Dual-Function Radar and Communication (DFRC) system with full-duplex (FD) sensing and half-duplex (HD) communication is regarded as a promising way to improve safety and efficiency in vehicular networks, which have been widely used in vehicular networks. To achieve higher communication capacity, this paper extends the beamforming design of full-duplex sensing and communication DFRC systems to general scenarios within vehicular networks. Compared to the fully-connected hybrid beamforming architecture, the partially-connected hybrid beamforming architecture holds great potential of reducing hardware costs, thereby facilitating practical implementation in real-world wireless systems. However, the unique block-diagonal structure of its analog beamforming matrix introduces additional design challenges. This paper investigates an efficient partially-connected hybrid beamforming algorithm for FD-DFRC multi-input multi-output (MIMO) systems. Specifically, we formulate a multi-objective optimization problem to jointly optimize the dual-functional transmit beamformers for information receiving vehicles (IRVs) and the base station (BS), while also optimizing the received beamformers for information transmission vehicles (ITVs) at the full-duplex vehicle terminal (FD-VT). In particular, we decompose the original problem of maximizing the sum rate into several subproblems. We design an efficient algorithm based on lower bound maximization, Riemannian manifold optimization methods, and the alternating direction multiplier method (ADMM) algorithm. Numerical simulations demonstrate that the proposed scheme effectively optimizes the sum rate while maintaining radar performance thresholds, requiring fewer radio frequency chains, and adhering to energy offloading constraints.

Zero-Touch Networks (ZTNs) represent a transformative paradigm toward fully automated and intelligent network management, providing the scalability and adaptability required for the complexity of sixth-generation (6G) networks. However, the distributed architecture, high openness, and deep heterogeneity of 6G networks expand the attack surface and pose unprecedented security challenges. To address this, security automation aims to enable intelligent security management across dynamic and complex environments, serving as a key capability for securing 6G ZTNs. Despite its promise, implementing security automation in 6G ZTNs presents two primary challenges: 1) automating the lifecycle from security strategy generation to validation and update under real-world, parallel, and adversarial conditions, and 2) adapting security strategies to evolving threats and dynamic environments. This motivates us to propose SecLoop and SA-GRPO. SecLoop constitutes the first fully automated framework that integrates large language models (LLMs) across the entire lifecycle of security strategy generation, orchestration, response, and feedback, enabling intelligent and adaptive defenses in dynamic network environments, thus tackling the first challenge. Furthermore, we propose SA-GRPO, a novel security-aware group relative policy optimization algorithm that iteratively refines security strategies by contrasting group feedback collected from parallel SecLoop executions, thereby addressing the second challenge. Extensive real-world experiments on five benchmarks, including 11 MITRE ATT&CK processes and over 20 types of attacks, demonstrate the superiority of the proposed SecLoop and SA-GRPO. We will release our platform to the community, facilitating the advancement of security automation towards next generation communications.

The fluid antenna system (FAS) is a reconfigurable antenna technology that enhances wireless communications by adapting to time-varying channel conditions. However, existing FAS research focuses on physical-layer performance, neglecting link-layer quality-of-service (QoS) guarantees like latency/delay. This paper develops a cross-layer model to investigate the low-latency performance of a slow fluid antenna multiple access (s-FAMA) system in a multi-user downlink scenario, employing the effective capacity (EC) framework. We first derive approximate expressions for the total EC in a multi-users-FAMA system under both complex and simplified channel models. To validate them, we propose a quasi-Monte Carlo (QMC) method to compute multidimensional integrals, overcoming the limitations of conventional numerical methods and solving a class of multidimensional integral numerical simulation problems for large-scale FAMA systems. Then, we formulate an optimization problem to maximize the total EC while satisfying the total power constraint and each user’s minimum EC requirement. To jointly optimize port selection and power allocation, an iterative algorithm based on alternating optimization (AO) and quadratic transform (QT) is proposed to solve the non-convex problem. Simulation results validate our approximations and show that our joint port selection and power allocation scheme outperforms the conventional baseline algorithms, confirming our proposed algorithm’s effectiveness and FAS’s superiority in ensuring the QoS in wireless communications.

Online model training is pivotal for enabling multiuser semantic communication systems to adapt to dynamic channel conditions. However, conventional frameworks suffer from prohibitive communication overhead and vulnerabilities to privacy attacks, hindering practical deployment. This paper proposes semantic information mixup (SIMix), a secure and efficient training framework that integrates Over-the-Air Mixup (OAM) with label-aware user grouping to jointly optimize spectral efficiency and semantic security. The OAM mixes semantic features of multiple users via wireless channels, inherently obfuscating sensitive data while reducing communication overhead. A closed-form Tx-Rx scaling optimization minimizes the mean square error (MSE) of over-the-air computation under channel noise, ensuring stable convergence in low-SNR regimes. Furthermore, an extended max-clique algorithm dynamically partitions users into groups with minimal intra-label similarity, reducing model inversion attack success rates. Experiments on CIFAR-10 and Tiny ImageNet demonstrate that the proposed approach is superior in terms of communication efficiency and security, reducing communication overhead by up to 25% and attaining 17.58 dB PSNR (20.98 dB reduction) under inversion attack and reducing 13.44% attack success rate under label inference attack, while achieving comparable transmission accuracy.

High peak-to-average ratio (PAPR) is a major drawback in orthogonal frequency division multiplexing (OFDM) systems. Despite that non-linear transformation techniques achieve low PAPR, it meanwhile degrades the bit error rate (BER) performance. To tackle this issue, deep learning based tightly coupled architectures have been proposed, but their significant computational overheads make them impractical for real-world deployments. This paper explores the ability of separated architecture to efficiently reduce PAPR while maintaining a good BER performance. Specifically, we analyze the distribution of OFDM signals to find an interesting phenomenon that the transmitter can achieve a large PAPR reduction (about 2.4 dB) as long as masking a small percentage (0.55%) of high-PAPR symbols. Driven by this finding, we propose a mask-based scheme including a PAPR reduction module at the transmitter and a mask-location assisted signal reconstruction module at the receiver. To capture the mask-location information (MLI) at the receiver, we outline two ways tailored for scenarios with different communication quality and computing capabilities, respectively. Simulation results reveal that our proposed scheme can achieve satisfactory BER performance with substantially reduced PAPR against conventional schemes. Moreover, the robustness of our scheme is demonstrated across various channels, including an additional white Gaussian noise channel, the Extended Pedestrian A channel and the Extended Vehicular A channel.

Terahertz (THz) communication technology is regarded as a promising enabler for achieving ultra-high data rate transmission in next-generation communication systems. To mitigate the high path loss in THz systems, the transmitting beams are typically narrow and highly directional, which makes it difficult for a single beam to serve multiple users simultaneously. To address this challenge, reconfigurable intelligent surfaces (RIS), which can dynamically steer the incident electromagnetic waves, have been integrated into THz communication systems to extend coverage. Existing works mostly remain theoretical analysis and simulation, while prototype validation of RIS-assisted THz systems is scarce. In this paper, we designed a kind of liquid crystal-based RIS operating at 220 GHz supporting both single-user and multi-user communication scenarios, followed by a 220 GHz THz-RIS communication system prototype. To support simultaneous multi-user transmission, we designed an OFDM-based resource allocation scheme. Beamforming optimization is performed in both single and muti-user scenarios. Specifically, to enhance the system performance of multi-user scenario, we developed a received power based feedback control method. In our experiments, the received power gain with RIS is no less than 10 dB in the single-beam mode, and no less than 5 dB in the multi-beam mode, compared to the case without RIS. With the assistance of RIS, the achievable rate of the system could reach 2.341 Gbps with 3 users sharing 2 GHz bandwidth and the bit error rate (BER) of the system decreased sharply. Finally, an image transmission experiment was conducted to vividly show that the receiver could recover the transmitted information correctly with the help of RIS. The experimental results also demonstrated that the received signal quality was enhanced through power feedback adjustments.

Simultaneous wireless information and power transfer (SWIPT) has been regarded as a highly promising technology for delivering reliable energy supply for low-powered Internet-of-Thing devices (IoTDs). In this paper, we originally investigate the performance of SWIPT systems in multi-Reconfigurable Intelligent Surface (RIS) aided cell-free IoT networks with fluid antenna (FA).Our objective is to simultaneously maximize the sum transmission rate and harvested energy by optimizing the selection of FA ports, transmit beamforming vectors at access points (APs), and reflect beamforming at RISs, which is a notorious trade-off and is impossible to simultaneously maximize at the same IoTD under the traditional Power splitting (PS) or Time switching (TS) based SWIPT techniques. By converting the harvested energy into throughput, we formulate a novel equivalent-sum-rate (ESR) maximization problem. To handle this sum-of-logarithmic function and nested multiple-ratio fractional nonconvex optimization problem, we develop a Lagrangian dual transform (LDT) and quadratic transform (QT)-based alternating optimization (AO) framework to tackle it. Simulation results demonstrate that the proposed method outperforms benchmarks, highlighting the enhanced system performance in both information and energy reception facilitated by RISs and FA. Moreover, our research also indicates that the deployment of FA at IoTDs can achieve better performance than the traditional fixed-position antennas in such a network, as long as it can provide an adequate number of ports and FA size.

This paper advocates a fluid antenna system (FAS)-assisted long-range communication (LoRa-FAS) for Internet-of-Things (IoT) applications. In the proposed system, FAS provides spatial diversity gains for LoRa, eliminating the necessity for integrating multiple-input multiple-output (MIMO) technologies into the system. It consists of a traditional LoRa transmitter with a fixed-position antenna and a LoRa receiver employing the FAS (Rx-FAS). The pilot sequence overhead and placement for FAS are also considered. Specifically, we consider embedding pilot sequences within symbols to reduce the impact of pilot overhead on system throughput and the physical layer (PHY) frame structure, leveraging the fact that the pilot sequences do not convey source information and correlation detection at the LoRa receiver need not be performed across the entire symbol. The achievable performance of LoRa-FAS is thoroughly analyzed under both coherent and non-coherent detection schemes. We obtain new closed-form approximations for the probability density function (PDF) and cumulative distribution function (CDF) of the FAS channel under the block-correlation model. Furthermore, the approximate SER, equivalently the bit error rate (BER), of the proposed LoRa-FAS is also derived in closed form. Simulation results indicate that substantial SER gains can be achieved by FAS within the LoRa framework, even with a limited size of FAS. In addition, our analytical results align well with Clarke’s exact spatial correlation model. Finally, when utilizing the block-correlation model, we suggest that the correlation factor should be selected as the proportion of the eigenvalues of the exact correlation matrix greater than 1 for higher accuracy.

Fluid antenna system (FAS) is regarded as one of the key enabling technologies for supporting massive communications in next-generation networks, owing to its advantages of compact design, low power consumption, and significant performance gains. This paper investigates a FAS-assisted downlink non-orthogonal multiple access (NOMA) satellite network, where terminal users are equipped with FAS to receive superimposed signals from the satellite. The satellite-to-FAS channels are modeled as spatially correlated shadowed-Rician fading distributions, where the spatial correlation matrix is obtained from the von Mises–Fisher model. By utilizing block-correlated approximation method, the cumulative distribution function of the maximum spatially correlated shadowed-Rician fading channel gain is derived. On this basis, closed-form expressions for outage probability and ergodic data rate of the FAS-NOMA satellite network are derived under both imperfect and perfect successive interference cancellation scenarios. Moreover, asymptotic expressions for outage probability and ergodic data rate of the FAS-NOMA satellite network are derived in the high signal-to-noise ratio region to unveil the achievable diversity gain and multiplexing gain. Simulation results reveal that: 1) Compared to conventional antenna system with selection combining scheme, FAS can effectively exploit the fluctuations of the channel response and provide additional selection diversity, thereby achieving superior performance in NOMA satellite networks; and 2) By adjusting the power allocation factor under different antenna apertures, the performance of FAS-NOMA satellite networks can be further enhanced.

A major limitation of physical layer security is the requirement for prior knowledge of the potential eavesdroppers' (Eves) channels, which makes its practical implementation challenging. Integrated sensing and communication systems can be exploited to address this issue by enabling both communication and sensing functionalities, sensing the physical environment to estimate Eves' directions and amplitudes, and further reconstructing their channel state information (CSI) to achieve sensing-assisted wiretap channel awareness, thus realizing secure communication. We analyze a more practical scenario where not only is reconstructed Eves' CSI inaccurate, but also the CSI of the legitimate user equipments (UEs) estimated by the base station is imperfect, and Eves are equipped with multiple antennas. To resolve this, we propose a robust optimization problem that jointly maximizes the sensing accuracy and the secrecy rate by co-designing the beamforming and the artificial noise matrix. For the highly challenging non-convex infinite objective function, the S-Procedure is introduced, which is then solved by an efficient algorithm based on alternating optimization and successive convex approximation. Numerical results validate the effectiveness of the proposed algorithm and demonstrate that the robust beamforming scheme can mitigate the impact of channel uncertainty (both UEs' and Eves') on the system performance.

Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly used auxiliary load balancing loss often leads to expert overlap and overly uniform routing, which hinders expert specialization and degrades overall performance during post-training. To address this, we propose a simple yet effective solution that introduces two complementary objectives: (1) an orthogonality loss to encourage experts to process distinct types of tokens, and (2) a variance loss to encourage more discriminative routing decisions. Gradient-level analysis demonstrates that these objectives are compatible with the existing auxiliary loss and contribute to optimizing the training process. Experimental results over various model architectures and across multiple benchmarks show that our method significantly enhances expert specialization. Notably, our method improves classic MoE baselines with auxiliary loss by up to 23.79\%, while also maintaining load balancing in downstream tasks, without any architectural modifications or additional components. We will release our code to contribute to the community.

The interoperability of satellite networks is crucial for achieving global low latency connections in B5G/6G communication. However, the use of different crypto-systems by multiple operators poses security threats such as cross domain identity spoofing and man-in-the-middle attacks. This article proposes a two-way anonymous and cross PKI-IBC authentication mechanism for the inter-satellite-networks interoperability scenarios. The proposed mechanism pre-establishes trust between authority centers of diverse operators. When authenticating, satellites online request the public key of the opponent’s operator’s authority center. Thus, satellites can guarantee the authenticity of each other’s public keys and then verify their identities mutually. We also generate temporary symmetric keys to conceal the public key and identity of the satellites, preserving identity privacy. Finally, the session key is derived through symmetric encryption to ensure the confidentiality of subsequent messages. ProVerif and BAN logic have formally verified that the proposed mechanism can achieve mutual authentication and key negotiation, while ensuring that private keys are not leaked. The numerical results indicate that our mechanism has lower authentication latency and communication overhead compared to existing schemes.

The fluid antenna system (FAS) enhances wireless communication through dynamic adaptation to channel conditions. However, most existing studies focus on physical-layer metrics without considering link-layer low-latency quality-of-service (QoS) guarantees. This paper develops a cross-layer model to analyze the QoS performance in a slow fluid antenna multiple access ($s$-FAMA) system using the effective capacity (EC) framework. We formulate and solve a joint port selection and power allocation problem to maximize total EC under total power and each user's minimum EC constraint. Simulation results demonstrate that our proposed algorithm significantly outperforms conventional uniform power allocation, highlighting the effectiveness of FAS in ensuring QoS.

With the widespread deployment of 5G networks, the dense layout of base stations has led to significant interference issues. Consequently, optimizing spectrum and power planning has become crucial for enhancing system channel capacity. This paper proposes a multi-agent proximal policy optimization method based on knowledge distillation, utilizing a globally shared reward function to promote cooperation among agents. The method employs a centralized training and distributed execution framework, allowing each agent to make effective decisions based solely on local state information, thereby achieving efficient spectrum selection and power allocation. Additionally, knowledge distillation techniques are introduced to simplify the model structure, optimizing memory usage and inference speed. Simulation results demonstrate that the proposed KD-MAPPO method exhibits significant advantages in performance and storage efficiency.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Rate-Splitting Multiple Access (RSMA)is a powerful technology to enhance spectral efficiency in short packet Ultra-Reliable and Low-Latency Communication (URLLC) systems with Finite Blocklength (FBL) codes.Enhancing system performance requires larger antenna arrays to meet increasingly stringent QoS requirements, which is very costly. To overcome this limitation, we propose a novel movable antennas (MAs)-assisted RSMA short packet transmission schemes to realize more flexible beamforming and higher spatial multiplexing gains with a small number of MAs. We formulate a sum rate maximization problem for the joint optimization of beamforming and antenna position under the specified URLLC requirements. An alternating optimization (AO) algorithmis developed to address this challenging high-dimensional non-convex optimization problem.Specifically, the Successive Convex Approximation (SCA) methodis utilized to design the beamforming matrix. Additionally, we proposeda novel worst position neighborhood variation-oriented particle swarm optimization (WPNVPSO) to optimize the MA positions efficiently.Numerical results demonstrate our proposed design outperforms other benchmarks by achieving high data rates with lower latency and higher reliability, while utilizing fewer antennas.

With the rapid rise in passenger demand and expanding flight routes, in-flight connectivity has become increasingly critical. Existing technologies—satellite communications with broad coverage but limited capacity, and ground-based networks offering high-speed, low-latency connections but constrained by geographic reach—each fall short of delivering seamless service independently. This paper introduces an innovative approach by integrating satellite and ground-based networks through a dynamic parameter and protocol-enhanced handover mechanism tailored for aeronautical communications in satellite-terrestrial integrated networks, and can be extended to Internet of Things scenarios such as disater response, remote sensing via unmaned aerial vehicle. Unlike conventional methods, our novel user-controlled location-based handover approach addresses the challenges of high aircraft-LEO satellite mobility and significant signaling latency by optimizing the handover decision process. This method reduces signaling overhead, improves target selection accuracy, and adapts dynamically to environmental variations, overcoming limitations posed by fluctuating satellite signal strength. Formulated as a multi-objective optimization problem, our approach minimizes handover interruption time while ensuring uninterrupted connectivity. Simulation results demonstrate that this approach lowers signaling overhead during handover requests and enhances connectivity throughout the flight, marking a novel step forward in aeronautical communication efficiency. Simulation results show the OLHO scheme achieving an 83.96% connection rate—higher than benchmarks—though at the cost of increased handover frequency, and 25% lower signaling overhead in SAGIN. This shows a clear trade-off between connection rate and handover intensity, where OLHO balances service continuity and stability via location-based handover triggers.

In semantic communication (SemCom), the gain of deep learning-based joint source and channel coding (JSCC) has been proved to partly come from the analog transmission of semantic features extracted directly from the source signal. While, the current mainstream digital communication system design results in the gains almost vanishing when bit-oriented modulation (e.g., QPSK) is employed. To tackle this problem, we propose a Semantic-Oriented Modulation (SOM) method that enables direct mapping from an analog value of a semantic feature to a sequence of discrete values without bit conversion. SOM employs a hierarchical design to mitigate discretization loss and optimizes resource allocation by exploiting the varying significance of semantic features, reducing the data volume by 29.17%. We provide an analysis that reveals the impact of modulation orders and hierarchical layers, guiding the SOM’s design. Simulations in four tasks show that SOM outperforms JSCC with bit-oriented modulation, and the implementation of the Software Defined Radio (SDR) platform confirms its compatibility with existing systems and its potential for latency reduction.

The rapid expansion and resource competition among proliferating Low Earth Orbit (LEO) satellite constellations have escalated adversarial jamming threats, critically challenging network reliability. However, most adaptive frequency hopping (AFH) strategies suffer from slow convergence and high switching overhead when confronted with dense and dynamic jamming scenarios. To tackle this problem, we utilize the temporal correlation of jamming from the fixed satellite orbits. Specifically, we derive the conditional outage probabilities between the adjacent time slots to characterize the temporal correlation of jamming. On this basis, the temporal correlation of jamming adaptive frequency-hopping (TCJ-AFH) strategy is proposed by integrating the correlation into the state exploration process of AFH. Simulation results demonstrate that the TCJ-AFH strategy achieves a 39.7% improvement in the average outage probability under jammed time and a 33.9% reduction in the frequency switching count versus the Q-learning baseline to accelerate the convergence process and minimize overhead, revealing its effectiveness in balancing performance and complexity under dense and dynamic jamming scenarios.

With the proliferation of smart devices and the expansion of human production and living areas, low Earth orbit (LEO) satellite computing is needed to meet the computing demands over a wide area. Due to the limited resources that a single LEO satellite can carry, it is essential to realize intersatellite collaborative computing to achieve efficient onboard processing. However, the high spatiotemporal dynamics of satellite networks pose significant challenges to the establishment of connection and offloading decisions in collaborative computing. Facing these challenges, we propose a multilayer LEO satellite collaborative computing framework by integrating LEO satellites from different orbits. Considering the continuity of task generation, we establish a temporal model for on-board processing. On this basis, we optimize intersatellite offloading decisions to minimize the average system cost over a long-term timescale. To address the constantly changing environment information and the strict constraints on task completion time, we propose using the proximal policy optimization (PF-PPO) algorithm to solve the problem. Extensive simulation results illustrate the effectiveness of the proposed algorithm, which can achieve lower system costs compared with benchmark methods under different conditions. It is also proved that our algorithm has stable performance for the system over a long-term continuous timescale.

Space-air-ground integrated network (SAGIN), which integrates satellite systems, aerial networks, and terrestrial communications, offers ubiquitous coverage for a multitude of applications. Nevertheless, the highly dynamic and open nature of SAGIN increases the network’s vulnerability. Hence, zero-trust security, operating on the principle of “never trust, always verify”, holds the significant potential of securing SAGIN. However, implementing zero-trust SAGIN in practice presents three primary challenges: 1) understanding massive unstructured threat information across diverse domains, 2) performing adaptive security assessments, and 3) making in-depth security decisions. This motivates us to propose SAG-Attack and LLM-SA to enhance zero-trust SAGIN. SAG-Attack serves as a simulator that aims to mimic various attacks in SAGIN. Our LLM-SA is a novel situation awareness method that explores the multiple agents of large language model (LLM). Specifically, the output logs of SAG-Attack will be fed into LLM-SA, and LLM-SA fuses vast amounts of heterogeneous threat information from various domains, thus tackling the first challenge. Then, our LLM-SA relies on multiple LLM-based agents to perform adaptive security assessments, utilizing the chain-of-thought capabilities of LLMs to automatically generate in-depth defense strategies, thereby addressing the second and third challenges. Experiments on five benchmarks demonstrate the superiority of the proposed SAG-Attack and LLM-SA. Notably, our method based on open-sourced Llama3-8B even outperforms ChatGPT-4 under the same setting, despite involving significantly fewer parameters. To foster further research in this area, we will release our platform to the community, facilitating the advancement of zero-trust SAGIN.

Low Earth orbit (LEO) satellite system has revolutionized the way to provide wireless seamless access on a global scale. One of the primary limitations is the low data rates resulting from Doppler shifts induced by the high mobility of LEO satellites. Although orthogonal time frequency space (OTFS) modulation has been proposed to deal with the serious Doppler problem by converting a time-variant fading channel in the time-frequency (TF) domain into a time-invariant channel in the delay-Doppler (DD) domain, it needs to be reconsidered in the LEO satellite system due to the facts that the scale of Doppler axes is not big enough and the velocity of satellite is too fast. In this paper, we analyze two interferences caused by Doppler that will be produced in OTFS-based LEO satellite system. Specifically, we establish a system model of LEO satellite-to-ground communication, involving the fractional Doppler interference (FDI) from the non-integer Doppler tap, and the other is the squint Doppler interference (SDI) from the frequency-dependent Doppler. By deriving the closed-form expressions of FDI and SDI respectively, we find that the simplest but most practical solution to mitigate interference is to increase the value of DD plane bins. Finally, numerical results showcase the significant impact of Doppler on transmission signals by quantifying the signal-to-interference (SIR) ratio and bit error rate (BER) and highlight the dominance of an applicable number of bins on alleviating Doppler in OTFS-based LEO satellite system.

The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can result in estimation errors that impact collaborative efforts among robots. In this paper, we propose a new metric termed Loss of Information Utility (LoIU) to quantify the freshness and utility of information critical for cooperation. The metric enables robots to prioritize information transmissions within bandwidth constraints. We also propose the estimation of LoIU using belief distributions and accordingly optimize both transmission schedule and resource allocation strategy for device-to-device transmissions to minimize the time-average LoIU within a robot team. A semi-decentralized Multi-Agent Deep Deterministic Policy Gradient framework is developed, where each robot functions as an actor responsible for scheduling transmissions among its collaborators while a central critic periodically evaluates and refines the actors in response to mobility and interference. Simulations validate the effectiveness of our approach, demonstrating an enhancement of information freshness and utility by 98%, compared to alternative methods.

Semantic communication (SemCom) lies in the emphasis on ensuring the correct semantic understanding rather than error-free bit transmission. However, traditional hybrid automatic repeat request (HARQ) mechanism relies on a bit-level check, and it cannot effectively address errors at the semantic level. In this paper, we propose a semantic codebook-based HARQ (SCB-HARQ) mechanism for the reliable and efficient SemCom. To enable semantic-level error detection, SCB-HARQ leverages a shared semantic codebook trained offline at both the transmitter and receiver. This codebook serves as prior information for evaluating the distortion of received semantic features, quantifying the extent to which they deviate from the intended meaning. To reduce the transmission overhead of features in the codebook, a weighted semantic feature index (WSFI) clustering method is introduced to map features into a compact index representation. Then, a masked rate-adaptive joint source-channel coding (JSCC) method is proposed to locate and retransmit the distorted features. The simulation results demonstrate that the proposed SCB-HARQ outperforms the traditional HARQ mechanism, achieving a 46.29% improvement in image reconstruction performance while reducing the transmission data volume by 51.27%.

Accurate and fast beam alignment is non-trivial in millimeter-wave (mmWave) communication systems. Deep learning models hold great promise to yield accurate beams within these systems. However, in practice, deep learning-based approaches suffer from generalization issues that the model trained under a given environment may not effectively adapt to new environments. This motivates us to propose a novel meta-learning-based algorithm to provide a general model for beam alignment, which aims to adapt to the unknown environments quickly. Concretely, this paper first proposes a Meta-Predictive Beam Alignment (MPBA) algorithm to assist model convergence and improve the quality of predicted beams. The MPBA simplifies the data partitioning process and training process when comparing traditional meta-algorithm, which is more conducive for actual deployment. This simplified and effective mechanism aids in identifying more general initial model parameters and transferring to new scattering environments without the need of heavy beam collection. Numerical simulation results reveal that the proposed MPBA has robust performance under various conditions and has about 21% accuracy gain and 2.2 bits/s/Hz spectral efficiency gain over direct transfer learning, as well as showcasing superior generalization capabilities when we transfer it to unseen base station (BS) environments.

Vertical Federated Learning (VFL) enables collaborative multi-agent model training with distributed feature spaces. Compared to horizontal federated learning (HFL), poisoning attacks in VFL confront the challenges of partial/incomplete data and model information, i.e., the adversary only has its local input feature space and model structure without knowing the ground-truth labels or server-side model. Existing attacks are inefficient in terms of stability and scalability under different learning configurations. In this paper, we design the poisoning framework via embedding manipulation in the model-partitioning structure of VFL, and propose two efficient poisoning attacks, i.e., layer embedding manipulation (LMP) and perturbation embedding manipulation (PMP). Both LMP and PMP meticulously transform the benign embeddings into the poisoned ones to dominate the model prediction. In particular, LMP blends a malicious trigger layer to poison the embedding. PMP exploits the gradients to generate a universal perturbation, which can be injected into the benign embeddings for poisoning. We conduct extensive experiments for attack performance evaluation. PMP and LMP achieve an average attack success rate of more than 0.9, and keep stability and robustness under different learning configurations. We further conduct six different defense methods to evaluate the proposed attacks. The proposed attacks are still shown to succeed against these defenses, necessitating the advanced defenses for VFL in future work.

Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: “Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?”. Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission.

Resilient operation of a wireless networked multirobot system (MRS) in a smart factory relies on the effective detection of physical anomalies from robots and cyber anomalies from wireless transmission errors or imprecise artificial intelligence decisions, which leads to a new technological frontier in data-driven industrial informatics: cyber-physical anomaly detection (AD). Furthermore, data patterns in a single smart factory are unlikely enough to train high-quality learning models for this new cyber-physical AD, which suggests the necessity to utilize operating data from multiple smart factories while keeping the privacy of each factory's data. To overcome the aforementioned technical challenges for cyber-physical AD in smart factories, this article proposes an integral mechanism of generative adversarial networks, federated learning, and fuzzy clustering acceleration. Generative adversarial networks facilitate data imputation to regenerate complete datasets alleviating anomalies caused by wireless communications. Federated learning enables rich privacy-preserving datasets to be jointly used among multiple collaborative factories. Furthermore, fuzzy clustering acceleration is embedded to speed up the factory selection algorithm such that efficient training and real-time physical AD in the large-scale operation of multiple smart factories can be achieved. Extensive computational experiments based on the KDD-99 dataset demonstrate the effective and efficient cyber-physical AD of wireless networked MRS in collaborative multiple smart factories.

Deep learning-based semantic communications (DLSC) leverage deep neural networks in transmitters and receivers, pushing the boundaries beyond Shannon limit. However, DLSC is extremely vulnerable to malicious physical-layer adversarial attacks due to the openness of wireless channels. Meanwhile, existing defense approaches still suffer from two challenges for robust DLSC. First, most methods require offline DLSC retraining to defend against various attacks, causing interruptions of online service. Second, they struggle to achieve effective defense in real-world time-varying channels, thus limiting DLSC reliability. We propose PBNet, integrating a pluggable protector and an adaptive protector to respectively address the above two challenges. First, the pluggable protector utilizes a novel denoising module to safeguard the transmitted signals, enabling hot-pluggable deployment without interrupting communication. Second, the adaptive protector leverages a novel alternating adaption strategy to achieve effective defense in time-varying channels, ensuring robust performances under real-world dynamic conditions. Evaluations involving symbols, images, texts, and speeches show the efficacy of our PBNet, which has respectively achieved an impressive 72.22% and 73.71% accuracy improvement in defending against unknown$l_{0}$-norm and$l_{2}$-norm attacks on image-based DLSC. Furthermore, we developed two real-world radio systems of PBNet to perform over-the-air signal generation, integrating hardware and software such as FPGA chips and GNU radio. We also implemented an interactive UI of PBNet based on QT5, aiming to demonstrate the effect of attacks and defense visually. This work achieves robust DLSC performances under various attacks and time-varying channels, taking a significant step towards the practical defense scheme for robust DLSC.

Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). Little attention has been given to wireless PFL (WPFL), where privacy concerns arise. Performance fairness of PL models is another challenge resulting from communication bottlenecks in WPFL. This paper exploits quantization errors to enhance the privacy of WPFL and proposes a novel quantization-assisted Gaussian differential privacy (DP) mechanism. We analyze the convergence upper bounds of individual PL models by considering the impact of the mechanism (i.e., quantization errors and Gaussian DP noises) and imperfect communication channels on the FL of WPFL. By minimizing the maximum of the bounds, we design an optimal transmission scheduling strategy that yields min-max fairness for WPFL with OFDMA interfaces. This is achieved by revealing the nested structure of this problem to decouple it into subproblems solved sequentially for the client selection, channel allocation, and power control, and for the learning rates and PL-FL weighting coefficients. Experiments validate our analysis and demonstrate that our approach substantially outperforms alternative scheduling strategies by 87.08%, 16.21%, and 38.37% in accuracy, the maximum test loss of participating clients, and fairness (Jain's index), respectively

Input diversity is an effective technique for crafting transferable adversarial examples that can deceive unknown AI models. Existing input-diversity-based methods typically use single input transformation, limiting targeted transferability and defense robustness. Combining different transformation types is challenging, as keeping increasing types would degrade semantic information and targeted transferability. This paper proposes a quality-awaretransformationcombinationattack (TCA) that selects high-quality transformation combinations. The quality-aware selection enables expansion of transformation types, enhances input diversity, and hence improves targeted transferability and defense robustness. We first design a quality-evaluation framework to quantify the effectiveness of transformation combinations, which jointly considers convergence, transferability, and robustness. Only a small group (up to 10) of images are required for computation-efficient quality evaluation. Experiments validate TCA's superiority over state-of-the-art baselines in adversarial transferability and robustness. When defenses are secured, the average targeted success rate of TCA with four transformation types (i.e., TCA-t4) outperforms the best baseline by 26%$\sim$42% on ImageNet.

As 6G networks actively advance edge intelligence, Federated Learning (FL) emerges as a key technology that enables data sharing while preserving data privacy and fostering collaboration among edge devices for intelligent service learning. However, the multi-dimensional heterogeneous and hierarchical network architecture brings many challenges to FL deployment, including selecting appropriate nodes for model training and designing effective methods for model aggregation. Compared with most studies that focus on solving individual problems within 6G, this paper proposes an efficient deployment scheme named hierarchical heterogeneous FL (HHFL), which comprehensively considers various influencing factors. First, the deployment of HHFL over 6G is modeled amid the heterogeneity of communications, computation, and data. An optimization problem is then formulated, aiming to minimize deployment costs in terms of latency and energy consumption. Subsequently, to tackle this optimization challenge, we design an intelligent FL deployment framework, consisting of a hierarchical aggregation deployment (HAD) component for hierarchical FL aggregation structure construction and an adaptive node selection (ANS) component for selecting diverse clients based on multi-dimensional discrepancy criteria. Experimental results demonstrate that our proposed framework not only adapts to various application requirements but also outperforms existing technologies by achieving superior learning performance, reduced latency, and lower energy consumption.

Multimodal Sarcasm Understanding (MSU) has a wide range of applications in the news field such as public opinion analysis and forgery detection. However, existing MSU benchmarks and approaches usually focus on sentence-level MSU. In document-level news, sarcasm clues are sparse or small and are often concealed in long text. Moreover, compared to sentence-level comments like tweets, which mainly focus on only a few trends or hot topics (e.g., sports events), content in the news is considerably diverse. Models created for sentence-level MSU may fail to capture sarcasm clues in document-level news. To fill this gap, we present a comprehensive benchmark for Document-level Multimodal Sarcasm Understanding (DocMSU). Our dataset contains 102,588 pieces of news with text-image pairs, covering 9 diverse topics such as health, business, etc. The proposed large-scale and diverse DocMSU significantly facilitates the research of document-level MSU in real-world scenarios. To take on the new challenges posed by DocMSU, we introduce a fine-grained sarcasm comprehension method to properly align the pixel-level image features with word-level textual features in documents. Experiments demonstrate the effectiveness of our method, showing that it can serve as a baseline approach to the challenging DocMSU.

Graph convolution networks (GCNs) are extensively utilized in various graph tasks to mine knowledge from spatial data. Our study marks the pioneering attempt to quantitatively investigate the GCN robustness over omnipresent heterophilic graphs for node classification. We uncover that the predominant vulnerability is caused by the structural out-of-distribution (OOD) issue. This finding motivates us to present a novel method that aims to harden GCNs by automatically learning Latent Homophilic Structures over heterophilic graphs. We term such a methodology as LHS. To elaborate, our initial step involves learning a latent structure by employing a novel self-expressive technique based on multi-node interactions. Subsequently, the structure is refined using a pairwisely constrained dual-view contrastive learning approach. We iteratively perform the above procedure, enabling a GCN model to aggregate information in a homophilic way on heterophilic graphs. Armed with such an adaptable structure, we can properly mitigate the structural OOD threats over heterophilic graphs. Experiments on various benchmarks show the effectiveness of the proposed LHS approach for robust GCNs.

Semantic communications rely on deep neural networks (DNNs) to reduce the amount of transmitted data by only transmitting the semantics of data rather than the whole data, showing the potential on image transmission even in low signal-to-noise-ratio (SNR) conditions. However, the performance deficiency happens once the real-time data do not follow the independent identical distribution (i.i.d) with the training dataset, which results from the poor generalization of DNNs. To tackle this problem, a promising solution is to align the real-time data to follow the similar distribution with the training dataset at the feature level. Thus, we propose a one-shot data adaptive semantic communication (ODASC), where domain adaptation is incorporated as a pre-processing module to cope with domain shift by aligning the distribution between the real-time data and the training dataset. Image transmission is considered as a case study to demonstrate the big plus of ODASC on data recovery and task accuracy.

Video anomaly understanding (VAU) aims to automat-ically comprehend unusual occurrences in videos, thereby enabling various applications such as traffic surveillance and industrial manufacturing. While existing VAU benchmarks primarily concentrate on anomaly detection and localization, our focus is on more practicality, prompting us to raise the following crucial questions: “what anomaly occurred?”,”why did it happen?”, and “how severe is this abnormal event?”. In pursuit of these answers, we present a comprehensive benchmark for Causation Understanding of Video Anomaly (CUVA). Specifically, each instance of the proposed benchmark involves three sets of human annotations to indicate the”what”, “why” and “how” of an anomaly, including 1) anomaly type, start and end times, and event descriptions, 2) natural language explanations for the cause of an anomaly, and 3) free text reflecting the effect of the abnormality. In addition, we also introduce MMEval, a novel evaluation metric designed to better align with human preferences for CUVA, facilitating the measurement of existing LLMs in comprehending the underlying cause and corresponding effect of video anoma-lies. Finally, we propose a novel prompt-based method that can serve as a baseline approach for the challenging CUVA. We conduct extensive experiments to show the superiority of our evaluation metric and the prompt-based approach. Our code and dataset are available at https://github.com/fesvhtr/CUVA.

Multi-hop relay is regarded as a promising and effective method to provide highly reliable communication service between the control center and the terminal under a fragile and unstable industrial wireless channel environment. However, wireless communication and control in the Industrial Internet of Things (IIoT) are tightly coupled, making multi-hop relay path construction more challenging. Therefore, we propose an efficient multi-hop relay node selection algorithm to decrease the control cost of the closed-loop system to ensure the stability of the system. Specifically, we establish the relationship between node selection and control cost using the age of information as an intermediary. Then, an improved elite ant colony (IEACO) algorithm is proposed to find a relay path to minimize control cost so as to ensure the system quickly converges to a stable state. Finally, we carried out extensive experiments to verify the effectiveness of the proposed algorithm, and the results reveal that the IEACO algorithm can achieve the approximately optimal control performance. Specifically, in the example of the temperature control system, the IEACO algorithm can also make the system reach the stable state faster and improve the convergence performance by about 5% to 30%.

Group-based authentication allows users within a single domain and group to access networks without repeating an individual authentication instance, greatly reducing the energy consumption of low-resource mobile devices in IoT and M2M communications. Nevertheless, in the case of the upcoming 6G massive communications with an exponentially larger number of connections, the computation and communication overhead of existing approaches on mobile devices are still significant. To this end, we propose GROSS, a novel GRoup-based authentication and key agreement (AKA) protocol that uses a One-time Secure Secret-sharing mechanism for more efficient authentication over massive wireless communications. Specifically, we employ a lightweight cryptographic operation for the above one-time secret sharing. The proposed GROSS significantly reduces both computation and communication overhead by consistently maintaining the validity of credentials for group-based authentication, thus enabling efficient verification of device legitimacy within a group. We also implement a simulation platform on JAVA for energy consumption evaluations for massive wireless communications. Our platform facilitates the flexible configuration of various energy components for authentication and supports up to million-level wireless connections. We conduct extensive experiments to show the effectiveness of our proposed GROSS.

Satellite being as a relay in non-terrestrial network (NTN) has been commonly considered as a feasible way to expand the coverage. However, one of the main challenges faced by satellite communications is the poor communication quality of the satellite-terrestrial link due to the long transmission distance. Semantic communication have shown initial advantages on transmission performance at poor channel quality (e.g., limited bandwidth or low signal-to-noise ratio), which provides us an idea to address the challenge faced by satellite communications. Current semantic communication mainly focuses on end-to-end communication (i.e., one-hop communication), but two-hop semantic communication in NTN, where satellite acts as a relay, has rarely been explored. In this paper, we devise a two-hop semantic communication system for NTN. Especially, two forwarding protocols, i.e., semantic amplify-and-forward (SAF) and semantic decode-and-forward (SDF), are proposed. On top of this technical contributions, this work provides a new insightful application for semantic communication. The simulation results demonstrate that the proposed two-hop semantic communication system for NTN achieve better recovery performance than traditional approaches.

The mobile communication network is gradually shifting from the deployment of dedicated physical facilities to the deployment of virtualized network functions on general infrastructure. Taking advantage of this transformation, operators can provide customized services to better meet the demands of vertical industries. However, the participation of vertical industry tenants introduces a new attack surface to the mobile networks. The centralized access control scheme currently employed in the 5G system has a high risk of single-point failure and privacy leakage. And it is inefficient when the scheme is applied in distributed core networks. To secure the mobile communication network and protect the privacy of vertical industry tenants, we propose a distributed policy-hidden (DPH) framework, which achieves flexible cross-trust-domain authorization with lower latency. Besides, Attribute-based Encryption is introduced to protect long-distance communications between network functions. Our evaluation demonstrates that DPH is suitable for virtualized 5G networks and effectively reduces the latency of authorization procedures.

Integrated Sensing and Communication (ISAC) can reuse the same spectrum and hardware resources for communication and radar sensing, which is a promising technology to alleviate spectrum congestion. Recently, there has been an increasing research interest in the physical layer secrecy aspects of ISAC systems. This paper studies a basic multiple-input single-output ISAC secrecy system, where a multi-antenna base station transmits a unified signal to sense a point target and communicate to a single-antenna receiver in the presence of$K$single antenna eavesdroppers. Under this setup, a sensing signal-to-noise ratio (SNR) constrained secrecy rate maximization problem has been formulated. We showed that the problem can be reformulated as a convex semidefinite programming problem and proved that beamforming is the optimal transmission scheme. We derived two low-complexity semiclosed-form optimal beamforming solutions and one suboptimal closed-form solution. Numerical results demonstrate that the proposed solutions attain the optimum of the sensing SNR constrained secrecy rate maximization problem and have lower computational complexity than the existing method.

The forthcoming 6G has necessitated more stringent requirements for network latency communication (0.1-1 ms). For this purpose, the Ultra-low Latency Mobile Network (ULMN) integrates open-loop communication, ultra-low latency mobile network association and the cell-free structure towards the minimum end-to-end latency, thus meeting the 6G network latency requirements. However, the ULMN cancels control signaling interactions and traditional centralized resource allocation mode, so both the sender and receiver cannot obtain real-time and accurate channel state information (CSI) in uplink and downlink transmission, which cannot guide reasonable resource allocation and seriously damage network reliability. Uplink communication is more challenging than downlink communication, because a large number of users will make transmission decisions autonomously and blindly to choose channels with poor quality and the same resources to transmit data, resulting in resource conflicts. Therefore, this paper focuses on machine-initiated uplink transmission in ULMN and exploits a reliability-guaranteed distributed resource management scheme. Specifically, we design an novel Multi-Agent Deep Deterministic Policy Gradient based Uplink Resource Management (MADDPG-URM) algorithm to maximize the average probability of success per transmission over time, where senders use historical transmission experience and additional sensor information for resource occupation, association decision-making and power control in a distributed way. The simulation results show that the proposed MADDPG-URM algorithm is more suitable for high network load than radio resource utilization information generation strategies with AN assistance (AA-RRUI) and random algorithm.

The next-generation wireless communication sys-tems require available bandwidth, reliability, and mobility. How-ever, the wider bandwidth and higher carrier frequency pose challenges for communication systems, including stricter peak-to-average power ratio (PAPR) requirements and more severe Doppler effects. This paper introduces an enhanced discrete Fourier transform spread (ED-OTFS) waveform that utilizes head-tail insertion sequences and frequency domain spectrum shaping (FDSS) methods to mitigate multipath delay variations and reduce high PAPR. Additionally, a reliable channel equalization algorithm for the ED-OTFS receiver is designed to mitigate the impact of the Doppler shift. Simulation results demonstrate that the proposed waveform significantly improves the bit error rate (BER) and PAPR performance compared with the existing schemes.

Dual-function radar-communication (DFRC) system has been recognized as a potential technology to address the issues of radio frequency spectrum congestion. Despite the advan-tages of the existing orthogonal frequency-division multiplexing (OFDM) chirp waveform, such as its high range resolution and low peak-to-average ratio, it is still plagued by issues related to ghost targets in complex communication environments with multiple targets. In this paper, a novel waveform, leveraging trapezoidal frequency modulation OFDM, is introduced to address the challenge of multi-target detection scenarios. Addition-ally, a power allocation and subcarrier assignment algorithm has been developed to maximize communication performance while adhering to the radar performance threshold, thereby achieving overall system optimization while enhancing multi-target detection capabilities. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation experiments, showcasing its ability to handle multi-target detection scenarios and achieve superior system performance while maintaining a delicate equilibrium between communication and radar considerations.

Smart manufacturing aims to support highly dynamic and flexible production processes where intelligent machines are expected to have the ability to complete quickly transition from old applications to new ones. Federated transfer learning is a promising solution that enables knowledge sharing in the above process to rapidly provide models required for new tasks. To achieve efficient transfer, the challenge lies in how to provide transferable and high-quality data for new tasks while preserving data privacy. In this paper, we propose a novel cross-application federated transfer learning framework based on collaborative generative adversarial networks named CPFTL-CGAN. Specifically, we innovatively propose a source domain selection scheme based on domain similarity, which aims to select the most optimal source domain among massive candidates for efficient knowledge sharing. Furthermore, a collaborative generative adversarial network has been designed to generate data similar to the source, which addresses the problem that private source domain data cannot be shared directly to the target domain and further enhances the quality of the generated data. Finally, domain adaptation based on generated data is performed on target clients. Extensive simulation results demonstrate that the proposed CPFTL-CGAN can effectively improve the learning efficiency and accuracy compared to single-device training locally, Fedavg and ACGAN-FTL.

In this paper, a novel joint energy and age of information (AoI) optimization framework for IoT devices in a non-stationary environment is presented. In particular, IoT devices that are distributed in the real-world are required to efficiently utilize their computing resources so as to balance the freshness of their data and their energy consumption. To optimize the performance of IoT devices in such a dynamic setting, a novel lifelong reinforcement learning (RL) solution that enables IoT devices to continuously adapt their policies to each newly encountered environment is proposed. Given that IoT devices have limited energy and computing resources, an unmanned aerial vehicle (UAV) is leveraged to visit the IoT devices and update the policy of each device sequentially. As such, the UAV is exploited as a mobile learning agent that can learn a shared knowledge base with a feature base in its training phase, and feature sets of a zero-shot learning method in its testing phase, to generalize between the environments. To optimize the trajectory and flying velocity of the UAV, an actor-critic network is leveraged so as to minimize the UAV energy consumption. Simulation results show that the proposed lifelong RL solution can outperform the state-of-art benchmarks by enhancing the balanced cost of IoT devices by 8.3% when incorporating warm-start policies for unseen environments. In addition, our solution achieves up to 49.38% reduction in terms of energy consumption by the UAV in comparison to the random flying strategy.

The deployment of mobile edge computing services in LEO satellite networks achieves seamless coverage of computing services. However, the time-varying wireless channel conditions between satellite–terrestrial channels and the random arrival characteristics of ground users’ (GUs) tasks bring new challenges for managing the LEO satellite’s communication and computing resources. Facing these challenges, a stochastic computation offloading problem of joint optimizing communication and computing resources allocation and computation offloading decisions is formulated for minimizing the long-term average total power cost of the GUs and the LEO satellite, with the constraint of long-term task queue stability. However, the computing resource allocation and the computation offloading decisions are coupled within different slots, thus making it challenging to address this problem. To this end, we first employ the Lyapunov optimization to decouple the long-term stochastic computation offloading problem into the deterministic subproblem in each slot. Then, an online algorithm combining deep reinforcement learning and conventional optimization algorithms is proposed to solve these subproblems. Simulation results show that the proposed algorithm can achieve the superior performance while ensuring the stability of all task queues in LEO satellite networks.

Split learning is a promising paradigm for privacy-preserving distributed learning. The learning model can be cut into multiple portions to be collaboratively trained at the participants by exchanging only the intermediate results at the cut layer. Understanding the security performance of split learning is critical for many privacy-sensitive applications. This paper shows that the exchanged intermediate results, including the smashed data (i.e., extracted features from the raw data) and gradients during training and inference of split learning, can already reveal the private labels. We mathematically analyze the potential label leakages and propose the cosine and Euclidean similarity measurements for gradients and smashed data, respectively. Then, the two similarity measurements are shown to be unified in Euclidean space. Based on the similarity metric, we design three label inference attacks to efficiently recover the private labels during both the training and inference phases. Experimental results validate that the proposed approaches can achieve close to 100% accuracy of label attacks. The proposed attack can still achieve accurate predictions against various state-of-the-art defense mechanisms, including DP-SGD, label differential privacy, gradient compression, and Marvell.

Federated learning is a popular distributed machine learning paradigm that enables collaborative model training at multiple entities via exchanging intermediate learning results. Security and communication efficiency are crucial for successful applications of federated learning in various privacy-sensitive services. However, existing work focused on gradient defense and communication efficiency separately, and also incurred additional computation, signaling, and accuracy overhead. A lightweight (in terms of time-complexity and signaling) technique that simultaneously achieves security and communication efficiency is critical for massive resource-constrained devices (e.g., Internet-of-Things generating the data), but has yet to be established. This paper proposes a secure and efficient federated learning framework with provable communication-accuracy-security performance guarantees. A low-complexity and signaling-free stochastic quantization module is added at the client side that quantizes the original local gradients to discrete values for communication-efficient global aggregation. The stochastic quantization module is shown to be interpreted as triangular or Gaussian-multiply-triangular noises under uniform or Gaussian distributions of local gradients, hence protecting data privacy. We prove that the proposed framework exhibits an {O(log21/δ),O(δ2),O(1/δ)}-tradeoff between the communication overhead, model accuracy, and data protection, where δ is an adjustable quantization interval. Experimental results validate the tradeoff and the superiority of the proposed stochastic quantization technique in terms of communication efficiency (only 14.1% of differential privacy and 0.2% of homomorphic encryption) and computation complexity (similar to differential privacy and only 0.03% of homomorphic encryption). Under the same data protection performance, the proposed approach also outperforms (in terms of accuracy) differential privacy in all the 9 comparison settings on CIFAR10 dataset.

Resource provisioning aims to efficiently allocate the infrastructure provider's communication, computation and caching resources to provide reliable (e.g., 99.999%) and responsive actions for massive delay-critical services. However, the challenges arise from the partial knowledge of uncertain traffic arrivals (even the distributions of estimation error may not be available) and computation complexity for massive numbers of emerging services. This paper aims to design efficient and robust resource provisioning for massive delay-critical services based on only partial knowledge on traffic uncertainty. We formulate the problem of robust resource provisioning to minimize the system cost with only partial (i.e., the first/second momentum) information of traffic estimation errors. We derive the robust approximation of the reliability constraint using the Bernstein approximation, which is proved to guarantee service reliability given the partial traffic knowledge. The problem is reformulated and solved via Lagrangian duality to obtain the closed-form expression for low-complexity solutions. Experimental results on both the simulation-based and trace-based datasets validate that the proposed approach can guarantee up to 99.999% service reliability with reduced time complexity.

Timely delivery of fresh data/information is fundamental and critical in the Industrial Internet of Things (1IoT) powered various applications, which face the challenges of how to meet the dynamically changing requirement of heterogeneous information urgency among different moving devices under time-varying channels. In this paper, we investigate a UAV-enabled mobile edge computing IloT system. To combat the above challenge, we firstly design a new metric, namely alterable Urgency of Information (aUol) to quantify the changeable heterogeneous urgency of information across various devices since the conventional Age of Information (Aol) is incapable. Further, we exploit a deep reinforcement learning algorithm to optimize the aU 01 of the system by adaptively determining the optimal user scheduling and UAV trajectory planning strategy. Extensive simulation results demonstrate that the proposed method can effectively reduce the aUol of system and improve heterogeneous information urgency service satisfaction rate by 42.5 % to 87.5 % as compared to other benchmark approaches. Additionally, the proposed approach is more effective than the conventional Aol-oriented method.

End-to-end semantic communication (ESC) system is able to improve communication efficiency by only transmitting the semantics of the input rather than raw bits. Although promising, ESC has also been shown susceptible to the crafted physical layer adversarial perturbations due to the openness of wireless channels and the sensitivity of neural models. Previous works focus more on the physical layer white-box attacks, while the challenging black-box ones, as more practical adversaries in real-world cases, are still largely under-explored. To this end, we present SemBLK, a novel method that can learn to generate destructive physical layer semantic attacks for an ESC system under the black-box setting, where the adversaries are imperceptible to humans. Specifically, 1) we first introduce a surrogate semantic encoder and train its parameters by exploring a limited number of queries to an existing ESC system. 2) Equipped with such a surrogate encoder, we then propose a novel semantic perturbation generation method to learn to boost the physical layer attacks with semantic adversaries. Experiments on two public datasets show the effectiveness of our proposed SemBLK in attacking the ESC system under the black-box setting. Finally, we provide case studies to visually justify the superiority of our physical layer semantic perturbations.

Deep learning based semantic communication (DLSC) systems have shown great potential of making wireless networks significantly more efficient by only transmitting the semantics of the data. However, the open nature of wireless channel and fragileness of neural models cause DLSC systems extremely vulnerable to various attacks. Traditional wireless physical layer key (PLK), which relies on reciprocal channel and randomness characteristics between two legitimate users, holds the promise of securing DLSC. The main challenge lies in generating secret keys in the static environment with ultra-low/zero rate. Different from prior efforts that use relays or reconfigurable intelligent surfaces (RIS) to manipulate wireless channels, this paper proposes a novel physical layer semantic encryption scheme by exploring the randomness of bilingual evaluation understudy (BLEU) scores in the field of machine translation, and additionally presents a novel semantic obfuscation mechanism to provide further physical layer protections. Specifically, 1) we calculate the BLEU scores and corresponding weights of the DLSC system. Then, we generate semantic keys (SKey) by feeding the weighted sum of the scores into a hash function. 2) Equipped with the SKey, our proposed subcarrier obfuscation is able to further secure semantic communications with a dynamic dummy data insertion mechanism. Experiments show the effectiveness of our method, especially in the static wireless environment.

Network-based intrusion detection system (NIDS) monitors network traffic for malicious activities, forming the frontline defense against increasing attacks over information infrastructures. Although promising, our quantitative analysis shows that existing methods perform inconsistently in declaring various unknown attacks (e.g., 9% and 35% F1 respectively for two distinct unknown threats for an SVM-based method) or detecting diverse known attacks (e.g., 31% F1 for the Backdoor and 93% F1 for DDoS for a GCN-based state-of-the-art method), and reveals that the underlying cause is entangled distributions of flow features. This motivates us to propose 3D-IDS, a novel method that aims to tackle the above issues through two-step feature disentanglements and a dynamic graph diffusion scheme. Specifically, we first disentangle traffic features by a non-parameterized optimization based on mutual information, automatically differentiating tens and hundreds of complex features of various attacks. Such differentiated features will be fed into a memory model to generate representations, which are further disentangled to highlight the attack-specific features. Finally, we use a novel graph diffusion method that dynamically fuses the network topology for spatial-temporal aggregation in evolving data streams. By doing so, we can effectively identify various attacks in encrypted traffics, including unknown threats and known ones that are not easily detected. Experiments show the superiority of our 3D-IDS. We also demonstrate that our two-step feature disentanglements benefit the explainability of NIDS.

Aerial and terrestrial heterogeneous networks (Het-Nets), millimeter wave (mmWave) communications and multi-antenna techniques are viewed as promising components of the solution for future communications. By leveraging the power of stochastic geometry, this paper provides an effective framework for modeling and analyzing an aerial and terrestrial HetNet, where the terrestrial base stations (TBSs) and the UAV base stations (UBSs) co-exist, and the UBSs are equipped with multi-antenna operating at mmWave frequency band. By modeling the TBSs and UBSs as Poisson point processes (PPPs), the expressions for the coverage probability and the average data rate of the typical user of both tiers are derived. Monte Carlo simulations are then performed to validate the analytical expressions. The effects of some relevant parameters on network performance are discussed in detail, and the proposed model can provide some insights into the deployment and optimization of real aerial and terrestrial HetNets.

Due to the flexible deployment of unmanned aerial vehicles (UAVs) and the wide-area coverage of satellites, the space–air–ground (SAG) communication network can provide flexible and pervasive connectivity, especially in remote areas. In this work, we investigate the uplink transmission in a SAG network, where the nonorthogonal multiple access mechanism is adopted at the UAVs to enhance the number of access from ground user equipments (UEs) and a low-earth orbit satellite offers the wireless backhaul for UAVs. In particular, the energy efficiency (EE) of the considered network is maximized by optimizing the user association (UA), power allocation (PA), and UAV 3-D trajectory jointly with the consideration of the movement of the satellite. To tackle the formulated problem, by leveraging the block coordinate descent (BCD) method, we develop a joint UA, PA, and UAV trajectory (namely, JUPT) optimization algorithm, i.e., the original problem is decomposed into three subproblems, and the subproblems are solved iteratively until convergence. Specifically, we propose to include the virtual UEs in the system and develop a low-complexity matching algorithm to effectively solve the UA problem. A successive convex approximation (SCA)-based Dinkelbach algorithm is then adopted to address the PA problem. Later, with the introduction of the auxiliary variables, the UAV 3-D trajectory subproblem is iteratively solved by the SCA method. Our numerical results demonstrate the superiority of the proposed JUPT algorithm, which obtains significantly higher EE compared to the benchmark schemes. Moreover, the rapid convergence of the JUPT algorithm is verified.

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare. However, robustifying semantic interpretation is challenging as ESC is extremely vulnerable to physical-layer adversarial attacks due to the openness of wireless channels and the fragileness of neural models. Toward ESC robustness in practice, we ask the following two questions: Q1: For attacks, is it possible to generate semantic-oriented physical-layer adversarial attacks that are imperceptible, input-agnostic and controllable? Q2: Can we develop a defense strategy against such semantic distortions and previously proposed adversaries? To this end, we first presentMobileSC, a novel semantic communication framework that considers the computation and memory efficiency in wireless environments. Equipped with this framework, we proposeSemAdv, a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria, thus answering the Q1. To better characterize the real-world effects for robust training and evaluation, we further introduce a novel adversarial training method$\texttt {SemMixed}$to harden the ESC againstSemAdvattacks and existing strong threats, thus answering the Q2. Extensive experiments on three public benchmarks verify the effectiveness of our proposed methods against various physical adversarial attacks. We also show some interesting findings, e.g., ourMobileSCcan even be more robust than classical block-wise communication systems in the low SNR regime.

Proactive Mobile Network (PMN) has been proposed to support extremely low latency communications with multi-tier computing architectures, machine-centricity, and data-driven operation features. Nevertheless, the communication reliability of the PMN introduces new substantial technological challenges. As PMN employs unique proactive open-loop communication, any feedback-based control is avoided to enhance end-to-end latency. This paper focuses on machine-initiated uplink transmission in PMN and proposes a reliability-guaranteed resource management scheme. Without requiring feedback control information, our scheme uniquely decomposes conventional resource management into two collaborative decision processes: predictive resource allocation suggested by network anchor nodes (ANs) and proactive smart resource utilization by smart equipment (SE). These two decision-making processes are constructed as independent reinforcement learning (RL) problems, but implicitly share the states of radio resources according to operating environments. Different algorithms for different operating scenarios have been investigated for this dual-decision solution. Simulation results in various scenarios show that our scheme enables PMN’s reliability close to the theoretical optimal value and successfully serves radio resource utilization in the PMNs.

In the future mobile edge networks, the Internet of things (IoT) applications will be latency-sensitive and computationally intensive. Given the resource limitation of IoT devices, mobile edge computing (MEC) servers are critical to support the efficient processing of IoT tasks. Since MEC servers attached to the ground base stations are generally deployed in fixed locations and vulnerable to physical damage, the unmanned aerial vehicle (UAV) and satellite-assisted MEC framework has been proposed to leverage the flexibility of UAVs and the broad coverage of satellites. However, efficient utilization of the UAV/satellite resources is challenging for the static ground IoT devices because of the dynamic in terms of aerial and space network topology and IoT task arrival rates. To adapt to the changing environment and utilize the interaction among multiple UAVs, we propose a multi-agent deep deterministic policy gradient (MADDPG) framework to jointly optimize the traveling routes of multi-UAVs and the offloading decision of IoT devices. To minimize the processing cost in terms of task processing latency and energy consumption of IoT devices, cooperative UAVs can help find the optimal task offloading location for each IoT device. Simulation results show the proposed algorithm based on MADDPG can averagely decrease 20% of the above processing cost compared with the benchmark approach.

Continuously increasing demands from enhanced mobile broadband, machine-to-machine and internet of things pose a growing threat to 5G communication. Satellites integrated with 5G and beyond are pivotal to achieve global coverage, reliable and continuing service access, with the booming low-orbit satellite (LEO) constellations. While previous works largely focus on rural or remote areas, the orbiting satellites cover the ground evenly. This paper is then interested in unserved or underserved users in urban hotspots arising from large-scale access. Under the large delay of the ground-to-satellite link and the dynamic characteristic of satellites, we propose a satellite-terrestrial cooperative, geographical dispersed and regional centralized batch access control scheme. A data-driven QoS indicator, responsivity, is proposed to complement the semi-online batch scheme from a network perspective. It combines subsiding access control from LEO satellite to ground with LEO satellite resources to provide auxiliary access for higher responsivity users relatively. We model the on-demand batch access mechanism as a multidimensional knapsack problem (MKP) solved by a hybrid genetic algorithm. Collectively, the cooperative batch mechanisms compress signaling transmission and reduce optional power consumption. Simulation results show that the new indicator achieves a better balance between spectral efficiency and global OoS compared to existing methods.

Network Slicing (NS) is an enabling technology to support vertical industries in 5G-and-beyond (B5G) systems. The management of Radio Access Network (RAN) slices re-lies on extensive awareness of network load status and data analysis. With increasingly diversified services and ubiquitous user data, centralized slice management is unsustainable. This paper presents a new distributed load prediction and slicing framework based on Federated Learning (FL). Specifically, we predict the slice-level traffic by designing a Federated Long Short Time Memory (Fed-LSTM) algorithm with a tailored loss function to reduce Service Level Agreement (SLA) violation rate. Given the predicted traffic load, we model slice resource orchestration as an improved two-dimensional Polygon Knapsack (2D- PK) problem, split slice traffic at different granularities, and solve the problem using the Maximal Rectangles Bottom-Left (MAXRECTS-BL) algorithm. Experimental results on real-world captured dataset show that our approach can achieve significant improvement in prediction accuracy, slicing SLA violation and resource utilization, compared to existing techniques.

The handover process in an integrated terrestrial-satellite network (ITSN) faces many security threats, such as eavesdropping, impersonating, replaying, and privacy leakage due to the link exposure and network heterogeneity of ITSN. This paper proposes a lightweight and privacy-preserving ITSN handover authentication mechanism based on elliptic curve cryptography to eliminate the security threats. Specifically, we propose to directly authenticate mobile users at access nodes and on a batch basis, hence avoiding backhaul and concurrent authentication overhead. User anonymous identity and private information encryption are adopted to protect users’ privacy. A regular key update scheme is developed to mitigate a risk of private key breach. As demonstrated by a formal security proof based on the AVISPA tool and security analysis, the proposed mechanism resists various attacks (e.g., replay attacks and impersonation attacks). Numerical results show that our mechanism is more efficient than the standard and the existing alternatives. The proposed mechanism has the great potential to secure the handover processes of emerging ITSN.

Deep learning-based semantic communications (DLSC) replace the physical blocks in traditional communication systems as end-to-end neural networks. DLSC significantly boost communication efficiency by only transmitting the meaning of data, showing great potentials for applications like automatic driving, digital twin and smart health. However, DLSC are fragile to black-box adversarial attacks due to the openness of wireless channel and sensitivities of neural models. To this end, this paper proposes SemBAT, a novel approach for crafting physical layer black-box adversarial attacks for semantic communication systems. The key ingredients of our method include the training of surrogate encoder and generation of adversarial perturbations. Specifically, we train our surrogate encoder by directly estimating the gradients based on Jacobian-matrixs, and then generate the adversarial perturbations by the particle swarm optimizations. Extensive experiments on a public benchmark show the effectiveness of our proposed SemBAT. We observe that our SemBAT with black-box adversaries can sharply decrease the classification accuracy of the semantic communication system from 78.4% to 11.6%. Meanwhile, such attacks are also imperceptible in terms of image quality metrics measured by the Structural similarity index measure (SSIM) and Peak Signal to Noise Ratio(PSNR).

Deep learning-based semantic communications (DLSC) significantly improve communication efficiency by only transmitting the meaning of the data rather than a raw message. Such a novel paradigm can brace the high-demand applications with massive data transmission and connectivities, such as automatic driving and internet-of-things. However, DLSC are also highly vulnerable to various attacks, such as eavesdropping, surveillance, and spoofing, due to the openness of wireless channels and the fragility of neural models. To tackle this problem, we present SemKey, a novel physical layer key generation (PKG) scheme that aims to secure the DLSC by exploring the underlying randomness of deep learning-based semantic communication systems. To boost the generation rate of the secret key, we introduce a reconfigurable intelligent surface (RIS) and tune its elements with the randomness of semantic drifts between a transmitter and a receiver. Precisely, we first extract the random features of the semantic communication system to form the randomly varying switch sequence of the RIS-assisted channel and then employ the parallel factor-based channel detection method to perform the channel detection under RIS assistance. Experimental results show that our proposed SemKey significantly improves the secret key generation rate, potentially paving the way for physical layer security for DLSC.

In the emerging Internet-of-Things (IoT) paradigm, the freshness of sensory information plays a crucial role in online data-analyzing and application-level decision-making. As the tailor-made performance metric of information freshness, the age of information (AoI) depends on the data transmission efficiency and data update frequency, which are energy demanding for IoT devices with limited battery capacity. To alleviate the energy constraints of low-power IoT devices, we propose an AoI-oriented unmanned aerial vehicle (UAVs)-enabled wireless power transmission scheme, where UAVs are deployed to wirelessly charge IoT devices. With the harvested energy, the devices will upload their fresh information to UAVs. The proposed system aims for sustainable IoT networks with practical device-specific energy limitation, which has been long neglected by existing AoI optimization works. In addition, to explore the influence of dynamic time-varying channels on AoI, a practical line-of-sight (LoS)/NLoS channel model is established to accurately depict the dynamic channel characteristics and precisely capture the efficiency of both data transmission and energy harvesting. To achieve the optimal system-level AoI under dynamic channel conditions, a novel deep reinforcement learning-based proactive UAV trajectory planning (PUTP) algorithm is proposed to automatically adjust the UAV fight policy according to the channel variations and the trade-off between the energy transmission and data collection. Extensive simulation results demonstrate that the proposed PUPT algorithm can significantly reduce the AoI by approximately 20% to 65% compared to three other existing trajectory planning algorithms.

Abstract With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals’ driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.

Federated learning (FL) through its novel applications and services has enhanced its presence as a promising tool in the Internet of Things (IoT) domain. Specifically, in a multiaccess edge computing setup with a host of IoT devices, FL is most suitable since it leverages distributed client data to train high-performance deep learning (DL) models while keeping the data private. However, the underlying deep neural networks (DNNs) are huge, preventing its direct deployment onto resource-constrained computing and memory-limited IoT devices. Besides, frequent exchange of model updates between the central server and clients in FL could result in a communication bottleneck. To address these challenges, in this article, we introduce GWEP, a model compression-based FL method. It utilizes joint quantization and model pruning to reap the benefits of DNNs while meeting the capabilities of resource-constrained devices. Consequently, by reducing the computational, memory, and network footprint of FL, the low-end IoT devices may be able to participate in the FL process. In addition, we provide theoretical guarantees of FL convergence. Through empirical evaluations, we demonstrate that our approach significantly outperforms the baseline algorithms by being up to 10.23 times faster with 11 times lesser communication rounds, while achieving high-model compression, energy efficiency, and learning performance.

With the increasing data traffic in the fifth-generation (5G) communication system, the 5G new radio extended to unlicensed bands (5G NR-U) has become a promising approach to relieve the heavy pressure on the cellular system. To achieve the efficient coexistence with the WiFi system and improve the efficiency of temporal, spectral and spatial resource utilization, we first divide the transmission space into two subspaces by leveraging spatial reuse, where the data transmitted by cellular user equipments (UEs) falls into one subspace and the data transmitted by Internet of Things (IoT) devices coexisting with WiFi users through power control is in the other subspace. Then, the coexistence among cellular UEs, IoT devices, and WiFi users is formulated as an optimization model with the aim of maximizing the cellular system throughput via the joint power and subchannel allocation under the interference constraint. Although the resulting optimization problem is a mixed-integer nonlinear programmming, we decompose it into two subproblems and develop an alternating iterative approach to effectively solve them. Also, the closed-form allocations of the power and subchannels are obtained. Simulation results confirm that the proposed scheme can improve the cellular system performance and guarantee the coexistence in the unlicensed band.

Consensus is one of the most important issues of a blockchain system because it is a necessary process to reach an agreement between a group of separated nodes that do not trust each other in a decentralized framework. Most existing blockchain consensus works assume that the time of block propagation among separated nodes during the consensus process is ignorable, i.e., a block always successfully reaches every participating node during a period of time that is far shorter than the mining time. However, when blockchain is used in vehicularad hocnetworks (VANETs), the block propagation time is no longer negligible since the dynamic connectivity of the moving nodes in a wireless environment brings opportunistic communication to blockchain consensus. In this article, we study the impact of mobility on block propagation under the single-chain structure in VANET. Specifically, we investigate the dynamics of block propagation from the macroscopic view and derive the closed-form expression of the single-block propagation time. Then, we characterize the blockchain forking as the multiblock competitive propagation. In this way, an approximate result on multiblock propagation time is discussed. An interesting finding is that higher mobility and more moving vehicles can speed up the block propagation. In addition, we also discover that distinct propagation capabilities of moving nodes contribute to the forking reduction in the blockchain consensus.

In the case of natural disasters such as floods and earthquakes, ground infrastructure is vulnerable to damage, so a large amount of IoT data cannot be processed in time. Therefore, aerial equipment such as UAV or satellite is needed to provide temporary emergency services. This paper considered a satellite/UAV-assisted computing and offloading networks with spectrum sharing, which can make full use of the deployment flexibility of UAVs, the broad coverage of satellite and the expansibility of unlicensed band. For the UAV and IoT devices, system energy consumption is critical, as well as ensuring each mission is handled timely. So we proposed a scheme to minimize the total energy consumed by the system for computation and transmission while satisfying the maximum tolerable total delay constraint. We jointly optimize UAV location, uploading decisions, computing resource allocation and frequency band selection, without decreasing the WiFi transmission rate in the unlicensed band. The simulation result shows that the proposed scheme can effectively reduce energy consumption, while guarantee the total delay.

In this paper, a lifelong learning problem is studied for an Internet of Things (IoT) system. In the considered model, each IoT device aims to balance its information freshness and energy consumption tradeoff by controlling its computational resource allocation at each time slot under dynamic environments. An unmanned aerial vehicle (UAV) is deployed as a flying base station so as to enable the IoT devices to adapt to novel environments. To this end, a new lifelong reinforcement learning algorithm, used by the UAV, is proposed in order to adapt the operation of the devices at each visit by the UAV. By using the experience from previously visited devices and environments, the UAV can help devices adapt faster to future states of their environment. To do so, a knowledge base shared by all devices is maintained at the UAV. Simulation results show that the proposed algorithm can converge 25% to 50% faster than a policy gradient baseline algorithm that optimizes each device’s decision making problem in isolation.

To achieve the minimal latency, proactive wireless communication has been proposed to facilitate proactive mobile networks. Due to lacking closed-loop control, random selection of radio resource units (RRUs) serves the only way to the critical radio resource allocation (RRA), which inevitably suffers collisions of simultaneous utilizing the same RRUs to result in loss of packets, particularly in the uplink. Macroscopic view on the uplink and downlink network operation cycle insightfully suggests that it is still possible to construct a delayed version of semi-closed-loop operation to predictively utilize radio resource in the uplink of proactive mobile network. A two-stage reinforcement learning mechanism to enable the recommendation of radio resource utilization from network infrastructure to mobile smart machines is proposed, which consists of the multi-armed bandit scheme for RRA and the neural network to learn historical utilization of RRUs. Simulations verify machine learning enables smart and predictive RRA with superior performance that can lead to the minimal latency of mobile networking.

The Satellite communication network is not limited by geographical factors and is an indispensable part of global interconnection. This paper analyzes a hybrid of cloud computing and edge computing LEO satellite network architecture, where the three-tier network is composed of ground users, LEO satellites, and remote cloud servers. Users can not only forward computing tasks to remote cloud servers through LEO satellites for processing, but also directly offload computing tasks to LEO satellites for processing. Based on this, we study the problem of user computing offloading in LEO satellite network and construct a joint optimization problem of offloading decision and computing resource allocation, which aims to reduce the user processing delay and energy consumption when the total computing resources of edge nodes are limited. This problem is a mixed-integer nonlinear problem, which is difficult to be solved in finite time. With the increase in the number of users, the complexity of the solution is very high. Therefore, we reconstruct the problem based on the linear reconstruction technique and relax the binary variables to transform the original non-convex problem into a convex problem. The simulation results show that the algorithm can effectively reduce user delay and energy consumption compared to the case when the tasks are processed locally.

The development of car-sharing system using electric vehicles (EVs) is a promising solution to mitigate traffic pressure and reduce carbon emissions. As the market scale of car-sharing expands gradually, the electricity refueling of EVs in car-sharing system becomes quite vital. We propose the concept of car-sharing battery swapping station (CSBSS), which has both the functions of a car-sharing station and a battery swapping station. An individual CSBSS is modeled as a coupled queuing network. Deep Q-Network (DQN) is implemented in this paper to control the charging operation of replaced batteries (RBs). The proposed charging control strategy can fetch more profit than the baseline scheme. Moreover, the simulation results show that more profits can be obtained by adjusting the state of charge (SOC) threshold or the total number of batteries. Our work provides a practical perspective and guidance for the problem of refueling car-sharing EVs.

This paper aims to produce an effective online scheduling technique, where a base station (BS) schedules the transmissions of energy harvesting-powered Internet-of-Things (IoT) devices only based on the (differently outdated) in-band reports of the devices on their states. We establish a new primal-dual learning framework, which learns online the optimal proactive schedules to maximize the time-average throughput of all the devices. Batch gradient descent is designed to enable stochastic gradient descent (SGD)-based dual learning to learn the network dynamics from the outdated reports. Replay memory is deployed to allow online convex optimization (OCO)-based primal learning to predict channel conditions and prevent over-fitting. We also decentralize the online learning between the BS and devices, and speed up learning by leveraging the instantaneous knowledge of the devices on their states. We prove that the proposed framework asymptotically converges to the global optimum, and the impact of the outdated knowledge of the BS diminishes. Simulation results confirm that the proposed approach can increasingly outperform state of the art, as the number of devices grows.

The development of 5G network and rapid growth of mobile traffic bring lucrative opportunities for Micro Operators (μOs), the novel local operators who own local spectrum, deploy and manage small cells (e.g., femtocells) in a specific area. Collaborating with traditional mobile network operators (MNOs), μOs gain profits from helping to offload the traffic carried by macro base stations and providing the MNOs customers with seamless service. However, due to the demand uncertainty and the sensitivity of individual user's demand profile, it is challenging for μOs to allocate the resource properly to avoid the under-and over-utilized situations. To address this issue, in this paper, we propose to employ data-driven approach to characterize the demand uncertainty, exploit differential privacy protocols to protect user's demand profile, and formulate the small cell planning problem into two-stage stochastic programming optimization with the objective of minimizing the capital and operational costs of μOs. Based on the formulated problem, we develop feasible solutions and conduct extensive simulations using real-world base station accessing real cellular data (i.e., data of 4G LTE network in Zhengzhou city, China) to verify the effectiveness of the proposed model.

Backhaul capability is usually considered as a bottleneck for system performance in dense wireless networks, caching popular file in base stations is an effective solution. In the dense deployed network, it is necessary to consider the interaction between cache size and file placement, which will lead to a rise of cache deployment cost. In order to solve this problem, this paper introduces the concept of user group and proposes a scheme with joint optimization of cache size and file placement. In this scheme, considering constraint of file delivery latency and other factors, joint optimization for minimizing the total cache size is established, with iteration of the coordinate axis descent algorithm and cache value competition algorithm. Simulation results show that, compared with the greedy algorithm, the proposed algorithm can effectively reduce the total cache size and and is very close to the optimal algorithm. Influences of user group size and preference on file placement are also discussed.

Ultra-low latency communication for mobile intelligent machines, such as autonomous vehicles and robots, is a central technology in Internet of Things (IoT) to achieve system reliability. Proactive network association and communication has been suggested to achieve ultra-low latency under the assistance of mobile edge computing. Highly dynamic and stochastic nature of IoT mobile machines suggests applying machine learning methodology to effectively enhance the proactive network association. In this paper, an online proactive network association is proposed for this distributed computing and networking scenario, in order to minimize the average task delay subject to time-average energy consumption. We first formulate an event-triggered delay model for mobility-aware anticipatory network association mechanism that takes future possible handovers into account. Based on the Markov decision processes (MDP) and Lyapunov optimization, a two-stage online decision algorithm for proactive network association is innovated for individual mobile machine without the statistical knowledge of random events that may lack of enough prior data. Theoretical analysis proves that the delay performance of proposed algorithm attains asymptotic optimality within the bounded deviation. Furthermore, an asynchronous online distributed association decision algorithm based on the nonlinear problem transformation is proposed to support more general scenarios of multi-machine event-triggered associations. Simulations verify the effectiveness of the proposed methodology.

The meta distribution (MD) of the signal-to-interference ratio (SIR) provides more fine-grained information about the link performance than the standard success probability. This paper focuses on a fundamental extension of the SIR MD-the joint MD of the SIR at different locations, and studies its applications to physical layer security and cooperative reception. The concept of the joint MD of the SIR is formally introduced for two or more users (locations), as well as the joint conditional success probability and the n -th order product MD, which is a simpler version of the joint MD. The joint MD is first applied to physical layer security. The network reliability of the secrecy transmission based on the MD is studied, taking into account the signal and interference correlations between the legitimate user and the eavesdropper. Next the joint MD is applied to cooperative reception, where we consider the scenario that the base station sends a message to a group of users, and the goal is that at least one user successfully receives the message. The moments of the conditional probability of the event that the transmission succeeds at at least one of the two users are derived. The results demonstrate the practical relevance of the joint MD.

The development of plug-in electric vehicles (PEVs) brings lucrative opportunities for charging station operators (CSOs). To attract more CSOs to the PEV market, provision of reasonable pricing policy is of great importance. However, dynamic environments and uncertain behavior of competitors make the pricing problem of CSOs challenging. In this paper, we focus on the dynamic pricing policy for maximizing the long-term profits of CSOs. Firstly, we propose a hierarchical framework to describe the economic association of PEV market, which is composed of smart grid, CSOs and charging stations (CSs) serving PEVs from top to bottom. Next, we leverage the Markov game to model the layer of CSOs as a competitive market. Finally, we design a dynamic pricing policy algorithm (DPPA) based on multi-agent reinforcement learning to achieve higher long-term profits of CSOs. Based on the real data of PEVs in Beijing, the experiment results show that DPPA has a significant improvement in long-term profit of CSOs, and the improvement gains increase over time. Moreover, DPPA can reduce the profit loss of CSOs effectively while involving more competitors.

This paper exploits the inter beam handover(IBH) mechanism for 5G mmWave mobile communication system in high speed rail(HSR) scenario. Considering higher speed and narrower coverage of serving beam in mmWave system, more frequent handover(HO) will happen, so in order to reduce inter beam handover failure rate of the network, a novel jointly optimized dynamic inter beam handover(JOD-IBH) scheme based on multiple beams cooperation was proposed in this paper, where handover failure rate and resource occupation rate were jointly optimized. Simulation results show that the proposed JOD-IBH brings improvements to the inter beam handover performance for HSR scenario, which can be observed in reducing handover failure rate and achieving the balance between handover failure rate and resource occupation rate.

Artificial intelligence (AI) and big data analytics enable autonomous vehicles (AVs) to dramatically change future intelligent transportation in smart cities. AVs are envisaged to evolve to a service rather than a product in the future. To provide best user experience of such services, three primary factors, namely, waiting time, travel time, and supply of AV services, are taken into consideration in a multiobjective optimization. Conventional optimization of services relies on traffic flow analysis over a queuing network model. However, due to the mobility of vehicles and the transfer uncertainty of road networks, the queuing network analysis is too complicated and practically intractable. For accuracy and convenient processing, network calculus (NC) is extended to model the queueing problem in this paper. The optimal number of available AVs can be identified by guaranteeing the waiting time of customers. The satisfaction of AV services can be viewed as a supply and demand problem, and optimized by bipartite graph matching. In order to reduce the average travel time, especially for rush hours with heavy traffic, we further propose a new online AVs fleet management scheme with congestion control for smart cities. It is shown that the intelligent management of AV fleet can be efficiently achieved, outperforming the cases of traditional vehicles. NC-assisted AI enables an efficient intelligent transportation paradigm in smart cities, while achieving substantial energy saving.

Massive machine-type communication (mMTC) is a new focus of services in fifth generation communication networks. The associated stringent delay requirement of end-to-end (E2E) service deliveries poses technical challenges. In this paper, we propose a joint random access and data transmission protocol for mMTC to guarantee E2E service quality of different traffic types. First, we develop a priority-queueing-based access class barring (ACB) model and a novel effective capacity is derived. Then, we model the priority-queueing-based ACB policy as a noncooperative game, where utility is defined as the difference between effective capacity and access penalty price. We prove the existence and uniqueness of Nash equilibrium (NE) of the noncooperative game, which is also a submodular utility maximization problem and can be solved by a greedy updating algorithm with convergence to the unique NE. To further improve the efficiency, we present a price-update algorithm, which converges to a local optimum. Simulations demonstrate the performance of the derived effective capacity and the effectiveness of the proposed algorithms.

Open Shortest Path First (OSPF) is a widely used routing protocol, which uses cost to find the shortest path. In this paper, we develop an extension to OSPF to support QoS guarantees by replacing the cost metric with end-to-end (E2E) delay. Particulary, we propose a new stream-based Dijkstra's algorithm to find the minimum E2E delay path for any traffic stream and implement this algorithm in Quagga OSPF, a network routing software suite. Experimental results show that our proposed protocol reduces E2E delays compared with unmodified OSPF routing protocol.

Mobile self-organizing networks, such as vehicular ad-hoc network (VANET), in most cases rely on infrastructure deployment to provide access to internet services and other resource. Therefore, it is crucial to optimize the deployment of roadside units (RSUs) in vehicle network. In this paper, we propose a RSU optimized deployment scheme based on large vehicle data, which considers the deployment cost and latency performance synthetically. We deploy the RSU problem as a multiobjective optimization problem for mathematical modeling. On this basis, two-step solution is proposed: firstly, the RSU candidate positions can be obtained by considering the road topology and large vehicle data; secondly, the branch and bound algorithm is used to obtain the better RSU deployment based on the mathematical model. The simulation results show that the proposed RSU deployment scheme uses a small amount of RSU can achieve high coverage and greatly reduce the deployment cost. Moreover, the low latency performance of the vehicle access network and the quality of the latency sensitive application service can also be ensured.

Due to the exponential growth of data traffic and high computational requirements, mobile edge computing (MEC) has become a promising approach to improve system performance in 5G networks. Latency and energy consumption are two important performance metrics for MEC. Due to the inhibition of buildings and other factors, the path loss in different directions are not the same, which leads to that the coverage regions of BSs are not circles. Integral geometry is an effective approach to analyze the system performance of irregular-shape region and is more suitable for the realistic scenario. In this paper, we propose MEC networks with both static UEs and a dynamic vehicle and analyze the average computation latency and energy consumption in both networks. Our difficulty lies in the handover in dynamic network. Our simulation results validate the analytical results well and our results shows that the density of static UEs and the speed of dynamic vehicle have main effect to average latency and energy consumption. The results provide useful guidelines for performance analysis of MEC network.

Cell discontinuous transmission (Cell DTx) improves network performance because it help mitigate inter-cell interference. However, the relationship between this technology and network performance has been little studied. The aim of this work is to understand the impact of Cell DTx on users’ quality of service (QoS) performance of multiuser ultra-dense networks (UDNs). We extend the traditional one-dimensional effective capacity model and develop a new multidimensional framework for UDNs with Cell DTx, which can be applied to different scheduling policies. Under the round-robin and the max-C/I scheduling examples, computer simulation shows that the analytical and simulation results are in good agreement and thus validate the accuracy of our proposed framework.

5G networks require 100x energy efficiency enhancement. Much work on energy efficiency was done with a single-source assumption, however, information received at UEs usually comes from multiple sources. Therefore, their work for energy efficiency analysis shall be adjusted to the multiple sources scenario. In this paper, based on the effective capacity and the effective bandwidth models, we propose a new method to approximate systems' effective energy efficiency for multiple data sources. It is shown that the approximation results based on our method overlap with simulation results and are more accurate than existing methods.

In this paper, a caching network is established based on OpenAirInterface (OAI) system. A caching server is introduced to locally cache the requested service, which is embedded into the core network. The user behavior as well as the user request profiles are collected and analyzed to dynamically update the cache list. In the demo system, we test the performance of different caching strategies according to different application scenarios using commercial smartphones. The latency performance and user throughput are demonstrated to show the caching influence in the limited storage environment and can be easily integrated in current cellular systems.

In recent years, convolutional neural network (CNN) has made a breakthrough development and been widely used in various fields, such as image recognition, target classification and natural language processing. However, with the continuous development of CNN, the complexity of CNN is gradually increasing. The ordinary hardware processors cannot meet the speed requirements of CNN. The hardware platform about CNN based on FPGA has gradually become the focus of research because of its parallel computing advantages. However, it is difficult and not friendly to implement CNN on FPGA for software developers. In this paper, we propose a FPGA-Cloud architecture for CNN. We implement CNN platform based on FPGA, and deploy the platform in the cloud. CNN resources based on FPGA can be utilized by local users through the network, which is language-friendly for software developers in hardware development and satisfies the user's requirement for CNN processing task.

The buffer-aided wireless relay technology is widely used in the 4G and the 5G wireless systems. This technology has advantages of throughput enhancement and coverage extension, but it will introduce additional delays because of buffering. Therefore, it is important to understand the end-to-end delay behavior when this technology is in use. In this paper, we consider a three-node relay system with block-fading channels and a random arrival process, and then derive a new upper bound of the two-hop end-to-end delay distribution function. Simulation results show that our bound provides the closest approximation of delay outage probability values.

The meta distribution of the signal-to-interference ratio (SIR) provides more fine-grained information about the link performance than the standard success probability. This paper studies the reliability in cellular networks with physical layer security constraints using the meta distribution. We consider a cellular network where the BSs are distributed as Possion point process. We provide the distribution of the opportunistic secure spectrum access probability when connection success and secrecy success occurs simultaneously, taking into account the interference correlation. We gain insights on the links reliability in the network under different security levels and the effect on the link reliability of the distance between legitimate user and eavesdropper.

LTE/WiFi Aggregation (LWA) provides a promising approach to relieve data traffic congestion in licensed bands by leveraging unlicensed bands. Critical challenges arise from provisioning quality-of-service (QoS) through heterogenous interfaces of licensed and unlicensed bands. In this paper, we minimize the required licensed spectrum without degrading the QoS in the presence of multiple users. Specifically, the aggregated effective capacity of LWA is firstly derived by developing a new semi-Markov model. Multi-band resource allocation with the QoS guarantee between the licensed and unlicensed bands is formulated to minimize the licensed bandwidth, convexified by exploiting Block Coordinate Descent (BCD) and difference of two convex functions (DC) programming, and solved efficiently with a new iterative algorithm. Simulation results demonstrate significant performance gain of the proposed approach over heuristic alternatives.

Grant-free transmission is considered as a promising technology to support sporadic data transmission in massive machine-type communications (mMTC). Due to the distributed manner, high collision probability is an inherent drawback of grant-free access techniques. Non-orthogonal multiple access (NOMA) is expected to be used in uplink grant-free transmission, multiplying connection opportunities by exploiting power domain resources. However, it is usually applied for coordinated transmissions where the base station performs coordination with full channel state information, which is not suitable for grant-free techniques. In this paper, we propose a novel distributed layered grant-free NOMA framework. Under this framework, we divide the cell into different layers based on predetermined inter-layer received power difference. A distributed layered grant-free NOMA based hybrid transmission scheme is proposed to reduce collision probability. Moreover, we derive the closed-form expression of connection throughput. A joint access control and NOMA layer selection (JACNLS) algorithm is proposed to solve the connection throughput optimization problem. The numerical and simulation results reveal that, when the system is overloaded, our proposed scheme outperforms the grant-free-only scheme by three orders of magnitude in terms of expected connection throughput and outperforms coordinated OMA transmission schemes by 31.25% with only 0.0189% signaling overhead of the latter.

In this paper, we propose a novel Adaptive Transmission Strategy to improve energy efficiency (EE) in a wireless point-to-point transmission system with Quality of Service (QoS) requirement, i.e., delay-outage probability considered. The transmitter is scheduled to use the channel that has better coefficients, and is forced into silent state when the channel suffers deep fading. The proposed strategy can be easily implemented by applying two-mode circuitry and is suitable for massive machine type communications (MTC) scenarios. In order to enhance EE, we formulate an EE maximization problem, which has a single optimum under a loose QoS constraint. We also show that the maximization problem can be solved efficiently by a binary search algorithm. Simulations demonstrate that our proposed strategy can obtain significant EE gains, hence confirming our theoretical analysis.

In this paper, considering non-ideal power amplifier (PA) and circuit power, we optimize the energy efficiency (EE) of two-way full-duplex relaying (FDR) systems with decode-and-forward (DF) protocol. With higher cooperative gain, the direct link (DL) of the regenerative FDR is decoded at the receiver and can contribute to the useful signal rather than the interference, which can further improve the system performance significantly. With this mechanism, the joint transmit power and duration optimization problem is formulated to maximize the EE of the FDR system, which is proved to be non-convex and cannot be resolved by the standard convex methods. Therefore, a two step optimization algorithm (TSOA) is proposed to solve the original non-convex problem, where the optimality of the solution is ensured due to the rigorously proved convexity. Simulations are carried out to verify the EE performance of our proposed scheme, compared with the existing schemes. It is also revealed that the FDR systems are basically comparable to the half-duplex relaying (HDR) counterparts in terms of EE if the residual self-interference (RSI) is below a certain level, and have the ability to achieve higher data rate demand.

The meta distribution provides fine-grained information on the signal-to-interference ratio (SIR) compared with the SIR distribution at the typical user. This paper first derives the meta distribution of the SIR in heterogeneous cellular networks with downlink coordinated multipoint transmission/reception, including joint transmission (JT), dynamic point blanking (DPB), and dynamic point selection/dynamic point blanking (DPS/DPB), for the general typical user and the worst-case user (the typical user located at the Voronoi vertex in a single-tier network). A more general scheme called JT-DPB, which is the combination of JT and DPB, is studied. The moments of the conditional success probability are derived for the calculation of the meta distribution and the mean local delay. An exact analytical expression, the beta approximation, and simulation results of the meta distribution are provided. From the theoretical results, we gain insights on the benefits of different cooperation schemes and the impact of the number of cooperating base stations and other network parameters.

In the heterogeneous cloud radio access network (H-CRAN), which consists of multiple access points (APs) providing smaller coverage and a high power node (HPN) providing ubiquitous coverage, the mobile machines can connect to multiple APs and HPN by coordinated multi-point transmission (CoMP) concurrently to achieve ultra-reliable and low-latency communication. However, the current network association (or priorly known as handovers), which only focuses on switching between two base stations, may not be an efficient scheme in H-CRAN. In this paper, we innovate a proactive network association mechanism by taking CoMP into consideration under the H-CRAN architecture. We consider two scenarios under the H-CRAN architecture: with and without the assistance of HPN in the network. By regarding APs/HPN in H-CRAN as resources that allocated to mobile machines, a novel proactive network association concept is proposed, and then generalized from one-to-one to multiple-to-multiple case. With the assistance of Lyapunov optimization theory, effective bandwidth, and capacity theory, we can prove that this proactive network association scheme can guarantee that the queueing delay performance and the delay violation probability can be both smaller than a corresponding upper bound. That is, both low-latency and ultra-reliable communication can be guaranteed. We also conduct experiments by using real trace from taxis movement data to verify the analytical results. Our results suggest the guidelines to design the proactive network association scheme in H-CRAN.

This paper presents a new approach to efficiently maximizing the energy efficiency (EE) of hybrid arrays under a practical setting of non-ideal power amplifiers (PAs) and non-negligible circuit power, where coherent and non-coherent beamforming are considered. As a key contribution, we reveal that a bursty transmission mode can be energy-efficient to achieve steady transmissions of a data stream under the practical setting. This is distinctively different from existing studies under ideal circuits and PAs, where continuous transmissions are the most energy-efficient. Another important contribution is that the optimal transmit duration and powers are identified to balance energy consumptions in the non-ideal circuits and PAs, and maximize the EE. This is achieved by establishing the most energy-efficient structure of transmit powers, given a transmit duration, and correspondingly partitioning the non-convex feasible region of the transmit duration into segments with self-contained convexity or concavity. Evident from simulations, significant EE gains of the proposed approach are demonstrated through comparisons with the state of the art, and the superiority of the bursty transmission mode is confirmed especially under low data rate demands.

The uncertainty in the locations of vehicles on streets induced by vehicles passing and queueing make the spatial modeling of vehicles a difficult task. To analyze the performance of vehicle-to-vehicle (V2V) communication for vehicular ad hoc networks (VANETs), accurate spatial modeling is of great importance. In this paper, we concentrate on spatial point process modeling for random vehicle locations in large and small cities, performing empirical experiments with real location data of mobile taxi trajectories recorded by the global positioning system (GPS) in Beijing city of China and Porto city of Portugal. We find that the empirical probability mass functions (PMFs) of the number of taxis in test sets in different regions of Beijing or in Porto all follow a negative binomial (NB) distribution. Based on the above, we show that the Log Gaussian Cox Process (LGCP) model, whose empirical PMF nicely fits the NB distribution, accurately characterizes diverse spatial point patterns of random vehicle location in both large and small cities. This is verified by the minimum contrast method. The LGCP model can be applied to analyze performance metrics (i.e., connectivity, coverage, and capacity) and optimize the practical deployments of VANETs.

An accurate energy efficiency analytical model based on a two-mode circuitry was recently proposed; and the model showed that the use of this circuitry can significantly improve a system's energy efficiency. In this paper, we use this analytical model to develop a new power control scheme, a scheme that is capable of allocating a minimum transmission power precisely within the delay-outage probability constraint. Precision brings substantial benefits as numerical results show that the energy efficiency using our scheme is much higher than other schemes. Results further suggest that data rate values affect energy efficiency non-uniformly, i.e., there exists a specific data rate value that achieves maximum energy efficiency.

In future mobile networks, the device-to-device (D2D) communication is considered as an efficient technique emerging direct and high-speed data transmission among devices (e.g. mobile social network services and machine type communications). Due to spectrum reuse among devices, the D2D however also brings in more interference, which will cause degradation in network performances. Therefore the concept of cooperative D2D communication is recently proposed to this issue and efficient radio resource allocations are further required. In this context, this paper focused on the bandwidth and power allocation problem for cooperative D2D communication and proposed a dynamic allocation scheme according to the channel status information (CSI) and data rate requirement. The goal is to minimize the total transmission power consumption in a cooperative D2D network with M transmitting devices(TDs) under respective data rate constraints of N receiving devices(RDs). Based on the KKT (Karush-Kuhn-Tucker) conditions of this problem, the dynamic allocation scheme is designed both for the cooperative diversity and multiplex modes. This scheme gives closed-form solutions for bandwidth and power allocation in cooperative diversity mode, and reduces the computational complexity of allocation in cooperative multiplex mode.

Coordinated multipoint (CoMP) joint transmission (JT) can save a great deal of energy especially for cell-edge users due to strengthened received signal, but at the cost of deploying and coordinating cooperative nodes, which degrades energy efficiency (EE), particularly when considerable amount of energy is consumed by nonideal hardware circuit. In this paper, we study energy-efficient cooperation establishment, including cooperative nodes selection (CNS) and power allocation, to satisfy a required data rate in coherent JT-CoMP networks with non-ideal power amplifiers (PAs) and circuit power. The selection priority lemma is proved first, and then the formulated discrete combinatorial EE optimization is resolved by proposing node selection criterion and deriving closedform expressions of optimal transmission power. Therefore, an efficient algorithm is provided and its superiority is validated by Monte Carlo simulations, which also show the effects of non-ideal PA and the data rate demand on EE and optimal number of active nodes.

To relieve the traffic burden and improve the system capacity, licensed-assisted access (LAA) has been becoming a promising technology to the supplementary utilization of the unlicensed spectrum. However, due to the densification of small base stations (SBSs) and the dynamic variety of the number of Wi-Fi nodes in the overlapping areas, the licensed channel interference and the unlicensed channel collision could seriously influence the Quality of Service (QoS) and the energy consumption. In this paper, jointly considering time-variant wireless channel conditions, dynamic traffic loads, and random numbers of Wi-Fi nodes, we address an adaptive spectrum access and power allocation problem that enables minimizing the system power consumption under a certain queue stability constraint in the LAA-enabled SBSs and Wi-Fi networks. The complex stochastic optimization problem is rewritten as the difference of two convex (D.C.) program in the framework of Lyapunov optimization, thus developing an online energy-aware optimal algorithm. We also characterize the performance bounds of the proposed algorithm with a tradeoff of [O(1/V), O(V)] between power consumption and delay theoretically. The numerical results verify the tradeoff and show that our scheme can reduce the power consumption over the existing scheme by up to 72.1% under the same traffic delay.

Autonomous vehicles (AVs) and crowdsourcing big data analytics may dramatically change future intelligent transportation in smart cities. AVs may evolve more like a service than a product. To provide best user experience of such service, three primary factors including waiting time, travel time, and supply of AVs are taken into consideration to for multi-objective optimization facilitated in three steps. With the queueing network model and traffic flow analysis, optimal operation of service could be achieved with minimum average travel time. The optimal number of available AVs could be identified while guaranteeing the waiting time of customers. To manage the supply and demand of the service in each geographical area, bipartite graph matching is adopted to accomplish optimal resource allocation. It is further shown that the optimization of operation of autonomous vehicle fleet can be successfully achieved, to outperform what human- driving vehicles can possibly do. A new intelligent transportation paradigm of great energy saving emerges.

Future mobile networks are converging toward heterogeneous multi-tier networks, where various classes of base stations (BS) are deployed based on user demand.So it is quite necessary to utilize the BSs resources rationally when BSs are sufficient.In this paper, we develop a more realistic model that fully considering the inter-tier dependence and the dependence between users and BSs, where the macro base stations (MBSs) are distributed according to a homogeneous Poisson point process (PPP) and the small base stations (SBSs) follows a Matern cluster process (MCP) whose parent points are located in the positions of the MBSs in order to offload the users from the over-loaded MBSs.We also assume the users are just randomly located in the circles centered at the MBSs.Under this model, we derive the association probability and the average ergodic rate by stochastic geometry.An interesting result that the density of MBS and the radius of the clusters jointly affect the association probabilities in a joint form is obtained.We also observe that using the clustered SBSs results in aggressive offloading compared with previous cellular networks.

In wireless networks, Spectrum Efficiency (SE) and Energy Efficiency (EE) can be affected by the channel estimation that needs to be well designed in practice. In this paper, considering channel estimation error and non-ideal backhaul links, we optimize the pilot symbols ratio in terms of SE and EE in uplink Coordinated Multi-point (CoMP) networks. Modeling the channel estimation error, we formulate the SE and EE maximization problems by analyzing the system capacity with imperfect channel estimation. The maximal system capacity in SE optimization and the minimal transmit power in EE optimization, which both have the closed-form expressions, are derived by some reasonable approximations to reduce the complexity of solving complicated equations. Simulations are carried out to validate the superiority of our scheme, verify the accuracy of our approximation, and show the effect of pilot symbols ratio.

License-assisted access (LAA) is a promising technology to offload dramatically increasing cellular traffic to unlicensed bands. Challenges arise from the provision of quality-of-service (QoS) and the quantification of capacity, due to the distributed and heterogeneous nature of LAA and legacy systems (such as Wi-Fi) coexisting in the bands. In this paper, we develop new theories of the effective capacity to measure LAA under statistical QoS requirements. A new four-state semi-Markovian model is developed to capture transmission collisions, random backoffs, and lossy wireless channels of LAA in distributed heterogeneous network environments. A closed-form expression for the effective capacity is derived to comprehensively analyze LAA. The four-state model is further abstracted to an insightful two-state equivalent which reveals the concavity of the effective capacity in terms of transmit rate. Validated by simulations, the concavity is exploited to maximize the effective capacity and effective energy efficiency of LAA, and provide significant improvements of 62.7% and 171.4%, respectively, over existing approaches. Our results are of practical value to holistic designs and deployments of LAA systems.

In this paper, we maximize the energy efficiency (EE) of full-duplex (FD) two-way relay (TWR) systems under non-ideal power amplifiers (PAs) and non-negligible transmission-dependent circuit power. We start with the case where only the relay operates full duplex and two timeslots are required for TWR. Then, we extend to the advanced case, where the relay and the two nodes all operate full duplex, and accomplish TWR in a single timeslot. In both cases, we establish the intrinsic connections between the optimal transmit powers and durations, based on which the original non-convex EE maximization can be convexified and optimally solved. Simulations show the superiority of FD-TWR in terms of EE, especially when traffic demand is high. The simulations also reveal that the maximum EE of FD-TWR is more sensitive to the PA efficiency, than it is to self-cancellation. The full FD design of FD-TWR is susceptible to traffic imbalance, while the design with only the relay operating in the FD mode exhibits strong tolerance.

This paper is devoted to the end-to-end performance analysis, optimal power allocation (OPA), and energy-efficiency (EE) optimization of decode-and-forward (DF)-based full-duplex relaying (FDR) and half-duplex relaying (HDR) systems. Unlike existing analyses and works that assume simplified transmission over symmetric fading channels, we consider the more realistic case of asymmetric multipath fading and shadowing conditions. To this end, exact and asymptotic analytic expressions are first derived for the end-to-end outage probabilities (OPs) of the considered DF-FDR set ups. Based on these expressions, we then formulate the OPA and EE optimization problems under given end-to-end target OP and maximum total transmit power constraints. It is shown that OP in FDR systems is highly dependent upon the different fading parameters and that OPA provides substantial performance gains, particularly, when the relay self-interference (SI) level is strong. Finally, the FDR is shown to be more energy-efficient than its HDR counterpart, as energy savings beyond 50% are feasible even for moderate values of the SI levels, especially at larger link distances, under given total transmit power constraints and OP requirements.

In this paper, energy efficiency (EE)-oriented partial spectrum reuse (PSR) scheme for heterogeneous cellular networks (HCN) with wired backhaul is studied with the aid of stochastic geometry theory. We first derive the total expected delay for a typical user by taking into account retransmissions over the wireless link, as well as the backhaul delay incurred from wired backhaul. Furthermore, we minimize the whole delay experienced by the users from the perspective of small cell density and the minimization of the total energy cost then can be addressed relying on the optimal small cell density and PSR factor. The main takeaway is that if we account for the backhaul, it can significantly reverse the relationship between the total network energy cost and the delay experienced by the users with varying signal to interference ratio (SIR) threshold. Theoretical analysis results indicate that, unlike previous known conclusions, sacrificing delay cannot always be traded for energy saving, and the feasible conditions under which the optimal energy-delay tradeoff exists are also obtained. Moreover, the optimal pair of PSR factor and SIR threshold to achieve the optimal energy-delay tradeoff is also derived. Both the analytical and simulation results show that significant energy saving can be achieved through tolerable sacrifice of delay performance with careful designs according to our analysis.

Cooperative communication has been shown to provide significant increase of transmission reliability and network capacity while expanding coverage in cellular networks. The present work is devoted to the investigation of the end-to-end performance and power allocation of a maximum-ratio-combining based regenerative multi-relay cooperative network over non-homogeneous scattering environment, which is the realistic case in many practical wireless communication scenarios. Novel analytic expressions are derived for the end-to-end symbol-error-rate of both M-ary phase-shift keying and M-ary quadrature amplitude modulation over independent and non-identically distributed generalized fading channels are given by exact analytic expressions that involve the Lauricella function and can be readily evaluated with the aid of a proposed computing algorithm. Simple analytic expressions are also derived for the corresponding symbol-error-rate at asymptotically high signal-to-noise ratios. The derived expressions are corroborated with respective results from computer simulations and are subsequently employed in formulating a sum-power optimization problem that enhances the system performance under total sum-power constraint within the multi-relay cooperative system. It is also shown that asymptotically optimum power allocation provides substantial performance gains over the corresponding equal power allocation, particularly, when the source-relay and relay-destination paths are highly unbalanced.

The authors have become aware that several equations in the original above-named work were presented with errors. As confirmed in this brief note, neither do the errors invalidate the game theoretic approach proposed, nor violate the equilibrium of the approach. However, the parameter selection, which is required to preserve the validity and unique equilibrium of the approach, is affected by the errors. We correct the equations with detailed mathematical derivations in the note.

This work is devoted to the outage probability analysis of full-duplex (FD) regenerative relay systems over multipath fading channels. Unlike the majority of analyses that assume basic symmetric fading conditions, the present work considers asymmetric generalized fading conditions, which are more realistic in practical communications scenarios. To this end, we assume that the source-relay path is subject to κ - μ multipath fading conditions, that can also account for line-of-sight communications, whereas the source-to-destination and relay-to-destination paths are subject to η-μ fading conditions that typically hold for non-line-of-sight communications. Novel analytic expressions are derived for the outage probability (OP) of the considered FD as well as for the corresponding half-duplex (HD) relay case for comparisons. These expressions are given in closed-form and have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. Based on this, they are subsequently employed in analyzing the corresponding performance for different communication scenarios. It is shown that the OP of the FD relay system is highly dependent upon the severity of fading and that its performance outperforms significantly the spectral efficiency increases.

This paper investigates cooperative forwarding in large highly dynamic vehicular networks. Unlike traditional coordinated cooperative forwarding schemes that require a large amount of coordination information to be exchanged before making the forwarding decision, this paper proposes an uncoordinated cooperative forwarding scheme where each node, a random transmission range, decides whether or not to forward a received packet independently based on a forwarding probability determined by its own location. Analytical results are derived on the successful end-to-end transmission probability and the expected number of forwarding nodes involved in the cooperative forwarding process. The multi-hop correlations and multi-path correlations, which constitute major challenges in the analysis, are carefully considered in our analysis. Simulations are conducted to establish the performance of the proposed scheme assuming different forwarding probability functions. In addition to developing an uncoordinated cooperative forwarding scheme, which is particularly suited for the highly dynamic vehicular networks, this paper also makes important theoretical contributions on analyzing the connectivity of networks with nodes of variable and random transmission ranges.

This work is devoted to the error rate and energy-efficiency analysis of regenerative cooperative networks in the presence of multipath fading and spatial correlation. To this end, exact analytic expressions are firstly derived for the symbol-error-rate of M-ary quadrature amplitude modulation in a dual-hop decode-and-forward relay system under spatially correlated Nakagami-m fading channels and maximum ratio combining at the destination. The derived expressions are subsequently employed in quantifying the energy consumption of the considered system, incorporating both transmit energy and the energy consumed by the transceiver circuits. The overall energy consumption is also minimized for certain quality-of-service requirements and it is shown that depending on the degree of spatial correlation, severity of fading, transmission distance, and relay location, a substantial overall energy reduction is sought when compared to conventional direct transmission.

Multicell cooperative processing (MCP) can overcome the detrimental effects of inter-cell interference (ICI). However, since mobile terminals (MTs) need to estimate and feed back channel state information (CSI) related to several cooperative base stations (BSs), reductions in CSI feedback overhead and delay are more demanding in MCP enabled networks. In this paper, a novel overlaid-pilot based CSI feedback scheme for multicell cooperative networks is proposed. MTs directly overlay the received downlink pilot data of all the cooperative BSs on the uplink pilot data and then broadcast it to the cooperative BSs. Each cooperative BS can recover multiple CSIs from the overlaid pilot by a presented overlaid-pilot based channel estimation algorithm. Because the overlaid pilot is fed back on the uplink pilot channel, no extra uplink channel is occupied and the downlink CSI feedback process is with zero-overhead regardless of the number of cooperative BSs. In addition, as it just needs one broadcast to feed back multiple CSIs to BSs, the feedback delay is decreased significantly. Numerical simulations show that the performance of the proposed CSI feedback scheme outperforms the traditional feedback scheme.

The recently emerged compressive sensing (CS) theory provides a whole new avenue for data gathering (DG) in wireless sensor networks (WSNs) with the benefits of energy efficient. The exiting CS based DG approaches design the routing processes based on the assumption that there is a transform that can sparsely represent the sensor readings. However, most of the real sensor readings are well approximated by the sparse signals, which increases the amount of the data traffic to obtain the exact reconstruction, specially in the noisy situation. In this paper, we present an adaptable CS based DG scheme in WSNs. The autoregressive (AR) model based optimization problem is considered for the successive reconstruction and the reduction of the data traffic, in which AR model and the routing processes are jointly introduced into the reconstruction of the sensor readings. The AR parameters are evaluated with the historical sensor readings at the sink, by taking advantage of the similar spatial correlation. Then, with the known AR parameters, the sensor readings are reconstructed by extending the BPDN algorithm to the AR model based optimization problem. Our experimental results indicate that the proposed DG scheme outperforms the exist random walk (RW) based DG scheme in terms of reconstruction error and energy efficient, which results in better reconstruction accuracy with the RWs with the significantly smaller length.

This paper investigates transmit antenna selection (TAS) in a multiple-input multiple-output (MIMO) wiretap channel based on a measure with dual thresholds at the legitimate receiver and the eavesdropper, respectively. Due to the fact that pilots are separated from transmit signals in time, weighting errors at the combiners are considered here. Closed-form expressions for the exact and asymptotic secure connection outage probability are derived and analysed. It is demonstrated that antenna channel hopping with fountain codes exploitation achieves a desirable secrecy diversity order, which is related to the weighting correlation coefficient and the additional transmit packet number. At last, simulation results validate the security enhancement and the actual impact of weighting errors.

Deterministic methods in indoor-localization systems based on the received signal strength (RSS) almost utilize the average value of the RSS, such as the k- nearest neighbor (KNN) algorithm. However, the distribution of RSS is not always normal Gaussian in the real complex indoor environment so the average value may not represent the location well. To solve this problem, we present a novel algorithm, named as probabilistic KNN (pKNN) algorithm. The algorithm uses the probability of RSS in the Radio-map as a weighting to calculate the Euclidean distance, and it filters the RSS value whose probability is less than 3%. At the same time, we propose a new application environment called as passive indoor-localization scenario. In this scenario, the access point (AP) collects the RSS when the mobile terminal (MT) is not connecting to the AP. Experiment and results analysis for different k values show that p-KNN algorithm is feasible and effective in passive indoor- localization scenario. Finally, comparing to the KNN algorithm, p-KNN algorithm can achieve a better average location accuracy.

Wireless communication is particularly susceptible to eavesdropping due to its broadcast nature. Security and privacy issues have become increasingly critical for wireless networks. This paper considers the problem of secret communication in the multiuser downlink eavesdropped by a passive eavesdropper (EVE). The well-known zero-forcing preceding is adopted at the transmitter to produce concurrent data streams to the users, and at the same time an artificial noise (AN) is generated to prevent EVE from intercepting the information. We first derive an analytical closed-form expression for the secrecy sum-rate in the large system limit. We then use it as the objective function to optimize the power allocation (PA) between the information signals and the AN to maximize the secrecy sum-rate. A simple analytical expression of the optimal PA is derived in the high transmit power regime, which proves to be a near-optimal and generic strategy. The large system results are quite accurate for finite-size systems and thus can provide useful insights into system analysis and design. Our analytical and simulation results show that the AN assisted strategy achieves the same multiplexing gain as in the multiuser downlink without eavesdropping. Moreover, more power should be allocated to AN when the system serves fewer users and EVE has more antennas.

This paper is devoted to the analytic investigation of a maximum-ratio-combining based regenerative multi-relay cooperative wireless network over non-homogeneous scattering environments. Such propagation conditions are rather realistic as they are encountered often in practical wireless transmission scenarios. Novel analytic expressions are derived for the symbol-error-rate of M-ary phase shift keying (M-PSK) over independently and non-identically distributed fading channels. The derived expressions are based on the moment-generating-function (MGF) approach and are given in closed-form in terms of the generalized Lauricella series. A simple algorithm for computing this special function is also proposed while the offered results are validated extensively through comparisons with respective results from computer simulations. Based on this, they are particularly useful in the analytic performance evaluation of such cooperative systems. To this end, it is shown that the performance of the cooperative system is significantly affected, as expected, by the number of employed relays as well as by the value of the involved fading parameters η and μ.

The present paper analyzes the outage probability of full-duplex (FD) regenerative relay systems over multipath fading channels. Unlike the majority of investigations that assume basic symmetric fading conditions, this analysis considers asymmetric generalized fading conditions, which are more realistic as they are encountered more often in practical wireless transmissions. To this end, it is assumed that the source-relay and source-destination links are subject to k -μ multipath fading conditions, which can represent generalized line-of-sight communication scenarios; on the contrary, the relay-to-destination link is subject to η-μ fading conditions that typically represent generalized non-line-of-sight communication scenarios. Novel analytic expressions are derived for the outage probability (OP) of the considered FD relaying system. These expressions are given in closed-form and have a relatively tractable algebraic form which renders them convenient to handle both analytically and numerically. To this effect, they are subsequently employed in analyzing the corresponding performance for various communication scenarios. It is shown that the OP of the FD relay system is, as expected, highly dependent upon the severity of fading, the relay self-interference and the interference from the direct link. Furthermore, it is shown that at relatively high average signal-to-noise ratio values, the outage probability at low fading severity and at high relay self interference outperforms the respective performance for the case of high fading severity, but with low relay self-interference. Based on this, the offered results can be useful in the design and deployment of future full-duplex based cooperative communication systems.

Energy efficiency is becoming more and more important to the development of wireless communication. In this paper, we study energy-efficient resource allocation in an Orthogonal Frequency Division Multiplexing (OFDM) system with mixed traffic. Considering both the energy efficiency and spectral efficiency, we model the allocation of sub-carriers and power as a mixed integer nonlinear programming (MINLP) problem maximizing the utility per Watt. The formulated problem is non-convex and is computationally forbidden. To simplify the solution of the problem, a two-step resource allocation (TSRA) algorithm is proposed. In the first step, we simplify the problem to a convex one and obtain the sub-carrier allocation using Karush-Kuhn-Tucker (KKT) conditions. On this basis, we solve the power allocation problem in the second step by transforming it into a parametric programming problem and applying Dinkelbach's theory. Simulation results show that our algorithm can provide 28% energy efficiency improvement compared to conventional algorithms.

In cellular wireless networks, base station (BS) cooperation is a promising technique to realize high spectral efficiency, whose potential gain, however, is restricted by the backhaul network. In this paper, we consider combining different decentralized cooperative strategies to fully utilize the limited backhaul. Firstly, the impact of limited backhaul capacity on different cooperative strategies is modeled. Then, we develop a analytical framework that gives a mathematical expression for the combination idea. Afterwards, we consider the establishment of the combination to fully exploit the backhaul capacity as well as the wireless channel realizations, which is a scheduling problem consisting of strategy selection and backhaul capacity allocation. A heuristic algorithm with polynomial time complexity is proposed. Finally, the validity of the combination idea and the proposed algorithm are confirmed by Monte Carlo simulations.

Energy efficiency is becoming an increasingly important issue in wireless networks. With the increasing diversity of types of wireless services, a user has the requirement to transmit various hybrid services simultaneously. In this paper, we propose a Cross-layer Cooperative delay-energy Tradeoff (CCT) scheme to achieve total energy consumption minimization for hybrid types of services while satisfying their quality of service (QoS) requirements in cellular networks. We exploit the enormous difference of delays and the diversity of the amounts of data between delay-sensitive services and delay-tolerant ones to conduct dynamic resource allocation. Experimental simulation results can verify that our CCT scheme can save total energy consumption of hybrid services significantly outperforming existing energy efficient schemes.

Owning to the advantage of an increase in spectral efficiency by reducing the transmitted pilot tones, compressed sensing has been widely applied to pilot-aided sparse channel estimation in OFDM systems. In this paper, we focus on increasing the accuracy of channel estimation and propose a novel sparse channel estimation method that takes the effect of the additive noise into consideration. In the proposed method, the channel impulse response (CIR) is represented as a combination of ideal channel paths and additive noise. We firstly use the Orthogonal Matching Pursuit (OMP) algorithm to estimate the path delays and path gains. Then, the Mahalanobis distance discriminant analysis is applied to channel paths identification. The proposed method could effectively distinguish the significant channel paths especially those with small amplitude from the noise. Simulation results demonstrate the effectiveness of the proposed sparse channel estimation method. Compared with the conventional CS-based channel estimation algorithms, the new method proposed here enjoys superior performance in terms of bit error rate (BER) and mean square error (MSE).

Resource allocation in Orthogonal Frequency Division Multiplexing (OFDM) system with mixed traffic is studied in this paper. The sub-carriers and power of a base station (BS) are allocated to users to maximize energy efficiency, which is measured by utility per Watt. We model the allocation of sub- carriers and power as a mixed integer nonlinear programming (MINLP) problem, which is computationally forbidden due to its non-concave objective function, non-convex feasible region and integer variables. To simplify the solution of the problem, a two-step resource allocation (TSRA) algorithm is proposed. In the first step, the integer variables are transformed into continuous variables and uniform power allocation is assumed. On this basis, sub-carrier allocation is obtained using KKT condition. Knowing the allocation of sub- carrier, the optimal power allocation is solved by an iterative sub-gradient method in the second step. Simulation results show that our algorithm can provide 25% energy efficiency improvement compared to conventional algorithms.

Orthogonal experimental design (OED) is a well-known method in statistical experiment which aims to reduce the testing complexity. This paper tries applying OED to reduce the computational complexity in Multiple-input Multiple-output (MIMO) detection. Several times of sorted QR decomposition (SQRD) algorithm are applied firstly to get the diverse solutions. Then a subspace is constructed with the diverse results. Inside the subspace, OED shows its potential and value in reducing the computational complexity compares with the exhaustive method. The proposed QR-OED MIMO scheme provides nearly 8 dB gain compared with the traditional sorted QRD while the minimal computational complexity needed is only 3.7% compared with exhaustive method for the 6×6 MIMO system and 4-QAM modulation scheme.

In this paper, we contribute to the secrecy outage analysis of transmit antenna selection (TAS) with switch-and-examine combining (SEC) in the Multiple-input multiple-output (MIMO) wiretap channel. The complexity of SEC is lower than maximum-ratio combining (MRC) and selection combining (SC). The transmit antenna which maximizes the instantaneous signal-to-noise ratio (SNR) at the receiver is selected. Two scenarios are studied here: 1) SEC at both the receiver's side and the eavesdropper's side (SEC/SEC); 2) SEC at the receiver's side while MRC at the eavesdropper's side (SEC/MRC). Expressions for the exact and asymptotic secrecy outage probability are derived and validated by simulation results. It is demonstrated that SEC may achieve competitive secrecy performance in the low SNR regime with less estimation cost. In the high SNR regime, it achieves full secrecy diversity order NaNb with careful threshold's design even under the condition of MRC applied at the eavesdropper.

In cooperative localization, distributed algorithm is more attractive than its centralized counterpart due to the low complexity and robustness. However, the position ambiguity of cooperative mobile terminal (MT) is usually neglected by related works, causing the loss of accuracy in distributed algorithms. In this paper, we establish an element-wise-weighted total least-squares (EW-TLS) model for distributed cooperative localization. Considering the impact of position ambiguity and distance measurement error, this localization model yields an optimal consistent estimator. Meanwhile, a general geometric dilution of precision (G-GDOP) metric is derived to estimate MT's coordinate error. Then a distributed cooperative localization algorithm is proposed by integrating the EW-TLS model with the G-GDOP metric. Simulation results indicate that the proposed algorithm outperforms traditional methods in high accuracy and strong robustness.

Clustering is a promising interference mitigation scheme in dynamic TDD systems. However, most previous works just took large-scale path loss or coupling loss as criteria of the clustering schemes, thus the throughput performance would be limited by the varying traffic requirements among different small cells within one cluster. In this paper, a novel dynamic cluster-based Interference Mitigation and Traffic Adaptation (IMTA) scheme is proposed and evaluated with dense deployment of small cells (SCs). Firstly, a new clustering criterion named Differentiating Metric (DM) is defined. Based on the defined DM value, a DM matrix is formed and further presented by a clustering graph. In the clustering graph, the dynamic clustering strategy is mapped to a MAX K-CUT problem, which is addressed in polynomial time by a proposed heuristic clustering algorithm. Furthermore, the system level simulation results demonstrate a promising improvement on uplink traffic throughput (UTP) in our proposed scheme compared with traditional clustering schemes.

The virtual multiple-input multiple-output (V-MIMO) and coordinated multipoint transmission and reception (CoMP) have been adopted by the LTE- Advanced as these techniques can significantly improve the uplink spectral efficiency. Given the poor cell-edge performance of the conventional determinant pairing scheduling (DPS), and the heavy backhaul brings about by performing coordinated reception (CR) to all UEs, this paper deals with a novel distributed scheduling algorithm for uplink multi-user MIMO (MU-MIMO) systems. The proposed algorithm differentiates the scheduling approaches based on a new criterion to distinguish UEs and a redefined pairing scheduling metric, which allows for further fine control over the scheduling algorithm. Simulation results shows that the proposed algorithm outperforms DPS with more considerable improvement of spectral efficiency and more flexibility.

Due to high system capacity requirement, 3GPP Long Term Evolution (LTE) is likely to adopt frequency reuse factor 1 at the cost of suffering severe inter-cell interference (ICI). One of combating ICI strategies is network cooperation of resource allocation (RA). For LTE uplink RA, requiring all the subcarriers to be allocated adjacently complicates the RA problem greatly. This paper investigates the joint multicell RA problem for LTE uplink. We model the uplink RA and ICI mitigation problem using pure binary-integer programming (BIP), with integrative consideration of all users' channel state information (CSI). The advantage of the pure BIP model is that it can be solved by branch- and- bound search (BBS) algorithm or other BIP solving algorithms, rather than resorting to exhaustive search. The system-level simulation results show that it yields 14.83% and 22.13% gains over single-cell optimal RA in average spectrum efficiency and 5th percentile of user throughput, respectively.

Device to Device (D2D) underlaying cellular networks have drawn a lot of attention recently, in both Long Term Evolution (LTE) standardization as well as academic research, with its huge potential in increasing the system capacity. However, intra-cell interference is quite a challenging issue since D2D user equipment (DUE) may share the same resources with the traditional cellular user equipment (CUE). Moreover, due to the lack of convexity, globally optimal sharing strategy is generally unattainable. In this paper, we apply bipartite matching strategy from graph theory to optimize the resource allocation in D2D underlay network. Two different situations are considered in detail, namely optimal resource allocation with fixed power transmission (FPT) and optimal resource allocation with uplink power controlled transmission (PCT). Practical constraints such as minimum spectral efficiency and maximum transmit power limitations are also considered in the optimization. Simulations and analyses show that the proposed Bipartite Matching based Allocation (BMA) can improve the system capacity significantly comparing to those traditional methods.

Consider secure transmissions over the downlink of a multiuser MISO system with independent confidential messages, each of which is intended for one of the users and should keep secret from others. We are interested in a scenario where users have individual secrecy rate requirements. This scenario is practical but has drawn little attention in the literature so far. In this paper, we propose a secrecy rate balancing algorithm which tries to fulfill the secrecy rate requirements of users, and maximizes the minimum margin which is defined as the difference between the available secrecy rate and the required rate for each user. This algorithm balances out all the secrecy rate margins until an equilibrium is reached. We derive a necessary condition for the optimal solution. Finally, theoretical results are illustrated by numerical simulations.

Denoising recovery algorithms are very important for the development of compressed sensing (CS) theory and its applications. Considering the noise present in both the original sparse signal x and the compressive measurements y, we propose a novel denoising recovery algorithm, named Regularized Subspace Pursuit (RSP). Firstly, by introducing a data pre-processing operation, the proposed algorithm alleviates the noise-folding effect caused by the noise added to x. Then, the indices of the nonzero elements in x are identified by regularizing the chosen columns of the measurement matrix. Afterwards, the chosen indices are updated by retaining only the largest entries in the Minimum Mean Square Error (MMSE) estimated signal. Simulation results show that, compared with the traditional orthogonal matching pursuit (OMP) algorithm, the proposed RSP algorithm increases the successful recovery rate (and reduces the reconstruction error) by up to 50% and 86% (35% and 65%) in high noise level scenarios and inadequate measurements scenarios, respectively.

The present work is devoted to the analysis of a regenerative multi-node dual-hop cooperative system over enriched multipath fading channels. Novel analytic expressions are derived for the symbol-error-rate for M-ary quadrature modulated signals in decode-and-forward relay systems over both independent and identically distributed as well as independent and non-identically distributed Nakagami-q (Hoyt) fading channels. The derived expressions are based on the moment-generating-function approach and are given in closed-form in terms of the generalized Lauricella series. The offered results are validated extensively through comparisons with respective results from computer simulations and are useful in the analytic performance evaluation of regenerative cooperative relay communication systems. To this end, it is shown that the performance of the cooperative system is, as expected, affected by the number of employed relays as well as by the value of the fading parameter q, which accounts for pre-Rayleigh fading conditions that are often encountered in mobile cellular radio systems.

Distributed cooperative localisation, owing to its low computational burden, is more attractive in wireless networks than the centralised paradigm. However, the distributed algorithms tend to have worse location accuracy among existing works. In this study, the authors focus on the performance dissimilarity between distributed and centralised localisation algorithms, and analyse the Cramér–Rao lower bound. Then, their performance relationship is investigated, and verified by theoretical proof and extensive simulations. Furthermore, it is observed that the position ambiguity of cooperative mobile terminal is the primary cause of performance loss in the distributed algorithm, in contrast with the centralised one. To suppress the impact of position ambiguity, the authors establish an element‐wise‐weighted total least‐squares model and devise the corresponding distributed localisation algorithm. Numerical simulations indicate that the proposed algorithm outperforms traditional methods.

Recently, two cooperative transmission strategies, Coordinated Multi-Point (CoMP) and wireless relaying, are widely expected to be deployed in future LTE-Advanced (LTE-A) systems to improve the coverage of high data rates and cell-edge throughput. Motivated by the common cooperative diversity feature, in this paper we propose a novel OFDM-based relay system with coordinated wireless backhaul (CoMP backhaul), in which two cooperative base stations (CBS) simultaneously transmit information to an infrastructure-based cell-edge relay node (RN), so as to enhance the rate of backhaul link, as well as improve the end to end (e2e) throughput. In order to maximize the achievable e2e rate, a new Co-Phasing waterfilling power allocation scheme is developed for the multi-carrier CoMP backhaul. Meanwhile, with Taylor series expansion, a simplified iterative Co-Phasing waterfilling (ICPWF) algorithm is proposed to seek the optimal power allocation solutions. Numerical results are presented to verify the optimality of the derived scheme and to show e2e throughput gains over non-coordinated schemes.

Recently, the recovery performance of analog Compressed Sensing (CS) has been significantly improved by representing multiband signals with the modulated and merged Slepian basis (MM-Slepian dictionary), which avoids the frequency leakage effect of the Discrete Fourier Transform (DFT) basis. However, the MM-Slepian dictionary has a very large scale and corresponds to a large-scale measurement matrix, which leads to high recovery computational complexity. This paper resolves the above problem by modulating and band-limiting the multiband signal rather than modulating the Slepian basis. Specifically, instead of using the MM-Slepian dictionary to represent the whole multiband signal, we propose to use the non-modulated Slepian basis to represent the modulated and band-limited version of the multiband signal based on the recently proposed Modulated Wideband Converter (MWC). Furthermore, based on the analytical derivation with the non-modulated Slepian basis, we propose an Interpolation Recovery (IR) algorithm to take full advantage of the Slepian basis, whereas the Direct Recovery (DR) algorithm using the Moore-Penrose pseudo-inverse cannot achieve this. Simulation results verify that, with low recovery computational load, the non-modulated Slepian basis combined with the IR algorithm improves the recovery SNR by up to 35 dB compared with the DFT basis in noise-free environment.

The hybrid mobile satellite system operating in a single frequency network (SFN) mode is more and more popular, since the combination of satellite component (SC) and terrestrial component (TC) can provide better Quality of Service (QoS). This paper presents a novel way to realize the dual polarization MIMO transmission by using layered space time block code (LSTBC). The theoretical analysis and simulations indicate that the application of LSTBC can improve the system performance dramatically by making use of the space, time and polarization diversity. It provides a higher diversity gain than the previous space time (ST) codes when satellite and terrestrial link are both available. Even if one of the links is lost, it can still work properly and offers a performance equals to the satellite-only or terrestrial-only broadcasting system. Furthermore, it can be extended to the hybrid systems with multiple SCs and TCs.

Green radio has attracted much attention currently, and among the many factors to be considered, power consumption reduction is of great significance. In this paper, we consider a power minimization problem with rate requirement and power constraints for downlink multi-cell cooperative MIMO(Co-MIMO) systems. A transmit signal covariance design scheme is proposed, and an integrated algorithm is developed to search the optimal covariance for power minimization. Numericalsimulations verify the optimality of our proposed transmit signal covariance scheme. A significant performance gain is observed over the uniform power allocation (UPA) and the noncooperative geometric mean decomposition (GMD)-based schemes.

Relay-assisted cooperative network coding systems are widely investigated as an effective framework to improve the system performance in IMT-Advanced system. On the other hand, green communication issues have been paid much attention due to the large energy consumption in wireless network. In this paper, an energy-efficient resource allocation scheme is proposed for two-way relay-assisted OFDM system with physical network coding. Considering subcarrier-pairing, subcarrier assignment to relay node and power allocation, the resource allocation scheme is formulated to minimize the total power consumption with the individual required QoS, which can further the system performance. The optimization problem is effectively solved by the dual approach and the computational complexity is analyzed. In order to reduce the computational complexity, two suboptimal algorithms are also proposed. Simulations are provided to demonstrate our theoretical analysis and the performance of the proposed optimal iterative algorithm.

In this paper, cooperative power allocation is investigated in a two-transmitter multi-receiver broadcast multiple input single output (MISO) channel. With shared data information and shared channel quality indication (CQI), rather than sharing the full channel state information (CSI), the two transmitters jointly send information to multiple receivers by fixing the transmit beamforming weights according to Zero Forcing (ZF) principle. The power allocation is then studied to maximize the sum throughput (capacity), which can be modeled as a non-convex optimization problem. Through an application of Karush-Kuhn-Tucker (KKT) conditions, the nonconvex optimization problem is reformulated as a convex one. Then the closed form solution is derived, which takes a form similar to classic water-filling principle but is also combined with an interesting cooperation feature. Moreover, based on the derived solution, an optimal cooperative power allocation algorithm is presented. Finally, numerical simulation results are given to evaluate and demonstrate the performance of the derived joint power allocation scheme.

Due to the prohibition of instantaneous channel state information (CSI) at the satellite gateway resulting from the long round trip time, current cooperative MIMO techniques for the integrated mobile satellite service system focus on diversity transmission. In this work, partial precoding is proposed, which can solve this problem to some degree. In this method, the satellite retains the conventional transmission without any knowledge of the CSI, while linear precoding is executed at the complementary ground component based on the CSI of the satellite and terrestrial channel. Both perfect and imperfect CSIT are investigated, and corresponding transceiver designs are proposed. Simulation results demonstrate that CSIT of the satellite channel can help dramatically improve system performance.

In mobile satellite service (MSS) system, channel using efficiency is still an important factor because the system's frequency reuse factor can't reach 1 exactly. Here we try to introduce channel borrowing technique into MSS system. In channel borrowing technique, if channels in adjacent cells are allocated, these channels can't be borrowed. Therefore, in high spectrum reuse scenario, this may result in insufficient borrowable channels because only when none of the adjacent cells is using this channel can this channel be borrowed. In this paper, we propose a channel reallocation scheme based on matching theory in graph theory to solve this problem by lowering the interference level. Further, we put forward a simplified channel reallocation scheme to reduce the computational complexity. As the simulation results show, our schemes can efficiently improve the system's performance.

A novel Multiple-input Multiple-output (MIMO) detection scheme based on orthogonal experimental design (OED) is proposed in this paper. By exploiting the initial solutions given by linear detection and non-linear detection algorithms, a searching subspace is constructed to obtain additional performance gain. Exhaustive searching method is optimal in the subspace, but with quite high complexity. Then OED, which is widely used in chemical experiments and statistics, is applied to reduce the computational complexity. The minimal required complexity of OED-MIMO is only 6.25% needed but just with insignificantly performance lost compared with exhaustive method for the 8×8 MIMO system and 16-QAM modulation scheme. The proposed OED-MIMO scheme provides nearly 10 dB gain compared with MMSE and 5 dB gain with ZF-VBLAST.

Recently, the performance bounds of the compressed sensing (CS) recovery algorithms have been investigated in the noisy setting. However, most of the papers only focus on the noisy measurement model where the signal is noiseless and the noise enters after the CS operation. The noisy signal model where both the signal and the compressed measurements are contaminated by the different noises is not considered. This paper works on the noisy signal model and provides the performance bounds for the following popular recovery algorithms: thresholding and orthogonal matching pursuit (OMP), Dantzig selector (DS) and basis pursuit denoising (BPDN). The performance of the recovery algorithms is quantified as the ℓ2distance between the reconstructed signal and the true noisy signal. Next, the impacts of the noise are analyzed on the basis of the quantified performance. The analysis results show that the effective way to restrain the impact of the noise is to choose the measurement matrix with low correlation between the columns or the rows. Finally, the theoretical bounds are verified with numerical simulations by calculating the mean-squared-error for the different noise variances. The simulation results show that OMP owns the better performance than the other three recovery algorithms under the noisy signal model.

It is well known that traditional water-filling provides a closed form solution for capacity maximization in orthogonal frequency division multiplex (OFDM) system. In this paper, cooperative power allocation is investigated in a two-transmitter multi-receiver model for OFDM systems. The local full channel state information (CSI) is available at the two transmitters respectively, where each transmitter has an individual power constrain. The transmitters first cooperate by sharing CSI, and then jointly optimize power allocation in the metric of sum throughput, which can be modeled as a convex optimization problem. Through an application of Karush-Kuhn-Tucker (KKT) conditions, the convex optimization problem is reformulated as a simplified convex one. Then the closed form solution is derived, which takes a form similar to classic water-filling principle. Based on the solution, the optimal cooperative power allocation algorithm is constructed, the structure of which can be explained as a cooperative water-filling relative to the traditional water-filling. Finally, numerical simulation is given to evaluate and demonstrate the performance of the optimal cooperative water-filling scheme.

Recently, due to the exponentially increasing overall energy consumption of the wireless networks, much attention has been directed to the energy efficiency metrics and minimization of energy consumption, both in battery powered terminal devices as well as in infrastructure devices. In this article, we present an analysis of energy-optimized cooperative network where nodes in the environment share and coordinate their resources to relay the source signal, and compare the achievable performance with the reference performance of direct transmission alone. To improve the energy-efficiency further, the optimal power allocation (OPA) scheme to minimize the energy consumption under certain quality-of-service (QoS) constraint, namely destination symbol error rate (SER), over the Nakagami-m fading channels is derived. Stemming from these derivations, we show that depending on the fading parameter m, significant performance gain can be achieved over the equal power allocation (EPA) scenario and direct transmission schemes. Moreover, the effect of relay location on the energy efficiency over the Nakagami-m fading environment is investigated.

Recently, distributed data storage (DDS) for Wireless Sensor Networks (WSNs) has attracted great attention, especially in catastrophic scenarios. Since power consumption is one of the most critical factors that affect the lifetime of WSNs, the energy efficiency of DDS in WSNs is investigated in this paper. Based on Compressed Sensing (CS) and network coding theories, we propose a Compressed Network Coding based Distributed data Storage (CNCDS) scheme by exploiting the correlation of sensor readings. The CNCDS scheme achieves high energy efficiency by reducing the total number of transmissions Nttot and receptions Nrtot during the data dissemination process. Theoretical analysis proves that the CNCDS scheme guarantees good CS recovery performance. In order to theoretically verify the efficiency of the CNCDS scheme, the expressions for Nttotand Nrtotare derived based on random geometric graphs (RGG) theory. Furthermore, based on the derived expressions, an adaptive CNCDS scheme is proposed to further reduce Nttot and Nrtot. Simulation results validate that, compared with the conventional ICStorage scheme, the proposed CNCDS scheme reduces Nttot, Nrtot, and the CS recovery mean squared error (MSE) by up to 55%, 74%, and 76% respectively. In addition, compared with the CNCDS scheme, the adaptive CNCDS scheme further reduces Nttotand Nrtotby up to 63% and 32% respectively.

Analog Compressed Sensing (CS) has attracted considerable research interest in sampling area. One of the promising analog CS technique is the recently proposed Modulated Wideband Converter (MWC). However, MWC has a very high hardware complexity due to its parallel structure. To reduce the hardware complexity of MWC, this paper proposes a novel Random Circulant Orthogonal Matrix based Analog Compressed Sensing (RCOM-ACS) scheme. By circularly shifting the periodic mixing function, the RCOM-ACS scheme reduces the number of physical parallel channels from m to 1 at the cost of longer processing time, where m is in the order of several dozen to several hundred in MWC. It is proved that the m×M measurement matrix of RCOM-ACS scheme satisfies the Restricted Isometry Property (RIP) condition with probability 1-M-O(1)when m = O(rlog2Mlog3r), where M is the length of the periodic mixing function, r denotes the sparsity of the input signal. Furthermore, to make a good tradeoff between processing time and hardware complexity, a short processing time RCOM-ACS scheme is proposed in this paper. Simulation results show that, the proposed schemes outperform MWC in terms of recovery performance.

In this paper, we propose a novel compressive sensing (CS) based indoor positioning approach, which uses the signal strength differentials (SSDs) as location fingerprints (LFs). The target location is regarded as an unknown sparse location vector in the discrete spatial domain. Then it just takes a little number of online noisy SSD measurements for the exact recovery of the sparse location vector by solving an ℓ1-minimization program. In order to mitigate the influences of large measurements noise on the recovery accuracy, an LF space denosing algorithm is proposed to discriminate the localization contribution rate of every LF according to its SSD variation. Moreover, an LF space filtering strategy is also exploited to lower the high computational complexity of the CS recovery algorithm. Both experimental results and simulations demonstrate that we achieve remarkable improvements on the positioning performance of the CS based approach by using the two proposed algorithms.

Employing active antenna array system (AAS) on 3GPP LTE base station (BS) has some potential impact on existing BS RF requirements. As each active antenna element in the array is connected to a separate transceiver, RF requirements such as in-band blocking should be tested on antenna element place. Furthermore, vertical sectorization brought by AAS in LTE leads to larger inter-system interference due to additional adjacent vertical sector, thus the existing Adjacent Channel Interference Ratio(ACIR) requirement between two LTE systems needs to be reconsidered. In this paper, 3GPP uplink LTE coexistence simulations are conducted to evaluate the above two RF performances. From simulation results, the blocking level on antenna element is higher than the current 3GPP requirement, whereas the ACIR results in vertical sectorization scenario can meet the existing requirement. It is concluded that AAS will have critical impact on current LTE BS blocking requirement and deploying vertical sectorization in LTE is feasible in the aspect of ACIR level.

Coordinated Multi-Point (CoMP) transmission is considered as a tool for 3GPP LTE-Advanced to increase the cell edge throughput. In CoMP JT (Joint Transmission) mechanism, multiplied time-frequency resources are occupied by JT users. Additionally, scheduling relevancy exists among the users, i.e., scheduling orders of different users influence on the cell average and cell edge throughput. So the impact of scheduling relevancy should be considered among the users in the same coordination cluster. Otherwise, the CoMP system throughput will decline significantly. To address this issue, an effective scheduling scheme is proposed for CoMP in heterogeneous scenario. Taking both the user fairness and the throughput of the coordination cluster into account, a suitable scheduling scheme is proposed to improve the system throughput. System-level simulation results prove that, the proposed scheduling scheme outperforms the conventional scheduling scheme in terms of both cell average and cell edge spectral efficiency.

It is known that Coordinated Multiple Point (CoMP) transmission can increase data rates as more than one copies of signals are coordinated transmitted by multiple points. For the wideband communication system with frequency selective fading channel, the optimal power allocation will be different for each fading block. The Water-Filling (WF) power allocation is optimal for single transmission point. However, it is hard to get closed-form optimal solution for the CoMP transmission as the power allocation for fading blocks of one point is simultaneously affected by other coordinated points. This paper analyzes the power allocation of coherent coordinated transmission in CoMP block fading communication system with two transmission points, and the optimal solution is given by Lagrange method. The numerical simulations show that it has a near constant performance gap between the optimal allocation and equal power allocation, and the performance of separate WF power allocation is between optimal allocation and equal power allocation.

In restricted communication environment, multiple-target localization is of important practical significance. So the cooperative group localization (CGL) was firstly put forward, which has been verified the effectiveness on localization performance gain and simultaneous multiple-target localization in ill conditions. However, it exists two inherent difficulties: the strict demand for CGL topology and the high complexity. By the rational use of information to relax restrictions on topology, and by dividing the complex problem into some simple local ones, the factor graph (FG) together with the sum-product algorithm is a perfect candidate for the problems above. This paper firstly proposes a novel FG-based CGL algorithm. Numerical results indicate that compared with the existing CGL algorithm, the proposed algorithm not only performs better in relaxing CGL topology requirement, but also enjoys high localization accuracy under low complexity.

It is known that traditional water-filling provides a closed form solution for capacity maximization in frequency-selective block fading channels or multicarrier system with adaptive modulation. This waterfilling solution is derived from a maximum mutual information argument with single transmission point. Motivated by the new technology of coordinated multiple point (CoMP) transmission , a new closed form solution named as joint-waterfilling is derived for a multicarrier system with multiple coordinated transmission point (CTP) [2]. However, like traditional waterfilling, to utilize this joint-waterfilling in practical system, an important issue is the complex transmitter and receiver design, such as variable-rate variable-power MQAM modulation and coding. In this paper we investigate a new constant-power joint-waterfilling scheme for a coordinated transmission system, in which two constant power levels are used across a properly chosen subset of subchannels. A rigorous worst-case performance bound of the constant-power joint-waterfilling is given based on the \emph{duality gap} analysis. Furthermore, a low-complexity constant-power adaptation algorithm is also developed. Numerical simulation result shows that the proposed constant-power joint-waterfilling has a negligible performance loss compared with true joint-waterfilling.

Coordinated multi-point (CoMP) has been adopted in 3GPP LTE-Advanced to improve the coverage of high data rates. Downlink CoMP is mainly categorized into joint transmission (JT) and coordinated beamforming (CB). Previous research shows that JT does not benefit cell average throughput. Moreover, traditional CB algorithms are based on iterative evaluation, resulting in very high computational complexity. To address these issues, in this paper, we propose an effective inter-cell interference coordination scheme for downlink CoMP, which includes two phases. Firstly, in order to improve the performance of all user equipments (UEs), cell edge UEs employ JT, while CB is adopted by both cell edge and cell center UEs. Secondly, we design linear CB precoders by maximizing a signal to leakage and noise ratio (SLNR) metric, so as to reduce processing complexity. System-level simulations validate that, in contrast to non-CoMP, the proposed scheme brings a cell average spectral efficiency (SE) gain up to 21.50% and a cell edge SE gain up to 119.2%.

Multi-User Multiple Input Multiple Output (MU- MIMO) technique is introduced into 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) to enhance the system capacity and spectrum efficiency. On the other hand, Coordinated Multi-Point transmission and reception (CoMP) technique which can reduce inter-cell interference (ICI) significantly, has been adopted in LTE-Advanced as one of the promising techniques to increase the throughput of the cell edge user. However, some new problems appear with the promotion of MU-MIMO technique in the CoMP scenario. Considering the necessity that an enhanced algorithm should be proposed to fit the CoMP scenario, this paper presents a novel capacity maximization criterion for multiuser pairing strategy based on particle swarm optimization algorithm for CoMP MU-MIMO in uplink LTE-A system. Under this criterion, this paper gives two kinds of receivers which are zero forcing (ZF) equalizer and minimum mean square error (MMSE) equalizer in the frequency-domain. Simulation results show that the proposed PSO strategy achieves nearly the same performance to the exhaustive search (ES) algorithm in terms of capacity performance, but with lower complexity.

In cognitive radio (CR) networks, two main challenges faced by cooperative spectrum sensing are low detection performance and long detection time in low SNR scenario. To the best of our knowledge, little study analyzes these two problems simultaneously and thoroughly. Therefore, this paper proposes a novel spectrum sensing scheme, termed multistep detection (MD) scheme, which aims to resolve the above two problems. For MD scheme which consists of five steps, we focus on the following two key parts: two-threshold detection and iteration detection. For the two-threshold detection, we make theoretical analysis about the establishment of the two thresholds. And for the iteration detection, a novel spectrum sensing algorithm is proposed to improve the detection performance in low SNR. The iteration algorithm utilizes the following property to improve the signal's SNR: the variance of the mean of independent and identity Gaussian random variables is 1/K of one variable's variance. The simulation results indicate that the proposed MD scheme outperforms the traditional cooperative scheme both on the detection performance and the detection time significantly.

This paper proposes a novel wideband spectrum sensing (WSS) scheme, termed multi-antenna compressed wideband spectrum sensing (MCWSS) scheme, which utilizes compressed sensing (CS) to reduce the extremely high sampling rate of wideband signal. Although there are studies on compressed wideband spectrum sensing, they only focus on single antenna signal. Since multi-antenna technology can enhance the detection performance, this paper investigates the multi-antenna scenario. However, existing CS recovery algorithms are designed only for single antenna signal and are not suitable for recovering multi-antenna signals. Therefore, the paper proposes two novel CS recovery algorithms from different angles, namely CRL2(combining relevance via L2norm) algorithm and CBS (combining before sampling) algorithm. The CRL2algorithm jointly recovers the multi-antenna signals and performs better than single antenna scenario. Whereas, CBS algorithm can significantly improves the recovery performance with an additional analog combining operation. Since existing WSS algorithms are too complicated, we devise a novel WSS algorithm, i.e. DA (divided-averaged) algorithm, which has good performance with low complexity. Simulation results show that the MCWSS scheme performs well at low sampling rate.

Maximizing system sum-rate or minimizing error rate with constrained power are the main research focuses in the previous system-oriented power allocation schemes. However, link-oriented power allocation schemes that minimize power consumption with constrained rates for every link, attracted researchers in recent energy efficiency system design. In this paper, a link-oriented optimal power allocation scheme is firstly proposed for physical layer network coding with DeNoise-and-Forward protocol in multicast system, that two sources and two destinations communicating with the assistance of arbitrary number of relays. We also consider a suboptimal power allocation scheme with low complexity, where a single relay is chosen to minimize the power consumption. The numerical simulation shows that the suboptimal power allocation scheme also can achieve relatively low power consumption, even approach to the optimal scheme in some special case.

In this paper, we investigate a new power allocation scheme for a multi-user orthogonal frequency division multiplex (OFDM) system, in which two cooperative power sources (CPS) coordinately transmit their power to multiple orthogonal subchannels. In order to maximize the sum rate, a closed form solution is obtained by analytical derivation. Based on the derived solution, we present a novel joint optimal power allocation scheme named as joint water-filling (Jo-WF) which has a remarkably simple nature. Furthermore, a new iterative Jo-WF algorithm is also proposed. Motivated by the regular theoretical derivation of the two-CPS case, we subsequently extend the closed form solution of Jo-WF into arbitrary K-CPS case. Numerical simulation results verify that, compared to traditional non-cooperative water-filling (WF) and equal power allocation (EPA), the proposed Jo-WF scheme provides a significant sum rate gain.

Coordinated multi-point transmission/reception (CoMP) has been considered in 3GPP LTE-Advanced to improve system performance, especially cell edge throughput. The conventional uplink power control (PC) scheme cannot work well since a UE can be served by multiple cells. This paper analyzes uplink PC issues in detail, and proposes an effective scheme aiming to obtain the reception diversity gain from CoMP. System-level simulation results have shown that CoMP systems with both the conventional scheme and the proposed scheme can achieve a better cell edge performance, compared with non-CoMP systems. Furthermore, the proposed scheme considerably outperforms the conventional scheme, it can bring an additional cell average throughput gain up to 15.78% and an additional cell edge throughput gain up to 69.22% over the conventional scheme, thus is recommended to be adopted in CoMP systems.

Adaptive modulation and coding (AMC) scheme suffers performance degradation with the signaling feedback delay, as channel state information (CSI) is outdated with time variant channel. In this paper, we propose a robust AMC scheme to mitigate the effect of signaling feedback delay, especially in vehicular environments. Channel information estimated in current time interval is utilized to obtain the distribution of channel fading in upcoming time interval. Average block error rate (BLER) for given modulation and coding scheme (MCS) is derived. Simulation results indicate that the proposed scheme guarantees reliable performance even in a vehicular environment with maximum Doppler frequency shift of 300 Hz.

Closed-loop MIMO system can provide dramatic gains in channel capacity. However, mobile terminal (MT) must feedback the downlink channel state information (CSI) to Base station (BS), and transmitting CSI is a heavy burden consuming considerable uplink bandwidth. To address this bottleneck problem, a zero-overhead downlink CSI feedback scheme is proposed in this paper. After receiving the downlink pilots, MT directly superposes the uplink pilot on the received analog downlink pilot, forms the hybrid pilot (HP), and then transmits HP to BS via uplink pilot channel. Because downlink CSI can be recovered from HP at BS, and HP is fed back to BS via the uplink pilot channel, no extra uplink channel is occupied and the overhead for CSI feedback is zero. Moreover, since no CSI estimation process is performed at MT, the terminal's complexity can be reduced. Numerical Simulations verify that, the proposed downlink CSI feedback has the higher precision than the traditional feedback schemes, and the MSE of CSI feedback can be reduced significantly.

With the advent of multimedia services featured 4th generation (4G) mobile communications, multiple input multiple output (MIMO) combined with multi-level quadrature amplitude modulation (M-QAM) technique gains great popularity for its capability of supporting high data rate. In this paper, the statistical distribution of post signal to noise plus interference ratio (SINR) in each iterative step of optimal ordered zero forcing perfect successive interference cancellation (ZF-OOPSIC) detection is deduced based on order statistics theory. Resultantly, the bit error rate (BER) of M-QAM MIMO system with ZF-OOPSIC detector is analyzed under flat Rayleigh fading channel. This precise analytical BER result could be taken as the lower bound of various zero-forcing ordered successive interference cancellation (ZF-OSIC) receivers. Monte Carlo simulations validate the analytical results and prove the conclusions.

A user pairing method is proposed to improve the throughput and users' fairness performance in virtual multiple input multiple output (VMIMO) in 3rd Generation (3G) long-term evolution (LTE). This approach is based on post-processing signal to interference plus noise ratio (SINR) of each user, and takes both large scale and small scale fading into account, which renders more spatial multi-user diversity than that of the traditional paring criterions. To simplify the method, a parameter D is constructed from each user's raw SNR and channel matrix H. As shown in the simulation results, the average system transmission rate obtained by the proposed simplified SINR-based pairing scheduling (SSINRPS) is obviously larger than that of traditional strategies such as random pairing scheduling (RPS), determinant pairing scheduling (DPS) and SINR-based pairing scheduling as the optimal one. Meanwhile, the fairness of the paired users is improved significantly.

The wireless cooperative positioning scheme based on information fusion is proposed in this paper. The scheme combines large-scale TOA and small-scale distance measured in the converged networks. The asymptotically efficient estimator based on maximum likelihood (ML) estimation is introduced in the proposed scheme, and the position of mobile terminal is estimated with closed-form solution. Moreover, the Cramer-Rao lower bound (CRLB) of location error is analyzed in theory. Theoretical analysis and simulation results verify that the proposed scheme can significantly enhance the location accuracy, especially in NLOS environment, and the MSE performance of the location estimator will approximate the CRLB as the number of reference terminals increases.

In traditional TOA or TDOA-based cellular localization, if mobile terminal (MT) can not connect well with 3 or more than 3 BSs, the localization will fail, or positioning accuracy is very poor. To solve this problem in 4G wireless networks, the cooperative group localization (CGL) scheme is proposed in this paper, where it is assumed that different MTs can directly communicate with each other in a peer-to-peer manner and perform the distance measurement, forming a terminal group (TG). When each MT in a TG connects with less than 3 BSs, CGL makes it possible to locate all the MTs in the TG by other MTs' cooperation. The proposed scheme can effectively enhance the robustness and precision of localization. Based on rigid graph theory, the unique solvability of CGL scheme is analyzed in theory. A searching and positioning algorithm for CGL is introduced, and numerical simulations indicate that the proposed algorithm has significantly advantages, compared with traditional localization methods.

This paper presents a cognitive location scheme based on Taylor-series method (TS-CL) for future wirelss networks . When the location of the destination mobile station (DS) is measured by the network, some extra mobile stations around the DS are involved in and assist the base stations (BSs) to complete the positioning process in TS-CL. The proposed method jointly estimates both DS and the extra mobile stations, named as reference mobile station (RS) in this paper. Moreover, the time-differential-of-arrival (TDOA) estimation between DS and pairs of RSs are measured by short-range communication signal with high accuracy. The high-accuracy short-range measurement information are applied into improving the accuracy of the existing positioning techniques using cellular systems' signals .According to the theoretic analysis, the Cramer-Rao bound (CRLB) of TS-CL reduces significantly.

Various characteristics of applications determine different requirements of mobility management schemes. Realtime applications especially lay strict limitation on handoff latency and latency stretch. This limitation is exacerbated when system robust, scalability and load balancing are required simultaneously. In this paper, we present a peer-to-peer based mobility support scheme that keeps advantages of P2P on robustness and scalability as well as optimizes handoff latency and latency stretch. Moreover, a proxy handoff mechanism is proposed to ensure the optimal performance in movement. Analysis and experiments based on OpenDHT reveal proxy handoff works effectively and handoff latency about 50 ms with latency stretch value 1.14 can be achieved.

In this paper, we propose a non-unitary codebook based precoding scheme for multi-user MIMO (MU-MIMO) downlink transmission with limited feedback The proposed MU- MIMO scheme will perform scheduling and precoding just according to the limited feedback of the signal to interference and noise ratio (SINR) of each user to enhance the system capacity. A precoder codebook design method is related to Grassmannian line packing criterion. We group vectors from the Grassmannian codebook into precoding matrices according to the correlation coefficient of the vectors to suppress the multi-user co-channel interference (CCI). It terms as non-unitary precoding and outperforms the traditional single user MIMO system as well as the unitary codebook based MU-MIMO scheme with limited feedback and low complexity.

A novel location model for 4G mobile communication networks is proposed in the paper. The model utilizes the more precise relative distance estimated by short-range signals between the destination mobile terminal to be located (abbr. DT) and the other mobile terminals near DT to be the reference mobile terminal assisting DT (abbr. RT) to improve the location accuracy of DT. Besides, Triangle Similarity Theorem is firstly introduced into the model to search the desired optimal /suboptimal triangle. It is shown that by analysis and simulation that more accurate estimated position for DT in stationary status can be achieved based on the model above.

In this paper, we investigate a novel MIMO transceiver with space-time-hopping code combing pulse position modulation (STH-PPM) for UWB time division multiple access (TDMA) system. Moreover, we propose a new approach called second-order statistics (SOS) with unknown channel state information (CSI) for receiver detection. This new transceiver does not require any training or pilot symbols or decision feedbacks, and it performs well in flat fading channel.