Traditional covert ambient backscatter communication (AmBC) systems suffer from a fundamental throughput limitation governed by the square root law (SRL), restricting reliable covert transmission toO(√n) bits overnchannel uses. To overcome this limitation, we introduce a non-colluding replay node that retransmits ambient radio frequency (RF) signals with randomized power, significantly increasing channel uncertainty faced by an adversarial warden (Willie) while preserving compatibility with low-power AmBC architectures. Through rigorous theoretical analysis, we demonstrate that this approach enables linear scaling of covert throughput without necessitating power reduction or prior knowledge of ambient RF signal characteristics. Furthermore, it guarantees that Willie’s total detection error probability can be driven arbitrarily close to 1, specificallyPFA+PMD= 1 − ϵ for any ϵ > 0, simultaneously achieving an arbitrarily low decoding error probability at the legitimate receiver (Bob). Unlike conventional jamming-based solutions requiring stringent synchronization or complex multi-antenna configurations, our replay mechanism operates independently from covert communication participants, substantially simplifying the decoding architecture for the legitimate receiver and reducing synchronization overhead. By increasing the ambient signal power uncertainty, the proposed architecture provides a robust, scalable framework suitable for high-rate covert communication scenarios in IoT and privacy-sensitive applications, achieving an effective balance among covertness, energy efficiency, and system robustness.

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

Task-oriented communications for multi-modal applications emerge with the intelligence-oriented evolution of Internet of Things, where massive computation offloading with heterogeneous streaming requirements greatly challenges the existing mobile networks. Compared with semantic coding which reduces intra-modality redundancy by feature extraction, cross-modal coding further exploits inter-modality association and thus acts as a promising solution. However, unresolved information-theoretic issues hinder its full promise: 1) how to characterize the achievable region of cross-modal coding for task-oriented communications, and 2) to what extent can task-oriented communications benefit from exploiting inter-modality association. Therefore, this work first establishes a cross-modal rate-distortion function for task-oriented communications, and proves the feasibility of optimizing its information-bottleneck inspired transformation for guiding the design of learnable codec. In particular, the optimal feature representation is specified by a converging iterative solver under perfect statistical knowledge. Second, we prove a new bound on compression gains of cross-modal coding in task-oriented communications, based on a sufficient condition for cross-modal representation to be effective. Furthermore, a typical learnable codec is designed, whose loss function can be theoretically interpreted by our derived results. Finally, experimental evaluations verify the positive correlation between cross-modal coding gains and inter-modality association levels.

Incorporating tactile feedback into traditional audio-visual multimedia services has gradually become the killer application in the Beyond 5G era. However, current state-of-the-art tactile coding approaches struggle to achieve extreme compression, thereby significantly impacting the transmission quality of visual streams due to resource competition. To address this challenge, this paper proposes an efficient cross-modal coding method that fully leverages semantic correlations between visual and tactile modalities. Specifically, we first construct a crossmodal coding architecture based on the predictive coding, enabling flexible and scalable bitrates to meet diverse compression requirements and adapt to dynamic network conditions. By introducing explainable surface semantics (e.g., friction, texture) as intermediates, we then associate visual modality with tactile modality to extract their potential correlations from the perspectives of multiple physical properties. Finally, we design a cross-modal feature fusion module through exploiting semantic correlations to further improve reconstruction quality of tactile signals, thereby reducing the bitrates required for tactile residuals and facilitating more efficient transmission. Experimental results demonstrate that the proposed tactile coding method achieves high bitrate compression, with almost no impact on visual stream quality.

With the increasing demand for secure communication in 5G and beyond, authentication of wireless devices has become a crucial task for communication security. Radio frequency fingerprint identification (RFFI) leverages the hardware-specific features in radio frequency (RF) signals, known as radio frequency fingerprints (RFF), to achieve highprecision device identification. However, the dependence of RFFI on the physical characteristics of devices makes it vulnerable to physical spoofing attacks. This paper proposes an innovative physical spoofing attack framework that combines spoofed transmitter and legitimate transmitter models. It performs RFF modeling, RFF concealment (RFFC), and RFF spoofing (RFFS) sequentially to achieve precise spoofing of the original baseband signal. We validate the effectiveness of the proposed physical spoofing mechanism through simulations of seven types of transmitters using MATLAB Simulink. The performance is further evaluated on an RFFI model based on complexvalued convolutional neural networks (CVCNN). Experimental results show that neural networks (NN) significantly outperform the generalized memory polynomial (GMP) model in nonlinear data fitting and temporal relationship modeling. Consequently, NN-based physical spoofing methods exhibit superior attack effectiveness. Specifically, under the signal-to-noise ratio (SNR) of 15 dB, the NN-based physical spoofing method achieves a target attack success rate (TSR) as high as 98%, which is superior to adversarial attack methods. NN-based methods also enhanced performance in terms of stealthiness metrics.

Automatic modulation classification (AMC) is a key technique for identifying the modulation schemes of wireless signals, enabling improved performance and security in communication systems by accurately classifying signal types. However, most existing AMC research assumes modulation classes are part of a closed set, which can cause classifiers to misidentify unknown modulation schemes as known ones, undermining both the security and reliability of communication systems. To address this, we propose a novel open set AMC (OS-AMC) method based on deep metric learning and OpenMax (M-OpenMax). The proposed M-OpenMax-based OS-AMC method utilizes crossentropy loss and center loss to extract separable and discriminative signal features and uses OpenMax to adjust the nonnormalized score output of the model to achieve the classification of known signals and removal of unknown signals. Experimental results demonstrate that the proposed M-OpenMax-based OSAMC method outperforms other open-set AMC techniques, particularly in its ability to handle unknown modulation types.

With unmanned aerial vehicles (UAVs) widely applied in diverse fields, their potential safety risks are more prominent. Accurate UAV identification is crucial. This paper presents a method combining wavelet decomposition and the channel-enhanced attention mechanism. Two-dimensional discrete wavelet transform (2D-DWT) analyzes UAV radio frequency (RF) signal spectrograms for multi-resolution, extracting key information while decomposition data volume and computational complexity. The efficient channel attention (ECA) mechanism boosts the model’s expressiveness. Together with attention-based multi-scale convolution network (AMSCNet), it extracts multi-scale features, reducing information loss and enhancing identification. Experimental results show an average accuracy of 97.00%, outperforming residual network (ResNet) and efficient neural network (EfficientNet). It also has low computational complexity and stable performance across various scenarios, offering an efficient and reliable UAV identification solution.

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

In this paper, we propose a new scheme for uplink transmission of low bit rate local data in wireless systems assisted by reconfigurable intelligent surface (RIS) arrays. With an incoming user signal that has a plane polarization (horizontal or vertical), the RIS array dynamically changes the reflected signal polarization and maps the local data on the polarization state. The base station employs two antennas, one with horizontal polarization and the other with vertical polarization, and detection of the RIS data is performed using a simple power comparator. Our results show that a bit error rate (BER) floor appears when the local data is transmitted at the user symbol rate, but the BER floor vanishes when the local data rate is reduced. The proposed technique thus turns out to be particularly suitable for transmission of low bit rate local data, and it features strong robustness to imperfections of the signal polarization.

Radio frequency fingerprinting (RFF) presents a promising solution for advancing specific emitter identification (SEI) methods, which are crucial for securing the Internet of Things (IoT). While deep learning (DL)-based SEI approaches have demonstrated strong potential, they heavily depend on large, labeled datasets, which are often difficult to obtain in real-world scenarios. This reliance limits the robustness of existing SEI methods. To overcome this challenge, we propose a robust few-shot SEI (FS-SEI) method leveraging multi-view feature fusion with attention (MFFA). By integrating interpretable signal processing (SP) features with DL features and incorporating an attention mechanism for adaptive multi-view fusion, the proposed approach enhances both identification accuracy and robustness in few-shot scenarios. Experimental results validate the effectiveness of the method, showing consistent robustness under noisy conditions and significant gains in identification accuracy. These findings highlight its strong potential for practical applications in dynamic and challenging environments.

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

Specific Emitter Identification (SEI) is pivotal for ensuring the security of the Internet of Things (IoT). Traditional deep learning-based SEI techniques often falter in real-world applications, particularly when distinguishing between legitimate and rogue devices amid noisy conditions and low Signal-to-Noise Ratios (SNR). To surmount these challenges, we propose a novel open-set SEI (OS-SEI) strategy that utilizes a Metric-enhanced Denoising Auto-encoder (MeDAE) architecture. This advanced framework incorporates a deep residual shrinkage network, significantly augmenting the denoising autoencoder’s capability, thereby bolstering its resilience against noisy environments. Further, the integration of discriminative metrics, such as center loss, markedly enhances feature discrimination, resulting in heightened accuracy of device identification. Our comprehensive experimental assessments, conducted on an Automatic Dependent Surveillance-Broadcast (ADS-B) dataset, underscore the superiority of our proposed OS-SEI method over existing models. The findings confirm our approach’s enhanced robustness to noise and its superior accuracy in device identification within open-set scenarios.

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

In this article, a novel multiuser multiple-input-multiple-output (MIMO) communication system for Internet of Things (IoT) is proposed, where the aerial reconfigurable intelligent surface (ARIS) and nonorthogonal multiple access (NOMA) are used as the sum rate enhancement pathway. The base station (BS) has multiple antennas that transmit superimposed signals to multiple users. The passive ARIS serves as a flexible transmit relay to reduce path loss and improve channel gains. Users are divided into several groups based on their channel status, each sharing a radio frequency (RF) chain. To maximize the sum rate of all users, the placement of ARIS, the passive/active beamforming design and the power allocation among users are jointly optimized. As the joint optimization for user grouping, passive/active beamforming and power distribution is formulated as a mixed-integer nonlinear program (MINLP) which is nonconvex and coupled and hence, obtaining an optimal solution is challenging. In this article, the problem is decoupled into three subproblems and solved alternately efficiently. The numerical results demonstrate that the suggested MIMO-ARIS-NOMA system can achieve higher sum rate performance than traditional schemes.

The expansion of the Industrial Internet of Things (IIoT), driven by informatization and intelligence, has made real-time monitoring for ensuring system safety and efficient production more critical than ever. However, in extreme industrial environments, harsh communication conditions and limited computing power impede reliable data transmission and processing, thus posing significant challenges to precise and continuous monitoring for massive IIoT. To this end, this paper proposes a digital twin (DT)-enabled hierarchical monitoring scheme by fully considering communication, computing, and control (3C) collaboration. Specifically, we first propose a DT-enabled 3C collaboration architecture, which builds an accurate digital representation of physical entities, and then completes the missing state based on shared-specific features to evolve continuously across the device lifecycle. Next, we design a multi-agent reinforcement learning (MARL)-based collaborative monitoring method, which formulates the joint optimization problem of compression rate and region assignment for monitoring nodes according to 3C performance. Then, we propose a mask state-assisted MARL scheduling method to refine the mask state space, take advantage of MARL’s distributed decision-making and centralized evaluation, and ensure timely monitoring of massive IIoT. Finally, we build a mine industry simulation platform and verify the effectiveness of the proposed method. The numerical results demonstrate a marked improvement in monitoring utility over traditional methods.

Multi-modal services that integrate signals such as audio, video, and haptic are poised to dominate the 5G and beyond era. Due to the presence of inter-modal redundancy and interference, it is highly challenging to simultaneously meet the demands for real-time, reliability, and high capacity in multi-modal services. Cross-modal communication, taking full use of inter-modal correlations, has been viewed as a promising solution. This paper aims to provide theoretical support for this approach by addressing two key issues: i) the benefits of cross-modal source coding (CMSC) in eliminating inter-modal redundancy, and ii) the benefits of cross-modal channel coding (CMCC) in alleviating inter-modal interference. Specifically, we first construct a cross-modal communication system model to illustrate how inter-modal correlations can be leveraged by incorporating common semantic information in both source coding and channel coding. Next, we analyze the reduction in error probability for a target modality’s symbols at the same coding rate and coding delay, both with and without assistance from other modalities and common semantics, to quantify the benefits of CMSC. Finally, we analyze the differences in achievable channel degrees of freedom and sum capacities under the same channel conditions, both with and without the utilization of common semantics, to quantify the advantages of CMCC. In the three case studies, numerical results validate the theoretical soundness of CMSC and CMCC for binary sources over a Gaussian channel, as well as their feasibility on a practical audio, video and haptic teleoperation platform.

Specific emitter identification (SEI), a crucial technology at the physical layer of communication protocols, exploits unique radio frequency fingerprints (RFFs) to distinguish between individual emitters. Deep learning (DL) has been widely applied to SEI due to its remarkable capability in uncovering hidden features and distinguishing between different devices. However, DL-based SEI approaches typically require extensive labeled datasets, which are difficult to obtain in real-world scenarios, thus limiting their practical applicability. To address this challenge, we propose a novel few-shot SEI (FS-SEI) method based on bi-interpolative metric learning (Bi-InterML), highly reducing the amount of data needed to adapt the algorithm to a new environment and simultaneously avoiding pretraining. Our approach enhances data quality through wavelet coefficient-based and sequence bi-interpolation, generating enriched data used alongside the original dataset for classification via a complex-valued convolutional neural network (CVCNN). Additionally, interpolative metric learning (IML) is employed to constrain feature distances, enhancing feature discriminability. Experimental results on a real-world Wi-Fi dataset demonstrate the effectiveness of the proposed Bi-InterML-based FS-SEI method, achieving an identification accuracy of 91.48% with 10 samples per category, while it outperforms comparative methods by a margin of 9.64% to 43.18% in the case of 1 sample per category. Furthermore, its generalizability is validated on the base station (BS) dataset, where the proposed method consistently outperforms existing approaches in few-shot scenarios.

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

Wireless emergency networks play a crucial role in natural disasters, enabling seamless communication between explorers (e.g., rescue robots) and remote command centers. However, unstable communication links and limited computational resources hinder explorers from directly transmitting massive real-time content for remote human observation or locally computing the latest detection results. To address this challenge, this paper introduces a cross-modal semantic communications paradigm, where our highlights are characterized by precise semantic extraction, low-complexity implementation, and progressive semantic transmission. Specifically, by simplifying the desired task from human-oriented signal recovery to the machine-oriented decision, we firstly deploy a lightweight cross-modal semantic encoder on the explorer, which precisely extracts compact semantics relevant to the decision-making based on inter-modal correlations. Then, to mitigate the impact of intermittent network connections, we develop a scalable semantic transmission strategy that encodes extracted semantics as base and enhanced semantics, progressively delivering them once the network becomes active. Numerical results indicate remarkable benefits of cross-modal semantic communications in terms of decision accuracy, model size, and transmission latency.

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

In this paper, we investigate the effect of spatial correlation on the performance of wireless systems with reconfigurable intelligent surface (RIS) arrays using pilot-aided channel estimation. The RIS array is partitioned into several tiles, a pilot is inserted at or near the center of each tile, and the phase of the channel coefficient estimate at the pilot location is used to determine a common phase shift for configuring all RIS elements of that tile. The analysis shows that while a large number of pilots are needed in the absence of spatial correlation to approach the performance of optimally configured RIS arrays, a small number is sufficient in the presence of spatial correlation. The implication of this is that spatial correlation between the RIS array elements appears as a desirable feature, which not only reduces the number of pilots needed for channel estimation at the base station and the receiver complexity, but also the overhead involved in the feedback of the phase information for configuring the elements of the RIS array.

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

Malware traffic classification (MTC) plays an important role in cyber security and network resource management for the secure Internet of Things (IoT). Many deep learning (DL)-based MTC methods have been proposed due to their robustness and effectiveness with self-designed model architecture. However, to completely adjust complex parameters in the DL model, the architecture design of the DL model requires substantial professional knowledge and effort from human experts. To solve these problems, we propose an automatic and efficient MTC method using neural architecture search via proximal iterations (NASP), which can automatically and efficiently search the optimal model architecture according to the network traffic in the realistic environment. Specifically, we first describe NAS as a constrained optimization problem by keeping the search space differentiable and forcing the architecture to be discrete in the search process. Second, a suitable regularizer is introduced to balance the complexity and performance of the model architecture. Finally, the simulation results show that the proposed NASP-aided MTC method not only can efficiently and accurately search the optimal classification model architecture on the USTC-TFC2016 data set and the Egde-IIoTset data set but also compared with the typical MTC methods it can achieve the optimal classification performance with the fewer parameters as well as the floating-point operations (FLOPs).

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

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

The implementation of Scenario Classification (SC) plays a pivotal role in various edge intelligence applications, notably in fields such as autonomous driving, navigation, and remote sensing. With recent advancements, deep learning (DL) techniques have substantially improved SC, delivering remarkable results in classification tasks. However, the integration of DL in SC brings significant computational demands, posing challenges for deployment on edge devices where resources are constrained. Addressing this issue, we propose a novel Low-Resource Scenario Classification (LR-SC) approach, primarily focused on model pruning. This strategy aims to reduce computational power and storage needs, thus optimizing resource utilization in edge intelligence applications. Our approach involves the application of an ℓ2 regularization and a threshold-based pruning method, which selectively eliminates non-essential connections. This is followed by a systematic process of alternating pruning and fine-tuning to mitigate any performance loss due to the pruning. Experimental evaluations of the LR-SC method have shown its effectiveness; it substantially lowers the parameter count to merely 24% of the original model, while simultaneously achieving a 0.42% increase in classification accuracy.

Direction-of-arrival (DOA), also referred to as angle-of-arrival (AOA), is an excellent choice for unmanned aerial vehicle (UAV) localization and has garnered significant attention recently. In this article, we propose a novel two-dimensional (2D)-DOA auxiliary framework for anonymous UAV localization. At its core, this framework relies on measuring 2D-DOA using a monostatic multiple-input–multiple-output (MIMO) radar configured with electromagnetic vector sensors (EMVSs). Differing from existing mainstream methods, the multipath effect of the UAV is taken into account. A rearrangement multiple signal classification (R-MUSIC) algorithm is developed. The algorithm recovers the covariance matrix rank by connecting spatial responses from both transmitting (Tx) or receiving (Rx) arrays with radar cross-section (RCS) coefficients. Subsequently, rough 2D-DOA estimates are obtained using the vector cross-product (VCP) technique. These rough estimates are then used to establish good initialized values for refined 2-D spectral peak searching. Finally, leveraging the relationship between 2D-DOA and Tx/Rx array coordinates, a UAV’s 3-D position can be directly computed. This framework remains insensitive to the geometric configuration of Tx/Rx arrays while striking a balance between complexity and accuracy. Numerical simulation experiments confirm the improvements of our developed R-MUSIC algorithm.

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

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

The increasing significance of unmanned aerial vehicles (UAVs) in mobile edge computing (MEC) has captured considerable attention. Nevertheless, the effectiveness of UAVs-assisted MEC networks is hampered by challenges, such as limited communication capacity and onboard power. To tackle these issues, this study develops a constrained multiobjective optimization model designed to enhance the performance of UAVs-assisted MEC networks, focusing on system capacity, energy consumption, and task latency. As a result, this problem manifests as a complex constrained multiobjective optimization problem. The study then proposes a constrained multiobjective decomposition evolutionary algorithm (CMODEA) with low-computational complexity. This algorithm employs an adaptive individual comparison strategy, balancing diversity and convergence, and integrates an optimally guided differential evolution strategy for efficiently approximating optimal solutions. Additionally, it incorporates an adaptive constraint handling method, effectively managing existing constraints. The CMODEA aims to simultaneously optimize system capacity, energy consumption, and task latency while meeting the computational resource requirements of UAVs and ensuring acceptable user task latency levels. Simulation results demonstrate the algorithm’s effectiveness in significantly enhancing capacity, reducing energy consumption and latency, without greatly increasing algorithm complexity.

This article proposes a new framework of aerial reconfigurable intelligent surface (ARIS) enhancing the nonorthogonal multiple access (NOMA) system. The base station (BS) transmits superimposed signals to multiple users with different channel gains through ARIS which can flexibly change channel conditions and perform intelligent NOMA operations. It ensures that our system can perform well in providing services to multiple users simultaneously. In this system, the placement of the unmanned aerial vehicle (UAV) is jointly optimized along with the AIRS passive beam and the multiuser power allocation in order to maximize the communication sum rate. Since the joint optimization problem is nonconvex and coupled, it is hence disintegrated into three subproblems and it is solved alternately through the successive convex approximation (SCA). Moreover, semi definite programming (SDP) is used to deal with the rank one constraint of RIS reflection matrix and comparisons are made using particle swarm optimization (PSO). The numerical results show that the proposed ARIS-NOMA framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.

Multi-modal services, integrating various modalities such as audio, visual, and haptic, have emerged as leading multimedia applications in the 5G era and beyond. To fulfill the demands for low latency, high reliability, and large capacity, cross-modal transmission schemes have been proposed. Typically, these schemes emphasize on either audio-visual or haptic modality, and prioritize flawless transmission of one modality to assist the other modality streaming. However, these prerequisite and assumption do not hold for generic multi-modal services and communication environments, where determining the priority of modality and guaranteeing flawless transmission becomes challenging. To address this fundamental problem, in this paper, we introduce a strategy toward generic cross-modal transmission, enabling visual and haptic modalities to assist each other as needed. The strategy includes a visual-haptic mutual stream delivery mechanism at the sender and a visual-haptic mutual signal reconstruction approach at the receiver. The former aims to eliminate redundancy in visual and haptic streams through mutual assistance, while the latter adaptively handles impaired, missing, or delayed visual or haptic signals by leveraging modality-aware knowledge transfer and semantic-aware signal generation techniques. The proposed strategy demonstrates excellent performance through experiments conducted on a standard multi-modal dataset and a practical visual-haptic communication platform.

Specific emitter identification (SEI) plays a crucial role in the security of the Industrial Internet of Things (IIoT). In recent years, research on applying deep learning (DL) methods for signal identification has mushroomed. However, DL-based SEI methods rely on a huge amount of training data and powerful computing devices, limiting their application scenarios. In addition, DL models are considered black box models with poor interpretability. To solve the above problems, this paper proposes a novel few-shot SEI solution using knowledge graph-driven incremental broad learning (KG-IBL). Specifically, this paper uses a deep belief network (DBN) to dig deep into features and expand the broad structure with additional enhancement nodes. Furthermore, the proposed KG-IBL does not need to retrain all data to achieve dynamic incremental update learning. To our knowledge, this is the first endeavor to integrate KG with broad learning for addressing the few-shot SEI problem. The experimental results demonstrate that the proposed KG-IBL surpasses existing incremental methods in both identification performance and computational overhead. Last but not least, the accuracy of the proposed KG-IBL is 97.5%, which is only 1.67% lower than the theoretical upper limit, and the training time is nearly 267 times lower than that of deep learning models. The code and dataset are available for download athttps://github.com/Lollipophua/KG-IBL.

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

Cyber security has always been an important issue in the Internet of Everything topic. In the physical layer of the Internet, specific emitter identification (SEI) technology is widely researched as a simple and effective intrusion prevention technology. Existing SEI research only focused on radio frequency (RF) signals from a single receiver. However, in real scenes such as the Industrial Internet of Things (IIoT), vehicle-to-everything applications, and intelligent sensing systems, etc., RF signals are received from different types of sensors deployed at different locations. Therefore, this paper proposes a multisource heterogeneous SEI (MH-SEI) method and proposes a multi-source heterogeneous attention-based feature fusion network (MHAFFN) to achieve excellent identification performance. The proposed MHAFFN utilizes a multi-channel convolutional network as the RF fingerprinting (RFF) extraction module for multisource heterogeneous RF signals and equips an attention-based RFF fusion module to obtain mixed RFF for the automatic classifier. The experimental results show that the identification accuracy of MHAFFN is 99.196% in a perfect environment. Furthermore, robustness verification has proved that MHAFFN keeps advantages in noisy environments. Through fault tolerance mechanism verification experiment, it is proved that MHAFFN is able to work stably in real-world complex scenarios.

Specific emitter identification (SEI) uses advanced techniques to identify radio equipment by analyzing unique characteristics in radio frequency signals. Recently, deep learning (DL) has been considered a promising tools for designing various intelligent SEI methods. This is primarily due to its ability to fully exploit hidden data features and make autonomous classification decisions, leading to effective performance. The existing DL-SEI methods are based on the availability of extensive labeled datasets, however, collecting and annotating such data is challenging and time-consuming in real-world scenarios. Furthermore, these datasets often contain both device-specific and irrelevant features, which limits the adaptability of models to fixed channels. To overcome these challenges, we propose a robust variable-channel SEI (VC-SEI) method. This method uses semantic consistency-powered semi-supervised domain adaptation (SSDA). We introduce domain adversarial training to ensure global semantic consistency (GSC), allowing the extraction of channel-irrelevant features. Additionally, we design two loss functions to maintain local semantic consistency (LSC) for extracting category-relevant features. This approach enables effective domain adaptation. Our SSDA-based VC-SEI method has been rigorously evaluated using the ORACLE RF fingerprinting datasets from 16 USRP X310 radios. When only 1% of training samples in the target domain are labeled, our method achieves 84.20% identification accuracy in the target domain and 92.00% identification accuracy in the source domain. These results surpass those of current state-of-the-art methods. Simulation results confirm the robust identification performance of our proposed VC-SEI method in both source and target domains across all scenarios. Our code can be downloaded fromhttps://github.com/frownean/VC-SEI-based-SSDA.

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

Intelligent Reflecting Surface (IRS) represents a significant breakthrough in wireless communications, allowing the reconstruction of wireless channels even for occluded users to the base station (BS). Estimating the Direction-of-Arrival (DOA) of a source oriented toward Non-Line-of-Sight (NLOS) propagation is an intriguing topic in an IRS-aided wireless communication scenario. However, the existing optimization-based approaches are overly complex to be practically implemented. In this paper, we propose a polarized IRS architecture, in which both IRS and BS are equipped with arbitrarily placed Electromagnetic Vector Sensor (EMVS) arrays. A Normalized Vector-Cross Product (NVCP) estimator is developed for DOA estimation, which avoids the need for complicated data recovery or exhaustive grid search. The proposed framework enables Two-Dimensional (2D) DOA estimation for NLOS signals without requiring prior knowledge of the BS-IRS channel. Numerical simulations have been conducted to verify its effectiveness.

Reconfigurable intelligent surfaces (RIS) are currently drawing a lot of attention in the research community as a key technology for future wireless networks. In addition to boosting the signal-to-noise ratio and improving coverage for cellular users, they can also be used to transmit locally collected data either by partitioning the RIS array into tiles and mapping the local data to the indexes of the activated tiles (or groups of tiles) following the concept of spatial modulation (SM), or by activating all RIS elements and mapping the local data onto a set of common phase shifts. The problem of the first technique, which we refer to as RIS-SM, is that spatial correlation between elements of the RIS array strongly degrades the bit error rate performance. In this paper, we focus on the second technique, and we investigate the transmission of low bit rate local data using the concept of hierarchical transmission. The basic idea behind this technique is that the magnitude of the common phase shifts must be maintained at a very small value in order to keep the performance degradation caused by the local data on the user data within an acceptable limit. For the local RIS data, this constraint leads to a small minimum Euclidean distance in the signal constellation plane, but despite this small minimum distance, the desired performance is achieved by limiting the speed of the local RIS data to a fraction of the user symbol rate. Analytic minimum distance calculations and simulation results are provided to demonstrate the efficiency of the proposed hierarchical transmission technique.

Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications. Existing deep learning-based RED methods have been proposed under friendly environments. However, these methods perform unstably under low signal-to-noise ratio (SNR) scenarios. To address this problem, we propose a robust RED method, which is a hybrid network of denoising autoencoder and deep metric learning (DML). Specifically, denoising autoencoder is adopted to mitigate noise interference and then improve its robustness under low SNR while DML plays an important role to improve the feature discrimination. Several typical experiments are conducted to evaluate the proposed RED method on an automatic dependent surveillance-Broadcast dataset and an IEEE 802.11 dataset and also to compare it with existing RED methods. Simulation results show that the proposed method achieves better RED performance and higher noise robustness with more discriminative semantic vectors than existing methods.

The topic of anonymous unmanned aerial vehicle (UAV) localizing based on angle estimation has been frequently discussed in the past few years. However, the existing methodologies are inefficient in a massive sensor arrays scenario. To avoid such drawback, a cooperative 3-D positioning methodology is introduced. The critical idea of the proposed localizing method is to estimate the 2-D angle of the anonymous UAV via a polarized massive–multi-input multi-output (MIMO) system. To reduce the computational burden and explore the nature of the multidimensional data, a tensor compressive sampling (TCS) framework is proposed. Moreover, a closed-form estimation strategy is developed for 2-D direction finding. Our framework is shown to be more efficient than the existing algorithm in terms of hardware/software complexity. Besides, it is suitable for a polarized MIMO system with an arbitrary array geometry. Several simulation examples are provided to show its improvement of the new methodology.

The polarized massive multiple-input multiple-output (MIMO) technique has been regarded as a promising solution to millimeter wave (mmWave) communication systems, because it experiences more degrees-of-freedom than the scalar configuration, and it represents a significant opportunity for secure communication. To deliver smart service to terminals, it is essential to provide base stations (BS) with the capability of terminal’s direction-of-arrival (DOA) awareness. In this paper, a compressive sampling (CS) framework is proposed for two-dimensional (2D) DOA and polarization estimation in mmWave polarized massive MIMO systems. The proposed approach first reduces the data volume via a reduced-dimension matrix. Then it computes the signal subspace via the eigendecomposition of the compressed array measurement. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization steering vector. Finally, 2D-DOA and polarization are estimated by incorporating the Poynting vector and the least squares (LS) techniques. The proposed architecture is computationally much more economical than existing algorithms. Besides, it allows a mmWave BS to provide comparable estimation performance with arbitrary sensor geometry, which is more flexible than most of the existing architectures. Furthermore, it is robust to the sensor position error. Numerical simulations verify the advantages of the proposed framework.

Blind signal recognition (BSR) is a significant research topic in the field of intelligent signal processing. However, existing BSR of space-time block codes (STBC) mainly depends on conventional algorithms, which require priori information and can only identify a relatively limited amount of STBC. Although deep learning (DL) has been widely used in signal recognition, so far there are few studies on BSR of STBC in multiple-input multiple-output (MIMO) systems using DL. In this paper, a blind recognition approach for STBC based on multichannel convolutional neural network (MCNN) is proposed. By leveraging the structure of multiple input channel, the in-phase and quadrature (IQ) channel information of STBC signals can be comprehensively extracted. Simulation results demonstrate that the proposed algorithm extends the recognizable STBC codes to 6, and can also improve the recognition accuracy in comparison to traditional convolutional neural network (CNN). The model proposed in this paper has been validated with two datasets and experimentally proved to be well generalized.

This paper analyzes the power imbalance factor on the uplink of a 2-user Power-domain NOMA system and reveals that the minimum value of the average error probability is achieved when the user signals are perfectly balanced in terms of power as in Multi-User MIMO with power control. The analytic result is obtained by analyzing the pairwise error probability and exploiting a symmetry property of the error events. This result is supported by computer simulations using the QPSK and 16QAM signal formats and uncorrelated Rayleigh fading channels. This finding leads to the questioning of the basic philosophy of Power-domain NOMA and suggests that the best strategy for uncorrelated channels is to perfectly balance the average signal powers received from the users and to use a maximum likelihood receiver for their detection.

To address the problem of spectrum resources and transmitting power for vehicular networks, this paper proposes a resource allocation (RA) method based on dueling double deep-Q network (D3QN) reinforcement learning (RL). Due to the high mobility of the vehicle, the channel changes rapidly which makes it difficult to accurately collect high-accuracy channel state information at the base station and to perform centralized management. In response of this difficulty, we construct a multi-intelligence model, using Manhattan Grid Layout City Model as the basis of environment and with each vehicle-to-vehicle (V2V) link as an intelligence. They work together to interact with the environment, receive appropriate observations, get rewards, and finally learn to improve the allocation of power and spectrum to enable users to achieve a better entertainment experience and a safer driving environment. Experimental results demonstrate that with proper training mechanism and reward function construction, cooperation among multiple intelligence can be performed in a distributed manner, with improvements in both the capacity of total vehicle-to-infrastructure links and the effective payload delivery success rate of the V2V links compared to common Q-network.

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

Specific emitter identification (SEI) is a promising physical layer authentication technique based on unintentionally hardware impairments of transmitters. These impairments are independent of the data’s content, so they are difficult to forge and analyze. Recently, most deep learning (DL) based SEI methods have been proposed, and have shown their great performance. However, these methods are big data-driven which means they have poor performance with limited training samples, and the vulnerability of neural networks to adversarial attacks is also a problem worth considering. In this paper, we propose an innovative few-shot SEI method based on class-reconstruction classification network and adversarial training (CRCN-AT) without the support of auxiliary dataset. Simulation results show that the proposed method achieves better identification performance and robustness in few-shot scenarios compared to traditional methods. The Pytorch code is released at https://github.comLIUC-000/CRCN-AT.

The fast development of intelligent wireless communications enables many devices to access various networks. It often leads to the security risks of malicious access of illegal devices. To ensure a secure and reliable wireless access, it is necessary to identify illegal devices and prevent their attacks accurately. To improve the performance of specific emitter identification (SEI), this paper proposes a multi-scale convolution neural network (MSCNN) based on convolution layers of three branches with different convolution kernel sizes. MSCNN extracts radio frequency fingerprints (RFF) in three receptive fields through different convolution kernels. We verify the identification accuracy using the RF signals conforming to long term evolution (LTE) standard. The experimental results show that our proposed MSCNN-based SEI method can improve the absolute accuracy by 15% and the relative accuracy by 22% in perfect communication environment. In addition, we verify the robustness of proposed MSCNN by comparing identification performance in imperfect environment. Simulation results show that the proposed MSCNN can extract more hidden features through convolution kernels of different sizes, and thus achieves better SEI performance than existing methods.

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

Radio frequency fingerprint (RFF) is regarded as a key technology in physical layer security in various wireless communications systems. Deep learning (DL) has achieved great success in the field of signal identification, particularly in improving performance and eliminating manual feature extraction. However, the training cost of these DL-based methods is usually large. It is unwise to retrain the network with whole data when it comes to new data. Therefore, we propose a novel RFF identification method based on incremental learning (IL), which uses continuous data stream to update the identification model, constantly. Experimental results show that with the increase of increment times, the accuracy of the proposed IL-based method gradually approaches the performance of joint training, and finally reaches 96.79%, which is only 1.9% lower than the performance upper bound.

As exploitation of low and medium airspace for air traffic management (ATM) is gaining more attention, aerial vehicles’ security issues pose a major challenge to the air–ground-integrated vehicle networks (AGIVNs). Traditional surveillance technology lacks the capacity to support the intensive ATM of the future. Therefore, an advanced automatic-dependent surveillance-broadcast (ADS-B) technique is applied to track and monitor aerial vehicles in a more effective manner. In this article, we propose a grouping-based conflict detection algorithm based on the preprocessed ADS-B data set, and analyze the experimental results and visualize the detected conflicts. Then, in order to further improve flight safety and conflict detection, the trajectories of the aerial vehicles are predicted based on machine learning-based algorithms. The results are fed into the conflict detection algorithm to execute conflict prediction. It was shown that the trajectory prediction model using long short-term memory (LSTM) can achieve better prediction performance, especially when predicting the long-term trajectory of aerial vehicles. The conflict detection results based on the trajectory prediction methods show that the proposed scheme can make it possible to detect whether there would be conflicts within seconds.

Due to the computing capability and memory limitations, it is difficult to apply the traditional deep learning (DL) models to the edge devices (EDs) for realizing lightweight automatic modulation classification (AMC). Recently, many works attempt to use different ways to realize lightweight AMC methods for EDs. However, the lightweight seems to be a contradiction with the classification performance in these lightweight networks. In this article, we propose an efficient lightweight decentralized-learning-based AMC (DecentAMC) method using spatiotemporal hybrid deep neural network based on multichannels and multifunction blocks (MCMBNN). Specifically, the lightweight network is designed from the perspectives of comprehensive consideration of lightweight and classification performance, which is composed of three parts to extract different features for realizing high classification performance and they are phase estimator and transformer (PET) block, spatial feature extraction block and temporal feature extraction & Softmax block. In addition, we use a multichannel input to extract complementary features of different channels for a better classification performance. The proposed DecentAMC method is an efficient training method, which is achieved by the cooperation in which multiple EDs update and upload the model weight to a central device (CD) for model aggregation to avoid the data privacy disclosure and reduce the computing power and storage pressure of CD. Experimental results show that the proposed MCMBNN can obtain an improved classification accuracy while reducing model complexity with the contributions of three blocks. Moreover, the proposed DecentAMC method can be deployed on EDs efficiently. Thus, the method has the advantages of avoiding data leakage on EDs and relieving the computing pressure of CD with relatively lower communication overhead. The simulation code and datasets are shared on GitHub.

As a typical Internet of Things application, network traffic prediction (NTP) plays a decisive role in congestion control, resource allocation, and anomaly detection. The trend of network traffic is different at different scales, so multiscale is an important characteristic of network traffic. In addition, the network traffic is nonlinear on each scale and dependent between scales. The existing NTP methods cannot comprehensively consider these characteristics, which limits their performance. In view of the characteristics of network traffic, such as multiscale, nonlinearity, and scale dependence, this article proposes a new multiscale NTP method based on a deep echo-state network (ESN). First, a multiscale parallel layered structure based on deep ESN is designed to fully consider the influence of each scale on the prediction result and then reduce the prediction error. Second, a feature extraction algorithm is proposed to improve the nonlinear approximation ability by extracting more abundant dynamic features with multiple reservoirs. Third, an NTP model based on scale dependence is proposed to reduce the influence from partial scale missing and then improve the prediction accuracy. Finally, simulation results demonstrate that compared with the state-of-the-art NTP methods, the proposed method significantly improves the prediction performance of network traffic with a slight increase in running time.

With the rapid growth of the number of flights, the traditional radar system has been unable to meet the needs of flight supervision. At the same time, it also puts forward higher requirements for air traffic management (ATM). Automatic dependent surveillance-broadcast (ADS-B) is a promising technology in the next generation of air traffic control (ATC). However, the openness of ADS-B system brings the opportunities for terrorists to tamper with data. In this paper, we propose a novel aircraft coordinate prediction hybrid model based on deep learning. The proposed model combines inception modules and long short-term memory (LSTM) modules. Inception modules are used to extract the spatial features of dataset, and LSTM modules are used to extract the temporal features of dataset. In addition, we use the ADS-B signal strength instead of its specific information to obtain aircraft coordinates. Signal strength is not easily tampered with, but it carries limited information. Therefore, this scheme sacrifices a certain precision for reliability. Inception modules and LSTM modules are combined in different ways to perform experiments on the real-world ADS-B datasets from OpenSky network. The experimental results show that the proposed 2-Inception-LSTM is the local optimal model. The prediction error is within 10 km. It can be suitable for situations where the positioning accuracy of aircraft coordinates is not pursued, but the positioning reliability must be guaranteed.

Energy-efficiency (EE) is a critical metric within wireless optimization. Power control over fading channels is considered as a promising EE-improving technique, but requires optimization of a series of fractional functional optimization problems which are hard to handle by existing optimization techniques. In this paper, we propose a novel EE power control method with unsupervised learning. Firstly, the original fractional problems are decomposed into sub-problems by Dinkelbach and quadratic transformations. Then, these sub-problems are reformulated into unconstrained forms through Lagrange dual formulation. Furthermore, unsupervised primal-dual learning method is applied to handle these unconstrained problems with strong duality. Finally, The unsupervised primal-dual learning is implemented by the deep neural network (DNN) with low computational complexity. Simulation results verify the effectiveness of the proposed approach on a number of typical wireless optimizing scenarios. It is shown that compared to conventional algorithms our method achieves better performance in cognitive radio networks, interference networks, and OFDM networks.

Reconfigurable intelligent surfaces (RIS) are currently drawing a lot of attention in the research community as a key technology for future wireless networks. In addition to boosting the signal-to-noise ratio, they can also be used to transmit data in the same way spatial modulation (SM) transmits data by mapping it to the activated antenna indices in MIMO systems. The problem of this transmission technique which we refer to as RIS-SM is that spatial correlation between elements of the RIS array strongly degrades bit error rate performance. In this paper, we analyze this degradation and introduce two techniques to compensate for spatial correlation. The first employs tile-specific phase shifts in the RIS elements and the second employs dynamic phase shifts that are specific to the RIS patterns activated by the information bits to be transmitted. The analysis and the simulation results show that the proposed techniques provide substantial performance improvements and make RIS-SM transmission reliable even in the presence of very strong spatial correlation.

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

Scenario identification plays a crucial role in enhancing the performance of vehicle-to-vehicle (V2V) communication systems. It enables smart vehicles to adjust driving speed in allowable range according to the surrounding circumstance automatically and avoid possible crashes. However, existing methods for scenario identification in vehicular networks have cumbersome processing of information sequence and huge energy consumption. This paper proposes a novel scenario identification method using joint multislice and cooperative detection aided residual network (Resnet), which can extract features from non-equalized signal at the receiver (Rx. signal) automatically and realize scenario recognition. Simulation results demonstrate that the proposed Resnet-based scenario identification method can achieve high classification accuracy with small model size.

Adaptive modulation and coding (AMC) technique plays an important role in vehicle-to-vehicle (V2V) systems. It enables smart vehicles to keep a good quality of communication for a better driving experience. However, the existing AMC methods for V2V system did not consider multiple scenarios and the amount of calculation is relatively large. In this paper, we propose a simple convolutional neural networks (CNN)-based AMC method which can extract features of channel and noise estimation from receiver, the transmitter will adjust in the light of modulation strategy to ensure the quality of V2V communication. Simulation results reveal that our proposed method performs better in terms of packet error rate (PER), throughput, classification accuracy with a lower prediction time.

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

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

Reconfigurable Intelligent Surfaces (RIS) have been a hot research topic in recent years, being widely advocated to represent a promising technology for beyond 5G cellular networks. In this paper, we propose a modulation scheme called RIS-IM in which Index Modulation (IM) is applied using RIS. However, to overcome the strong channel correlation caused by the small separation distance between the RIS elements a precoding stage based on the concept of signature constellations is added at the transmitter. The precoding stage is shown to counteract the degradations caused by these correlations providing interesting gains. We further improve the performance by passive beamforming, optimizing the phase shifts of the RIS elements using Semidefinite Programming (SDP) to improve the receiver SNR. We then provide performance analysis for this IM scheme using computer simulations. A comparison of RIS-IM with Spatial Modulation (SM), which is another member of the IM family, is also included, showing that RIS-IM provides substantial SNR gains.

Since it uses active antenna indices to transmit information, spatial modulation (SM) is highly sensitive to transmit antenna and channel correlations, and precoding techniques to reduce this sensitivity are now well documented in the literature for single-stream SM with one active antenna. In this paper, we address the correlation issue for multi-stream SM (MSM) in which multiple antennas are active at a time. We show that conventional techniques fall short of efficiently compensating antenna and channel correlations in this case, and we introduce a novel precoding technique that significantly improves performance. Considering an MSM system with 4 transmit antennas and 2 parallel streams, we describe three variants of the proposed technique and evaluate its performance on Rayleigh fading and Rician fading channels. The results show a substantial improvement over conventional techniques, particularly on Rician fading channels.

Nonorthogonal multiple access (NOMA) significantly improves the connectivity opportunities and enhances the spectrum efficiency (SE) in the fifth generation and beyond (B5G) wireless communications. Meanwhile, emerging B5G services demand for higher SE in the NOMA-based wireless communications. However, traditional ground-to-ground (G2G) communications are hard to satisfy these demands, especially for the cellular uplinks. To solve these challenges, this article proposes a multiple unmanned-aerial-vehicles (UAVs)-aided uplink NOMA method. In detail, multiple hovering UAVs relay data for a half of ground users (GUs) and share the spectrums with the other GUs that communicate with the base station (BS) directly. Furthermore, this article proposes a K-means clustering-based UAV deployment scheme and location-based user pairing (UP) scheme to optimize the transceiver association for the multiple UAVs-aided NOMA uplinks. Finally, a sum power minimization-based resource allocation problem is formulated with the lowest Quality-of-Service (QoS) constraints. We solve it with the message-passing algorithm and evaluate the superior performances of the proposed scheduling and paring schemes on SE and energy efficiency (EE). Extensive simulations are conducted to compare the performances of the proposed schemes with those of the single UAV-aided NOMA uplinks, G2G-based NOMA uplinks, and the proposed multiple UAVs-aided uplinks with a facility location framework-based UAV deployment. Simulation results demonstrate that the proposed multiple UAVs deployment and UP-based NOMA scheme significantly improves the EE and the SE of the cellular uplinks at the cost of only a little relaying power consumption of UAVs.

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

This article aims to improve spectrum efficiency (SE) for the unmanned aerial vehicle (UAV)-relayed cellular uplinks, through distinguishing both line-of-sight (LoS) and non-LoS (NLoS) links. Meanwhile, aiming to accommodate the air-to-ground (A2G) cooperative nonorthogonal multiple access (NOMA)-based cellular users (CUs) with a high energy efficiency (EE), a joint resource allocation (RA) problem is further considered for the UAV and the CUs. To solve the problem, first, an access-priority-based receiver determination (RD) method is derived. According to the RD result, the heuristic user association (UA) strategies are given. Then, based on the UA result, transmission powers of the CUs and the UAV are initialized based on their quality-of-service (QoS) demands. Furthermore, the subchannels are assigned to the associated CUs and the UAV with the reweighted message-passing algorithm. Finally, the transmission power of the CUs and the UAV is jointly fine-tuned with the proposed access control schemes. Compared with the traditional orthogonal frequency-division multiple access (OFDMA) scheme and the traditional ground-to-ground (G2G) NOMA scheme, simulation results confirm that the UAV-aided NOMA with the proposed joint RA scheme yields better performances in terms of the SE, the EE, and the access ratio of the CUs.

The increasing demands for massive connectivity, low latency, and high reliability of future communication networks require new techniques. Multiple-input-multiple-output non-orthogonal multiple access (MIMO-NOMA), which incorporates the NOMA concept into MIMO, is an appealing technology to enhance system throughput and energy efficiency. However, rapidly changing channel conditions and extremely complex spatial structure degrade the system performance and hinder its application. Thus, to tackle these limitations, in this paper, we propose a deep learning-based MIMO-NOMA framework for maximizing the sum data rate and energy efficiency. To be specific, we design an effective communication deep neural network (CDNN) in which several convolutional layers and multiple hidden layers are included. Thanks to the impressive representation ability of the deep learning technique, the CDNN framework addresses the power allocation problem for achieving higher data rate and energy efficiency of MIMO-NOMA with the aid of training algorithms. Additionally, simulation results corroborate that the proposed CDNN framework is a good candidate to enhance the performance of MIMO-NOMA in term of power allocation, and extensive simulations show that it realizes larger sum data rate and energy efficiency compared with conventional strategies.

Non-Orthogonal Multiple Access (NOMA) has been a hot research topic in recent years, because it is widely advocated that this technique represents a promising technology for 5G cellular networks and beyond. The NOMA literature today is heavily based on the so-called Power-Domain NOMA (PD-NOMA), which requires a strong power imbalance between the signals assigned to different users. Also, the focus in the literature has been on the derivation of achievable rates, which represent an information theoretic measure. In contrast, the signal-to-noise ratio (SNR) degradation that is caused by multiuser interference at practical bit error rate (BER) values has not attracted much attention. In some recent papers, the present authors revived an early NOMA concept, which had been rather overlooked in the recent NOMA literature. This concept, which we refer to as NOMA-2000, consists of superposing the signals of two user groups with different signal waveforms rather than the signals of two users. In our earlier papers, performance of NOMA-2000 was investigated in various conditions, but no comparisons were provided with PD-NOMA. The purpose of this paper is to compare the BER performances of the two schemes using several values of the power splitting factor between users. The results confirm that PD-NOMA suffers a strong SNR degradation and that NOMA-2000 provides substantially better performance in general.

In this paper, we propose a novel scheme to guarantee the security of UAV-relayed networks with caching via jointly optimizing the UAV trajectory and time scheduling. For the two users that have cached the required file for the other, the UAV broadcasts the files together to these two users and the eavesdropping can be disrupted. For the user without caching, we maximize its secrecy rate by jointly optimizing the trajectory and scheduling, with the secrecy rate of the caching users satisfied. The corresponding optimization problem is difficult to solve due to its non-convexity, and we propose an iterative algorithm via successive convex optimization to solve it approximatively. Simulation results are provided to show the effectiveness and efficiency of our proposed scheme.

In this paper, we present a three-dimensional (3D) wideband geometry-based channel model for multiple-input and multiple-output (MIMO) vehicle-to-vehicle (V2V) Ricean fading channels, where the received signal is constructed as a sum of line-of-sight (LoS) and non-LoS (NLoS) propagation rays. We first introduce multiple confocal semi-ellipsoid models to depict roadside environments, which is able to efficiently model scatterers with identical delays on the same semi-ellipsoid. Therefore, the V2V channel characteristics for different propagation delays can be investigated. Moreover, the proposed models can easily be reduced to various simplified channel models by properly adjusting model parameters. Using this channel model, the channel characteristics, i.e., the spatial correlation functions (CFs) and Doppler power spectral densities (PSDs), are investigated. The numerical results are very close to the previous results and measurements, thereby demonstrating the accuracy of the proposed channel model.

In some recent papers, the present authors revived an early non-orthogonal multiple access (NOMA) concept, which uses two sets of orthogonal signal waveforms and iterative interference cancellation. The beauty of this concept, which was introduced back in the year 2000, is that it fully avoids the power imbalance requirements of power-domain NOMA on which the current NOMA literature is heavily based. Using different type of receivers, these papers reported channel overload factors up to 25% on additive white Gaussian noise (AWGN) channels. In this paper, we investigate uplink performance on Rayleigh fading channels and we introduce a dynamic user grouping strategy, which leads to a substantial increase of the channel overload capability. Using this strategy, we show that the channel overload factor can be increased up to 100% at the expense of a virtually zero degradation of the signal-to-noise ratio (SNR).

In this paper, user selection and transceiver design are proposed to guarantee the secure transmission in a multiple-input multiple-output interference network with an eavesdropper. First, user selection is performed to select the most suitable user to transmit confidential information according to the topology and path loss in each time slot. Then, based on user selection, the transceivers are jointly designed to maximize the secrecy rate of the selected user while guaranteeing a minimum transmission rate for other users. Due to the non-convexity of the problem, an alternate iteration algorithm is proposed to obtain the optimal solution with the help of successive approximations. Finally, simulation results are presented to show the effectiveness and efficiency of the proposed schemes.

Spatial modulation (SM) has a strong sensitivity to transmit antenna and channel correlations, because some of the information bits are assigned to active antenna selection, and the correlation limits the detection reliability of these bits. Recent approaches for the solution of this problem rely on either unequal error protection (UEP) of antenna and symbol bits with the addition of a channel encoder/decoder pair to the transceiver or precoding in the form of antenna-dependent rotation (or joint rotation and amplitude scaling) of the signal constellation. The UEP approaches have been shown to offer only limited efficiency in compensating for the adverse channel effects while increasing the latency and complexity due to the addition of the encoder/decoder. The precoding based approaches achieve good results for BPSK and QPSK signals, but the performance quickly degrades for higher-level QAM signal constellations. Also, the complexity of the precoder optimization problem increases with the number of transmit antennas and the modulation order, making this approach not very practical to use for large spectral efficiencies. This paper introduces a novel approach to this problem whose performance is independent of the modulation order. The key idea is to use signature constellations for different transmit antennas with an inter-constellation minimum Euclidean distance that is independent of the modulation order. The theoretical analysis and the simulation results show that compared to previous methods the new approach gives significant performance improvements in terms of robustness to transmit antenna correlation, particularly for Rician fading channels.

Spatial Modulation (SM) was first introduced to reduce the number of radio frequency (RF) chains in a multiple-input multiple-output (MIMO) transmitter and thereby reduce cost and power consumption. Although it is very appealing in theory, this concept actually has two main problems, which are not sufficiently highlighted in the existing literature: The first is that the antenna switching involved destroys the spectral shaping of the transmitted signal, and the second is the limited spectral efficiency due to the presence of silent antennas. In order to remedy the second problem, Enhanced Spatial Modulation (ESM) was introduced. As for the problem of antenna switching, it actually disappears when SM is implemented in the frequency-domain, because the switching operation in Frequency-Domain SM (FD-SM) occurs at baseband. But the number of RF chains needed becomes equal to the number of transmit antennas. In this paper, we investigate Frequency-Domain ESM (FD-ESM), which avoids both the spectral efficiency limitations and the antenna switching of the original SM. Exploiting the property that switching occurs at baseband and no savings in terms of the number of RF chains can be achieved in frequency-domain implementation, we design FD-ESM schemes which provide spectacular gains with respect to conventional Multi-Stream SM (MSM) and also significant gains compared to spatial multiplexing.

Unmanned aerial vehicle (UAV) can be utilized as a relay to connect nodes with long distance, which can achieve significant throughput gain owing to its mobility and line-of-sight (LoS) channel with ground nodes. However, such LoS channels make UAV transmission easy to eavesdrop. In this paper, we propose a novel scheme to guarantee the security of UAV-relayed wireless networks with caching via jointly optimizing the UAV trajectory and time scheduling. For every two users that have cached the required file for the other, the UAV broadcasts the files together to these two users, and the eavesdropping can be disrupted. For the users without caching, we maximize their minimum average secrecy rate by jointly optimizing the trajectory and scheduling, with the secrecy rate of the caching users satisfied. The corresponding optimization problem is difficult to solve due to its non-convexity, and we propose an iterative algorithm via successive convex optimization to solve it approximately. Furthermore, we also consider a benchmark scheme in which we maximize the minimum average secrecy rate among all users by jointly optimizing the UAV trajectory and time scheduling when no user has the caching ability. Simulation results are provided to show the effectiveness and efficiency of our proposed scheme.

Non-Orthogonal Multiple Access (NOMA) has been a hot research topic over the past few years, particularly because it is widely recognized that this technique represents a promising technology for massive Machine-Type Communications (mMTC) in future 5G cellular networks. The NOMA literature today is heavily focused on the so-called Power-Domain NOMA, which requires a strong power imbalance at the receiver between user signals. In some recent papers ([1] and [2]), the present authors revived a NOMA concept introduced back in the year 2000 and completely overlooked in the recent NOMA literature. This NOMA concept, which uses two sets of orthogonal signal waveforms and iterative interference cancellation at the receiver, fully avoids the power imbalance requirements of power-domain NOMA and makes it possible to grant the same data rates and performance levels to different users. In this paper, we first shed further light on the limitations of today's power-domain NOMA and we give insight on the potential of superposing the signals of two user groups with different characteristics instead of superposing two user signals. Next, we propose a new variant of the NOMA technique proposed in [1] and [2], which avoids the use of a complex interference canceler. This scheme achieves a 25% channel overloading factor at a negligible degradation of the signal-to-noise ratio (SNR) using a very simple maximumlikelihood (ML) receiver.

Massive MIMO is considered as one of the key enablers of the next generation 5G networks.With a high number of antennas at the BS, both spectral and energy efficiencies can be improved. Unfortunately, the downlink channel estimation overhead scales linearly with the number of antenna. This does not create complications in Time Division Duplex (TDD) systems since the channel estimate of the uplink direction can be directly utilized for link adaptation in the downlink direction. However, this channel reciprocity is unfeasible for the Frequency Division Duplex (FDD) systems where different physical transmission channels are existent for the uplink and downlink. In the aim of reducing the amount of Channel State Information (CSI) feedback for FDD systems, the promising method of two stage beamforming transmission was introduced. The performance of this transmission scheme is however highly influenced by the users grouping and selection mechanisms. In this paper, we first introduce a new similarity measure coupled with a novel clustering technique to achieve the appropriate users partitioning. We also use graph theory to develop a low complexity groups scheduling scheme that outperforms currently existing methods in both sum-rate and throughput fairness. This performance gain is demonstrated through computer simulations.

The requirement of the increasing capacity of the communication networks promotes the massive multiple input multiple output (MIMO), which has attracted a lot of attention among academic and industry communities. Due to the inherent sparsity features of channel structure in uplink massive MIMO systems, conventional methods often bring about high computational complexity and also fail to make full use of the structural information. In order to solve this problem, this paper proposes a novel deep learning (DL) based super-resolution direction of arrivals (DOA) estimation method. Specifically, it is realized with the aids of the well-designed deep neural network (DNN). Then we employ the DNN to carry out offline learning and online deployment procedures. This learning mechanism can learn the features of the wireless channel and the spacial structures efficiently. Finally, simulation results are provided to show that the proposed DL based scheme can achieve better performance in terms of the DOA estimation compared with conventional methods.

Non-orthogonal multiple access (NOMA) is recognized today as a most promising technology for future 5G cellular networks and a large number of papers have been published on the subject over the past few years. Interestingly, none of these authors seems to be aware that the foundation of NOMA actually dates back to the year 2000, when a series of papers introduced and investigated multiple access schemes using two sets of orthogonal signal waveforms and iterative interference cancellation at the receiver. The purpose of this paper is to shed light on that early literature and to describe a practical scheme based on that concept, which is particularly attractive for machine-type communications (MTC) in future 5G cellular networks. Using this approach, NOMA appears as a convenient extension of orthogonal multiple access rather than a strictly competing technology, and most important of all, the power imbalance between the transmitted user signals that is required to make the receiver work in other NOMA schemes is not required here.

Compared with the conventional degrees of freedom, the orbital angular momentum (OAM), which describes the helical phase structure of electromagnetic wave, provides a new degree of freedom. As a new multiple access scheme, mode division multiple access (MDMA) is constructed in millimetre wave frequency band utilising the orthogonality and high dimensionality in this study. Various traditional resources such as frequency, time and code pattern have been shared. Therefore, addresses of signals from different terminal users can be distinguished by OAM mode to realise multi‐address connection. In this study, the theoretical analysis of the number of terminals in MDMA scheme is carried out. According to the analysis results, infinite terminals can be connected together in the ideal case. Moreover, the simulation results show that compared with the conventional multi‐input multi‐output millimetre wave communication systems, the performance indicators of MDMA millimetre wave communication systems are improved remarkably.

The ‐norm penalised (LP) normalised least mean square algorithm converges faster than the LP normalised least mean fourth algorithm does, but the latter can achieve better steady‐state performance, particularly in regions with low signal‐to‐noise ratios (SNRs). To simultaneously take advantage of both merits, a sparse hybrid adaptive filtering algorithm is proposed in various SNR environments. Specifically, the authors construct a cost function that uses the statistical error term and sparse penalty term. The first term is designed by a hybrid error function of the second‐ and fourth‐order statistical errors, respectively, and the second term is obtained using a sparse constraint function. The hybrid error term can be easily balanced by a proportional parameter . Moreover, they devise a non‐uniform step size in the proposed algorithm to further balance the convergence speed and estimation error. Simulation results are provided to validate the proposed algorithm in various SNR environments.

Unmanned aerial vehicles (UAVs) can help small-cell base stations (SBSs) offload traffic via wireless backhaul to improve coverage and increase rate. However, the capacity of backhaul is limited. In this paper, UAV assisted secure transmission for scalable videos in hyper-dense networks via caching is studied. In the proposed scheme, UAVs can act as SBSs to provide videos to mobile users in some small cells. To reduce the pressure of wireless backhaul, UAVs and SBSs are both equipped with caches to store videos at off-peak time. To facilitate UAVs, a single antenna is equipped at each UAV and thus, only the precoding matrices of SBSs should be cooperatively designed to manage interference by exploiting the principle of interference alignment. On the other hand, the SBSs replaced by UAVs will be idle. Thus, in order to guarantee secure transmission, the idle SBSs can be further exploited to generate jamming signal to disrupt eavesdropping. The jamming signal is zero-forced at the legitimate users through the precoding of the idle SBSs, without affecting the legitimate transmission. The feasibility conditions of the proposed scheme are derived, and the secrecy performance is analyzed. Finally, simulation results are presented to verify the effectiveness of the proposed scheme.

We present a precoding approach for spatial modulation to provide robustness against both the Rayleigh/Rician fading effects and also spatial correlations among transmit antennas. This approach, based on phase-rotation and/or amplitude scaling of the transmitted symbols according to the active transmit antenna, can be implemented while preserving the average power budget and without any explicit knowledge of the channel coefficients at the transmitter. The optimum values of the precoding coefficients are determined so as to minimize the asymptotic average bit-error rate. Both theoretical analysis and simulation results indicate significant performance improvements even in the case of heavily correlated transmit antennas. Moreover, it is also shown that optimal precoding significantly compensates for the vulnerability of the antenna index bits against direct line-of-sight channel components and/or heavy inter-antenna correlations at the transmitter.

We present a precoding approach for spatial modulation (SM) to provide robustness against both the Rician fading effects and also spatial correlations among transmit antennas. This approach, based on phase-rotation and/or amplitude scaling of the transmitted symbols according to the active transmit antenna, can be implemented while keeping the average power budget and without any explicit knowledge of the channel state information at the transmitter. The optimum values of the precoding coefficients are determined so as to minimize the asymptotic average bit-error rate (ABER). Both theoretical analysis and simulation results indicate significant performance improvements even in the case of heavily correlated transmit antennas.

We address multiple-input multiple-output (MIMO) communication employing spatial modulation (SM) with dual-polarized (DP) antennas. The proposed architecture adds the polarization dimension to the conventional SM mappings and offers performances, which are comparable to or under certain conditions even better than those of the uni-polarized systems while occupying half as much space. We consider the generalized spatially correlated Rayleigh and Rician fading channel models and present an average bit-error probability upper bounding framework for the proposed DP SM-MIMO system. The theoretical error analysis is also extended to the case where the channel coefficients are estimated with Gaussian estimation errors. This upper bounding method is also used to determine the conditions in which the dual-polarized SM is better than equivalent systems with uni-polarized antennas. Theoretical derivations are also validated by extensive simulations, both corroborating that SM combined with dual-polarization forms an attractive alternative not only for its improved multiplexing gains and space efficiency but also for performance gains over correlated channels.

In this paper, we present a new type of Spatial Multiplexing (SMX) schemes based on the multiple signal constellation concept, which was recently introduced in the context of Spatial Modulation (SM) by the present authors. The proposed technique, which we refer to as Enhanced SMX or E-SMX, conveys information not only by the transmitted symbols, but also by the antenna and constellation combinations used. In addition to the primary constellation, these schemes make use of one or more specifically-designed secondary constellations, obtained through geometric interpolation in the signal constellation plane. We present the general concept and describe specific schemes for different numbers of transmit antennas and using 16QAM as primary modulation. Our analysis and the simulation results indicate that the proposed schemes provide a significant performance gain over conventional SMX.

In this paper, we present three new signal designs for enhanced spatial modulation (ESM), which was recently introduced by the present authors. The basic idea of ESM is to convey information bits not only by the index(es) of the active transmit antenna(s) as in conventional SM, but also by the types of the signal constellations used. The original ESM schemes were designed with reference to single-stream SM and involved one or more secondary modulations in addition to the primary modulation. Compared with single-stream SM, they provided either higher throughput or improved signal-to-noise ratio (SNR). In this paper, we focus on multi-stream SM (MSM) and present three new ESM designs leading to increasing SNR gains when they are operated at the same spectral efficiency. The secondary signal constellations used in the first two designs are derived through a single geometric interpolation step in the signal constellation plane, while the third design also makes use of additional constellations derived through a second interpolation step. The new ESM signal designs are described for MIMO systems with four transmit antennas out of which two are active, but we also briefly present extensions to higher numbers of antennas. Theoretical analysis and simulation results indicate that the proposed designs provide a significant SNR gain over MSM.

In this work, multiple-input multiple-output (MIMO) communication employing spatial modulation (SM) is considered with dual-polarized antennas over correlated Rayleigh and Rician channels. A general average bit-error probability (ABEP) upper bounding framework is presented for the dual-polarized SM-MIMO systems. This framework is used to derive a closed form condition for the asymptotic feasibility regions for employing dual-polarization versus using uni-polarized antennas. Theoretical derivations are validated with extensive simulation results. Both analysis and simulation results indicate that especially in the case of correlated channels, the use of dual-polarized antennas for SM is more feasible in terms of both allocated space and error performance than equivalent systems implemented with uni-polarized antennas.

In this paper, we introduce a new signal design for the Enhanced Spatial Modulation (ESM) concept, which was recently proposed by the present authors. The basic idea of ESM is to convey information bits not only by the indexes of the active transmit antennas, but also by the types of the multiple constellations used. Design of the original ESM schemes involved one or more secondary modulations, which were derived using a single step of geometric interpolation in the signal constellation plane. In the new design, we go one step further in the interpolation process and derive additional modulations leading to a significant increase of the number of active antenna and modulation combinations used. A design example is given for 10 bits per channel use (bpcu) transmission using four transmit antennas two of which are active at a time. The new design is compared to conventional multi-stream spatial modulation (MSM) and to our previously proposed ESM scheme. The analysis and the simulation results confirm a signal-to-noise ratio (SNR) gain of 2.2 dB over MSM and 0.4 dB over ESM when the three schemes are operated at the same spectral efficiency.

In this paper, we introduce a new spatial modulation (SM) technique using one or two active antennas and multiple signal constellations. The proposed technique, which we refer to as Enhanced SM or ESM, conveys information bits not only by the index(es) of the active antenna(s), but also by the constellations transmitted from each of them. The main feature of ESM is that it uses a primary signal constellation during the single active antenna periods and some other secondary constellations during the periods with two active transmit antennas. The secondary signal constellations are derived from the primary constellation by means of geometric interpolation in the signal space. We give design examples using two and four transmit antennas and QPSK, 16QAM, and 64QAM as primary modulations. The proposed technique is compared to conventional SM as well as to spatial multiplexing (SMX), and the results indicate that in most cases, ESM provides a substantial performance gain over conventional SM and SMX while reducing the maximum-likelihood (ML) decoder complexity.

Interference cancellation is a key design concern for next-generation communication systems. One practical approach is the so-called interference rejection combining (IRC) scheme which treat interference as a stationary Gaussian process to simplify interference suppression design. However, this stationary assumption does not hold in practice; in particular, the statistics of the pilot and the data parts from an interfering eNodeB (eNB) are quite different. This considerably impacts the possible improvements of interference-aware receivers. In this paper, novel interference suppression schemes are proposed, which handle separately the interfering pilot and data signals. The proposed schemes take into account channel estimation errors and also the errors in covariance estimation of interference plus noise.

Co-channel interference suppression is a key design concern for next-generation communication systems. It is particularly challenging in the presence of frequency-selective fading as the covariance matrices of the interference plus noise vary across subcarriers. The moving average technique is an effective scheme for estimating the frequency- selective covariance matrices. But the problem of choosing the optimal window size is far from trivial. In this work, we propose a window selection scheme based on the mean square error (MSE) of the covariance estimate. It only requires information of the signalto-noise ratio (SNR) and signal-to-interference ratio (SIR) and is robust against variations of the interfering channel's power delay profile. Moreover, the results can be applied to time-varying channels by simply replacing the frequency correlation function by the time correlation function.

Interference rejection combining (IRC) is an effective technique to suppress the spatial interference in multiple input multiple-output (MIMO) systems. In general, more receive antennas result in better interference-suppression capability. However, as the number of receive antennas increases, the receiver becomes more complex and loses its scalability. Therefore, an increased attention was turned by industry to find efficient ways to enable the use of more receive antennas, e.g., scaling the number of receive antennas to increase interference rejection capability. In this paper, a baseband preprocessing scheme is proposed by maximizing the signal-to- interference-plus noise ratio (SINR). It involves minimum modification of the existing receiver structure when additional receive antennas become available. Substantial improvements are provided by exploiting the channel estimates or the SINR feedback from the original receiver. The main idea is to generate a new channel matrix for the original receiver such that the interference can be suppressed after the proposed processing. Since the proposed scheme does not rely on the receiver structure, it can be easily generalized to different kind of receivers.

Interference cancellation is a key design concern for next-generation communication systems. One practical approach is the interference rejection combining (IRC) scheme, which is already being considered as the base receiver for next-generation 3GPP specifications. IRC treats interference as a stationary Gaussian process to facilitate simple interference suppression. However, this stationary assumption does not hold in practice; for example, the variances of the pilot signal and of the data signal from the interfering base station (eNB) are quite different. This considerably impacts the possible improvements of an interference-aware receiver. In this paper, novel interference suppression schemes are proposed, which handle separately the interfering pilot and the interfering data signals. The proposed schemes take into account channel estimation errors and the errors in estimating the covariance of interference plus noise. The performance of the proposed schemes is validated by using realistic channel models and asynchronous scenarios for MIMO-OFDM systems.

We present a general framework for the analysis of the effects of channel estimation errors on the average bit error probability (ABEP) of spatial modulation over correlated Rayleigh and Rician channels. The proposed approach is useful in obtaining the upper bound on the ABEP exactly for M-ary PSK constellations and with a very close approximation for M-ary QAM constellations. The framework is applicable to any number of transmit/receive antennas and to other linear constellations as well. Theoretical derivations are validated via simulation results.

Space-Time Block Codes (STBCs) suffer from a prohibitively high decoding complexity unless the low-complexity decodability property is taken into consideration in the STBC design. For this purpose, several families of STBCs that involve a reduced decoding complexity have been proposed, notably the multi-group decodable and the fast decodable (FD) codes. Recently, a new family of codes that combines both of these families namely the fast group decodable (FGD) codes was proposed. In this paper, we propose a new construction scheme for rate-1 FGD codes for 2atransmit antennas. The proposed scheme is then applied to the case of four transmit antennas and we show that the new rate-1 FGD code has the lowest worst-case decoding complexity among existing comparable STBCs. The coding gain of the new rate-1 code is optimized through constellation stretching and proved to be constant irrespective of the underlying QAM constellation prior to normalization. Next, we propose a new rate-2 FD STBC by multiplexing two of our rate-1 codes by the means of a unitary matrix. Also a compromise between rate and complexity is obtained through puncturing our rate-2 FD code giving rise to a new rate-3/2 FD code. The proposed codes are compared to existing codes in the literature and simulation results show that our rate-3/2 code has a lower average decoding complexity while our rate-2 code maintains its lower average decoding complexity in the low SNR region. If a it time-out sphere decoder is employed, our proposed codes outperform existing codes at high SNR region thanks to their lower worst-case decoding complexity.

In this paper we propose a new construction method for rate-1 Fast-Group-Decodable (FGD) Space-Time-Block Codes (STBC)s for 2atransmit antennas. We focus on the case of a = 2 and we show that the new FGD rate-1 code has the lowest worst-case decoding complexity among existing comparable STBCs. The coding gain of the new rate-1 code is then optimized through constellation stretching and proved to be constant irrespectively of the underlying QAM constellation prior to normalization. In a second step, we propose a new rate-2 STBC that multiplexes two of our rate-1 codes by the means of a unitary matrix. A compromise between rate and complexity is then obtained through puncturing our rate-2 code giving rise to a new rate-3/2 code. The proposed codes are compared to existing codes in the literature and simulation results show that our rate-3/2 code has a lower average decoding complexity while our rate-2 code maintains its lower average decoding complexity in the low SNR region at the expense of a small performance loss.

We address coded spatial modulation (CSM) and present a novel approach denoted as bit-interleaved coded spatial modulation (BICSM) with iterative demodulation/decoding. The proposed transceiver architecture alleviates some drawbacks of the previously proposed CSM systems, such as being limited to a particular class of trellis codes or being effective only in limited channel scenarios. We specifically address the performance of BICSM over correlated Rayleigh and Rician fading channels and provide a general framework for the error performance analysis. Simulation results illustrate that BICSM provides not only significant performance improvements against channel fading in comparison to other CSM approaches but also higher robustness against antenna correlation effects.

We present a general upper bounding framework for the average bit error probability of spatial modulation over correlated Rayleigh and Rician channels. The proposed approach provides a closed form upper bound for correlated Rayleigh fading conditions whereas for correlated Rician channels it leads to the numerical evaluation of a single integral formula. The framework is applicable to a general class of linear modulation alphabets and any number of transmit/receive antennas. Theoretical derivations are validated via simulation results.

Complex Orthogonal Design (COD) codes are known to have the lowest detection complexity among Space-Time Block Codes (STBCs). However, the rate of square COD codes decreases exponentially with the number of transmit antennas. The Quasi-Orthogonal Design (QOD) codes emerged to provide a compromise between rate and complexity as they offer higher rates compared to COD codes at the expense of an increase of decoding complexity through partially relaxing the orthogonality conditions. The QOD codes were then generalized with the so called g-symbol and g-group decodable STBCs where the number of orthogonal groups of symbols is no longer restricted to two as in the QOD case. However, the adopted approach for the construction of such codes is based on sufficient but not necessary conditions which may limit the achievable rates for any number of orthogonal groups. In this paper, we limit ourselves to the case of Unitary Weight (UW)-g-group decodable STBCs for 2atransmit antennas where the weight matrices are required to be single thread matrices with non-zero entries ∈{± 1,± j} and address the problem of finding the highest achievable rate for any number of orthogonal groups. This special type of weight matrices guarantees full symbol-wise diversity and subsumes a wide range of existing codes in the literature. We show that in this case an exhaustive search can be applied to find the maximum achievable rates for UW-g-group decodable STBCs with g>;1. For this purpose, we extend our previously proposed approach for constructing UW-2-group decodable STBCs based on necessary and sufficient conditions to the case of UW-g-group decodable STBCs in a recursive manner.

The use of Space-Time Block Codes (STBCs) increases significantly the optimal detection complexity at the receiver unless the low-complexity decodability property is taken into consideration in the STBC design. In this paper we propose a new low- complexity decodable rate-5/4 full-diversity 4 × 4 STBC. We provide an analytical proof that the proposed code has the Non-Vanishing-Determinant (NVD) property, a property that can be exploited through the use of adaptive modulation which changes the transmission rate according to the wireless channel quality. We compare the proposed code to the best existing low-complexity decodable rate-5/4 full-diversity 4 × 4 STBCs in terms of performance over quasi-static Rayleigh fading channels, worst-case complexity, average complexity, and Peak-to-Average Power Ratio (PAPR). Our code is found to provide better performance, lower average decoding complexity, and lower PAPR at the expense of a slight increase in worst-case decoding complexity.

Space-time coding techniques have become common-place in wireless communication standards as they provide an effective way to mitigate the fading phenomena inherent in wireless channels. However, the use of Space-Time Block Codes (STBCs) increases significantly the optimal detection complexity at the receiver unless the low complexity decodability property is taken into consideration in the STBC design. In this letter we propose a new low-complexity decodable rate-1 full-diversity 4 × 4 STBC. We provide an analytical proof that the proposed code has the Non-Vanishing-Determinant (NVD) property, a property that can be exploited through the use of adaptive modulation which changes the transmission rate according to the wireless channel quality. We compare the proposed code to existing low-complexity decodable rate-1 full-diversity 4 × 4 STBCs in terms of performance over quasi-static Rayleigh fading channels, detection complexity and Peak-to-Average Power Ratio (PAPR). Our code is found to provide the best performance and the smallest PAPR which is that of the used QAM constellation at the expense of a slight increase in detection complexity w.r.t. certain previous codes but this will only penalize the proposed code for high-order QAM constellations.

Fast Maximum Likelihood Decodable Space-Time Block Codes (STBCs) have recently gained a lot of interest for use in wireless communication systems. Several strategies have been introduced in the literature to build such codes, among them we mention the conditional detection strategy, where the code matrix can be expressed as the sum of two Orthogonal STBCs by means of a unitary matrix. Thus, the decoder benefits from the orthogonality by estimating an orthogonal set of symbols assuming knowledge of the other set of symbols. Even if this strategy permits high rates, numerical optimization of the coding gain becomes unrealizable for high-order constellations. Another strategy has been proposed in the literature, the g-group decodable codes, where the minimization of the Maximum Likelihood (ML) metric can be made over several independent subsets of symbols. Moreover, the g-group decodability strategy enables us to optimize the coding gain for each subset of symbols separately which permits the optimization of the coding gain for high-order constellations. On the other hand, the rate of g-group decodable codes is limited. In this paper, we reformulate the problem of finding Unitary Weight (UW)-2-group decodable codes in a simpler way based on necessary and sufficient conditions. Then, a simple routine is used to search for the desired code. We determine the maximum achievable rate for a specified type of weight matrices.

Spatial diversity techniques are now used in most wireless communications systems to improve robustness to signal fading. On the downlink of cellular systems, low-cost requirements of the user terminal tend to favor transmit diversity over receive diversity, and Alamouti's transmit diversity technique appears today in most wireless communications systems standards. But in terms of the total transmit power, Alamouti's technique loses 3 dB with respect to optimum transmit diversity which requires perfect channel state information at the transmitter. In this paper, we introduce a simple transmit diversity technique which performs significantly better than both Alamouti's transmit diversity and switched diversity schemes and comes very close to optimum transmit diversity performance. In this technique, the same power is transmitted from the two antennas and the phase difference of the signals received from the two channels is constrained to be smaller than π/4. Its performance is studied in both narrowband fading channels and in OFDM systems operating on frequency- selective channels.

From a practical point of view, fast Maximum Likelihood Decoding (MLD) and low Peak-to-Average-Power- Ratio (PAPR) are two important design criteria for Space-Time Block Codes (STBCs). The use of the principle of conditional detection allows for having high-rate STBCs while keeping the complexity of detection at a reasonable level. Recently, we proposed a structure for a STBC with fast MLD having a rate of 3/2 complex symbols per channel use. In this paper, the PAPR criterion is included in the code design, and a modified version is proposed which has a lower PAPR.

Single-Carrier Frequency Division Multiple Access (SC-FDMA) is a recent modulation technique combining most of the advantages of Orthogonal Frequency Division Multiple Access (OFDMA) with the low Peak-to-Average Power Ratio (PAPR) of single-carrier transmission. This paper presents the key design criteria for space-time and space-frequency transmit diversity precoding in SC-FDMA with four transmit antennas. In particular, we propose a novel quasi-orthogonal space-time-frequency precoding. We show that our proposed precoding conserves the single-carrier property of the signal on all of the four transmit antennas with improved performance at the expense of a slight increase of the frame granularity with respect to existing space-frequency solutions.

Recently, Space-Time Block Codes (STBCs) with fast Maximum-Likelihood Decoding (MLD) have gained a lot of interest from a practical perspective. B. Rajan et.al proposed a systematic approach to build such codes from the representations of the real Clifford algebras. In the Sari-Sezginer code, another approach was proposed to build fast MLD STBC's. Indeed, the authors used the conditional detection principle and numerical optimization in order to have a large coding gain (thus full diversity) while maintaining a reduced complexity level at the receiver. In this paper we are interested in extending the principle of conditional detection to the case of four transmit antennas. We propose here a full-diversity 4×4 STBC achieving a rate of 3/2 complex symbols per channel use. To the best knowledge of the authors, the highest rate for full transmit diversity 4×4 codes with the same complexity level was reported to be 5/4 complex symbols per channel use (such codes were found independently).

Due to overlap of antenna radiation patterns and the resulting interference between sectors, different frequency bands are typically used in different sectors of a cell in cellular systems. In this paper, a full frequency reuse concept, which is based on multiple-input multiple-output (MIMO) techniques, is introduced and analyzed for the uplink and downlink. The results indicate a significant increase of the cell capacity.

The 2times2 MIMO profiles included in mobile WiMAX specifications are Alamouti's space-time code (STC) for transmit diversity and spatial multiplexing (SM). The former has full diversity and the latter has full rate, but neither of them has both of these desired features. An alternative 2times2 STC, which is both full rate and full diversity, is the Golden code. It is the best known 2times2 STC, but it has a high decoding complexity. Recently, the attention was turned to the decoder complexity, this issue was included in the STC design criteria, and different STCs were proposed. In this paper, we first present a full-rate full-diversity 2times2 STC design leading to substantially lower complexity of the optimum detector compared to the Golden code with only a slight performance loss. We provide the general optimized form of this STC and show that this scheme achieves the diversity multiplexing frontier for square QAM signal constellations. Then, we present a variant of the proposed STC, which provides a further decrease in the detection complexity with a rate reduction of 25% and show that this provides an interesting trade-off between the Alamouti scheme and SM.

Innovative mapping schemes for Space-Frequency Block Codes (SFBC) which are compatible with the structure of Single-Carrier Frequency Division Multiple Access (SC-FDMA) systems are introduced. We first show that existing space-time and space-frequency block codes lack flexibility in terms of framing or cause a degradation of the signal envelope properties when combined with SC-FDMA. Then, we present an Alamoutibased orthogonal code designed for 2 transmit antennas that makes use of an innovative mapping in the frequency domain to preserve the low envelope properties of SC-FDMA. Next, an extension of this concept to a quasi-orthogonal code for 4 transmit antennas is presented and analyzed. We prove the good performance of the proposed schemes over multiple-input multiple-output (MIMO) channels both in static and in highmobility scenarios.

Single-carrier frequency division multiple access (SC-FDMA) is a recent modulation technique combining most of the advantages of orthogonal frequency division multiple access (OFDMA) with the low peak-to-average power ratio (PAPR) of single-carrier transmission. For these reasons, it has been adopted as a possible air interface on the uplink of future wireless networks. It is also suitable for any other system in which a low PAPR is desired. In this paper, we highlight the severe limitations of existing space-time and space-frequency block codes when combined with SC-FDMA, and we propose a novel quasi-orthogonal space-frequency block code compatible with a SC-FDMA system with four transmit antennas. We show that our proposed preceding keeps the single-carrier property of the signal on all of the four transmit antennas and we also prove the good performance of our scheme on frequency selective channels with spatial correlation.

Single-carrier frequency division multiple access (SC-FDMA) has been adopted as a possible air interface for future wireless networks. It combines most of the advantages of orthogonal frequency division multiple access and the low peak to average power ratio (PAPR) of single carrier (SC) transmission. Existing transmit antenna diversity techniques such as space-time block coding and space-frequency block coding are incompatible either with the system constraints or with the SC nature of SC-FDMA. We propose a novel space-frequency flexible coding scheme compatible with SC-FDMA and we prove its good performance both in terms of PAPR and bit error rate (BER) on frequency selective multiple input multiple output channels.

In this paper we investigate the performance of single-carrier space-frequency block coding (SC-SFBC), a new diversity technique compatible with single-carrier frequency division multiple access (SC-FDMA). SC-FDMA has been adopted as a possible air interface for future wireless networks as it combines the advantages of orthogonal frequency division multiple access (OFDMA) and the low envelope variation properties of single carrier (SC) transmission. Existing transmit antenna diversity techniques as space-time block coding (STBC) and space-frequency block coding (SFBC) are incompatible either with the system constraints or with the low envelope variations of SC-FDMA. We describe the new proposed SC-SFBC technique and we prove its good performance both in terms of peak to average power ratio (PAPR) and bit error rate (BER) performance on frequency selective multiple input multiple output channel. Our new scheme is compared to existing open-loop transmit antenna selection, cyclic delay diversity (CDD), STBC and classical SFBC schemes.

Mobile WiMAX systems are based on the IEEE 802.16e specifications, which include two mandatory MIMO profiles for the downlink. One of these is Alamouti's space-time code (STC) for transmit diversity, and the other is a 2times2 spatial multiplexing MIMO scheme. In this paper, we compare the two schemes assuming that the latter employs maximum-likelihood detection. The analysis shows that at the same spectral efficiency, Alamouti's STC combined with maximum-ratio combining (MRC) at the receiver significantly outperforms the 2times2 spatial multiplexing scheme at high values of the signal-to-noise ratio (SNR). Next, selection of a MIMO option is included in link adaptation to maximize network capacity, and operating SNR regions are determined for different modulation, coding and MIMO combinations.

This paper investigates the performance of linearly precoded OFDMA systems. Based on the pairwise error probability (PEP) analysis, a new design criterion, namely, the good shaping criterion, is introduced. Existing precoding techniques are discussed and an ad-hoc constellation shaping strategy is presented in order to satisfy the proposed criterion. Since maximum-likelihood (ML) detection has the ability to exploit the diversity achieved with the most effective spreading matrices, small size precoders are considered to keep the complexity reasonable. Simulation results have shown that linear precoding recovers most of the frequency diversity loss that is inherent to OFDMA. Moreover it is observed that simple constellation shaping strategies may be effective for small sizes of precoders.

In this paper, we present a novel metric-based symbol predistortion algorithm and describe three variants for peak-to-average power ratio (PAPR) reduction in OFDM transmission. The algorithm consists of predistorting a set of input symbols per block using simple metrics, which measure how much each symbol contributes to the output signal samples of large magnitudes. The symbols to be predistorted in each block are selected as those with the largest positive-valued metrics. Predistortion of input symbols is performed either by scaling only the amplitude or by scaling separately the real and/or the imaginary parts of the selected symbols. The simple metric-based structure of the proposed algorithm gives high flexibility which enables various tradeoffs between performance and complexity. Another important feature is that the algorithm does not require transmitting any side information to the receiver and it does not involve any additional complexity for symbol detection at the receiver side. It is shown by simulations that a considerable improvement can be achieved with these simple techniques, which can be implemented as one-shot or iterative procedures.

The main drawback of Orthogonal Frequency Division Multiplexing (OFDM) systems is the high peak-to-average power ratio (PAPR), which significantly reduces the efficiency of the transmit high power amplifier (H PA). Several methods have been proposed in the literature to reduce the peak power of OFDM signals and substantial gains were reported. In this paper, the effectiveness of some recently proposed methods is evaluated for WiMAX systems. Using typical HPA models and spectral masks, these PAPR reduction methods are evaluated in terms of the total system degradation.

The performance of some channel overloading schemes based on using two sets of orthogonal signal waveforms is investigated on a Rayleigh fading channel. The achievable overload factor is evaluated for quaternary phase-shift keying (QPSK) modulation. It is shown that when used in conjunction with soft iterative interference cancellation, the investigated channel overloading techniques offer substantial channel overloads without requiring any user sorting according to the fading amplitudes.

Multiple transmit and receive antenna arrays can be used to form multiple input and multiple output (MIMO) systems for diversity and multiplexing in wireless communications. In this paper, we develop iterative signal-detection schemes based on energy spreading transform (EST) (T. Hwang and Y. Li) for MIMO channels. The EST in a MIMO system improves signal-detection performance by spreading the symbol energy over the space and time domain. It also enables iterative signal detection without employing channel coding. Analytical and simulation results demonstrate that the performance of the proposed schemes is very close to that of the genie-aided receiver when there are a sufficiently large number of receive antennas and signal-to-noise ratio (SNR) is above a threshold

In this paper, we propose a new peak-to-average power ratio (PAPR) reduction technique for OFDM systems. It consists of predistorting the amplitude of a set of symbols per block using a simple metric-based algorithm, and it does not require transmitting any side information to the receiver. The metrics measure how much each symbol contributes to the output signal samples of large magnitude, and the predistorted symbols are selected as those with the largest metrics. The proposed technique can be implemented as a one-shot procedure or as an iterative algorithm. Its performance is investigated using the QPSK signal constellation.

In this paper, we propose a simple peak-to-average power ratio (PAPR) reduction algorithm for code-division multiple access (CDMA) systems. Based on constellation extension, the described algorithm does not need sending any side information to the receiver and approaches the performance of the optimum PAPR reduction procedure, while avoiding its excessive complexity. Performance of the proposed algorithm is investigated using binary phase-shift keying (BPSK) modulation.

Presents the welcome message from the conference proceedings.

We present some new results on a recently introduced multiple access technique which accommodates more than TV users on a channel whose bandwidth is N times the bandwidth of the individual user signals. For K = N + M users, the technique makes use of two sets of orthogonal signal waveforms, one for the first N users and one for the M additional users. Considering a combination of time-division multiple access (TDMA) and orthogonal code-division multiple access (OCDMA), this multiple access scheme is evaluated using quaternary phase-shift keying (QPSK) and 16-state quadrature amplitude (16-QAM) modulations. When used in conjunction with soft iterative interference cancellation (IC), the technique offers substantial overloads.

We present a receiver architecture for transmission over the upstream channel of hybrid fiber coax (HFC) networks affected by ingress noise and channel distortion. The receiver uses a noise prediction algorithm. It is shown that the proposed receiver makes it possible to transmit a QPSK signal over a cable channel affected by three interferers with an individual power of -10 dBc, with an SNR degradation of only 1.8 dB. The SNR degradation corresponding to 16-QAM transmission over a channel affected by three interferers of -15 dBc is 2.1 dB. We also describe an adaptation technique that makes use of empty time slots to track changes of the ingress noise continuously. The noise canceller involves only a small additional complexity.

In this paper, we present a code-division multiple access (CDMA) system with a spreading factor of N that can accommodate up to mN users, where m/spl ges/2. The m sets of orthogonal spreading sequences are constructed by overlaying the same N orthogonal Walsh-Hadamard sequences with a set-specific pseudo-noise (PN) sequence. Interference between user signals from different sets is cancelled by means of soft-decision iterative interference cancellation. We show that this technique, which is referred to as m-OCDMA, can accommodate more users and is computationally less complex than conventional PN-CDMA with the same type of interference cancellation. Assuming BPSK transmission over an additive white Gaussian noise (AWGN) channel and perfect power control, a degradation of 0.4 dB at the BER of 10/sup -5/ in m-OCDMA is obtained with 2.56N users in the uplink or 3.03N users in the downlink. In PN-CDMA, this figure is 2.16N for both the uplink and the downlink.

We present a new encoded signaling concept which consists of transmitting an additional data rate of 50 % to the original data rate that is determined by the channel bandwidth, the modulation scheme, and other physical-layer functions of the transmission system. This is accomplished by overlaying an encoded version (using a rate 1/2 code with Hamming distance 4) of N/2 excess symbols to each block of N primary symbols. The resulting interference between primary and excess symbols is handled using a joint symbol detection technique with low complexity for N=8 and N=10. Our analysis shows that the transmission of the excess data rate involves an asymptotic gain in SNR of 0.8 dB as compared to conventional BPSK or QPSK signaling. Our simulation results indicate that this gain is noticeable at bit error rate (BER) values below about 10/sup -7/.

This paper investigates the application of CDMA to broadband wireless access systems commonly known as LMDS networks. After a review of the intercell interference in the emerging LMDS networks based on TDMA, we analyze the interference in CDMA-based networks and show that, while conventional CDMA is superior to TDMA in terms of the worst-case interference on the upstream channel, it turns out to be inferior to TDMA in terms of interference on the downstream channel. Next, based on the observation that strong interference occurs only for a small range of user locations, we introduce a multimode CDMA concept that efficiently handles this interference by assigning orthogonal spreading sequences whose number and length are a function of the user position with respect to the base station. We show that in terms of worst-case SIR, the proposed technique leads to a significant improvement with respect to conventional CDMA and TDMA.

This paper investigates the sensitivity of several multiple-access techniques to narrow-band interference. The analysis covers time-division multiple access (TDMA), code-division multiple access (CDMA), and orthogonal frequency-division multiple access (OFDMA). The study is carried out under the assumption that all the considered multiple-access systems occupy the same total bandwidth, and the bit rates of all active users are identical. A major finding of this study is that CDMA with pseudonoise spreading sequences is more sensitive to narrow-band interference than TDMA. We point out that the signal-to-jammer power ratio at the decision device input is in fact identical for both multiple-access techniques, but the amplitude distribution of the jammer term at the threshold detector input is more favorable to TDMA, which turns out to be more robust in terms of bit-error rate. Another finding is that in terms of sensitivity to narrow-band interference, orthogonal CDMA (OCDMA) is closer to TDMA than to CDMA with pseudonoise sequences, because the degradation is not the same for all users. Finally, we discuss the relationship of OCDMA and TDMA and highlight the superiority, in terms of capacity over the narrow-band interference channel, of TDMA to the other multiple-access techniques considered in this paper.

We present a new signaling concept which consists of transmitting an additional data rate to the original data rate that is determined by the channel bandwidth, the modulation scheme, and other physical-layer functions of the transmission system. This is accomplished by overlaying M excess symbols to each block of N primary symbols, with the excess symbol period being equal to N times the primary symbol period. The resulting interference between primary and excess symbols is handled using a joint symbol detection technique. Our analysis shows that for BPSK or QPSK modulation and M=1 or M=3, transmission of the excess data rate does not degrade the minimum Euclidean distance when the excess symbol ratio M/N does not exceed 25%. Furthermore, our simulation results indicate that at bit error rate (BER) values in the order of 10/sup -3/ to 10/sup -5/, transmission of the additional data in these cases is achieved at the expense of a moderate to very small penalty in signal-to-noise ratio (SNR).

We present a multiple access technique which accommodates more than N users on a channel whose bandwidth is N times the bandwidth of the individual user signals. It makes use of two sets of orthogonal signal waveforms, one for the first N users, and one for the additional users. An iterative multistage detection technique is used to cancel interference between the two sets of users. At each stage of the detection process, the best estimate of the multiuser interference (MUI) is synthesized using the decisions available from the previous stages, and this interference is subtracted from the user signals of interest before entering these signals to a threshold detector. The new concept is described using a combination of time-division multiple access (TDMA) and orthogonal code-division multiple access (OCDMA), but it is equally applicable to any other sets of orthogonal signal waveforms. Using a binary phase-shift keying modulation (BPSK), it is shown that the proposed technique supports an excess user rate of 40% at the expense of a very small degradation of the signal-to-noise ratio (SNR).

This paper gives an overview of a previously introduced channel overloading concept that is applicable to both multiuser communications and single-user communications. We first describe it for multiuser communications using a hybrid multiple access scheme based on a combination of time-division multiple access (TDMA) and code-division multiple access (CDMA). Next, we extend it to full CDMA by defining two sets of orthogonal signal waveforms. Using a binary phase-shift keying (BPSK) modulation, it is shown that this multiple access concept can increase the number of users by 40% with respect to the hard limit of orthogonal waveform multiple access (OWMA) which includes TDMA, orthogonal CDMA (OCDMA), and orthogonal frequency-division multiple access (OFDMA). Then, we apply the same concept to single-user communications to transmit an excess bit rate in addition to the primary data rate. It is shown that an excess data rate of up to 25% can be transmitted at the price of a very small penalty in signal-to-noise ratio (SNR).

We investigate the sensitivity to carrier phase jitter of an orthogonal frequency-division multiple-access (OFDMA) system. When all OFDMA carriers have the same power level and jitter spectrum, the degradation caused by the jitter is shown to be equal to the degradation of an OFDM system. Also, traditional FDMA is found to be slightly more robust than OFDMA.

We present two multilevel constant-envelope continuous-phase modulation (CPM) schemes with four-dimensional (4-D) trellis coding. The receiver is composed of a simple quadrature demodulator, followed by a symbol-rate sampler and a Viterbi decoder matched to the code trellis. The first modulation is a quaternary CPM scheme whose phase transitions over a symbol interval are those of /spl pi//4-shift quaternary phase-shift keying (QPSK). The demodulator filter is optimized so as to minimize the combined effect of intersymbol interference (ISI) and noise at the decision instants. We use Wei's (1987) 16-state 4-D trellis code, and redefine the set partitioning tree so as to maintain the same minimum distance between parallel transitions as in quadrature amplitude modulation (QAM) signal sets. The resulting modulation outperforms minimum-shift keying (MSK) by as much as 3.5 dB, in addition to reducing the 30-dB signal bandwidth by 20%. Next, we introduce an octonary (8-level) CPM scheme whose phase transitions are those of /spl pi//8-shift 8PSK. The same trellis code and receive filter optimization are also applied to this modulation which is shown to achieve better error rate performance than MSK, while saving some 60% of the transmitted signal bandwidth.

In addition to the conventional matched filter, implementation of Gardner's (1990) frequency detector (GFD) based on the maximum-likelihood principle also involves a frequency-matched filter. the present authors give a reduced-complexity frequency detector (RCFD) derived from Gardner's detector through a simple approximation to the frequency-matched filter. They analyze its steady-state jitter properties and frequency acquisition performance and compare it to the original GFD as well as to the well-known balanced quadricorrelator. The results show that performance of the RCFD is close to that of the original GFD and that this detector significantly outperforms the balanced quadricorrelator which involves a similar hardware complexity. Another contribution of the paper is the analysis of GFD and RCFD when their outputs are computed at twice the symbol rate.>

Using static and time-varying channel impulse responses, the authors analyze the performance of orthogonal frequency-division multiplexing (OFDM) on multipath fading channels. They show that, provided it employs a frequency domain equalizer, single-carrier transmission substantially outperforms OFDM signalling, a result that contradicts the usual claims that OFDM signalling is more resistant to multipath fading than is standard single-carrier transmission. This finding is supported by computer simulation results and analytical arguments related to the decision process. The implication of the results is that not only OFDM signalling increases the system sensitivity to nonlinear distortion and carrier synchronization errors, but it also turns out to offer no performance advantage on fading radio channels.>

The timing jitter induced by pointer adjustments in the basic STM-1 frame represents a serious technical problem in SDH-based networks. This paper describes two jitter reduction techniques to cope with this phenomenon. The first technique is based on digital phase-lock loop (PLL) theory, and obtained through two structural modifications of a previously proposed desynchronizer. The second technique is entirely novel, and it avoids the generation of the random noise with uniform probability density, which is required in PLL-type desynchronizers to smooth the 1-b phase steps at the output of the first stage. We describe two different methods to adapt the speed of this desynchronizer to the incoming pointer adjustment statistics. The performance of both jitter reduction techniques is investigated in both the normal mode and the degraded mode of operation of the network. Using a design example, it is shown that the peak-to-peak jitter in the presence of isolated pointer adjustments that characterize the normal operation mode is kept below 0.1 b. It is also shown that with frequency offsets up to 4.6 ppm in the degraded mode, the peak-to-peak jitter does not exceed 0.6 b. Lower jitter values are achievable if the complexity and memory requirements of the desynchronizer are allowed to increase.>

A family of 6-D trellis-coded modulation (TCM) schemes which involve a 2-step partitioning of the constituent QAM signal alphabet is presented. With infinite constellations without shaping, the asymptotic coding gain is 3 dB for the 2-state code, 4 dB for the 4- and 8-state codes, and 5 dB for the 16- and 32-state codes which involve a smaller alphabet expansion. The authors also describe a rotationally invariant 16-state code that achieves the same asymptotic gain as its linear counterpart. Practical signal constellations are described for 6-D TCM with the spectral efficiency of uncoded 64-QAM, and the performance of these schemes is studied by means of computer simulations. It was found that they achieve an additional coding gain of 0.2-0.3 dB over infinite hypercube-type constellations. The performance of the presented schemes at practical signal-to-noise ratio values is evaluated using transfer function techniques.>

The authors present an efficient data predistortion technique with memory for compensation of high-power amplifier (HPA) nonlinearities in digital microwave radio systems employing quadrature amplitude modulation (QAM) signal formats. A practical implementation method is described which trades off performance against complexity and which makes it possible to implement this kind of predistorter in 256-QAM, and higher-level QAM systems. Using the 16-, 64-, and 256-QAM signal constellations, it is shown that the proposed technique achieves a considerably higher performance than that of conventional memoryless data predistortion of the predistortion technique with memory based on finite-order inverses of nonlinear systems. Specifically, numerical results show that the proposed technique achieves a gain that is in excess of 2 dB over conventional memoryless data predistortion.>

Two block-coded modulation (BCM) families particularly suited for high-capacity digital microwave radio systems are presented. The first family is based on one-step partitioning, and the second family is based on two-step partitioning of the signal constellation. The alphabet expansion is a decreasing function of the block length, and the constellation is constructed in such a way as to minimize both the average and peak signal powers. Using short block lengths, specific modulation schemes are described that transmit 4, 6, and 8 information bits per symbol. The asymptotic coding gain is only on the order of 2 dB in the first family, and of 3 dB in the second family, but their detection simplicity makes the presented BCM schemes particularly attractive for high-speed applications where trellis-coded modulation (TCM) decoders may be difficult to implement.>

Two data predistortion techniques are presented that compensate for high-power amplifier (HPA) nonlinearities in digital microwave radio systems by employing quadrature amplitude-modulation (QAM) signal formats. The first one is a T/2-spaced predistortion technique that ensures distortion-free HPA output at two points per symbol interval T. The second is a T/3-spaced predistortion technique which cancels nonlinear distortion at the HPA output at three points per symbol interval. As opposed to conventional data predistortion, which can only compensate for warping of the signal constellation, the new techniques are effective against intersymbol interference. Using the 64- and 256-QAM signal constellations, it is shown that the proposed techniques lead to a very efficient utilization of the available HPA power. It is shown that, of the two techniques, the T/3-spaced data predistortion employs narrower transmit-pulse shaping and achieves higher protection against adjacent-channel interference at the expense of some additional complexity.>

An analysis made of the performance of predistortion, equalization, and intersymbol interference (ISI) cancellation techniques in compensating for the transmit amplifier nonlinearity in digital microwave radio systems. The study is carried out using the 64 QAM and 256 QAM signal formats and two values of the roll-off factor in the Nyquist pulse shaping. The simulated compensation techniques include three types of predistortion, two ISI cancellers, and several nonlinear equalizers with or without decision feedback. A basic result is that decision-feedback equalizers do not offer any significant advantage over nonrecursive equalizers. It is also shown that ISI cancelers with a memoryless equalizer as first-stage decision device do not perform any better than nonlinear equalizers of similar complexity. Another contribution an improved fifth-order analog signal predistortion technique is analyzed. The gain that can be achieved using a modified 256 QAM signal constellation that is more robust to nonlinear distortion is quantified.>

For asymmetric channels, the performance of symmetric and asymmetric baseband equalizers is analyzed. For both structures, the frequency response and the output mean-square error (MSE) of the optimum equalizer of infinite length are given. Several specific modem imperfections are considered, and the associated CNR (carrier-to-noise ratio) degradation is computed for 256-QAM systems. The results show that while asymmetric equalizers lead to a very limited compensation of modem imperfections, asymmetric equalizers lead to a dramatic improvement and, in the range of practical interest, reduce the CNR degradation almost to zero.>

Phase and frequency detectors (PFDs) are presented that considerably extend the acquisition range of carrier-recovery loops in digital communication systems. Based on a simple modification of conventional phase detectors (PDs), the proposed detectors are applicable to a large variety of modulation schemes, including the popular PSK and QAM signal formats. Their application to QPSK and 16- and 64-QAM is extensively discussed, and simulated frequency-detector (FD) characteristics, as well as acquisition behavior of several PFDs, are reported for QPSK and 16 QAM. The results of an experimental evaluation using a 16-QAM laboratory modem are also reported which show that the detectors increase the acquisition range achievable by conventional PDs by more than one order of magnitude. In PSK, the improved acquisition performance is obtained with no penalty in steady-state phase jitter. In combined amplitude- and phase-shift keying, it generally leads to increased jitter, but this is easily avoided by incorporating a lock indicator and switching back to the original PD after lock is acquired.>

This paper presents and investigates the performance of a reduced-bandwidth 16 QAM (RB-16 QAM) signaling technique which employs severe narrow-band filtering and decision-feedback equalization in the receiver to compensate for the resulting intersymbol interference. The overall filtering is designed so as to provide the spectral efficiency of 64 QAM. RB-16 QAM is compared to 64 QAM in terms of its performance on additive white Gaussian noise channels, in multipath fading environment, as well as in terms of its sensitivity to modem imperfections including carrier and timing phase errors, filter imperfections, nonlinear distortion and sinusoidal interference. The results show that depending on the spectral shaping filters and the equalizer used, RB16 QAM can be significantly more advantageous than 64 QAM. A most interesting finding is that while RB-16 QAM is comparable to 64 QAM in terms of its spectral efficiency, its robustness against system imperfections is very much like that of conventional 16 QAM.

The influence of adaptive baseband equalization on decision-feedback carrier recovery performance is investigated. First, it is shown that the delay introduced by the equalizer in the carrier recovery loop does not have basic consequences on system performance. Second, the interaction between the two adaptive circuits is studied for four different equalizer adaptation algorithms. It is found that algorithms based on the zero-forcing criterion are unstable unless they are used with an appropriate constraint. With gradient-type algorithms based on the minimum mean square error criterion, the interaction leads to an improved carrier acquisition performance at the expense of a larger phase jitter. It is therefore concluded that even with these latter algorithms it is generally worthwhile to avoid the interaction by forcing the imaginary part of the equalizer reference tap to zero. The theoretical analysis (carried out with a single complex tap equalizer) is supported by experimental results and computer simulation.

Two algorithms are presented for optimum timing recovery in digitally implemented equalizers. The first one is a polarity-type algorithm based on the conventional minimum mean-square error criterion. A theoretical analysis is made to characterize the algorithm phase detector and evaluate its steady-state phase jitter variance. Influence of various channel and system design parameters on the algorithm performance is illustrated using phase jitter probability densities obtained by means of computer simulations. Interaction of the algorithm with decision-directed carrier recovery is also examined. It is shown that interaction with carrier recovery may considerably degrade the timing acquisition performance, and a second algorithm is then presented which eliminates this interaction. The second algorithm is based on the minimization of a modified mean-square error criterion which provides a measure of the intersymbol interference, independently of the carrier phase. Decision-directed timing and carrier recoveries are thus decoupled and the system startup period is considerably reduced. Phase detector characteristic and steady-state jitter performance of the second algorithm are evaluated by analytical means and computer simulations, as in the first algorithm.

The performance of four decision-feedback carrier recovery techniques is evaluated in the presence of additive noise and intersymbol interference (ISI). For QAM signal constellations, a closed-form expression is given for the phase jitter variance (PJV) of each loop, and the loop tracking performance is examined. The analytic results are then computed in the case of a 16 QAM digital radio system subjected to multipath fading. Two cases are considered: in the first case no countermeasure techniques are used against selective fading, while in the second case a three-coefficient decision-feedback equalizer (DFE) is used. Computer simulations using a pseudorandom sequence to estimate loop performance are also reported which support the theoretical results.

The three simplified versions of the stochastic gradient algorithm are analyzed. Assuming gaussian probability distributions for the input signal and the output error signal, their stability is studied, their output mean square error performance is evaluated and their optimal step-size parameters are given. For digitally implemented equalizers we also indicate the minimum number of bits required to store the equalizer coefficients. Computer simulation results are reported which confirm the validity of the theoretical results.