The rapid proliferation of Internet-of-Things (IoT) systems has led to increasingly heterogeneous, resource-constrained, and security-sensitive software deployments. In this context, detecting vulnerabilities in embedded and system-level IoT code has become particularly critical, as even a single exploitable flaw may compromise entire networks or devices. Vulnerability detection in real-world software continues to pose significant challenges due to the intricate nature of program semantics, the diversity of vulnerability patterns, and the limited explainability offered by current machine learning models. Although Large Language Models (LLMs) have exhibited remarkable capabilities in comprehending and reasoning about source code, their effectiveness is frequently compromised by inadequate domain knowledge and instances of hallucinated outputs. To address these limitations, we propose VulKnow, a retrieval-augmented framework for vulnerability detection that harnesses structured vulnerability knowledge alongside multi-stage prompt-based reasoning. Specifically, VulKnow first constructs a structured knowledge base derived from authoritative sources such as CWE/CVE reports and standard library specifications. Sub-sequently, it conducts context-aware knowledge retrieval and integrates the retrieved items into an LLM-based initial filtering module. To ensure high-confidence and verifiable results, we design a multi-stage reasoning pipeline that progressively validates vulnerabilities through semantic prompts and consistency checks. Experiments conducted on multiple real-world datasets (Linux, Qemu, Big-Vul) demonstrate that VulKnow significantly enhances precision, recall, and explainability compared to existing static analysis tools as well as LLM-only baselines. This positions VulKnow as a reliable solution for practical vulnerability auditing scenarios.

Distributed computing has been widely applied in distributed edge networks for reducing the processing burden of high-dimensional data centralization, where a high-dimensional computational task is decomposed into multiple low-dimensional collaborative processing tasks or multiple edge nodes use distributed data to train a global model. However, the computing power of a single-edge node is limited, and collaborative computing will cause information leakage and excessive communication overhead. In this paper, we design a parallel collaborative distributed alternating direction method of multipliers (ADMM) and propose a three-phase parallel collaborative ADMM privacy computing (3P-ADMM-PC2) algorithm for distributed computing in edge networks, where the Paillier homomorphic encryption is utilized to protect data privacy during interactions. Especially, a quantization method is introduced, which maps the real numbers to a positive integer interval without affecting the homomorphic operations. To address the architectural mismatch between large- integer and Graphics Processing Unit (GPU) computing, we transform high-bitwidth computations into low-bitwidth matrix and vector operations. Thus the GPU can be utilized to implement parallel encryption and decryption computations with long keys. Finally, a GPU-accelerated 3P-ADMM-PC2 is proposed to optimize the collaborative computing tasks. Meanwhile, large-scale computational tasks are conducted in network topologies with varying numbers of edge nodes. Experimental results demonstrate that the proposed 3P-ADMM-PC2 has excellent mean square error performance, which is close to that of distributed ADMM without privacy-preserving. Compared to centralized ADMM and distributed ADMM implemented with Central Processing Unit (CPU) computation, the proposed scheme demonstrates a significant speedup ratio.

In this paper, we propose a federated learning (FL) system enhanced by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which is designed to protect client-side data privacy and reinforce the security of data exchange over wireless channels. Assuming an honest-but-curious central server that may infer private information from user gradients, we adopt differential privacy (DP) by injecting noise into local updates to safeguard user data. Theoretical results are derived to characterize the systematic privacy guarantees provided by the DP noise power and the gradient information in the proposed STAR-RIS-enabled DP-FL systems. Building on these results, the secrecy sum rate of local information is formulated by jointly optimizing the STAR-RIS coefficient matrices, users’ transmission power, artificial jamming power, and the power of DP noise introduced by the FL users. To tackle the non-convex optimization challenge, we develop a block coordinate descent algorithm that partitions the original problem into four manageable subproblems. The closed-form expressions are obtained for users’ transmit power, artificial jamming power, and the power of DP noise. For the STAR-RIS phase shift design, approximate solutions are derived through semidefinite relaxation combined with a surrogate lower bound method. Finally, simulation results demonstrate that the proposed STAR-RIS-enabled DP-FL systems achieve significantly improved secrecy performance compared to conventional FL systems with randomly configured STAR-RIS amplitude, phase shifts, and transmit power. Furthermore, the proposed FL algorithm achieves model training and testing performance that closely approximates that of FL without DP, highlighting its effectiveness in preserving both data privacy and model utility.

In this paper, a novel artificial noise (AN)-aided secure and covert integrated sensing and communication (ISAC) framework is established for uncrewed aerial vehicle (UAV) systems, to against malicious radar signal detection and communication eavesdropping. Specifically, we consider that besides the communication and sensing signals, the AN signal, which is used to interfere with the eavesdropper and conceal the existence of radar signal, will be transmitted by the UAV-enabled base station (UBS) with uncertainty on its power level. The closed-form expressions of intercept probability (IP) as well as the minimum detection error probability (M-DEP) are derived. Moreover, an efficient communication and sensing performance maximization strategy is designed by optimizing the beamforming vector of communication, covariance matrix of sensing, and UBS receiver filter jointly, to satisfy the IP, power and M-DEP constraints. Simulation results are provided to verify the effectiveness of our joint design by comparing it to benchmark strategy. Moreover, the impact of AN power uncertainty is examined via simulations.

ABSTRACT Traditional codebooks used for tracing information leakage in text documents often suffer from limitations in embedding capacity, robustness, and efficiency due to their manual generation process. This paper proposes a robust text watermarking method based on the stroke components of Chinese characters. By designing an innovative approach, Chinese character strokes are divided into several distinct components, with only specific ones being selectively modified to generate new glyphs, thus forming a unique codebook. The watermark signals are embedded by substituting the carrier glyph with the newly generated one, and the signals are extracted using a template matching method. Experimental results demonstrate that, compared to traditional manually designed codebooks, the proposed method significantly reduces human labor and computational overhead while maintaining high visual quality. Moreover, it exhibits superior robustness and adaptability across various challenging scenarios, including digital noise attacks, print‐scanning attacks, and print‐camera capture, making it a highly effective solution for protecting textual information.

Tibetan Jiu chess, recognized as a national intangible cultural heritage, is a complex game comprising two distinct phases: the layout phase and the battle phase. Improving the performance of deep reinforcement learning (DRL) models for Tibetan Jiu chess is challenging, especially given the constraints of hardware resources. To address this, we propose a two-stage model called JFA, which incorporates hierarchical neural networks and knowledge-guided techniques. The model includes sub-models: strategic layout model (SLM) for the layout phase and hierarchical battle model (HBM) for the battle phase. Both sub-models use similar network structures and employ parallel Monte Carlo tree search (MCTS) methods for independent self-play training. HBM is structured as a hierarchical neural network, with the upper network selecting movement and jump capturing actions and the lower network handling square capturing actions. Human knowledge-based auxiliary agents are introduced to assist SLM and HBM, simulating the entire game and providing reward signals based on square capturing or victory outcomes. Additionally, within the HBM, we propose two human knowledge-based pruning methods that prune parallel MCTS and capture actions in the lower network. In the experiments against a layout model using the AlphaZero method, SLM achieves a 74% win rate, with the decision-making time being reduced to approximately 1/147 of the time required by the AlphaZero model. SLM also won the first place at the 2024 China National Computer Game Tournament. HBM achieves a 70% win rate when playing against other Tibetan Jiu chess models. When used together, SLM and HBM in JFA achieve an 81% win rate, comparable to the level of a human amateur 4-dan player. These results demonstrate that JFA effectively enhances artificial intelligence (AI) performance in Tibetan Jiu chess.

Intelligent Omni-Surface (IOS) has attracted considerable attention for its advantages of high energy efficiency, which are similar to those of Reconfigurable Intelligent Surface (RIS), while also being able to overcome the limited scope of RIS services. In this paper, we provide a security energy efficiency (SEE) maximization design for IOS and artificial noise (AN) assisted cell-free massive MIMO (CF-mMIMO) networks, via jointly optimizing the transmission beamforming and AN covariance matrix of the AP, the reflection and transmission phase-shift matrices of the IOS, and the reflection-transmission power ratio of the IOS. To handle the formulated problem with non-convexity and high complexity, we first decouple it into two sub-problems. Then, we design low-complexity algorithms for each sub-problem i.e., an AP transmission beamforming and AN noise covariance matrix joint optimization algorithm based on the SSNCG-ALM, and an IOS reflection and transmission phase-shift matrix joint optimization algorithm based on the RPM-TR. Finally, a SEE maximization iterative algorithm based on block coordinate descent and successive convex approximation is established. The simulation results demonstrate that the proposed design significantly enhances the SEE of CF-mMIMO networks.

Online action detection aims to identify ongoing actions within untrimmed video streams, with extensive applications in real-life scenarios. However, in practical applications, video frames are received sequentially over time and new action categories continually emerge, giving rise to the challenge of catastrophic forgetting - a problem that remains inadequately explored. Generally, in the field of video understanding, researchers address catastrophic forgetting through class-incremental learning. Nevertheless, online action detection is based solely on historical observations, thus demanding higher temporal modeling capabilities for class-incremental learning methods. In this paper, we conceptualize this task as Class-Incremental Online Action Detection (CIOAD) and propose a novel framework, TS-ILM, to address it. Specifically, TS-ILM consists of two components: task-level temporal pattern extractor and temporal-sensitive exemplar selector. The former extracts the temporal patterns of actions in different tasks and saves them, allowing the data to be comprehensively observed on a temporal level before it is input into the backbone. The latter selects a set of frames with the highest causal relevance and minimum information redundancy for subsequent replay, enabling the model to learn the temporal information of previous tasks more effectively. We benchmark our approach against SoTA class-incremental learning methods applied in the image and video domains on THUMOS'14 and TVSeries datasets. Our method outperforms the previous approaches.

This article investigates the use of unmanned aerial vehicles (UAVs) in assisting hybrid non‐orthogonal multiple access (NOMA) systems to enhance spectrum efficiency and communication connectivity. A joint optimization problem is formulated for UAV positioning and user grouping to maximize the sum rate. The formulated problem exhibits non‐convexity, calling for an effective solution. To address this issue, a two‐stage approach is proposed. In the first stage, a particle swarm optimization algorithm is employed to optimize the UAV positions without considering user grouping. With the UAV positions optimized, a game theory‐based approach is utilized in the second stage to optimize user grouping and improve the sum rate of the hybrid NOMA system. Simulation results demonstrate that the proposed two‐stage method achieves solutions close to the global optimum of the original problem. By optimizing the positions of UAVs and user groups, the sum rate can be effectively improved. Additionally, optimizing the deployment of UAVs ensures better fairness in providing communication services to multiple users.

With the wide application of deep learning (DL) across various fields, deep joint source–channel coding (DeepJSCC) schemes have emerged as a new coding approach for image transmission. Compared with traditional separated source and CC (SSCC) schemes, DeepJSCC is more robust to the channel environment. To address the limited sensing capability of individual devices, distributed cooperative transmission is implemented among edge devices. However, this approach significantly increases communication overhead. In addition, existing distributed DeepJSCC schemes primarily focus on specific tasks, such as classification or data recovery. In this paper, we explore the wireless semantic image collaborative nonorthogonal transmission for distributed edge networks, where edge devices distributed across the network extract features of the same target image from different viewpoints and transmit these features to an edge server. A two‐view distributed cooperative DeepJSCC (two‐view‐DC‐DeepJSCC) with or without information disentanglement scheme is proposed. In particular, the two‐view‐DC‐DeepJSCC with information disentanglement (two‐view‐DC‐DeepJSCC‐D) is proposed for achieving balancing performance between multitasking of image semantic communication; while the two‐view‐DC‐DeepJSCC without information disentanglement only pursues outstanding data recovery performance. Through curriculum learning (CL), the proposed two‐view‐DC‐DeepJSCC‐D effectively captures both common and private information from two‐view data. The edge server uses the received information to accomplish tasks such as image recovery, classification, and clustering. The experimental results demonstrate that our proposed two‐view‐DC‐DeepJSCC‐D scheme is capable of simultaneously performing image recovery, classification, and clustering tasks. In addition, the proposed two‐view‐DC‐DeepJSCC has better recovery performance compared to the existing schemes, while the proposed two‐view‐DC‐DeepJSCC‐D not only maintains a competitive advantage in image recovery but also has a significant improvement in classification and clustering accuracy. However, the proposed two‐view‐DC‐DeepJSCC‐D will sacrifice some image recovery performance to balance multiple tasks. Furthermore, two‐view‐DC‐DeepJSCC‐D exhibits stronger robustness across various signal‐to‐noise ratios.

To meet the needs of high energy efficiency (EE) and various heterogeneous services for 6G, in this article, we probe into the EE of reconfigurable intelligent surfaces (RISs) subsurface (SSF) architecture-aided cell-free Internet of Things (CF-IoT) networks. Specifically, we jointly optimize the base station (BS)-RIS-IoT device (ID) joint associations, the RIS’s phase shift matrix (PSM), and the BS’s transmit power to enhance CF-IoT’s EE. The elevated complexity (NP-hard) and nonconvexity of the formulated problem pose significant challenges, making the solution highly difficult and intricate. To handle this challenging problem, we first develop an alternating optimization framework based on block coordinate descent, which can decouple the original problem into several subproblems. We then carefully design the corresponding low-complexity algorithm for each subproblem to solve it. Moreover, the proposed joint optimization framework serves as a versatile solution applicable to a wide range of scenarios aiming to maximize EE with the assistance of RISs. Simulations confirm that deploying RISs in CF-IoT scenarios is beneficial for improving the EE of the system, and the SSF architecture can further enhance the EE of the system.

Online inspection of industrial parts becomes increasingly important for factories to improve production quality, where small sizes and high computations increase difficulties in the defect detection process. In order to solve these issues, we propose a defect detection model to identify detailed defects with edge computations, named fast attention segmentation classification network (FASCNet). In the model, we design skip connection attention (SCA) with edge average attention (eAA), edge sum attention (eSA), and attention for segmentation (AS) to catch complex features of extremely tiny defects. Additionally, global mixed pooling (GMP) operation is explored to adaptively obtain severe mapping into low dimensional feature domains. Furthermore, a tensor freeze decomposition (TFD) is discovered to reduce model computation and complexity for edge devices. Finally, we achieve an average precision (AP) of 97.86% and giga floating point operations (GFLOPs) of 64.4495 on the real-world sprocket surface data set, which has 16.06% of GFLOPs of the current state-of-the-art method while only lowering the AP by 0.58%. On the public data set, we achieve an AP of 98.83% and GFLOPs of 92.0379 on the Severstal Steel data set. The experimental results indicate that our model performs more effectively than other state-of-the-art approaches in terms of both accuracy and computational cost simultaneously.

Intelligent electromagnetic signature recognition is one of the key technologies in Internet of Things (IoT) device connection, which can improve system security and speed up the authentication process. In practical scenarios, as the number of IoT devices increases, electromagnetic features, such as fingerprint and modulation signals also increase substantially. However, since intelligent recognition technology, such as automatic modulation classification (AMC), requires a large amount of labeled data to train the neural network classifier, it is challenging to collect so much labeled data. To address the performance degradation challenges with small training data, we propose an efficient semisupervised electromagnetic recognition framework to break the performance gap with the fully supervised learning scheme. This framework can fully use the unlabeled electromagnetic data collected during the authentication process for self-training to improve the classifier’s performance. According to the idea of consistency regularization, we design a signal augmentation method and propose an ensemble pseudolabel design algorithm to improve confidence. Moreover, we perform a convex combination of electromagnetic features to smooth the model decision boundary while generalizing to unknown data distribution regions. Experimental results on the modulated data demonstrate the performance superiority of the proposed algorithm, i.e., use less than 5% of data with no more than 10% performance drop.

In highly interconnected large-scale event and other Internet of Things (IoT) device-intensive scenarios, traditional terrestrial base stations have difficulty meeting the requirements of IoT devices for network speed and security, and have exacerbated carbon pollution. To this end, a blockchain-enabled unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) system is introduced to enhance communication efficiency and ensure the privacy of IoT devices. In this system, the Byzantine consensus algorithm is applied in the blockchain. Considering the pollution of reducing carbon dioxide emissions, a strategy for jointly optimizing the flight trajectories of UAVs, task offloading scheduling, and MEC computing resource allocation is formulated to minimize the system’s carbon emissions and time delay while meeting MEC and blockchain computing tasks. However, due to the coupling of variables, this problem is very complex. Therefore, the original problem is decoupled into multiple subproblems, and the block coordinate descent method (BCD) and successive convex approximation method (SCA) are used for solving. Specifically, the UAV flight trajectories, task offloading scheduling, and MEC computing resource allocation are alternately optimized until convergence. Simulation results verify the effectiveness and good performance of the proposed algorithm in this article.

Power efficiency for license plate recognition (LPR) under unconstrained scenarios is a crucial factor in many edge-based real-world applications, e.g., autonomous vehicles whose power budget is limited. While a bulk of prior works have explored LPR approaches for unconstrained situations on CPU and GPU servers, these methods result in huge power dissipation, and are ineffective in challenging scenes. In this work, we present a mixed-precision (MP) Transformer hardware architecture to meet the requirements of power efficiency and satisfactory LPR accuracy in unconstrained scenarios, dedicated to implementing power-efficient edge accelerators for difficult LPR tasks. Firstly, MP-LPR Transformer model is proposed, where novel mixed-precision quantization and non-linear approximation techniques are tailored for low bit-width inference. Secondly, vector tiling systolic array Transformer architecture (VTSATA) is proposed with unique vector tiling systolic array (VTSA) and vector ALU (VALU) designs, where VTSA is used to accelerate matrix multiplications, and VALU is used to process non-linear operations. Finally, a FPGA accelerator prototype based on our approach is developed. Experimental results demonstrate that our hardware platform can reduce power consumption more than$20\times $compared to GPU platforms, and for challenge subset on CCPD dataset, our method can further improve nearly 1% accuracy with respect to the state-of-the-art performance.

The intelligent transportation system (ITS) was supported by the sixth generation (6G) wireless networks, since it has great potential to realize intelligent transportation with the benefit for the society and economy. In order to overcome the practical problem of spectrum scarcity, ultra-low latency, large-scale connectivity in ITS, we propose a cooperative multiple-input single output non-orthogonal multiple access (MISO-NOMA) for ITS with intelligent reflecting surface (IRS) and simultaneous wireless information and power transfer (SWIPT). An user fairness optimization problem is formulated to maximize the fairness rate of the vehicles, subject to the quality of service requirements of the vehicles and the successive interference cancellation. The optimization problem involves the transmit beamformers design, the IRS reflection matrix design, and the power splitting ratio of the SWIPT, which lead to the problem is difficult to solve. For solving the challenging problem, an iterative successive convex approximation and semi-definite relaxation based algorithm is proposed. Explicitly, we firstly adopt the method of reconstructing epigraph for simplification due to the objective function is non-convex, and then the original problem is decomposed into two sub-problems that are easy to solve. Finally, Experimental results illustrate that the user fairness of the proposed cooperative MISO-NOMA for ITS with IRS and SWIPT is better than that of both the IRS-NOMA for ITS without SWIPT and the IRS-OMA for ITS.

Online action detection plays a vital role in video action understanding and can be widely used in various video analysis applications. This task aims to detect actions at the current moment within long untrimmed video streams. However, accurately identifying action-background transitions that are ambiguous in terms of time during detection can be challenging due to the similarity between the action and background clips, adding to the difficulty in finding a suitable division between them. To address this issue, we propose a hard video clip mining method based on deep metric learning for online action detection named HCM. The HCM method first selects video clips that are hard to distinguish to determine the optimization objects. Then, a hard clip mining loss is adopted to push the features toward the centers of the categories to which they belong and away from others. Furthermore, we introduce an intra-class feature compaction loss to constrain the divergence of action features, ensuring the stability of their distribution. We evaluated the proposed method on two challenging online action detection datasets, THUMOS14 and TVSeries. The results show that HCM is effective and efficient in online action detection and action anticipation tasks.

This paper considers reconfigurable intelligent surface (RIS)-enabled federated learning (FL) system, where the FL users communicate with the access point (AP) via RIS. To reveal the impact of RIS and learning rate on FL aggregation, the theoretical result of minimum global communication rounds and local iteration rounds are derived. Based on the obtained convergence results of FL, we formulate an optimization problem to minimize the energy consumption of the proposed RIS-assisted FL system by jointly optimizing the passive beamforming of RIS, the CPU computing frequency, the bandwidth, and the transmit power of users. To solve the non-convex problem, we propose a block coordinate descent (BCD) optimization algorithm based on successive convex approximation (SCA) to decompose the original problem into four sub-problems. Specifically, the closed-form solutions are derived for the CPU frequency, RIS reflection matrix, and communication bandwidth. For the transmit power sub-problem, we propose a linear approximation algorithm based on the first-order Taylor expansion to ensure solution accuracy. Finally, simulation results show that: 1) the energy consumption of the proposed RIS-assisted FL system can be greatly reduced compared to that without optimizing the passive beamforming of RIS and the transmit power; 2) The learning performance of the proposed RIS-enabled FL system is closed to the FL without wireless communication interference; and 3) The proposed algorithm can not only significantly reduce energy consumption, but also fast convergence in terms of the FL model training and testing.

The loss function of Generative adversarial network (GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure (MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant-reconstruction score is composed of discrimination and reconstruction loss. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1-score.

In this paper, we study an uplink reconfigurable intelligent surfaces-non-orthogonal multiple access (RIS-NOMA) assisted mobile edge computing (MEC) network with simultaneous wireless information and power transfer (SWIPT), where the users want to offload their computing tasks to the BS via a RIS and a relay based on the SWIPT technique. The goal of the paper is to minimize the delay concerning the computing tasks of the users by jointly optimizing the power allocation ratio, the phase shift matrix of the RIS, the offloading task ratio, and the offloading transmit power. For solving the challenging optimization problem, we conceive a low-complexity algorithm by optimizing two subproblems separately, based on the penalty method as well as the successive convex approximation. Simulation results demonstrate that the proposed RIS-NOMA assisted MEC network with SWIPT outperforms both the conventional RIS-NOMA assisted MEC network without SWIPT and the RIS-orthogonal multiple access assisted MEC network.

Abstract Unsupervised anomaly detection refers to the discovery of unconventional images that are globally or locally different from the training set. Recently, reconstruction‐based anomaly detection methods have made great progress. However, most of the existing methods take reconstructing the original image as the goal of latent feature learning. Due to lack of effective semantic guidance, latent features have intrinsic characteristics which retain redundant details of spatial structure. Such information is too general and cause over‐expression problem. To solve this problem, in this paper, dual transformation‐aware embeddings are coined which aims to achieve a stable model to learn high‐level latent features in a self‐supervised manner. To be more specific, the authors try to extract transformation‐detectable feature embeddings for both structure and content views which explore the regular pattern under different transformations in normal situations. In addition, the relationship between the original feature and the transformed feature is established. Based on such relationship, the latent feature of generated image to predict transformation parameter is extracted. Then, a transformation‐consistency regularization is proposed to constrain decoder to generate high‐quality image with high‐level consistency and achieve a more stable model. Experiments on MVTec‐AD and CIFAR10 datasets prove the effectiveness and robustness of the proposed method.

We investigate the activity detection and channel estimation issues for cell-free Internet of Things (IoT) networks with massive random access. In each time slot, only partial devices are active and communicate with neighboring access points (APs) using non-orthogonal random pilot sequences. Different from the centralized processing in cellular networks, the activity detection and channel estimation in cell-free IoT is more challenging due to the distributed and user-centric architecture. We propose a two-stage approach to detect the random activities of devices and estimate their channel states. In the first stage, the activity of each device is jointly detected by its adjacent APs based on the vector approximate message passing (Vector AMP) algorithm. In the second stage, each AP re-estimates the channel using the linear minimum mean square error (LMMSE) method based on the detected activities to improve the channel estimation accuracy. We derive closed-form expressions for the activity detection error probability and the mean-squared channel estimation errors for a typical device. Finally, we analyze the performance of the entire cell-free IoT network in terms of coverage probability. Simulation results validate the derived closed-form expressions and show that the cell-free IoT significantly outperforms the collocated massive MIMO and small-cell schemes in terms of coverage probability.

An adaptive power allocation by minimizing grid power consumption for the Orthogonal Frequency Division Multiplexing (OFDM) system with the hybrid energy supply under a fast fading channel is proposed in this paper. To solve the non-convex problem, two adaptation algorithms have been presented. The first is Lagrangian algorithm and the second is a successive convex approximation (SCA) algorithm. The theoretical analysis and simulation results show that the proposed algorithms are effective. Compared with traditional OFDM systems with grid energy supply only, the proposed algorithms can reduce the power consumption of the grid. Also, different proposed algorithms have different performance. Lagrangian algorithms with higher computational complexity have better performance. However, there is a trade-off between computational complexity and performance.

Ultra-dense HetNets (UDN)-based Millimeter-Wave (mmWave) massive MIMO is considered a promising technology for 5th generation (5G) wireless communications systems since it can offer massively available bandwidth and improve energy efficiency (EE) substantially. However, in UDN, the power consumption of the system increases sharply with the increase of network density. In this paper, we investigate the optimization of the EE in the mmWave massive MIMO systems with UDN. To develop the functions of massive MIMO, we first propose a system model where the massive MIMO harvests electromagnetic energy from the environment employing simultaneous wireless information and power transfer (SWIPT) technology, implemented at the base station (BS). Then, the EE optimization problem is formulated for 5G mmWave massive MIMO systems within the UDN. Considering the nonconcave feature of the objective function, an iterative EE algorithm is developed, based on Dinkelbach method. To utilize the role of coordinated multi-point transmission and reception (CoMP) for improving the EE, a coordinated user(UE)-BS association algorithm-based CoMP with maximum energy efficiency (MaxEE) is proposed. The simulation results demonstrate that the proposed algorithm has a substantially faster convergence rate and is very effective, compared with existing methods.

In this paper, the challenging problem of data detection for orthogonal frequency division multiplexing (OFDM) systems under rapidly time- varying channels is considered. Time-varying channels within a multicarrier symbol will lead to a loss of sub-channel orthogonality, and result in inter-channel interference (ICI) and an irreducible error floor in traditional receivers. The genetic algorithm (GA) assisted data detection for the single input single output (SISO) and single input multiple output (SIMO) OFDM systems are presented, respectively. Theoretical analysis and simulations show that the proposed algorithms are valid compared with minimum mean square error (MMSE) and minimum mean square error successive interference cancellation (MMSE-SIC). The GA-assisted data detection for OFDM systems under rapidly time- varying channels is flexible to provide tradeoff between performance and complexity. To accelerate the convergence of GA for SIMO OFDM, the individuals of GA are selected based on the concept of Pareto optimality.

In this paper, a new partial power and rate adaptation scheme for orthogonal frequency division multiplexing (OFDM) systems is proposed in the presence of carrier frequency offset (CFO). The conventional adaptive scheme is shown to be a special case of the partially adaptive scheme technique which enables the resulting non-convex optimization problem, solved in a feasible way. It leads to a solution for optimal power adaptation that maximizes the spectral efficiency of an OFDM system using M-ary quadrature amplitude modulation (MQAM) under average power and instantaneous BER constraints. Closed-form expressions for the average spectral efficiency (ASE) of adaptive OFDM systems are derived. The theoretical results and computer simulations show that the range of the partial adaptation becomes narrow and the performance of constant power and continuous rate is very close to that of the partially adaptive power and continuous rate for higher CFO or high signal noise ratio (SNR) values.

In this paper, the effect of power and rate adaptation on the spectral efficiency of orthogonal frequency division multiplexing (OFDM) systems using M-ary quadrature amplitude modulation (MQAM) is investigated, in the presence of the fast fading channels, under power and instantaneous bit error rate (BER) constraints. A lower bound on the maximum spectral efficiency of adaptive OFDM/MQAM systems is obtained, together with a closed-form expression for the average spectral efficiency of adaptive OFDM systems. Theoretical and numerical results show that the adaptive MQAM/OFDM systems under fast fading channel have substantial gain in spectral efficiency over the non-adaptive counterparts.