Fluid antenna (FA) is an emerging technology in recent years to switch physical locations of antennas in a predetermined small space. This paper proposes a reconfigurable intelligent surfaces (RIS)-cooperative framework with FA-assisted non-orthogonal multiple access (NOMA) system, namely FA-RIS-NOMA. Targeting multi-interference scenarios in urban environments, the considered system incorporates a base station (BS) equipped with a conventional antenna and users each equipped with an FA. Additionally, the RIS is utilized to forward signals between BS and users blocked by obstacles. The non-line-of-sight multipath fading characteristics of the RIS-assisted link are modeled as a Nakagami-mchannel. To overcome the multiuser interference and improve the system performance, we develop the NOMA technique for resource allocation. Meanwhile, to reduce the signal processing complexity at the receiver, a group optimization greedy detection method is proposed. To evaluate the system’s reliability, the outage probability for each user is analyzed by using the copula function. This method enables the derivation of the cumulative distribution and probability density functions for the equivalent user-side channel, from which closed-form outage probability expressions are obtained. Numerical results demonstrate that the proposed framework achieves signal-to-noise ratio gains of approximately 9 dB and 8 dB over fixed-antenna and relay-assisted systems, respectively. Furthermore, the proposed detection method reduces computational complexity by over 75% with less than 0.5 dB performance degradation.

Fluid antenna-enabled multiple-input multiple-output (FA-MIMO) systems hold significant application potential; however, they also introduce substantial costs in channel state information (CSI) acquisition. In this paper, we propose a novel FA-assisted rectangular differential index modulation (FA-RDIM) scheme for MIMO systems, aiming to achieve high spectral efficiency while addressing the problem of the increased CSI acquisition cost. The transmitted information is mapped to modulation symbols and cyclic shifts of FA pattern indices. A multi-stage detection method is introduced, which reduces computational complexity by identifying the most likely candidates for FA pattern indices. We derive a closed-form expression for the bit error rate (BER) theoretical performance considering error propagation, and present an optimization algorithm based on rank and determinant criterion (RDC), Hamming distance (HD), and gradient descent (GD) to optimize the FA pattern vector set. Simulation results demonstrate that the proposed scheme exhibits a minimal performance loss compared to conventional coherent modulation schemes under static channel conditions, while offering a performance advantage in time-varying channels with outdated CSI.

We introduce DenoMAE2.0, an enhanced denoising masked autoencoder designed to significantly improve representation learning for Automatic Modulation Classification (AMC) in wireless communications. Unlike standard Masked Autoencoders (MAEs), which solely reconstruct masked inputs, DenoMAE2.0 jointly performs denoising and reconstruction by incorporating a position-aware local patch classification objective. This approach enables the model to simultaneously denoise corrupted signals and accurately reconstruct missing information, effectively capturing both global context and local structural patterns crucial for modulation classification under noisy and data-scarce scenarios. Extensive experiments demonstrate that DenoMAE2.0 achieves superior denoising and classification performance, outperforming baseline methods and its predecessor, DenoMAE, particularly under extremely low Signal-to-Noise Ratios (SNR) (down to -7dB), where other approaches significantly degrade. Our method achieves state-of-the-art accuracy of 82.4%, improving upon the original DenoMAE by 1.1%, while consistently enhancing reconstruction quality across all modulation classes. Additionally, DenoMAE2.0 exhibits robust transfer learning capabilities, achieving consistent improved accuracy over a Vision Transformer (ViT) at different SNRs with a 6.13% highest gain at noise-signal equilibrium point. These results highlight the effectiveness of our dual-objective, self-supervised framework for robust AMC in challenging wireless communication environments.

Spectral efficiency (SE) and energy efficiency (EE) are two major challenges faced by the sixth-generation wireless communication systems. In this paper, we propose a reconfigurable intelligent surfaces-assisted simultaneous wireless information and power transfer scheme based on fluid antenna port grouping index modulation (RIS-FA-PGIM). The flexible port switching capability of FA overcomes the spatial limitations of traditional antennas, significantly improving the SE. Further-more, in order to improve the SE and EE of the system, this paper jointly optimizes the beamforming matrix at the base station and RIS. Due to the coupling relationship between variables, the optimization problem is non-convex and difficult to solve. In order to solve this problem, an alternating optimization algorithm is proposed, which gradually approaches the global optimal solution through an iterative optimization process. Simulation results show that the system not only achieves outstanding SE performance but also realizes low energy consumption, which verifies the effectiveness and superiority of the scheme.

The combination of positron emission tomography (PET) and computed tomography (CT) can accurately reflect the metabolic and anatomical information of a variety of tumors, including nasopharyngeal carcinoma, lymphoma and lung cancer, which plays an important role in the diagnosis, staging and efficacy evaluation of tumors. Accurate and automatic delineation of target tumors is crucial for radiotherapy, however, the tumor segmentation task is extremely challenging due to the fuzzy tumor boundaries, uncertain locations, and the scattered distribution of multiple tumors throughout the body. To this end, this study extended the STUNet pre-trained on the TotalSegmentator dataset and proposed M-AECA, which integrates a Mamba-based auxiliary encoder (M-AE) to provide multi-scale global features for enhanced feature extraction. In addition, an Inter-Branch Feature Fusion Module (IBFFM) is designed to achieve more comprehensive global and local feature fusion through cross attention (CA) and feature subspace projection. The method was evaluated on Hecktor and AutoPET datasets, demonstrating superior performance compared to other comparison methods. In the test sets of these two datasets, the average Dice similarity coefficients of the proposed method were 70.86% and 64.91%, respectively. In addition, the results of the ablation experiment show that the proposed M-AE and IBFFM demonstrate strong performance and significant advantages.

The differences between cross-modality medical images are significant, so several studies are working on unsupervised domain adaptation (UDA) segmentation, which aims to adapt a segmentation model trained on a labeled source domain to an unlabeled target domain. The conventional UDA segmentation strategy aims to integrate image generation and segmentation. However, conventional image generation modules only consider information from a single domain (source or target), resulting in visual inconsistencies. The image generation module may also lack anatomical constraints, leading to incorrect pseudo-label generation. To address these issues, we propose an improved symmetric generative adversarial network (ISGAN). Unlike conventional approaches that perform domain adaptation only in the source or target domain, ISGAN adopts a symmetric architecture using two-path domain adaptation to reduce the visual difference. In addition, ISGAN adopts a bidirectional training strategy to optimize the image generation and segmentation modules. The bidirectional training strategy introduces the anatomical constraints into the image generation module, thereby reducing the generation of incorrect pseudo labels. Finally, we validate ISGAN on two cross-modality datasets (the MMWHS cardiac dataset and Abdomen dataset). ISGAN delivers promising segmentation and generalization performance compared with state-of-the-art UDA methods.

Summary COVID‐19 is a novel coronavirus‐induced disease and automatic identification of COVID‐19 using computer‐assisted methods can facilitate faster diagnostic efficiency. Current research typically employs a single model for COVID‐19 identification, while implicit and complementary knowledge between heterogeneous networks is neglected. To address these issues, we propose a new model based on deep mutual learning with online feature alignment called DML‐OFA to more effectively diagnose COVID‐19. First, we use a traditional deep mutual learning (DML) framework to allow two parallel heterogeneous networks to learn from each other to form two effective feature extractors. In addition, we embed the adaptive feature fusion classifier and logits ensembling module in the proposed DML‐OFA, which can simultaneously learn implicit complementary knowledge from feature maps and logits. We evaluated DML‐OFA on four public datasets: Covid‐chestxray‐dataset, ChestXRay2017, Coronavirus‐dataset and COVIDx. The results showed that our model attains 97.10 Accuracy, 97.28 Specificity, 96.21 Recall, 97.45 Precision, and 96.82 F1‐score, which outperforms other previous related works.

Background and Objective. Currently, depression is a widespread global issue that imposes a significant burden and disability on individuals, families, and society. Deep learning (DL) has emerged as a valuable approach for automatically detecting depression by extracting cues from audiovisual data and making a diagnosis. PHQ-8 is considered a validated diagnostic tool for depressive disorders in clinical studies, and the objective of this experiment is to improve the accuracy of PHQ-8 prediction. Furthermore, this paper aims to demonstrate the effectiveness of expert knowledge in depression diagnosis and discuss a novel multimodal network architecture. Methods. This research paper focuses on multimodal depression analysis, proposing a flexible parallel transformer (FPT) model capable of extracting data from three distinct modalities (i.e., one video and two audio descriptors). The FPT-Former model incorporates three paths, each using expert-knowledge-based descriptors from one modality as inputs. These descriptors are represented into 32 features by the encoder part of a transformer module, and these features are fused to realize the final regression of PHQ-8 score. The extended distress analysis interview corpus (E-DAIC) is an expansion of WOZ-DAIC which comprises semiclinical interviews intended to assist in the diagnosis of psychological distress conditions. It encompasses a sample size of 275 participants, and in this study, it was utilized to test the model in a way of 10-fold cross-validation. Results. The FPT presented herein achieved comparable performance to the state-of-the-art works, with a root mean square error (RMSE) of 4.80 and a mean absolute error (MAE) of 4.58. The ablation experiments demonstrate that the three-modality-fused model outperforms other two-modality-fused and single-modality models. While using a PHQ-8 score threshold of 10, the accuracy of the depression classification is 0.79. Conclusions. Leveraging the strength of expert-knowledge-based multimodal measures and parallel transformer structure, the FPT model exhibits promising performance in depression detection. This model improved the accuracy of depression diagnosis through audio and video, and it also proved the effectiveness of using expert-knowledge in the diagnosis of depression. The traits of flexible structure, high predictive efficiency, and secure privacy protection make our model a promotable intelligent system in mental healthcare.

In this paper, a novel active fully-connected reconfigurable intelligent surface assisted space shift keying and code index modulation (AFRIS-SCIM) scheme is proposed. On one hand, by introducing joint space-code index modulation while maintaining low power consumption and complexity, the proposed scheme achieves higher data rates compared to existing one-dimensional index modulation. On the other hand, the proposed active fully-connected architecture achieves a desirable trade-off between reliability and power consumption compared to conventional passive RIS and active RIS architectures. Additionally, to reduce detection complexity at the receiver, a low-complexity detection algorithm is proposed and the upper bound for the bit error rate (BER) of the system is derived. Mathematical models characterizing the system complexity and power consumption are also established to analyze the overall performance. Both theoretical analyses and simulation results demonstrate that the AFRIS-SCIM scheme outperforms existing RIS-IM schemes as well as multidimensional index modulation systems in terms of BER performance.

The reconfigurable intelligent surface (RIS) aided index modulation (IM) is a promising technology for next-generation wireless communications. However, acquiring channel state information (CSI) for RIS-based IM requires expensive pilot overhead, especially for the IM in multiple domains. In this paper, a novel rectangular differential reflecting spatial modulation (RDRSM) system is proposed for the RIS-aided multiple-input multiple-output (MIMO) system. Specifically, the proposed multi-antenna-activated RDRSM (M-RDRSM) scheme utilizes a rectangular dispersion matrix (DM) to jointly map a digital beamforming (DBF) weight vector and a RIS reflection pattern to perform the rectangular differential modulation. A thorough analysis presents that the proposed M-RDRSM scheme can achieve high-spectral efficiency, low complexity of the system, and noncoherent decoding without prior knowledge of CSI in the space-reflection dual-domain IM. Further, the proposed M-RDRSM scheme is simplified to a single-antenna-activated RDRSM (S-RDRSM) scheme, which can reduce the hardware cost and avoid the inter-antenna synchronization problem more effectively. Simulation results demonstrate that the proposed RDRSM scheme performs better in terms of bit error rate performance and achieve a lower decoding complexity than the existing correlated differential IM schemes with the same achievable spectral efficiency.

Unsupervised domain adaptation (UDA) methods have shown great potential in cross-modality medical image segmentation tasks, where target domain labels are unavailable. However, the domain shift among different image modalities remains challenging, because the conventional UDA methods are based on convolutional neural networks (CNNs), which tend to focus on the texture of images and cannot establish the global semantic relevance of features due to the locality of CNNs. This paper proposes a novel end-to-end Swin Transformer-based generative adversarial network (ST-GAN) for cross-modality cardiac segmentation. In the generator of ST-GAN, we utilize the local receptive fields of CNNs to capture spatial information and introduce the Swin Transformer to extract global semantic information, which enables the generator to better extract the domain-invariant features in UDA tasks. In addition, we design a multi-scale feature fuser to sufficiently fuse the features acquired at different stages and improve the robustness of the UDA network. We extensively evaluated our method with two cross-modality cardiac segmentation tasks on the MS-CMR 2019 dataset and the M&Ms dataset. The results of two different tasks show the validity of ST-GAN compared with the state-of-the-art cross-modality cardiac image segmentation methods.

With the widespread popularity of the Internet of Things and various intelligent medical devices, the amount of medical data is rising sharply, and thus medical data processing has become increasingly challenging. Mobile edge computing technology allows computing power to be allocated at the edge closer to users, which enables efficient data offloading for healthcare systems. However, existing studies on medical data offloading seldom guarantee effective data privacy and security. Moreover, the research equipping data offloading architectures with Blockchain neglect the delay and energy consumption costs incurred in using Blockchain technology for medical data offloading. Therefore, in this paper, we propose a data offloading scheme for healthcare based on Blockchain technology, which achieves optimal medical resource allocation and simultaneously minimizes the cost of offloading tasks. Specifically, we design a smart contract to ensure secure data offloading. And, we formulate the cost problem as a Markov Decision Process, solved by a policy search-based deep reinforcement learning (Asynchronous Advantage Actor-Critic) scheme, where we jointly consider offloading decisions, allocation of computing resources and radio transmission bandwidth, and Blockchain data security audits. The security of our smart-contract-based mechanism is theoretically and empirically proved, while extensive experimental results also show that our solution can obtain superior performance gains with lower cost than other baselines.

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings.

Narrowband Internet of Things (NB-IoT) is an emerging standard serving massive wireless communications devices. It is implemented based on the legacy long-term evolution (LTE) technology and thus shares many system configurations with it. In fact, NB-IoT is deployed on some of the spectrum allocated to LTE. This sometimes introduces interference to the frequency bands with LTE transmission. Signal identification has been found as a critical and effective method for spectrum awareness and the improvement of resource allocation performance thus mitigating potential interference. While not been studied in previous work, this article fills the hole in identifying NB-IoT transmissions in different communications scenarios. In particular, we develop a convolutional neural network (CNN)-based signal identification method to distinguish NB-IoT signals from other cellular signals. The performance of the signal identification method is tested with different CNN training setups. Our experiments demonstrate that the proposed model can successfully identify the existence of NB-IoT signal with 98.13% accuracy in the Rayleigh fading channel where the signal-to-noise ratio (SNR) = 10 dB. Our results also show that the proposed signal identification method is able to provide promising identification accuracy under various and unknown SNR environments in both additive white Gaussian noise (AWGN) and Rayleigh fading channels.

A novel temporal convolutional network (TCN) model is utilized to reconstruct the central aortic blood pressure (aBP) waveform from the radial blood pressure waveform. The method does not need manual feature extraction as traditional transfer function approaches. The data acquired by the SphygmoCor CVMS device in 1,032 participants as a measured database and a public database of 4,374 virtual healthy subjects were used to compare the accuracy and computational cost of the TCN model with the published convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) model. The TCN model was compared with CNN-BiLSTM in the root mean square error (RMSE). The TCN model generally outperformed the existing CNN-BiLSTM model in terms of accuracy and computational cost. For the measured and public databases, the RMSE of the waveform using the TCN model was 0.55 ± 0.40 mmHg and 0.84 ± 0.29 mmHg, respectively. The training time of the TCN model was 9.63 min and 25.51 min for the entire training set; the average test time was around 1.79 ms and 8.58 ms per test pulse signal from the measured and public databases, respectively. The TCN model is accurate and fast for processing long input signals, and provides a novel method for measuring the aBP waveform. This method may contribute to the early monitoring and prevention of cardiovascular disease.

Positron emission tomography-computed tomography (PET/CT) is an essential imaging instrument for lymphoma diagnosis and prognosis. PET/CT image based automatic lymphoma segmentation is increasingly used in the clinical community. U-Net-like deep learning methods have been widely used for PET/CT in this task. However, their performance is limited by the lack of sufficient annotated data, due to the existence of tumor heterogeneity. To address this issue, we propose an unsupervised image generation scheme to improve the performance of another independent supervised U-Net for lymphoma segmentation by capturing metabolic anomaly appearance (MAA). Firstly, we propose an anatomical-metabolic consistency generative adversarial network (AMC-GAN) as an auxiliary branch of U-Net. Specifically, AMC-GAN learns normal anatomical and metabolic information representations using co-aligned whole-body PET/CT scans. In the generator of AMC-GAN, we propose a complementary attention block to enhance the feature representation of low-intensity areas. Then, the trained AMC-GAN is used to reconstruct the corresponding pseudo-normal PET scans to capture MAAs. Finally, combined with the original PET/CT images, MAAs are used as the prior information for improving the performance of lymphoma segmentation. Experiments are conducted on a clinical dataset containing 191 normal subjects and 53 patients with lymphomas. The results demonstrate that the anatomical-metabolic consistency representations obtained from unlabeled paired PET/CT scans can be helpful for more accurate lymphoma segmentation, which suggest the potential of our approach to support physician diagnosis in practical clinical applications.

Accurate segmentation of the hepatic vein can improve the precision of liver disease diagnosis and treatment. Since the hepatic venous system is a small target and sparsely distributed, with various and diverse morphology, data labeling is difficult. Therefore, automatic hepatic vein segmentation is extremely challenging. We propose a lightweight contextual and morphological awareness network and design a novel morphology aware module based on attention mechanism and a 3D reconstruction module. The morphology aware module can obtain the slice similarity awareness mapping, which can enhance the continuous area of the hepatic veins in two adjacent slices through attention weighting. The 3D reconstruction module connects the 2D encoder and the 3D decoder to obtain the learning ability of 3D context with a very small amount of parameters. Compared with other SOTA methods, using the proposed method demonstrates an enhancement in the dice coefficient with few parameters on the two datasets. A small number of parameters can reduce hardware requirements and potentially have stronger generalization, which is an advantage in clinical deployment.

Collaborative learning methods for medical image segmentation are often variants of UNet, where the constructions of classifiers depend on each other and their outputs are supervised independently. However, they cannot explicitly ensure that optimizing auxiliary classifier heads leads to improved segmentation of target classifier. To resolve this problem, we propose a structured collaborative learning (SCL) method, which consists of a context-aware structured classifier population generation (CA-SCPG) module, where the feature propagation of the target classifier path is directly enhanced by the outputs of auxiliary classifiers via a light-weighted high-level context-aware dense connection (HLCA-DC) mechanism, and a knowledge-aware structured classifier population supervision (KA-SCPS) module, where the auxiliary classifiers are properly supervised under the guidance of target classifier's segmentations. Specifically, SCL is proposed based on a recurrent-dense-siamese decoder (RDS-Decoder), which consists of multiple siamese-decoder paths. CA-SCPG enhances the feature propagation of the decoder paths by HLCA-DC, which densely reuses previous decoder paths' output predictions to belong to the target classes as inputs to the latter decoder paths. KA-SCPS supervises the classifier heads simultaneously with KA-SCPS loss, which consists of a generalized weighted cross-entropy loss for deep class-imbalanced learning and a novel knowledge-aware Dice loss (KA-DL). KA-DL is a weighted Dice loss broadcasting knowledges learnt by the target classifier to other classifier heads, harmonizing the learning process of the classifier population. Experiments are performed based on PET/CT volumes with malignant melanoma, lymphoma, or lung cancer. Experimental results demonstrate the superiority of our SCL, when compared to the state-of-the-art methods and baselines.

Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data. However, NMF pays the same attention to all attributes of a data point, which inevitably leads to inaccurate representations. For example, in a human-face dataset, if an image contains a hat on a head, the hat should be removed or the importance of its corresponding attributes should be decreased during matrix factorization. This article proposes a new type of NMF called entropy weighted NMF (EWNMF), which uses an optimizable weight for each attribute of each data point to emphasize their importance. This process is achieved by adding an entropy regularizer to the cost function and then using the Lagrange multiplier method to solve the problem. Experimental results with several datasets demonstrate the feasibility and effectiveness of the proposed method. The code developed in this study is available at https://github.com/Poisson-EM/Entropy-weighted-NMF.

End-to-end learning of communications systems is a promising new paradigm for future communications, in which deep neural networks (DNNs) are implemented in the transmitter and receiver as an autoencoder architecture. However, due to DNN’s natural vulnerability to adversarial perturbations, the end-to-end communications system exhibits security and robustness issues in terms of adversarial attacks over the air. The common defensive method, known as adversarial training, is to augment training data with adversarial perturbations, but it is hard to cover all possible perturbations and also hurt the system generalization. In this paper, we propose a novel and defensive mechanism based on a generative adversarial network (GAN) framework1to achieve robust end-to-end learning of a communications system. We utilize a generative network to model a powerful adversary and enable the end-to-end communications system to combat the generative attack network via a minimax game. We show that the proposed system not only works well against white-box and black-box adversarial attacks but also possesses excellent generalization capabilities to maintain good performance under no attacks. The results also show that our GAN-based system outperforms the conventional communications system and the autoencoder communications system with/without adversarial training.

Lymphoma is cancer originated in the lymphatic system. Clinically, automatic and accurate lymphoma segmentation is critical yet challenging. Recently, UNet-like architectures are widely used for medical image segmentation. The pure UNet-like architectures can model the spatial correlation between the feature maps very well, whereas they discard the critical temporal correlation. Some prior works combine UNet with recurrent neural networks (RNNs) to utilize the spatial and temporal correlation simultaneously. However, it is inconvenient to incorporate some advanced techniques proposed for UNet to RNNs, which hampers their further improvements. In this paper, we propose a recurrent dense siamese decoder architecture, which simulates RNNs and can densely utilize the spatial temporal correlation between the decoder feature maps following a "UNet" approach. We combine it with a modified hyper dense encoder. Therefore, the proposed model is a UNet with a hyper dense encoder and a recurrent dense siamese decoder (HD-RDS-UNet). To stabilize the training process, we propose a weighted Dice loss with stable gradient and self-adaptive parameters. We perform patient-independent five-fold cross-validation on 3D volumes collected from whole-body PET/CT scans of patients with lymphomas. The experimental results show that the volume-wise average Dice score and sensitivity are 85.58% and 94.63%, respectively. The patient-wise average Dice score and sensitivity are 85.85% and 95.01%, respectively. The different configurations of HD-RDS-UNet consistently show superiority in the performance comparison. Besides, a trained HD-RDS-UNet can be easily pruned, resulting in significantly reduced inference time and memory usage, while keeping very good segmentation performance.

Modulation classification is one of the key tasks for communications systems monitoring, management, and control for addressing technical issues, including spectrum awareness, adaptive transmissions, and interference avoidance. Recently, deep learning (DL)-based modulation classification has attracted significant attention due to its superiority in feature extraction and classification accuracy. In DL-based modulation classification, one major challenge is to preprocess a received signal and represent it in a proper format before feeding the signal into deep neural networks. This article provides a comprehensive survey of the state-of-the-art DL-based modulation classification algorithms, especially the techniques of signal representation and data preprocessing utilized in these algorithms. Since a received signal can be represented by either features, images, sequences, or a combination of them, existing algorithms of DL-based modulation classification can be categorized into four groups and are reviewed accordingly in this article. Furthermore, the advantages as well as disadvantages of each signal representation method are summarized and discussed.

Voice assistant devices function as interaction gateways in the Internet-of-Things (IoT) smart home. By using voice assistants, users are able to control smart homes via speech commands. However, voice assistants introduce potential security risks and privacy disclosures. For example, malicious actors could impersonate genuine users to send smart home speech commands. Speaker/user verification thus becomes a critical issue for smart home security. This article proposes a secure method for speaker verification in IoT smart homes using millimeter-wave (mmWave) radar. Specifically, we utilize the radar to capture both vocal cord vibration (VCV) and lip motion (LM) as multimodal biometrics for identifying speakers. Traditional voice-based speaker verification methods are vulnerable to impostor attacks, such as replay attacks and voice synthesis attacks, that use recorded or imitated voice audio to spoof the system. Our approach is able to protect IoT smart homes from these attacks by continuously detecting the liveness of the user using mmWave sensing and deep learning techniques. Extensive experiments show that the proposed approach can achieve high verification accuracy and be more robust against imposter attacks.

As Internet-of-Things (IoT) applications today utilize many diverse devices to collect information, Bluetooth low energy (BLE), featuring low power and low cost, is one of the most promising wireless solutions. To meet the requirements of diverse IoT applications, the neighbor discovery process (NDP) in BLE networks requires low cost and low latency, which is one of the most challenging tasks in supporting such a large number of BLE devices. Since the choice of BLE parameters is essential for achieving the required performance of BLE NDP, many performance analysis models have been proposed, aiming to provide guidance for the parameter configuration in IoT applications. This article reviews and studies the BLE NDP models and BLE performance analysis models proposed over the period 2012-2020, considering the advantages and constraints in utilizing these models in IoT. The performance analysis models are divided into two categories: 1) probabilistic models and 2) Chinese reminder theory-based models. The model design, performance metrics, deployment constraints, analysis results, and use cases are discussed for research, development, and applications.

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

Accurate lymphoma segmentation on Positron Emission Tomography (PET) images is of great importance for medical diagnoses, such as for distinguishing benign and malignant. To this end, this paper proposes an adaptive weighting and scalable distance regularized level set evolution (AW-SDRLSE) method for delineating lymphoma boundaries on 2D PET slices. There are three important characteristics with respect to AW-SDRLSE: 1) A scalable distance regularization term is proposed and a parameter q can control the contour's convergence rate and precision in theory. 2) A novel dynamic annular mask is proposed to calculate mean intensities of local interior and exterior regions and further define the region energy term. 3) As the level set method is sensitive to parameters, we thus propose an adaptive weighting strategy for the length and area energy terms using local region intensity and boundary direction information. AW-SDRLSE is evaluated on 90 cases of real PET data with a mean Dice coefficient of 0.8796. Comparative results demonstrate the accuracy and robustness of AW-SDRLSE as well as its performance advantages as compared with related level set methods. In addition, experimental results indicate that AW-SDRLSE can be a fine segmentation method for improving the lymphoma segmentation results obtained by deep learning (DL) methods significantly.

The ever-growing security issues in various mobile applications and smart devices create an urgent demand for a reliable and convenient user verification method. Traditional verification methods request users to provide their secrets (e.g., entering passwords and collecting fingerprints). We envision that the essential trend of user verification is to free users from active participation in the verification process. Toward this end, we propose a continuous user verification system, which re-uses the widely deployed WiFi infrastructure to capture the unique physiological characteristics rooted in user's respiratory motions. Different from the existing continuous verification approaches, posing dependency on restricted scenarios/user behaviors (e.g., keystrokes and gaits), our system can be easily integrated into any WiFi infrastructure to provide non-intrusive continuous verification. Specifically, we extract the respiration-related signals from the channel state information (CSI) of WiFi. We then derive the user-specific respiratory features based on the waveform morphology analysis and fuzzy wavelet transformation of the respiration signals. Additionally, a deep learning based user verification scheme is developed to identify legitimate users accurately and detect the existence of spoofing attacks. Extensive experiments involving 20 participants demonstrate that the proposed system can robustly verify/identify users and detect spoofers under various types of attacks.

This paper investigates the problem of energy efficiency maximization (EEM) for the small cells that coexist with a macro cell in an underlay heterogeneous cellular network, where a macro base station and a number of small base stations transmit signals to a macro user and small users through their shared spectrum. We propose a joint power and bandwidth allocation (JPBA) scheme for the sake of maximizing the energy efficiency (EE) of small cells under the constraint of a guaranteed quality-of-service requirement for the macro cell. Considering that our formulated EEM-based JPBA (EEM-JPBA) problem is non-convex, we convert the original fractional problem into an equivalent subtractive form by adopting the Dinkelbach's method, which is addressed through the augmented Lagrange multiplier approach. Moreover, a new two-tier iterative algorithm is presented to obtain the optimal solution of our EEM-JPBA scheme. Simulation results demonstrate that the proposed two-tier iterative algorithm can quickly converge to the optimal EE solution. In addition, it is shown that the proposed EEM-JPBA scheme significantly outperforms the conventional power and bandwidth allocation methods in terms of their EE performance.

In this paper, we study a multiuser uplink network consisting of one base station (BS), multiple users and one eavesdropper (E), where the users are intended to transmit their confidential messages to BS, while the eavesdropper attempts to tap their transmissions. To improve the transmission secrecy, we propose two friendly jammer selection-aided multiuser scheduling schemes, namely, the random jammer selection-aided multiuser scheduling (RJS-MUS) without knowing the eavesdropper's channel state information (CSI) and the optimal jammer selection-aided multiuser scheduling (OJS-MUS), where the CSIs of eavesdropper are available. For comparison purposes, the conventional non-jammer selection-aided multiuser scheduling (NJS-MUS) scheme is considered as a benchmark. We derive exact and asymptotic closed-form secrecy outage probability expressions for the conventional NJS-MUS as well as proposed RJS-MUS and OJS-MUS schemes. The numerical results show that the proposed RJS-MUS and OJS-MUS schemes with equal power allocation between the selected friendly jammer and scheduled user perform worse than the conventional NJS-MUS approach in terms of the secrecy outage probability in the low signal-to-noise ratio (SNR) region. As the SNR increases, the secrecy outage performance of RJS-MUS and OJS-MUS schemes substantially improves, which is, in turn, better than that of conventional NJS-MUS approach. Moreover, the secrecy advantage of RJS-MUS and OJS-MUS over NJS-MUS becomes more significant with an increasing SNR. Also, it is shown that for both the RJS-MUS and OJS-MUS schemes, a better secrecy performance can be achieved through an optimal power allocation (OPA) between the scheduled user and friendly jammer. In addition, the proposed RJS-MUS and OJS-MUS schemes with OPA strictly outperform the conventional NJS-MUS approach in terms of the secrecy outage probability.

Analysis of the morphology and function of the right ventricle (RV) can be used for the prediction and diagnosis of cardiovascular disease. Accurate description of the structure and function of heart can be provided by analyzing cardiac magnetic resonance imaging (MRI) images. Noise interference and intensity inhomogeneity of MRI images can be addressed by using a local intensity clustering (LIC) model. However, the segmentation of the RV in MRI images still remains a challenge mainly due to its ill-defined borders. To address such a challenge, an algorithm for segmenting the RV based on a local motion intensity clustering (LMIC) model is proposed in this paper. The LMIC model combines the LIC model with the motion intensity information, due to cardiac motion and blood flow. The motion intensity is calculated by using the Lucas Kanade optical flow method and utilized in the LMIC model as an energy parameter. Because the motion intensity of the RV region is stronger than other areas, the RV can be accurately segmented by this approach. Experimental results demonstrate that the LMIC model is able to address the challenge of the ill-defined RV borders in cardiac MRI images and improved RV segmentation accuracy over existing methods.

Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.

Exploiting the Security Aspects of Compressive Sampling

Organ segmentation on computed tomography (CT) images is of great importance in medical diagnoses and treatment. This paper proposes organ location determination and contour sparse representation methods (OLD-CSR) for multiorgan segmentation (liver, kidney, and spleen) on abdomen CT images using an extreme learning machine classifier. First, a location determination method is designed to obtain location information of each organ, which is used for coarse segmentation. Second, for coarse-to-fine segmentation, a contour gradient and rate change based feature point extraction method is proposed. A sparse optimization model is developed for refining the contour feature points. Experimentations with 153 CT images demonstrate the performance advantages of OLD-CSR as compared with related work.

Biological sequence alignment is an important research topic in bioinformatics and continues to attract significant efforts. As biological data grow exponentially, however, most of alignment methods face challenges due to their huge computational costs. HMMER, a suite of bioinformatics tools, is widely used for the analysis of homologous protein and nucleotide sequences with high sensitivity, based on profile hidden Markov models (HMMs). Its latest version, HMMER3, introduces a heuristic pipeline to accelerate the alignment process, which is carried out on central processing units (CPUs) and highly optimized. Only a few acceleration results are reported on the basis of HMMER3. In this paper, we propose a five-tiered parallel framework, CUDAMPF++, to accelerate the most computationally intensive stages in HMMER3's pipeline, multiple/single segment Viterbi (MSV/SSV), on a single graphics processing unit (GPU) without any loss of accuracy. As an architecture-aware design, the proposed framework aims to fully utilize hardware resources via exploiting finer-grained parallelism (multi-sequence alignment) compared with its predecessor (CUDAMPF). In addition, we propose a novel method that proactively sacrifices L1 Cache Hit Ratio (CHR) to get improved performance and scalability in return. A comprehensive evaluation shows that the proposed framework outperforms all existing work and exhibits good consistency in performance regardless of the variation of query models or sequence datasets. For MSV (SSV) kernels, the peak performance of CUDAMPF++ is 283.9 (471.7) GCUPS on a single K40 GPU, and impressive speedups ranging from 1.8x (1.7×) to 168.3× (160.7×) are achieved over the CPU-based implementation (16 cores, 32 threads).

Hardware limitation at the secondary user (SU) terminal makes multiband (wideband) spectrum sensing more challenging. This paper considers spectrum sensing under SU hardware limitation, where the SU can only sense a small portion of the multiband spectrum for a given time period. This introduces a design issue of selecting channels to sense at a given time. A random spectrum sensing strategy (RSSS) is presented to select the subchannels to sense in a totally random fashion. Considering the Markov property of the state transition of a primary user (PU), an adaptive spectrum sensing strategy (ASSS) is then proposed to take advantage of the PU traffic patterns in determining the subchannels to sense. In the proposed ASSS, a novel decision rule is designed and two decision combinations are obtained. The ASSS decision rule adaptively selects a decision combination to determine the subchannels to sense for SU such that the selected subchannels are more likely to be available for the SU network. A metric called spectrum sensing capability (SSC) is defined to evaluate the performance of any spectrum sensing strategies. The SSC expressions for both RSSS and ASSS are derived. Numerical results show significant performance gain of ASSS as compared to RSSS.

A massive multiple-input multiple-output (MIMO) system, which utilizes a large number of antennas at base stations to communicate with multiple user terminals each with a single antenna, is one of the most promising techniques for future wireless communications systems. A successful massive MIMO implementation relies on accurate channel estimation, which is typically performed through pilot sequences. However, the channel estimation performance or massive MIMO performance is limited by pilot contamination due to unavoidable reuse of pilot sequences from terminals in neighboring cells. In this paper, a semi-blind decoding method based on independent component analysis (ICA), channel energy levels, and reference bits is proposed. Specifically, the proposed decoding method uses ICA to separate and decode the received signals and to estimate the channels. The estimated channel energy is used to differentiate the in-cell signals and the neighboring cell signals, and reference bits are applied to identify a desired signal among signals within a cell. The analytical performance results of the proposed decoding method are derived. Numerical results show that the proposed semi-blind decoding method has better bit error rate performance and higher transmit efficiency than the traditional minimum mean-square error decoding method and the singular value decomposition-based decoding method.

By introducing amplify‐and‐forward relaying into a cognitive radio system, typical cognitive relay networks are studied for the optimisation problems of the spectrum and power allocation. By applying the mixed spectrum access of overlay and underlay approaches, an interference evaluation strategy is proposed to use different spectrum and power allocation methods while the secondary users (SUs) are located in different service regions of the primary users (PUs). In the interference evaluation strategy, the service area of the PUs is divided according to the possible interference strength of the PUs from the SUs compared with the location‐aware strategy in which the service area is divided only by the location information. An optimal power allocation algorithm is developed to maximise the throughput of the SUs under the condition of anti‐interference performance of the PUs and the total power constraints of the SUs. A type of computing algorithm that joins the subgradient and the Newton's method is used in order to resolve the complex optimisation problem. Numerical results show that the performance using the interference evaluation strategy, such as the throughput, the power consumption, and the energy efficiency, outperforms that using the location‐aware strategy.

This paper presents a systematic approach to exploiting TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a lookup table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is first introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking and D2D communications in hotspots such as subway, game stadiums, and airports.

Spectrum sharing and aggregation among authorized secondary users (A-SUs) are important tasks in operating effective cognitive radio networks. Furthermore, in protecting spectrum sharing/aggregation against unauthorized secondary users (UA-SUs), secondary user access control (SUAC) is needed, which is investigated in this paper. A jamming signal is injected to degrade the spectrum sensing performance of UA-SUs, while reliable spectrum sensing performance for A-SUs can be achieved through an oblique projection-based jamming cancellation method. An orthogonal frequency division multiplexing-based transmission model is considered in this paper. The generalized likelihood ratio test algorithm is used for both authorized and unauthorized SUs in spectrum sensing. Numerical results show the effectiveness of the proposed SUAC in degrading the spectrum sensing performance of the unauthorized SUs.

Heart disease is one of the most severe health illnesses. Developing accurate and efficient methods to diagnose heart disease is crucial in providing good heart healthcare to patients. In this paper, a data mining based technique for diagnosing heart disease is introduced, in which heart disease related patient data sets are utilized. A matrix factorization based technique for missing data reconstruction is presented. Numerical results show that recovery data sets are able to achieve reliable diagnosis or classification performance comparable to using original completed patient datasets.

Cooperative spectrum sensing improves the performance of cognitive radio (CR) networks. In cooperative sensing, there are two important phases, detecting and reporting. In the detecting phase, multiple CR users detect the presence of primary users. In the reporting phase, one or multiple channels are needed for CR users to transmit local detection results to a fusion center (FC). In this paper, a reporting channel design, based on a random access protocol, is introduced for CR users and the fusion center. Analytical and simulation evaluations are performed considering the collision and capture in the reporting channels. In addition, the rule of K out of N is implemented in FC in determining global detection results. Numerical results demonstrate the feasibility of the random access based reporting channel in implementing cooperative spectrum sensing.

Radio resource management (RRM) is crucial for improving resource utilization in heterogeneous wireless networks. Existing work attempts to exploit the network diversity to gain throughput improvement for users, which, however, neglects the impact of user demand on RRM. Armed with the idea that the ultimate goal of communications is to serve users with personalized demand, we introduce another dimension of potential performance gain, user demand diversity gain. This gain derives from the elaborate matching between user demand and radio resource, which can not be directly attained in existing throughput-centric optimization due to users' blindness in maximizing throughput. Aiming at obtaining this gain, we propose the user demand-centric optimization, where users seek to maximize quality of experience (QoE), instead of throughput. This shift enables us to propose a novel game formulation, QoE game. We derive the condition on the existence of the QoE equilibrium, validate the user demand diversity gain and propose a distributed QoE equilibrium learning algorithm. Finally, a cloud assisted learning framework is proposed to accommodate the learning algorithm with significantly reduced cost. Simulation results validate the existence of user demand diversity gain and the effectiveness of the proposed learning algorithm in improving the system efficiency and QoE fairness.

Using relays to receive and forward packets between a network base station and network users is a promising solution to address the cellular coverage issue. In this study, we consider time division duplexing (TDD) networks and explore the use of network coding for implementing the relays. Several transmission scenarios are investigated, including direction transmissions, one‐way relay and two‐way relay. In addition, we propose an adaptive TDD network, which enables the network to adaptively configure frame structures based on channel conditions to support direct transmissions, cooperative relay and two‐hop relay (one‐way and two‐way). An opportunistic decoding scheme and a relay operation scheme are introduced to realise the proposed adaptive TDD network in a distributed manner. Both throughput and outage probability are derived. Performance evaluations show that the proposed adaptive TDD network based on network coding achieves the maximum throughput, as compared with one‐way relay (without network coding), and extends the cell coverage range (compared with direct transmissions).

To support delay-sensitive traffic in multi-channel cognitive radio systems, designing a channel access scheme faces two major challenges, namely, the long waiting time due to continuous channel occupancy of primary users (PUs) and the performance degradation due to transmission collisions among secondary users (SUs). To address both issues, we propose a two-phase channel access scheme, which consists of a distributed channel negotiation phase and a hopping-based channel access phase for each SU. Specifically, in its first phase, an SU attempts to negotiate a specific initial slot/channel differing from the ones chosen by other SUs. Then, in its second phase, the SU chooses a channel in each time slot in a hopping-based manner to transmit data, where the hopping starts from its initial channel and follows a common hopping sequence. Virtual channels are introduced to accommodate the situation when the number of SUs is larger than that of actual channels. The average maximal waiting time due to the channel negotiation phase is derived, and the effective capacity of the service process for each SU in the channel access phase is analyzed. Numerical results show that the proposed scheme can support a higher traffic load under the statistical delay constraint, as compared with fixed or random channel access schemes.

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing data with an arbitrary abnormal component. Under this model, we then analyze the impact of general abnormal data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal component pursuit, and develop a data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal data parameter configurations.

The unused time slots in a primary time division multiple access (TDMA) network are regarded as the potential channel access opportunities for secondary users (SUs) in cognitive radio (CR). In this paper, we investigate the medium access control protocols that enable SUs to access a common TDMA channel with primary users (PUs). The primary traffic is assumed to follow a Bernoulli random process. A two-state Markov chain is used to model the secondary traffic, and two different scenarios are considered. The first scenario assumes that the secondary packet arrivals are independent and follow a Bernoulli random process and a cognitive carrier sensing multiple access (Cog-CSMA) protocol is proposed. A Rayleigh fading channel is considered in evaluating Cog-CSMA, and its throughput expression is derived in this paper. The second scenario assumes that the packet arrivals follow a correlated packet arrival process and a cognitive packet reservation multiple access (Cog-PRMA) protocol is proposed. A Markov chain is used to model the different system states in Cog-PRMA and derive the throughput. Numerical results show that the Cog-CSMA and Cog-PRMA protocols achieve the objective of supporting secondary transmissions in a TDMA network without interfering the PUs' transmissions and improve the network bandwidth utilization. Copyright © 2012 John Wiley & Sons, Ltd.

This paper investigates the issue of spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks, where at a given time secondary users (SUs) at different locations may experience different spectrum access opportunities. Most prior studies address either spatial or temporal sensing in isolation and explicitly or implicitly assume that all SUs share the same spectrum opportunity. However, this assumption is not realistic and the traditional non-cooperative sensing (NCS) and cooperative sensing (CS) schemes are not very effective in a more realistic setting considering the heterogeneous spectrum availability among SUs. We define new performance metrics to guide the spatial-temporal opportunity detection and propose a two-dimensional sensing (TDS) framework to improve the opportunity detection performance, which exploits correlations in time and space simultaneously by effectively fusing sensing results in a spatial-temporal sensing window. Furthermore, in terms of maximum interference constrained transmission power (MICTP), we classify the spatial opportunities for SUs into three groups: black, grey, and white, and propose a TDS-based distributed power control scheme to further improve the spectrum utilization by exploiting both grey and white spectrum opportunities. The effectiveness of the proposed scheme is demonstrated through in-depth numerical simulations under a variety of scenarios.

Cognitive radio (CR) networks provide an open architecture for effectively utilizing communication resources through flexible opportunistic spectrum access methods. To successfully realize its benefits and minimize the misuses of a CR network, distinguishing radio/user classes (legacy radios/users versus secondary radios/users) and individual radio/user terminals (within one class/type) is a critical and challenging task in CR network operation. In this paper, we propose a radio frequency fingerprinting (RFF) based approach combined with machine learning algorithms to differentiate radio/user classes and terminals. In our experiments, the proposed method is implemented for distinguishing radio class (MOTOROLA walkie talkies (as legacy radios) versus Universal Software Radio Peripheral (USRP) (as secondary radios)) and distinguishing individual radio terminals within one radio class. The experimental results demonstrate that the proposed method is very effective in differentiating radio types and radio terminals.

Cognitive radio (CR) provides an open architecture for efficiently utilizing communication resources through flexible opportunistic access methods. However, such flexibility and dynamic access approach could lead to potential communication resource misuses and security threats. In order to successfully deploy a CR network and realize its benefits, distinguishing/classifying radio terminals and the radio behaviors is an important research issue. This paper explores unique radio characteristics in CR networks, radio access behavior (RAB) characteristics (radio access bandwidth, access time and access response time), in identifying CR terminals in a CR network. Using machine learning algorithms, the proposed RAB based CR classification method can be used for CR network monitoring and CR identification. A GNURadio/Universal Software Radio Peripheral (USRP) test bed is developed to implement and evaluate the performance of the RAB feature extraction and CR identification. The experimental results demonstrate that the proposed method is effective in CR classifications/identifications (differentiating radio types and radio terminals in a CR network).

Cognitive radio (CR) is regarded as a solution to address the spectrum scarcity issue in wireless communications. In CR, an unlicensed network user is enabled to dynamically/adaptively access the frequency channels according to the current state of the radio environment and, therefore, CR refers to a smart radio as defined in an “observe-learn-decision” cognitive cycle. In this paper, we consider the medium access control (MAC) protocols as radio parameters in the cognitive cycle, and propose a new approach called MAC protocol identification to implement smart cognitive MAC. The MAC protocol identification approach enables CR users to sense and identify the network MAC protocol types. The identification results will be used by CR users to adaptively change their transmission parameters in order to improve the spectrum hole utilization efficiency, save energy, minimize the interference to the primary users, as well as facilitate communications among heterogeneous CR networks. To verify that the MAC protocol identification is feasible, we propose a MAC identification process based on machine learning techniques in this paper, and some experimental results are presented.

In this paper, we first explore a selective cooperation framework for secondary user transmissions in a cognitive radio network with single relay. In the selective cooperation framework, two transmission modes (i.e., relay diversity transmission and non-relay direct transmission) are considered. We study two specific selective cooperation schemes with and without an acknowledgement (ACK) from a cognitive destination as to if it succeeds in decoding or not, called ACK and non-ACK based selective cooperation, respectively. We derive closed-form outage probability expressions for the two schemes with imperfect spectrum sensing, showing that an outage probability floor occurs in high signal-to-noise ratio (SNR) regions due to mutual interference between primary and secondary users. We consider the use of the outage probability floor to generalize the traditional diversity-multiplexing tradeoff (DMT) definition, based on which a DMT analysis is conducted for the non-ACK and ACK based selective cooperation schemes. We then extend the selective cooperation framework to a multiple-relay cognitive radio network considering the best cognitive relay only to participate in assisting secondary transmissions, referred to as the selective best-relay cooperation. We also consider the non-ACK and ACK based selective best-relay cooperation schemes and develop their DMTs by using the generalized DMT definition.

This paper investigates a type of attacks on a cognitive radio (CR) network, most active band (MAB) attack, where an attacker or a malicious CR node senses/determines the most active band within a multi-band CR network and targets this band through a denial of service (DoS) attack. We propose a countermeasure strategy, coordinated concealment strategy (CCS), to counter the MAB attack. Our results show that CCS significantly outperforms CR's inherent capability of signal/interference avoidance under a MAB attack. We also introduce power control in CCS to further improve the countermeasure performance in terms of the percentage of survival nodes.

We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.

Abstract With the objective to minimize the energy consumption for packet based communications in energy‐constrained wireless networks, this paper establishes a theoretical model for the joint optimization of the parameters at the physical layer and data link layer. Multilevel quadrature amplitude modulation (MQAM) and automatic repeat request (ARQ) techniques are considered in the system model. The optimization problem is formulated into a three dimensional nonlinear integer programming (NIP) problem with the modulation order, packet size, and retransmission limit as variables. For the retransmission limit, a simple search method is applied to degenerate the three dimensional problem into a two dimensional NIP problem, for which two optimization algorithms are proposed. One is the successive quadratic programming (SQP) algorithm, combining with the continuous relaxation based branch‐and‐bound method, which can obtain the global optimal solution since the continuous relaxation problem is proved to be hidden convex. The other is a low‐complexity sub‐optimal iterative algorithm, combining with the nearest‐neighboring method, which can be implemented with a polynomial complexity. Numerical examples are given to illustrate the optimization solution, which suggests that the joint optimization of the physical/data link layer parameters contributes noticeably to the energy saving in energy‐constrained wireless networks. Copyright © 2010 John Wiley & Sons, Ltd.

In cognitive radio networks, cooperative spectrum sensing typically requires two essential phases: the phase of primary user's signal detection by cognitive users and the phase of initial detection result reporting from the cognitive users to a fusion center, which are referred to as detection and reporting phases, respectively. Common control channels (also called dedicated reporting channels) from the cognitive users to fusion center are assumed in previous research to avoid interfering with the primary user in the reporting phase. This, however, requires additional channel resources and increases implementation complexity due to the dedicated reporting channels management. In this paper, we propose an alternative cooperative spectrum sensing framework without dedicated reporting channels and present an interference analysis of its impact on primary users. We show that the interference caused by the proposed scheme is controllable and can be constrained to satisfy a given primary user's quality-of-service (QoS) requirement. By jointly considering the detection and reporting phases, we further examine the receiver operating characteristics (ROC) performance of the proposed cooperative spectrum sensing scheme in Rayleigh fading environment. Numerical results illustrate that, with a guaranteed detection probability constraint, a minimized false alarm probability can be achieved through an optimization of the time durations between the detection and reporting phases.

In recent years, the security issues of the cognitive radio (CR) networks have drawn a lot of research attentions. Primary user emulation attack (PUEA), as one of common attacks, compromises the spectrum sensing, where a malicious user forestalls vacant channels by impersonating the primary user to prevent other secondary users from accessing the idle frequency bands. In this paper, we propose a new cooperative spectrum sensing scheme, considering the existence of PUEA in CR networks. In the proposed scheme, the sensing information of different secondary users is combined at a fusion center and the combining weights are optimized with the objective of maximizing the detection probability of available channels under the constraint of a required false alarm probability. We also investigate the impact of the channel estimation errors on the detection probability. Simulation and numerical results illustrate the effectiveness of the proposed scheme in cooperative spectrum sensing in the presence of PUEA.

In cognitive radio networks, each cognitive transmission process typically requires two phases: the spectrum sensing phase and data transmission phase. In this paper, we investigate cognitive transmissions with multiple relays by jointly considering the two phases over Rayleigh fading channels. We study a selective fusion spectrum sensing and best relay data transmission (SFSS-BRDT) scheme in multiple-relay cognitive radio networks. Specifically, in the spectrum sensing phase, only the initial spectrum sensing results, which are received from the cognitive relays and decoded correctly at a cognitive source, are selected and used for fusion. In the data transmission phase, only the best relay is utilized to assist the cognitive source for data transmissions. Under the constraint of satisfying a required probability of false alarm of spectrum holes (for the protection of the primary user), we derive an exact closed-form expression of the spectrum hole utilization efficiency for the SFSS-BRDT scheme, which is used as a measure to quantify the percentage of spectrum holes utilized by the cognitive source for its successful data transmissions. For the comparison purpose, we also examine the spectrum hole utilization efficiency for a fixed fusion spectrum sensing and best relay data transmission (FFSS-BRDT) scheme, where all the initial spectrum sensing results are used for fusion without any refined selection. Numerical results show that, under a target probability of false alarm of spectrum holes, the SFSS-BRDT scheme outperforms the FFSS-BRDT scheme in terms of the spectrum hole utilization efficiency. Moreover, the spectrum hole utilization efficiency of the SFSS-BRDT scheme always improves as the number of cognitive relays increases, whereas the FFSS-BRDT scheme's performance improves initially and degrades eventually after a critical number of cognitive relays. It is also shown that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time durations between the spectrum sensing and data transmission phases for both the FFSS-BRDT and SFSS-BRDT schemes.

Typically, each cooperative spectrum sensing process requires two phases: the primary user's signal detection phase, in which all cognitive users attempt to detect the presence of the primary user within a certain observation window (called signal detection overhead); and the initial detection result reporting phase, in which the cognitive users forward their detection results to a fusion center. To avoid interfering with the primary user in the reporting phase, previous research assumed that there is a common control channel (also known as dedicated reporting channel) between the cognitive users and fusion center, which, however, requires extra channel resources and introduces an additional complexity due to the dedicated channel resource management. In this paper, we propose a selective-relay based cooperative spectrum sensing scheme, which is able to control and reduce the interference from cognitive reporting users to primary user without the dedicated channel. We analyze the interference impact on the primary user and show that the interference induced by the reporting users is controllable and can be reduced to satisfy a given outage probability requirement of the primary transmissions. In addition, we investigate the receiver operating characteristics (ROC) of the traditional cooperative sensing scheme (with dedicated reporting channel) and the proposed scheme (without dedicated reporting channel) by jointly considering the signal detection and reporting phases. It is proven that, given a target detection probability, a unique optimal signal detection overhead exists to minimize an asymptotic overall false alarm probability in high SNR regions. We illustrate that, compared to the traditional scheme, the selective-relay based cooperative sensing scheme can save the dedicated channel resources without sacrificing ROC performance. Numerical results also show that, under a guaranteed overall detection probability, an overall false alarm probability can be minimized through an optimization of the signal detection overhead.

In this paper, we propose a cognitive transmission scheme with the best relay selection in a multiple-relay cognitive radio network to improve the secondary transmission performance while guaranteeing the quality-of-service (QoS) of the primary transmissions. We derive the closed-form expressions of outage probability for the secondary transmissions, called secondary outage probability, with the constraint of ensuring a target outage probability of primary transmissions (primary outage probability) for both the traditional non-relay and proposed best-relay selection based cognitive transmission schemes. Numerical results illustrate that, under a primary outage probability constraint, a secondary outage probability floor of the cognitive transmission occurs in high signal-to-noise ratio regions. Besides, the secondary outage probability floor of the proposed scheme is lower than that of the non-relay transmission, which is further reduced with an increasing number of cognitive relays.

In this paper, an incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks. This approach can effectively detect unknown radio signals in the uncertain communication environment. The adaptability of ISOM can improve the real-time learning performance, which provides the advantage of using this approach for on-line learning and control of cognitive radios in many real-world application scenarios. Furthermore, we propose to integrate the ISOM with the hierarchical neural network (HNN) to improve the learning and prediction accuracy. Detailed learning algorithm and simulation results are presented in this work to demonstrate the effectiveness of this approach.

In this paper, we study efficient rate control schemes for delay sensitive communications over wireless fading channels based on reinforcement learning. Our objective is to find a rate control scheme that optimizes the link layer performance, specifically, maximizes the system throughput subject to a fixed bit error rate (BER) constraint and longterm average power constraint. We assume the buffer at the transmitter is finite; hence packet drop happens when the buffer is full. We assume the fading channel under our study can be modeled as a finite state Markov chain, however the transition probability of channel states is not known, and the only information available about the wireless channel is the instantaneous channel gain, which is estimated and fed back from receiver side to the transmitter side on the fly. In this paper, we use reinforcement learning approach to learn the time-varying channel environment and search for the optimal control policy on line. Simulation results show that starting from an arbitrary control policy, the learning agent gradually modifies its estimation about the system model and adjusts the control policy to its optimality.

In cognitive radio networks, a cognitive source node requires two essential phases to complete a cognitive transmission process: the phase of spectrum sensing with a certain time duration (also referred to as spectrum sensing overhead) to detect a spectrum hole and the phase of data transmission through the detected spectrum hole. In this paper, we focus on the outage probability analysis of cognitive transmissions by considering the two phases jointly to examine the impact of spectrum sensing overhead on system performance. A closed-form expression of an overall outage probability that accounts for both the probability of no spectrum hole detected and the probability of a channel outage is derived for cognitive transmissions over Rayleigh fading channels. We further conduct an asymptotic outage analysis in high signal-to-noise ratio regions and obtain an optimal spectrum sensing overhead solution to minimize the asymptotic outage probability. Besides, numerical results show that a minimized overall outage probability can be achieved through a tradeoff in determining the time durations for the spectrum hole detection and data transmission phases. In this paper, we also investigate the use of cognitive relay to improve the outage performance of cognitive transmissions. We show that a significant improvement is achieved by the proposed cognitive relay scheme in terms of the overall outage probability.

Slotted Aloha is an effective random access protocol and can also be an important element of more advanced media access protocols. This paper investigates slotted Aloha in a radio environment with multiple access points. Specifically, we examine the impact of multi-access-point (multi-AP) diversity on the performance of slotted Aloha. The paper considers both omnidirectional (OM) and beamforming (BF) antennas at transmission nodes. This leads to the investigation and comparison of four different network scenarios, i.e., OM with multi-AP diversity, OM without multi-AP diversity, BF with multi-AP diversity and BF without multi-AP diversity. Performance evaluations and comparisons are presented in terms of throughput.

The power adaptation issue for multi-hop sensor networks is considered in this paper to minimize the energy consumption with a given requirement of the average end-to-end bit error rate (BER) performance. To optimize the power adaptation, a nonlinear programming problem is formulated and an analytical result is derived from its Karush-Kuhb-Tucker (KKT) necessary conditions. Numerical examples are presented to compare the networks with the proposed power adaptation scheme and a distributed power adaptation scheme, in which the transmitter of each individual hop adjusts its transmission power based on its own channel state information (CSI) and a specified BER requirement for this hop. A significant energy saving is achieved with the use of the proposed scheme.

In this paper, the outage performance of a dual-hop amplify-and-forward (AF) cooperative diversity system with a maximal-ratio combining receiver at the destination terminal in Rician fading environments with different K-factors is investigated. The signal-to-noise ratio (SNR) at the receiver is upper bounded. Closed-form expressions for the probability density function (PDF) and the moment generating function (MGF) of the bounded SNR are derived. These statistical results are then applied through numerical evaluations to obtain the outage probability. Numerical results of the outage probabilities are presented to illustrate the performance improvement due to cooperative diversity.

This paper studies the throughput, average packet delay and average power consumption for opportunistic slotted ALOHA (OS-ALOHA) in Rayleigh fading channels. The basic idea of the opportunistic transmission is to control the transmission probability of each user based on the channel quality. If the channel is good, a user transmits the packet; otherwise it holds the packet. A finite state Markov chain (FSMC) channel is modeled and the capture effect is considered in this paper. The channel fading is assumed to be correlated from slot to slot and channel state transition is incorporated into the analysis of system performance. The system performance for OS-ALOHA is investigated considering packet traffic statistics. Compared to conventional slotted ALOHA, OS-ALOHA systems show a higher throughput, smaller average delay and higher power efficiency.

In this paper, the performance of multicarrier (MC) code-division multiple access (CDMA) systems in the presence of carrier frequency offset (CFO) with beamforming techniques is evaluated. A conventional uniform linear array (ULA) beam- former is utilized. An independent Nakagami fading channel is assumed for each subcarrier of all users. The performance of outage probability is investigated under a scenario where perfect beamforming is assumed. A closed-form expression of the outage probability performance is derived. Numerical results show that the performance of outage probability improves significantly as the number of antenna elements increases. The effect of CFO on the outage probability is reduced significantly when the beamforming technique is employed.

This paper investigates the outage probability of a wireless system with linear constrained minimum variance (LCMV) beamforming using a uniform linear array beamformer. LCMV beamforming is able to perfectly cancel a number of dominant interferers while other interferers remain. A simplified beamforming model is used to derive closed-form outage probability expressions considering the impact of LCMV beam patterns on various interferers. Fading statistics of Rayleigh, and Nakagami are used to characterize the desired signal, whereas interferers are assumed to be subject to Rayleigh fading. One important aspect of this paper is the consideration of the directions of arrivals (DOA) of the dominant interferers and the exact beam patterns in the outage performance evaluations of LCMV beamforming systems. Numerical results of the outage probability are presented to illustrate the impact of DOA's of the dominant interferers and the impact of different fading scenarios. The paper also presents performance comparison between LCMV beamforming and conventional beamforming considering different interference scenarios (DOA's of dominant interferers).

This paper investigates the outage probability of a wireless system with linear constrained minimum variance (LCMV) beamforming using a uniform linear array beamformer. LCMV beamforming is able to perfectly cancel a number of dominant interferers while other interferers remain. A simplified beamforming model is used to derive closed-form outage probability expressions considering the impact of LCMV beam patterns on various interferers. Fading statistics of Rayleigh, Rician, and Nakagami are used to characterize the desired signal, whereas interferers are assumed to be subject to Rayleigh fading. One important aspect of this paper is the consideration of the directions of arrivals (DOA) of the dominant interferers and the exact beam patterns in the outage performance evaluations of LCMV beamforming systems. Numerical results of the outage probability are presented to illustrate the impact of DOA's of the dominant interferers and the impact of different fading scenarios. The paper also presents performance comparison between LCMV beamforming and conventional beamforming considering different interference scenarios (DOA's of dominant interferers).

Low probability of intercept (LPI) performance of a direct-sequence code division multiple access (DS-CDMA) system is investigated in this paper; both chaotic and pseudorandom binary spreading sequences are considered. Several intercept receiver structures, including energy detector, synchronous and asynchronous, coherent and noncoherent, are examined, and the expressions of the detection probabilities are derived. The bit error rate (BER) of the chaotic CDMA system is also investigated in the paper.

Common packet channel (CPCH) access is an efficient approach to support packet data transmissions in a wideband code division multiple access (W-CDMA) system. Rather than using a continuous-time analysis approach, this paper presents a discrete-time analysis of the CPCH access scheme to fully characterize the complete CPCH operation process. Previous studies using the continuous-time analysis only models a portion of the CPCH process. We assume that a packet arrival process is Poisson distributed and the service time of each packet is geometrically distributed. The study focuses on examining the number of packet arrivals in each CPCH access slot. Performance is evaluated in terms of normalized throughput and it is observed that CPCH performs better when packet mean service time is larger. The performance results are also compared with previous studies using continuous-time analyses

An efficient channel allocation policy that prioritizes handoffs is an indispensable ingredient in future cellular networks in order to support multimedia traffic while ensuring quality of service requirements (QoS). In this paper we study the application of a reinforcement-learning algorithm to develop an alternative channel allocation scheme in mobile cellular networks that supports multiple heterogeneous traffic classes. The proposed scheme prioritizes handoff call requests over new calls and provides differentiated services for different traffic classes with diverse characteristics and quality of service requirements. Furthermore, it is asymptotically optimal, computationally inexpensive, model-free, and can adapt to changing traffic conditions. Simulations are provided to compare the effectiveness of the proposed algorithm with other known resource-sharing policies such as complete sharing and reservation policies

A reduced-rate retransmission (RRR) scheme is proposed for improving the throughput performance of spread-spectrum packet radio networks. The scheme takes advantages of the available multi-rate scalable source coding techniques. It assumes that several versions of a data packet with different sizes (number of information bits) are available. The transmission of a packet starts from its full-size version. If the full-size version is not correctly received, its half-size version is used in the retransmission. If further retransmissions are needed, the quarter-size version and so on are used. The shrunk packets are transmitted either in a minislot if the processing gain is kept the same, or occupying a slot duration by increasing the processing gain proportionally. In both cases, the effective signal to interference ratio for a packet is increased. As a result, the system throughput is improved. Theoretical and numerical results are provided in this paper which illustrate the throughput improvement. Another advantage of the proposed RRR scheme is that the packet-size reduction provides finer granules for link adaptation. Therefore, it is especially suitable for multimedia applications for which codes of variable rate for the source data are available and which can tolerate gracefully degraded quality of service. The performance of the proposed scheme in fading channels is also addressed.

In this paper, the performance of wireless communication systems with antenna arrays and multiple interferers in correlated Nakagami fading channels is investigated. Closed-form expressions of the probability density function (PDF) of signal-to-noise-plus-interference ratio (SNIR), taking account of fading correlations and mutual coupling, are derived and both maximal ratio combining (MRC) and optimal combining (OC) are considered in the derivation. The expressions for the outage probabilities are then derived. Since fading correlations and mutual coupling are closely related to array geometry, performance for both linear and circular arrays are analyzed and numerical results are shown to illustrate the effects of array geometry, angular spread, fading, and so on.

Low probability of intercept (LPI) performance of a direct-sequence (DS) spread-spectrum (SS) system with chaotic spreading sequences is investigated in this paper. Several intercept receivers, including energy detectors, synchronous and asynchronous, coherent and noncoherent structures, which are typically used to detect binary DS SS signals, are examined here to detect the presence of chaotic DS SS signals. A simple detection approach using a binary correlating function to detect nonbinary chaotic sequences is proposed. The expressions of detection probabilities of chaotic spreading signals using those intercept receivers are derived. Comparisons between systems using chaotic and binary sequences are given in terms of the LPI performance, and the performance improvement with chaotic spreading sequences is observed.

In orthogonal frequency division multiple access (OFDMA), the total spectral resource is partitioned into multiple orthogonal subcarriers. These subcarriers are assigned to different users for simultaneous transmission. OFDMA is very sensitive to frequency synchronization errors. In an unsynchronized OFDMA uplink, each user has a different carrier frequency offset (CFO) relative to the common uplink receiver. The orthogonality among subcarriers is thus destroyed resulting in multiple access interference as well as self interference. This problem is tackled in this paper by constructing the orthogonal spectral signals that would have been received if all users were frequency synchronized. A generalized OFDMA is described first considering arbitrary subcarrier assignments. A new signal model is formulated to characterize the interference on the generalized OFDMA uplink due to multiuser frequency synchronization errors. Least squares (LS) and minimum mean square error (MMSE) criteria are used to construct the orthogonal spectral signals from one received OFDMA block. An efficient implementation method is developed based on a banded matrix approximation, which is computationally inexpensive and provides good performance.

In orthogonal frequency-division multiple access (OFDMA), closely spaced multiple subcarriers are assigned to different users for parallel signal transmission. An interleaved subcarrier-assignment scheme is preferred because it provides maximum frequency diversity and increases the capacity in frequency-selective fading channels. The subcarriers are overlapping, but orthogonal to each other such that there is no intercarrier interference (ICI). Carrier-frequency offsets (CFOs) between the transmitter and the receiver destroy the orthogonality and introduces ICI, resulting in multiple-access interference. This paper exploits the inner structure of the signals for CFO estimation in the uplink of interleaved OFDMA systems. A new uplink signal model is presented, and an estimation algorithm based on the signal structure is proposed for estimating the CFOs of all users using only one OFDMA block. Diversity schemes are also presented to improve the estimation performance. Simulation results illustrate the high accuracy and efficiency of the proposed algorithm.

In this paper, reverse link capacity of a signal-to-interference ratio (SIR) based power-controlled direct-sequence code-division multiple-access (DS-CDMA) system, with the use of an antenna array and a Rake receiver in a multiple cell environment, is investigated. Both transmit and receive beamforming in the reverse link are considered. Instead of using tedious iterative methods, reverse link user capacity represented by a simple closed-form expression is derived, which relates to the number of antennas, the number of Rake receiver fingers, a target SIR, and the processing gain. The most efficient distribution of antenna elements between the base stations and the mobile stations to maximize the user capacity is observed through the numerical results, which also show significant capacity improvement by increasing the number of the antennas and Rake receiver fingers.

In orthogonal frequency division multiple access (OFDMA), closely spaced multiple subcarriers are assigned to different users for parallel data transmissions. The subcarriers are overlapping but orthogonal to each other such that there is no inter-carrier interference. Carrier frequency offsets between the transmitter and the uplink receiver will cause the loss of the orthogonality among subcarriers, hence introduce inter-carrier interference resulting in multiple access interference. This paper proposes a carrier frequency offset estimation algorithm for the uplink of interleaved OFDMA systems. This algorithm requires only one OFDMA block and utilizes the inner structure of the signals without any aid from training symbols or the knowledge of channels. Simulation results illustrate the high accuracy and efficiency of the proposed estimation algorithm.

The slotted ALOHA (S-ALOHA) scheme in Nakagami fading channel with the presence of in-cell and cochannel-cell interference is studied. The cases of asynchronous cochannel-cells are especially considered. The analysis is based on the signal capture model and gives closed-form expressions for the system throughput. Additional channel conditions and system parameters are examined in the study, including a minimal signal power requirement, lognormal shadowing and the cellular cluster size.

Abstract In this paper, reverse link capacity, in terms of user capacity, of a code‐division multiple access (CDMA) system with both beamforming and antenna diversity under a multipath environment is analyzed and compared. Signal‐to‐interference ratio (SIR) based power control is assumed in the system. Antenna elements are divided into several groups to achieve antenna diversity gain. Antenna elements in a group are closely spaced to perform beamforming in order to suppress the own‐cell and other‐cell interference, while those groups are widely spaced to achieve the diversity gain. Based on the comparison, a most efficient use of the antenna array according to the distribution of antenna elements among the groups is obtained. Instead of using tedious iterative methods to evaluate user capacity, a simple closed‐form capacity expression with respect to antenna diversity and beamforming gains, a target SIR and the CDMA processing gain is derived. Numerical results indicate significant capacity improvement with the antenna array. Copyright © 2003 John Wiley & Sons, Ltd.

As a coding technique designed for use with multiple transmit antennas, space-time coding (STC) has been gaining more and more attention recently due to its attractive characteristics. One of these characteristics is to provide diversity at the receiver and coding gain over an uncoded system without sacrificing bandwidth. It is also attractive because it increases the effective transmission rate as well as the potential system capacity. This paper presents powerful and computationally efficient linear and decision feedback equalization schemes, considering both zero-forcing (ZF) and minimum mean-square error (MMSE) criteria to combat intersymbol interference (ISI) and obtain diversity gain in a system with a transmit diversity technique using symbol-level space-time block coding. General linear and decision feedback equalizers are derived for the transmit diversity case with two transmit antennas and M receive antennas. The conditions are investigated under which FIR channels can be equalized perfectly using linear FIR filters in noise-free environments. BPSK modulation scheme is assumed to simulate the proposed diversity and equalization methods. Numerical results show significant performance improvements compared to the cases without equalization.

Common packet channel (CPCH) access is an efficient approach to support packet data transmissions in a wideband code division multiple access (W-CDMA) system. This paper investigates a modified CPCH access method which provides prioritized services for various traffic classes. Each traffic type has a distinct transmit permission probability that is estimated at the new call initiation stage based on the status of CPCH channel occupancy. The differentiated service qualities are evaluated in terms of packet blocking rates. The overall system performance is also evaluated in terms of throughput.

Abstract It is of great interest to provide high data rate services in wireless communication systems. In order to support such services, it is desirable to extend space‐time (ST) coding, originally proposed for known, frequency‐nonselective fading channels, to unknown, multipath channels. In this paper, we consider the problem of interference suppression for wireless TDMA (time division multiple access) systems equipped with multiple transmit antennas and receive antennas in frequency‐selective fading channels. A novel scheme with space‐time block coding based transmit diversity (STTD) is presented to estimate the multipath channel, coherently demodulate information symbols, and meanwhile suppress radio interference. The proposed scheme is simple to implement and able to mitigate interference of various origins, including intersymbol interference (ISI), cochannel interference (CCI), and others. Numerical examples are presented to illustrate the performance of the proposed estimator and detector in multipath Rayleigh‐fading channels. Copyright © 2002 John Wiley & Sons, Ltd.

As an effective technique to combat adverse effects of fading, provide diversity and increase the transmission rate, space-time coding (STC) has been gaining more and more attention. This paper presents efficient zero-forcing (ZF) and minimum mean-square error (MMSE) equalization schemes to combat intersymbol interference (ISI) and obtain diversity gain in a system using symbol-level space-time block coding. General linear and decision feedback equalizers are derived with two transmit antennas and M receive antennas. The conditions are explored under which FIR channels can be equalized perfectly in noise-free environments. In order to estimate the system performance, upper bounds of bit error rate (BER) are derived. A training-aided method are proposed as well to estimate the channel state information (CSI) utilizing training sequences. Numerical results of the proposed techniques show significant performance improvement compared to the case without equalization, and show the tightness of the upper bounds along with the effectiveness of the channel estimation scheme.

In this paper we propose an alternative approach for finding a near-optimal call admission policy that prioritizes handoff requests over new calls in a generic mobile cellular network. The performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities. The problem is formulated as a semi-Markov decision process with average cost criterion. Then, a simulation-based learning algorithm based on temporal difference methodology is used to determine a near-optimal control policy online from interaction with the network without a priori knowledge or estimation of the dynamical model of the network. Simulations are provided to compare the effectiveness of the proposed algorithm with two well-known resource-sharing policies: complete sharing and reservation policies (guard threshold). The learning algorithm adapts to traffic variations and this paper shows that it also gives very close blocking probabilities to the optimal guard threshold approach.

Wireless signals are subject to multipath propagation, which results in Rayleigh fading characteristics and the fading signal fluctuates around its mean signal level. The level crossing rate (LCR) and average fade duration (AFD) are often used to characterize the signal-fading statistics. This paper explores the LCR and AFD issue in an interference environment. New definitions of LCR and AFD are introduced and the corresponding expressions are derived for an interference-limited environment.

This paper investigates ARQ operation in a satellite communications system with inter-satellite links. The satellite is considered to have on-board processing (OBP) capability that differs from a conventional bent-pipe satellite system. The inter-link delay is analyzed for OBP ARQ via the technique of signal-flow graph and the result is compared to bent-pipe ARQ both with the selective-repeat scheme. A general scenario with multiple inter-satellite links is examined. The effect of buffer blocking to both throughput efficiency and delay are considered. An analytical result indicates that OBP ARQ although having an advantage over bent-pipe ARQ in throughput efficiency suffers in delay due to buffer queueing.

This paper studies the performance of negative acknowledgement- (NAK-) based ARQ which is a conventional selective repeat ARQ except sending an acknowledgment message only when a transmission error occurs. A Markovian error channel model is considered for both forward and feedback channels. The throughput and mean extra delay in terms of RTD (round trip delay) are obtained. The result shows that the throughput of NAK-based ARQ in a noisy feedback environment is as good as that of a selective repeat ARQ scheme with a perfect feedback channel.

In the next generation cellular mobile multimedia networks, a resource allocation policy, which prioritizes handoff requests over new calls while making efficient use of the network resources, will be an essential component for successful operation. In this paper we develop a new handoff prioritized scheme which adapts the allocation policy according to the current traffic conditions. The goal is to minimize the new call blocking while keeping the handoff failures close to a targeted objective. This problem is formulated as a constrained semi-Markov decision process (SMDP) with average cost criterion. A simulation-based learning algorithm is developed to determine a control policy from direct interaction with the network without a priori knowledge of the network dynamics or traffic. Extensive simulations test the effectiveness of the algorithm under a variety of traffic conditions. Comparisons with other resource allocation policies, such as complete sharing and channel reservation, are presented.

As a coding technique designed for use with multiple transmit antennas, space-time coding (STC) has been gaining more and more attention due to its attractive characteristics to provide diversity at the receiver and coding gain over an uncoded system without sacrificing the bandwidth, and increase the effective transmission rate as well as the potential system capacity. This paper presents powerful and computationally linear intersymbol and cochannel interference suppression schemes, considering both zero-forcing (ZF) and minimum mean-square error (MMSE) criteria to combat intersymbol interference (ISI) and suppress cochannel interference (CCI) and obtain diversity gain in a system with the transmit diversity technique using symbol-level space-time block coding (STBC). General linear schemes are derived for the transmit diversity case with two transmit antennas and M receive antennas. The conditions are investigated under which interference can be suppressed perfectly using linear FIR filters in noise-free environments. The binary phase shift keying (BPSK) modulation scheme is assumed to simulate the proposed diversity and interference suppression methods. Numerical results show significant performance improvements compared to the cases without interference suppression.

This work gives an analytical performance measure of the common packet channel (CPCH) in 3rd Generation Partnership Project (3GPP) wideband code division multiple access (W-CDMA) systems. The CPCH procedure of the channel assignment (CA) mode is modeled as an Erlang loss network, and a technique for analyzing Erlang loss networks is applied to get the expression of the system throughput and delay. The theoretical results are verified with computer simulations.

In nonstationary channels, error rates vary considerably. The author proposes an effective go-back-N ARQ scheme which estimates the channel state in a simple manner, and adaptively switches its operation mode in a channel where error rates vary slowly. It provides higher throughput than other comparable ARQ schemes under a wide variety of error rate conditions.>

This paper investigates mobile satellite system performance under multipath fading, foliage shadowing and cochannel interference. Up-link and down-link interference scenarios are examined considering independent and correlated signal and interference. System performance is evaluated in terms of the outage probability and bit error probability under extremely slow fading and moderately slow fading conditions.>

Investigates the performance of direct-sequence spread-spectrum packet radio networks in the presence of the near/far problem. It is found that the maximum throughput of the network suffers degradation due to the near/far problem. However, analysis also shows that, under high traffic conditions, a network with the near/far problem delivers higher throughput than another without the near/far problem. This suggests that the direct-sequence spread-spectrum packet radio network with the near/far problem retains stability for heavier traffic conditions. Following these findings, a new adaptive power control scheme is suggested to enhance network throughput under both low and heavy traffic conditions. The mean and variance of the packet delay are derived and network stability and deadlock avoidance issues are discussed. The impact of channel fading on the network behavior is also studied.>