper, we propose an unsupervised representation learning-based fetal DUS SQA for the improvement of FHR estimation performance. We firstly learn representations of pre-processed fetal DUS data from variational autoencoder (VAE) and then combine these representations as one signal quality index (SQI) using a self-organizing map (SOM). Finally, we apply the combined SQI and a Kalman filter (KF) to estimate fetal RR intervals (FRRI) for reducing the errors of FHR estimation. The experimental results showed that our proposed method could reduce the averaged root mean squared error (RMSE) of FRRI and averaged absolute error (AAE) of FHR.

Non-contact measurement of the heart rate (HR) refers to the usage of wireless sensors (e.g., Doppler sensors) to identify the heart-related faint movements and to reconstruct the R-peaks or estimate the HR. Conventional work in the field requires the chest of the subject being monitored to be in a specific position in front of the sensor. In our previous work, we have proposed to use a Multiple-Input Multiple-Output (MIMO) Doppler Radar to perform the detection of heartbeats even when the Signal-to-Noise Ratio (SNR) is not very high. By exploiting the fact that different beams have different SNR of heartbeat components, our previous method detects heartbeats by aggregating several received signals. In general, it is essential to use beam directions toward the a subject’s chest. However, the chest location estimation is challenging, when there exist several subjects or other moving objects. In the current paper, to deal with this issue, we narrow down the direction of beams to use by using a system composed of a MIMO Doppler sensor and a Light Detection and Ranging (LiDAR) device. By means of transfer learning and automatic annotation of data, the LiDAR is trained to identify the relative chest angle and distance to the subject from the devices, while the MIMO Doppler Radar can adjust the beam towards the identified direction. Throughout experiments, we show the effectiveness of the combination of both the Lidar and radar in detecting the direction and distance to the subject as well as the heartbeat.

In the 5th generation mobile communications (5G) and 5G and beyond (B5G), a high altitude platform station (HAPS) is expected to serve as a flying base station (BS) to provide communications over wide areas. In the HAPS system, a multi-cell configuration with multiple beams is considered to increase system throughput. When the HAPS is subjected to wind pressure, the cell range moves accordingly, causing degradation of received signal power and handover to the user equipment (UE). To suppress such degradation and handover, beam control of HAPS is necessary. However, it is not easy to control the beam because multiple antenna parameters affect each other and determine the cell range. In this paper, we propose a beam control method for HAPS using fuzzy Q-learning in multi-cell configuration. In this type of learning, the variable states are controlled by the use of fuzzy sets, which allows multiple searches to be performed in one setup, thus reducing the cost of search, compared with conventional Q-learning. In the proposed beam control method, antenna parameters are controlled by fuzzy Q-learning so that the number of users having a received signal power larger than a predetermined threshold becomes larger in each cell. We evaluate the proposed method by computer simulation and show that the proposed method can improve the number of users having a received signal power larger than a predefined threshold and thus reduce the number of users with low throughput compared to before learning.

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. However, most of the current FL-based intrusion detection methods still suffer from three limitations: (1) model parameters transmitted in each round may be used to recover private data which leads to security risks, (2) not independent and identically distributed (non-IID) private data seriously adversely affects the training of FL (especially distillation-based FL), and (3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this paper develops an intrusion detection method based on semi-supervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a CNN-based model for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, discriminator is designed to improve the quality of each client’s predicted labels, to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of hard-label strategy and voting mechanism further reduces communication overhead. Experimental results on the real-world traffic dataset show that our proposed method can achieve better classification performance as well as lower communication overhead than state-of-the-art methods.

Accessing the accurate downlink channel state information (CSI) is essential to take full advantage of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its weak channel reciprocity. Meanwhile, great computational burdens will happen, which is accompanied by continuous CSI feedback. The existing compressive sensing (CS)-based and deep learning (DL)-based methods try to solve such problems, but do not achieve desired effect to get ideal CSI feedback or decrease the overhead. An adaptive deep neural network (DNN)-based CSI feedback method is proposed in this paper to address this. A classification block of the compression ratio is adopted and modified to apply to a more complex channel model named Clustered-Delay-Line (CDL), which helps decrease the computational overhead of the network. Besides, the reconstruction accuracy of the CSI feedback is further improved by proposing a new structure of the encoder. Quantization and dequantization modules are also applied to make the whole network more robust and effectively minimize the quantization distortion in the real communication scenario, respectively. The simulation results show that the proposed method performs better than the conventional ones on the CSI reconstruction accuracy in terms of normalized mean square error (NMSE), even though the quantization module is added.

Matrix completion-assisted channel estimation is considered one of promising techniques in millimeter wave (mmWave) massive multiple input multiple output (MIMO) system by exploiting the low-rank property of channel matrix in the angle domain. However, existing channel estimation approaches are hard to achieve high accuracy due to the inevitable bias solution caused by nuclear norm based minimization (NNM). To address this problem, this paper proposes a novel matrix completion-assisted mmWave massive MIMO channel estimation method. We employ an effective and flexible rank function named joint weighted and truncated nuclear norm as relaxation of nuclear norm, and then construct an novel matrix completion model for channel estimation problem. Moreover, a popular framework of alternating direction method of multipliers (ADMM) is derived for minimization of the resulting optimization problem. Simulation results are provided to verify the proposed method that can flexibly and effectively improve the channel estimation accuracy with reliable convergence.

With the increasing popularity of Internet of things (IoT), the emergence of many IoT devices has led to security vulnerabilities. The classification of wireless signals is very important for secure communications. Most of existing signal classification tasks only focus on single signal classification task, while ignoring the relationship between radio frequency fingerprinting identification (RFFI) and automatic modulation classification (AMC). To solve the multi-task classification problem, this paper designs a multi-task learning convolutional neural networks (MTL-CNN). Real-radio datasets are generated by Signal Hound VSG60A and collected by Signal Hound BB60C to solve the lack of RFF samples with numerous modulation types. Experimental results confirm that the MTL-CNN method can work well by using the generated dataset. The MTL network designed in this paper improves the accuracy of RFFI by 1xs% relative to the single-task learning (STL) network. The keras code is released at https://github.comLiuK1288/1hw-000.

High-Altitude Platform Stations (HAPSs) are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Single-cell frequency reuse using multiple cells can increase capacity to cover a wide area. Thus far, we have proposed a cell configuration optimization method based on a genetic algorithm (GA). We clarified the optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution scenario. Whereas user distributions differ depending on location. Thus, cell configuration optimization is required for a non-uniform user distribution. Each cell needs different antenna parameters for non-uniform user distributions, resulting in an increase in the number of parameters compared with a uniform user distribution, making the optimization difficult even by GA in some cases. To address this problem, we propose a co-evolutionary dynamic cell optimization algorithm. Co-evolution is one of the divide-and-conquer methods. The proposed method divides multiple cells into several groups to decrease the number of parameters to optimize at a time, and each group is optimized in order. The simulation results show that the cell configuration with the proposed method can increase the sum of the square root of throughput for non-uniform user distributions compared to that for uniform user distributions. Furthermore, the proposed method with three sub-areas can improve the sum of the square root of throughput while reducing the number of combinations performed compared to the method without subarea division for a nine-cell scenario.

Human activity recognition (HAR) is one of the most promising technologies in the smart home, especially radio frequency (RF-based) method, which has the advantages of low cost, few privacy concerns and wide coverage. In recent years, deep learning (DL) has been introduced into HAR and these DL-based HAR methods usually have outstanding performance. However, as the recognition scenarios and target change, the model performance drops sharply. To solve this problem, we propose a generalized method for cross-person activity recognition (CPAR), which is called snapshot ensemble learning based an attention with bidirectional long short-term memory (SE-ABLSTM). Specifically, by defining the cosine annealing learning rate, the models with diversity are saved and integrated in the same training process. In addition, we provide a dataset for CPAR and simulation results show that our method improves generalization performance by 5% compared to the original method. The source code and dataset for all the experiments can be available at https://github.com/NJUPT-Sivan/Cross-person-HAR.

In this research, we propose a novel antenna control method for reducing the number of low throughput User Equipments (UEs) caused by the movement and rotation of High-altitude platform station (HAPS). We assume that each HAPS has three antenna arrays for serving one region that consists of three cells and that we know the UE locations in each cell. In the proposed method we first redesign the cell configuration for each HAPS and then divide all UEs into three cells based on the UE locations. Based on the radius and center location information of three new cells, we can mathematically calculate the antenna parameters by the desired coverage model. Thus, each antenna array will be controlled to serve a new cell, respectively. We evaluate the proposed method under 5 different UE distribution scenarios. The simulation results show that, in 5 different UE distribution scenarios, the proposed method can reduce the number of UEs with low throughput. Compared with the conventional method, the proposed method can achieve good throughput performance in all the scenarios.

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, the existing methods need a massive specific emitter dataset to alleviate model overfitting during the training stage. In this paper, we propose data augmentation (DA) aided few-shot learning method and validate the proposed method using automatic dependent surveillance-broadcast (ADS-B) signals. Specifically, according to the characteristics of ADS-B signals, four DA methods, i.e., flip, rotation, shift, and noise are studied for the proposed method. Experimental results are provided to show that the proposed method improves the recognition accuracy and the model robustness.

Activity detection is a key task in the monitoring of elderly people living alone. This is because it helps locate them and identify any accident that might occur to them. In this article, we propose a novel approach that uses 2-D light detection and ranging (LIDAR) and deep learning to perform activity detection. In a first step, our approach processes and interpolates the data collected using the 2-D LIDAR following an algorithm we propose to locate the person and identify the useful data points. In the next steps, the data are transformed into two types of representations: 1) a time-series type and 2) an image type. The time-series data are used to train different long short-term memory (LSTM) networks to identify the person and to recognize his/her activity, while the image type is used to fine-tune a convolutional neural network (CNN) for fall detection. Throughout our experiments, we show that our approach allows for the identification of people from their gait, and the detection of unsteady gait or unstable walk (i.e., when the person is about to fall or feeling dizzy) as well as the detection of up to four activities: 1) walking; 2) standing; 3) sitting; and 4) falling. The results obtained from our experiment show that the proposed method reaches an accuracy equal to 94.1% for multiclass activity detection, 98.6% for fall detection, 93.2% for person identification (for three different people), and 92.5% for unsteady walk detection.

Preventing COVID-19 disease from spreading in communities will require proactive and effective healthcare resource allocations, such as vaccinations. A fine-grained COVID-19 vulnerability map will be essential to detect the high-risk communities and guild the effective vaccine policy. A mobile-crowdsourcing-based self-reporting approach is a promising solution. However, an accurate mobile-crowdsourcing-based map construction requests participants to report their actual locations, raising serious privacy concerns. To address this issue, we propose a novel approach to effectively construct a reliable community-level COVID-19 vulnerability map based on mobile crowdsourced COVID-19 self-reports without compromising participants’ location privacy. We design a geo-perturbation scheme where participants can locally obfuscate their locations with the geo-indistinguishability guarantee to protect their location privacy against any adversaries’ prior knowledge. To minimize the data utility loss caused by location perturbation, we first design an unbiased vulnerability estimator and formulate the location perturbation probability generation into a convex optimization. Its objective is to minimize the estimation error of the direct vulnerability estimator under the constraints of geo-indistinguishability. Given the perturbed locations, we integrate the perturbation probabilities with the spatial smoothing method to obtain reliable community-level vulnerability estimations that are robust to a small-sampling-size problem incurred by location perturbation. Considering the fast-spreading nature of coronavirus, we integrate the vulnerability estimates into the modified susceptible-infected-removed (SIR) model with vaccination for building a future trend map. It helps to provide a guideline for vaccine allocation when supply is limited. Extensive simulations based on real-world data demonstrate the proposed scheme superiority over the peer designs satisfying geo-indistinguishability in terms of estimation accuracy and reliability.

Wi-Fi channel state information (CSI)-based fall detection systems have a great potential compared with other alternatives since they are nonintrusive and nonspace limited. However, in the conventional work on Wi-Fi CSI-based fall detection, a phenomenon is commonly observed: the classification performance degrades when data in different environments are used for learning and testing. Nonetheless, when the signal-to-noise-power ratio (SNR) is small, the conventional methods cannot capture features of motion and cannot segment signals accurately. Therefore, there is a need to address these problems in order to build a robust fall detection system. In this article, we propose a spectrogram-image-based fall detection using Wi-Fi CSI. Unlike the conventional method, CSI is segmented with a certain sliding-time window, and then the classifier detects fall by using the spectrogram image generated from the segmented CSI. We use a pretrained convolutional neural network (CNN) optimized for binary classification of the spectrogram images of the fall and nonfall motions. We carried out experiments to evaluate the classification performance of our proposed method against the conventional one by using motion data in two different rooms for learning and testing. As a result, we confirmed that our proposed method outperforms the conventional one and reaches over 0.92 accuracy. In addition, compared with the conventional method, the fall detection performance of our method does not degrade even when using different environment data for learning and testing.

The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder the actual deployment of DL-based high-complexity models. In this article, we propose a novel NID method for IoT based on the lightweight deep neural network (LNN). In the data preprocessing stage, to avoid high-dimensional raw traffic features leading to high model complexity, we use the principal component analysis (PCA) algorithm to achieve feature dimensionality reduction. Besides, our classifier uses the expansion and compression structure, the inverse residual structure, and the channel shuffle operation to achieve effective feature extraction with low computational cost. For the multiclassification task, we adopt the NID loss that acts as a better loss function to replace the standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID data sets demonstrate that our method has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios.

With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.

Fetal heart rate monitoring is an enormous challenge since the observed fetal electrocardiography (ECG) signal is typically characterized by a very low signal-to-noise ratio (SNR). In this letter, we aim to improve the accuracy of heartbeat detection by proposing an adaptive template for removing the maternal cycle. The template is formed by a matrix, each row of which consists of an abdominal recorded signal (ADS). It can be updated by integrating the incoming cycle while removing the contribution of the previous recording. This process is conducted by considering a discriminator to adapt the non-stationarity of each incoming cycle. Furthermore, to suppress the morphological change caused by noise, we propose a novel multi-layer hybrid network to reconstruct the chest maternal ECG (chest mECG) morphology from a set of templates. The approach has a deep structure of each layer consisting of a reservoir layer and an encoder layer. The reservoir layer explores multi-scale dynamics by transforming the input series into a high-dimensional space. The encoder layer achieves the collection of the encoder features from the output of the reservoir layer. Once the model is built, the output weight of a direct connection is trained by solving a regression problem. Experimental results show that the proposed method has a better performance compared with some classical approaches.

Device-to-Device (D2D) communication enabling direct data transmission between two mobile users has emerged as a vital component for 5G cellular networks to improve spectrum utilization and enhance system capacity. A critical issue for realizing these benefits in D2D-enabled networks is to properly allocate radio resources while coordinating the co-channel interference in a time-varying communication environment. In this paper, we propose a Stackelberg game (SG) guided multi-agent deep reinforcement learning (MADRL) approach, which allows D2D users to make smart power control and channel allocation decisions in a distributed manner. In particular, we define a crucial Stackelberg Q-value (ST-Q) to guide the learning direction, which can be calculated based on the equilibrium achieved in the Stackelberg game. With the guidance of the Stackelberg equilibrium, our approach converges faster with fewer iterations than the general MADRL method and thereby exhibits better performance in handling the network dynamics. After the initial training, each agent can infer timely D2D resource allocation strategies with distributed execution. Extensive simulations are conducted to validate the efficacy of our proposed scheme in developing timely resource allocation strategies. The results also show that our method outperforms the general MADRL based approach in terms of the average utility, channel capacity, and training time.

Accurate localization of devices in 5G cellular networks is of that utmost importance. This is because location information is a key component of a variety of new emerging applications. In particular, collocation (or co-location) refers to the idea of identifying devices that are located within a certain range from one another. In this paper, we propose a novel technique for inter-user distance estimation that uses low-resolution and high-resolution beam energy-based images as location fingerprints. Our approach uses the beam energy-based images generated by different users to estimate the distance between each pair of them. Nevertheless, we explore the idea of using a deep learning technique referred to as super resolution applied on low-resolution beam energy-based images to enhance their resolution, thus identify collocated users with an accuracy comparable to that of higher resolution ones. More specifically, throughout our experiments, we generate images of resolution$4\times 4$and$8\times 8$and use these for distance estimation between users. Afterwards, we apply super resolution on images with size$4\times 4$to improve their resolution, and compare their results to the ones obtained with the original$8\times 8$images. For an area roughly equal to$60\times 30\ \mathrm{m}$, our proposed approach reaches an average mean squared error equal to 0.13 m. We also demonstrate how our proposed approach outperforms the conventional ones that rely on user location detection to measure the inter-user distance.

Monitoring elderly people living alone is of the utmost importance given the amount of risk they are exposed to. Being aware of the activities of the elderly person in real time could help prevent/detect dangerous event that might occur such as falling. In this paper, we propose a method for activity detection using a 2D LIght Detection and Ranging (LIDAR) and deep learning. Unlike conventional work, where an activity refers to moving from one position to another, we use the term “activity” to refer to a set of movements including walking, standing, falling and sitting. Not only does our approach detect these activities, but it also identifies a given person from his gait, and identifies unsteady gait (i.e., when he is about to fall or feeling dizzy). Throughout our experiments, we show that the proposed approach could reach an accuracy equal to 92.3% and 91.3% in activity and unsteady gait detection, respectively. It is also capable of identifying up to 3 people's gait with an accuracy equal to 92.4% using 10 seconds of walking data.

Heartbeat detection is a promising technology for health care and is in great demand in many applications. For non-contact and non-invasive heartbeat detection, Doppler sensor-based heartbeat detection methods have been investigated extensively. However, since the conventional methods use a single beam Doppler sensor, the accuracy of heartbeat detection tends to degrade, when the SNR (Signal-to-Noise Ratio) of heartbeat components for the beam direction is low. In this paper, we propose a non-contact heartbeat detection method based on beam diversity using a multibeam Doppler sensor. The effects of noises and the corresponding SNRs of heartbeat components over the received signal differ from one beam to another. Based on this fact, the proposed method detects heartbeat by exploiting beam diversity of the received signals. The experimental results showed that compared to the detection method using a single beam, our proposed method using multiple beams detected heartbeat accurately, which indicates that exploiting the beam diversity can lead to the improvement of heartbeat detection accuracy.

Remote sensing of heartbeat offers various applications in the medical and health care fields. To realize non-contact heartbeat detection, an FMCW (Frequency Modulated Continuous Wave) radar-based heartbeat detection method has been investigated. The conventional FMCW radar-based heartbeat detection method estimates a range from an FMCW radar to a subject and extracts heartbeat components from phase changes for the range. However, the range suitable for extracting heartbeat components can change over time due to respiration and body fluctuation. Thus, when the SNR (Signal-to-Noise Ratio) of heartbeat components over phase changes is low at the estimated range, the accuracy of heartbeat detection tends to degrade. In this paper, we propose a MIMO (Multiple-Input Multiple-Output) FMCW radar-based heartbeat detection method based on space diversity. A MIMO FMCW radar can estimate the range for multiple beam directions and obtain phase changes for a space specified with the range and the beam direction. The SNR of heartbeat components over phase changes differs from one space to another. Taking it into account, the proposed method detects heartbeat by exploiting the space diversity of phase changes. The experimental results showed that compared to the detection method using only one phase change, the proposed method using phase changes for multiple spaces detected heartbeat accurately, which is brought by the diversity effect of phase changes for multiple spaces.

Heartbeat is one of essential vital signs to assess our health condition. Noncontact heartbeat detection is thus receiving a lot of attention in recent years, which motivates many researchers to investigate heartbeat detection via a Doppler radar. In this paper, to detect heartbeat with a high accuracy, we propose a Doppler radar-based heartbeat detection method by the Viterbi algorithm with a fusion of Beat-Beat Interval (BBI) and deep learning-driven Branch Metrics (BM). The Viterbi algorithm is a technique to estimate a sequence with maximum likelihood by using a pre-defined metric, namely, a BM. In the proposed method, we combine two BMs defined based on (i) a difference between two adjacent BBIs and (ii) an output probability of a deep learning model that judges whether a peak is caused by heartbeat or not. We apply the VIterbi algorithm with the fusion of the two BMs to the signal obtained by some signal processing. We experimentally confirmed that our method performed heartbeat detection with small Root Mean Squared Error (RMSE) between the estimated and actual BBIs.

We develop mobility-aware routing and caching strategies to solve the network cost minimization problem for dense small-cell networks. The challenge mainly stems from the insufficient backhaul capacity of small-cell networks and the limited storing capacity of small-cell base stations (SBSs). The optimization problem is NP-hard since both the mobility patterns of the mobilized users (MUs), as well as the MUs’ preference for contents, are unknown. To tackle this problem, we start by dividing the entire geographical area into small sections, each of which containing one SBS and several MUs. Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs, and make caching decision. Notably, FRPL enables the completion of the multi-tasks in one shot, thereby reducing the average processing time per global aggregation.1Theoretical and numerical analyses show the effectiveness of our proposed approach.

Recently, non-contact blood pressure measurement is attracting increasing interests because it is suitable for the blood pressure measurement on a daily basis. A Doppler radar is a key device to enable the non-contact blood pressure measurement, and it can observe pulse waves due to aortic vasomotion. Specifically, PTT (Pulse Transit Time) is known to be correlated with the blood pressure, and thus the blood pressure can be measured by calculating the PTT from the pulse waves. However, to avoid the effects of slight body movements and breathing, the conventional method requires the subject to lie down on a bed and to stop breathing. In this paper, we propose a Doppler radar-based blood pressure measurement method that works against a subject with slight body movements and breathing. Due to respiration and slight body movements, the pulse wave could be distorted, which makes it difficult to calculate PTT. To solve this issue, in the proposed method, by relating features of the distorted pulse wave to those of the clean pulse wave using a deep learning model with LSTM (Long Short-Term Memory), the distorted pulse wave is transformed to a clean pulse wave that provides PTT correlated with the blood pressure strongly. The experimental results showed that PTT calculated using the proposed method has a strong correlation with actual blood pressure and the accuracy of blood pressure estimation is improved, compared with the conventional method.

Millimeter wave (mmWave) and massive multiple input multiple output (MIMO) are recognized as key technologies in the forthcoming beyond the fifth-generation (B5G) and the sixth-generation (6G) wireless networks. In this paper, a multibeam MIMO non-orthogonal multiple access (NOMA) scheme with weighted beam superposition for mmWave is proposed to enhance the system sum rate while ensuring fairness of users as much as possible. Specifically, a method of power allocation is adopted to guarantee the minimum demanded for the quality of service (QoS) of the weak users (lower channel gain)in each group. Furthermore, to improve further the sum rate after the QoS of the weak users is satisfied, the coefficient of strong users' beam gain are set to the largest value. In system simulations, we compare the performance of three multi-beam schemes, i.e, beam splitting, beam superposition and the proposed scheme, together with a single beam scheme, and a TDMA scheme at different levels of the SNR. The simulation results demonstrate that the system sum rate of the proposed method is much higher than TDMA scheme and competitive compared to the best scheme.

Intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) multiple-input single-output (MISO) is considered one of the promising techniques in next-generation wireless communication. However, existing beamforming methods for IRS-aided mm Wave MISO systems require high computational power, so it cannot be widely used. In this paper, we combine an unsupervised learning-based fast beamforming method with IRS-aided MISO systems, to significantly reduce the computational complexity of this system. Specifically, a new beamforming design method is proposed by adopting the feature fusion means in unsupervised learning. By designing a specific loss function, the beamforming can be obtained to make the spectrum more efficient, and the complexity is lower than that of the existing algorithms. Simulation results show that the proposed beamforming method can effectively reduce the computational complexity while obtaining relatively good performance results.

The problem of unknown modulation signal recognition has been received intensely attentions in next-generational intelligent wireless communications. The deep learning (DL) has been widely used in unknown modulation signal recognition due to its excellent performance in solving classification problems and the DL-based automatic modulation classification (AMC) had been proposed. However, DL-based AMC method usually has high space complexity and computational complexity, which limits DL-based AMC to miniaturized devices with limited storage and computing capability. Therefore, a lightweight residual neural network (LResNet) for AMC is proposed in this paper. The simulation results show that the model parameters of LResNet is about 4.8% of the traditional CNN network, and about 14.9% of the ResNet and the classification performance of LResNet improves more than 3% compared with the traditional CNN network and decreases less than 1.5% compared to the ResNet.

Convolutional neural network (CNN) is one of the most important tools to accomplish high-spatial-resolution remote sensing (HSRRS) image classification tasks with their unique feature extraction and feature expression capabilities. However, the CNN-based classification method is very limited due to the acquisition of HSRRS images is difficult and the sample size is limited. In addition, the extraction of features by a single model is very limited, which limits the further improvement of classification performance. To solve the above problems, we propose ResNet50-InceptionV3 based on deep transfer learning and multi-feature fusion (TLMFFRI) model to apply for high-spatial-resolution remote sensing image classification. First, both ResNet50 and InceptionV3 are trained on the ImageNet dataset. Then, transfer the trained convolutional layers weights to the TLMFFRI model to fuse the features and realize the HSRRS image classification. Finally, we evaluate the method on the HSRRS dataset. Compared with ResNet50 based on transfer learning (TL-ResNet50) and InceptionV3 based on transfer learning (TL-InceptionV3), the proposed method achieved better classification performance.

Accurate downlink channel state information (CSI) is one of the essential requirements for harnessing the potential advantages of frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The current state-of-art in this vibrant research area include the use of deep learning to compress and feedback downlink CSI at the user equipments (UEs). These approaches focus mainly on achieving CSI feedback with high reconstruction performance and low complexity, but at the expense of inflexible compression rate (CR). High training overheads and limited storage capacity requirements are some of the challenges associated with the design of dynamic CR, which instantaneously adapt to propagation environment. This paper applies transfer learning (TL) to develop a multi-rate CSI compression and recovery neural network (TL-MRNet) with reduced training overheads. Simulation results are presented to validate the superiority of the proposed TL-MRNet over traditional methods in terms of normalized mean square error and cosine similarity.

Deep learning-based radar signal recognition is considered one of the important technologies in the field of electronic countermeasure (ECM). However, existing deep learning-based methods require much time to design a specific neural network by experts for recognizing radar signals. It is difficult to employ these methods in real application scenarios. To solve this problem, we proposed an effective radar signal recognition method using neural architecture search (NAS) to automatically design convolutional neural networks (CNN). Experiments are given to validate the proposed method via comparing with both machine learning and deep learning-based methods. Experimental results show that the proposed method can achieve the optimal accuracy with low parameters and floating-point operations.

Scenario identification (SCI) is one of key techniques for intelligent vehicular communications (IVC) to maintain an effective and reliable operating state. Based on the deep learning (DL), it is a hotspot to identify scenarios of wireless communication using the characteristic quantity inherent in wireless channels. This paper proposes a decentralized learning-based SCI (DecentSCI) for IVC, relying on the algorithm of lightweight and model aggregation. By improving training efficiency and meanwhile reducing model complexity, the proposed method achieves low computing and communication, which is applicable for vehicular devices. Simulation results show that the training efficiency is upgraded by 97.15% and the model complexity is decreased by 90.25% at the cost of slight performance loss, i.e., 0.15%.

This paper proposes a decentralized automatic modulation classification (DecentAMC) method using light network and model aggregation. Specifically, the lightweight network is designed by separable convolution neural network (S-CNN), in which the separable convolution layer is utilized to replace the standard convolution layer and most of the fully connected layers are cut off, the model aggregation is realized by a central device (CD) for edge device (ED) model weights aggregation and multiple EDs for ED model training. Simulation results show that the model complexity of S-CNN is decreased by about 94% while the average CCP is degraded by less than 1% when compared with CNN and that the proposed AMC method improves the training efficiency when compared with the centralized AMC (CentAMC) using S-CNN.

Accurate channel state information (CSI) is necessary for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. Existing deep learning-based CSI feedback methods, e.g., CSI sensing and recovery neural network (CsiNet), designed based on an autoencoder architecture, achieves higher feedback accuracy and reconstruction speed. However, this network needs to be retrained due to different communication scenarios and channel conditions, which is costly in practical deployment. To solve this problem, this paper proposes a deep learning-based modular adaptive multiple-rate (MAMR) compression CSI feedback framework. Extra padding modules are added at the base station to pad compressed CSI into different compression rates into the same dimensions, thereby realizing a general autoencoder performing variable-rate compression. Simulation results are given to confirm the effectiveness of the proposed method in terms of normalized mean square error.

Automatic modulation classification (AMC) is an essential and meaningful technology in the development of cognitive radio. It can judge the modulation mode according to the signal acquired by the receiver. In recent years, the deep learning (DL) method has been used to take the place of modulation signal recognition based on decision theory and pattern recognition, which has achieved very effective results. The development of the neural network classification model focuses on architectural engineering. Discovering state-of-the-art neural network architectures requires substantial prior knowledge and effort of human experts. Neural architecture search (NAS) can be viewed as a subdomain of automatic machine learning (AutoML), which uses a neural network to automatically adjust the structures and parameters to obtain a network that researchers need by following search strategies that maximize performance. In this paper, we propose a differentiable architecture search (DARTS) based AMC method. In addition, we also consider six other methods, including convolutional neural network (CNN), simple recurrent unit (SRU), a convolutional-recurrent neural network (CRFN-CSS), Residual Networks (ResNet), Inception Modules (Inception) and MobileNet. Simulation results show that the proposed method can achieve the optimal classification accuracy at low parameters and floating-point operations (FLOPs) without manual architecture engineering.

With the ubiquitous network applications and the continuous development of network attack technology, all social circles have paid close attention to the cyberspace security. Intrusion detection systems (IDS) plays a very important role in ensuring computer and communication systems security. Recently, deep learning has achieved a great success in the field of intrusion detection. However, the high computational complexity poses a major hurdle for the practical deployment of DL-based models. In this paper, we propose a novel approach based on a lightweight deep neural network (LNN) for IDS. We design a lightweight unit that can fully extract data features while reducing the computational burden by expanding and compressing feature maps. In addition, we use inverse residual structure and channel shuffle operation to achieve more effective training. Experiment results show that our proposed model for intrusion detection not only reduces the computational cost by 61.99% and the model size by 58.84%, but also achieves satisfactory accuracy and detection rate.

Nowadays, microgrids (MG) have attracted much attention, as a key technology of the Internet of Energy (IoE). A great deal of research have shown that the hierarchical microgrid is a more novel structure of IoE. Although the hierarchical microgrid model solves the problem of weak power scheduling capability across microgrids, it suffers from severe communications uncertainty, which can lead to communication delay and fluctuation. To obtain the accurate result of the renewable energy accommodation assessment capacity, a hierarchical microgrid model considering communication uncertainty is proposed in this article. The solution to solve the problem of the assessment renewable energy accommodation capacity for hierarchical MG is a hybrid control based on distribution deep reinforcement learning. The temporal difference (TD) generation adversarial network (TD-GAN) is proposed as a value-based method. Compared with the policy-based method, it can better solve the distributed problem in hybrid control with a generation adversarial network (GAN). Moreover, the challenge that the method cannot handle a continuous action space is solved by using a normalized advantage function (NAF). The method similar with the TD error method is employed to train the GAN network. Simulation results using real power grid data demonstrate the effectiveness and accuracy of the proposed method.

Specific emitter identification (SEI) is a promising technology to discriminate the individual emitter and enhance the security of various wireless communication systems. SEI is generally based on radio frequency fingerprinting (RFF) originated from the imperfection of emitter's hardware, which is difficult to forge. SEI is generally modeled as a classification task and deep learning (DL), which exhibits powerful classification capability, has been introduced into SEI for better identification performance. In the recent years, a novel DL model, named as complex-valued neural network (CVNN), has been applied into SEI methods for directly processing complex baseband signal and improving identification performance, but it also brings high model complexity and large model size, which is not conducive to the deployment of SEI, especially in Internet-of-things (IoT) scenarios. Thus, we propose an efficient SEI method based on CVNN and network compression, and the former is for performance improvement, while the latter is to reduce model complexity and size with ensuring satisfactory identification performance. Simulation results demonstrated that our proposed CVNN-based SEI method is superior to the existing DL-based methods in both identification performance and convergence speed, and the identification accuracy of CVNN can reach up to nearly 100% at high signal-to-noise ratios (SNRs). In addition, SlimCVNN just has 10% ~ 30% model sizes of the basic CVNN, and its computing complexity has different degrees of decline at different SNRs; there is almost no performance gap between SlimCVNN and CVNN. These results demonstrated the feasibility and potential of CVNN and model compression.

This paper proposes an unmanned aerial vehicle (UAV)-aided full-duplex non-orthogonal multiple access (FD-NOMA) method to improve spectrum efficiency. Here, UAV is utilized to partially relay uplink data and achieve channel differentiation. Successive interference cancellation algorithm is used to eliminate the interference from different directions in FD-NOMA systems. Firstly, a joint optimization problem is formulated for the uplink and downlink resource allocation of transceivers and UAV relay. The receiver determination is performed using an access-priority method. Based on the results of the receiver determination, the initial power of ground users (GUs), UAV, and base station is calculated. According to the minimum sum of the uplink transmission power, the Hungarian algorithm is utilized to pair the users. Secondly, the subchannels are assigned to the paired GUs and the UAV by a message-passing algorithm. Finally, the transmission power of the GUs and the UAV is jointly fine-tuned using the proposed access control methods. Simulation results confirm that the proposed method achieves higher performance than state-of-the-art orthogonal frequency division multiple-access method in terms of spectrum efficiency, energy efficiency, and access ratio of the ground users.

Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality.

Automatic modulation classification (AMC) is a critical algorithm for the identification of modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Deep learning (DL)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work under the corresponding condition. In this paper, a novel multi-task learning (MTL)-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from knowledge-sharing-based MTL in varying noise scenarios. In detail, multiple CNN models with the same structure are trained for multiple SNR conditions, but they share their knowledge (e.g. model weight) with each other. Thus, MTL can extract the general features from datasets in different noise scenarios. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

In a time division duplex (TDD) based massive multiple-input multiple-output (MIMO) system, a base station (BS) needs accurate estimation of channel state information (CSI) for a user terminal (UT). Due to the time-varying nature of the channel, the length of pilot signals is limited and the number of the orthogonal pilot signals is finite. Hence, the same pilot signals are required to be reused in neighboring cells and thus its channel estimation performance is deteriorated by pilot contamination from the neighboring cells. With the minimum mean square error (MMSE) channel estimation, the influence of pilot contamination can be reduced by the fully known covariance matrix of channels for all the UTs using the same pilot signal. However, this matrix is unknown to the BS a priori, and has to be estimated. In this paper, we propose two methods of deep learning aided channel estimation to reduce the influence of pilot contamination. One method uses a neural network consisting of fully connected layers, while the other method uses a convolutional neural network (CNN). The neural network, particularly the CNN, plays a role in extracting features of the spatial information from the contaminated signals. In terms of the speed of training, the former method is better than the latter one. We evaluate the proposed methods under two scenarios, i.e., perfect timing synchronization and imperfect one. Simulation results confirm that the proposed methods are better than the LS and the covariance estimation method via normalized mean square error (NMSE) of the channel.

Fall detection system has a great demand for elderly people living alone. Wi-Fi CSI (Channel State Information) based fall detection method can be used to build non-intrusive and nonspace- limited fall detection systems. In the conventional work on Wi-Fi CSI based fall detection, a classification performance degradation has been observed when data in different environments is used for learning and testing data. Also, that method can not capture accurate features of motion due to the signal distortion during the noise reduction, and it can not segment signals accurately when the SNR (Signal to Noise power Ratio) is small. In this paper, we propose a spectrogram image-based fall detection using Wi-Fi CSI. Unlike the conventional method, CSI is segmented with a certain sliding time window, and then the classifier detects fall by using the spectrogram image generated from segmented CSI. We use a CNN (Convolutional Neural Network) for binary classification of the spectrogram images of the fall and non-fall motions. We carried out experiments to evaluate the classification performance of our proposed method against the conventional one by using motion data in two different rooms for learning and testing data. As a result, we confirmed that our proposed method outperformed conventional one and reached 0.90 accuracy.

Spatia1 modulation (SM) has attracted enormous research attention due to its notable merits in terms of enabling higher spectral- and energy-efficiencies relative to the conventional MIMO technique. To provide higher system performance for SM, linear precoder is designed to shape the APM constellations observed at the receiver. However, numerical results reveal that: (1) The performance of precoding based approaches degrades dramatically for higher-level QAM constellations, which implicitly affect the spectral-efficiency improvement of SM. (2) The complexity of precoder optimization increases greatly with the system dimensions and the APM constellation dimensions, which indeed is cost inefficient. In this context, this paper introduces the signature spatial modulation (SSM) technique that inherits the properties of both the SM and the analog shift weighting techniques. To guarantee superior trade-offs among spectral-and cost-efficiencies, and bit error rate (BER) performance for SSM, we first develop efficient multi-configuration selection mechanisms by exploiting the spatial degree of freedoms (DoFs), as well as by leveraging the benefits of SM. To provide a global optimum solution against the constant-modulus constrained analog precoding problem, we next relax the precoding problem into unconstrained alternating minimization subproblems, followed by proposing Broyden-Fletcher-Goldfarb-Shanno (BFGS) aided alternating minimization algorithm to solve those subproblems. It should be noted that our proposed mechanism can realize an L-fold reduction in terms of the number of transmit antennas. In particular, theoretical results reveal that our proposal can reduce the complexities brought by the configuration selection operation up to 50% when considering L-QAM with L=4.

Polar codes are attracting much attention and being used for control channels of the 5th generation of mobile communication system (5G). As a feature, it is easier to implement encoder and decoder than Turbo codes and LDPC (Low Density Parity Check) codes. One of the decoding methods of polar codes is BP (Belief Propagation) decoding, which can decode in parallel, so that decoding can be performed at high speed. However, due to hardware limitation, calculations on the decoder get very complicated. This issue can be solved by using the information bottleneck method. This method compresses an observation variable to a quantized one while attempting to preserve the mutual information shared with a relevant random variable. In the conventional research, the information bottleneck method is applied to the BP decoding of the LDPC codes. In this paper, the information bottleneck method is used for the BP decoding of polar codes. The BP decoding of polar codes is distinct from that of LDPC codes. It has several types of the messages, and each time a message is updated, the decoding becomes more complex. By using the information bottleneck method, the decoder can compress the channel outputs and the messages of BP into unsigned integers while preventing degradation of the error correcting performance. Thus, we can reduce the complexity of calculation in the decoding process and easily implement the decoder. This paper also investigates the minimum bit width for quantization with negligible degradation and the suboptimal Eb/N0for designing the lookup tables. These lookup tables are used for updating the messages. The simulation results show that the error correcting capability of the discrete polar decoders of the proposed method is negligibly degraded compared to the BP decoding without compression.

So far, most separation approaches of vital signs such as heartbeat and respiration, are implemented based on linear mixtures. However, some literatures have reported that non-linear mixtures actually occur in the associated applications, e.g., heart rate (HR) estimation with Doppler radar, where the simple linear demixing architecture may limit the effect of source separation. In addition, the human motions during HR measurement further complicate the mixing processes. The issue motivates us to exploit a more suitable separation approach to deal with contact-free HR estimation, considering non-linear mixtures including motions. A semi-supervised deep clustering (DC) is proposed to separate the three mixed sources of heartbeat, respiration, and motions, by segmenting the spectrogram of Doppler signal. First, through training a deep recurrent neural network (RNN) with long short-term memory (LSTM) via heartbeat/respiration-only data, the embeddings to each frame-sample from spectrogram can be acquired, which enables feature optimization in a lower dimensional space. Then, in the test phase, K-means clusters the embeddings associated with each source, to infer the masks used for spectrogram segmentation. The proposed deep clustering has three main strengths: It (i) gets rid of the restriction of mixture class, relying on data mining; (ii) can handle three-source mixtures by training two sorts of source-independent samples; (iii) only requires the mixtures from single-channel. The HR measurement experiments on subjects' sitting still and typing, validate the improvements of accuracy and robustness by our proposal, over some prevailing approaches in signal decomposition or separation.

The large spectral bandwidth at millimeter-wave (mmWave) frequencies provides a mean to achieve very high data rates in wireless communication systems. A unique characteristic of mmWave is that mmWave links are very sensitive to blockage and have large propagation path loss, which exhibits low line-of-sight (LoS) probability, unstable connectivity and unreliable communication. To overcome such challenges, one of the existing solution is to associate the user equipment (UE) with other available Base Stations (BSs) by handover (HO) if the serving BS is blocked. In this paper, for a pedestrian scenario, we propose two reinforcement learning (RL) based user association algorithms, which accounts for the past experience of the blockage on the position of the UE. One focuses on the reward to increase the sum LoS probability and is named as Blockage-Aware User Association (BAUA). The other focuses on the reward to balance the tradeoff between the throughput and the LoS probability and is named as modified BAUA. Simulation results show that the BAUA algorithm increased sum LoS probability and the modified BAUA algorithm show better trade-off between the throughput and the LoS probability than the maximum Signal-to-Interference-plus-Noise Ratio (SINR) based and maximum-throughput based user association algorithms.

Identifying objects with radar is in great demand to avoid road accidents. Recent research tried to identify moving objects on a road by inputting radar information to a machine learning classifier. In the conventional method, the features used in the machine learning are extracted from the observed radar information with a short time interval. Since the movement of the objects is different depending on the objects, time series information is effective for classification, which has not been exploited before. In this paper, we propose an on-road object identification considering time series of radar information. We measured objects with 79.5 GHz millimeter-wave radar and extract features from a series of time windows by calculating the mean and variance of object information, i.e., velocity, distance, and signal power. The classification performance was evaluated with a dataset obtained by on-road experiments. It is shown that our method outperforms the conventional one and the proposed features significantly contribute to the accurate identification.

In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection performance deteriorates when there is a difference in the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can reduce the detection performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method can improve the detection performance compared to the conventional DNN-dBP with the same computational complexity. By computer simulation, it is shown that the proposed method significantly reduces the bit error rate (BER) performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method has the better BER performance than the conventional DNN-dBP with the same computational complexity.

LTE-LAA (LTE-Licensed Assisted Access) is the LTE (Long Term Evolution) to use an unlicensed band. However, in addition to the currently used license band, to use the unlicensed band, interference control with Wi-Fi that also uses the unlicensed bandwidth is required. A technique called LBT (Listen Before Talk) is considered as a technique of LTE-LAA interference control. However, since the channel occupation time (TXOP) of LTE-LAA is larger than that of Wi-Fi, there is a problem that Wi-Fi channel access is hindered. As a solution to this problem, one way is to increase the chances of channel access to Wi-Fi by establishing a waiting time of Tmutein LTE-LAA to reduce TXOP of LTE-LAA and postpone transmission. In this paper we propose an interference control method for LTE-LAA using Q-learning, which is one of the reinforcement learning techniques. In the proposed method, we select TXOP and Tmuteusing Q-learning. In the proposed method, the number of ACKs and that of NACKs in HARQ (Hybrid Automatic Repeat Request) are used as reward. In LTE-A the user terminal generates ACK / NACK according to the received radio frame and transmits it to the BS (base station). BS retransmits according to the number of ACKs / NACKs. Learning values of TXOP, Tmuteof the LTE-LAA BS can be obtained so as to increase the number of ACKs or NACKs. When the LTE-LAA BS learns to increase the number of ACKs, the throughput of LTE-LAA is controlled to be high and that of Wi-Fi is controlled to be low. On the other hand, when the LTE-LAA BS learns to increase the number of NACKs, the throughput of LTE-LAA is controlled to be low and that of Wi-Fi is controlled to be high. Simulation result shows the proposed method using LTE-LAA UE (User Equipment) and Wi-Fi UE.

With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.

In this article, we investigate the dual-ascent inspired transmit precoding (TPC) for multiple-access spatial modulation (MASM) in multiple-input multiple-output (MIMO) systems. Note that several novel TPC techniques have been developed in earlier works, to provide a solution for either the maximal-minimum Euclidean distance, or the quadratically constrained quadratic program problems. However, numerical results expose that the system performances degrade distinctly when applying these TPC techniques into MASM-MIMO. The main reason behind is that these TPC techniques are sensitive to the system dimensions and the quadratic constraints. In this context, we first recast the above challenging problems as an unconstrained problem by imposing a penalty over the quadratic constraints. Based on the primal-dual optimality theory, we next propose a Broyden-Fletcher-Goldfarb-Shanno (BFGS) aided dual-ascent approach for finding a global optimum solution to the unconstrained problem. Further, we introduce non-stationary time-varying TPC parameters to characterize an evolving MASM-MIMO in which the signals are multiplexed over a small coherence time, and thereby resulting in dual-ascent aided non-stationary TPC approach. Numerical results manifest that the proposed algorithms possess an inherent robustness to the increasing system dimension and quadratic constraint. Besides, simulation results show the benefits of our algorithms under different kinds of performance metrics.

In this paper, we investigate the multiple access spatial modulation (MASM) in multiple-input multiple-output (MIMO) systems with the aid of dual-ascent inspired iterative transmit precoding (TPC) methods. To find a global optimum against the maximum minimum Euclidean distance (MMD) problem, as well as to reach a fast convergence rate with low-complexity, we first study the peculiarities of the convex optimization methods that take the dual-ascent method into account. This beneficially provides us an insight into developing a new dual-ascent method which is capable of bringing robustness to the existing dual-ascent method. Then, we introduce an evolving MASM-MIMO system by imposing non-stationary time-varying TPC parameters, and thereby resulting in a guaranteed Euclidean distance (GED) aided TPC method. Numerical results reveal that our proposals are capable of promoting a significant bit error rate (BER) performance improvement comparing with the existing methods for the MASM-MIMO systems, as well as facilitating a fast convergence with low-complexity.

With the increase in the population of elderly, a technique for monitoring them is receiving more and more attention all over the world. In particular, it is highly demanded to develop the system that detects a fainting elderly in a toilet, since such an accident is likely to get worse due to the late detection. In this paper, we propose an anomaly detection method in toilet using an FMCW(Frequency Modulated Continuous Wave) radar, which can measure the distance between an object and an FMCW radar. As anomalies, we determine as the situations where a subject faints while stooping on the toilet seat, leaning on the backrest, leaning on the side wall, and lying on the floor after the fall. With an FMCW radar, a fainting subject could be detected based on whether the estimated distance between a subject and an FMCW radar is almost constant. However, a subject might be sitting still, and also the distance to not a subject but a floor could be estimated depending on a subject's posture. To address this problem, in the proposed method, the time when the estimated distance is almost constant is detected, and then the detected time is classified into the time due to a faint or a non-faint by a supervised machine learning classifier. The used features are extracted based on the data, e.g., the estimated distance, just before the detected time. To evaluate the anomaly detection accuracy of the proposed method, we conducted the experiments to observe not only the aforementioned abnormal behavior but also the normal behavior, e.g., the button operation and sitting down. As a result, in terms of the classification of abnormal and normal behavior, our proposed method achieved the F- measure of 0.91, where F-measure is the overall score of recall and precision rates.

Respiration is known to reflect our health condition, which motivates researchers to develop various radar-based respiration rate estimation methods. However, these conventional methods do not work, when a subject is not right in front of the radar. In this paper, we propose a novel CNN (Convolutional Neural Network)-based respiration rate estimation method in indoor environments via a MIMO (Multiple-Input Multiple-Output) FMCW (Frequency Modulated Continuous Wave) radar. A MIMO FMCW radar can estimate the DoA (Direction of Arrival) and the distance between a MIMO FMCW radar and an object. Thus, the respiration can be captured based on the phase variation at a subject's location. However, even when the advanced signal processing, e.g., MUSIC (MUltiple SIgnal Classification) algorithm, is used, it is difficult to estimate the DoA and the distance in indoor environments due to the large effect of multipath. To deal with this problem, in the proposed method, phase variations against various locations are calculated from the received signals of a MIMO FMCW radar, and then spectrograms are calculated from the phase variations. Each spectrogram is subsequently fed into the CNN that outputs the respiration rates, e.g., 0.1 Hz, 0.2 Hz, and non-respiration, i.e., a spectrogram without the effect of respiration, where is one of the deep learning techniques that have been successfully applied to the image recognition. Through the experiments we confirmed that except for when microwaves were not transmitted directly toward a subject's chest due to furniture, the proposed method accurately estimated the respiration rate, regardless of the situation.

Similar to the deep architectures, a novel multi-layer architecture is used to extend the linear blind source separation (BSS) method to the nonlinear case in this paper. The approach approximates the nonlinearities based on a polynomial network, where the layer of our network begins with the polynomial of degree 1, up to build an output layer that can represent data with a small bias by a good approximate basis. Relying on several transformations of the input data, with higher-level representation from lower-level ones, the networks are to fulfill a mapping implicitly to the high-dimensional space. Once the polynomial networks are built, the coefficient matrix can be estimated by solving an l1-regularization on the coding coefficient vector. The experiment shows that the proposed approach exhibits a higher separation accuracy than the comparison algorithms.

Blind source separation (BSS) is a technique to recognize the multiple talkers from the multiple observations received by some sensors without any prior knowledge information. The problem is that the mixing is always complex, i.e., nonlinear, underdetermined mixture, such as the case where sources are mixed with some direction angles, or where the number of sensors is less than that of sources. In this paper, we propose a multi-subspace representation based BSS approach that allows the mixing process to be nonlinear and underdetermined. The approach relies on a multi-layer representation and sparse representation in time-frequency (TF) domain. By parameterizing such subspaces, we can map the observed signals in the feature space with the coefficient matrix from the parameter space. We then exploit the linear mixture in the feature space that corresponds to the nonlinear mixture in the input space. Once such subspaces are built, the coefficient matrix can be constructed by solving an l1-regularization on the coding coefficient vector. Relying on the TF representation, the target matrix can be constructed in a sparse mixture TF vectors with a fewer computational cost. The experiments are run on the observations that are generated from nonlinear functions, and that are collected with some direction angles in a virtual room environment. The proposed approach exhibits a higher separation accuracy than that of the conventional algorithms.

In this paper, we divide antennas at the transmitter into K transmit antenna groups (TAGS) and extend the generalized spatial modulation (GSM) idea to a new preceding aided multiple-input multiple-output (MIMO) system, which is referred to as preceding aided GSM (PGSM) system. To select the receive antenna subset with low complexity, a class of efficient receive antenna selection techniques with low computational complexity are devised. To reduce the complexity of maximum likelihood (ML) detector, a low complexity iterative greedy (IG) algorithm relied ML (IG-ML) detector is also derived. Simulation results show that the bit error rate (BER) performance gain up to 3.5 dB is obtained by the proposed PGSM with IG-ML detector over the PGSM with ordered block minimum mean-squared error (OBMMSE) detector at a given BER of 10-4. It is also shown that the proposed PGSM with IG-ML detector achieves a similar BER performance in high signal-to-noise power ratio (SNR) region compared to that of an ideal preceding for the GSM with optimal ML detector.

Heartbeats and a blink are two major physiological signals that provide crucial information on our stress and drowsiness. Hence, it is highly demanded to detect simultaneously heartbeats and blinks in many applications. A Doppler sensor could be a key device to facilitate the non-contact heartbeat and blink detection in daily life. Although many Doppler sensor-based heartbeat and blink detection methods have been independently proposed, when heartbeats and blinks are detected simultaneously with one Doppler sensor, the detection accuracies of such heartbeat and blink detection methods get degraded because of at least two issues: (i) the low SNR (Signal-to-Noise Ratio) of each signal reflected from a subject's chest and face, and (ii) the similarity of the spectrum distribution of heartbeats and a blink. In this paper, we propose a spectrogram-based simultaneous heartbeat and blink detection using one Doppler sensor. In the proposed method, to extract the spectra that might be due to heartbeats and a blink, the spectra on a spectrogram are integrated. Blink detection is then performed by classifying the peaks of the integrated spectrum into a peak due to a blink or a non-blink based on a supervised machine learning classifier trained with a set of the time domain and the time-frequency domain features. Based on the non-blink peaks, heartbeats are detected considering the RRI (R-R Interval) estimated before the investigated peak to prevent the incorrect heartbeat detection. Through the experiments in the case where microwaves are transmitted from one Doppler sensor toward a body including a chest and a face, our proposal has been shown to be able to simultaneously detect heartbeats and blinks with high accuracy.

Recently, through exploiting the spectral sparsity of heartbeat component, a heartbeat detection method using a stochastic gradient approach has enabled a high-resolution of heartbeat spectrum reconstruction by Doppler radar signal, which also suppresses the residual noises after signal decomposition. However, the interference from respiration and/or body motion often corrupts the decomposition of signal by singular spectrum analysis (SSA), resulting in an inaccurate extraction of heartbeat component. In this paper, a non-negative matrix factorization (NMF)-based blind source separation (BSS) is first applied to non-contact heartbeat detection for better heartbeat extraction, incorporating the stochastic gradient approach. Specifically, motion noise is taken into account as one of sources, achieving relatively stable separation in various scenarios. In our proposed BSS approach, the spectrogram originated from radar signal is decomposed twice by NMF, which is used to learn the basis spectra (BS) relying on spectral correlation. Experimental results showed the improved accuracy and robustness of our method over conventional methods, on the heart rate (HR) measurement against subjects' sitting still or typewriting.

Massive multiple-input multiple-output (MIMO) is a technology that uses many antennas at base station (BS), and can realize advanced beamforming and spatial multiplexing. However, since Massive MIMO has many antennas, there exist problems such as the increase of overhead including pilot signals used for channel estimation and feedback of channel state information (CSI). To reduce the overhead, there is a technique of transmitting pilot signals only from some antennas and obtaining channels of the remaining antennas by the minimum mean square error (MMSE) interpolation based on spatial correlation. In addition, to perform the MMSE interpolation with low computational complexity, there is a method of interpolating channels by dividing the uniform rectangular antenna array (URA) into the vertical and horizontal linear directions. However, there is a problem that channel interpolation accuracy becomes low with dividing into the linear arrays. In this paper, we propose a method of channel interpolation with dividing the entire URA into a set of smaller URAs where interpolation is performed in each URA. Through computer simulation we show that the proposed method reduces the computational complexity compared with that without dividing, and improves the channel interpolation accuracy compared with that with dividing into linear arrays.

Injuries caused by falls are one of the major threats for the elderly. Thus, the demand for a fall detection system without requiring a user to wear or equip any devices is rapidly increasing. In this paper, a fall detection system using Ultra-High Frequency (UHF) passive Radio-Frequency IDentification (RFID) is presented. With the RFID reader on the ceiling and multiple RFID tags on the floor, anomaly scores are computed based on the neighborhood preservation principle. This focuses only on the correlation between RSSI fluctuations between a pair of tags. Therefore, unlike the conventional methods, our method manages to detect falls without requiring a large number of reference data. To detect falls in several areas of the room, our system only requires a simple reference data with one subject walking around the room. Experiments are conducted in an indoor environment with two different setups to show the effectiveness of our method. Our method improves the conventional one, and achieves a high Area Under Curve (AUC) of 0.98.

In Cognitive Radio Networks (CRNs), the available channels for the unlicensed Secondary Users (SUs) may be varying. When SUs want to communicate with each other, they must first access the same channel simultaneously. The process of accessing the same channel is referred to as a rendezvous process, by which SUs can exchange control information for establishing data transmission link. Channel Hoping (CH) is one of the most representative techniques for letting SUs rendezvous with each other. At the beginning of each time slot, SUs access available channels according to their CH Sequences (CHSs) generated by the CH algorithm. In our previous work, we have proposed a Heterogeneous Radio Rendezvous (HRR) algorithm to address the rendezvous problem for heterogeneous CRNs, where SUs may be equipped with different numbers of radios. In this paper, we propose an Enhanced HRR (EHRR) algorithm, which can further shorten the length of period for the CHSs. Compared with the HRR algorithm, the EHRR algorithm lowers the upper bounds of Maximum Time To Rendezvous (MTTR). Moreover, the upper bounds of MTTR for the EHRR algorithm are derived by theoretical analysis. In addition, the performance of the EHRR algorithm in terms of MTTR is evaluated by simulation. Simulation results show the superiority of the EHRR algorithm compared with the HRR algorithm in terms of MTTR.

Cognitive Radio (CR) technique has been proposed for improving spectrum efficiency by dynamic spectrum access. In Cognitive Radio Networks (CRNs), unlicensed Secondary Users (SUs) with CR can utilize licensed spectrum without interfering licensed Primary Users (PUs). For effectively avoiding interference with licensed PUs and malicious attacks from jammers, a two-stage Learning-based Optimal Channel Selection (LOCS) algorithm for unlicensed SUs in distributed heterogeneous CRNs is proposed in this paper. The LOCS algorithm enables SUs to obtain real states of the licensed channels without knowing their information. Hence, SUs using LOCS algorithm can efficiently avoid collision and attack with PUs and jammers. Besides, the LOCS algorithm considers hardware limitation of the SUs, i.e., SUs can only sense and access parts of the license spectrum during any given time. SUs can select the optimal channels for spectrum sensing and data transmission by using the LOCS algorithm. Simulation results show the efficiency of our proposed algorithm in terms of collision and attack avoidance.

Interference alignment (IA) is a technique that can suppress interference with a small number of antennas by aligning interference signals using transmit weights. These weights are designed based on the channel state information (CSI) fed back from each receiver, however, under the timevarying channel, the estimated CSI can be delayed/outdated, which will result in an imperfect IA. Therefore, IA with channel prediction has attracted much attention. The auto regressive (AR) model is known as a prediction method that predicts a future state based on only the past states. In the conventional channel prediction based IA method, the past channels are used directly for prediction. Therefore, the number of calculations for prediction can be too large. In this paper, based on the AR model, we describe a low complexity and high accuracy channel prediction method for IA. To predict the future channel, we only use the differences of channels between adjacent times instead of using the past channels directly. This will lead to a very low channel prediction error. Simulations show that the proposed method improves prediction accuracy and requires less calculation than the conventional one. Moreover, the IA with the proposed channel prediction method will achieve a higher transmission rate.

Signal restoration involves the removal or minimization of degradation such as attenuation, interference, and noise. Blind signal restoration is the process of estimating either the original signals or mixture functions from the degraded signals, without any prior information about original sources. In this paper, we present a novel approach to tackle the ill-posedness of the nonlinear blind source separation problem. The derivation of our algorithm is inspired by the idea of an efficient layer-by-layer representation to approximate the nonlinearity. Once such representations are built, a final output layer is constructed by solving a convex optimization problem. Thus, the projected data can break a nonlinear problem down into the version of generalized joint diagonalization problem in the feature space. Importantly, the parameters and forms of polynomials depend solely on the input data, which guarantee the robustness of the structure. We thus address the general problem without being restricted to any specific mixture or parametric model. Experimental results show that the proposed algorithm is able to recover the nonlinear mixture with higher separation accuracy on audio datasets from the real world.

Heartbeat detection are receiving a lot of attention in the field of health care, since cardiac activity reflects various information of a subject, e.g., the stress. Many Doppler sensor-based heartbeat detection methods have been proposed so far. As one of such methods, the MUSIC (MUltiple SIgnal Classification)-based HR (Heart Rate) estimation method has been proposed. However, the conventional MUSIC-based HR estimation method not only needs a long time window, but also requires to know the number of sinusoidal signals composing the analyzed signals, P, in advance of applying MUSIC to the analyzed signal, which is challenge. In this paper, we propose a MUSIC-based HR estimation method with the DCT (Discrete Cosine Transform)-based parameter P selection. In the proposed method, the analyzed signal is firstly decomposed by DCT. The inverse DCT is then performed based on only components that might be related with heartbeats. The number of components used in the inverse DCT is selected as P. Through the experiments, we confirmed that our method outperformed the conventional one by the estimation accuracy of the HR and the stress indexes such as CVI (Cardiac Vagal Index) and CSI (Cardiac Sympathetic Index).

The number of elderly people who live alone is increasing in many countries. Furthermore, many of their accidents occur at home. Hence, it is an urgent demand for a system monitoring their activities to detect accidents indoor such as falling. In conventional systems of fall detection using array antennas, falling after standing still can be detected with high accuracy by leveraging the features indicating the change of radio wave propagation. However, it is difficult to detect falling after walking correctly. In this paper, to improve fall detection accuracy including falling after walking, we propose an accurate fall detection system by leveraging the features indicating the change of Doppler signals during human activities in detail. Analyzing Doppler signals is useful to detect falling since they are observed when a radio wave reflects by moving objects. We conducted experiments in actual rooms to demonstrate that the proposed method can detect falling after both standing and walking with high accuracy.

It is known that the blink duration is highly related to drowsiness, where the blink duration is the entire duration of one blink. Hence, it is important to measure the blink duration without any special wearable devices in various application, e.g., driver's monitoring. Although a Doppler sensor could be a key device to realize it, no such blink duration estimation method has been realized so far, since estimating the blink duration is difficult because of the low SNR (Signal to Noise Ratio) of the signal reflected from eyelids. In this paper, we propose a Doppler sensor-based method that estimates the duration, tblink, that is proportional to the actual blink duration. In the proposed method, a spectrogram is firstly calculated from a received signal, and then the timings when eyelids close and open are extracted from that. More specifically, tblink is calculated based on the center-of-gravity of the energy on a spectrogram. We conducted the experiments on five subjects to show the estimation accuracy of our proposed method. We confirmed that our method achieved the average correlation coefficient R of 0.95, furthermore, the average RMSE (Root Mean Square Error) of 46 ms.

Heart-rate variability (HRV) is closely related with physical or mental conditions. It is rather necessary to develop remote monitoring technologies to reduce subjects' pressure, e.g., heart-rate (HR) monitoring to drivers. Recently, the contactless heartbeat detection with Doppler radar has drawn extensive attention, due to less burden on subjects. However, the received signals of Doppler radar are easily contaminated by respiration or body motion, resulting in performance degradation. In this paper, to realize robust heartbeat detection with Doppler radar, a stochastic gradient approach is first proposed to reconstruct heartbeat spectrum. By correcting the gradient of cost function constructed by recursion error, and utilizing the sparse characteristics of heartbeat spectrum, the reconstructed spectrum can be obtained with minimum deviation. Furthermore, the zero-attracting sign least mean square (ZA- SLMS) algorithm based on stochastic gradient descent (SGD) is proposed, to accomplish more stable sparse spectrum reconstruction (SSR), by quantizing the updating of recursion error. Finally, HR is estimated by spectral peak tracking consisting of peak selection and verification. Experimental results validate that the proposed method can significantly improve detection accuracy over the conventional methods, based on the measurements from 5 subjects during sitting still or typing with a laptop.

Device-to-device (D2D) underlay massive multipleinput multiple-output (MIMO) is a notable technology to improve spectrum efficiency by using the same frequency among both cellular user equipments (CUEs) and D2D user equipments (DUEs). In terms of communication resources, it is required to avoid assigning orthogonal pilot sequences to all CUEs and DUEs. Although sharing the same pilot sequences among user equipments (UEs) can save the communication resources, interference arises among the same pilot sequences, which is known as pilot contamination. Because the impact of this interference is still large in the conventional pilot reuse, it is highly demanded to mitigate the effect of pilot contamination. In this paper, we propose a novel pilot reuse scheme based on a graph coloring technique to mitigate the effect of pilot contamination. By leveraging the fact that the interference is alleviated when the number of antennas is large and the angle of arrivals (AOAs) of the same pilot sequences does not overlap, our method assigns the same pilot sequences so that they are not overlapped. Furthermore, to improve the channel estimation accuracy between D2D pairs, our scheme restricts reusable pilot sequences by constructing an interference graph based on graph coloring. In this graph, the nodes near to each other are connected and assigned orthogonal pilot sequences. Simulation results show that the proposed scheme outperforms the conventional one in terms of channel estimation accuracy for UEs and spectral efficiency of the system.

Rate control technology could adaptively adjust the bit rate of video according to the current throughput, which could better solve the video playout interruption. The playout threshold of video playout buffer influences the overflow and underflow probabilities of the video playout and result in video playout interruption or video frame skip. In this paper we propose an optimization method that considering Quality of Experience (QoE), joint bit rate and playout threshold control under Long Term Evolution (LTE) network. An analytical framework is presented to investigate the impact of the limited playout buffer dynamics on the wireless video QoE by analyzing the transient queue length of the buffer with the diffusion approximation method. The simulation results show that the proposed method could lower the occurrence of the video playout interruption and video frame skip under the constraints of limited resources so as to improve the QoE of video.

Massive multiple-input multiple-output (MIMO) is a promising technology for the next generation of wireless communication systems, which improves the spectrum efficiency and reduces the energy consumption. However, because of the shortage of both time and frequency resources, it is inevitable to reuse pilot sequences among users, which leads to a degradation in terms of channel state information (CSI). This degradation is known as pilot contamination. For the purpose of solving this issue, the pilot reuse (PR) scheme was proposed to alleviate the pilot contamination by non-overlapping the angles of arrivals (AOAs) between the signals of the identical pilot sequences. In the conventional AOA-based PR schemes, there is a difference in channel estimation accuracy between users, according to the order of pilot assignment, because these PR schemes assign greedily pilot sequences to user, which provides the most benefit for the first user, and less benefit for later users. In this paper, we propose a location-based PR scheme to alleviate the pilot contamination by assigning pilot sequences based on the path-loss as well as the AOAs. Furthermore, a low-complexity coordinated pilot assignment algorithm that brings benefits across multiple cells is introduced. Simulation results show that the proposed scheme provides a good channel estimation accuracy and fairness among users.

Superposed multicarrier transmission is a potential solution to the spectra overcrowding problem in wireless communications. Powerful FEC coding using a turbo code is one way to overcome the increased noise power due to the interference. Reliable decoding of turbo codes requires accurate knowledge of the channel coefficients and the power of the interference if it is present. This paper presents soft-decision-aided, joint MAP estimation of these parameters, which requires only a small pilot overhead. A novel superposed band detection method is also proposed. The accuracy of the estimators and the effectiveness of the superposed band detection are demonstrated via simulations.

Channel Hopping (CH) is a representative technique to solve the rendezvous problem for Cognitive Radio Networks (CRNs). Multiple radios technique were utilized in several latest researches on CH owing to the fact that it can significantly reduce the Time-To-Rendezvous (TTR) while the cost of the device is low. However, the radios of one unlicensed Secondary User (SU) may access same channel at the same time for most of the existing multi-radio CH protocols, which is a waste of energy. Moreover, the number of radios for the SUs is implicitly assumed same or must be more than one, which is unrealistic for heterogeneous CRNs. In this paper, an energy-efficient CH protocol, Hybrid Radio Rendezvous (HRR) protocol is proposed to address the above issues. Furthermore, theoretical analysis is presented to derive the upper bound on the Maximum TTR (MTTR) for the HRR protocol. In addition, the theoretical analysis is corroborated by extensive simulations while the simulation results show that the HRR protocol outperforms the state- of-the-art CH protocols in terms of the TTR and the energy efficiency.

Although Bitcoin is one of the most successful decentralized cryptocurrency, recent research has revealed that it can be used as fraudulent activities such as HYIP (High Yield Investment Program). To identify such undesired activities, it is important to obtain Bitcoin addresses related with fraud. So far, the identification of such activities is based upon relating Bitcoin addresses with graph mining procedures. In this paper, we follow a different approach for identifying Bitcoin addresses related with HYIP by analyzing transactions patterns. In particular, based on the individual inspection of HYIP activity in Bitcoin, we propose a number of features that can be extracted from transactions. In particular, a signed integer called pattern is assigned to each transaction and the frequency of each pattern is calculated as key features. By evaluating the classification performance with more than 1,500 labeled Bitcoin addresses, it is shown that about 83% of HYIP addresses are correctly classified while maintaining false positive rate less than 4.4%.

In this paper, we derive the analytical expressions for the secure outage probability in a single-input single-output (SISO) system over fading, in which both the main and eavesdropper channels are subject to κ-μ fading. Many authors have analyzed the secrecy performance of such a SISO system over various fading models. More recently, a lower bound on the secure outage probability over κ-μ fading has been reported. However, the exact analytical expression of secure outage probability over κ-μ fading has not been obtained. The problem with using the lower bound is that we are not sure about the tightness of the bound. Our result is an exact expression which is verified by Monte-Carlo simulations. Furthermore, it enables us to examine the behavior of the secure outage probability as a function of the fading parameters of both the main and eavesdropper channels for given values of the average signal-to-noise power ratio (SNR) over these channels. Exact expressions for the case of Nakagami-m and Rician fading models for both the main and eavesdropper channels are also obtained and verified by simulations.

A variability of R-R intervals that represent the peak-to-peak intervals of the heartbeats indicates the mental condition. Doppler radar can capture the information of heartbeats with less burden on subjects, which leads to less stress of subjects. However, non-contact heartbeat detection using Doppler radar is easily affected by respiration and body movements. In this paper, we propose a detection algorithm of R-R intervals based on the spectrogram. Our algorithm determines the frequency bands containing the heartbeats components from the frequencies that might respond to heartbeats in the spectrogram. We integrate the amplitudes of frequencies due to heartbeats within the frequency band to eliminate the noise caused by respiration and small body movements. Then, we detect peaks in the integrated amplitudes of frequencies corresponding to heartbeats. In general, the R-R intervals do not largely change between two adjacent intervals. Thus, we set a threshold to the difference of two adjacent peak-to-peak intervals that are detected. If the peak-to-peak interval is judged not corresponding to an R-R interval by the threshold, we remove the corresponding peak and interpolate a peak based on the adjacent peak-to-peak intervals. Through experiments, we show that when the subjects were sitting still, our algorithm improved the detection accuracy of the R-R intervals compared with our previous algorithm that was able to achieve a better detection accuracy than the other existing algorithms. Moreover, we confirmed that the improvement of the detection accuracy is effective to accurately calculate the stress index.

Superposed multicarrier transmission is a known method to improve frequency utilization efficiency when several wireless systems share the same spectrum. Obviously, an enhanced spectral efficiency comes at the expense of interference. To suppress the effect of interference, forward error correction (FEC) metric masking can be applied. In FEC, the corresponding log-likelihood (LLR) of the superposed band is set to zero or to other proper values determined by the other parameters such as the desired to undesired power ratio (DUR). To be able to apply the FEC metric masking, the information on the superposed band sub-carriers is required at the receiver side. Therefore, in this paper, we propose a novel method for detecting the superposed bands of multicarrier transmissions using Q-learning. We present the simulation results that show a higher rate of superposed band detection accuracy in lower DUR over the conventional method, as well as similar accuracy over other DUR.

Bitcoin is a decentralized currency system that does not need any central authorities. All transactions issued by users have been recorded in the common ledger, called blockchain, which is shared by all users. In Bitcoin, an SPV (Simplified Payment Verification) client, which is a lightweight client that does not possess the entire blockchain, are developed for storage constrained devices such as a mobile phone. For an SPV client to check if there are transactions related to it, a Bloom filter where their Bitcoin addresses are involved is sent to a full client that possesses the entire blockchain. The full client only transfers transactions of which Bitcoin addresses are positive on the received Bloom filter. However, it is necessary to preserve the privacy of SPV clients when designing a Bloom filter because SPV clients' Bitcoin addresses will be identified by a full client with high probability. In this paper, we propose a privacy-preserving Bloom filter design for SPV clients based on γ-Deniability. γ-Deniability is a privacy metric that shows how much true positive Bitcoin addresses are hidden by the false positives in a Bloom filter. Furthermore, in order to design a Bloom Filter that satisfies a certain γ-Deniability, it is necessary to know the number of unique Bitcoin addresses that appear for the first time since the queried time. Based on our manual inspection, we propose to estimate it based on the linear regression. We show that our scheme achieves good estimation accuracy and γ through the simulation with a real Bitcoin blockchain.

Increased environmental awareness coupled with the rising cost of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning (FQL) based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network (HetNet). The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The introduction of sleep mode for BSs may lead to a large-scale coverage loss, unless a specific remedial solution is exploited at the same time. To this end, we also propose to use device-to-device (D2D) communications to extend network coverage to the service areas of the switched-off BSs. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE.

Blink is a physiological signal that reflects drowsiness and concentration. It is important to detect driver's blinks without any wearable devices. For this purpose, a Doppler sensor has been used and several blink detection methods where a subject sits in front of such sensor have been proposed. However, it is challenging to detect driver's blinks because of face and body movement. In this paper, we propose a Doppler sensor-based driver's blink detection method in existence of face and body movement in a car. In the proposed method, blinks are detected through two steps: pre-detection and classification. In the first step which we call pre-detection, the time candidates of subject's blinks are detected based on spectrograms calculated from a received signal. Then, in the second one which we call classification, a set of features are calculated from a spectrogram and are fed into a supervised machine learning classifier to identify which time candidates are truly blinks. We leverage the fact that the distribution of the energy on a spectrogram differs between a blink and non-blink. Specifically, features are extracted based on the distribution of energy on a spectrogram. We conducted a series of experiments for the evaluation in the situation where a subject drives a real car in public road. As a result, we confirmed our method outperforms the conventional one in terms of F-measure calculated from recall rate and precision rate.

Power line communication (PLC) is an emerging technology for the realization of smart grid and home automation. It utilizes existing power line infrastructure for data communication in addition to the transmission of power. The PLC channel behaves significantly different from the wireless channel; and it is characterized by signal attenuation as well as by additive noise and multiplicative noise effects. The additive noise consists of background noise and impulsive noise; while the multiplicative noise results in fading of the received signal power. This paper focuses on the impact of the PLC channel characteristics on the outage and BER performance of a PLC system over Rayleigh fading channel with frequency- distance dependent attenuation and colored Nakagami-m distributed additive noise. We derive the exact closed-form expressions for the distribution of the instantaneous signal-to-noise ratio (SNR) and show the detector based the maximum Likelihood (ML) criterion as well as a simple but efficient suboptimal detector. Monte Carlo simulation results are used to verify the derived analytical expressions.

With the huge increases in traffic volumes and subscribers, diverse devices, and rich media applications, manual management of mobile network becomes highly challenging in terms of optimization and management. Self-organizing networks (SON) has been introduced to optimize the network in an automatic manner. In this paper, we address the coverage and capacity joint optimization (CCO) by adaptively and simultaneously adjusting both antenna tilt and power. To this end, we propose: · a multi-player multi-armed bandit (MAB) framework (decentralized restless upper confidence bound (RUCB) algorithm) with a change point detection test based on Page-Hinkley (PH) statistics used to decide whether some change has occurred in the environment. Then, the strategy is designed to deal with such a change. · a central unit to deal with simultaneous conflicting actions when many cells decide to start the optimization process at the same time. · a Pareto search framework to deal with multi-objective optimization (CCO). To evaluate our work, we compared our proposal with the fixed antenna parameter scheme and with the linear scalarization function that transforms the multi-objective optimization problem into a scalar function. Simulation results show that the proposed method could improve user experience in terms of cell-center capacity and cell-edge coverage compared to different conventional methods and under different number of users.

Recently, some papers that apply a multi-armed bandit algorithm for channel selection in a cognitive radio system have been reported. In those papers, channel selection based on Upper Confidence Bound (UCB) algorithm has been proposed. However, in those selection, secondary users are not allowed to transmit data over same channels at the same time. Moreover, they do not take security of wireless communication into account. In this paper, we propose secure channel selection methods based on UCB algorithm, taking secrecy capacity into account. In our model, secondary users can share same channel by using transmit time control or transmit power control. Our proposed methods lead to be secure against an eavesdropper compared to conventional channel selections based on only estimated channel availability. By computer simulation, we evaluate average system secrecy capacity. As a result, we show that our proposed channel selections improve average system secrecy capacity compared to conventional channel selection.

In this paper, we investigate the secrecy performance of single-input single-output (SISO) system over fading, such that the main channel and the eavesdropper channel are subject to Rician/Nakagami-m fading or Nakagami-m/Rician fading, and derive the analytical expressions of the probability of the existence of the non-zero secrecy capacity and the outage probability of secrecy capacity. Although many authors have analyzed the secrecy performance of SISO system over various fading, the secrecy performance in the cases we assume has not been clarified. Furthermore, the simulation and numerical results show that the results using the approximation between Rician and Nakagami-m fading is insufficient in evaluating those probabilities because of only approximation to main bodies of the probability density function, and that our analyses are valid.

Almost blank subframe (ABS) techniques emerge as inter‐cell interference mitigation techniques in time region. One of the conventional ABS techniques is dynamic ABS assignment, which is able to adapt to interference state to protect the outage users more. However, the number of ABS assignments increases to protect a lot of outage users in the dense BS deployment. Therefore, the resource utilisation of base stations (BSs) decreases. Moreover, the convergence time of subframe assignment should be short enough to adapt to the change of environments. In this study, the authors propose a dynamic ABS assignment method to improve the resource utilisation of BSs and the convergence time of subframe assignment. Each BS receives feedback from surrounding BSs and users, and selects its subframe assignment based on the received feedback. Simulation results show that the proposed method improves the resource utilisation of BSs and the convergence time of subframe assignment compared with the conventional dynamic ABS assignment one, while achieving a similar user outage ratio.

Co-localization of mobile users combines methods of detecting nearby users and providing them interesting and useful services or information. By exploiting the massive use of smartphones, nearby users can be co-localized using only their captured ambient radio signals. In this paper, we propose a real-time co-localization system, in a centralized manner, that leverages co-located users with high accuracy. We exploit the similarity of radio frequency measurements from users' mobile terminal. We do not require any further information about them. Our co-localization system is based on a nonparametric Bayesian method called infinite Gaussian mixture model that allows the model parameters to change with observed input data. In addition, we propose a modified version of Gibbs sampling technique with an average similarity threshold to better fit user's group. We design our system in a completely centralized manner. Hence, it enables the network to control and manage the formation of the users' groups. We first evaluate the performance of our proposal numerically. Then, we carry out an extensive experiment to demonstrate the feasibility, and the efficiency of our approach with data sets from a real-world setting. Results on experiment favor our algorithm over the state-of-the-art community detection-based clustering method.

We propose a Coordinated Non-Orthogonal Multiple Access (CO-NOMA) scheme for resource allocation in the context of mobile networks. NOMA refers to schemes where multiple users can access the wireless channel in the same frequency band simultaneously. In this study, we consider NOMA with power domain multiplexing, where the near- far property in space is exploited through appropriate power allocations. Moreover, the space domain is further exploited by coordinating transmissions from several distributed radio units. This approach, known as Coordinated Multi- Point (CoMP), can complement NOMA in order to further make use of the available degrees of freedom. Therefore, we discuss and propose a suboptimal scheduling strategy that achieves NOMA with coordinated transmitters in the downlink, with linear complexity, and compare performances with other schemes. The proposed scheme is shown to enhance performances for low to medium number of users per cell, as observed from system level simulations.

Twitter is attracting significant interests from the research community in the last few years. Sentiment analysis of tweets is among the hottest topics of research nowadays. State of the art approaches of sentiment analysis present many shortcomings when classifying tweets, in particular when the classification goes beyond the binary or ternary classification. Multi-class sentiment analysis has proven to be a very challenging task. This is mainly for the simple reason that a tweet usually does not contain a single sentiment, but many ones. In this paper, we propose a pattern-based approach for sentiment quantification in Twitter. By quantification, we refer to the detection of the existing sentiments within a tweet and the detection of the weight of these sentiments. In a first step, we classify tweets into positive, negative, or neutral. Our approach reaches an accuracy of 81%. We then perform the sentiment quantification on the sentimental tweets (i.e., positive and negative ones) to extract the sentiments within them: we define 5 positive sentiment sub-classes 5 negative ones and detect which exist in each tweet. We define 2 metrics to measure the correctness of sentiment detection, and prove that sentiment quantification can be a more meaningful task than the regular multi-class classification.

We present centralized iterative algorithms that jointly determine the optimal transmit and receive filters as well as the optimal power allocation for a K-user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is based on the achievable sum-rate and the average per user multiplexing gain in the MIMO IC. By allowing channel state information (CSI) exchanged between base stations (BSs) and a central unit (CU), we design a feedback topology where CU collects local CSIs from all BSs, computes all transmit and receive filters and sends them to corresponding user-BS pairs. Note that the local CSIs at BSs are obtained from the estimation of the channel states during the so- called uplink-training phase. At the CU, we propose iterative algorithms utilizing alternating optimization strategy to design the filters. In most of the studies on the MIMO IC, choice of equal transmit powers for all user-BS pairs ignores the essential need to search for the optimal power allocation policy; they do not take the full advantage of the system's total power. Thus, how to allocate power among all the user-BS pairs in the network based on the sum-rate maximization strategy and under a sum power constraint is another key to this paper.

A blink is one of physiological signals that indicates a consciousness level or a drowsiness. Using a Doppler sensor is one solution to realize non- contact blink detection without cameras. However, it is difficult to detect blinks using a Doppler sensor because of low signal to noise power ratio (SNR) of reflected signal from an eyelid. We previously proposed the blink detection method based on machine learning and show that the method provides high probability of detection of blinks. However, it is necessary for the method to do a prior learning process for each person or environment. In this paper, we propose a high accuracy blink detection method without prior learning process. By applying constant false alarm rate (CFAR) processing to the blink signal detection, the method can be robust against the fluctuation of a body or noise, and the probability of detection can be improved. Moreover, we apply two kinds of classification based on the characteristic of the signal waveform of the blink. Thereby, a subtle body movement that is easy to be wrongly detected as a blink is excluded, and false positive in detection can be reduced. We conducted three experiments to evaluate the detection accuracy. As a result, we show that our proposal achieved high detection accuracy of around 99 %.

Nowadays people are carrying their mobile devices wherever they go, and as social beings they interact with others all day long. Thus, by exploiting this massive use of smart devices they provide a way to be co-located using only their captured environmental radio signals. In this paper, we design a co-location system that finds groups of people, in real-time, with high accuracy, by exploiting the similarity of their measured radio signals. Our method is based on a nonparametric Bayesian (NPB) method called infinite Gaussian mixture model (IGMM) that allows the model parameters to change with observed input data. This system is designed in a completely centralised manner. Hence, it enables the network to control and manage the formation of the all users' groups. We analyze the performance of our framework, in terms of clustering accuracy, with datasets from a real-world setting to demonstrate its feasibility. We also compare its performance against community detection based clustering method. Results on experiment with real datasets show a better accuracy favoring our approach against its counterpart.

Proximity information of users can be used in various applications (e.g., user interactions in social networks). Applying conventional works to room-level proximity detection is difficult because there is a limitation of the proximity range within 5 m. In this paper, we propose a room-level proximity detection method based on the similarity of received information of Wi-Fi signals between users. We use Wi-Fi signals in both 2.4 GHz band and 5 GHz band, and use relevant features that indicate the similarity of received signal strength (RSS) of beacon frames and access points (APs) sets from which users receives them. Through extensive experiments, in which we recognize whether or not users exist in the same room with a size approximately from 10 to 15 m square, we demonstrate that our proposed method can realize room-level proximity detection with high robustness to relative location of users and APs.

Most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented towards the classification of texts into positive and negative. In this paper, we propose a pattern-based approach that goes deeper in the classification of texts collected from Twitter (i.e., tweets). We classify the tweets into 7 different classes; however the approach can be run to classify into more classes. Experiments show that our approach reaches an accuracy of classification equal to 56.9% and a precision level of sentimental tweets (other than neutral and sarcastic) equal to 72.58%. Nevertheless, the approach proves to be very accurate in binary classification (i.e., classification into “positive” and “negative”) and ternary classification (i.e., classification into “positive”, “negative” and “neutral”): in the former case, we reach an accuracy of 87.5% for the same dataset used after removing neutral tweets, and in the latter case, we reached an accuracy of classification of 83.0%.

We present centralized iterative algorithms that jointly determine the optimal transmit and receive filters as well as the optimal power allocation for a K-user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is based on the achievable sum-rate and the average per user multiplexing gain in the MIMO IC. By allowing channel state information (CSI) exchanged between users and a central unit (CU), we design a feedback topology where the CU collects local CSIs from all base stations (BSs), computes all transmit and receive filters and sends them to corresponding user-BS pairs. At the CU, we propose iterative algorithms utilizing alternating optimization strategy to design the filters. In most of the studies on the MIMO IC, choice of equal transmit powers for all user-BS pairs ignores the essential need to search for the optimal power allocation policy; they do not take the full advantage of the system's total power. How to allocate power among all the user-BS pairs in the network based on the sum-rate maximization strategy is the key to this paper. Thus, while the filters are designed at the CU, we propose a novel power allocation problem for sum-rate maximization under sum power constraint.

Recently, multimedia communication by smart-phones and tablets via mobile networks have become popular in daily life. Therefore, an effective method to use the limited wireless resources is necessary. When using wireless resources among multiple users, the schedulers allocate resources to each user. When allocating resources, assuring quality of experience (QoE) and achieving high system throughput are fundamental. In this paper, we present a two-step radio resource allocation algorithm for the downlink of a long term evolution (LTE) network, by taking into account the user's and the service provider's perspective. At the first step, it allocates resources dynamically to each user so that a minimum QoE is guaranteed for every user. In the second step, it allocates resources dynamically to improve the system throughput while achieving a high-level of QoE. We show that our proposed algorithm achieves a higher level of QoE than the conventional allocation algorithms based on quality of service (QoS) metrics, as well as a higher throughput compared to the conventional allocation algorithms based on QoE.

Massive MIMO (multiple-input multiple-output) is one of the key technologies to realize 5G (5th Generation). Massive MIMO can be implemented with many antennas at a transmitter and receiver sides, and it can improve transmission quality at high frequency band by transmitting with superposing shift of radio wave toward the direction of the receiver. However, there exists an issue such as the increase of the amount of feedback of channel state information (CSI) from the receiver to the transmitter, due to the enormous number of antennas. For the purpose of solving this issue, there exists the technique to compress CSI to a lower dimension matrix and decrease the amount of feedback, by using principal component analysis (PCA). In the conventional method, the compression matrix to compress a channel matrix is calculated on the basis of PCA, and the compressed channel is fed back from the receiver to the base station (BS). In this method, the compression matrix used in PCA is generated based on the past CSI at the receiver, which leads to the degradation of transmission rate. This is because there is a mismatch between the CSI acquired at the transmitter and that when the transmitter transmits a signal, due to the channel variation during the feedback from the receiver to the transmitter. In this paper, to solve this problem, we propose the method based on PCA with the channel prediction. As the channel prediction, the forward-backward AR (Auto Regressive) model is used, and the compression matrix in PCA is generated from the predicted channels. By the computer simulation, it is shown that the system capacity is increased by generating the compression matrix from the predicted channel that improves the accuracy of channel restoration.

Recently, wrist-worn smart devices such as smart-watches are becoming popular, and there is a growing need for authentication systems on such devices. Many wrist-worn devices contain a photoplethysmographic (PPG) sensor with a purpose of measuring the user's heart rate. In this paper, in addition to using the PPG sensor to measure the heart rate, we use it to detect the user's hand movement. In our proposed method, we use not only the information of the hand movement, but also that of the stationary state to improve the robustness of authentication. We obtained the PPG signals from 15 subjects, whom in the experiment were asked to bend their wrist and put it back for several times, continuously. The results of the experiments revealed that a continuous wrist movement of three times achieved an equal error rate of 11.6 %, and a movement of nine times achieved an equal error rate of 8.8 %.

Vital signs are related to mental stress conditions. In particular, the variation of R-R interval (the peak-to-peak interval of heartbeats) reflects mental stress. For measuring heart rate, non-contact detection using Doppler radar has been studied in many works. In this paper, we propose an algorithm to detect periodic heartbeats for some periods in which the R-R intervals are almost equal. In the long term, the R-R intervals gradually vary, however, the R-R intervals are almost equal within a short period. To detect those periods, we assume that the R-R intervals always have almost equal intervals. Then, we detect periodic peaks appearing at almost the equal intervals by using the Viterbi algorithm. After detecting periodic peaks, we detect the periods during which the R-R intervals are almost equal, based on the variance of the detected peak-to-peak intervals. Moreover, we estimate an average R-R interval and find peaks that appear at the interval nearly equal to the estimated average R-R interval within each detected period. Experimental results show that the proposed algorithm improves RMSEs (root mean square error) of the R-R interval compared with the conventional methods.

In Physical Layer Network Coding (PLNC), since the two sources access the relay station at a time, the relay station can hardly demodulate each information owing to the mutual interference between two transmitted signals. The PLNC achieves the high secure relay communication based on physical layer security (PLS). The source needs the transmit power control based on the channel state information (CSI) for maintaining the secure mutual interference. The untrusted relay, however, may impersonate the CSI for breaking it. So far, the authors have evaluated the amount of the leaked information to the untrusted relay by the impersonation of CSI but the leaked information is not maximal. For the countermeasure to the impersonation of CSI, the most powerful model of the untrusted relay to maximizing the leaked information is necessary. This paper constructs the optimal impersonation of CSI by a linear programing problem.

In long term evolution (LTE), heterogeneous network (HetNet) is constructed to increase network capacity. In HetNet, low transmit power base stations (BSs) such as femto-BSs are deployed in an indoor within the coverage of macro cells. Since BSs are close to each other in HetNet, indoor or cell-edge users are likely to experience outage due to inter-cell interference. Therefore, almost blank subframe (ABS) techniques emerge as inter-cell interference mitigation technique in time region. One of the conventional ABS techniques is dynamic ABS assignment, which is able to adapt to interference circumstances to protect the cell-edge users more. In the dynamic ABS assignment method, a BS dynamically allocates ABS to its subframe based on surrounding circumstances. However, the resource utilization of BSs decreases, because the number of ABS assignments increases in the dense BS deployment. This results in low sum rate. In addition, if the number of ABS assignments increases, user fairness becomes impaired due to the difference of the number of users which each BS serves. In this paper, we propose a dynamic ABS assignment method with power control subframe to improve sum rate and user fairness together. Each BS receives feedback from surrounding BSs and users, and chooses its subframe assignment based on the received feedback. Simulation results show that the proposed method improves the sum rate and user fairness compared with the conventional dynamic ABS assignment method, while achieving a similar user outage ratio.

Interference Alignment (IA) in heterogeneous networks (HetNets) is a promising technique that improves the spectral efficiency significantly. We showed in [1] that transmit antennas at pico BSs could be utilized more efficiently by clustering pico cells in IA in HetNet where the clustering formation was optimized so as to minimize the rate loss caused by inter-cluster interference. In [1], the optimum clustering formation was selected by comparing all possible formations, that is, the value of the objective function for all possible formations was calculated. Therefore, we required the enormous complexity to construct pico cell clusters. In this paper, we propose a novel algorithm for clustering pico cells that reduces the complexity of the clustering process. In particular, we define rate-loss matrix that represents the rate loss caused by inter-pico interference, and translate the optimization problem to the construction of rate-loss matrix. Clearly, the proposed algorithm is sub-optimum in terms of achievable rate compared to all-search algorithm used in [1]. However, the simulation results show that the difference of achievable rate between the proposed algorithm and all-search algorithm is negligible and becomes smaller as the cluster size decreases. We evaluate the complexity of proposed algorithm quantitatively comparing to all-search algorithm, and show that our algorithm reduces the complexity of clustering process significantly while achieving almost same performance.

Superposed multicarrier transmission scheme is known to improve frequency utilization efficiency where several wireless systems share spectrum. On superposed band, log likelihood ratio (LLR) cannot be set correctly due to interference, which results in BER (Bit Error Rate) degradation. Forward error correction (FEC) metric masking is proposed to suppress the effect of interference. In this technique, LLRs corresponding to superposed band is set to zero, because received bits corresponding to superposed band is unreliable. This scheme requires superposed band detection and does not consider channel estimation error. We proposed an iterative estimation technique for undesired signal power in [6]. Although this scheme does not require superposed detection beforehand, due to the estimation error of undesired signal power, BER is degraded. In this paper, we propose an estimation technique for undesired signal power and superposed band to calculate LLR correctly. This scheme estimates the superposed band within 1 packet and based on the information about the superposed band, undesired signal power is estimated using pilot symbols. Simulation results show that as the number of pilot symbols increases, BER of our proposed scheme becomes better than that of [6] and gets closer to the BER when the estimation of undesired signal power is perfect.

Opinion mining and sentiment analysis refer to the identification and the aggregation of attitudes or opinions expressed by internet users towards a specific topic. However, due to the limitation in terms of characters (i.e. 140 characters per tweet) and the use of informal language, the state-of-the-art approaches of sentiment analysis present lower performances in Twitter than that when they are applied on longer texts. Moreover, presence of sarcasm makes the task even more challenging. Sarcasm is when a person conveys implicit information, usually the opposite of what is said, within the message he transmits. In this paper we propose a method that makes use of a minimal set of features, yet, efficiently classifies tweets regardless of their topic. We also study the importance of detecting sarcastic tweets automatically, and demonstrate how the accuracy of sentiment analysis can be enhanced knowing which tweets are sarcastic and which are not.

Sarcasm is a special form of irony by which the person conveys implicit information, usually the opposite of what is said, within the message he transmits. Sarcasm is largely used in social networks and microblogging websites, where people mock or criticize in a way that makes it difficult even for humans to tell if what is said is what is meant. Recognizing sarcastic statements can be very useful when it comes to improving automatic sentiment analysis of data collected from social networks. It helps also enhance the efficiency of after-sales services or consumer assistance through understanding the intentions and real opinions of consumers when browsing their feedbacks or complaints. In this paper we propose a method to detect sarcasm in Twitter that makes use of the different components of the tweet. We propose four sets of features that cover different types of sarcasm we defined, and that will be used to classify tweets into sarcastic and non-sarcastic. We evaluate the performances of our approach. We study the importance of each of the proposed sets of features and evaluate its added value to the classification.

Vehicular ad hoc networks (VANETs) which are deployed along roads make traffic systems safer and efficient. Existing theoretical results on capacity scaling laws provide insights and guidance for the design and deployment of VANETs. In this paper, we propose a novel fundamental framework RVWNM (Real Vehicular Wireless Network Model), which enables a more realistic capacity analysis in VANETs. We first introduce a Euclidean planar graph which can be constructed from any real map of urban area, and represents the practical geometry structure of the urban area. Then, an interference relationship graph is abstracted from the Euclidean planar graph which considers the transmission interference relations among the nodes in the network. Finally, we analyze theoretically the interference relationships in the interference relationship graph. As far as we know, we are the first to use a practical geometry structure to calculate the asymptotic capacity of VANETs. To verify the feasibility of RVWNM, we calculate the asymptotic capacity of urban area VANETs with the consideration of social- proximity based mobility of vehicles.

In interference alignment (IA), interference signals are aligned in a certain signal subspace of each receiver using transmit weights and eliminated using receive weights. The transmit and receive weights are typically calculated at transmitters with an iterative optimization based on estimated channel state information (CSI) from receivers. However, the estimated CSI is different from the channel when a signal is transmitted by the transmitter, because the channel varies during a CSI feedback and the iterative weight calculation. In IA with the weights based on the estimated CSI, the interference signals are not perfectly aligned and remains at the receiver. As a result, a rate decreases due to the remaining interference signals. In this paper, we propose three IA methods having the capability of adapting to time-varying channel using auto regressive (AR) model by which next state is predicted with some past states. In the first method, the channel when a signal is transmitted at the transmitter is predicted from the estimated CSI at the receiver based on AR model. Transmit and receive weights are calculated with the predicted CSI so that the interference signals are aligned and eliminated. In the second method, transmit and receive weights are predicted with past transmit and receive weights based on AR model, respectively. In the third method, only the transmit weight is predicted with past transmit weights based on AR model and receive weight is calculated with the estimated CSI. The prediction methods are able to calculate transmit and receive weights with low amount of calculation compared with the iterative optimization. Through computer simulation, compared with general IA methods that do not use prediction process, the proposed ones are shown to improve the rate irrespective of signal to noise power ratio (SNR) and decrease the calculation amount.

Nowadays, the population of elderly persons who live alone is increasing in Japan. Some systems are available to monitor those persons' activities, however, they have some limitations; physical burden and need to wear sensors. In our previous work, we have proposed a fall detection method using an array antenna. Array antenna is an antenna, which detects radio wave propagation by observing the direction of arrival (DOA) of the received signal. In the conventional method, it can only detect whether the activity is fall or not, so it is impossible to recognize which kind of non-fall activity the person does. In addition, the detection accuracy of the non-fall activity is not so high, because of the difficulty to detect the difference of fall and sit. In this paper, we propose an activity recognition system by using array antenna. We detect the person's activity (fall, walk, sit, and stand still). To improve the accuracy, we set thresholds for the continuous number of each activity, and correct the result which continues less than the threshold. Also, we use the state transition of the activity, and correct the impossible activity transition.

Now, aging society is a worldwide problem, and the population of people aged over 60 years is growing faster than any other age group. Therefore, monitoring services for elderly people are attracting a great deal of attention. We have proposed a fall detection method using a low resolution infrared array sensor to inform an unexpected falling in our previous work. However, knowing daily fundamental activities of elderly people is also important to prevent future accidents. In this paper, we propose an activity recognition method using a low resolution infrared array sensor. This sensor can detect temperature on a two dimensional area. From the viewpoint of general versatility (available in darkness), cost, size, privacy (low resolution), and availability (commercial off-the-shelf), this sensor is better than other sensing devices like video cameras, Doppler radars, acceleration sensors, and so on. In the proposed method, temperature distribution obtained from the sensor is analyzed and classified into five fundamental states: “No event”, “Stopping”, “Walking”, “Sitting”, and “Falling” (emergency situation). As a result of experiments, our proposed method achieved recognition accuracy of 100 %, 94.8 %, 99.9 %, and 78.6 % respectively. In particular, 100 % accuracy of “Falling” recognition was achieved.

In this paper, we focus on the scale factor in the wavelet transform. When we observe the time series of wavelet coefficients on each scale factor, the wavelet coefficients are affected by the respiration or the body motion on some scale factors. On the other hand, on some scale factor, the wavelet coefficients increase only when the heartbeats appear. We search the scale factor whose wavelet coefficients increase only when the heartbeats appear, in advance. In the learning phase, the subject sits still without body motion. We avoid using a pseudo frequency to obtain the heart rate. Instead, we use the time interval of each peak on the wavelet coefficients whose scale factor is decided in the learning phase. Thus, although the subject has body motion, the heartbeats are able to be detected. For the evaluation, four types of activities are tested. The R-R interval is used for the evaluation of heartbeat detection. As a result, we confirm that RMSE of the R-R interval reduced on all activities compared to the conventional method. Moreover, the RMSE of the R-R interval did not deteriorate when the distance between the Doppler radar and the subject becomes long.

In this paper, we explore the use of full-duplex technique to improve spectrum efficiency in amplify-forward multiple-input multiple-output (MIMO) relay channel. The full-duplex operation can reduce the overall communication to only one phase but suffers from self-interference. We investigate an asymptotic ergodic capacity of full-duplex amplify-forward MIMO relay using Tracy-Widom distribution. Instead of using numerical solution, we derive an asymptotic expression of ergodic capacity by exploiting eigenvalue distributions. We consider singular value decomposition (SVD) based scheme with perfect channel state information (CSI) with three scenarios: i) Large relay, and fixed source and destination antennas, ii) Fixed source, and large relay and destination antennas, and iii) Large relay and source, and fixed destination antennas. We show the simulation results that the capacity of full-duplex model is almost twice the capacity of half-duplex. We show that increasing the number of destination antennas does not help much for improving the capacity when the number of source antennas is fixed. Moreover, we also show that increasing the number of source antennas can decrease the capacity when the number of destination antennas is fixed.

We consider Multiple-input multiple-output (MIMO) wiretap channel with one transmitter, one receiver, one cooperative jammer, and one eavesdropper, where each node equips with multiple antennas. Eigenbeam-space division multiplexing (E-SDM) and cooperative jamming are known as the techniques improving secrecy rate. E-SDM enables the transmitter and the legitimate receiver to communicate with each other in parallel using channel state information (CSI) known to them to maximize the legitimate receiver's mutual information. On the other hand, cooperative jamming is to jam only the eavesdropper to degrade eavesdropper's mutual information. However, co-channel interference and residual interference from the cooperative jammer are caused by channel estimation error, which degrades legitimate receiver's mutual information. How these interference influence on the secrecy rate has not been clarified. In this paper, we derive lower and upper bounds on secrecy rate using legitimate receiver's mutual information in the presence of channel estimation error and eavesdropper's mutual information who uses minimum mean square error (MMSE) receiver. We analyze the effect of co-channel interference and residual interference from the cooperative jammer on the secrecy rate.

Superposed multicarrier transmission is proposed as one of the schemes that improve frequency utilization efficiency. In this scheme some wireless systems share the same spectrum, and interference suppression is necessary. One conventional scheme for the interference suppression applies the EM (Expectation Maximization) algorithm for the estimation of interference parameters, and another scheme iterates the estimation of the parameters based on undesired signal power. However, these schemes assume the same average power for all interferences, which makes accuracy of the estimated power decrease and results in degradation of BER (Bit Error Rate) when the average power of each interference is not same. In this paper, we propose a suppression schemes for multiple interferences with different average power levels. In our scheme, undesired signal power is averaged in both the time and the frequency domain, after which it is compared to threshold to determine if the corresponding subcarrier is superposed or not. According to the result of this comparison, the power of each interference is estimated. Furthermore, the threshold is iteratively recalculated when the estimated parameters are updated. Through simulations we show that the proposed scheme taking the different average power of interference into account shows better BER performance than that of conventional ones assuming the same average power of interference. In addition, we show that averaging undesired signal power in both the time and the frequency domain and iterative calculation of threshold lead to improvement of the accuracy of superposed band detection.

Heart rate variability gives information about health and mental condition. Noncontact detection of heartbeat using Doppler sensor has been researched in many studies. There is a major issue which is how to reduce the influence of body movement. A conventional algorithm uses the continuous wavelet transform. To extract heartbeat, a constant scale factor is selected during a learning phase which is then used to detect heartbeat during a test phase. However, to select the scale factor, the authors do not consider the difference of heart rate between learning and test. Thus, the root mean square error (RMSE) of R-R interval which represents the peak-to-peak of heartbeat is deteriorated. In this paper, we propose a method to improve the RMSE of R-R interval compared with the conventional one. During learning, we search for a scale factor interval corresponding to the heart rate obtained with the Doppler sensor. To take the difference of heart rate between learning and test into consideration, we extend the scale factor interval depending on the action during test. After we select a certain scale factor from some scale factors in the extended interval, we detect heart rate during test by counting the peaks of wavelet coefficients of the selected scale factor. Through experiments, when a subject is sitting still or doing a typing game, we show that the RMSE of R-R interval is improved by about 60 msec and 65 msec, respectively, compared with the conventional method.

Interference alignment (IA) is a technique shown to be able to achieve a significant overall throughput in a μ — user multiple-input multiple-output (MIMO) interference channel (IC). In this paper, we try to modify the conventional IA designs to achieve enhanced sum-rate performance. We jointly design transmit and receive IA filters (precoding and suppression filters) at a central unit (CU) to reduce the channel state information (CSI) feedback/sharing overhead. At the CU, in a one-way iterative strategy (left ‘L’ to right ‘R’, see Fig. 1), the precoding filters are optimized (on side ‘L’) in the direction of the gradient of the sum-rate, and the suppression filters are chosen (on side ‘R’) to minimize the leakage interferences. Then, in a two-way iterative strategy, the proposed scheme alternates between ‘L’ and ‘R’ sides to design the IA filters through the joint sum-rate maximization and interference leakage minimization on each side. Finally, a modified version of the two-way iterative strategy is proposed to design the IA filters on the basis of signal-to-interference-plus-noise ratio (SINR) (instead of the interference minimization) and the sum-rate maximization techniques. Comparing to other conventional IA designs, the proposed designs with provable convergence show a significant sum-rate performance improvement and often outperform other widely used algorithms, as shown in the simulation examples.

In this paper, we present a cognitive radio (CR) based statistical framework for a two-tier (femto- macro) heterogeneous cellular network. In this framework, the coverage probability of an arbitrary femto user is determined. Using tools from stochastic geometry and point process theory (in this paper, the spatial Poisson point process (PPP) theory is used) we model the random locations and topology of both the femto and macro networks. A considerable improvement of system performance can be generally achieved by mitigating interference, as a result of applying the CR idea over the above model. We also study the implication of a Reinforcement Learning (RL) based power control (PC) strategy per femto user in interference-limited networks over the above model to guarantee a certain value of coverage probability for a given signal-to-interference- plus-noise-ratio (SINR) target.

Interference alignment (IA) in heterogeneous networks (HetNet) is a promising technology that can achieve a significant increase of network capacity. However, there are some issues on IA in HetNet. One of them is that each pico base station (BS) requires a large number of transmit antennas, because it tries to align all inter-pico interference. In actual environments, the strength of inter-pico interference varies depending on the distance between pico cells, i.e., inter-pico interference from the remote pico cell is much smaller than that from the close one, so that aligning all interference is not necessarily the best. In addition, the residual interference from macro BS that is not eliminated by IA may decrease the capacity. In this paper, we propose 1) to reduce the required number of transmit antennas at pico BS by clustering pico cells based on the strength of inter-pico interference and 2) to apply combined weight, that comprises ZF (zero forcing) weight and MMSE (minimum mean square error) weight, to pico user equipment (UE) so as to mitigate the residual interference that is not eliminated perfectly by IA. Simulation results show that by applying combined weight to pico UEs, the capacity increases compared to the case of applying only ZF weight, and that the average rate per pico cell normalized by the required number of transmit antennas at each pico BS, that is, the rate per transmit antenna can be increased with pico cell clustering, which indicates that each pico BS can utilize its transmit antennas more efficiently.

Abstract In sparse mobile wireless networks, normally, the mobile nodes are carried by people, and the moving activity of nodes always happens in a specific area, which corresponds to some specific community. Between the isolated communities, there is no stable communication link. Therefore, it is difficult to ensure the effective packet transmission among communities, which leads to the higher packet delivery delay and lower successful delivery ratio. Recently, an additional ferry node was introduced to forward packets between the isolated communities. However, most of the existing algorithms are working on how to control the trajectory of only one ferry work in the network. In this paper, we consider multiple ferries working in the network scenario and put our main focus on the optimal packet selection strategy, under the condition of mutual influence between the ferries and the buffer limitation. We introduce a non‐cooperative Bayesian game to achieve the optimal packet selection strategy. By maximizing the individual income of a ferry, we optimize the network performance on packet delivery delay and successful delivery ratio. Simulation results show that our proposed packet selection strategy improves the network performance on packet delivery delay and successful delivery ratio. Copyright © 2013 John Wiley & Sons, Ltd.

Network coding is a way of transmitting information where nodes in a network combine incoming packets into a single one to increase throughput in some scenarios, nodes wishing to get the original information can perform decoding when enough packets have been received. Given its efficiency, the exclusive or (XOR) operation is very popular for network coding. One security concern for networks using network coding is the so called “pollution attack”, where an adversary introduces packets that are not combinations of the original ones. In this paper, we present a construction to prevent pollution attacks in XOR network coding that is suitable for networks where nodes must perform fast verifications. Unlike existing constructions in the literature which are based on XOR-homomorphic authentication functions, our construction can be instantiated with existing cryptographic primitives that are not related to the XOR operation. The core insight of our proposal is a carefully selected set of authenticated packets that are used to authenticate the network coding stream. We show that our proposal is computationally efficient at the intermediate nodes and that can be computed efficiently at the nodes which are generating the content.

Proofs of Data Possession (PDPs) are protocols that allow a file owner to verify that a file stored at an outsourced server is stored entirely. From a security perspective, it must be difficult for the server to pass the verification protocol if the file is not available. Even though several efficient PDPs exist in the literature, to the best of our knowledge no special algorithms, besides the existing combinatorial approaches have been designed to find what exact blocks of the file are defective. In this article we present an efficient method to find what blocks are defective in a server, even when the server might lie; we show that by taking advantage of the homomorphic properties of existing PDPs, we can improve existing combinatorial methods to find the defective blocks. Our method involves a single invocation of the PDP's verification protocol and an additional communication overhead, which is never larger than the number of blocks of the file regardless of the number of missing blocks. For cases where few blocks have been corrupted, the transmission overhead is proportional to the the number of missing block times the logarithm of the length of the file. This is a significant improvement from existing combinatorial methods which exhibit worse performance than the naive approach (where the result of the PDP for each block is sent independently) as the number of corrupted blocks increases.

Device-free passive (DFP) localization systems are a key solution for location-based services because they do not require any wireless device on a human body. Most of the existing DFP localization systems are based on the received signal strength (RSS) measurement only. However, the localization accuracy of RSS only-based systems is easily affected by the spatial and temporal variances of RSS due to multipath fading and noise, even in a static environment. In this paper, we propose a novel localization system for DFP using signal subspace eigenvectors from an antenna array. We present a fingerprinting technique using multiclass support vector machines (SVMs) based on a combination of array signal features with spatial and temporal averaging. We then evaluate the localization accuracy of our proposed system in different propagation environments: line-of-sight (LOS) and non-line-of-sight (NLOS). In addition, we analyze two types of receive antenna placement: centralized and distributed antennas. The experimental results show that the localization accuracy can be improved by the proposed system, particularly in the centralized antenna case. Moreover, they show that the proposed system can improve localization accuracy compared to the conventional RSS-only based system.

In this paper, we present a cognitive radio (CR) based statistical framework for a two-tier heterogeneous cellular network (femto-macro network) to model the outage probability at any arbitrary secondary (femto) and primary (macro) user. A system model based on stochastic geometry (utilizing the spatial Poisson point process (PPP) theory) is applied to model the random locations and topology of both secondary and primary networks. A considerable performance improvement can be generally achieved by mitigating interference, in result of applying the CR idea over the above model. Novel closed form expressions are derived for the outage probability of any typical femto and macro user considering the Nakagami-m fading for each desired and interference links. We also study the effect of some important design factors which play vital roles and are usually ignored in determination of outage and interference. We conduct simulations to evaluate the performance of our proposed schemes in terms of outage probability for different values of signal-to-interference-plus-noise-ratio (SINR) target.

Interference detection and suppression schemes are proposed for coded OFDM systems in the presence of narrowband interference. Previously proposed interference detection schemes perform with a recursive forward error correcting (FEC) decoding, thus its calculation is heavy. The proposed scheme is based on an expectation maximization (EM) algorithm to detect the interference before the FEC decoding process. The complexity is reduced because the FEC decoding is performed only once. Simulation results indicate that the proposed scheme achieves almost the same bit error rate performance as the conventional scheme, while decreasing the times of the FEC decoding.

To increase spatial frequency efficiency and transmit capacity, multiple-input multiple-output (MIMO) heterogeneous networks (HetNets) are extensively applied in modern cellular networks. Due to consisting of K-tiers of random base-stations (BSs), MIMO HetNets certainly introduce cross-tier interference. For this problem, fractional frequency reuse (FFR), as an interference management technique, can mitigate effectively the impact of the interference. Recently, Poisson point process (PPP) is more and more used to model HetNets, because it can naturally capture the randomness of the BSs' locations. In this paper, we first develop a general downlink model based on PPP for MIMO HetNets utilizing FFR technique. This paper uses different independent PPP to model BSs' locations of each tier and considers different MIMO techniques. We derive tractable expressions of coverage probability of cell-edge user under both closed and open access cases and discuss the impact of the main parameters on the coverage probabilities.

Interference alignment (IA) is known as having a great effect on the capacity achieved at each receiver in interference channel, when used in conjunction with multiple-input multiple-output (MIMO) technology. Using coordinating base station(BS) transmission, the system generates the beamforming vector to align interference signals into confined subspace at each receiver, where the beamforming and subspace vectors are calculated using the relative magnitude coefficients. It is difficult to design these coefficients since it is needed to solve non-linear equations. In [12], we propose the design algorithm of the relative magnitude coefficients. Using Brent's method iteratively, our design algorithm improves system capacity largely. We assume the worst-case situation where all of 4 users receive the large interference signals from all of the 3 adjacent BSs, and the system model that a base stations has 5 transmit antennas and the other 3 BSs have 4 transmit antennas, where the sum of the number of the transmit antennas is 17. However, this required system is not general because one BS is assumed to have one more transmit antenna than the other BSs. In this paper, we extend the algorithm in [12] to be applicable for more general system where all the base stations have the same number of transmit antennas and the sum of the number of the transmit antennas is 16. As the extended algorithm, we propose how to select an un-eliminated interference signal and calculate the beamforming vectors and the interference signal spaces. In the extended algorithm, not all the interference signals are canceled; one interference signal with the smallest effect on the capacity is not canceled. That is because we can cancel 11 interference signals at maximum when there are 16 transmit antennas though there are 12 interference signals in our system model. We compare the capacities of the conventional algorithm and the extended one, and evaluate how much system capacity the proposed design algorithm can achieve in the situation where there is an un-eliminated interference signal. Through simulation, we show that the proposed design algorithm improves the degradation of the system capacity and achieve the fairness of capacities among users for the increase of the number of designed coefficients.

In this paper, we present a Cognitive Radio (CR) based statistical framework for a two-tier heterogeneous cellular network (macro-femto network) to model the outage probability at any arbitrary secondary user (femto user) and primary user (macro user). A system model based on stochastic geometry (utilizing the theory of a Poisson point process (PPP)) is introduced to model the random locations and topology of both primary and secondary networks (macro-femto networks). We provide an overview of how CR idea facilitates interference mitigation in two-tier heterogeneous networks in the presented model. We also study the effect of several important design factors which play vital roles and are usually ignored in determination of outage and interference. We conduct simulations to evaluate the performance of our proposed schemes in terms of outage probability for different values of signal-to-interference-plus-noise-ratio (SINR) target.

Spectrum leasing is one of the cognitive radio models, where a primary user leases its spectrum resources for secondary user in exchange for cooperation. In [1] cooperative communication with beamforming (BF) in spectrum leasing was proposed for primary secure communications. However, interference is caused to the primary user from secondary user with its scheme. Therefore, we focus on cooperative communications where secondary users use orthogonal BFs that do not affect primary user. We consider the effects of secondary cooperation and the knowledge of eavesdropper's channel state information (CSI) on the cooperative communications. By simulation, we evaluate primary secrecy capacity, primary secrecy capacity outage probability, secondary sum throughput, and primary throughput. We show that secondary transmit BFs in spectrum leasing are effective for both primary and secondary systems in terms of primary secure communications without any interference from secondary user and with small degradation of the secondary throughput.

Nowadays, the population of the elderly people is increasing in Japan. The system to protect an elderly person who is living alone is needful. The bigger part of the accident in their house is falling. There are several kinds of products to detect a person's fall. However, there are some problems that it does not have enough detection range and privacy. Array antenna is an antenna, which detects radio wave propagation by observing the direction of arrival (DOA) of the signal. We developed previously the fall detection using array antenna. In the conventional fall detection method, it is needed to learn and observe the whole activity scenario, which includes how the person moves before and after the fall. As a result, when the unexpected fall scenario happens, it is not able to detect the fall correctly. In this paper, we propose a detection algorithm for unexpected fall using array antenna. We detect fall for every fixed time, and by the results of the detection, we decide whether the activity is falling or not. By using this method, it can detect the unexpected fall scenario, which is not learned. In addition, we use new features for support vector machine (SVM) to distinguish confusing activities and improve the fall detection accuracy.

Nowadays, aging society is a big problem and demand for monitoring systems is becoming higher. Under this circumstance, a fall is a main factor of accidents at home. From this point of view, we need to detect falls expeditiously and correctly. However, usual methods like using a video camera or a wearable device have some issues in privacy and convenience. In this paper, we propose a system of fall detection using a low resolution infrared array sensor. The proposed system uses this sensor with advantages of privacy protection (low resolution), low cost (cheap sensor), and convenience (small device). We propose four features and based on them, classify activities as either a fall or a non-fall using k-nearest neighbor (k-NN) algorithm. We show a proof-of-concept of our proposed system using a commercial-off-the-shelf (COTS) hardware. Results of experiments show the detection rate of higher than 94% irrespective of training data contains object's data or not.

Pedestrian dead reckoning is a method of localization that uses inertial sensors to estimate the travelled distance from the previous position. This method can estimate the position when GPS or WiFi is not available such as indoor. However, pedestrian dead reckoning is subject to cumulative errors. In this paper, we exploit the fact that most of lights are located regularly in indoor environments. We calibrate the estimated position by pedestrian dead reckoning by the estimated distance between lights when we judge that lights are located regularly from the information obtained from illuminance sensor. In our proposed method, we do not need to know the position of lights. We judge whether lights are regularly located from the change of illuminance obtained by illuminance sensor in smartphone. When we judge that lights are regularly located, we calibrate the estimated position by the average estimated distance between lights. Our experimental results show that the accuracy of localization with our proposed method is higher than that of the conventional one that uses only inertial sensors.

Superposed multicarrier transmission scheme is known to improve frequency utilization efficiency when several wireless systems share the same spectrum. To suppress the effect of interference, forward error correction (FEC) metric masking is proposed. In the technique, log-likelihood ratio (LLR) that corresponds to superposed band is set to zero, because received bits that correspond to superposed band is unreliable. However, to apply FEC metric masking, the information about superposed band must be known at the receiver beforehand. Furthermore, the received bits contain channel estimation error, which is a cause of degradation. In this paper, we propose an iterative estimation technique of power of undesired signal (noise, interference, and channel estimation error) for superposed multicarrier transmission. We use the estimated power of the undesired signal to calculate the LLR that takes the channel estimation error into account, since including this extra information about the channel improves the BER. The proposed scheme estimates the power of undesired signal on each subcarrier, thus the information about superposed band is not required. Simulation results show that the accuracy of estimating undesired signal becomes more reliable as the number of estimation increases, so that BER becomes better as a result of iterative estimation.

There have been a lot of attention paid to health and safety, such as dry eye syndrome or falling asleep while driving. Blinks are the physiological signal that reflects the degree of concentration or drowsiness, and thus the blink detection system is expected to get blink information. In many blink detection systems it is necessary to contact the equipment or use with a camera and the feeling of oppression and resistance will arise. In this paper, we propose the contactless blink detection system with Doppler sensor. With the setting of the threshold for each person and the classification of a blink and non-blink, the proposed system can detect and count blinks. It is shown that the system achieves the recall rate of 92 % as for the detection accuracy by experiment.

In heterogeneous network where picocells are overlaid onto macrocells, combined usage of cell range expansion (CRE) and enhanced inter-cell interference coordination (eICIC) is very effective in improving the system capacity. Two parameters of the CRE bias value and the ratio of ABS (almost blank subframe) affect each other. Thus, the comprehensive control of both parameters is very important to improve the system capacity and fairness among user equipments (UEs). The conventional parameter optimization method, which uses the Nash bargaining solution, can keep fairness among UEs, while improving system throughput. However, a fixed pair of these parameters used in this method is not always appropriate due to varying distribution of UEs. In this paper, we focus on a convex characteristic of the cell-edge UE capacity for the ABS ratio and propose a parameter optimization using local search with small computational complexity. Simulation results show that parameter setting by the proposed scheme improves system capacity and cell-edge UE capacity, compared with the conventional approach.

Cell range expansion (CRE) is a load balancing technique that virtually expands a pico cell range by adding a bias value to the pico received power, instead of increasing transmit power of the pico base station (PBS); It can make cell-edge throughput and overall network throughput improved. CRE disperses the load of macro base stations (MBSs) on PBSs, so that it can reduce the number of UE outages. Although the configuration of the bias values of each user equipment (UE) has potential to reduce UE outages compared with the common bias value configuration among UEs, the common one is applied in the majority of related works for simplicity. In this article, we propose a scheme to select a cell by using Q- learning algorithm where each UE learns to which cell to send a service request to reduce the number of UE outages from its past experience independently. Simulation results show that the proposed scheme has the minimum number of UE outages in the system. Moreover, they show that it reduces the number of UE outages and the required memory size, compared with our previous proposed method.

In this paper, we propose a tractable and flexible model for K-tier multiple-input-multiple-output (MIMO) heterogeneous networks (HetNets), with the fractional frequency reuse (FFR) technique, based on the spatial Poisson point process (PPP). The MIMO HetNets consist of K tiers of base stations (BSs), where each tier may differ in terms of the transmit power, the BSs' deployment density, the target signal-to-interference ratio, the number of antennas, and the MIMO technique. Since HetNets experience serious cross-tier interference, FFR, as an interference management technique, is found as a suitable solution. Due to the randomness of the BSs' locations, the PPP is more and more used to model them in HetNets. In this paper, we use different independent PPPs to model the BSs' locations of each tier, and we take different MIMO techniques into consideration. We focus on two main types of FFR techniques, i.e., strict FFR and soft frequency reuse, and we derive the coverage probability expressions of cell-edge users (the users at the cell edge). We also derive the average rate expressions and show the impact of the main parameters on the coverage probability under closed-access and open-access cases.

Regenerating Codes strip a file in several servers, such that it is possible to recover the file when at least a given number of them is online. The difference between these codes and traditional erasure codes such as Reed-Solomon (RS), is that they require less bandwidth to repair failed nodes. This property is meant to improve storage reliability in cloud storage data systems. In this article, we present a method to check the availability of files that have been encoded using linear regenerating codes, by implementing two protocols that prove with high probability a node is in possession of a particular combination of data units from the original file. The constructions only use fast linear operations and are suited for real world files. Our proposal is based on the linear properties of the dot product among vectors and smart key assignments based on Combinatorics as well as Linear Algebra.

In physical-layer security, communication secrecy is commonly achieved by providing the legitimate receiver a physical-layer advantage over the eavesdropper or, equivalently, by making the eavesdropper's channel more degraded than the legitimate receiver's, a setting traditionally known as the degraded wiretap channel. There may be situations, however, where such advantages cannot be guaranteed. One example is the wireless channel where the legitimate receiver's channel may be more degraded than the eavesdropper's owing to a deep fade (outage), additive interference at the edge of a network cell, etc. To our knowledge, such scenarios have not been remedied in the related literature. Thus, we propose in this work the first physical-layer security scheme that achieves communication in a setting where the eavesdropper has the physical-layer advantage over the legitimate receiver, a scenario that we call the reversely-degraded wiretap channel. Precisely, we show that by constraining the transmitter's input to be discrete uniformly distributed (such as an on-off keying modulation) and by using nonlinear threshold-based signal detection, the legitimate receiver is guaranteed to have a positive secrecy capacity even though the eavesdropper has a better signal-to-noise ratio (SNR). The former requirement on the input distribution is to limit the capacity gains of the eavesdropper at high SNR. The latter requirement allows the communication channel to exhibit supra-threshold stochastic resonance, a phenomenon whereby noise improves signal detection and increases the mutual information. A numerical example is provided to confirm our claims.

Capacity scaling laws of wireless networks have attracted a lot of attention. In this paper, we study the multicast capacity of bus-assistant VANETs (vehicular ad hoc networks) with two-hop relay scheme, which has not been addressed before. Assume that n ordinary vehicles and nbbuses are deployed in a grid-like road framework while the number of roads increase linearly with n. All the ordinary vehicles obey the restricted mobility model. Thus, the spatial stationary distribution decays as power law with the distance from the centre spot (home-point) of a restrict region of each vehicle. All the buses deployed in all roads as intermediate nodes. They are used to forward packets for ordinary vehicles. Each ordinary vehicle randomly chooses k - 1 vehicles from the other ordinary vehicles as receivers. The packets could be transmitted directly from source to destination or be transmitted to an intermediate vehicle or bus, then be forwarded to the destination. We found that the social-proximity urban bus-assisted VANET has three routing methods. For each routing method, we derive the matching asymptotic upper and lower bounds of multicast capacity of bus-assisted VANET.

We propose a human activity classification and localization algorithm based on temporal-spatial virtual array. By analyzing the synthetic wave of the echo reflected from each human body part, we can classify various human activities. In addition by using the estimation result of target direction with temporal-spatial phase shift that depends on relative target velocity and position, the proposed algorithm can estimate the position of a human being simultaneously with a smaller amount of computational complexity than the conventional least-square approach. We demonstrate the effectiveness of the proposed algorithm via simulation results.

We propose a human activity classification and localization using a bistatic three frequency continuous wave (CW) radar. In the proposed algorithm each transmit antenna transmits signals with unique frequency. Thus, we can obtain Doppler signals for each transmit frequency, that is, each transmit antenna. Based on Doppler spectrums calculated from the received signals, we can estimate the number of human beings from the number of spectrum lines concerning translational motion, and motion interval from peaks of the spectrum. In addition, by using the relative velocity transformed from Doppler frequency and the estimated range information, we can classify human activities and track multiple human beings. Note that the conventional system using two frequency CW algorithm can estimate the position of a target by using phase information of the Doppler signal extracted from echo waves, however it can estimate only single target. Through computer simulation, we show that the proposed algorithm using a bistatic three frequency CW radar can classify various human activities and track multiple targets.

In a cognitive radio (CR) network, the channel sensing scheme to detect the appearance of a primary user (PU) directly affects the performances of both CR and PU. However, in practical systems, the CR is prone to sensing errors due to inefficient sensing scheme. This may lead to interfering with primary user and low system performance. In this paper, we present a learning based scheme for channel sensing in CR network. Specifically, we formulate the channel sensing problem as a partially observable Markov decision process (POMDP), where the most likely channel state is derived by a learning process called Fuzzy Q-Learning (FQL). The optimal policy is derived by solving the problem. The simulation results show the effectiveness and efficiency of our proposed scheme.

In this article, we explore the use of a special type of erasure codes known as Regenerating Codes (RCs), as a way to perform load balancing among servers of a multimedia content streaming site. The goal of our construction is to reduce the costs of keeping the redundant servers by minimizing the amount of information that must be stored on disks as well as main memory. This proposal is advantageous for devices connecting through several means, such as a specialized video streaming device with two different internet connections or a smartphone connecting through Wi-Fi and a carrier simultaneously. To achieve this goal, we use a regenerating code based on a technique introduced by Rashmi et al. known as the Twin Code Framework. The use of this technique allows any user to connect to any k servers containing encoded fragments of the file to decode a particular part of it, while keeping the information overhead at the servers low. The construction also allows new copies to be created when demand increases; copies created at a later stage can be used in conjunction with previously created copies for load balancing purposes. In addition, any node in the system can be repaired with a relatively low amount of transmitted information compared to the length of the stored contents. Our instantiation of the framework uses Network Coding with coefficients drawn from a Hilbert Matrix.

Best-to-worst (BTW) subcarrier pairing (SCP) has been discussed as a technique in orthogonal frequency division multiplexing (OFDM)-based amplify-and-forward (AF) relay networks, owing to a good bit error rate (BER) performance. However, the technique does not consider peak to average power ratio (PAPR), where the PAPR must be kept as low as possible to use a low cost power amplifier (PA) at relay node (RN). PAPR reduction technique should be applied in RN, because lower PAPR in source node (SN) using PAPR reduction technique might not be maintained in RN owing to channel effects between SN and RN. We propose a new approach of BTW SCP technique, S-BTW (shifted BTW) that can perform a low PAPR as well as good BER performance. After BTW reordering, we calculate PAPR and compare the value with a threshold value. If the PAPR cannot achieve the required PAPR value, the subcarrier is one-by-one circularly shifted until reaching a maximum number of shifts. For the case that the required PAPR cannot be achieved after reaching maximum number of shifts, we propose a combination of the S-BTW and clipping. Both simulation and theoretical analysis is performed to prove the validity of our proposal.

Doppler radar-based fall detection has been attractive due to its low cost and high detection performance. However, fall detection based on Doppler signatures is affected by the spatial variance due to the multipath and non-line-of-sight (NLOS) effects, which has been one of the key issues for detection performance in current Doppler radar-based systems. Moreover, the drawbacks of Doppler radar are the limited measurement range and sensitivity to the target's falling directions. In this paper, we present a cooperative fall detection system that uses a Doppler radar and an array sensor which can be used even for multipath and NLOS environments. We analyze the impact of Doppler signatures in multipath and NLOS environments and account for undesirable Doppler measurements. We use the temporal-spatial characteristics of the signals using an array sensor and propose a novel fall detection system, which cooperates with the Doppler radar to enhance fall detection performance in multipath and NLOS environments. We evaluate the proposed system performance in a typical laboratory environment in LOS and NLOS conditions. The experimental results show that the proposed system reduces the false alarm rate and improves true positive rate. Moreover, our proposed system can significantly enhance the fall detection accuracy compared with the corresponding only Doppler radar-based approach.

Superposed multicarrier transmission is known to improve frequency utilization efficiency when several wireless systems share spectrum. In the superposed multicarrier transmission scheme, interference can be suppressed by setting the corresponding initial log likelihood ratio (LLR) for forward error correction (FEC) decoder to zero or proper values determined by the desired to undesired power ratio (DUR). Since this technique needs information of superposed subcarriers and DUR, in this paper we propose a superposed band and interference power estimation technique based on the residual power. Our proposed scheme adopts robust LLR for initial LLR and averages the residual power over all packets even if these contain bit errors. The superposed band is detected by comparing the averaged residual power with a threshold. Simulation results indicate that the proposed method performs faster interference detection than the conventional scheme.

We consider distributed estimation of a random vector signal in a power constraint wireless sensor network (WSN) that follows multiple-input and multiple-output (MIMO) coherent multiple access channel model. We design linear coding matrices based on linear minimum mean squared error (LMMSE) fusion rule that accommodates correlated sources. We obtain a closed-form solution that follows water-filling strategy. We also derive a lower bound distortion to this model. Simulation results show that when the sources are more correlated, the distortion in terms of mean squared error (MSE) degrades. By taking into account the effects of correlation, observation, and channel matrices, the proposed method performs better than equal power method.

Cell range expansion (CRE) is a technique to expand a pico cell range virtually by adding a bias value to the pico received power, instead of increasing transmit power of pico base station (PBS), so that coverage, cell-edge throughput, and overall network throughput are improved. Many studies have focused on inter-cell interference coordination (ICIC) in CRE, because macro base station's (MBS's) strong transmit power harms the expanded region (ER) user equipments (UEs) that select PBSs by bias value. Optimal bias value that minimizes the number of UE outages depends on several factors such as the dividing ratio of radio resources between MBSs and PBSs. In addition it varies from UE to another. Thus, most papers use the common bias value among all UEs determined by a trial and error method. In this paper we propose a scheme to determine the bias value of each UE by using Q-learning algorithm where each UE learns its bias value that minimizes the number of UE outages from its past experience independently. Simulation results show that, compared to the scheme using optimal common bias value, the proposed scheme reduces the number of UE outages and improves network throughput.

In this work we present two t-collusion resistant schemes to detect nodes performing “Diversity Attacks” in network coding. This attack was introduced by Popa et al. in [1] and occurs when nodes performing network coding, do not code from some of their neighbors or they do not use random coefficients, which reduces network performance; this attack is fundamentally different from Pollution Attacks and it cannot be detected by current pollution prevention schemes. Our construction differs from the original solution in that it makes use of the broadcast nature of wireless networks; which allows us to reduce the computational load of both receivers and senders compared to their non-probabilistic solution; as an additional advantage, one of our constructions works without knowledge of the topology beyond the neighbors of a node, improving one of the shortcomings of the original solution. Tools employed in our constructions involve threshold digital signatures and aggregate Message Authentication Codes (MACs).

In this paper, we focus on user-centered handover decision making in open-access non-stationary femtocell networks. Traditionally, such handover mechanism is usually based on a measured channel/cell quality metric such as the channel capacity (between the user and the target cell). However, the throughput experienced by the user is time-varying because of the channel condition, i.e. owing to the propagation effects or receiver location. In this context, user decision can depend not only on the current state of the network, but also on the future possible states (horizon). To this end, we need to implement a learning algorithm that can predict, based on the past experience, the best performing cell in the future. We present in this paper a reinforcement learning (RL) framework as a generic solution for the cell selection problem in a non-stationary femtocell network that selects, without prior knowledge about the environment, a target cell by exploring past cells behavior and predicting their potential future state based on Q-learning algorithm. Our algorithm aims at balancing the number of handovers and the user capacity taking into account the dynamic change of the environment. Simulation results demonstrate that our solution offers an opportunistic-like capacity performance with less number of handovers.

Recently, to improve the capacity of cell-edge user, interference alignment (IA) is an intense research interest. We focus on the IA extended to the multiple-input multiple-output (MIMO) interference network. In this method, each coordinated transmitter generates the beamforming vector to align interference from different transmitters into confined subspace at each receiver. Then, using singular value decomposition (SVD) with the relative magnitude coefficients, transmitters calculate the beamforming vectors and the received vectors. However, this method can not decide the value of the relative magnitude coefficients so that the system capacity is improved, because it is necessary to solve non-linear function of multivariable. In this paper, we propose the design method of the relative magnitude coefficient of interference channels to improve the system capacity using Brent's method. The proposed method can improve the system capacity, however, the system complexity increases because Brent's method needs multiple SVD calculation to calculate the null space. Therefore, in addition, we propose the complexity reduction method to calculate the null space of the matrix instead of SVD. Through simulation, we show that the proposed method achieves a higher system capacity than the conventional one. We also show that the proposed method that calculates the null space needs much lower complexity than SVD.

Recently, various kinds of healthcare systems for the elderly have been developed. Falling detection is one of the important tasks to protect them from crucial accidents. Cameras, acoustic sensors, and accelerometers are mainly used to detect the falling. However, from the viewpoint of false alarm rate, privacy issues, and intrusiveness of the devices, each method has its own shortcomings. Doppler sensor is a palm-sized device, and can be implemented for highly accurate human activity recognition without wearable sensors. Doppler sensor is less sensitive to the movements orthogonal to the irradiation direction. Thus, a method to compensate this characteristic is needed. We propose falling detection using multiple Doppler sensors to raise the precision of falling detection covering the multi-directions of the target movement. Two or three sensors are exploited, and the extracted sensor data is processed by a feature combination or selection method. The resulting data are classified by support vector machine (SVM) or k-nearest neighbors (k-NN). We evaluate several kinds of falling, “Standing - Falling,” “Walking - Falling,” and “Standing up - Falling,” and non-falling like “Walking,” “Lying on floor,” “Picking up,” and “Sitting on a chair.” These activities are tested toward 8 directions spaced at respective intervals of 45 degrees. The results show that the combination method, using three sensors, achieves 95.5 % accuracy of falling detection, and the selection method, using three sensors, achieves 93.3 % accuracy. We also discuss the accuracy of each activity direction and the viability of these methods for the practical use.

In intermittently connected mobile Ad Hoc networks, how to guarantee the packet delivery ratio and reduce the transmission delay has become the new challenge for the researchers. Epidemic-theory based routing has shown the better performance in terms of improving packet delivery ratio and reducing the delay, when infinite node buffer and network bandwidth model is assumed. Typically, epidemic routing adopts the 2-hop or multi-hop forwarding mode to deliver a packet. However, these two modes have the intrinsic disadvantage on too much redundant copies or too long delivery delay. In this paper, we introduce a novel H+1 hop forwarding mode that is based on the epidemic theory. Firstly, we utilize the Susceptible-Infective-Recovered (SIR) model of epidemic theory to estimate the amount of relay nodes (epidemic equilibrium) and the delivery delay within the epidemic process. Secondly, we formulate the quantities of relay nodes into a single absorbing Markov Chain model, facilitating the estimation of the expected delay for the packet transmission. Simulation results show that our H+1 hop forwarding mode has the better performance on delay and packet delivery ratio.

The ability to track, detect and monitor human activities in real time conditions is very important for security and surveillance operation. The movements of the human body and limbs generate unique microDoppler features which enable the identification and classification of a wide diversity of human motions. In this paper, we propose a method to simulate the signal generated by human motion, by modeling human movement using equation for each limb. The simulation method is tested on five activities: walking in circle, falling, walking straight, punching, and making squats. The simulated signals are compared with measurement results. The results end up being satisfying.

We present a hidden Markov model based localization using array sensor. In this method, we use the eigenvector spanning signal subspace as a feature for location. The eigenvector does not depend on received signal strength (RSS) but on direction of arrival (DOA) of incident signals. As a result, the eigenvector is robust to fading and noise. In addition, the eigenvector is unique to the environment of propagation due to indoor reflection and diffraction of the electric wave. The conventional method based on fingerprinting does not take previous information into account. In this paper, we propose an algorithm that applies HMM to conventional fingerprinting of the eigenvector. This algorithm takes previous state of estimation into account by comparing the eigenvector obtained during observation with the one stored in the database. The database has the eigenvector obtained at each reference location according to setting in advance. In an indoor environment represented in a quantized grid, we decide the HMM transition probabilities denoting the possible moving range from previous estimation location. The most likely trajectory is calculated by means of the Viterbi algorithm. The results show that the localization accuracy is improved owing to the use of a possible moving range from the previous location.

In network coding, intermediate nodes are allowed to transmit a function of the packets, instead of the traditional scheme where unmodified packets travel through the network. Some of the advantages of this mechanism are: a unified way to represent Broadcast, Multicast and Unicast, robustness in link and node failures and robustness to routing loops. However, allowing intermediate nodes to change information, introduces new points where byzantine attackers may try to disrupt the network. In this paper, we present a Message Authentication Code (MAC) Based protocol which can identify misbehaving nodes. Our construction uses only fast symmetric cryptographic operations, making it suitable for multicast networks, where latency is an important factor. For its construction, we used an efficient key assignment based on Blom's scheme, and a Merkle tree to provide authenticity during our identification routine. We show our construction is relevant in the context of network coding, by showing its execution time compared to that of pollution detection routines and other schemes used for authentication.

In open-access non-stationary femtocell networks, cellular users (also known as macro users or MU) may join, through a handover procedure, one of the neighboring femtocells so as to enhance their communications/increase their respective channel capacities. To avoid frequent communication disruptions owing to effects such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell selection method. Traditionally, such selection method is usually a measured channel/cell quality metric such as the channel capacity, the load of the candidate cell, the received signal strength (RSS), etc. However, one problem with such approaches is that present measured performance does not necessarily reflect the future performance, thus the need for novel cell selection that can predict the \textit{horizon}. Subsequently, we present in this paper a reinforcement learning (RL), i.e, Q- learning algorithm, as a generic solution for the cell selection problem in a non-stationary femtocell network. After comparing our solution for cell selection with different methods in the literature (least loaded (LL), random and capacity-based), simulation results demonstrate the benefits of using learning in terms of the gained capacity and the number of handovers.

We consider a problem of secure communications for the communication system consisting of multiple outputs for a source and a relay and multiple inputs for the relay, a destination and an eavesdropper. For the above-mentioned communication system, we establish a lower bound on the secrecy capacity at which secure communications between the source and the destination are attainable. We make use of the singular value decomposition (SVD) and its generalization to decompose the whole system into parallel independent channels. At the source, the generalized singular value decomposition (GSVD) is performed to simultaneously diagonalize the channel matrices of the relay and the destination and independently code across the resulting parallel channels. At the relay, the SVD is performed to beamform the signal towards the destination. The scalar case of what we are considering in this paper has been investigated in previous literature, to prove that the introduction of a fourth party, the relay, in the wire-tap channel facilitates secure wireless communications. Our simulation results are in line with the scalar case's and prove to be successful in achieving secrecy capacity where the conventional model failed, i.e. when no relay is introduced and the eavesdropper's channel incurs as little noise as the legitimate receiver.

Multiple-input multiple-output (MIMO) systems with antenna selection are practical ones that can intuitively alleviate the computational complexity at the receiver and achieve good reception performance. Channel correlation, not just carrier-to-noise ratio (CNR), has a great impact on the reception performance in MIMO channels. We propose a simple antenna selection algorithm that exploits the condition number of the channel matrix and a predetermined threshold CNR. This paper describes the hardware implementation of the proposed algorithm and its performance evaluation, which was conducted in an indoor measurement using received signals obtained in the actual mobile outdoor experiment. The results confirm that our proposed method provides good bit error rate performance by setting a threshold CNR properly.

Superposed multicarrier transmission scheme is known to improve frequency utilization efficiency where several systems share spectrum without any spectrum spreading techniques. In superposed multicarrier transmission scheme forward error correction (FEC) coding is applied over subcarriers so that the effects of interference caused by superposition is mitigated by interleaving effects. When the interference power is large, the effects of FEC become smaller particularly owing to the mis-setting of initial log likelihood ratio (LLR). To solve this problem, FEC metric masking is proposed where initial LLRs for superposed subcarriers are replaced by neutral value in the receiver followed by decoding. This technique is effective, however, it needs the information about superposed frequency band. In this paper we propose a scheme to detect superposed band by using FEC metric masking. In the proposed scheme, the subcarriers where FEC metric masking is applied are changed and packet error rate (PER) is measured. When the FEC metric masking is applied to the superposed subcarriers, PER is minimized. Based on the change of PER, we can detect the superposed band. We evaluate the proposed scheme by computer simulation and show that it can detect the superposed band with high probability.

Cooperative automatic repeat request (ARQ) communication has been studied as a way to improve reliability of networks compared to conventional ARQ transmission scheme. We consider cooperative ARQ via Vickrey auction-based spectrum leasing, which is a communication scheme with a decentralized mechanism that motivates other non-cooperative nodes to participate as relay nodes in cooperative ARQ. Conventional researches of this model did not focus on fairness for amount of traffic flows among relay nodes' data. In this paper, we propose a new protocol considering this fairness by making autonomousdecentralized decision based on behavioral economic by each relay node evaluating own degree-of-satisfaction according to their acquired profits up to previous time auction. With computer simulation, we show that our proposal protocol has superiority to conventional schemes from the viewpoint of throughput and fairness index.

Cooperative communication has been studied as a way to improve reliability of networks compared to conventional direct transmission scheme. We consider amplify-and-forward (AF) cooperative communication with multiple relays. In this system, there is a problem that which relay nodes should be selected and how much power should be allocated to the selected relay nodes. Most of conventional researches on this model have focused on only improving channel capacity or reliability, rather than saving power consumption in all over the network with maintaining such communication qualities. In this paper, we propose a new power allocation scheme for multiple relay nodes using game theory that focuses on transmission power efficiency. With computer simulation, we show that our proposed game theoretical approach has superiority to some conventional schemes from the viewpoint of transmission power efficiency.

In sparse mobile social networks, the moving activity of nodes always happen in a specific area, which corresponds to some specific community. How to guarantee the higher packet delivery ratio while reducing forwarding delay in such networks, is a challenging issue and has not been widely investigated yet. Recently, additional super-node was introduced to ferry packets between the isolated areas. However, most existing solutions assume the super-node is always moving according to the fixed trajectory. In this paper, we put some special mobile nodes in the networks, they are called postmen, and their responsibility is to carry packets for normal nodes which belong to specific communities. Our work focus on the optimization of the moving trajectory by considering the minimum transmission delay. We formulate the optimal issue into semi-Markov Decision Process model. The decision process includes two parts: Packets-choosing strategy and trajectory of packet-ferrying-determination strategy. By maximizing the individual reward of a postman, its optimal trajectory will be found. Furthermore, the proposed solution guarantees the packet delivery ratio and delay for isolated communities. Simulation results show that the proposed packet-ferrying solution outperforms two existing ferrying solutions in terms of packet delivery ratio.

We have been developing a minimum mean square error macro-diversity (MMSE-MD) reception system using distributed remote antennas and radio-on-fiber (RoF) links for use in live broadcasts of road races. For the system to be feasible, we have to consider the propagation delay differences among diversity branches due to RoF links and radio propagation. This paper describes a hardware implementation of an MMSE-MD receiver embodying our delay difference correction (DDC) technique and indoor and outdoor performance evaluations. Our prototype receiver perfectly corrected the propagation delay difference among diversity branches in less than 11 symbols duration in a time-varying multipath fading environment, and it was capable of the MD reception without outage on an actual road race course.

Switching estimated receiver position (SwERP) scheme is proved to be a promising solution for indoor positioning system, owing to its high achievable accuracy and consistency. The high positioning accuracy is achieved from sensitivity (Rx,S) limit, field-of-view (FOV) limit, and assisted azimuth and tilt information from 6-axis sensor. In this paper, we propose a physical simulation model that can predict the latter three factors. The conventional visible light communication (VLC) model uses only geometric optics (GO) to predict light propagation, which is not enough to define the FOV limit. Thus, we propose a novel method using rotation matrix with cone function and support vector machines (SVMs) to classify the boundary of FOV limit. Based on sensitivity and FOV limits, possible azimuth and tilt angulations are mathematically defined. Moreover, by including FOV limit into the simulation, transmitters and their mirrors that are outside FOV limit can be neglected, of which reduce at least 80% of computation during GO calculation.

Vector Coding (VC) is a transmission scheme that can obtain path diversity gain while orthogonalizing multiple symbols. It has been shown that VC can provide good bit error rate (BER) performance compared to orthogonal frequency division multiplexing (OFDM). In addition VC has been proven to be the optimal partition of time domain channel. However, In VC, the eigenvectors of the channel matrix must be available at both transmitter and receiver. Thus, orthogonalization and path diversity gain can be achieved only if perfect channel state information (CSI) is available at both ends. However, there exists channel estimation error in practice. In addition feedback delay of estimated CSI causes mismatch of CSI at both ends, which degrades the performance of VC severely. To see the feasibility (implementability) of VC, it is very important to evaluate the effects incurred by channel estimation error and feedback delay on the performance of VC. In this paper, we analyze VC system in the presence of channel estimation error and feedback delay. We show how the transmit and receive weights selection affects the performance of VC. Numerical (Theoretical) results are confirmed by computer simulations.

In this paper, we develop a new subspace based multiuser carrier frequency offset (CFO) estimation scheme for multi-input multi- output (MIMO) orthogonal frequency division multiple access (OFDMA) uplink transmission. We exploit the rank reduction approach by equipping multiple antennas at the receiver, in which the CFO of each user is derived blindly using one dimension (1-D) search individually. The proposed scheme supports the generalized subcarrier assignment scheme and full loaded transmission with all subcarriers being allocated to users. Numerical results are provided to corroborate the proposed studies.

In traditional delay tolerant networks (DTNs), there exists a potential assumption that the nodes are willing to help others for packet forwarding. However, in the real application scenarios, such as civilian DTNs, selfish behaviors always widely exist. Therefore, the assumption that nodes are cooperative is not realistic in all applications. Currently, most of the existing incentive mechanism focuses on individual selfish behaviors. Few research work is proposed on social selfish behavior in DTNs. In this paper, we stimulate the nodes to cooperate with others by using a virtual bank mechanism. This incentive mechanism can effectively avoid individual selfish behaviors. Meanwhile, we observe that under this individual selfish incentive mechanism, the social distribution is unfair. That means the poverty nodes would appear in the networks, and become the internal threats for the social DTNs. To avoid this, we introduce the Gini coefficient to measure the inequality of the social distribution. Furthermore, by using the taxation strategy, we avoid the internal threats caused by social selfishness. To demonstrate the selfish behavior, we introduce the forwarding protocol which is based on social relations of nodes. We verify the proposed methods using simulation evaluations.

In this paper, we propose a distributed angle estimation method for wireless sensor network localization with multipath fading. The multiple antenna system is equipped at the anchor and the angle of departure (AOD) can be estimated individually at each sensor node with single antenna system. Further, we exploit the space diversity to improve the estimation performance by deploying multiple parallel arrays at the anchor. Analysis and simulations demonstrate the effectiveness of our method.

In this paper we focus on improving state classification methods that can be implemented in elderly care monitoring systems. The authors group has previously proposed an indoor monitoring and security system (array sensor) that uses only one array antenna as the receiver. The clear advantages over conventional systems are improvement of privacy concern from the usage of closed-circuit television (CCTV) cameras, and elimination of installation difficulties. Our approach is different from the previous detection method which uses an array of sensors and a threshold that can classify only two states: nothing and something happening. In this paper, we present a state classification method that uses only one feature obtained from the radio wave propagation, and assisted by multiclass support vector machines (SVM) to classify the occurring states. The feature is the first eigenvector that spans the signal subspace of interest. The proposed method can be applied to not only indoor environments but also outdoor environments such as vehicle monitoring system. We performed experiments to classify seven states in an indoor setting: “No event,” “Walking,” “Entering into a bathtub,” “Standing while showering,” “Sitting while showering,” “Falling down,” and “Passing out;” and two states in an outdoor setting: “Normal state” and “Abnormal state.” The experimental results show that we can achieve 96.5 % and 100 % classification accuracy for indoor and outdoor settings, respectively.

Nearest transmitter information is a mandatory requirement for utilizing Switching Estimated Receiver Position (SwERP) scheme, of which was proposed to enhance accuracy of Visible Light ID (VLID) positioning system. The conventional approach is achieved by controlling Sensitivity (RxS) limit. Conversely, general distance estimation by optical received power can also be used. Both of the mentioned methods have disadvantage on implementation complexity and inaccuracy due to noise fluctuation, respectively. In this paper, we propose a nearest transmitter classification (NTC) method by utilizing Optical Orthogonal Code (OOC) with On-Off Keying (OOK) modulation at transmitters (Txs), and perform oversampling at a receiver (Rx) to overcome the limitation of bandwidth resolution (B). The results confirm that the proposed method can classify the nearest Txaccurately, with some tradeoff with computation complexity from the increment of oversampling factor (Oc).

In multi-cell cellular system, users suffer from inter-cell interference coming from neighboring cells, particularly at cell boundary. Base station (BS) cooperation is a promising technique to mitigate inter-cell interference for users located at cell-edge area and currently under discussion for further releases of the 3GPP Long Term Evolution (LTE) system. In this paper, using BS cooperation approach, we propose an inter-cell interference coordination technique to mitigate the intercell interference in multi-cell cellular network. Coordinating BSs cooperatively choose their antenna weights based on requested information from users to compromise between reducing the inter-cell interference to neighboring cell-edge users and maximizing received signal power to its own users to achieve maximum network sum-rate. Simulation results show that average sum-rate of multi-cell cellular system is increased by our proposed method particularly when users are located at cell-edge area.

To achieve a high speed transmission with limited frequency bandwidth, multiple-input multiple-output (MIMO) has been gaining much attention. QR-decomposition based successive interference canceller (SIC) can provide good detection performance with reasonable complexity. In MIMO system, there could be spatial antenna correlation among antennas and this degrades the transmission performance. The performance severely degrades owing to the correlation among transmit antennas in the case of uplink transmission. In this paper, we propose a frequency band allocation in MIMO system based on received power difference among users that each user utilizes several frequency bands that overlap with other users'. At the receiver, QR-decomposition based SIC is performed by utilizing the received power difference. It is shown that the proposed scheme yields superior error rate performance over the conventional scheme by computer simulations.

Simultaneous transmission to multiple users using orthogonal beamforming, known as space-division multiple-access (SDMA), is capable of achieving very high throughput in multiple-input multiple-output (MIMO) broadcast channel. In this paper, we propose a new orthogonal beamforming algorithm to achieve high capacity performance in MIMO broadcast channel. In the proposed algorithm, the base station generates a unitary beamforming vector set using Gram-Schmidt orthogonalization. We extend the algorithm of LF-OSDMA (Opportunistic SDMA with Limited Feedback) to guarantee that the system never loses multiplexing gain for fair comparison with the proposed algorithm by informing unallocated beams. Finally, we show that the proposed method can achieve a significantly higher sum capacity than LF-OSDMA and the extended LF-OSDMA without a large increase in the amount of feedback bits and latency.

Single-carrier frequency-division multiple-access (SC-FDMA) with frequency domain equalization (FDE) can provide good performance even in a frequency selective fading channel. In this paper, we focus on a special kind of SC-FDMA, i.e., interleaved frequency-division multiple-access (IFDMA) be cause of its peak-to-average power ratio (PAPR) property and frequency diversity gain. FDE requires accurate channel estimation. In the case of fast time varying fading channel, decision directed channel estimation (DDCE) is effective. However, in IFDMA, amplitude of each sub carrier may fluctuate depending on the combination of data symbols, then estimation accuracy may degrade. That can be avoided by multiplying information data sequence by random phase sequence. The performance depends on the selection of the sequence. In this paper, selection criterion suitable for IFDMA with DDCE is proposed. The effect of the proposed method is evaluated by computer simulation. The results will show the proposed method can lower the error floor of the system.

A novel Genie-aided capacity outer bound is provided for MIMO Gaussian interference channels. Rather than requiring each side-informed receiver to cancel the interference from its received signal, it is suggested that each receiver views the interference as the useful signal undergoing a different channel, which we call an equivalent channel representation. Reflections on the existence and capacity enhancement of such equivalent channel representation are made. Subsequently, an application to relay-assisted communications is provided for which an achievable capacity region is determined. Finally, numerical examples are provided to demonstrate an increase of capacity owing to the proposed interference processing method.

In this paper, a new direction-of-arrival (DOA) estimation method for wideband signals is introduced. This new method uses both the signal subspace projection and the reference frequency selection in squared test of orthogonality of projected subspaces (Squared-TOPS) and estimates DOAs by measuring the orthogonal relation between the signal and noise subspaces of multiple frequency components of the signals. The signal subspace projection and the reference frequency selection reduce some error terms in the matrix used for testing the orthogonality between signal subspaces and noise subspaces. The resolution and root mean square error (RMSE) of the proposed method are compared with those of the coherent signal subspace method (CSSM), test of orthogonality of projected subspaces (TOPS) and test of orthogonality of frequency subspaces (TOFS) through computer simulations, respectively. The simulation results show that the resolution of the proposed method is better than that of the others except CSSM in whole signal-to-noise ratio (SNR) ranges. They also show that RMSE of the proposed method is the best in whole SNR ranges.

In this paper, we study the performance of Network MIMO in the presence of transmit desynchronization between the base stations (BS), in Rayleigh fading radio environments. We model the transmit desynchronization using lower shift matrices and we explain how a receiver can reliably detect space-time codewords while mitigating synchronization errors. Subsequently, we derive a closed form of the system capacity and bounds on the capacity scaling. Interestingly, we find out that maximum capacity scaling can be preserved even in the presence of desynchronization, provided that the latter be uniformly distributed over [0,T-1], where T is the codeblock length. Thus, our work suggests that, rather than synchronizing all BSs to a common clock, it would be more beneficial, from a capacity scaling perspective, to have their signal clocks independent from each other.

A MIMO keyhole is a propagation environment such that the channel gain matrix has unit rank (single degree of freedom), irrespective of the number of deployed antennas or their correlations (spacing), thereby reducing the MIMO channel capacity to that of a SISO channel. Related literature seems to consider such degeneration hopeless. Contrary to this general belief, this paper demonstrates that cooperative diversity can mitigate keyhole effects. Precisely, provided that the source-relay channel is keyhole-free, we show that there exists a ``cutoff'' relay transmit power above which keyhole effects can be mitigated even when both the source-destination and the relay-destination channels incur keyhole effect. Furthermore, assuming Rayleigh fading, the devised closed form of this power threshold is found to be independent of the channel gain matrices, thereby applying to situations where channel state information (CSI) is unreliable or unavailable at the transmitters. Numerical examples confirm the relevance of our claims.

Relay transmission can enhance a communication reliability, increase a transmission rate and save power at a sending station in wireless networks. The scenario under consideration is a simple relay system that sending station sends coded data to a receiving station through a relay station. The relay station makes hard decision and transmits it. The receiving station decodes the error-correcting-coded bits using log-likelihood ratio (LLR). Here, it is important to set initial LLR to decode the coded data accurately at the receiving station. In this paper, we present how to set initial LLR for belief propagation decoding in the relay system to achieve better decoding error rate performances. We calculate initial LLR based not only on the channel from the relay station to the receiving station, but also on the channel from the sending station to the relay station when the station decodes error-correcting coded data. When we use turbo code or low-density parity-check (LDPC) code as error-correcting code, we decode the coded data accurately by the use of accurate initial LLR. Through computer simulation, we showed that the proposed method achieves better error rate performances compared to the conventional method both when the channel estimation is perfect and when the channels are estimated. Moreover, we showed that the average number of decoding iterations of the proposed method decreases compared to those of the conventional method.

Recently, the integration of cooperation with coding has proven to be a very useful technique to enhance relay system performance. Relay systems based on low-density parity check (LDPC) codes have the potential to approach the theoretical information limits very closely. However, LDPC codes have a disadvantage in that the encoding complexity is high. To solve this problem, serially-concatenated low-density generator matrix (SCLDGM) codes are attracting attentions due to its low encoding complexity. However, the performance of SCLDGM coded relaying has not been investigated. In this paper, we apply SCLDGM codes to relay channel with the motivation of lowering the encoding complexity. Because SCLDGM codes are concatenated codes, we cannot use the same method as LDPC coded relaying. We propose a new relaying system and three cooperation protocols suited for SCLDGM codes. Computer simulation results show that the BER performance of the proposed system equals to that of conventional LDPC coded relaying system.

Recently, Echo-MIMO, a delay-free feedback scheme has been proposed for Closed-Loop MIMO systems, where the receiver echoes the received signal on the fly to the transmitter without any processing. While this reduced feedback latency allows for more use of the channel's coherence time for data transmission, it comes at high power-and-bandwidth costs, as two MIMO transmissions are required in the feedback phase. In this paper, we present a feedback scheme that preserves the advantages of Echo-MIMO while requiring only one feedback transmission. The echoed signals are judiciously combined with the receiver's signals such that their separation at the transmitter be lossless, and that no extra transmit power nor bandwidth be required. In addition, we highlight the estimation accuracy degradation in Echo-MIMO owing to the echoed noise, and analytically confirm the intuition that removing the noise prior to echoing the received signal provides better estimation than echoing the noisy received signal as is and later account for the noise effect upon echo reception. Simulation results show that the proposed scheme outperforms Echo-MIMO in terms of channel estimation accuracy and achievable capacity of up to 5 dB and 10 bit/sec/Hz, respectively.

Network coding has been investigated as a way to improve throughput and power efficiency of wireless networks by mixing of various traffic flows. The scenario under consideration is Y-topology denoise-and-forward relay channel that two sending stations transmit independent data to the receiving station directly and through the relay station. The receiving stations decode the error-correcting coded bits using the extrinsic information. Here, it is important to set the extrinsic information to decode the coded data accurately. In this paper, we present how to set the extrinsic information for belief propagation decoding with network coding to achieve better decoding error rate performance. We calculate the extrinsic information based not only on channels from the relay station to receiving station, but also on all channels from two sending stations to the receiving station when the receiving station decodes error-correcting coded data. When we use turbo code or low-density parity-check (LDPC) code as error-correcting code, we decode the coded data accurately by the use of accurate extrinsic information. We provide computer simulation results and show that the proposed method improves bit error rate performance and decreases the average number of decoding iterations.

You-Chiun Wang, Tomoaki Ohtsuki, Athanasios (Thanos) Vasilakos, Ashutosh Sabharwal, Yuh-Shyan Chen, Yu-Chee Tseng; Algorithms, Protocols and Future Applica

In this correspondence, we consider the problem of scheduling, in closed-loop multiuser MIMO systems, a large set of users with different Quality of Service (QoS) requirements, supplied by a single base station (BS) by means of Zero-Forcing Beamforming (ZFBF). We provide a power-and-bandwidth efficient feedback scheme in which only ZFBF-optimal users feedback their CSI, thereby reducing the number of required feedbacks and the computational burden of exhaustive search for best users at the BS. Afterwards, we show that conventional sum-capacity maximizing scheduling policies fall short to meet the requisites of delay-sensitive applications, and we provide appropriate scheduling scheme for such-constrained users. Simulation results demonstrate that the proposed scheduling approach successfully meets both average and instantaneous delay constraints of delay-sensitive applications.

A MIMO keyhole is a propagation environment such that the channel gain matrix has unit rank (single degree of freedom), irrespective of the number of deployed antennas or their correlations (spacing), thereby reducing the MIMO channel capacity to that of a SISO channel. Related literature seems to consider such degeneration hopeless. Contrary to this general belief, this paper demonstrates that cooperative diversity can mitigate keyhole effects. Precisely, provided that the source-relay channel is keyhole-free, we show that there exists a "cutoff" relay transmit power above which keyhole effects can be mitigated even when both the source-destination and the relay-destination channels incur keyhole effect. We explicit the closed form of this power threshold as function of the source transmit power and the channel matrices brought into play in the relay channel. Numerical examples confirm the relevance of our claim.

We introduce the two-way MIMO wire-tap channel and convey its potential to provide information-theoretic secure communications. We mainly address two challenges, namely the channel estimation and the single-user decodability issues. For the former, as the channel estimation becomes trickier owing to self-interference, we propose that users project their training signals on orthogonal subspaces to ensure their separability upon reception. For the latter, we suggest that each user uses, among the available antennas only the antenna subset that ensures both peers are not single-user decodable while maximizing their achievable secrecy sum-rate. Thus, unlike the result reported in literature about the two-way single-antenna wire-tap channel, we demonstrate that it is possible to achieve a positive secrecy rate even when a user is single-user decodable, provided that a sufficient number of transmit antennas be available. Finally, we observe that the proposed scheme, unlike secrecy schemes based on Wyner's degraded wire-tap channel, provides positive secrecy sum-rate even when the eavesdropper's channels incur as little noise as the legitimate users', and maintains the secrecy rate's growth in the high SNR regime, a fact we demonstrate by computer simulations. This makes the proposed scheme a better candidate for high-throughput secrecy applications.

In this paper, we introduce a novel routing algorithm which is based on ant colony system. The objective of this novel algorithm is to solve the problem of energy and congestion control on wireless sensor network routing process. This novel algorithm is able to achieve better load balance and prolong the network lifetime. In this novel algorithm we combine the pheromone released by multi-ant colonies and residual energy as the algorithm control factor. Furthermore, we also introduce the competition mechanism among multiant colonies to avoid the simplex convergence in our algorithm. In this way, the novel algorithm controls the network traffic congestion effectively and balances the energy consumption for sensor networks. Simulation results in this paper demonstrate that this novel algorithm has better performance on load balance comparing with fundamental ant colony algorithm.

Target localization is one of the interesting applications of sensor networks. Localization algorithms that use received signal strength (RSS) measurements at individual sensor nodes have been proposed. The maximum likelihood (ML) algorithm is known as a popular algorithm for target localization. In uniform propagation environments, the ML algorithm has high accuracy to estimate a target location. Meanwhile, in nonuniform propagation environments, the ML algorithm has low accuracy, because this algorithm uses RSS from all the sensor nodes equivalently. The residual weighting (RWGH) algorithm has been proposed to reduce the effect of nonuniform propagation environments. This algorithm first sets subsets of sensor nodes. The target location is estimated using the sensor nodes in each subset. The final estimated result is the averaged value of the estimated results of all the subsets weighted by their reliabilities. The reliability of each subset is the residual error of the distances between the target and each sensor node estimated by two ways. In the RWGH algorithm, there may be a case that the reliability of each subset is high although the estimation error of the subset is large. This degrades the final estimated result. In this paper, we propose a localization algorithm to reduce the effect of the subsets that have large errors. The proposed algorithm tries to detect the area where the density of the estimated results of subsets is high and reflect this information to the final estimated result. In this algorithm, the sensor field is split into cells. Each subset votes its reliability for a cell that includes the estimated location with the subset and the reliability of the subset is added to the cumulative reliability of the voted subsets. The cumulative reliabilities of cells are used to calculate the final estimated result. We show that the proposed algorithm has higher localization accuracy than the ML and RWGH algorithms by computer simulation.

This paper proposes signal-subspace-partition event filtering for the event detection system with signal subspace spanned by eigenvector in [3][4]. To detect an event with small changes of signal subspace components, we emphasize the changes of signal subspace components by selecting an appropriate component of signal subspace with signal-subspace partition. In addition, to detect an event in the presence of the changes of signal subspace components due to some undesired movements, such as by a pet, we mask or attenuate the changes of signal subspace components by selecting an appropriate component of signal subspace with signal-subspace partition. In the proposed method, firstly, we estimate direction of arrival (DOA) to obtain the basis of the signal subspace. Next, we make the parted signal subspace by combining each base of the signal subspace. The parted signal subspace is used as the filter. The experimental results reveal that the proposed method can emphasize or mask or attenuate events using signal-subspace partition.

In the orthogonal frequency division multiplexing (OFDM) the multipath exceeding a guard interval (GI) is one of obstacles for high data rate transmission. For the purpose of the interference suppression the authors have proposed the canceller algorithm naming DWCC, where the canceller operation is based on decision feedback equalization (DFE), but the performance of the canceller is still subject to the frequency selectivity since the tentative symbol detection at the deep faded subcarriers could not be reliable. In this paper turbo equalized double window cancellation and combining (TE-DWCC) is proposed. Throughout the paper, it is found that the error rate performance of TE-DWCC depends on how to incorporate the channel decoding gain from turbo equalization into DWCC. In addition, it is also shown that TE-DWCC is more compatible with the hard decision feedback (HDF) rather than the soft decision feedback (SDF) due to the ICI elimination method. Finally, by varying interference level, code rate, and decision feedback type, the performances of TE-DWCC are compared with the conventional canceller that adopts turbo equalization in the exponentially distributed slow fading channel.

In multiuser MIMO-BC (Multiple-Input Multiple-Output Broadcasting) systems, user selection is important to achieve multiuser diversity. The optimal user selection algorithm is to try all the combinations of users to find the user group that can achieve the multiuser diversity. However, the calculation amount of the optimal algorithm is too large to implement. Thus, instead of the optimal algorithm, some suboptimal user selection algorithms were proposed based on semiorthogonality of user channel vectors. The purpose of this paper is to achieve multiuser diversity with a small amount of calculation. For this purpose, we propose a user selection algorithm that can improve the orthogonality of a selected user group. Simulation results show that the proposed user selection algorithm achieves higher sum rate capacity than the SUS (Semiorthogonal User Selection) algorithm. As the result of a discussion on the calculation complexity, the proposed algorithm is shown to have a calculation complexity almost equal to that of the SUS algorithm, and they are much lower than that of the optimal user selection algorithm.

In recent years, network coding has been investigated as a way to improve throughput and power efficiency of wireless networks by mixing of various traffic flows. The scenario under consideration is a half-duplex decode-and-forward relay channel. The receiving stations decode the error-correcting-coded bits using Log-Likelihood Ratio (LLR). Here, it is important to set initial LLR to decode the coded data accurately. In this paper, we present how to set the initial LLR for belief propagation decoding with network coding to achieve better decoding error rate performance. We calculate initial LLR based not only on channels from relay station to receiving stations, but also on all channels from sending stations to receiving stations when stations decode error-correcting coded data. When we use turbo code or low-density parity-check (LDPC) code as error-correcting code, we decode the coded data accurately by the use of accurate initial LLR. We provide computer simulation results both under perfect and imperfect channel state information. We show that the proposed method improves bit error rate (BER) performance and that the proposed method decreases the number of decoding iterations.

This paper considers the problem of scheduling, in closed-loop multiuser MIMO systems, a large set of users with different Quality of Service (QoS) requirements, supplied by a single base station (BS) by means of Zero-Forcing Beamforming (ZFBF). We provide a power-and-bandwidth efficient feedback scheme in which only ZFBF-optimal users feedback their CSI, thereby reducing the number of required feedbacks and the computational burden of exhaustive search for best users at the BS. Afterwards, we show that conventional sum-capacity maximizing scheduling policies fall short to meet the requisites of delay-sensitive applications, and we provide appropriate scheduling scheme for such-constrained users. Simulation results demonstrate that the proposed scheduling approach successfully meets both average and instantaneous delay constraints of delay-sensitive applications.

The location estimation in sensor networks is of great current interest. A general approach to location estimation is to gather Time-of- Arrival (TOA) measurements from a number of nodes and to estimate a target location. The two major sources of range measurement errors in geolocation techniques are measurement error and Non-Line-of-Sight (NLOS) error. NLOS errors caused by blocking of direct paths have been considered as one of serious issues in the location estimation. Therefore, Iterative Minimum Residual (IMR) method, which identifies NLOS nodes and removes them from the data set used for localization, has been proposed. IMR improves location estimation precision in comparison with the technique that does not identify and remove NLOS nodes. However, IMR needs a lot of calculation to identify NLOS nodes. In this paper, we propose a low complexity localization algorithm based on NLOS node identification using minimum subset for NLOS environments. We evaluate our proposed algorithm by computer simulation. We show that the proposed method achieves almost the same root mean square error (RMSE) as the conventional method with lower complexity.

In line-of sight (LOS) environments, the performance of Multiple-Input Multiple-Output (MIMO) systems depends largely on the difference of the phase difference of direct paths from transmit antennas to each receive antenna. When the phase difference of direct paths are close to each other, the spatial division multiplexing (SDM) channel is not orthogonal to each other so that the signal detection becomes difficult. In this paper, we propose a MIMO system with relative phase difference time-shift modulation (RPDTM) in Rician fading environments. The proposed scheme transmits independent signals from each antenna at each time slot where relative phase difference between signal constellations used by transmit antennas varies with a pre-determined pattern. This transmission virtually changes the phase difference of direct paths from transmit antennas to each receive antenna without lowering data rate and without knowledge of the channels. In addition, forward error correction coding (ECC) is applied to exploit the time slots where the receiver can detect the signals easily to improve the detection performance. From the results of computer simulation, we show that MIMO system with RPDTM can achieve the better bit error rate (BER) than the conventional MIMO system. We also show that the MEMO system with RPDTM is effective by about Rician factor K = 10 dB.

We propose a random beamforming technique in multiuser multi-antenna orthogonal frequency division multiplexing (OFDM) systems for a large system capacity and fairness among users. In the proposed technique, we allocate several beams on each subcarrier to users based on signal-to-interference plus noise ratio (SINR) and all the remaining beams on each subcarrier by the proportional fairness (PF) algorithm. We also use the random unitary matrices corresponding to the beams and subcarriers where each user with low capacity has its highest SINR in the next time slot. We show that compared to the conventional techniques, such as the SINR-based scheduling scheme and the PF scheduling scheme, the proposed technique achieves high system capacity with about the same variance of user capacity.

This paper proposes an indoor event detection scheme using an electric wave such as intrusion into home or office. The proposed system exploits an antenna array on the receiver side and detects events such as intrusion using signal subspace spanned by eigenvector obtained by the antenna array. The eigenvector is not based on received signal strengths (RSS) but on the direction of arrival (DOA) of incident signals on an antenna array. Therefore, in a static state, the variance of the eigenvector over time is smaller than that of RSS. The eigenvector changes only when the indoor environment of interest changes statically or dynamically. The installation cost is low, because the detection range is wide owing to indoor reflections and diffraction of electric wave and only a pair of transmitter and receiver are used. Experimental results reveal that the proposed method can distinguish the state when no event occurs and that when an event occurs clearly. The proposed method has a superior detection performance to the event detection method based on RSS.

The path loss exponent is very important parameter for localization using receive signal strength (RSS). In actual environments, path loss exponent for each link (target to each receive node) differs. However, the conventional localization methods use the same path loss exponent for all links. Hence, there are some mismatches between the real path loss exponent and the one used to estimate. We proposed the localization method that considers all the combinations of path loss exponents for each link and estimates the target location by averaging the target locations derived with all the combinations. However, the amount of calculation is huge. In this paper we propose RSS-based localization in environments with different path loss exponent for each link. The proposed method is a grid-based centralized localization using RSS. First the proposed method sets the minimum distance di,minand maximum distance di,maxfor each node i by using the RSS of each receive node i and the minimum and maximum path loss exponents set before estimation. Next, it calculates the distance di,(k,l)between the candidate target position (k, I) and each receive node i. If di,minles di,(k,l)les di,max, vote the grid (k,l). These processes are performed for all the receive nodes over the search area. Finally, the grid point with most voting is estimated to be the target location. According to the simulation results, we show that the proposed method achieves the higher localization accuracy than the conventional localization method using the same path loss exponent for all the links when the distribution of the path loss exponents over the field is uniform distribution.

In line-of-sight (LOS) environments, the signal detection of multiple-input multiple-output (MIMO) systems becomes difficult because the correlation among channels becomes higher when the phase differences of direct paths are close to each other. In this paper, we propose a MIMO system with precoding to maximize the minimum distance (denoted hereafter as Max-dminprecoding) to improve the performance of MIMO systems in LOS environments. Max-dminprecoding can improve the detection performance of maximum likelihood detection (MLD) by maximizing the minimum Euclidean distance between symbol points at the receiver side, but the design becomes complicated as the number of substreams increases. The design of our proposed Max-dminprecoding is easy by using the property that the first eigenvalue of channel matrix becomes dominant in LOS environments even when the number of substreams is large. We use long-term average channel state information (CSI) instead of instantaneous CSI for precoding. Moreover, we propose a reduced-complexity MLD that uses the ordered reliability of symbols that results from Max-dminprecoding. From the results of computer simulation, we show that our proposed scheme with reduced-complexity MLD can achieve low complexity and good bit error rate (BER) performance in LOS environments with Rician factor K = 5 dB.

In multiple-input multiple-output (MIMO) wireless systems, since different signals are transmitted by different antennas simultaneously, interference occurs between the transmitted signals. The receiver has to detect each signal from the multiplexed signal. MMSE-SIC combines MMSE filtering and soft interference cancellation (SIC) with soft replicas and can achieve good bit error rate (BER) performance. If an irregular LDPC code or a turbo code is used, the reliability and BER of the information bits output by the decoder are likely to be higher and better than the parity bits. In MMSE-SIC, bits with poor reliability lower the accuracy of soft replica estimation. When the soft replica is inaccurate, the gain obtained by SIC is small. For M-ary phase shift keying (PSK) and M-ary quadrature amplitude modulation (QAM), larger constellations such as 8PSK and 16QAM transfer more bits per symbol, and the number of bits per symbol impacts the accuracy of SIC. Unfortunately, increasing the number of bits per symbol is likely to lower the accuracy of soft replica estimation. In this paper, we evaluate three types of the mapping scheme for MMSE-SIC with either LDPC codes or turbo codes with the goal of effectively increasing the SIC gain. The first scheme is the information bit or the parity bit reliable mapping. In this scheme, either information bits or parity bits are assigned to strongly protected bits. The second scheme is the weight based mapping. When the number of information bits is larger than that of strongly protected bits, not all the information bits are assigned to strongly protected bits. In the weight based mapping, information bits are assigned to strongly protected bits with the increasing or decreasing orders of that weight. The last one is the random mapping. Computer simulations show that in MMSE-SIC with irregular LDPC codes or turbo codes, information reliable and high weight reliable mapping offer the highest SIC gain. We also show that in MMSE-SIC with the regular LDPC code, the gains offered by the mapping schemes are very small.

Multiple-input multiple-output (MIMO) systems that realize a high-speed data transmission with multiple antennas at both transmitter and receiver are drawing much attention. In MIMO systems, it has been reported that the scheme that weights transmitted signals based on minimum mean square error (MMSE) criterion at the transmitter using feedback channel state information (CSI) achieves good performance (denoted hereafter as MMSE precoding). A throughput maximization transmission control scheme (TMTC) that selects the transmission mode (modulation schemes and code rates) based on CSI has been proposed. We expect that its throughput performance can be improved by addition of MMSE precoding. However, throughput performance is degraded in the presence of feedback delay. In this paper, we propose TMTC with MMSE precoding; it reduces the throughput performance degradation by using channel prediction and the receive weights robust against feedback delay. The proposed scheme selects a transmission mode (the number of substreams, modulation schemes, and code rates) and configures the precoder to achieve maximum throughput based on the SINR given by channel prediction. Simulation results show that even when feedback delay exists, the proposed scheme attains high throughput.

In this paper, we propose optical wireless multiple- input multiple-output (OMIMO) with backward spatial filter (BSF) in diffuse channels. In the proposed system BSF is constructed based on the channel state information (CSI) in the transmitter so that the received signal is free from multipath interference, which results in less receiver complexity and good performance. We show that the proposed system can achieve better bit error rate (BER) performance than the conventional single-input single output (SISO) system in diffuse channels.

Array low density parity check (LDPC) codes are high-rate codes that can achieve good error rate performance in additive white Gaussian noise (AWGN) channels. However, array LDPC codes do not support arbitrary code lengths, because the code length of an array LDPC code with good error rate performance is limited to a multiple of a prime number. This paper proposes Size Compatible (SC)-array LDPC codes; they achieve good error rate performance while supporting arbitrary code lengths. We conduct computer simulations to evaluate the block error rate (BLER) performance of SC-array LDPC codes in AWGN channels. We also evaluate the corresponding performance of SC-array LDPC coded multiband OFDM systems for data transmission rates over 1 Gbps in the UWB multipath channel model CM 3. We show that if the submatrix size is a non- prime number, SC-array LDPC codes achieve better error rate performance than the conventional array LDPC codes in AWGN channels. We also show that the SC-array LDPC codes achieve better error rate performance than the punctured array LDPC codes in AWGN channels. We also show that if the submatrix size is a prime number, SC-array LDPC codes achieve the same error rate performance as conventional array LDPC codes in AWGN channels. Moreover, we show that for multiband OFDM systems with data transmission rates of over 1 Gbps, SC-array LDPC codes achieve better error rate performance than conventional array LDPC codes.

We propose a log likelihood ratio (LLR) based iterative reduced-complexity maximum likelihood detection (MLD) algorithm with parallel interference cancellation (PIC) to improve detection performance in multiple-input multiple-output (MIMO) systems. In the proposed algorithm, we estimate the initial channel responses with pilot symbols and detect the initial transmitted symbols by list detection algorithm that is the reduced-complexity suboptimal detection algorithm with detection performance close to maximum likelihood detection (MLD). In the proposed algorithm, we update the channel estimates based on the minimum LLR absolute value given by turbo decoder output only when it is larger than a threshold. Using the updated channel estimates and the transmitted symbol replica, we detect the transmitted symbols by MLD algorithm after performing PIC. In the iterative detection process, we reduce the complexity by reducing the number of candidate symbols of MLD and determine the detection ordering based on LLR. We consider the proposed algorithm in an bit-interleaved turbo coded MIMO system. Simulation results show that the frame error rate (FER) performance of the initial list detection algorithm is improved by performing iterative reduced complexity MLD with PIC.

Among new technologies, ultra wideband (UWB) is gathering interests as a short-reach transmission system. At present, as one of the prevailing UWB systems, the multiband-orthogonal frequency division multiplexing (MB-OFDM) system has attracted much attention where convolutional code (CC) is used. The main contribution of this paper is to evaluate the bit error rate (BER) and decoding complexity performances of MB-OFDM with concatenated coding scheme and dual carrier modulation (DCM) on IEEE 802.15.3a UWB multipath channel model. We employ the CC for the inner code and the Reed Solomon (RS) code for the outer code in the concatenated coding scheme. From the results of our computer simulation, we show good combination of coding rates in the concatenated coding scheme at the data rates of about 320, 400 480 Mbps. We also show that MB-OFDM with concatenated coding scheme can achieve the better BER than MB-OFDM with only CC. In addition, we show that the BER improvement of MB-OFDM with concatenated coding scheme over that with only CC becomes larger as the data rate becomes higher.

Random unitary beamforming is one of the schemes reducing the amount of feedback information in multiuser diversity techniques with multiple-antenna downlink transmission. In multiple-input multiple-output (MIMO) systems, throughput performance is greatly improved using AMC (adaptive modulation and coding). Throughput performance is also improved by allocating power among streams appropriately. In random unitary beamforming, transmitter has only partial channel state information (CSI) of each receiver because of reducing the amount of feedback information. Thus, the power allocation scheme needs to work with only partial CSI and low latency. In this paper, we propose a new power allocation scheme for random unitary beamforming assuming a discrete transmission rate with a small amount of feedback information and low latency. Simulation results show that the proposed scheme can improve throughput compared to conventional power allocation scheme.

Random unitary beamforming is one of the schemes reducing the amount of feedback information in multiuser diversity techniques with multiple-antenna downlink transmission. In multiple-input multiple-output (MIMO) systems, throughput performance is greatly improved using AMC (adaptive modulation and coding). Throughput performance is also improved by allocating power among streams appropriately. In random unitary beamforming, transmitter has only partial channel state information (CSI) of each receiver. Thus, it is difficult for random unitary beamforming to use conventional power allocation methods that use full CSI at all the receivers. In this paper, we propose a new scheduling algorithm with power allocation for downlink random unitary beamforming that improves throughput performance without full CSI. We provide numerical results of the proposed scheduling algorithm and compare them to those of the conventional random unitary beamforming scheduling algorithm.

In this paper, we propose the low computational complexity 2D pilot-symbol-assisted (2D PSA) MMSE channel estimation with extended estimation size for MIMO-OFDM systems. In the proposed scheme, we assume the estimated channel frequency responses of some adjacent subcarriers in an OFDM symbol and those in some adjacent OFDM symbols to be constant to reduce the computational complexity, and extend MMSE channel estimation size to improve the mean square error (MSE) performance. We show that the proposed channel estimation achieves the better MSE performance with the same or lower computational complexity than the conventional 2D PSA MMSE channel estimation.

Likelihood Ratio Test (LRT) and Best Linear Unbiased Estimator (BLUE) have been researched as estimation methods of observation event in sensor network. LRT and BLUE can estimate the observation event with high accuracy at the Fusion Center (FC) when the FC has a perfect knowledge of observation statistics of each sensor node. However, all the sensor nodes' observation statistics are not always available at the FC. In this paper, we propose a method to estimate false alarm probability and observation noise variance of each sensor node at the FC, and present the performance of LRT and BLUE using the proposed estimation method. We show that the LRT and BLUE using the proposed method achieve almost the same bit error rate (BER) as the conventional LRT and BLUE with perfect knowledge of them.

In multiple-input multiple-output (MIMO) wireless systems, the receiver must extract each transmitted signal from received signals. An iterative signal detection with belief propagation (BP) is an attractive technique for MIMO systems. This technique can improve the error rate performance with increasing the number of detection and decoding iterations. This number is, however, limited in actual systems because each additional iteration increases the latency, receiver size, and so on. This paper proposes a convergence acceleration technique that can achieve better error rate performance with fewer iterations than the conventional iterative signal detection. Since the log-likelihood ratio (LLR) or a probability of one bit propagates to all other bits with BP, improving some LLRs improves overall decoder performance. In our proposal, all the coded bits are divided into groups and only one group is detected in each iterative signal detection whereas in the conventional approach, each iterative signal detection run processes all coded bits, simultaneously. Our proposal increases the frequency of initial LLR update (for BP) by increasing the number of iterative signal detections and decreasing the number of coded bits that the receiver detects in one iterative signal detection. Computer simulations show that our proposal achieves better error rate performance with fewer detection and decoding iterations than the conventional approach.

The reliability-based hybrid ARQ (RB-HARQ) scheme, which can be used with error correcting codes using soft-input soft-output (SISO) decoders such as convolutional codes and turbo codes, has been proposed. In the RB-HARQ scheme, the error rate performance is improved by selecting retransmission bits based on log likelihood ratio (LLR) of each bit in the receiver. However, the receiver has to send the bit positions of retransmission bits to the transmitter. Therefore, the RB-HARQ scheme requires a large number of feedback bits. On the other hand, low density parity check (LDPC) codes are recently attracting a lot of interest, because LDPC codes can achieve near Shannon limit performance. In this paper, we evaluate the RB-HARQ scheme using LDPC codes. Moreover, we propose a RB-HARQ scheme that requires a fewer feedback bits by utilizing a code structure of LDPC code. We refer to the scheme as the RB-HARQ (row base) scheme. We show that the RB-HARQ and RB-HARQ (row base) schemes using LDPC codes have better error rate performance and higher throughput performance than no ARQ scheme. We also show that the RB- HARQ (row base) scheme has a good trade-off between error rate performance and the number of feedback bits compared to the RB-HARQ scheme.

Recently, the superimposed pilot channel estimation has attracted attention for wireless communications, where the pilot symbol sequence is superimposed on a data symbol sequence and transmitted together, and thus there is no drop in information rate. In the superimposed pilot channel estimation, the receiver correlates the received symbol sequence with the pilot symbol sequence, and obtains the channel estimate. However, the correlation between the pilot symbol sequence and the data symbol sequence deteriorates the channel estimation accuracy. In particular, the channel estimation accuracy of the superimposed pilot channel estimation scheme is significantly deteriorated in MIMO systems, because the pilot symbol power of each transmit antenna to the total transmit power of all transmit antennas becomes smaller as the number of transmit antennas increases. On the other hand, it has been well known that the SAGE algorithm is an effective method for channel estimation and data detection. This algorithm is particularly effective in MIMO systems, because the operation of this algorithm can cancel the interference from other transmit antennas. In this paper, we propose a SAGE algorithm for channel estimation and data detection using superimposed pilot channel estimation in MIMO systems. From the results of computer simulations, we show that the proposed system can achieve the good BER performances by using the SAGE algorithm with iteration.

In this paper, we propose a concatenated code by combining a Recursive Systematic Convolutional (RSC) code with a Low-Density Parity-Check (LDPC) code. The proposed concatenated code is encoded in parallel by an RSC encoder and an LDPC encoder without interleavers between them. When decoding the proposed concatenated code, the information bits are decoded by two decoding algorithms, and soft information is exchanged between the RSC decoder and the LDPC decoder. This allows for the elimination of wrong codewords output by each decoder. The use of an LDPC code allows us to halt the decoding process when the valid codeword is obtained. We evaluate the error rate performance of the proposed concatenated code by computer simulations. We show that the proposed concatenated code does not have a high error floor, and achieves better BLock Error Rate (BLER) performance than either conventional LDPC codes or conventional turbo codes in the high Eb/No region. We also show that since the proposed concatenated code can correct wrong codewords output by each decoder, it achieves better BLER than the regular LDPC code at the same Bit Error Rate (BER).

Recently, multiple-input multiple-output (MIMO) systems that realize a high bit rate data transmission with multiple antennas at both transmitter and receiver have drawn much attention for high spectral efficiencies. In MIMO systems, eigen-beam space division multiplexing (E-SDM) that achieves good performance by weighting at the transmitter using channel state information (CSI) has been studied. Early studies for E-SDM have assumed perfect CSI at the transmitter. However, in practice, CSI fed back to the transmitter is not identical to that when the signals are transmitted owing to the time-varying nature of channels and feedback delay. As a result, the performance of E-SDM is degraded. In this paper, as methods that reduce the performance degradation of E-SDM in the presence of feedback delay, we evaluate the performance of a method that predicts CSI when the signals are transmitted at the receiver and feeds the predicted CSI back to the transmitter (denoted by channel prediction method). We also evaluate the performance of a method that uses the receive weight based on zero-forcing (ZF) or minimum mean square error (MMSE) criterion instead of those based on singular value decomposition (SVD) criterion (denoted by ZF or MMSE receive weight method). Simulation results show that bit error rate (BER) degradation of E-SDM in the presence of feedback delay is reduced by three methods. We also show that the ZF and MMSE receive weight methods achieve the identical BER with smaller amount of calculation than the channel prediction method.

In this paper, we evaluate the performance of layered space-frequency equalization (LSFE) with iterative cancellation. LSFE is a combination of V-BLAST (Vertical Bell Laboratories Layered Space-Time) that is one of the signal detection methods for MIMO systems and frequency domain equalization (FDE) that is one of the techniques for frequency selective fading channels. We use two iterative cancellation methods: forward iterative cancellation (FIC) and backward iterative cancellation (BIC). From the results of computer simulations, we show that in frequency selective fading channels, bit error rate (BER) of LSFE is improved by FIC and BIC. We also show that when the number of paths is large, LSFE with BIC achieves the larger diversity gain and the better BER than LSFE with FIC

In mobile communications, the quality of signal is deteriorated by fading. The spatial diversity technique is used to reduce the degradation of the signal by fading. There are several techniques to realize the spatial diversity gain, such as Single- Input Multiple-Output (SIMO), Space-Time Block Code (STBC), and so on. The SIMO system realizes the receive diversity. The error correcting code can be combined with spatial diversity gain. Low-Density Parity Check (LDPC) codes can achieve good performance approaching Shannon limit. In particular, an irregular LDPC code with optimum degree distribution achieves better performance than a regular LDPC code. The optimum degree distribution of the irregular LDPC code depends both on the channel and the system. In this paper, we derive the Eb/No thresholds of a regular LDPC codes and the irregular LDPC codes for SIMO systems with several diversity orders. The Eb/No threshold is the smallest Eb/No that the decoder can achieve the error free. We also derive the optimum degree distributions of the irregular LDPC codes for SIMO systems with several diversity orders. We show that with low diversity order, the optimum degree distributions of the irregular LDPC code depends on the diversity order largely, while with high diversity order, the optimum degree distribution depends both on the diversity order and the combining scheme largely. We also show that comparing the optimum degree distributions for MRC and STBC, when the diversity orders are the same, the optimum degree distributions are also the same.

A low-density parity check (LDPC) code with the belief propagation (BP) or the log-likelihood ratio belief propagation (LLR-BP) can achieve good bit error rate (BER) performance approaching the Shannon limit. When a parity check matrix of the LDPC code has the cycle, the BP and LLR-BP decoding algorithms achieve approximate maximum a posterior probability (MAP) decoding. Although the decoding algorithms are approximate MAP decoding, LDPC codes can achieve very good BER. For the short and middle length LDPC codes, BER and block error rate (BLER) performances are affected by cycle largely. In each iteration, the magnitudes of a posterior LLRs of some bits oscillate owing to cycles. The oscillation is the dominant error factor in the high Eb/Noregion for short and middle length LDPC codes. In this paper, we extend the definition of oscillation to extrinsic LLR (ex-LLR) derived in the bit node process and propose the modified LLR-BP and the modified UMP-BP decoding algorithms. To reduce effects of oscillating ex-LLRs on decoding, for oscillating ex-LLRs, we add the previous ex-LLR to the current ex-LLR. From the computer simulation, we show that for short and middle length LDPC codes, with a simple modification, our proposed decoding algorithms can improve the conventional LLR-BP and UMP-BP decoding algorithms. In particular, we show that the modified UMP-BP decoding algorithm with low complexity can achieve better BER and BLER than the conventional LLR-BP decoding algorithm

Acoustic source localization is one of the interesting applications of sensor networks. Localization algorithms that use acoustic signal energy measurements at individual sensor nodes are proposed. The maximum likelihood (ML) algorithm is known as an algorithm for source localization. The ML algorithm has high accuracy to estimate source location, however the calculation amount of the ML algorithm is large. The expectation- maximization (EM) algorithm is proposed to realize the ML algorithm with less amount of calculation. An EM algorithm processed at a center, that is, the centralized EM algorithm is proposed in which each sensor node sends observed information to the center. The total cost of the centralized data processing is expensive, because the communication cost is large when the transmission distance from each sensor node to the center is long. On the other hand, the distributed data processing can reduce the transmission cost and thus the total cost, because data is processed in each sensor node and thus the amount of communication can be reduced. In this paper, we propose a distributed EM algorithm for acoustic source localization that is applicable to a source with unknown signal energy. We show that the proposed algorithm has high accuracy and reduces the communication cost.

Multiple-input multiple-output (MIMO) systems can achieve high data-rate and high capacity transmission. In MIMO systems, the scheme that weight substreams based on minimum mean square error (MMSE) criterion at the transmitter using feedback channel state information (CSI) achieves good performance (denoted hereafter as MMSE precoder). However, if the optimum number of substreams is not selected based on CSI and methods for reducing the performance degradation due to feedback delay is not used, MMSE precoder cannot achieve the full performance possible. In this paper, we propose an MMSE precoder with mode selection for MIMO systems, which selects both the number of substreams and the modulation scheme for each substream to minimize the average BER at a fixed rate. We evaluate the BER of the proposed MMSE precoder with two methods for reducing performance degradation due to feedback delay, channel prediction and receive weight robust to feedback delay. We also evaluate the BER of the proposed MMSE precoder with the method that combines channel prediction with receive weight robust to feedback delay. Simulation results show that the BER of the proposed MMSE precoder is improved compared to that of the conventional MMSE precoder using the fixed number of substreams. We also show that the method that combines channel prediction with receive weight robust to feedback delay can achieve good BER even when the large feedback delay exists.

One typical use of sensor networks is monitoring targets. The sensor networks classify, detect, locate, and track targets. The ML (maximum likelihood) algorithm is one of the estimation algorithms of target location and has high accuracy to estimate target location. However, the calculation amount of the ML estimation algorithm is large. Energy-ratios source localization nonlinear least square (ER-NLS) is proposed to realize the ML algorithm. ER-NLS is the algorithm of estimating source location by using the ratio of sensors' receiving energies. However, ER-NLS has to search all the areas, so that the calculation amount of ER-NLS is large. In this paper we propose a method of reducing search area for localization. The proposed method uses the ratio of sensors' receiving energies. It can be used with the ML algorithm. We show that the proposed method with the ML algorithm can reduce the search areas to estimate the target location and thus reduce the complexity, while achieving the RMSE (root mean square error) close to that of the ML algorithm

In recent years, multiple-input multiple-output (MIMO) systems, which use several transmit and receive antennas, have attracted much attention for high performance radio systems. In MIMO systems, the channel estimation is important to distinguish transmit signals from multiple transmit antennas. While the space-alternating generalized expectation-maximization (SAGE) algorithm is known to offer good channel estimation and data detection. We proposed earlier the minimum mean square error (MMSE)-based SAGE algorithm for MIMO systems where the MMSE estimation is used for channel estimation. We showed that the proposed MMSE-based SAGE algorithm can achieve the better bit error rate (BER) than the maximum likelihood (ML) detection with training symbols. The MMSE channel estimation needs the knowledge of the maximum Doppler frequency Fdfor deriving the covariance matrix of the channel and the variance sigma2of additive white Gaussian noise (AWGN). Additionally, the computation of the MMSE channel estimation requires O(L3) operations where L is the transmitted frame length. Thus, its computational complexity is high. In this paper, we propose a zero-forcing (ZF)-based SAGE algorithm for channel estimation and data detection in MIMO systems that does not need the knowledge of Fd and sigma2. Since the computation of the proposed ZF-based SAGE algorithm requires O(N3) operations where N is the number of transmit antennas, its computational complexity is low. We show that the proposed ZF-based tracking SAGE algorithm with less computational complexity can achieve almost the same BER as that of the MMSE-based tracking SAGE algorithm

In multiple-input multiple-output (MIMO) systems, we need the scheme that attains high quality and high throughput data transmission. We proposed the throughput maximization transmission control scheme (TMTC) for MIMO systems. The proposed transmission control scheme selects a transmission scheme with maximum transmission rate based on signal to interference and noise ratio (SINR) and signal to noise ratio (SNR). In time-varying channels, however, the selected transmission scheme fed back to the transmitter becomes outdated. That is, there is a mismatch between "the selected transmission scheme at the receiver" and "the optimum transmission scheme at the transmitter." Therefore, the throughput performance of TMTC may decrease. In this paper, we propose TMTC using a minimum-mean square error (MMSE) channel prediction for MIMO systems. From the computer simulation, we show that even when the large feedback delay exists, the proposed scheme attains high throughput

In wireless sensor networks, if the local sensor node transmits wrong decided data to a fusion center, the energy-efficiency of the network is degraded. In this paper, we propose wireless sensor networks with local fusion to reduce the degradation caused by observation noise, channel noise, and fading. In the proposed systems, first, information transmitted from local sensor nodes are decided by a local fusion center allocated in the neighborhood of local sensor nodes. Here, we consider two signal decision schemes (local fusion) for use at the local fusion center: majority decision and likelihood decision. By using local fusion, even if the local sensor node makes an error in the decision of the event, the probability that the local fusion center makes an error in the decision of the event becomes small. Thus, the probability that the local fusion center transmits wrong decided data becomes small. Then, from the local fusion center to a destination node (a global fusion center), error correcting coded bits are transmitted in multihop communication. We show that the proposed systems can achieve good energy-efficiency as compared with the system without local fusion. We also show that the total energy consumption of the system with likelihood decision is smaller than that of the system with majority decision when the influence of observation noise is large. Moreover, we show that the optimal number of hops minimizing the total energy consumption of the proposed systems depends on the signal attenuation parameter k and a long-haul transmission distance between the local fusion center and the global fusion center.

Previously, we have proposed the optical wireless multiple-input multiple-output communications (OMIMO) to achieve a high speed transmission with a compact transmitter and receiver. In OMIMO, the transmission signal is degraded by optical beat interference (OBI) when we use the same wavelength at the optical transmit antennas. However, the influence of OBI has not been clarified. In this paper, we evaluate the influence of OBI on OMIMO, where we consider the linear array and square array of optical transmit and receive antennas. We analyze the signal-to-interference-plus-noise ratio (SINR) and the bit error rate (BER) of OMIMO when the same wavelength is used at me optical transmit antennas. We employ on-off keying (OOK) and subcarrier multiplexing (SCM) for each optical transmit antenna. From the numerical result, we clarify the influence of OBI on OMIMO and show that by using zero-forcing (ZF) or singular-value-decomposition (SVD), we can reduce the size of transmitter and receiver compared to space division multiplexing (SDM).

Recently, multi-carrier code-division multipleaccess (MC-CDMA) has attracted much attention. To make smoothly connection with a base station (BS), time division duplex (TDD) systems using the reciprocity of uplink and downlink in the same frequency band is considered. If the coherence time is large compared with the duplexing time, TDD systems can provide the channel characteristics of uplink from that of downlink. Thus, the channel distortion can be compensated in advance as a pre-equalization at mobile terminals (MTs). The pre-equalization technique using zero-forcing equalization (ZFE) was proposed for the TDD/MC-CDMA system. The channel parameters obtained from the last pilot (LP) symbol in the downlink frame have the highest time correlations with the uplink channel parameters. Therefore, they can be used as the channel parameters of all the symbols in the following frame. However, when the coherence time is not so large compared with the duplexing time, it is expected that the system suffers from the channel mismatch by time-selective fading. In this paper, we propose a pre-equalization using MMSE channel prediction for TDD/MC-CDMA. As a derivation of the pre-equalization coefficients, we employ ZFE, Controlled Equalization (CE), and Minimum Mean Square Error Equalization (MMSEE). Simulation results show that the proposed channel prediction techniques are effective on time-selective fading channels.

We propose a multiple-subcarrier (MS) optical communication system with subcarrier signal-point sequence (SSPS). We use the SSPSs having a large minimum value and large Euclidean distances, so that the required dc bias is minimized and the error-rate performance is improved. Note that in the proposed system, the signal points having the larger minimum value are selected, while the signal points having a lower peak-to-mean-envelope-power ratio (PMEPR) are selected in orthogonal frequency-division multiplexing (OFDM) systems. Therefore, the SSPSs good for OFDM with phase shifting by /spl pi/ rad are not necessarily effective for MS optical communication systems. The main contributions of our paper are: 1) we derive transmit sequences having a large minimum value and large Euclidean distances by using 8-phase-shift keying and (8+1)-amplitude phase-shift keying; and 2) since designing optimal sequences would be prohibitively complex, we introduce a reasonable procedure for suboptimal sequence design, obtaining good results. We show that the normalized power requirements and normalized bandwidth requirements of the MS systems with SSPS are smaller than those of the conventional MS systems.

Ultra wideband (UWB) has recently attracted much attention as an indoor short range high-speed wireless communication. Of all UWB systems, ultra wideband-impulse radio (UWB-IR) and direct sequence-ultra wideband (DS-UWB) use extreme short pulses and have the advantage of the extremely high path resolutions. The RAKE receiver is known as a technique that can effectively combine paths with different delays, obtain the path diversity gain, and improve the transmission characteristics. However, multipath is spread over dozens of symbols in the case of ultra high-speed communications of several hundreds Mbps, which causes the strong frequency selective channel. As a result, the transmission performance degrades. Meanwhile, the single-carrier (SC) transmission with frequency domain equalization (FDE) has also recently attracted much attention (Falconer et al. (2002)). It is reported that the SC with FDE has few problems of peak to average power ratio (PAPR) and also obtains an excellent performance as well as orthogonal frequency division multiplexing (OFDM) even in the strong frequency selective channel where multipath is spread over dozens of symbols. In this paper, we propose UWB-IR and DS-UWB with minimum mean square error (MMSE)-FDE. We evaluate the bit error rate (BER) or UWB-IR and DS-UWB with MMSE-FDE and compare it to that of UWB-IR and DS-UWB with maximal ratio combining (MRC)-RAKE and MMSE-RAKE. In consequence, we show that UWB-IR with MMSE-FDE can achieve the better BER than UWB-IR with MRC-RAKE and MMSE-RAKE. We also show that DS-UWB with MMSE-FDE has a better BER than DS-UWB with MRC-RAKE and MMSE-RAKE, particularly when M is large.

Space-time transmit diversity (STTD) and space-time block codes (STBC) are the attractive techniques for high bit-rate and high capacity transmission. Recently, low-density parity-check (LDPC) codes have attracted much attention as good error correcting codes achieving the near Shannon limit. Concatenation schemes of LDPC codes and STBC (LDPC-STBC), and turbo codes and STBC (turbo-STBC) have been proposed. The performance of the LDPC-STBC is better than that of the turbo-STBC on a flat Rayleigh fading channel. The performance of LDPC code can be analyzed by density evolution (DE). In this paper we analyze the performance of the LDPC-STBC by using DE. Also, we optimize the irregular LDPC codes for the LDPC-STBC by using DE. Furthermore, we evaluate the error rate performance of the optimized irregular LDPC codes and the regular LDPC codes for the LDPC-STBC. From the numerical and simulation results, we show that the LDPC-STBC with the irregular LDPC codes achieves better error rate performance than the LDPC-STBC with the regular LDPC codes.

Various channel estimation methods for multiple-input multiple-output (MIMO) systems have been proposed. In this paper, to prevent performance degradation for the space-time trellis coded OFDM (STTC-OFDM) system in fast fading channels, we propose a system that applies the added pilot semi-blind (APSB) channel estimation to all the OFDM data symbols except the preamble of a frame. We also propose a simplified system that applies the APSB channel estimation to the last OFDM symbol of a frame. We show that the proposed systems can track the channel variations without additional bandwidth because of the use of the APSB channel estimation and improve the word error rate (WER) performance compared to the conventional system that uses only DDCE.

In MIMO systems, channel estimation is important to distinguish transmit signals from multiple transmit antennas. The space-alternating generalized expectation-maximization (SAGE) algorithm is known to be good for channel estimation and data detection. However, the SAGE algorithm has not been applied to MIMO systems. In this paper, we propose a SAGE algorithm for the channel estimation and data detection in MIMO systems. In addition, we propose a simplified SAGE algorithm for the channel estimation and data detection by tracking the channel variation in MIMO systems. In the simplified SAGE algorithm, we divide a transmit frame into some subblocks and apply the SAGE algorithm to each subblock, and we use the channel estimates in the previous subblock as the initial channel estimates in the current subblock. According to the division of the transmit frame, the computational complexity is decreased. In addition, the simplified SAGE algorithm can track the channel variation by using the channel estimates transferred between the subblocks.

We propose optical wireless multiple-input multiple-output (OMIMO) communications to achieve high speed transmission with a compact transmitter and receiver. In OMIMO, by using zero forcing (ZF), we can eliminate the other signal interferences transmitted from other optical transmit antennas. Thus, we have no need to consider the frequency assignment for each optical transmit antenna. We analyze the signal-to-interference-plus-noise ratio (SINR) and the bit error rate (BER) of the proposed OMIMO with a linear array assignment of optical transmit and receive antennas, where we employ subcarrier multiplexing (SCM) for each optical transmit antenna. Note that the proposed OMIMO is applicable to other arrangements of optical transmit and receive antennas. We show that the proposed OMIMO system can realize MIMO multiplexing and achieve a higher speed transmission than one optical single-input single-output (OSISO) by setting the optical transmit and receive antennas and the transmitter semiangle appropriately.

We analyze the optical wireless sensor network system using corner cube retroreflectors (CCRs). A CCR consists of three concave mirrors. When a light beam enters the CCR, it bounces off each of the three mirrors and is reflected back parallel to the direction it entered. A CCR sends information towards a base station modulating the reflected beam by operation of vibrating CCR or shielding of light pass, and one can transmit an on-off-keying (OOK) modulated optical signal. In optical communications, a CCR is attractive, because of its small size, ease of operation, and low power consumption. In this analysis, we evaluate two decisions at the fusion center, the collective decision and the majority decision. The collective decision is that all the information detected by the sensors is collected by one photodetector (PD), and then the hard decision is done. The majority decision is that each information detected by the sensor is respectively received by each PD, the hard detection is done for each PD output, and decided by majority. We show that the bit error rates (BERs) of the systems are improved as the number of sensors increases. We also show that when the transmitted optical power is adequately large, the BERs of the systems depend on the accuracy of the sensors. We confirm that the BERs of the systems using the collective decision are better than those of the systems using the majority decision.

We analyze performance of multibits/sequence-period optical code division multiple access (MS-OCDMA) systems with double optical hardlimiters (DHL) in the presence of PD noise, thermal noise, and channel interference. We apply Reed-Solomon (RS) codes to MS-OCDMA to further improve the error rate performance. We show that the MS-OCDMA receiver with DHL improves the bit error probability of MS-OCDMA systems when the received laser power is large. We also show that the RS coded MS-OCDMA systems have the higher bit rate and the lower chip rate than the on-off keying OCDMA (OOK-OCDMA) systems for approximately the same bit error probabilities, respectively.

Ultra wideband-impulse radio (UWB-IR) systems transmit data by ultra short pulses. The UWB-IR systems can achieve a time diversity gain by transmitting some pulses for each information symbol. In L. Yang et al. (2002) the space time (ST)-UWB-IR system was proposed to achieve a space-time diversity gain by transmitting space-time block coded symbols with some antennas. In the ST-UWB-IR system, the transmit antennas transmit the same symbol over an ST code block simultaneously. If all the transmit antennas can transmit different symbols independently, that is, if it separates the symbols from each antenna, we can expect a higher transmission rate than the ST-UWB-IR system. Moreover, at the same transmission rate, we can expect a larger diversity gain by sending more pulses for each symbol. This paper proposes a space hopping (SH)-UWB-IR system. In the SH-UWB-IR system, to allow each user to transmit different symbols from different antennas, a unique time hopping sequence is assigned to each antenna, which is known to the receiver. In addition, to get the larger diversity gain in the SH-UWB-IR system, the transmitted symbols are interleaved over the transmit antennas. From the results of our computer simulation, we show that when the number of users is small, the bit error rate (BER) of the SH-UWB-IR system is superior to that of the ST-UWB-IR system at the same data rate.

In this paper the performance of the atmospheric optical subcarrier multiplexing (AO-SCM) systems and the atmospheric optical code division multiplexing (AO-CDM) systems is analyzed and the average received carrier-to-interference-plus-noise ratio (CINR) of the AO-SCM systems and the AO-CDM systems on a turbulence channel with the scintillation and the nonlinearity of an Ld is derived. We show that the received CINR of the AO-CDM systems is larger than that of the AO-SCM systems when the number of channels is small for the same optical modulation index (OMI), and vice versa when the number of channels is large for the same OMI. For instance, when the logarithm variance of scintillation /spl sigma//sub X//sup 2/ is 0.3, a constant of the nonlinearity of an LD a/sub 3/ is 0.17, and the number of channels K is 4, the maximum CINR of the AO-CDM systems is about 4 dB better than that of the AO-SCM systems. Also, when K is 16, the maximum CINR of the AO-SCM systems is about 3 dB better than that of the AO-CDM systems. The difference of CINR results in the difference of the maximum achievable number of channels for the systems. When the required BER is 10/sup -9/, AO-SCM and AO-CDM systems can accomodate 16 and 8 BPSK channels, respectively.

We propose the performance factors for space-time trellis codes (STTC) in block fading channels (BFC). The proposed performance factors are simpler than the conventional ones and good for the various situations: a short fading block length for a high signal-to-noise noise ratio (SNR) and a low SNR. They are inverse proportion to the coding gain. Moreover, based on the proposed performance factor, we propose a new STTC that outperforms the conventional STTC when the required SNR is low. For some STTC and the proposed STTC, we compute the values of the performance factors. From the computer simulation, we show the effectiveness of the proposed performance factors and the proposed STTC.

In the compressing and expanding (companding) method, at the transmitter, baseband signals are compressed for reducing peak-to-average power (PAPR) and at the receiver, the received signals are expanded for removing the distortion caused by the compressing. However, since the signals are distorted by the band pass filter (BPF), the high power amplifier (HPF), and the channel fading, the distortion caused by the compressing cannot be removed by expanding the received signals. We propose the novel companding system that compensates the distortions caused by not only the compressing but also the BPF, the HPF, and the fading. From the computer simulation, we show that the proposed companding system achieves better BER than the conventional one with the same level of the spectrum in the out-of-bandwidth.

Orthogonal frequency division multiplexing (OFDM) is a promising technique for achieving high bit-rate transmission in radio environments. Various techniques to estimate channel attenuation have been proposed for OFDM transmission. In these techniques, the added pilot semiblind (APSB) channel estimation has been proposed, which does not require any additional bandwidth. For an OFDM packet transmission that includes preambles, it is difficult to track the channel variation of the latter of packet due to time-varying channels in fast fading environments. We expect that channel estimation is improved by applying the APSB channel estimation technique to the last symbols of packets. In this paper, we propose an OFDM packet transmission system with APSB channel estimation technique to track the variation of channel attenuations. We show that the proposed system can track the channel variation and achieve better performance than the conventional system in fast fading channels.

We propose a multiple-subcarrier (MS) optical communication system with peak reduction carriers (PRCs) to reduce the optical power requirement on an additive white Gaussian noise (AWGN) channel. The proposed system transmits L subcarriers referred to as PRCs among N subcarriers for the d.c. bias reduction so that the optical power is reduced. Since information bits are mapped onto each subcarrier independently, the information bits of each subcarrier can be detected independently and the error rate of the proposed system is unaffected by PRCs. We show that with the fixed bias, the normalized power requirements of MS-PRC are smaller than those of the MS system with the normal block coding (MS-normal) and the MS system with the reserved-subcarrier block coding (MS-reserved) for all the total number of transmitted bits, and smaller than that of the MS system with the minimum-power block coding (MS-min.power) for four transmitted bits and below, respectively. We also show that with the time-varying bias, the normalized power requirements of MS-PRC are smaller than those of the conventional MS systems for all the total number of transmitted bits.

This paper proposes a new channel interference cancellation technique using interference estimation and double optical hardlimiters (DHL) for direct-detection optical code-division multiple-access (CDMA) systems. In the proposed system, when the output of the DHL is "0", the proposed system outputs "0". When the output of the DHL is "1", the proposed system reconstructs the interference and estimates whether the output "1" is owing to the interference or the transmitted signal. It is shown that the proposed system is effective to reduce the effects of the channel interference. Moreover, it is shown that the proposed system can improve the error floor of the systems with DHL.

Optical communications through clear atmospheric links can provide high-speed communications where cables cannot be installed easily. However, on atmospheric optical links, the turbulence-induced intensity fluctuations deteriorate the quality of received optical signals. In this paper, we propose two atmospheric optical subcarrier modulation systems using space-time block codes (STBC) with coherent and differential detection to realize high-speed communications on atmospheric optical links. In the proposed systems STBC is used to overcome the influence of the turbulence-induced fading and to get the space diversity gain. We show that the proposed systems can achieve the better bit error rate (BER) than the atmospheric optical subcarrier modulation systems with the single transmit aperture (antenna) in the presence of the scintillation. Moreover, we prove that the differential STBC scheme is 3 dB worse than coherent STBC scheme.

Multi-carrier code division multiple access (MC-CDMA) has been one of the candidates for the next generation wireless communication systems. In an uplink, the MC-CDMA system suffers from the different access timing, the different fading, and the different frequency offset of each active user. In this paper we analyze the effects of the frequency offset compensation with MMSE-MUD (minimum mean square error based multi-user detection) for MC-CDMA in a quasi-synchronous uplink. We consider the MC-CDMA system with two subcarrier mapping schemes, the continuous mapping scheme and the discrete mapping scheme. From our theoretical analysis and computer simulation, we show that the MMSE-MUD can compensate the different frequency offsets among users. We also show that the MMSE-MUD significantly improves the bit error rate (BER) for the MC-CDMA system with the continuous mapping scheme.

As a new spread spectrum system, an ultra wideband-impulse radio (UWB-IR) has attracted much attention in high speed indoor multiple access radio communications. Since the UWB-IR system repeats and transmits pulses for each bit, the UWB-IR system is considered as a coded scheme with a simple repetition block code. As an error correcting code, turbo-codes proposed by C. Berrou et al. in 1993 have attracted much attention beyond the field of coding theory, because the performance is very close to the Shannon limit with practical decoding complexity. In this paper, we propose an adaptive internally turbo-coded UWB-IR (AITC-UWB-IR) system. The proposed system employs a turbo code in addition to a repetition block code and shares the transmission bandwidth adaptively between them. We evaluate the performance of the AITC-UWB-IR by theoretical analysis and computer simulation. From our numerical and simulation results, we show that the BER of the AITC-UWB-IR is superior to that of the SOC-UWB-IR on an AWGN channel when the bit rate is high. We also showed that the AITC-UWB-IR system using low rate turbo codes improves the BER on an AWGN channel and a real Gaussian fading channel. Consequently, we confirm that for the same bit rate, we had better make the code rate of turbo codes lower and make the number of repetitions of the repetition codes smaller to achieve the better BER.

Design criteria on space-time trellis codes (STTC's) in fast fading channels have been derived by Tarokh, Seshadri and Calderbank (1998): the distance criterion and the product criterion. The design criteria of Tarokh et al. are based on optimizing the pairwise error probability (PWEP). However, a frame error rate (FER) of STTCs depends on the distance spectrum. We propose new design criteria on STTCs based on the distance spectrum in fast fading channels. The proposed design criteria are based on the product distance distribution for a high signal-to-noise ratio (SNR) and the trace distribution for a low SNR, respectively. Moreover, we propose new STTCs by the computer search based on the proposed design criteria in fast fading channels. By the computer simulation, we show that the proposed design criteria are more useful than the product criterion of Tarokh et al. in fast fading channels. We also show that the proposed STTCs achieve better FER than the conventional STTCs in fast fading channels.

We propose a multiple-subcarrier (MS) optical communication system with subcarrier signal point sequence (SSPS). We use the SSPS having the large minimum value and the large Euclidean distances, so that the required d.c. bias is minimized and the error rate performance is improved. Note that in the proposed systems, the signal points having the larger minimum value are selected, while the signal points having lower peak to mean envelope power ratio (PMEPR) are selected in orthogonal frequency division multiplexing (OFDM) systems. Therefore, the signal point sequences good for OFDM with phase shifting by /spl pi/ radian are not necessarily good ones for MS optical communication systems. We show that the normalized power requirement and normalized bandwidth requirement of the MS-SSPS are smaller than those of the conventional MS systems.

We propose multiple subcarrier modulation (MSM) for infrared wireless systems using punctured convolutional codes and variable amplitude block codes. The rate-compatible punctured convolutional (RCPC) code deletes the coded bits corresponding to zeros in the puncturing table and does not allocate them on amplitude of the subcarriers. On the other hand, the proposed system maps them to the amplitudes of subcarriers where the amplitudes are selected so that the required bias can be minimized. We propose two systems that use the block codes to map the coded bits corresponding to zeros in the puncturing table: one block code maps them to only zeros (proposed 1), and the other block code maps them to the appropriate values among 0, and /spl plusmn/1, so that the average optical power can be minimized (proposed 2). We show that the proposed 2 tan achieve the minimum required SNR at the same average optical power among all the systems.

Orthogonal frequency division multiplexing (OFDM) is a very attractive technique for high-bit-rate data transmission in multipath environments. Many error-correcting codes have been applied to OFDM. Recently, LDPC codes have attracted much attention. The performance of LDPC codes is very close to the Shannon limit, with practical decoding complexity. We proposed LDPC coded OFDM (LDPC-COFDM) systems with BPSK and showed that the LDPC codes are effective in improving the bit error rate (BER) of OFDM in multipath environments (see Futaki, H. and Ohtsuki, T., IEEE VTC2001 fall, vol.1, p.82-6, 2001). LDPC codes can be decoded using a probability propagation algorithm known as the sum-product algorithm or belief propagation. To clarify iterative decoding properties in LDPC-COFDM systems, we first investigate the distribution of the number of iterations where the decoding algorithm stops. In mobile communications, multilevel modulation is preferred for high bandwidth efficiency. However, it has not been clarified how to apply LDPC codes to OFDM systems with multilevel modulation. We propose a decoding algorithm for the LDPC-COFDM systems with M-PSK. By simulation, we show that LDPC-COFDM systems achieve good error rate performance with a small number of iterations on both AWGN and frequency-selective fading channels. We confirm that the algorithm for LDPC-COFDM systems with M-PSK work correctly.

We propose atmospheric (free-space) optical code-division multiple-access (CDMA) systems. We analyze the bit error rate (BER) of the proposed system using pulse position modulation (PPM) taking into consideration the effects of scintillation, avalanche photodiode (APD) noise, thermal noise, and multiuser interference. We show that atmospheric optical CDMA systems can realize communications at a bit rate of 156 Mbps when the logarithm variance of the scintillation /spl sigma//sub s//sup 2/ is smaller than, or equal to, 0.1. When /spl sigma//sub s//sup 2/ is large, we need to use forward error correction codes (FECs).

We propose two turbo-coded atmospheric (free-space) optical communication systems: a turbo-coded atmospheric optical subcarrier phase-shift keying (PSK) system and a turbo-coded atmospheric optical pulse position modulation (PPM) system. We obtain upper bounds on the bit-error rate (BER) for maximum-likelihood (ML) decoding of both turbo-coded systems on atmospheric optical channels where the effects of scintillation exist. We present iterative maximum a posteriori (MAP) probability decoding of the turbo-coded optical binary PPM (BPPM) system (see Yamamoto, N. and Ohtsuki, T., IEEE Global Telecom. Conf., GLOBECOM'01, 2001). We show that both turbo-coded atmospheric optical systems have better BER than convolutional coded atmospheric optical systems when the E/sub b//N/sub 0/ without scintillation is small. We also show that the turbo-coded atmospheric optical subcarrier PSK system has better BER than the turbo-coded atmospheric optical PPM systems. Moreover, we show that as the scintillation becomes larger, the information bit energy-to-noise ratio, E/sub b//N/sub 0/, where the transfer function bound of turbo codes on the atmospheric channel diverges, also becomes larger.

We propose a parallel combinatory multiple-subcarrier (PC-MS) optical communication system. The proposed system with N subcarriers selects at each symbol interval a subset of U null subcarriers and transmits /spl lfloor/log/sub 2/ (N/U)/spl rfloor/ bits for the selected subset. The other N-U=N/sub pc/ subcarriers are modulated by points from an M-PSK signal constellation. Thus, the proposed system can transmit more information at each symbol interval than the conventional MS system with N subcarriers. in addition the proposed system can decrease the bias that must be added to the MS electrical signal, because the number of subcarriers other than null subcarriers is smaller than that in the conventional MS systems and thus the minimum power is larger Therefore, the proposed system can decrease the required energy per bit.

Infrared wireless communication and optical communication usually adopt on-off keying (OOK) and pulse position modulation (PPM). Previously (see Yamamoto, N. and Ohtsuki, T., Proc. IEEE GLOBECOM'01, vol.3, p.1913-17, 2001), we presented the iterative maximum a posteriori (MAP) probability decoding of turbo coded OOK and turbo coded binary PPM (BPPM). We now present a soft-output Viterbi algorithm (SOVA) based iterative decoding of turbo coded OOK and turbo coded BPPM. We show that the BER of both turbo coded OOK and turbo coded BPPM are almost identical and improve as the number of iterations is increased.

Orthogonal frequency division multiplexing (OFDM) is a very attractive technique to achieve the high-bit-rate transmission required for future mobile communications. To improve the error rate performance of OFDM, forward error correction coding is essential. Recently, low-density parity-check (LDPC) codes, which can achieve the near Shannon limit performance, have attracted much attention. We proposed the LDPC coded OFDM (LDPC-COFDM) systems to improve the error rate performance of OFDM. We showed that LDPC codes are effective to improve the error rate performance of OFDM on a frequency-selective fading channel. In mobile communications high bandwidth efficiency is required, and thus multilevel modulation is preferred. We also proposed the decoding algorithm for the LDPC-COFDM systems with MPSK on an AWGN channel. In this paper, we evaluate the error rate performance of the LDPC-COFDM systems with M-PSK using the Gray and the natural mappings on an AWGN channel, and that of the systems with M-PSK using the Gray mapping on a flat Rayleigh fading channel. We show that the LDPC-COFDM systems with M-PSK using the Gray mapping have better error rate performance than the systems using the natural mapping on an AWGN channel. We also show that the LDPC-COFDM systems with QPSK is more effective than the other systems on a flat Rayleigh fading channel.

Infrared wireless communication and optical communication usually adopt on-off keying (OOK) and pulse position modulation (PPM). In this paper, we present iterative maximum a posteriori probability (MAP) decoding of Turbo coded OOK and Turbo coded binary PPM (BPPM). We show that the BER performances of both Turbo coded OOK and Turbo coded BPPM are almost identical and improved as the number of iterations is increased.

We analyze the performance of indoor infrared wireless systems using pulse position modulation (PPM) code-division multiple-access (CDMA) on diffuse channels by taking the effects of intersymbol interference (ISI) of more than ten chips into account. Then we analyze the system with a hard-limiter (HL) and the system with a linealizer with dead-zone (LDZ) on diffuse channels. We evaluate the performance of the system with HL, the system with LDZ, and that of the system with the decision-feedback equalizer (DFE) on diffuse channels. We show that the bit error rate (BER) performance of the system using PPM CDMA is worse than that of the system using on-off keying (OOK) CDMA on diffuse channels. We also show that the BER performance of the system with the HL and that of the system with the LDZ are both better than that of the unequalized system on diffuse channels. We also show that the BER performance of the system with HL is almost identical to that of the system with the DFE on diffuse channels. We further show that when the desired code sequence is (0,1,5) and the effects of ISI is large, the BER performance of the system with the LDZ is superior to that of the system with the HL. However, in the average BER performance over each code sequence, the system with the HL is better.

We evaluate the performance of indoor infrared wireless systems using on-off keying code-division multiple-access (OOK-CDMA) systems with three kinds of equalizers: decision-feedback equalizer (DFE), decision-feedforward-and-feedback equalizer (DFFE), and feedforward equalizer (FE). To estimate the impulse response, we use the training sequence that alternates '1' and '0' sequentially. Among three kinds of equalizers, we show that the system with the DFFE can achieve the best performance at high bit rate. When the bit rate is low or the transmitted optical power is low, the system with the FE achieves the best performance.

Orthogonal frequency division multiplexing (OFDM) is a very attractive technique for high-bit-rate data transmission in a multipath fading environment that causes intersymbol interference (ISI). There are many error-correcting codes applied to OFDM, convolutional codes, Reed-Solomon codes, turbo codes, and so on. Low-density parity-check (LDPC) codes have attracted much attention particularly in the field of coding theory. LDPC codes were proposed by Gallager (1962) and the performance is very close to the Shannon limit with practical decoding complexity like turbo codes. We propose LDPC coded OFDM (LDPC-COFDM) systems to improve the bit error rate (BER) of OFDM. We show that the BER of the LDPC-COFDM is worse than that of the TCOFDM on an AWGN channel, while that of the LDPC-COFDM is better than that of the turbo-coded OFDM (TCOFDM) on a frequency-selective fading channel.

To improve the bit error rate (BER) performance of the linearized constant power coded OFDM (LCP-COFDM),that is, the compensating technique used for nonlinear distortion, we propose a technique that combines the partial transmit sequence (PTS) technique with the LCP-COFDM technique. By means of computer simulation, we evaluate the BER and power spectrum density (PSD) performances of the proposed technique. In the BER performance evaluation, we consider both cases where the side information of the PTS technique is coded and uncoded by error correction codes. As a result, we show that the proposed technique can improve the BER performance, while keeping very low out-of-band emission.

To improve the bit error rate (BER) with a low outband emission, we propose an OFDM system with modified predistorter and maximum likelihood sequences (MLS). In the proposed system, input data are mapped to signal point series over multiple subcarriers. Then, the peak power is reduced with the partial transmit sequence (PTS) technique. After limited to the saturation amplitude of the high-power-amplifier (HPA), the signal is applied to the predistorter. Compared with the system using the conventional predistorter, the compression quantity of signal in the proposed system is small, because the peak power is reduced with the PTS technique beforehand. In addition, the minimum Euclidean distance in the proposed system is larger than that of the conventional system. Thus, the proposed system can improve the BER with the low outband emission. We show that only the proposed system can achieve the good performance in terms of both the BER and power spectrum density (PSD).

We obtain upper bounds on the bit-error rate (BER) for the performance of linear binary block coded optical pulse position modulation (PPM) code-division multiple-access (CDMA) systems with soft-decision decoding. We use a union bounding technique to obtain these bounds, so our results correspond to the upper bounds. We consider systems using binary PPM (BPPM) and an avalanche photodiode (APD), and treat APD noise, thermal noise, and multi-user interference using a Gaussian approximation. We compare the performance of BCH-coded BPPM CDMA systems with soft-decision decoding with that of BCH-coded BPPM CDMA systems with hard-decision decoding. We show that BCH-coded optical PPM CDMA systems using soft-decision decoding can achieve much lower BER than BCH-coded optical PPM CDMA systems using hard-decision decoding.

We propose an interference cancellation technique using a reference signal for optical synchronous code-division multiple-access (CDMA) systems. In the proposed system, we use the signature code sequences composed of the group information codes and the modified prime code sequences. The group information codes are added in the forefront of the signature code sequences to estimate the amount of the multiple access interference (MAI). The proposed cancellation technique can be realized with a simpler structure than the conventional canceller using the time division reference signal, because it can reduce the number of optical correlators from P to two, where P is the prime number. Considering the effects of the MAI, avalanche photodiode (APD) noise, and thermal noise, we analyze the performance of the proposed system. We show that the proposed canceller achieves better bit error probability than the conventional canceller.

We obtain upper bounds on the bit-error rate (BER) for maximum-likelihood (ML) decoding of binary turbo-codes followed by M-ary orthogonal signal mapping through a uniform interleaver. We use the transfer function bounding techniques to obtain the above bounds. Thus, the upper bounds correspond to the average bound over all interleavers of a given length. We apply the techniques to parallel concatenated coding (PCC) schemes where recursive convolutional codes are used as constituent codes. We present the average bounds on the BER of turbo codes with constraint length 3 for M-ary orthogonal signals on additive white Gaussian noise (AWGN) channels. We show that for a given increase in interleaver length, the improvement in BER is larger for turbo-coded 4-ary orthogonal signals than for turbo-coded binary orthogonal signals. We also show that the coding gains of turbo code for M-ary orthogonal signals becomes smaller as M is increased when the block length N is short.

We obtain upper bounds on the bit-error rate (BER) for turbo-coded optical code-division multiple-access (CDMA) systems using pulse position modulation (PPM.) We use transfer function bounding techniques to obtain these bounds, so our results correspond to the average bound over all interleavers of a given length. We consider parallel concatenated coding (PCC) schemes that use recursive convolutional codes as constituent codes. We consider systems using an avalanche photodiode (APD), and treat APD noise, thermal noise, and multi-user interference using a Gaussian approximation. We compare the performance of turbo coded systems with that of BCH-coded systems with soft-decision decoding. We show that turbo-coded systems have better performance than BCH-coded systems.

We propose a rate adaptive transmission scheme using repeated and punctured convolutional codes for indoor infrared wireless communications. The proposed system uses a coding scheme consisting of an outer punctured convolutional code and an inner repetition code of length RR used for each punctured convolutional code symbol, and varies the code rate of the punctured convolutional codes and the rate reduction factor of the repetition codes, that is, the bit rate varies adaptively depending on the channel conditions. We analyze the performance and show that the proposed system can realize communications in worse channel conditions at the expense of the bit rate, while maximizing the throughput available to a user on his position.

We propose a rate-adaptive indoor infrared wireless communication systems using punctured convolutional codes and adaptive pulse position modulation (PPM) to achieve high bit rate and realize communications even under bad channel conditions with limited transmitter power. The proposed system varies the code rate of the punctured convolutional codes and the modulation order L of L-PPM adaptively according to the channel conditions; when the channel condition is good, low-order PPM and high-rate codes are selected, while high-order PPM and low-rate codes are selected when the channel condition is bad. We analyze the performance of the proposed system on hybrid line-of-sight (LOS) channels. Our results show that the proposed system can achieve high bit rate and realize communications even under bad channel conditions with limited transmitter power by varying the code rate and the modulation order adaptively according to the signal-to-noise ratio (SNR).

This paper proposes new channel interference cancellation technique using an electrooptic (EO) switch and optical hard-limiters for direct-detection optical CDMA systems. In the proposed system the channel interference in a reference path is reduced by double optical hard-limiters. The output of a reference path drives the EO switch to reduce the effect of the channel interference in a signal path. When the reference signal is larger than or equal to the threshold of the driver circuit, the EO switch is turned on and the signal in a signal path is passed to an avalanche photodiode (APD); otherwise, the EO switch is turned off and the signal is not passed to the APD. It is shown that the proposed system is effective to reduce the effect of the channel interference when the received optical power is large.

Performance of optical asynchronous CDMA systems with double optical hard-limiters is analyzed under the assumption of Poisson shot noise model for the receiver photodetector where the noise due to the detector dark currents exists. The performance is analyzed in the chip synchronous case and thus the derived performance results in the upper bounds. The results show that the optical asynchronous CDMA systems with double optical hard-limiters have much better performance than other conventional asynchronous CDMA systems with and without the optical hard-limiter even when the number of simultaneous users is large, which is different from the case of optical synchronous CDMA systems.

This paper proposes Viterbi decoding differential detection (Viterbi decoding DD) with space diversity for 16DAPSK. Power ratio combing (PRC) is employed as a diversity combining scheme. The bit error probability performance is evaluated on Rayleigh fading channels by the computer simulation. It is shown that extending the length of a trellis path is effective for bits encoded in phase by 8DPSK to improve the bit error probability, while not effective for bits encoded in amplitude by 2DASK. However, Viterbi decoding DD with N/sub A/=2 for bits encoded in amplitude by 2DASK can achieve better performance than conventional DD. The proposed system with the lengths of trellis path N/sub A/=2 for bits encoded in amplitude by 2DASK and N/sub P/=100 for bits encoded in phase by 8DPSK is shown to achieve gains for the DD with space diversity systems when the fading is fast or not. Therefore, the Viterbi decoding DD with PRC diversity is shown to be effective for 16DAPSK to improve the bit error probability performance on Rayleigh fading channels.

An optical code-division multiple-access (CDMA) system with double optical hard-limiters is proposed where the optical hard-limiters are placed before and after an optical correlator. Moreover, the-effect of the optical hard-limiter on the performance of the optical synchronous CDMA systems using modified prime sequence codes as signature codes is analyzed under the assumption of a Poisson shot noise model for the receiver photodetector where the noise due to the detector dark currents exists. We evaluate the performance under average power and bit rate constraints. Our results show that using the single optical hard-limiter slightly degrades the performance of the optical CDMA systems under the assumption of Poisson shot noise model for the receiver photodetector where the noise due to the detector dark currents exists. Moreover, we show that the optical CDMA systems with double optical hard-limiters have better performance than other conventional CDMA systems with and without the optical hard-limiter when the number of simultaneous users is not so large.