Deep reinforcement learning (DRL) is a powerful method for Dynamic Power Allocation (DPA) due to its adaptability to changing wireless environments. Crafting proficient DRL agents necessitates a well-structured problem representation, learning scheme, and agent interaction framework. Additionally, the environment, as the second pivotal component in DRL, must mirror realistic channel dynamics faithfully. This research tackles these often-neglected aspects. We introduce Het-DRL, a heterogeneous multi-agent DRL framework with a competitive interaction paradigm for DPA, moving away from conventional collaborative and centralized learning schemes. The independent learning scheme eliminates information exchange among agents, empowering autonomous decision-making. Moreover, we present our CELLUCARLA wireless simulator, an extension of the CARLA (Car Learning to Act) simulator with a cellular network layer, enabling realistic urban mobility modeling. We leverage CELLUCARLA to evaluate the proposed Het-DRL, highlighting its effectiveness for DPA in Orthogonal Frequency-Division Multiplexing (OFDM) systems, with up to 153% sum rate relative to the Weighted Minimum Mean Square Error (WMMSE) algorithm.

Dynamic Power Allocation (DPA) in wireless networks has been tackled utilizing model-driven optimization, reinforcement learning, and deep learning methods. However, these methods still face some limitations, including dependence on task-specific designs and limited adaptability to diverse network configurations. In this paper, we optimize three adaptation variants of a pre-trained 3.82-billion-parameter Large Language Model (LLM) for DPA. These strategies include LLM with Prompt Engineering (LLM-PE), which employs DPA-specific prompt engineering with detailed network and channel information; LLM with Retrieval Augmented Generation (LLM-RAG), which enhances prompts with retrieved optimal power allocation examples; and LLM with Fine-tuning (LLM-FT), which fine-tunes the model on a small DPA-specific dataset. Our results show that LLM-FT excels in adaptability across various network configurations and resilience to channel estimation errors, maintaining a sum rate performance above 99% compared to the optimal water-filling algorithm, using only 5000 fine-tuning examples.

In modern phased-array systems, limited resolution of digital attenuators and phase shifters can degrade beamforming accuracy, increase interference, and disrupt beam tracking, impacting overall performance. While existing methods like rounding, dithering, and optimization-based approaches have addressed phase quantization, most have overlooked amplitude errors and are often limited to specific configurations, such as linear arrays. This paper introduces MISOCP-QBeam, a unified optimization framework for joint amplitude and phase quantization, applicable to both 1D and 2D arrays. Using a Mixed-Integer Second-Order Cone Program (MISOCP), the method provides deterministic, hardware-compliant solutions that enhance main-lobe accuracy and suppress sidelobes. Numerical results demonstrate significant improvements, with up to 5.8x lower pointing error and 3.4 dB reduction in sidelobe level compared to traditional rounding methods.

Multi-task large language models (MTLLMs) are important for many applications at the wireless edge, where users demand specialized models to handle multiple tasks efficiently. However, training MTLLMs is complex and exhaustive, particularly when tasks are subject to change. Recently, the concept of model fusion via task vectors has emerged as an efficient approach for combining fine-tuning parameters to produce an MTLLM. In this paper, the problem of enabling edge users to collaboratively craft such MTLMs via tasks vectors is studied, under the assumption of worst-case adversarial attacks. To this end, first the influence of adversarial noise to multi-task model fusion is investigated and a relationship between the so-called weight disentanglement error and the mean squared error (MSE) is derived. Using hypothesis testing, it is directly shown that the MSE increases interference between task vectors, thereby rendering model fusion ineffective. Then, a novel resilient MTLLM fusion (R-MTLLMF) is proposed, which leverages insights about the LLM architecture and fine-tuning process to safeguard task vector aggregation under adversarial noise by realigning the MTLLM. The proposed R-MTLLMF is then compared for both worst-case and ideal transmission scenarios to study the impact of the wireless channel. Extensive model fusion experiments with vision LLMs demonstrate R-MTLLMF's effectiveness, achieving close-to-baseline performance across eight different tasks in ideal noise scenarios and significantly outperforming unprotected model fusion in worst-case scenarios. The results further advocate for additional physical layer protection for a holistic approach to resilience, from both a wireless and LLM perspective.

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.

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.

Despite recent advancements in millimeter-wave (mm Wave) communication systems, the practical validation of research ideas in this field is still challenging. This is mostly due to the simplifying assumptions in their system modeling and overlooking hardware limitations in the simulation environment. In this paper, we propose a high-speed and programmable mm Wave testbed with multi-user (MU) multiple-input multiple-output (MIMO) beamforming capability. The baseband signal processing is preformed fully on a general-purpose processor. The radio frequency (RF) frontend holds a hybrid MIMO architecture, where each RF chain is connected to one phased-array antenna. The testbed allows to plug and play different physical layer signal processing algorithms or radio resource allocation techniques, including machine learning models, to assess their performance under real-life conditions.

Edge computing is a sustainable network paradigm that supports resource constrained Industrial Internet of Things (IIoT) devices in performing data-driven tasks. The distributed architecture of computing resources, combined with the heterogeneous and sporadic characteristics of IIoT tasks, presents a substantial challenge to achieving energy-efficient and low-latency task execution. In this paper, we demonstrate that modeling the Age of Information (AoI) as an exponential function can significantly enhance the performance of task scheduling and execution in edge computing systems. Furthermore, we introduce a low over-head Multi-Agent Deep Reinforcement Learning (DRL) algorithm with No Observation (MA-NO) for distributed edge computing resource allocation, where agents (IIoT devices) operate independently without communicating with each other. However, to prevent the divergence of the agents and ensure a Pareto-optimal decision-making, we adopt a common reward that is shared among all agents. That is, all agents receive the same reward, based on their collective performance. Simulation results show that our proposed AoI model reduces the average execution latency of the tasks by 35.2% compared to a linear AoI ones. Furthermore, our proposed MA-NO algorithm reduces the total energy consumption of the IIoT devices by 59.9% and 63.4% compared to the single-agent DRL algorithm and multi-agent DRL algorithm with partial observations, respectively.

We propose an extremely energy efficient and reconfigurable accelerator for performing Deep Neural Network (DNN) inferences on a Field-Programmable Gate Array (FPGA). Our design allows on the fly reconfigurability of the model to adapt it to any arbitrary DNN inference task and perform it with an extremely low latency, on the scale of tens of micro seconds. Our design can be adopted by resource constrained Industrial Internet of Things (IIoT) devices (e.g., mobile robots) to fulfill different DNN inference tasks. By parallelization of the matrix multiplications in each layer of the DNN and adopting an adjustable sliding window at the synthesize stage, our accelerator strikes a balance between the computing latency and the scarce resource utilization on the FPGA. It also uses a low-overhead control scheme for the accelerator’s internal interactions to update the DNN model on-the-fly. The results show that for a DNN model with 64 input features, 9 hidden layers (each with 64 neurons), and 64 output variables, our proposed inference accelerator speeds up the inference by 28.8 times and decreases the energy consumption by 31 % compared to an AMD Deep Learning Processing Unit.

Deep reinforcement learning has emerged as a pow-erful tool for dynamic power allocation, as it allows continuous learning and realtime responsiveness to environmental changes through its core element, experience replay. Experience replay involves two critical hyperparameters: the replay buffer and mini-batch sizes. While state-of-the-art solutions primarily concentrate on designing input features and reward-shaping methods, the impact of experience replay parameters on system performance has often been overlooked. This paper aims to address this gap by exploring the effects of experience replay parameters in the context of dynamic power allocation in multi-carrier wireless systems. To address the power allocation problem, we propose a multi-agent cooperative deep reinforcement learning framework. The results show that a minimum of 2000 experiences in the replay buffer is necessary for the proposed solution to outperform conventional approaches. Moreover, many obsolete experiences within a larger replay buffer slightly decrease system performance. Interestingly, the increase in batch size does not significantly affect the learning models' training time due to parallel execution, yet, it improves performance.

Multi-agent deep reinforcement learning has pre-dominantly been applied to tackle the challenges of power allocation within complex and dynamic wireless networks. These intelligent agents rely on exploration as a fundamental tool to gather crucial information about their environment, reducing uncertainty and facilitating more informed power allocation decisions. Importantly, as a wireless channel is inherently dynamic, exploration is an indispensable mechanism for agents to continually adapt by acquiring fresh environment insights. Traditionally, most research has leaned towards employing semi-uniform distributed exploration for discrete action spaces and noise perturbation exploration for continuous action spaces. In this paper, we set out to question the validity of this default choice. We modify both exploration methods to work in both action spaces and with different exploration speeds. We demonstrate that semi-uniform distributed exploration yields comparable results to noise perturbation exploration in a continuous action space while significantly simplifying the exploration factor's fine-tuning. In contrast, noise perturbation exploration excels primarily in continuous action space.

Mobile edge computing (MEC) is expected to provide low-latency computation service for wireless devices (WDs). However, when WDs are located at cell edge or communication links between base stations (BSs) and WDs are blocked, the offloading latency will be large. To address this issue, we propose an intelligent reflecting surface (IRS)-assisted cell-free MEC system consisting of multiple BSs and IRSs for improving the transmission environment. Consequently, we formulate a min–max latency optimization problem by jointly designing multiuser detection (MUD) matrices, IRSs’ reflecting beamforming vectors, WDs’ offloading data size and edge computing resource, subject to constraints on edge computing capability and IRSs phase shifts. To solve it, an alternating optimization algorithm based on the block coordinate descent (BCD) technique is proposed, in which the original nonconvex problem is decoupled into two subproblems for alternately optimizing computing and communication parameters. In particular, we optimize the MUD matrix based on the second-order cone programming (SOCP) technique, and then develop two efficient algorithms to optimize IRSs’ reflecting vectors based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques, respectively. Numerical results show that employing IRSs in cell-free MEC systems outperforms conventional MEC systems, resulting in up to about 60% latency reduction can be attained. Moreover, numerical results confirm that our proposed algorithms enjoy a fast convergence, which is beneficial for practical implementation.

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.

Despite the success of Deep Reinforcement Learning (DRL) in radio-resource management within multi-cell wireless networks, applying it to power allocation in ultra-dense 5G and beyond networks poses challenges. While existing multi-agent DRL-based methods often adopt a fully centralized approach, they often overlook communication overhead costs. In this paper, we model a multi-cell network as a collaborative multi-agent DRL system, implementing a centralized training-decentralized execution approach for accurate and real-time decision-making, thereby eliminating communication overhead during execution. We carefully design the DRL agents’ input observations, actions, and rewards to address potential impractical power allocation policies in multi-carrier systems and ensure strict compliance with transmit power constraints. Through extensive simulations, we assess the sensitivity of the proposed DRL-based power allocation to various exploration methods and system parameters. Results indicate superior performance of DRL-based power allocation with continuous action space in complex network environments. Conversely, simpler network settings with fewer subcarriers and users require fewer power allocation actions, ensuring rapid convergence. By leveraging a fast exploration rate, DRL-based power allocation with discrete action space outperforms conventional algorithms, achieving a 36% relative sum rate increase within 60,000 training episodes.

Integrating distributed machine learning tools with sensing, communication, and decision-making operations in networked and cooperative intelligent machines has opened up novel avenues for research. The cross-fertilization of these components is essential for enabling collaborative task management that requires safety, reliability, scalability, and low latency. Data privacy preservation and high spectral efficiency are required for various Industrial Internet of Things (IIoT) applications. Most previous works have focused on joint optimization of task-offloading and resource allocation while neglecting the data privacy and data overhead of the decision-making process in task-offloading. Considering the robos as agents, this paper proposes Prioritized Spectral efficient Federated Reinforcement Learning (PSFRL)-based partial task-offloading in a heterogeneous industrial robotic Mobile Edge Computing (MEC) system. Due to its definition, PSFRL keeps data private while achieving high spectrum efficiency. Additionally, we define a Value of Information (VoI) metric so that the PSFRL agents allocate their resources more wisely for more valuable data. As robots have limited battery and computing resources for task processing, multiple edge computing-enabled access points assist the robots in accomplishing tasks. Simulation results show that our proposed PSFRL outperforms other learning-based task-offloading methods concerning energy consumption, spectral efficiency, and VoI. Moreover, we demonstrate the superiority of the proposed cooperative approach over its non-cooperative counterpart.

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.

This paper considers an application of deep learning for channel estimation with imperfect frame synchronization in mobile communication systems. Without prior knowledge of the channel model and its characteristics, the proposed method can dynamically estimate and track channel transfer function variations based on received pilot symbols. Furthermore, this method is applicable in practical scenarios, as it considers imperfect frame synchronization and channel estimation for high-speed wireless communication scenarios. The performance and practical feasibility of the deep learning (DL)-based models are assessed by taking into account realistic frequency-selective fading scenarios. Numerical results demonstrate that the proposed method performs better for practical signal-to-noise ratios than the state-of-the-art approaches. In addition, the fine frame offsets are estimated and compensated in the synchronization block with a DL-based algorithm, which outperforms the traditional fine frame synchronization algorithms.

Hardware channel emulators are essential for developing and testing transceiver prototypes in laboratory settings. They should be able to monitor the dynamic motion of the mobile terminal. This paper presents a narrowband Multiple-Input Multiple-Output (MIMO) channel emulator implemented on Field-Programmable Gate Array (FPGA). The proposed emulator is based on Non-Line-of-Sight (NLoS) non-isotropic scattering with arbitrary motion dynamics of the mobile terminal. Its standalone hardware architecture ensures the flexibility and scalability of further hardware realization. Our proposed emulator is validated by comparing the emulated statistics of the channel gain against simulated results under a circular antenna trajectory. The Probability Density Function (PDF) of the fading envelope is observed to perfectly match the theoretical Rayleigh distribution(stationary channel) and the auto-correlation function(non-isotropic scattering) also shows a close agreement. The successful alignment of the eigenvalue of the channel gain matrix indicates our proposed emulator can perform correct MIMO characteristics.

Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Deep learning (DL) and deep neural networks (DNNs) can learn the hidden features of data and build the classifier automatically for decision making, which have been widely used in the SEI research. Considering the insufficiently labeled training samples and large-unlabeled training samples, the semi-supervised learning-based SEI (SS-SEI) methods have been proposed. However, there are few SS-SEI methods focusing on extracting the discriminative and generalized semantic features of radio signals. In this article, we propose an SS-SEI method using metric-adversarial training (MAT). Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open-source large-scale real-world automatic-dependent surveillance–broadcast (ADS-B) data set and Wi-Fi data set and is compared with the state-of-the-art methods. The simulation results show that the proposed method achieves better identification performance than existing state-of-the-art methods. Specifically, when the ratio of the number of labeled training samples to the number of all training samples is 10%, the identification accuracy is 84.80% under the ADS-B data set and 80.70% under the Wi-Fi data set. Our code can be downloaded from https://github.com/lovelymimola/MAT-based-SS-SEI .

Wi-Fi-based passive sensing is considered as one of the promising sensing techniques in advanced wireless communication systems due to its wide applications and low deployment cost. However, existing methods are faced with the challenges of low sensing accuracy, high computational complexity, and weak model robustness. To solve these problems, we first propose a robust channel state information (CSI)-based Wi-Fi passive sensing method using attention mechanism deep learning (DL). The proposed method is called as convolutional neural network (CNN)-ABLSTM, a combination of CNNs and attention-based bi-directional long short-term memory (LSTM). Specifically, CSI-based Wi-Fi passive sensing is devised to achieve the high precision of human activity recognition (HAR) due to the fine-grained characteristics of CSI. Second, CNN is adopted to solve the problems of computational redundancy and high algorithm complexity which are often occurred by machine learning (ML) algorithms. Third, we introduce an attention mechanism to deal with the weak robustness of CNN models. Finally, simulation results are provided to confirm the proposed method in three aspects, high recognition performance, computational complexity, and robustness. Compared with CNN, LSTM, and other networks, the proposed CNN-ABLSTM method improves the recognition accuracy by up to 4%, and significantly reduces the calculation rate. Moreover, it still retains 97% accuracy under the different scenes, reflecting a certain robustness.

Radio frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary dataset and the target dataset often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary datasets, in this paper, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN”. First, we analyze the causes of RFF, and model the RFF identification problem with augmented dataset. A non-auxiliary data augmentation method is proposed to acquire an extended dataset, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long-short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.

The Angle-of-Arrival (AoA)-based approach is an appealing solution for unmanned aerial vehicle (UAV) positioning, and has received significant interest recently. In this article, we propose a novel framework for UAV three-dimensional (3-D) positioning, the core of which is to measure the two-dimensional (2-D) Angle-of-Departure (2D-AoD) and 2D-AoA via a bistatic multiple-input multiple-output (MIMO) radar. Unlike the existing positioning architectures, the MIMO radar is equipped with polarized array antennas. An estimator based on the parallel factor (PARAFAC) decomposition is developed. It first obtains the direction matrices via performing the PARAFAC decomposition of the array data. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization response vector, from which the 2D-AoD, 2D-AoA, and polarization status of the UAVs are achieved via incorporating the vector cross-product method and the least squares (LSs) technique. Finally, the 3-D positions of the UAVs are easily calculated via the location relationship between the 2D-AoD, 2D-AoA, and the coordinates of transmitting/receiving (Tx/Rx) array. The proposed framework is computationally friendly, and is capable of positioning anonymous UAV. Moreover, it is insensitive to the geometry of the Tx/Rx array, indicating that the proposed framework supports configurable Tx/Rx antennas. Simulation results are provided to verify our theoretical advantages.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communications have been regarded as a promising candidate for future 6G networks because of its ultra-wide bandwidth and ultra-low power consumption. However, there exists the beam split problem, especially when the base station (BS) or RIS owns the large-scale antennas, which may lead to serious array gain loss. Therefore, in this paper, we investigate the beam split and beamforming design problems in the THz RIS communications. Specifically, we first analyze the beam split effect caused by different RIS sizes, shapes and deployments. On this basis, we apply the fully connected time delayer phase shifter hybrid beamforming (FC-TD-PS-HB) architecture at the BS and deploy distributed RISs to cooperatively mitigate the beam split effect. We aim to maximize the achievable sum rate by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS and reflection coefficients at the RISs. To solve the formulated problem, we first design the analog beamforming and time delays based on different RISs’ physical directions, and then it is transformed into an optimization problem by jointly optimizing the digital beamforming and reflection coefficients. Next, we propose an alternatively iterative optimization algorithm to deal with it. Specifically, for given the reflection coefficients, we propose an iterative algorithm based on the minimum mean square error technique to obtain the digital beamforming. After, we apply Lagrangian dual reformulation (LDR) and multidimensional complex quadratic transform (MCQT) methods to transform the original problem to a quadratically constrained quadratic program, which can be solved by alternating direction method of multipliers (ADMM) technique to obtain the reflection coefficients. Finally, the digital beamforming and reflection coefficients are obtained via repeating the above processes until convergence. Simulation results verify that the proposed scheme can effectively alleviate the beam split effect and improve the system capacity.

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.

With the explosive growth of advanced wireless technologies and computing device platforms, mobile sensing has gained huge attention. Indoor localization is actually considered as one of most valuable techniques in the field of contactless sensing. In this paper, we propose a novel graph convolutional network (GCN) empowered indoor localization method, which aggregates channel state information (CSI) features extracted from multiple multiple-input multiple-output (MIMO) links. CSI features from multiple antennas are basically converted into graph nodes in order to adopt GCN classification model. At the same time, graph attention mechanism is introduced to study and transfer spatial and frequency of CSI features. Eventually, output of graph is mapped with multiple measurement points through prediction network to provide final estimate position. 5GHz commercial Wi-Fi equipment is respectively utilized for data collection and experimental evaluation in two representative indoor scenarios. Experimental result shows that the proposed method has better performance in robust localization compared to other state-of-the-art deep learning methods.

Radio frequency-based device-free passive perception (RF-DFPP) is considered as one of the most promising techniques for ubiquitous smart applications in the WiFi field due to its extremely low deployment cost. Existing RF-DFPP methods typically employ received signal strength indicator (RSSI), ignoring the potential benefits of fine-grained sensing accuracy of channel state information (CSI). In addition, the robustness of such sensing methods is not good at present. To solve the problem, in this paper, we propose a robust CSI-based RF-DFPP method using a combination network of convolutional neural networks (CNN) and attention-based bi-directional long short term memory (LSTM). The combined network can extract the signal features of the collected CSI through CNN, and then realize RF-DFPP recognition through the training of LSTM and attention layers. Simulation results show that the proposed method significantly improves the recognition accuracy compared with the existing methods. Moreover, it performs robustly even if the model training is done under the different datasets.

Radio frequency fingerprint (RFF) identification is an emerging physical layer security technique, which provokes many promising applications in the internet of things (IoT). However, traditional machine learning-based RFF identification methods rely on complex manual feature extraction, while it is difficult for methods based on deep learning to deal with RFF identification under different channel environments. To solve these problems, we propose three different transfer learning-based RFF identification methods based on ConvMixer network, which is a mixture of different convolutional layers, using pre-trained model in the previous channel environment to assist in training under the new channel environment. Experimental results show that, compared with the previous retraining method, our proposed method reduces the number of training parameters and improves the identification performance at low SNR. Moreover, the proposed method can still have a certain performance guarantee with less training data.

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 automatic modulation classification (AMC). In this paper, a lightweight neural network for decentralized learning-based automatic modulation classification (DecentAMC) method is proposed. Specifically, group convolutional neural network (GCNN) is designed by replacing the standard convolution layer with the group convolution layer, replacing the flatten layer with the global average pooling (GAP) layer and removing part of fully connected layers. DecentAMC method 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. Experimental results show that the proposed GCNN-based DecentAMC method can improve training efficiency to about 4 times and 57 times than that of GCNN-based centralized AMC (CentAMC) and CNN-based DecentAMC respectively. GCNN-based DecentAMC method can effectively reduce the communication cost and save storage of EDs when compared with CNN-based DecentAMC. Meanwhile, the time complexity and the space complexity of GCNN is significantly decreased when compared with CNN and SCNN, which is suitable to be deployed in EDs.

Specific emitter identification (SEI) is developed as a potential technology against attackers in cognitive radio networks and authenticate devices in Internet of Things (IoT). It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Due to the strong capability of deep learning (DL) in extracting the hidden features of data and making classification decision, deep neural networks (DNNs) have been widely used in the SEI. Considering the insufficiently labeled training dataset and large unlabeled training dataset, we propose a novel SEI method using semi-supervised (SS) learning framework, i.e., metric-adversarial training (MAT). Specifically, two object functions (i.e., cross-entropy (CE) loss combined with deep metric learning (DML) and CE loss combined with virtual adversarial training (VAT)) and an alternating optimization way are designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset. The simulation results show that the proposed method achieves a better identification performance than four latest SS-SEI methods.

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.

Malware traffic classification (MTC) is a very important component of cyber security, and a number of the MTC techniques are based on deep learning (DL) with a strong capability of feature mining and classification. However, these DL-based MTC methods are heavily dependent on a large amount of network traffic samples. In the few-shot scenarios, these methods usually overfit and have poor classification performance. Considering that the update cycle of malware is faster and faster, and there are more and more types of malware, collecting enough training samples for all malware is very challenging, if not impossible. In this paper, a novel few-shot MTC(FS-MTC) method is proposed based on convolutional neural network (CNN) and model-agnostic meta-learning (MAML) algorithm. Specifically, the CNN is trained on samples from normal softwares by MAML rather than the conventional optimization methods, then the CNN is finetuned by a few samples from malware for MTC. Simulation results show that our proposed MAML-based FS-MTC can outperform the traditional MTC methods. The performance of our proposed method can reach up to 95.69%.

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.

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.

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.

We propose reliability and latency quantities as metrics to be used in the routing tree optimization procedure for Wi-Fi mesh networks. In contrast to state-of-the-art routing optimization methods, our proposal involves directly optimizing the data rates of individual mesh links according to underlying channel conditions such that reliability and latency requirements are satisfied for entire mesh paths. Moreover, to mitigate the channel contention problem that is common in Wi-Fi networks, we propose a multichannel (MC) assignment method. In this method, bandwidth is allocated to the individual mesh nodes based on the expected traffic load that they are expected to handle. Once the bandwidth for each node is determined, specific channels are assigned in a way to avoid co-channel interference. Furthermore, considerable efforts were spent for developing a system-level simulator that captures the features of the physical (PHY) layer and medium access layer defined in the IEEE 802.11 standard (Wi-Fi). Using this simulator, we were able to show that Wi-Fi mesh networks using the proposed routing metric based on reliability and latency quantities significantly outperform the state of the art. Finally, the mitigation of channel contention through the proposed MC assignment method results in further dramatic gains in performance.

Malware traffic classification (MTC) is a key technology for anomaly and intrusion detection in secure Industrial Internet of Things (IIoT). Traditional MTC methods based on port, payload, and statistic depend on the manual-designed features, which have low accuracy. Recently, deep-learning methods have attracted a significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep-learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this article proposes three methods based on semisupervised learning (SSL), transfer learning (TL), and domain adaptive (DA), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the classification accuracy with few labeled samples. Then, we use the DA method to solve the mismatch problem between the source domain and the target domain in the TL process. The proposed method is not only applicable to the shallow network but also to the deep neural network structure, and can achieve better classification results. Experimental results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples in IIoT. The source code for all the experiments is available at GitHub.The code of this article can be downloaded from GitHub link:https://github.com/yzjh/Keras-MTC-DA-Ladder.

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

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.

Wireless connectivity will soon enable people to consume augmented and virtual reality content anywhere and cloud-connected mobile robots to perform complex tasks collaboratively. This connectivity will come to enterprises, factory floors and digital homes first, powered by indoor networks that offer much higher data rates, greater reliability, and lower latency than today’s networks. In addition to providing traditional communication capabilities, the future indoor network will have a “sixth sense” that enables it to provide sensory information and insights to meet physiological needs such as lighting, heating, health and safety. It will serve as the core infrastructure for smart buildings and help enterprises operate more efficiently by further enabling capabilities such as immersive virtual workplaces, indoor navigation and asset tracking. This paper reviews current technologies, examines the enabling technologies of the future indoor network and presents our latest research results and our vision for implementing them in commercial and residential environments.

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.

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

Malware traffic classification (MTC) is a key technology for solving anomaly detection and intrusion detection problems. And hence it plays an important role in the field of network security. Traditional MTC methods based on port, payload and statistic depend on the manual-designed features, which have low accuracy. Recently, deep learning methods have attracted significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this paper proposes two methods based on semi-supervised learning (SSL) and transfer learning (TL), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the accuracy classification with few labeled samples. Through experiments, we obtained the best method to improve the accuracy of few labeled samples in different situations. Experiment results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples.

In this paper, we propose Federated Deep Learning (FDL) for intrusion detection in heterogeneous networks. Local Deep Neural Network (DNN) models are used to learn the hierarchical representations of the private network traffic data in multiple edge nodes. A dedicated central server receives the parameters of the local DNN models from the edge nodes, and it aggregates them to produce an FDL model using the Fed+ fusion algorithm. Simulation results show that the FDL model achieved an accuracy of 99.27 ± 0.79%, a precision of 97.03 ± 4.22%, a recall of 98.06 ± 1.72%, an F1 score of 97.50 ± 2.55%, and a False Positive Rate (FPR) of 2.40 ± 2.47%. The classification performance and the generalisation ability of the FDL model are better than those of the local DNN models. The Fed+ algorithm outperformed two state-of-the-art fusion algorithms, namely federated averaging (FedAvg) and Coordinate Median (CM). Therefore, the DNN-Fed+ model is preferable for intrusion detection in heterogeneous wireless networks.

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

In massive multiple input multiple output (MIMO) systems, the base station (BS) requires channel state information (CSI) to better utilize the available spatial diversity and multiplexing gains. However, in frequency division duplex (FDD) systems, user equipment (UE) needs to keep on feeding downlink CSI back to the BS, thereby consuming precious bandwidth resources. In this paper, we propose a deep learning (DL) based downlink CSI limited feedback scheme, called FullyConv, which is composed of all convolutional layers to compress and decompress the downlink CSI. FullyConv will improve reconstruction accuracy and robustness as well as reduce the time and space complexity, thus enhancing the system feasibility. Experimental results demonstrate that the FullyConv has a gain of nearly 5 dB compared to baseline. The performance of the FullyConv degrades slightly in the noisy uplink channel, which shows the robustness of FullyConv. Meanwhile, the complexity of the model composed of time complexity and space complexity is significantly reduced.

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

Acquisition of downlink channel state information (CSI) is an important procedure performed at the base station (BS) for high quality wireless communication in frequency division duplexing (FDD) communication system. Generally, the downlink CSI is fed back to the BS through the user equipment (UE). Compared with traditional methods, neural network (NN) can effectively compress the downlink CSI, thus greatly reducing the feedback overhead. However, the generalization of the NN is poor, hence it is necessary to train a NN from scratch whenever there is a change in the wireless channel environment. Nevertheless, training a NN this way requires huge data and time cost in 5G massive MIMO systems. In this paper, deep transfer learning (DTL) is proposed to solve the problem of high training cost of the downlink CSI feedback NN. In a new wireless environment, our proposed technique utilises relatively small number of samples to fine-tune a pre-trained model, in order to obtain a new model with low training cost. The performance of this model is shown to be comparable with that of the NN trained with large samples. Experiment results demonstrate the effectiveness and superiority of the proposed method.

Blockchain (BC) and artificial intelligence (AI) are often utilized separately in energy trading systems (ETSs). However, these technologies can complement each other and reinforce their capabilities when integrated. This article provides a comprehensive review of consensus algorithms (CAs) of BC and deep reinforcement learning (DRL) in ETS. While the distributed consensus underpins the immutability of transaction records of prosumers, the deluge of data generated paves the way to use AI algorithms for forecasting and address other data analytic-related issues. Hence, the motivation to combine BC with AI to realize secure and intelligent ETS. This study explores the principles, potentials, models, active research efforts and unresolved challenges in the CA and DRL. The review shows that despite the current interest in each of these technologies, little effort has been made at jointly exploiting them in ETS due to some open issues. Therefore, new insights are actively required to harness the full potentials of CA and DRL in ETS. We propose a framework and offer some perspectives on effective BC-AI integration in ETS.

Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large. It is, therefore, almost impossible to implement the DL method in memory-constrained Internet-of-Things (IoT) devices. In this article, we reduce the feature dimensionality of large-scale IoT network traffic data using the encoding phase of long short-term memory autoencoder (LAE). In order to classify network traffic samples correctly, we analyze the long-term inter-related changes in the low-dimensional feature set produced by LAE using deep bidirectional long short-term memory (BLSTM). Extensive experiments are performed with the BoT-IoT data set to validate the effectiveness of the proposed hybrid DL method. Results show that LAE significantly reduced the memory space required for large-scale network traffic data storage by 91.89%, and it outperformed state-of-the-art feature dimensionality reduction methods by 18.92-27.03%. Despite the significant reduction in feature size, the deep BLSTM model demonstrates robustness against model underfitting and overfitting. It also achieves good generalisation ability in binary and multiclass classification scenarios.

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.

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

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

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

In the last decade, the number of vehicles worldwide has increased every year, and this growth is projected to continue unabated. Thus, the congestions, incidents, and environmental pollution which are caused by the increasing number of road vehicles and traffics have resulted in hundreds of millions of losses and become a major challenge to the sustainable development of recent human society. Both academia and industry have already reached a consensus that vehicular communication is a vital element to extend the sensing ability of vehicles for ensuring safety driving. Unfortunately, current vehicular communication cannot meet the security, reliability, and effectiveness and other needs of ITS. The industry needs a more intelligent vehicular communication to support secure and reliable transmission of data. Therefore, the research community has to focus more on enhanced and completely new communication techniques.

The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent.

In this paper, we propose a convolutional neural network (CNN) aided automatic modulation recognition (AMR) method for a multiple antenna system. We also present two specific combination strategies, such as the relative majority voting method and arithmetic mean method to improve the classification performance in comparison with the state of the art. Our results are given to verify that the proposed method dominant exploits features and classify the modulation types with higher accuracy in comparison with the AMR employing high order cumulants (HOC) and artificial neural networks (ANN).

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.

Drones can be used for many assistance roles in complex communication scenarios and play as the aerial relays to support terrestrial communications. Although a great deal of emphasis has been placed on the drone-assisted networks, existing work focuses on routing protocols without fully exploiting the drones superiority and flexibility. To fill this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed regarding the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on the floating car data of Chengdu China, extensive experiments are conducted to examine the performance of the MNA-optimized drone-assisted VANET. The results demonstrate that our scheme outperforms its counterparts in terms of hop number, packet delivery ratio, and throughput.

With the development of smart cities, the demand for artificial intelligence (AI) based services grows exponentially. The existing works just focus on cloud- edge or edge-device cooperative AI which suffers low learning efficiency of AI, while edge-to-edge cooperative AI is still an unresolved issue. Moreover, the existing researches concentrate on the computation offloading of the AI-based task, ignoring that it is a brain-like task performing sophisticated processing to raw data, which leads to the high latency and low quality of the learning services. To address these challenges, this paper proposes an on-demand learning offloading mechanism for edge-to-edge cooperative AI. Firstly, the principle of the learning capability and its offloading are proposed for the formal description of the learning resources migration. Secondly, the proposed mechanism realizes the bilateral learning offloading utilizing edge-to-edge and cloud-edge collaborations to handle AI-based tasks with high learning efficiency and resource utilization rate. Moreover, we model the edge-to-edge learning offloading allocation based on the concatenation of deep neural network (DNN) subtasks and their heterogeneous requirement of learning resources. Simulation results indicate the rationality and efficiency of the proposed mechanism.

In this paper, we propose a two-dimensional pilot allocation scheme over frequency- and delay-time domains (2D-PFD) for channel estimation in massive multiple-input multiple-output (MIMO)/time division duplex (TDD) system, where two- dimensional pilot resources are simultaneously allocated to each user for their uplink channel estimation. We evaluate bit error rate (BER) performance of massive MIMO/TDD system using the 2D-PFD scheme by computer simulation in order to clarify the effectiveness of the proposed pilot allocation compared with single dimensional pilot allocation over either delay-time domain or frequency domain, respectively.

Orthogonal frequency-division multiplexing (OFDM) is a popular multi-carrier technique used in many digital communication systems such as wireless fidelity (Wi-Fi), long term evolution (LTE) and power line communication systems. It can be designed using fast Fourier transform (FFT) or wavelet transform (WT). The major drawback in using WT is that it is computationally inefficient. In this study, we introduce a simple and computationally efficient WT, harmonic wavelet transform, for OFDM signal processing. The new WT uses the orthogonal basis functions of conventional FFT-OFDM except that it involves translation and dilation of the input signal; the new wavelets is referred to as harmonic wavelets (HW). When compared with pilot-assisted OFDM system in terms of reduction in the peak-to-average power ratio, the results show that HW-OFDM outperforms FFT-OFDM by 3 dB at 10-4CCDF (complementary cumulative distribution function). Over Rayleigh fading channel with additive white Gaussian noise (AWGN), the bit error ratio of both FFT-OFDM and HW-OFDM perfectly matched, showing that the proposed HW-OFDM is better in terms of peak-to-average power ratio reduction.

The rapid growth of Internet-of-Things (IoT) in the current decade has led to the development of a multitude of new access technologies targeted at low-power, wide area networks (LP-WANs). However, this has also created another challenge pertaining to technology selection. This paper reviews the performance of LP-WAN technologies for IoT, including design choices and their implications. We consider Sigfox, LoRaWAN, WavIoT, random phase multiple access (RPMA), narrowband IoT (NB-IoT), as well as LTE-M and assess their performance in terms of signal propagation, coverage and energy conservation. The comparative analyses presented in this paper are based on available data sheets and simulation results. A sensitivity analysis is also conducted to evaluate network performance in response to variations in system design parameters. Results show that each of RPMA, NB-IoT, and LTE-M incurs at least 9 dB additional path loss relative to Sigfox and LoRaWAN. This paper further reveals that with a 10% improvement in receiver sensitivity, NB-IoT 882 MHz and LoRaWAN can increase coverage by up to 398% and 142%, respectively, without adverse effects on the energy requirements. Finally, extreme weather conditions can significantly reduce the active network life of LP-WANs. In particular, the results indicate that operating an IoT device in a temperature of -20 °C can shorten its life by about half; 53% (WavIoT, LoRaWAN, Sigfox, NB-IoT, and RPMA) and 48% in LTE-M compared with environmental temperature of 40 °C.

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.

Currently, blockchain technology has been widely used due to its support of transaction trust and security in next generation society. Using Internet of Things (IoT) to mine makes blockchain more ubiquitous and decentralized, which has become a main development trend of blockchain. However, the limited resources of existing IoT cannot satisfy the high requirements of on-demand energy consumption in the mining process through a decentralized way. To address this, we propose a decentralized on-demand energy supply approach based on microgrids to provide decentralized on-demand energy for mining in IoT devices. First, energy supply architecture is proposed to satisfy different energy demands of miners in response to different consensus protocols. Then, we formulate the energy allocation as a Stackelberg game and adapt backward induction to achieve an optimal profit strategy for both microgrids and miners in IoT. The simulation results show the fairness and incentive of the proposed approach.

The lattice L(A) of a full-column rank matrix A ∈ Rm×nis defined as the set of all the integer linear combinations of the column vectors of A. The successive minima λi(A), 1 ≤ i ≤ n, of lattice L(A) are important quantities since they have close relationships with the following problems: shortest vector problem, shortest independent vector problem, and successive minima problem. These problems arise from many practical applications, such as communications and cryptography. This paper first investigates some properties of λi(A). Specifically, we develop lower and upper bounds on λi(A), where A are, respectively, the Cholesky factor of G1+ G2and (G1+ G2)-1for two given symmetric positive definitive matrices G1and G2. The bounds are, respectively, expressed as the successive minima of L(A1) and L(A2), and L(A1) and L(A2), where A1, A2, A1and A2are, respectively, the Cholesky factors of G1, G2, G1-1, and G2-1. Then, we show how some properties of λi(A) are used to design a suboptimal integer-forcing strategy for cloud radio access network. Our approach provides much higher time efficiency while keeping the same achievable rate as the algorithm reported by Bakoury and Nazer (I. E. Bakoury and B. Nazer, “Integer-forcing architectures for uplink cloud radio access networks,” in Proc. 55th Annu. Allerton Conf. Commun. Control Comput., Oct. 2007, pp. 67-75). Simulation tests are performed to illustrate our main results.

With the ever increasing cell densities in wireless networks, it is desired to enable more self-X features, such as self-configuration. Setting the network parameters in an autonomous manner is not only more time efficient but also increases network performance and reduces the probability of a human error. Among numerous network settings, one of the key parameters that requires such autonomous configuration is the cell ID (CID). It is used during fundamental procedures, such as network access, decoding, or handover, and therefore, its configuration is crucial for network operation. A complete CID management framework together with a centralized method for CID assignment is presented in this paper. It is not only applicable to multiple mobile standards but is also compatible with multi-vendor equipment, since it is based on the TR-069 management protocol. The proposed approach mitigates CID conflicts when there is a high level of reuse. Moreover, in the 4G case it also prevents neighbors from using different CID but with the same reference signal pattern, which avoids significant interference. The proposed method is evaluated in an enterprise small cell scenario, and in comparison with the baseline third generation partnership project approach, significant reductions of assignment conflicts are demonstrated.

As the age of the Internet of Things (IoT) continues to flourish, the concept of smart healthcare has taken an unprecedented turn due to interdisciplinary thrusts. To carry the big healthcare data emanating from the plethora of bio-sensors and machines in the IoT sensing plane to the central cloud, next generation high-speed delivery networks are essential. On the other hand, once the IoT data are delivered to the cloud, the massive IoT healthcare data are processed and analyzed employing the state-of-the-art analytics tools such as deep machine learning and so forth. However, given the explosion of big data (from various sources in addition to the healthcare data), the delivery network as well the cloud may experience network and computational congestion, respectively. This may impact the realtime analytics of the healthcare data, e.g., critical for in-house patients and senior citizens aging at home. To address this issue, the emerging IoT edge analytics concept can be regarded as a promising solution to process the big healthcare data close to the source. For large-scale IoT deployments, this functionality is critical because of the sheer volumes of Data being generated. In this paper, we propose a deep learning based IoT edge analytics approach to support intelligent healthcare for residential users. The performance of the proposal is validated using computer-based simulation for online training of a real dataset. The reported results of our proposal exhibit encouraging performance in terms of low loss rate, high accuracy, and low execution time to support near real-time actionable decision making on the healthcare data.

The IEEE 1901 powerline standard can be deployed using orthogonal frequency division multiplexing (OFDM) since it is robust over impulsive channels. However, the powerline channel picks up impulsive interference that the conventional OFDM driver cannot combat. Since the probability density function (PDF) of OFDM amplitudes follow the Rayleigh distribution, it becomes difficult to correctly predict the existence of impulsive noise (IN) in powerline systems. In this study, we use companding transforms to convert the PDF of the conventional OFDM system to a uniform distribution which avails the identification and mitigation of IN. Results show significant improvement in the output signal-to-noise ratio (SNR) when nonlinear optimization search is applied. We also show that the conventional PDF leads to false IN detection which diminishes the output SNR when nonlinear memoryless mitigation scheme such as clipping or blanking is applied. Thus, companding OFDM signals before transmission helps to correctly predict the optimal blanking or clipping threshold which in turn improves the output SNR performance.

In this paper, we investigate pilot allocation problem in two-tier time division duplex (TDD) heterogeneous network (HetNet) with mMIMO. First, we propose a new pilot allocation scheme to maximize ergodic downlink sum rate of macro users (MUs) and small cell users (SUs), where the uplink pilot overhead and cross-tier interference are jointly considered. Then, we theoretically analyze the formulated problem and propose a low complexity one-dimensional search algorithm to obtain the optimum pilot allocation. In addition, we propose two suboptimal pilot allocation algorithms to simplify the computational process and improve SUs' fairness, respectively. Finally, simulation results show that the performance of the proposed scheme outperforms that of the traditional schemes.

In this paper, we investigate asynchronous coded caching in fog radio access networks (F-RAN). To minimize the fronthaul load, the encoding set collapsing rule and encoding set partition method are proposed to establish the relationship between the coded-multicasting contents in asynchronous and synchronous coded caching. Furthermore, a decentralized asynchronous coded caching scheme is proposed, which provides asynchronous and synchronous transmission methods for different delay requirements. The simulation results show that our proposed scheme creates considerable coded-multicasting opportunities in asynchronous request scenarios.

Future indoor networks need to provide broadband services either by increasing bandwidth, using higher order modulations and high spatial reuse. To this end, ultra-dense Wi-Fi networks need to be deployed. This work discusses the challenges of ultra-dense Wi-Fi networks, where inter-AP distances are 5 m or less, and discuss necessary requirements to maximize the high spatial reuse of radio resources through the use of adaptive Clear Channel Assessment (CCA) thresholds and downward facing directional antennas. We also examine the significant impact of uplink transmissions, and present the necessary uplink transmit power control in ultra-dense deployments to avoid the interference coming from hidden nodes in neighboring APs. With the proposed techniques and antennas in place, we show that the limitations placed by high contention and interference in ultra-dense deployments can be overcome to provide over 8 χ gains in downlink throughput when compared with APs with omni-directional and unadjusted CCA thresholds.

In this paper, we investigate the dynamic small cell (SC) clustering strategy and their precoding design problem for interference coordination in two-tier heterogeneous networks (HetNets) with massive MIMO (mMIMO). To reduce interference among different SCs, an interference graph-based dynamic SC clustering scheme is proposed. Based on this, we formulate an optimization problem as design precoding weights at macro base station (MBS) and clustered SCs for maximizing the downlink sum rate of SC users (SUs) subject to the power constraint of each SC BS (SBS), while mitigating inter-cluster, eliminating inter-tier, intra-cluster and multi-macro user (MU) interference. To eliminate the inter-tier and multi-MU interference simultaneously, we propose a clustered SC block diagonalization precoding scheme for the MBS. Next, each SU's precoding vector at clustered SCs is designed as the product of the following two parts. The first part is designed with singular value decomposition to remove the intra-cluster interference. The second part is designed to coordinate the inter-cluster interference for maximizing the downlink sum rate of SUs, which is a non-convex optimization problem and difficult to solve directly. A non-cooperative game-based distributed algorithm is proposed to obtain a suboptimal solution. Meanwhile, we prove the existence and uniqueness of Nash equilibrium for the formed game. Finally, simulation results verify the effectiveness of our proposed schemes.

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.

The upsurge in data traffic pushed Wi-Fi operators to adopt wireless extenders to improve indoor coverage. Existing deployment approaches, however, focused on coordinated scenarios (managed by the same operator) with single-hop communication. In this paper, we propose a self-deployment approach for finding the optimal placement of extenders in which both the wireless back-haul and front-haul throughputs of the extender are optimized. To that end, we propose an AI-CBR framework to enable autonomous self-deployment that allows the network to learn the environment by means of sensing and perception. New actions, i.e. extender positions, are created by problem-specific optimization and semi-supervised learning algorithms that balance exploration and exploitation of the search space. Wi-Fi standard compliant ns-3 simulations evaluated the proposed self-deployment AI approach and compared its performance against existing conventional coverage maximization approaches under practical uncoordinated scenarios. Throughput fairness and ubiquitous QoS satisfaction are achieved which provide the impetus of applying the AI-driven self-deployment in practice.

Cellular networks with hyper-dense deployment of small cells have been identified as the performance-optimizing architecture for fifth generation (5G) mobile systems. A thousand-fold capacity increase is projected when such networks benefit from aggressive spatial multiplexing and huge bandwidth, realizable with massive antenna arrays and millimeter-wave (mmWave) spectrum, respectively. As a precursor to the 5G projections, we show in this paper that network performance (cell capacity, user throughputs and spectral efficiency) can be significantly increased by overlaying mmWave small cells on microwave (μWave) macrocells, due to the elimination of cross-tier interference. This is without any increase in bandwidth or antenna configuration of legacy dense networks. Dramatic performance gains can further be achieved by employing more antenna arrays and larger bandwidth. In addition, we demonstrate that such networks are density-limited. Increasing the number of small cells per macrocell beyond the optimal cell density threshold leads to performance degradation. Adequate consideration should, therefore, be given to this limit in the design, planning and operation of future cellular networks.

This paper analyzes the energy efficiency performance of cooperative and non-cooperative decode-and-forward (DF) relaying power line communication (PLC) systems. In order to further minimize the energy consumption of such systems, we propose incremental DF (IDF) relying over the impulsive noise PLC channel. For a more realistic scenario, the PLC modems power consumption profile is assumed to consist of both dynamic power and static power. For the sake of comparison and completeness as well as to quantify the achievable gains, we also analyze the performance of a single-hop PLC system. In this respect, accurate analytical expressions for the outage probability and energy efficiency are derived. Monte Carlo simulations are provided throughout the paper to validate the analysis. Results reveal that the cooperative relaying PLC systems can provide better energy efficiency performance compared to the non-cooperative ones. It is also shown that increasing the noise probability or the modems static power can negatively impact the system performance.

Pilot contamination due to the pilot reuse in adjacent cells is a serious problem in time-division duplex (TDD) massive multi-input multiple-output (MIMO) system. Therefore, the pilot allocation is significant for improving the performance of the system. In this paper, we formulate the pilot allocation optimization problem for maximizing uplink sum rate of the system. To reduce the required complexity for finding the optimum pilot allocation, we propose a low-complexity pilot allocation algorithm, where the formulated problem is decoupled into multiple subproblems; in each subproblem, the pilot allocation at a given cell is optimized while fixing the pilot allocation in other cells. This process is continued until the achievable sum rate converges. Through multiple iterations, the optimum pilot allocation is found. In addition, to improve users' fairness, we formulate a fairness aware pilot allocation as maximization problem of sum of user's logarithmic rate and solve the formulated problem using a similar algorithm. Simulation results show that the proposed algorithms obtain good performance comparable to the exhaustive search algorithm, meanwhile the users' fairness is improved.

Self-configuration of network parameters is a desired network management feature, especially in dense small cell deployments. One of the key parameters that requires such autonomous set up is the cell ID. Its configuration to a large extent influences the network performance affecting such mechanisms as network access, decoding, handover, and is therefore of a very high importance. In this paper a new centralised method for Physical Layer Cell Identity (PCI) assignment is presented. The proposal is based on the network information available via the TR-069 management protocol, which makes it applicable also to multi-vendor deployments. In contrast with the existing schemes, the proposed algorithm mitigates not only the colliding and confusing assignments but also prevents neighbours from using different PCIs but introducing overlap in their reference signal pattern, which has not been addressed before. The results show a significant reduction of assignment conflicts when compared with the baseline 3rd Generation Partnership Project (3GPP) approach.

The use of small cells is a promising way of providing the required capacity in future cellular networks through densification. Self-organizing network (SON) techniques are required in small cells, particularly in femtocells where plug-and-play user deployments are used. However, these techniques have tended to be implemented by vendors with proprietary algorithms that use low-level UE measurements which are not easily accessible to network operators. This makes the management of femtocells in a multi-vendor environment difficult. In this paper, we present a centralized SON technique to perform load balancing, coverage optimization and manage sleep modes that utilizes high-level measurements made at the femtocells which are easily available through the widely used TR-069 device management protocol standard. This allows network operators the flexibility of implementing and coordinating the SON techniques in multi-vendor femtocell deployments. We show that, despite the limited amount of information used, the proposed solution is able to effectively optimize the coverage in order to balance load and manage sleep modes whilst ensuring that no coverage gaps occur.

Recently, an adaptive peak cancellation was proposed to reduce the high peak-to-average power ratio (PAPR), while keeping the out-of-band (OoB) power leakage as well as an in-band distortion power (EVM) below the pre-determined (permissible) level. However, the peak cancellation in MIMO-OFDM systems was not considered. In this paper, we propose a peak cancellation method for linearly pre-coded MIMO-OFDM systems using eigen-beam space division multiplexing (E-SDM). We evaluate and discuss the performance of the system using the proposed peak cancellation in terms of bit error rate (BER), complementary cum-mulative distribution function (CCDF) of PAPR and the system's computational complexity. Our results show the improvements with respect to both the achievable BER and PAPR with the proposed peak cancellation in E-SDM systems under the restriction of OoB power radiation.

Broadcast nature of the wireless channel enables wireless communications to make use of network coding at the physical layer (PNC) to improve the network capacity. Recently, narrowband and later broadband analog network coding (ANC) were introduced as a simpler implementation of PNC. The ANC schemes require two time slots while in PNC three time slots are required for bi-directional communication between two nodes and hence ANC is more spectrum efficient. The coherent detection and self-information removal in ANC require accurate channel state information (CSI). {In this paper, we theoretically analyze the bit error rate (BER) performance with imperfect knowledge of CSI for broadband ANC using orthogonal frequency division multiplexing (OFDM), where the channel estimation error is modeled as a zero-mean complex Gaussian random variable. We investigate the BER performance for three cases: (i) the effect of imperfect self-information removal due to channel estimation (CE) error with fading tracking errors, (ii) the effect of imperfect self-information removal due to CE error without fading tracking errors}, and (iii) the ideal CE case. We discuss how, and by how much, our results obtained by theoretical analysis can be used for design of broadband ANC system with the imperfect knowledge of CSI. Our results show that imperfect channel estimation due to the noise effect has less impact on self-information removal than the imperfect channel estimation due to fading tracking errors. The tracking against fading is an important problem for accurate self-information removal as well as coherent detection and thus, the effect of channel time-selectivity is also theoretically studied. The achievable BER performance gains due to the polynomial time-domain channel interpolation are investigated using the derived close-form BER expressions and it was shown that the broadband ANC schemes with practical CE in a time- and frequency-selective channel should include a more sophisticated channel interpolation techniques since the impact of Doppler shift has prevalent effect on the achievable BER performance.

ABSTRACT Orthogonal frequency division multiplexing (OFDM) signals have a problem with a high peak‐to‐average power ratio (PAPR). A distortionless selected mapping (SLM) has been proposed to reduce the PAPR, but a high computational complexity prohibits its application to an OFDM system with a large number of subcarriers. Recently, we proposed OFDM combined with time division multiplexing (OFDM/TDM) using minimum mean square error frequency‐domain equalization (MMSE‐FDE) to improve the bit error rate (BER) performance of conventional OFDM with a lower PAPR. The PAPR problem, however, cannot be completely eliminated. In this paper, we present an SLM combined with symbol re‐mapping for OFDM/TDM using MMSE‐FDE. Unlike the conventional OFDM, where SLM is applied over subcarriers in the frequency domain, we exploit both time and frequency dimensions of OFDM/TDM signal to improve the performance with respect to PAPR and BER. A mathematical model for PAPR distribution of OFDM/TDM with SLM is presented to complement the computer simulation results. It is shown that proposed SLM can further reduce the PAPR without sacrificing the BER performance with the same or reduced computational complexity. Copyright © 2010 John Wiley & Sons, Ltd.

ABSTRACT Orthogonal frequency division multiplexing (OFDM) has been adopted for several wireless network standards due to its robustness against multipath fading. Main drawback of OFDM is its high peak‐to‐average power ratio (PAPR) that causes a signal degradation in a peak‐limiting (e.g., clipping) channel leading to a higher bit error rate (BER). At the receiver end, the effect of peak limitation can be removed to some extent to improve the system performance. In this paper, a joint iterative channel estimation/equalization and clipping noise reduction technique based on minimum mean square error (MMSE) criterion is presented. The equalization weight that minimizes the mean square error (MSE) between the signal after channel equalization and feedback signal after clipping noise reduction is derived assuming imperfect channel state information (CSI). The MSE performance of the proposed technique is theoretically evaluated. It is shown that the BER performance of OFDM with proposed technique can be significantly improved in a peak‐limited and doubly‐selective (i.e., time‐ and frequency‐selective) fading channel. Copyright © 2011 John Wiley & Sons, Ltd.

Future wireless Internet services require a broadband frequency spectrum with high data rates. Cognitive radio (CR) concept is a novel approach to improve the spectrum efficiency. The CR is based on the opportunistic usage of frequency spectrum, which is not occupied by the primary users. Conventional multi-user access in bi-directional CR network may be done by using either time division multiple access (TDMA), frequency division multiple access (FDMA) or code division multiple access (CDMA). Without adaptive or dynamic frequency reuse, TDMA and FDMA have lower spectrum efficiency in comparison with CDMA. However, the problem of CDMA in a multipath channel is a multi-user interference (MUI). In this paper, we present a bi-directional CR network with wireless network coding (WNC) in a multipath channel. Unlike the conventional multi-user bi-directional CR network, where the users access the spectrum holes in different time-slot or frequency, the proposed method allows secondary users (SUs) to access the spectrum holes simultaneously. The performance of bi-directional CR network with WNC is theoretically analyzed in terms of spectrum efficiency and the maximum number of SUs. The numerical results show that the spectrum efficiency and the maximum number of SUs of the proposed method increases in comparison with conventional CR network.

Cooperative transmission, where spatially distributed users form a multi-antenna system and assist in forwarding the information from source to destination node, achieves the diversity gain of a multiple-input multiple-output (MIMO) system. Cooperative network based on OFDM has been proposed to cope with the channel frequency-selectivity but due to high peak-to-average power ratio (PAPR) of OFDM, expensive power amplifier are required. In this paper, we present cooperative network based on OFDM combined with time division multiplexing (OFDM/TDM) using minimum mean square error frequency-domain equalization (MMSE-FDE) in a frequency-selective fading channel. We theoretically analyze the network performance with respect to the bit error rate (BER). The conditional signal-to-interference plus noise ratio (SINR) expressions are derived based on Gaussian approximation of the residual inter-slot interference (ISI) after MMSE-FDE for the given set of channel gains. The average BER performance for OFDM/TDM is evaluated by Monte-Carlo numerical computation method using the derived theoretical expressions and confirmed by computer simulation. The results show that the lower BER is achieved with cooperative OFDM/TDM in comparison with OFDM based cooperative network because OFDM/TDM exploits both cooperative and frequency diversity gains.

Recently, multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) combined with time division multiplexing (OFDM/TDM) based on frequency domain equalization (FDE) has been proposed to improve the performance of conventional OFDM-based relaying in terms of high peak-to-average power ratio (PAPR) and the bit error rate (BER). The PAPR problem, however, is not completely eliminated and thus, the effect of peak-limited channel is not negligible. In this paper, we present the BER performance analysis of MIMO-OFDM/TDM in a peak-limited and frequency-selective channel. The theoretical equalization weights in a nonlinear channel for MIMO-OFDM/TDM are derived to capture the negative effect of peak-limitation. Performance analysis presented in this paper was confirmed by the computer simulation and it was shown that the achievable BER performance of MIMO-OFDM/TDM in a peak-limited and frequency-selective channel is a function of the OFDM/TDM design parameter.

Broadband analog network coding (ANC) has been recently introduced to increase the network capacity by exploiting the broadcasting nature of the wireless channel. However, channel state information (CSI) knowledge is required for self-information removal and signal detection. Recently, a pilot-assisted channel estimation (PACE) scheme has been presented for broadband ANC, where feedback of the channel estimates from the relay to the users is required. In this work, we introduce a PACE scheme without feedback from the relay for broadband ANC using orthogonal frequency-division multiplexing (OFDM). In the first time slot the users transmit their respective pilots to the relay and in the second time slot the relay simply amplifies and forwards the received pilot signals to both users. Each user can then estimate all the CSI it needs for self-information removal and coherent signal detection, without requiring any feedback from the relay. The bit error rate (BER) performance of broadband ANC using the proposed PACE is evaluated by computer simulation. It was shown that the proposed PACE scheme causes only a slight BER performance degradation compared to the conventional PACE scheme while eliminating the feedback channel requirement.

Vehicular networks are a promising field in wireless networks enabling connection vehicles among themselves or between a vehicle and an infrastructure. These networks aim to offer several potential applications ranging from road safety applications and driver assistance to infotainment. However, these networks have limited coverage and capacity. Among the options that may help overcome these limitations, we can quote cooperative communications and analog network coding (ANC). The idea of this paper is to combine these two concepts in order to improve the vehicular network connectivity and capacity. The proposed solution is divided into three stages. The first stage aims at identifying the coding and relaying opportunities. The second stage uses a distributed scheme to select the Best Vehicular Relay among potential candidates when relaying is required, while the last stage performs our joint relaying and coding strategy on the received signals. In order to validate our approach, numerical analysis is performed to evaluate the performances in terms of raw Bit Error Rate (BER) and throughput. This confirmed our expectation showing that cooperative relaying achieves better performance than the direct transmission in terms of raw BER but decreases the throughput. However, by deploying analog network coding on the Best Vehicular Relay, the throughput is improved considerably at the price of a slight deterioration of the raw BER.

Network coding at the physical layer (PNC) can be used to improve the network capacity in a wireless channel. Broadband analog network coding (ANC) was introduced as a simpler implementation of PNC. The coherent detection and self-information removal in ANC require accurate channel state information (CSI). In this paper, we theoretically investigate an impact of the channel time-selectivity on the bit error rate (BER) performance of broadband ANC with practical channel estimation (CE) scheme using orthogonal frequency division multiplexing (OFDM). The achievable BER performance gains due to the first and second order polynomial time-domain channel interpolation are evaluated using derived close-form BER expressions.

Recently, broadband analog network coding (ANC) was introduced to utilize high-data rate transmission over the wireless - frequency selective fading - channel. However, ANC requires the knowledge of channel state information (CSI) for self-information removal and coherent signal detection. In this paper, we propose a two-slot pilot-assisted CE for bi-directional broadband ANC. In the first slot, two users transmit their respective pilots to the relay, where the users' pilot signals are designed to avoid the interference and consequently, allow the relay to estimate the CSIs from both users. During the second slot the relay broadcast its pilot signal to both users that estimate the corresponding CSIs. It was shown by computer simulation that, even with imperfect CSI, the BER performance of broadband ANC gives a satisfactory performance for a low and moderate mobile terminal speed in a frequency-selective fading channel.

In this letter, we present the performance of broadband bi-directional transmission with analog network coding (ANC) in a frequency-selective fading channel. To cope with the channel frequency-selectivity we introduce the use of frequency domain equalization (FDE) with broadband ANC based on orthogonal frequency division multiplexing (OFDM) and single carrier (SC) radio access. We evaluate, by theory and computer simulation, the achievable bit error rate (BER) and ergodic capacity of bi-directional ANC scheme based on OFDM and SCFDE radio access in a frequency-selective fading channel. Our results show that SC-FDE achieves a better BER performance, but on the other hand, a lower ergodic capacity in comparison with OFDM in a frequency-selective fading channel. Through both analysis and computer simulation, our findings show that a drawback of ANC scheme is its lack of diversity combining at the destination, which causes a slightly lower ergodic capacity in comparison with cooperative relaying irrespective of radio access scheme.

Wireless communications are characterized by a multipath propagation that is suitable for application of network coding (NC) to improve the network performance. In particular, a physical layer network coding (PNC) is a promising technique to further improve network capacity for bi-directional information exchange between pairs of end users assisted by a relay terminal. In this paper, we present the performance of bi-directional transmission with PNC in a multipath channel. We introduce the use of frequency domain equalization (FDE) with orthogonal frequency division multiplexing (OFDM) and single carrier (SC) transmission to cope with the channel distortion. The equalization weights based on minimum mean square error (MMSE) criteria required for SC-FDE signaling are derived.

Orthogonal frequency division multiplexing (OFDM) has been adopted for several wireless network standards due to its robustness against multipath fading. Main drawback of OFDM is its high peak-to-average power ratio (PAPR) that causes a signal degradation in a peak-limiting (e.g., clipping) channel leading to a higher bit error rate (BER). At the receiver, the effect of peak-limitation can be removed to some extent to improve the system performance. In this paper, a combined decision-feedback equalization with clipping noise reduction technique is presented. An iterative equalization weight that minimizes the mean square error (MSE) with respect to residual clipping noise is derived. It is shown that the bit error rate (BER) performance of OFDM with proposed technique can be significantly improved in a peak-limited and frequency-selective fading channel.

Radio channel is characterized by a multipath propagation that is suitable for application of network coding (NC) to improve the network capacity. In particular, physical layer network coding (PNC) scheme is known to double the network capacity of bi-directional communication between pairs of end users assisted by a relay terminal in a Gaussian channel. Recently, analog network coding (ANC) has been proposed with a simpler implementation complexity in comparison with PNC scheme, but the same achievable network capacity. On the other hand, an important question is how much the channel capacity is improved over a conventional point-to-point communication in a frequency-selective fading channel. In this paper, we present the ergodic channel capacity analysis of bi-directional transmission with analog network coding in a frequency-selective fading channel. The channel capacity expression for transmission with frequency domain equalization (FDE) is derived based on the Gaussian approximation of the inter-symbol interference (ISI) after FDE.

Cooperative networking schemes provide spatial diversity gain (named cooperative diversity gain) using the antennas of spatially distributed users to form a multi antenna transmit situation. A variety of algorithms for orthogonal frequency division multiplexing (OFDM) networks have been developed to achieve cooperative diversity gain. OFDM signals, however, have a problem with a high peak-to-average power ratio (PAPR) leading to lower power efficiency, which may significantly increase the user power consumption. In this paper, instead of conventional OFDM, we present the performance of cooperative network based on OFDM combined with time division multiplexing (OFDM/TDM) using minimum mean square error frequency-domain equalization (MMSE-FDE) in a frequency-selective fading channel. To fully exploit the channel frequency-selectivity and achieve a larger frequency diversity gain the equalization weights required for OFDM/TDM signaling based on MMSE criteria are derived.

In this paper, frequency-domain interleaving on a frame-by-frame basis for orthogonal frequency division multiplexing combined with time division multiplexing (OFDM/TDM) using minimum mean square error frequency-domain equalization (MMSE-FDE) is presented. The OFDM/TDM frame signal (i.e., several concatenated OFDM signals) frequency components are interleaved to enhance channel frequency-selectivity and then, MMSE-FDE is applied at the receiver to obtain frequency diversity gain. The bit error rate (BER) performance of OFDM/TDM with and without channel coding is evaluated by computer simulation. It was shown, that frequency-domain interleaving on the frame-by-frame basis for OFDM/TDM using MMSE- FDE achieves a better BER performance in comparison with conventional OFDM due to enhanced frequency diversity gain.

Orthogonal frequency division multiplexing (OFDM) signals have a problem with high peak-to-average power ratio (PAPR). A distortionless selective mapping (SLM) has been proposed to reduce the PAPR, but a high computational complexity prohibits its application to OFDM with a large number of subcarriers. Recently, OFDM combined with time division multiplexing (OFDM/TDM) using minimum mean square error frequency-domain equalization (MMSE-FDE) was proposed to improve the transmission performance of conventional OFDM in terms of the bit error rate (BER) and the PAPR. The PAPR problem, however, cannot be completely eliminated. In this paper, we propose a new SLM to further reduce the PAPR of OFDM/TDM. Unlike the conventional OFDM, where SLM is applied over subcarriers in the frequency domain, we propose the new SLM for OFDM/TDM by exploiting both time and frequency dimensions of OFDM/TDM signal. It is shown, by computer simulation that proposed SLM for OFDM/TDM increases the number of candidate sequences in comparison with the conventional SLM, while reducing the PAPR. Furthermore, OFDM/TDM with proposed SLM achieves a lower PAPR than the conventional OFDM with same or reduced computational complexity.

Orthogonal frequency division multiplexing (OFDM) is currently under intense research for broadband wireless transmission due to its robustness against multipath fading. However, OFDM signals have a problem with high peak-to-average power ratio (PAPR) and thus, a power amplifier must be carefully manufactured to have a linear input-output characteristic or to have a large input power backoff. Recently, OFDM combined with time division multiplexing (OFDM/TDM) using minimum mean square error frequency domain equalization (MMSE-FDE) was proposed to improve the bit error rate (BER) performance of conventional OFDM while reducing the PAPR. In this paper, by extensive computer simulation, we present a comprehensive performance comparison between OFDM/TDM using MMSE-FDE and conventional OFDM over a frequency-selective fading channel. We discuss about the trade-off among the transmit peak-power efficiency, the spectrum splatter and the BER performance. Our results show that OFDM/TDM using MMSE-FDE achieves almost the same coded BER performance with a several decibels better peak-power efficiency than conventional OFDM, which is significant reduction of amplifier transmit-power backoff, but with a slight decrease in spectrum efficiency.

We present, in this paper, pilot-assisted channel estimation (CE) for orthogonal frequency division multiplexing combined with time division multiplexing (OFDM/TDM) using minimum mean square error frequency-domain equalization (MMSE-FDE) over a nonlinear and frequency-selective fading channel. Joint use of time-domain filtering to increase the signal-to-noise ratio (SNR) of pilot signal and frequency-domain interpolation for OFDM/TDM is presented. The simulation results show that OFDM/TDM with proposed pilot-assisted CE provides a better performance than OFDM since the peak-to-average power ratio (PAPR) problem can be reduced.

The OFDM signals have a problem of high peak-to-average power ratio (PAPR). Hence, a large transmit-power backoff or amplitude clipping is required. The amplitude clipping causes signal degradation and the BER performance increases. A trade-off between the PAPR reduction and the BER performance is present; the PAPR reduces as the level of clipping reduces, but the BER degrades due to signal distortion. Recently, we proposed OFDM combined with time division multiplexing (OFDM/TDM) to alleviate the high PAPR problem, while achieving better BER performance than OFDM. In this paper, a theoretical bit error rate (BER) analysis of clipped OFDM/TDM system in a frequency-selective fading channel is developed. The average BER performance is evaluated by numerical computation using the derived conditional BER and by computer simulation. It is shown that OFDM/TDM can significantly reduce the amplitude clipping level and the required average signal energy per bit-to-AWGN power spectrum density ratio Eb/Nofor the given BER in comparison to conventional OFDM.

This paper presents a hybrid automatic repeat request (HARQ) with variable spreading factor (VSF) suitable for a multicarrier code division multiple access (MC-CDMA). We consider a HARQ based on incremental redundancy (IR) strategy. The throughput of MC-CDMA HARQ is a tradeoff among the frequency diversity gain, the coding gain and the inter-code interference (ICI). In order to effectively exploit the channel frequency-selectivity and the channel coding, the spreading factor is changed between the initial transmission and the succeeding retransmissions. The throughput performance of HARQ with VSF in a frequency-selective Rayleigh fading channel is evaluated by the computer simulation and compared with that with fixed spreading factor (FSF). It is shown that HARQ with VSF can provide better throughput performance than with FSF and is particularly useful when high-level modulation (e.g., 16QAM and 64QAM) is used. The impacts of fading correlation between packet retransmissions and the channel frequency-selectivity on achievable throughput performance are discussed