The surge in low-altitude economic activities has spurred a significant interest in sensing Unmanned Aerial Vehicles (UAVs). With the widespread deployment of 5G infrastructure and the increasing prominence of integrated sensing and communication, monitoring UAVs via 5G base stations is a natural consideration. However, the rapid Doppler shifts of UAVs and sparse 5G reference signals violate Nyquist sampling requirements. To bridge this gap, we propose LaSen, which merges reference and downlink data signals for sensing. A key challenge stems from the fact that the combination of the periodic reference signals and stochastic data signals constitutes a non-uniform, time-varying measurement matrix. LaSen formulates the tracking of UAVs as a sparse recovery problem, where the target’s kinematics are reconstructed from non-uniform, sub-Nyquist observations. LaSen overcomes the challenges of volatile 5G signal patterns in an iterative way, starting from good measurements as an anchor and progressively refining the suboptimal measurements. Real-world experiments show that LaSen significantly extends the velocity sensing capability, where the measurable speed is up to 20.2 m/s. LaSen can detect drones at a distance of 108 m and can continuously track the distance and velocity of multiple targets, even when the downlink channel is sparsely and dynamically occupied. This work demonstrates the feasibility of high-speed target sensing in next-generation dual-function 5G/6G infrastructures.

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.

While integrated sensing and communication (ISAC) beamforming has become a research hotspot recently, most proposed approaches remain scenario-specific and suffer from limited generalizability. In this paper, we propose a flexible beamforming design framework for multiple-input multiple-output ISAC (MIMO-ISAC) systems. To characterize the optimal performance tradeoff between the sensing and communication (S&C), the objective is to maximize the weighted sum of S&C mutual information (MI). An integrated fractional programming (IFP) framework is first proposed, from which a semi-closed-form solution for the optimal beamformer is derived. The proposed framework is further extended to several representative ISAC scenarios, including fairness-aware tradeoffs, imperfect channel state information (CSI), dynamic radar cross-section (RCS) variations, and massive MIMO systems. By incorporating advanced optimization techniques, the IFP framework effectively addresses the challenges arising in these scenarios with only minor modifications while still yielding semi-closed-form solutions. Moreover, we show that the IFP framework is closely related to the projected gradient descent (PGD) method, and its computational efficiency can be further enhanced through gradient acceleration techniques. Simulation results demonstrate that the IFP framework and its variants consistently outperform benchmark schemes across diverse system settings. The proposed low-complexity and accelerated schemes reduce computation time by approximately 65% and 89%, respectively, with only marginal performance degradation. These results highlight the flexibility and generality of the proposed IFP framework for ISAC beamforming design and are expected to provide valuable insights for the development of future 6G systems.

This paper investigates an explicit polar coding scheme for a two-user discrete memoryless multiple access wiretap channel with partial rate-limited feedback (MAC-WT-PLF). Feedback is exploited to increase channel-input correlation, inject dummy messages, and encrypt messages using a one-time pad. Existing MAC-WT polar coding schemes assume independent channel inputs, which cannot support the correlation requirement. Therefore, we propose an explicit polar mapping that leverages feedback to introduce correlation between the channel inputs. This mapping allows both the transmitter and the receiver to restructure the correlation in opposite decoding directions. The proposed scheme relies on source polarization, block Markov coding, superposition coding, lossy source coding, and ratesplitting, without making symmetry or degradation assumptions on the channel model. Rigorous information-theoretic asymptotic analysis establishes that the proposed scheme ensures both reliability and strong secrecy, and attains the entire achievable secrecy rate region given in prior work.

Recent AI agents, such as ChatGPT and LLaMA, primarily rely on instruction tuning and reinforcement learning to calibrate the output of large language models (LLMs) with human intentions, ensuring the outputs are harmless and helpful. Existing methods heavily depend on the manual annotation of high-quality positive samples, while contending with issues such as noisy labels and minimal distinctions between preferred and dispreferred response data. However, readily available toxic samples with clear safety distinctions are often filtered out, removing valuable negative references that could aid LLMs in safety alignment. In response, we propose Positive–Toxic Self-Alignment (PT-ALIGN), a novel safety self-alignment approach that minimizes human supervision by automatically refining positive and toxic samples and performing fine-grained dual instruction tuning. Positive samples are harmless responses, while toxic samples deliberately contain extremely harmful content, serving as a new supervisory signal. Specifically, we utilize LLM itself to iteratively generate and refine training instances by only exploring fewer than 50 human annotations. We then employ two losses, i.e., maximum likelihood estimation (MLE) and fine-grained unlikelihood training (UT), to jointly learn to enhance the LLM’s safety. The MLE loss encourages an LLM to maximize the generation of harmless content based on positive samples. Conversely, the fine-grained UT loss guides the LLM to minimize the output of harmful words based on toxic samples at the token-level, thereby guiding the model to decouple safety from effectiveness, directing it toward safer fine-tuning objectives, and increasing the likelihood of generating helpful and reliable content. Experiments on 9 popular open-source LLMs demonstrate the effectiveness of our PT-ALIGN for safety alignment, while maintaining comparable levels of helpfulness and usefulness.

The emergence of beyond-fifth-generation (B5G) networks and the increasing demand for Internet of Things (IoT) requires deploying a cloud-edge-terminal computing network with the Software Defined Network as the controller. However, this network is vulnerable to various threats, notably SYN flood attacks. This paper adopts queuing theory and game theory to explore Mobile Edge Computing (MEC) attack and defense interaction in different IoT businesses. Moreover, we propose a utility model of the packet flow in MEC networks featuring the delay and packet loss rate in the SYN flood attacks. For the attacker and defender’s strategy, we use game theory to model the interaction between strategy and resource allocation. A search algorithm analyzing MEC cell impact on strategy selection is developed, and we investigate the impact of the attacker’s possession of prior knowledge versus lack thereof regarding MEC cell characteristics under SYN flood attacks. The proposed game models are solved, and the results show that under the defender’s strategy, the attacker has no chance to launch SYN flood attacks under the defender’s defense cost of four times MEC computing resources; the cost of defense resources is lower than other related schemes.

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.

this paper, we investigate the Inter Symbol and Carrier Interference (ISCI) of doubly-dispersive channels in highly dynamic scenarios from the continuous and discrete perspectives, respectively, and thoroughly analyze its impact on the performance of communications systems. Due to its robustness against the Doppler effect in time-varying channels, Orthogonal Time Frequency Space (OTFS) modulation has gained significant research interest, with many equalization algorithms proposed. However, existing studies either fail to fully account for ISCI or suffer from prohibitive complexity. In this study, we accurately quantify the ISCI of doubly-dispersive channels and provide an in-depth analysis from continuous and discrete channel models, respectively. Based on this, we propose a low-complexity Minimum Mean Square Error (MMSE) equalization to equalize ISCI in doubly-dispersive channels. The proposed algorithm demonstrates a reduction in complexity of the MMSE equalization fromO(M3N3)toO(MNL2bw), whereLbwdenotes the bandwidth of the time domain channel matrix. Concurrently, it has been demonstrated to significantly reduce the bit error rate (BER), thereby enhancing communication performance.

Stacked intelligent metasurface (SIM) and dual-polarized SIM (DPSIM) enabled wave-domain signal processing have emerged as promising research directions for offloading baseband digital processing tasks and efficiently simplifying transceiver design. However, existing architectures are limited to employing SIM (DPSIM) for a single communication function, such as precoding or combining. To further enhance the overall performance of SIM (DPSIM)-assisted systems and achieve end-to-end (E2E) joint optimization from the transmitted bitstream to the received bitstream, we propose an SIM (DPSIM)-assisted E2E orthogonal frequency division multiplexing (OFDM) system, in which traditional communication tasks such as channel coding, modulation, precoding, combining, demodulation, and channel decoding are performed synchronously within the electromagnetic (EM) forward propagation. Furthermore, inspired by the idea of abstracting real metasurfaces as hidden layers of a neural network, we propose the EM neural network (EMNN) to enable the control of the E2E OFDM communication system. In addition, transfer learning is introduced into the model training, and a training and deployment framework for the EMNN is designed. Simulation results demonstrate that both SIM-assisted E2E OFDM systems and DPSIM-assisted E2E OFDM systems can achieve robust bitstream transmission under complex channel conditions. Our study highlights the application potential of EMNN and SIM (DPSIM)-assisted E2E OFDM systems in the design of next-generation transceivers.

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.

Communication overhead is a major bottleneck in distributed AI training, particularly in multi-tenant environments, limiting GPU utilization. Existing job-level scheduling methods fail to address the varying urgency of individual communication operations. We propose Flux, a novel fine-grained scheduler that prioritizes communication operations based on their Urgency Score and job intensity. Our evaluation shows Flux improves GPU utilization by up to 10 % compared to state-of-the-art job-level algorithms. This demonstrates the significant advantage of fine-grained communication scheduling in multitenant AI clusters.

Multiple base stations (multi-BS) cooperative sensing is considered a promising solution for achieving high-accuracy sensing and robust connectivity in future sixth-generation (6G) mobile communication systems. In this paper, we investigate the design of a target localization scheme for a cooperative integrated sensing and communication (ISAC) system. Specifically, our objective is to achieve high-accuracy target localization without compromising communication performance. To accomplish this, we propose a high-accuracy sensing scheme consisting of two stages. In the first stage, we develop a novel noise subspace re-projection orthogonal matching pursuit (NSR-OMP) algorithm to enable simultaneous, high-accuracy estimation of multipath parameters in a high-dimensional signal space. In the second stage, we introduce a data fusion algorithm based on the weighted average method, which effectively mitigates the impact of ill-conditioned measurements within the bistatic ranges. Finally, simulation results demonstrate that the proposed sensing scheme enhances sensing accuracy by approximately 78.9% compared to benchmark schemes, effectively validating its feasibility in practical systems.

The balance between system performance and power consumption has always been a top priority, particularly in massive multiple-input multiple-output (MIMO) system. Besides, as one of the most critical modules in the radio-frequency (RF) chain, analog-to-digital converters (ADCs) hold significant importance. To find that tradeoff, mixed-ADC architecture, combining high-resolution ADCs and low-resolution ADCs, emerges as an effective approach. In this paper, we propose a novel three-stage near maximum likelihood (nML) detector to perform signal detection for the mixed architecture. In the first step, high-resolution ADCs are exploited to roughly find the transmitted signal vector. Subsequently, a convex optimization problem is further constructed based on the rough vector and projection gradient method is used to estimate the more likely one. An approximate expression for the cumulative distribution function is derived to simplify the optimization problem. Finally, the size of the transmitted signal vector collection is effectively minimized on the basis of the most possible vector and exhaustive search is carried out on the collection. Numerical results show that the proposed three-stage nML detector is efficient enough to simultaneously obtain high accuracy and low complexity.

This paper focuses on the performance analysis of multi-functional RIS (MF-RIS) assisted ambient backscatter communication (AmBC) networks. To be specific, we derive the closed-form and asymptotic expressions of outage probability for both data signal and backscatter signal with the consideration of imperfect and perfect successive interference cancellation (SIC) process. On this basis, the diversity orders, which are related to the number of MF-RIS elements and levels of imperfect SIC, are also derived. Numerical results indicate that the outage performance of MF-RIS-assisted AmBC networks outperforms that of passive and conventional relaying schemes.

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.

5G networks and Subband Full Duplex (SBFD) technology aim to enhance uplink transmission efficiency in IoT environments. However, large-scale random access users in SBFD systems exacerbates cross-link interference (CLI), which could undermine its potential performance advantages. To address this challenge, we propose a priority-aware random access (PA-RA) strategy, which comprises two key components. First, access users are selected through a dynamic priority evaluation method that considers Quality of Service (QoS), real-time resources availability, and historical access attempts. Second, a centralized subband allocation method preferentially assigns resources to central regions of the uplink subband to minimize CLI, while adaptively utilizing edge resources under low-interference conditions. The simulation results demonstrate that PA-RA strategy could significantly reduce CLI compared to conventional methods, achieving high success rates of access and low latency. Furthermore, it provides a robust framework for interference management in SBFD networks with high-density IoT devices.

In massive MIMO mmwave systems, the inherent conflict between high-latency conventional beam scanning in millimeter-wave communications and the critical need for user location privacy protection is addressed. In this paper, an implicit location-based beam selection method driven by deep learning is proposed, where raw user coordinates are transferred into irreversible implicit feature vectors through a triple protection mechanism involving Gaussian noise injection, differential privacy perturbation, and nonlinear transformation. A lightweight deep neural network integrated with channel attention is designed to establish end-to-end mapping from implicit location to optimal beam indices for latency-sensitive selection. Experiments with DeepMIMO dataset prove that the proposed method reduces beam search complexity by nearly 90% with comparable accuracy to exhaustive search. This work provides a novel framework for jointly optimizing privacy preservation and beam management efficiency in mmWave communication systems.

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.

To meet the increasing demand for communication and computing resources in delay-sensitive and computation-intensive applications, resource-limited users should take advantage of the extensive computing resources at the network edge. A user-centric cell-free network can fully utilize the communication and computing resources of multiple access points (APs) to provide better load balancing among users. Parallel offloading for divisible tasks is expected to offer greater flexibility and further reduce latency. When data dependencies between different tasks has to be considered, the strict temporal relationship makes the task offloading problem even more challenging, which has been ignored in existing studies. In this paper, we propose a task-dependent parallel task offloading strategy for the user-centric cell-free network. The total time latency is minimized by joint optimizing AP selection, power control, task assignment and computational resource allocation. The formulated problem is then solved by an intelligent optimization algorithm based on gray wolf optimization algorithm (GWO). Simulation results show that in user-centric cell-free networks with limited computational resources, the proposed scheme can effectively reduce user latency.

Intelligent Reflecting Surface (IRS) assisted challenge-response physical layer authentication (CR-PLA) offers a promising solution for enhancing the security and stability of authentication channels between base stations and legitimate users in wireless environments with obstacles and eavesdroppers. However, there is a tradeoff between communication efficiency and security, with improved security often reducing efficiency. To address this, we propose a power-budget IRS-assisted CR-PLA scheme that balances these competing factors. Our approach involves generating multiple optimal IRS configurations within transmit power budgets and applying them to the authentication channel. We enhance both efficiency and security by randomly employing IRS configurations used for channel reconstruction as a pre-shared key. Experiments demonstrate that our scheme achieves high authentication efficiency and security under power budget constraints. Notably, even if the eavesdropper knows the secret channel configurations in man-in-the-middle attack scenarios, our scheme reduces their authentication success rate by 15.7% to 34.1%, rendering the attack ineffective.

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.

The application of blockchain technology in the field of wireless communication authentication is becoming more and more widespread. The adoption of blockchain access schemes with composite security features is an important research direction in the current wireless access field. Meanwhile, the authentication scheme needs to reduce the process steps and improve the efficiency on the basis of protecting the identity privacy of the access device. In this article, with identity-based cryptography (IBC) as the core, a group signature algorithm supporting batch verification is constructed. Meanwhile, the proposed algorithm is the main component to design a blockchain-based heterogeneous network authentication scheme. Through security analysis, it is proved that the group signature algorithm can carry out multiuser batch authentication under the conditions of unforgeability, anonymity, and unlinkability. And through efficiency analysis, it can be seen that the authentication scheme can significantly reduce the number of pairwise operations in multiuser authentication, thus improving the efficiency of the authentication scheme by nearly 30.7%.

The recent surge in large-scale Distributed Denial-of-Service (DDoS) attacks, primarily driven by Internet of Things (IoT) botnets, has drawn significant concerns. Defending against IoT botnet DDoS attacks is challenging, especially as attackers’ techniques and strategies become increasingly sophisticated. We propose a novel traffic-level differential game model from holistic and dynamic perspectives to address these challenges, integrating both the botnet formation and DDoS attack stages. Based on the actual operation mechanism of IoT botnet DDoS attacks, we extract two key attack features: 1) the incubation period of infected devices and 2) the threshold effects of DDoS attacks. Considering these, the proposed model more effectively captures the dynamic strategic behaviors of attackers and defenders. Using optimal control theory, we derive the optimal timing and frequency for attackers to activate latent devices and for defenders to restore compromised ones. Numerical simulations show the effectiveness of our defense strategy under both traditional and intelligent attack scenarios, reducing the payoff function of the attack-defense system by approximately 48% compared to static and adaptive strategies. Additionally, we examine the impact of key parameters on network security, providing valuable insights for developing effective defense strategies against botnet-driven DDoS attacks.

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.

One-bit analog-to-digital converters (ADCs) reduce cost and power consumption in massive MIMO systems but introduce signal distortion that complicates data detection. Temporal oversampling at the receiver can mitigate performance loss from coarse quantization. In this paper, we analyze the performance of temporally oversampled massive MIMO systems with one-bit quantization in the presence of correlated noise, where the noise correlation arises from both filtered thermal noise and inter-symbol interference (ISI). Specifically, we propose a linear symbol-wise MMSE receiver and reveal how noise correlation affects its resulting MSE. Two approximations of the MSE are developed to offer valuable insights. The first approximation suggests that the effects of different noise correlation pairs on the MSE can be treated as independent. The second approximation demonstrates that the MSE variations induced by noise correlation are jointly determined by the pulse-shaping and receive filters. For a system employing a raised cosine pulse, noise correlation degrades the receiver performance. Moreover, the trade-off between noise correlation and the oversampling ratio is analyzed. The case of imperfect channel state information (CSI) is also considered, where the analysis developed for data detection is extended to a linear MMSE channel estimator. Finally, numerical results show that for a system employing a raised cosine pulse, an oversampling ratio of 2 is beneficial, offering practical guidelines for system design.

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.

The massive access in Internet of Things (IoT) introduces significant communication and computation overheads. Besides, the widespread IoT terminals placed in unattended area and with limited capabilities are vulnerable to various attacks such as physical attack. To alleviate the huge communication and computation overheads and to resist physical attacks, we propose a physically unclonable function (PUF)-based group authentication protocol in this paper, where a pre-stored PUF challenge scheme with PUF acting as the root key is proposed to limit the size of signalings in a group. Different from existing work with assumptions on secure communication channels and trusted group leader (GL), we consider untrusted GL and insecure communication channels among the device, the GL, and the home network (HN) and propose a simplified PUF-based device-to-device authentication scheme to perform mutual authentication and key sharing between the devices and the untrusted GL. Finally, the proposed protocol is evaluated with formal security analysis, where a novel threat model is presented for the physical attacker to overhear the secret in device’s memory. Results show that the proposed protocol can achieve desired authentication and confidentiality goals even the GL is under physical attacks and the communication channels are insecure. Further, simulations are demonstrated to show the outperformance of the proposed protocol in communication overhead, computation overhead, and security, compared with baseline solutions.

Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of “Shannon’s trap” by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields.

Interactive multimodal applications (IMAs), such as route planning in the Internet of Vehicles, enrich users’ personalized experiences by integrating various forms of data over wireless networks. Recent advances in large language models (LLMs) utilize mixture-of-experts (MoE) mechanisms to empower multiple IMAs, with each LLM trained individually for a specific task that presents different business workflows. In contrast to existing approaches that rely on multiple LLMs for IMAs, this paper presents a novel paradigm that accomplishes various IMAs using a single compositional LLM over wireless networks. The two primary challenges include 1) guiding a single LLM to adapt to diverse IMA objectives and 2) ensuring the flexibility and efficiency of the LLM in resource-constrained mobile environments. To tackle the first challenge, we propose ContextLoRA, a novel method that guides an LLM to learn the rich structured context among IMAs by constructing a task dependency graph. We partition the learnable parameter matrix of neural layers for each IMA to facilitate LLM composition. Then, we develop a step-by-step fine-tuning procedure guided by task relations, including training, freezing, and masking phases. This allows the LLM to learn to reason among tasks for better adaptation, capturing the latent dependencies between tasks. For the second challenge, we introduce ContextGear, a scheduling strategy to optimize the training procedure of ContextLoRA, aiming to minimize computational and communication costs through a strategic grouping mechanism. Experiments on three benchmarks show the superiority of the proposed ContextLoRA and ContextGear. Furthermore, we prototype our proposed paradigm on a real-world wireless testbed, demonstrating its practical applicability for various IMAs. We will release our code to the community.

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.

Network-based intrusion detection system (NIDS) monitors network traffic for malicious activities, formingthe frontline defense against increasing attacks over information infrastructures. Although promising, our quantitative analysis shows that existing methods perform inconsistently in attacks (e.g., 18% F1 for the MITM and 93% F1 for DDoS by a GCN-based state-of-the-art method), and perform poorly in few-shot intrusion detections (e.g., dramatically drops from 91% to 36% in 3D-IDS, and drops from 89% to 20% in E-GraphSAGE). We reveal that the underlying cause is entangled distributions of flow features. This motivates us to propose DIDS-MFL, a disentangled intrusion detection approach for various scenarios. DIDS-MFL involves two key components: a double Disentanglement-based Intrusion Detection System (DIDS) and a plug-and-play Multi-scale Few-shot Learning-based (MFL) intrusion detection module. Specifically, the proposed DIDS first disentangles traffic features by a non-parameterized optimization, automatically differentiating tens and hundreds of complex features. Such differentiated features will be further disentangled to highlight the attack-specific features. Our DIDS additionally uses a novel graph diffusion method that dynamically fuses the network topology for spatial-temporal aggregation in evolving data streams. Furthermore, the proposed MFL involves an alternating optimization framework to address the entangled representations in few-shot traffic threats with rigorous derivation. MFL first captures multi-scale information in latent space to distinguish attack-specific information and then optimizes the disentanglement term to highlight the attack-specific information. Finally, MFL fuses and alternately solves them in an end-to-end way. To the best of our knowledge, DIDS-MFL takes the first step toward disentangled dynamic intrusion detection under various attack scenarios. Equipped with DIDS-MFL, administrators can effectively identify various attacks in encrypted traffic, including known, unknown, and few-shot threats that are not easily detected. Comprehensive experiments show the superiority of our proposed DIDS-MFL. For few-shot NIDS, our DIDS-MFL achieves a 71.91% -125.19% improvement in average F1-score over 14 baselines and shows versatility in multiple baselines and multiple tasks.

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.

As one of the latest features of ultra-high-definition media services, high frame rate can significantly enhance perceptual quality, but also increases codec complexity in the transmission chain, leading to additional overhead. In this paper, we carry out comprehensive offline experiments in which the codec overhead (e.g., energy and delay) shows a linear or even quadratic increase trend with various frame rates, while correspondingly, when the frame rate increases to 75FPS, its bitrate is 24.2% lower than that under 15FPS for several scenarios. This illustrates that the overhead is more significant than the load from data traffic in the frame rate control problem. Thus, we propose a Bilateral Adaptive video Transmission framework that establishes Bilateral game-theoretic Control (BAT-BC) between sender and viewer. Through dynamically adjusting frame rate for sender and service payment for viewer, BAT-BC can flexibly adapt to the external environment such as computational state and scenario changes and it is expected to provide viewers with a smoother experience. Furthermore, we extend it to the scenario including concurrent multi-viewer and discuss the effects of grouping utility. Finally, we design a prototype system and the proposed solution is deployed on it to evaluate the performance. The frame drop rate is reduced by 61%, resulting in a 31% improvement in subjective QoE. The objective metric achieves the same level of actual experience as a fixed 60 FPS under dynamic environment.

Binary malware summarization aims to automatically generate human-readable descriptions of malware behaviors from executable files, facilitating tasks like malware cracking and detection. Previous methods based on Large Language Models (LLMs) have shown great promise. However, they still face significant issues, including poor usability, inaccurate explanations, and incomplete summaries, primarily due to the obscure pseudocode structure and the lack of malware training summaries. Further, calling relationships between functions, which involve the rich interactions within a binary malware, remain largely underexplored. To this end, we propose MALSIGHT, a novel code summarization framework that can iteratively generate descriptions of binary malware by exploring malicious source code and benign pseudocode. Specifically, we construct the first malware summary dataset, MalS and MalP, using an LLM and manually refine this dataset with human effort. At the training stage, we tune our proposed MalT5, a novel LLM-based code model, on the MalS and benign pseudocode datasets. Then, at the test stage, we iteratively feed the pseudocode functions into MalT5 to obtain the summary. Such a procedure facilitates the understanding of pseudocode structure and captures the intricate interactions between functions, thereby benefiting summaries’ usability, accuracy, and completeness. Additionally, we propose a novel evaluation benchmark, BLEURT-sum, to measure the quality of summaries. Experiments on three datasets show the effectiveness of the proposed MALSIGHT. Notably, our proposed MalT5, with only 0.77B parameters, delivers comparable performance to much larger Code-Llama.

Unsupervised network-based intrusion detection system (UNIDS) identifies suspicious traffic and alerts administrators without using any traffic labels. Existing Graph Convolutional Network (GCN)-based UNIDS approaches show great potential with collaboratively utilizing traffic features and network topologies. However, these methods suffer from an excessively high false-positive rate (FPR), e.g., 3.27% FPR in a supervised NIDS approach, while increases dramatically to 19.37% under the unsupervised setting. We reveal that the high FPR stems from a single-scale spatial-frequency learning paradigm, which blurs the distinction between benign and malicious traffic and misleads UNIDS systems. Therefore, we propose a Multi-scale Spatial-Frequency Intrusion Detection System (MSF-IDS) to mitigate the high FPR. Specifically, we propose multi-scale frequency encoders, thereby differentiating abnormal feature patterns. Then we propose NAPH as a spatial encoder, mining intrinsic abnormal topology patterns by tracking tens of thousands of evolving traffic nodes. To the best of our knowledge, NAPH takes the first step toward differentiable persistent homology analysis over dynamic network data. We also develop an executable application for NAPH to provide easy-access visualization insights. Finally, a self-supervised representation augmentor and an intrusion detector are proposed to refine and highlight the attack-specific information. Equipped with MSF-IDS, administrators effectively identify the unknown attack traffic, freeing security staff from labor-intensive engineering. Extensive experiments demonstrate the superiority of MSF-IDS, including binary classification, multi-classification, online intrusion detection, and visualized discussions. Our codes, datasets, and an executable application are available at https://github.com/qcydm/MSF-IDS.

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.

Physical layer (PHY) secret key generation (SKG) has been widely studied as a promising approach to achieving One-Time-Pad security. The improvement of SKG rate is quite a huge challenge, especially in scenarios with untrusted internal helpers or eavesdroppers that aim to wiretap the negotiated secret keys between legitimate parties. In this paper, we propose a token-based SKG scheme to deal with the problem of information leakage with internal eavesdropping attacks. The basic idea is to cover random pilots with protective tokens to confuse eavesdroppers. Three scenarios including passive external eavesdropping, active internal eavesdropping with a reconfigurable intelligent surface (RIS)-assisted untrusted helper, and active internal eavesdropping with an untrusted relay are considered and analyzed to evaluate the performance of the proposed anti-eavesdropping scheme. Theoretical analysis shows that the proposed token-based SKG scheme can perfectly secure the key negotiation, achieving zero information leakage even in the untrusted relaying scenario without a direct link between Alice and Bob. Moreover, closed-form expressions for secret key capacity (SKC) are obtained. Finally, numerical results indicate that the proposed scheme outperforms the state-of-the-art methods. Using a token-generation mapping function with greater diversity in amplitude and phase, our approach achieves enhanced SKC performance across various scenarios, including those with a passive eavesdropper, a RIS-assisted untrusted helper, and an untrusted relay.

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.

Time-sensitive networking (TSN), essential in industrial networks for its promise of reliable and deterministic data transmission, faces deployment challenges due to the limitations of existing time-aware shaper (TAS)-based scheduling algorithms. Specifically, the size of the generated gate control lists (GCLs) is usually too large to be deployed in actual devices. To bridge the gap between theory and practice, we propose FlexTAS, a flexible and practical solution for TSN. The key insight behind FlexTAS is that relaxing gating does not introduce uncertainty, as long as nonoverlap reserved time slots are guaranteed. FlexTAS is comprised of two main components: first, a novel gating model deviates from the conventional TAS model by incorporating selective relaxation of gating at certain nodes; and second, a deep reinforcement learning-based engine to rapidly generate valid schedules. We build a real testbed and validate the effectiveness of our proposed solution. Our evaluation demonstrates that FlexTAS effectively controls the number of gate entries within the GCL capacity of devices, while simultaneously meeting the Quality of Service(QoS) requirements of time-triggered streams. It significantly reduces the number of GCL entries by 60% to 80%, and facilitates deployment in heterogeneous networks, thus offering a practical solution for TSN.

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

Network performance monitoring and troubleshooting are crucial yet challenging tasks in datacenter management. Despite the numerous solutions that have been proposed in recent years, their efforts are often hindered by high costs and unreliable failure localization, making it difficult to deploy them in real-world environments. In this paper, we presentLMon, a highly reliable and efficient system for monitoring and troubleshooting in datacenter networks. LMon utilizes the characteristic of ECMP hashing linearity to control probe packet routing, enabling the monitoring of targeted paths without any modification of underlying protocols and devices. Additionally, LMon leverages a lightweight probing technique to reduce monitoring overhead, as well as integrates the improved LASSO regression and hypothesis testing for higher accuracy and faster processing in link failure localization. We evaluate the performance of LMon in our testing environment. Compared to the monitoring system Pingmesh, LMon generates only one-third probes while maintaining 99% accuracy and 1% false negatives.

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.

Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive the secrecy outage probability (SOP) and secrecy throughput expressions of users in MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS; ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity; and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case.

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.

Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to select appropriate collaborators for each FL participant (FL-PT) based on data complementarity. Recent studies have addressed this challenge. Similarly, it is imperative to consider the inter-individual relationships among FL-PTs where some FL-PTs engage in competition. Although FL literature has acknowledged the significance of this scenario, practical methods for establishing FL ecosystems remain largely unexplored. In this paper, we extend a principle from the balance theory, namely “the friend of my enemy is my enemy”, to ensure the absence of conflicting interests within an FL ecosystem. The extended principle and the resulting problem are formulated via graph theory and integer linear programming. A polynomial-time algorithm is proposed to determine the collaborators of each FL-PT. The solution guarantees high scalability, allowing even competing FL-PTs to smoothly join the ecosystem without conflict of interest. The proposed framework jointly considers competition and data heterogeneity. Extensive experiments on real-world and synthetic data demonstrate its efficacy compared to five alternative approaches, and its ability to establish efficient collaboration networks among FL-PTs.

To meet future ultra-low latency and extensive computing requirements, resource-constrained users should utilize ubiquitously available computing resources at the network edge. User-centric cell-free networks can effectively utilize the resources of multiple access points (APs) to serve users. However, the computing resources of a single AP are often limited due to deployment environment or cost, which makes the resource allocation problem more challenging. In this paper, we propose a parallel offloading scheme with limited AP computing resources under user-centric cell-free networks. Maximize the total users’ uplink rate and minimum delay margin by optimizing the users’ uplink transmission power and task allocation ratio. And we solve the non-convex optimization problem based on fractional programming (FP). Simulation results show that jointly scheduling the computing and communication resources of multiple APs can effectively alleviate problems such as task offloading failure caused by limited computing resources of a single AP.

Selecting access points (APs) and power control are crucial issues in resource allocation within cell-free massive multiple-input multiple-output (MIMO) networks. The aim of these challenges is to manage inter-user interference, lessen the fronthaul load, and ensure system performance is maintained as the number of users increases. Unlike previous works that address these issues separately, this paper introduces an integrated optimization approach for both AP selection and power control in a user-centric cell-free network, constrained by users’ minimum quality of service (QoS) to ensure system fairness. The problem at hand is a mixed-integer non-convex challenge, and we address it by utilizing fractional programming techniques for its transformation and solution. Simulations were carried out for environments, both indoors and outdoors, following 3GPP standards. The results indicate that the joint optimization approach greatly enhances the overall network sum spectral efficiency (SE) compared to solving these issues independently, emphasizing the proposed method’s effectiveness.

Active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASTARS) warrants in-depth research to enhance the reliability and efficiency of sixth-generation wireless communication. This paper investigates the performance of ASTARS-assisted uplink communication networks. We derive new closed-form expressions for the outage probability, considering both perfect and imperfect successive interference cancellation scenarios, and subsequently calculate the throughput and energy efficiency. Numerical results indicate that ASTARS-assisted networks significantly outperform passive counterpart networks.

Satellite-terrestrial integrated networks (STINs) offer wide coverage and ubiquitous connectivity for massive mobile equipments (UEs). However, the security issues in STINs should not be ignored. Authentication and key agreement is the fundamental way to protect STINs from unauthorized access. Nevertheless, high-speed UEs, such as those in vehicles and trains, may encounter frequent handovers among heterogeneous access points in STINs, resulting in reduced authentication efficiency and an increased rate of access failures. To tackle these challenging issues, this paper proposes DBC-Auth, a seamless and universal handover authentication scheme that relies on a dual-blockchain architecture supporting all handover scenarios over STINs. Specifically, the proposed dual-blockchain architecture involves two chains that reside on the ground base stations and satellites, respectively, to efficiently manage the handover authentication in different segments of STINs. Leveraging the blockchain’s global availability and tamper-resistance, the authentication receipts can be recorded on the blockchain in advance, which can significantly improve the handover authentication efficiency and alleviate the handover failures. Security analysis demonstrates that our scheme achieves various security requirements, and performance evaluation shows superior efficiency compared to existing works.

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.

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.

Over-the-air (OTA) attacks, such as flooding and jamming, significantly compromise the availability and reliability of radio access networks, especially with Quality of Service (QoS)-aware scheduling. Despite extensive research aimed at enhancing network performance and efficiency, the impact of OTA attacks on QoS performance has been insufficiently addressed. This paper conducts a thorough analysis of how OTA attacks influence the network performance. By leveraging a multi-class M/M/1 queuing model with selection and feedback mechanisms, the complex relationship between attack strategies and prioritized network scheduling is analyzed. Expressions for the delay and throughput are derived based on the result of the stationary distribution of Continuous Time Markov Chain (CTMC) model. Finally, numerical and simulation results are demonstrated to validate the theoretical analysis and to analyze the impact of OTA attack on the scheduling performance. Results show that the attacker can compromise the QoS of low-priority traffic by blocking high-priority traffic. Moreover, the QoS of high-priority traffic can be compromised by jamming low-priority traffic.

In this paper, we investigate the computation offloading problem in a distributed reconfigurable intelligent surface (RIS)-aided cell-free network, where users offload computing-intensive tasks to their associated base stations with the aid of multiple RISs. To minimize the long-term energy consumption of all users under the constraints of latency tolerance, we formulate a long-term non-convex problem by jointly optimizing the offloading strategy, transmit power at users, reflection matrix at the RIS, receive beamforming at the BS, and CPU resources at both users and the server. To solve this intractable time-varying problem with multiple coupling variables, we propose a two-layer distributed proximal policy optimization (DPPO) algorithm for solving the problem with a high-dimensional decision-making space. Simulation results show that the proposed algorithm effectively reduces the long-term energy consumption of all users while completing the computing task within a given time limit.

Federated Learning (FL) algorithms commonly sample a random subset of clients to address the straggler issue and improve communication efficiency. While recent works have proposed various client sampling methods, they have limitations in joint system and data heterogeneity design, which may not align with practical heterogeneous wireless networks. In this work, we advocate a new independent client sampling strategy to minimize the wall-clock training time of FL, while considering data heterogeneity and system heterogeneity in both communication and computation. We first derive a new convergence bound for non-convex loss functions with independent client sampling and then propose an adaptive bandwidth allocation scheme. Furthermore, we propose an efficient independent client sampling algorithm based on the upper bounds on the convergence rounds and the expected per-round training time, to minimize the wall-clock time of FL, while considering both the data and system heterogeneity. Experimental results under practical wireless network settings with real-world prototype demonstrate that the proposed independent sampling scheme substantially outperforms the current best sampling schemes under various training models and datasets.

The proliferation of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) and the emergence of variants such as Distributed Reflection Denial-of-Service (DRDoS) attacks severely threaten the fifth-generation (5G) networks. More importantly, as attack methods continue to escalate, attackers can obtain deployed critical defense strategies through continuous probing, increasing the defense difficulty. We propose a hybrid strategy model and corresponding Software Defined Network (SDN) paradigm-based framework based on existing defense strategies to deploy it flexibly and conveniently into the 3GPP-defined 5G architecture. In addition, after quantifying the proposed hybrid strategy's defense capacity, the Stackelberg game is employed to model the hybrid strategy and solve the optimal packet sampling rate. The simulation shows that the optimal packet sampling rate is effective and robust and can force a rational attacker to give up DRDoS attacks and achieve the effect of subduing the enemy without fighting.

Reconfigurable intelligent surface (RIS) facilitates the extraction of unpredictable channel features for physical layer key generation (PKG), securing communications among legitimate users with symmetric keys. Previous works have demonstrated that channel reciprocity plays a crucial role in generating symmetric keys in PKG systems, whereas, in reality, reciprocity is greatly affected by hardware interference and RIS-based jamming attacks. This motivates us to propose LoCKey, a novel approach that aims to improve channel reciprocity by mitigating interferences and attacks with a loop-back compensation scheme, thus maximizing the secrecy performance of the PKG system. Specifically, our proposed LoCKey is capable of effectively compensating for the CSI non-reciprocity by the combination of transmit-back signal value and error minimization module. Firstly, we introduce the entire flowchart of LoCKey and provide an in-depth discussion of each step. Following that, we delve into a theoretical analysis of the performance optimizations when our LoCKey is applied for CSI reciprocity enhancement. Finally, we conduct experiments to verify the effectiveness of the proposed LoCKey in improving channel reciprocity under various interferences for RIS-assisted wireless communications. The results demonstrate a significant improvement in both the rate of key generation assisted by the RIS and the consistency of the generated keys, showing great potential for the practical deployment of our LoCKey in future wireless systems.

Integrated sensing and communication (ISAC), as a promising component in 6G, however, is faced with security challenges that the critical communication information will be leaked to the sensing targets. This paper investigates an intelligent reflecting surface (IRS) aided bi-static ISAC system. The objective is to maximize the sensing signal-to-interference-and-noise ratio (SINR) with security and other constraints. A joint beamforming, reflection and receiving filter design is proposed. Alternating optimization (AO) based iterative algorithm leveraging semi-definite relaxation (SDR), Dinkelbach's transform, and successive convex approximation (SCA) techniques is utilized to solve the non-convex problem. It is found that the proposed scheme provides significantly better sensing performance with security constraint.

Reconfigurable intelligent surfaces (RIS), as new physical dimension transmission technology, can smartly adjust its reflection coefficients to achieve passive beamforming gains for signal enhancement and interference nulling. However, the instantaneous channel state information (CSI) is generally hard to obtain and involves huge training overhead. Besides, due to the passive and noncooperative manner of eavesdroppers, neither location nor CSI of the eavesdropping links can be perfectly known. Considering these practical restrictions, we propose a robust secure transmission strategy using only statistical CSI of the RIS assisted legitimate link with imperfect knowledge about the eavesdropper link. A bounded error model is established to characterize the uncertainty of eavesdroppers channel state information based on its potential location with uncertainty. The approximated ergodic secrecy rate is first derived in closed form, and then maximized by optimizing the active beamforming of the BS, the phase shifts of the RIS and the artificial noise (AN). To tackle this problem, a block coordinate descent (BCD) algorithm is proposed, and the original problem is decoupled into multiple subproblems, which are solved iteratively until convergence. Simulation results demonstrate that our proposed scheme can achieve approximately $59.1 \%$ secrecy gain compared to the benchmark schemes.

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.

With the rapid development of Internet of Things (IoT) technologies, increasing number of IoT devices are connected to the wireless network. Random access (RA) is the necessary initial step in establishing a connection. However, the RA process is vulnerable to various attacks due to the openness of the wireless environment. Distributed denial of service (DDoS) is one of the leading attacks significantly violating the usability of wireless networks. Traditional security methods are set up after the RA process and thus can neither detect the RA-oriented attacks nor protect RA from being attacked. In this paper, we consider a typical RA-oriented DDoS attack in which a large number of preambles are illegally occupied to prevent the normal access of legitimate users. We propose a DDoS detection method based on multiple physical layer attributes. The time advance (TA) calculated from the preamble sequences and the carrier frequency offset (CFO) extracted from the preamble signals are jointly utilized to describe the characteristics of different accessing devices. A density-based spatial clustering of applications with noise (DBSCAN) based algorithm is proposed. Simulation results show that the proposed method can detect DDoS attacks with a false alarm rate of 0.6% and a miss detection rate of 0% within 1s. Compared with different benchmarks, the method shows better time efficiency and higher identification accuracy. It can reduce the access failure rate by 40% when the attack situation is relatively mild and can reduce the access failure rate by 61% when the attack situation is relatively serious.

With the emergence of diverse services and the development of native artificial intelligence (AI), 6G network is expected to satisfy higher performance requirement by supporting deep convergence of computing and communication. As an important development trend, AI-embedded base station (BS) with computing resources should be considered. However, in some busy-traffic cells, it is difficult for a single BS to serve all users due to its limited computing resources. In this paper, a novel collaborative-computing based system model is proposed for computing-constrained BSs, in which the shared computing resources are introduced to finish both communication protocol and application processing. In addition, a new user association (UA) scheme based on a hierarchical double auction model is designed to improve the long-term network capacity. Simulation results demonstrate that, compared with several typical UA algorithms, the proposed scheme could increase the served user number and throughput significantly without energy efficiency degradation, which is proved a promising approach for wireless networks with integrated computing and communication.

In massive Multiple-Input Multiple-Output (MIMO) systems, the high power consumption of radio frequency (RF) chains is unbearable. Low-resolution analog-to-digital converters (ADCs) serve as a potential method, but they may lead to performance degradation. To get a tradeoff, mixed- ADC architecture is considered as a promising approach to balance performance and power consumption. In this paper, an optimal bits allocation scheme is proposed for uplink massive MIMO systems with mixed ADCs. Based on the derivation of closed-form expressions for achievable sum rate and energy efficiency (EE), optimization problems are formulated to reflect the relationship between system performance and different ADC resolutions on antennas with total bits constraint. Moreover, a hierarchical traversal algorithm is further provided to reduce the computation complexity. Numerical results validate that the proposed bits allocation scheme shows significant performance gain compared with other schemes. Furthermore, it has been proved feasible for practical massive MIMO systems due to its much lower complexity.

By exploiting the entropy of wireless channels, physical layer key generation (PLKG) has gained considerable attention in recent years. Due to the characteristic of dynamic controlling of wireless channels, reconfigurable intelligent surface (RIS) can improve the key generation rate (KGR). Simultaneous transmission and reflection reconfigurable intelligent surface (STAR- RIS) can extend the half-space coverage to full-space coverage. This paper proposes a STAR-RIS-assisted physical layer key generation (SRKG) scheme in a multi-user case. The objective is to maximize the sum KGR by jointly designing the transmitting and reflecting coefficients (TARCs). We propose an iterative algorithm based on alternating optimization (AO), utilizing successive convex approximation (SCA) and semi-definite relaxation (SDR) methods, to solve the non-convex optimization problem in the presence of correlated channels. We also propose a low-complexity algorithm based on Lagrange multipliers to jointly optimize TARCs considering independent fading. Finally, the simulation results validate that, compared with the PLKG scheme assisted by traditional RIS (T-RIS), the proposed SRKG scheme can achieve higher sum KGR in a wider range under correlated and independent channel conditions.

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.

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) have been envisioned as two promising technologies due to their high transmission reliability as well as energy-efficiency. This paper investigates the secrecy performance of RIS assisted AmBC networks. New closed-form and asymptotic expressions of secrecy outage probability for RIS-AmBC networks are derived by taking into account both imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) cases. On top of these, the secrecy diversity order of legitimate user is obtained in high signal-to-noise ratio region, which equals zero and is proportional to the number of RIS elements for ipSIC and pSIC, respectively. Numerical results are provided to verify the accuracy of theoretical analyses and manifest that the secrecy performance of RIS-AmBC networks exceeds that of conventional AmBC networks. In addition, due to the mutual interference between direct and backscattering links, the number of RIS elements has an optimal value to minimise the secrecy system outage probability.

Network slicing is an emerging architecture that allows multiple virtual networks to be created on top of a shared physical infrastructure, each tailored to a specific type of service or application. Managing network slices using a Software Defined Network (SDN) controller makes the network more scalable and manageable but also vulnerable due to the centralized control of SDN. Low-rate denial-of-service (LDoS) is periodic and stealthy, and its attack frequency is lower than that of ordinary distributed denial-of-service attacks, making it one of the most severe threats on SDN. To cope with the above challenges, we propose a real-time Lightweight LDoS Detection and Mitigation (L2DM) framework. Moreover, an Adaptive Weight XGBoost (AW-XGBoost) algorithm is designed to extract features and obtain the detection model via adjusting adaptive weights. We also leverage the architecture of network slicing to build honeypot slices to gather information from attackers. Experimental results show that the proposed framework and corresponding algorithm can be deployed on SDN controllers to achieve LDoS attack detection and mitigation at low cost with high accuracy and effectiveness.

Reconfigurable intelligent surface (RIS) is highly envisaged as a promising technology in millimeter wave (mmWave) communication systems for its capability of restructuring the wireless communication environment and mitigating the severe signal blockage. However, passive RIS beamforming design is still a challenge due to the non-convex property of the problem. This work presents a deep learning (DL) based codebook classification beam search algorithm, comprising a convolutional neural network (CNN) based codebook searcher and a mapper. The searcher determines the optimal codeword position range index based on the channel state information (CSI), while the mapper selects the optimal codeword based on this index. The proposed network is trained both with the DeepMIMO dataset and the Saleh-Valenzuela channel model, respectively, and imperfect CSI is utilized to enhance the system robustness. Simulation results demonstrate that the proposed algorithm significantly improves the beam search efficiency.

The emergence of fifth-generation (5G) networks and heterogeneous Internet of Things (IoT) demand requires deploying a Cloud-Edge-Terminal Computing distributed network with Software Defined Network (SDN) as the controller. However, this network faces threats, notably Denial-of-Service (DoS) attacks. This paper adopts game theory to explore Mobile Edge Computing (MEC) attack and defense interaction in diverse businesses. Moreover, we propose a three-layer 5G distributed Cloud-Edge- Terminal network featuring an attack-defense game model between DoS attackers and SDN defenders. For the defender's strategy, we use the Eisenberg-Gale (G-E) resource allocation model considering delay, packet loss rate, utility functions, values, sensitivities, and busyness. Heuristic search algorithms analyzing MEC cell impact on strategy selection are developed. The proposed game models are solved, and results are validated through simulations.

Deep neural network (DNN) is a promising technology for Internet of Visual Things (IoVT) devices to extrct their visual information from unstructured data. However, it is hard to deploy a complete DNN model at resource-constrained IoVT devices to fulfill their latency, energy, and inference accuracy demands. Exploiting the reachable and available computing resources of IoVT devices and mobile-edge computing (MEC) servers, we propose an edge-device collaborative DNN inference framework to empower resource-constrained IoVT devices to perform DNN-based inference. Especially, the DNN model partition separates the DNN model into two parts, which are deployed on both the IoVT devices and multiaccess MEC server for performing inference collaboratively. The DNN early exit and computation resource allocation are employed to accelerate the DNN inference while guaranteeing the inference accuracy. Moreover, a metric to measure the inference performance of average latency and accuracy (IPLA) is designed. Joint multiuser DNN partitioning, early exit point selection, and computation resource allocation are optimized to maximize the tradeoff performance of inference latency and accuracy. We model the optimized problem as an Markov decision process and propose a deep deterministic policy gradient-based edge-device collaborative DNN inference algorithm to solve the problem of huge state space and high-dimensional continuous actions. Experiments are conducted with the Alexnet model on the data set of CIFAR-10 and Resnet-50 model on the data set of ImageNet. Simulation results verify that the proposed algorithm speeds up the overall inference execution of IoVT devices while guaranteeing inference accuracy.

The wireless control system (WCS) operating with massive ultra-reliable and low-latency communications is viewed as a promising technology for the Industrial Internet of Things (IIoT). However, the co-design of sensing, control, and communications is full of challenges, and the trade-off between reliability and control performance in a closed-loop WCS under blind areas of the wireless network’s coverage is still to be solved. In this paper, we exploit reconfigurable intelligence surface (RIS) to assist the device located in the blind areas of the wireless network’s coverage in transmitting sensing and control information over the wireless channels. Furthermore, we formulate a joint optimization of reliability and control cost for a RIS-assisted closed-loop WCS. Specifically, we apply a linear quadratic regulator (LQR) cost to measure the control performance, and a trade-off performance metric called reliability-to-control efficiency (RCE) is proposed for the WCS. In addition, we maximize the minimum RCE of IIoT devices while meeting the requirements of reliability, control cost, and communication resources. An alternating optimization-based maximum the minimum RCE algorithm (AO-MmRCEA) is formulated to jointly optimize the transmission power, transmission time, beamforming, and reflecting coefficients of RIS. The convergence and complexity of the proposed AO-MmRCEA algorithm are analyzed. Simulation results demonstrate the convergence and effectiveness of the proposed AO-MmRCEA algorithm for closed-loop WCS.

This article proposes a secrecy and energy-efficient device-edge co-inference scheme for resource-constrained Artificial Intelligence of Things (AIoT) devices with physical layer security assistance. Our approach leverages split learning, where the AIoT device executes the initial part of the AI model, and the mobile edge computing server (MECs) computes the remainder, reducing energy consumption (EC) and inference delay. We measure secrecy capacity under the finite blocklength regime to address the vulnerability of intermediate feature data (IFD) to eavesdropping over wireless channels and its short block length characteristics. The objective is to minimize the average EC of the device-edge co-inference by jointly optimizing deep neural network (DNN) model partitioning and resource allocation. We formulate a distributed reinforcement learning-based joint DNN model partitioning and resource allocation (DRPA) algorithm, which uses knowledge-based reinforcement learning for optimal DNN partitioning and a convex optimization approach for resource allocation. Simulation results demonstrate that the DRPA algorithm achieves near-optimal performance, closely matching the results of exhaustive search methods.

Vehicular social network (VSN) has been emerged in recent years with the prosperity of Internet of Vehicles (IoV). VSN service providers can exploit the social relationships of vehicles to improve the performance of vehicular networks, such as improving the order-taking efficiency of taxis. However, when social relationships are captured by attackers, other contact information is passively disclosed without their knowledge, seriously threatening user privacy. This article proposes a$(\theta ,\epsilon)$privacy technique for protecting the social relationships of vehicles that can be used to shorten the average order-taking distance of taxis during order allocation. Two variants are included in the proposed technique, which combines graph theory and graph differential privacy (GDP) mechanisms. 1)$(\theta ,\epsilon)$-GDP–a projection-based GDP algorithm is proposed to protect social relationships among the vehicles. 2) Furthermore, an improved order allocation scheme named$(\theta ,\epsilon)$-PrivOT is proposed to reduce the average order-taking distance. Real data evaluations are provided to verify the outperformance of the proposed technique. The privacy level of the proposed algorithm is improved by up to 8.77% while its data utility is improved by up to 22.94% compared with the edge removal scheme. The proposed dispatching scheme can shorten the average order-taking distance by up to 6.89% compared with the scheme of original data with 100 vehicles.

Internet of Things (IoT) is one of the most representative application scenarios in the 5G and 6G era. The concurrent access of massive IoT devices definitely poses enormous communication, computation, and certificate management challenges to the wireless authentication. Moreover, the emergence of quantum computing makes classical cryptography-based authentication protocols, such as 5G-AKA, more easier to be broken. Facing the challenges posed by the massive concurrent authentication and quantum attacks, this paper proposes a lattice cryptography based group authentication scheme, where lattice-based aggregate signature algorithm and identity-based encryption (IBE) are leveraged to achieve simultaneous authentication of concurrent accessed devices. The proposed authentication scheme eliminates the process of public key certificate management, greatly reducing the storage overhead of core network. Moreover, the utilization of lattice cryptography enables the resistance of quantum attacks. The proposed solution does not rely on additional security assumptions such as security channel or trusted group center, making it more flexible to be deployed in actual network scenario. Finally, formal security analysis of the proposed protocol is provided with the tool ProVerif. It is demonstrated that the proposed protocol can satisfy the goals of identity privacy, authentication, data confidentiality and forward secrecy. In addition, compared with existing advanced solutions, the outperformance of the proposed scheme in terms of computation overhead, signaling overhead, communication overhead, and security properties is validated with simulations.

Many interconnected terminals in the Internet of Things (IoT) networks raise significant security risks. From a competitive perspective, the many heterogeneous nodes, including various wireless terminals, access points, and base stations are the competing targets between the defenders and potential attackers. This article proposes a security competition model based on the game theory to address the security competition problem. The model has two players, a defender and an attacker, who allocate resources to each IoT node according to their strategies. A novel metric, security entropy, derived from the security probability, quantifies each node’s security status. Based on the node heterogeneity, the overall security performance of the considered IoT network is evaluated with a topology-determined weighted security entropy. The defender and the attacker, respectively, aim to decrease and increase the weighted security entropy while balancing the cost, which constitutes their utility functions. The existence of the unique Nash equilibrium is proved. A best response selection algorithm for the optimal solutions is designed. The experimental results demonstrate that the proposed model effectively represents the goal orientation and interaction between the attackers and defenders in various scenarios. Additionally, increasing the cost for the attackers significantly reduces their resource allocation, especially to the attackers leading to a decrease in the system’s security entropy.

Channel state information (CSI) is differently available at each terminal in state-dependent wiretap channels (SD-WTCs). Considering a random channel state non-causally available only at the encoder, this paper investigates an explicit polar coding scheme for the discrete and memoryless SD-WTC, where Alice aims to transmit a secret message (SM) and a secret key (SK) to Bob while concealing them from Eve. Based on a two-layer superposition coding, which includes an inner layer and an outer layer, the proposed polar coding scheme can achieve the inner bound of the current optimal SM-SK capacity region for the discrete and memoryless SD-WTC with CSI non-causally available only at the encoder. A cross-layer construction is proposed to address the issue where the regular chaining construction used in wiretap polar codes cannot be applied in the inner layer. Results show that the decoding error probability (DEP) of the legitimate decoder vanishes to zero as the block length increases. In addition, the proposed scheme is proven to satisfy strong secrecy.

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.

Reconfigurable intelligent surface (RIS) is acknowledged as a promising technique in improving physical layer (PHY) secret key generation (SKG) performance by enhancing the randomness of radio electromagnetic environment. However, RIS can also be adopted by illegal eavesdropper to destroy the channel reciprocity between key-sharing parties. Thus, there is a tradeoff between the enhanced randomness and the decreased channel reciprocity with the application of RIS. This paper aims to answer the question that whether the adversarial RIS is harmful to SKG by studying the SKG performance in the RIS scenario where Alice and Eve each applies a RIS. To this end, the closed-form expression for the upper bound of secret key rate (SKR) and expressions for the autocorrelation coefficients of two probing channel samples within a coherent duration are provided. Results show that the relative positions of Eve and adversarial RIS to Alice and Bob play a key role in determining whether the adversarial RIS is harmful or beneficial to the PHY SKG system.

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.

Reconfigurable Intelligent Surface (RIS) assisted physical layer key generation has shown great potential to secure wireless communications by smartly controlling signals such as phase and amplitude. However, previous studies mainly focus on RIS adjustment under ideal conditions, while the correlation between the eavesdropping channel and the legitimate channel, a more practical setting in the real world, is still largely under-explored for the key generation. To fill this gap, this paper aims to maximize the RIS-assisted physical-layer secret key generation by optimizing the RIS units switching under the eavesdropping channel. Firstly, we theoretically show that passive eavesdropping significantly reduces RIS-assisted secret key generation. Keeping this in mind, we then introduce a mathematical formulation to maximize the key generation rate and provide a step-by-step analysis. Extensive experiments show the effectiveness of our method in benefiting the secret key capacity under the eavesdropping channel. We also observe that the key randomness, and unmatched key rate, two metrics that measure the secret key quality, are also significantly improved, potentially paving the way to RIS-assisted key generation in real-world scenarios.

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.

Millimeter Wave (mmWave) is usually combined with large-scale multiple antenna technology to produce a large number of narrow beams for better coverage. In order to improve the network throughput, an optimal beam or beam pair is selected to achieve high data rate. However, it is challenging to perform beam selection in vehicular systems with dynamic blocking scenario due to its complex geometric environment and variable number of multipaths. In this paper, a two-stage prediction-based beam selection (TS-PBBS) algorithm is proposed to find the optimal beam accurately and quickly in blocking scenarios. A deep neural network (DNN) is constructed to perform the mapping between the performance of predesigned probing beams and narrow beams. Furthermore, a local search method is introduced to improve the beam selection accuracy. Simulation results reveal that the proposed algorithm is able to recognize the blockage in the environment and achieve high accuracy for beam selection with relatively low searching complexity. In addition, it also appears to have the capability of resisting channel estimation error because the deep learning model weakens the effects of noise, which provides an effective solution for practical deployment of mmWave massive MIMO systems.

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.

Blockchain-based non-terrestrial networks (NTN) are expected to ensure the reliability of data in the process of data sharing between untrusted entities. Nevertheless, the low transactions per second (TPS) of blockchain makes it unable to support a large number of data sharing tasks in NTN. One of the key factors for low TPS is the single-chain structure of blockchain. In this way, block generation and block verification are only permitted to be executed sequentially, that means only a single valid block can be generated in the same period. To resolve this dilemma, we propose a twin-chain practical byzantine fault tolerance consensus strategy (TPBFT), in which the consensus process is executed in parallel at two aspects. The first parallel aspect permits block generation and block verification being executed in parallel by twin-chain structure. The second parallel aspect is that the block can be generated with sub-blocks being generated in parallel by replacing transactions with sub-blocks. Finally, the effectiveness of TPBFT is verified by simulation, and the TPS can reach up to 3×109, which means the problem of low TPS of blockchain-based NTN is solved.

The booming development of Internet of Vehicles (IoV) has brought new vitality to the construction of intelligent transportation systems (ITS). At the same time, a huge amount of data has been generated due to the gradual development of IoV toward large scale, complex, and diversified. These data are owned by the companies that vehicles belonging to or service providers, such as taxi companies own taxi data. Due to interest and privacy considerations, data owners are not willing to share data, thus a serious data isolated island problem is created, which is detrimental to the development of ITS. Therefore, this article focuses on how to prevent privacy disclosure of vehicles while sharing vehicle data to improve the service. Considering the amount of interactive data and privacy disclosure during data release, vehicle data are abstracted from text form into a graph-structured data form. At the same time, graph differential privacy (DP) together with anonymity protection is proposed innovatively to firmly protect vehicle privacy. Moreover, to solve the high complexity of big data graph-structure transformation, an accelerated nodes and edges combined graph DP (ACGDP) algorithm is proposed. Based on the simulations of real-world data that combine electric and nonelectric taxies, it is verified that our proposed scheme has a tradeoff between information availability and privacy protection. With the graph DP processed data, our proposed scheme reduces the average wasted mileage for charging by 3.87% and achieves a 44.28% increase in drivers’ income. Drivers’ satisfaction of receiving orders and charging preference reaches 68% after the graph-structured data reuse.

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.

In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

Cloud-assisted Internet of Things (IoT) overcomes the resource-constrained nature of the traditional IoT and is developing rapidly in such fields as smart grids and intelligent transportation. In a cloud-assisted IoT system, users can remotely control the IoT devices and send specific instructions to them. If the users’ identities are not verified, adversaries can pretend as legitimate users to send fake and malicious instructions to IoT devices, thereby compromising the security of the entire system. Thus, a sound authentication mechanism is indispensable to ensure security. At the same time, it should be noted that a gateway may connect to massive IoT devices with the exponential growth of interconnected devices in a cloud-assisted IoT system. The efficiency of authentication schemes is easily impacted by the computation capability of the gateway. Recently, several schemes have been designed for cloud-assisted IoT systems, but they have problems of one kind or another, making them not very suitable for cloud-assisted IoT systems. In this paper, we take a typical scheme (proposed at IEEE TDSC 2020) as an example to identify the common weaknesses and challenges of designing a user authentication scheme for cloud-assisted IoT systems. In addition, we propose a new secure user authentication scheme with lightweight computation on gateways. The proposed scheme provides secure access between remote users and IoT devices with many ideal attributions, such as forward secrecy and multi-factor security. Meanwhile, the security of this scheme is proved under the random-oracle model, heuristic analysis, the ProVerif tool and BAN logic. Compared with ten state-of-the-art schemes in security and performance, the proposed scheme achieves all the listed twelve security requirements with minimum computation and storage costs on gateways.

In heterogeneous Internet of Things (IoT) networks, various communication technologies lead to different transmission ranges of nodes, and they can cooperatively provide seamless communication. Unfortunately, the flexible communication mode offers more chances for malware to invade the IoT devices. To maintain its cyber security, two key issues, i.e. the critical threshold of the onset of malware propagation and the lowest-cost defense strategy after it occurs, need to be addressed. To solve the first challenge, we construct a dynamics model by using the degree-based mean-field theory and point process theory, to study the malware propagation-defense process among heterogeneous IoT devices. We analytically derive the closed-form expression of the critical malware transmission rate which can be used to predict whether the malware can propagate or not. For the latter problem, we investigate the equivalent conditions of degree-related patching strategies in defense effectiveness, which can be used to determine the lowest-cost one more directly. In addition, We compare different transmission ways of malware, and find that the malware infection is hard to avoid under the way of centralized transmission, but the decentralized transmission has a greater risk of large-scale infection. We also explore how the immunization measures and the different communication frequencies affect malware propagation. The results show that it is significant to identify important devices for immunization and infected cluster may form in some cases. Our results offer a theoretical foundation to predict and defense malware propagation in heterogeneous IoT networks.

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.

In this paper, we study the jamming aided physical layer security of the intelligent reflecting surface (IRS) assisted communication system in the presence of an eavesdropper. We aim to prove that the optimally designed jamming strategy is beneficial for the IRS assisted secure transmission. Specifically, we first derive the successful and secret transmission probability (SSP) in the closed form. Then we maximize the SSP by optimizing the jamming power allocation. When the number of the IRS elements or the total transmit power increases, less jamming power is required. It is also found that the jamming location and its corresponding optimal jamming power follow a certain quantitative relation, especially when the number of the IRS elements or the total transmit power is large. Finally, simulations are provided to validate our analytical derivations.

Non orthogonal multiple access (NOMA) is a potential solution for communication systems with massive connection requirement. It enables multiple users to share the same time-frequency resource block through user pairing, so as to improve the spectrum efficiency of the system. However, the advantage of NOMA can only be fully utilized when the difference of user channels are quite large, while it is rarely achieved when the user channels are similar due to the poor performance of successive interference cancellation (SIC) receiver. In this paper, a user pairing scheme based on power boosting is proposed for NOMA systems, which enables users with similar channel gains to get paired and obtain NOMA advantage. In addition to a feasibility analysis on promoting more paired users by increasing certain power, the relationship between the required power boosting and channel difference is also derived. Simulation results demonstrate that, compared with the hybrid transmission of NOMA and orthogonal multiple access (OMA), our proposed scheme can significantly improve the spectrum efficiency with little power efficiency degradation.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present WISE, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. WISE can also detect fleeting radar signals. We implement WISE and perform extensive evaluations with both controlled experiments and field tests. Results show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, Due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this demo, we present how to transform the low-cost Ultrawideband (UWB) devices into a spectrum sensor and showcase WISE, a low-cost wideband spectrum sensing system, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. Our demo will show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels and fleeting radar signals. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

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.

Space-air-ground integrated network (SAGIN) is a prominent architecture for the future wireless communication system. Due to the long distance transmission of the satellite communication and the limited battery capacity of aircrafts, the energy cost has become one of the dominant problems of SAGIN. Mobile edge computing (MEC) offers a potential solution by offloading partial tasks to nodes with higher computational capability. In this paper, we study a hybrid offloading problem where both unmanned aerial vehicles (UAVs) and ground users have tasks to be processed, and UAVs and the satellite can provide offloading service. The aim is to minimize the system energy consumption under time delay constraint by choosing the optimal offloading objects and jointly optimizing the offloading proportion and computing resource allocation. The optimization problem is highly nonconvex and difficult to solve optimally. To tackle the problem, we first derive the closed-form solution of computation resources allocation and further propose a low-complex algorithm based on successive convex approximation (SCA). Simulation results show that the proposed hybrid of-floading scheme can significantly reduce energy consumption compared to benchmark schemes. Moreover, by increasing the transmission power of users and UAVs in a certain range, total energy consumption can be effectively reduced. And increasing the number of UAVs can improve the energy efficiency.

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.

Multi-access edge computing (MEC) architecture is emerging as a promising paradigm to improve the computing quality for mobile devices. Nevertheless, the offloading process of MEC encounters privacy and security problems. Independent protection for security and privacy will lead to different offloading policies which may contradict with each other. Besides, high unpredictability of channel information and the threaten of vicious edge nodes make it very challenging to offload securely. To address the problem about privacy and security, this paper proposes a Lyapunov-based privacy-aware secure offloading scheme. The scheme minimizes average energy consumption with known and unknown channel information of the eavesdropper. Lyapunov optimization is resorted to design an efficient online task offloading algorithm which can reduce the energy consumption under the strict security and privacy requirements. When compared to other schemes in the simulation, the proposed algorithm achieves fewer energy consumption and guarantees the security and the privacy in MEC offloading process.

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.

Abstract The evolution of the fifth-generation (5G) new radio (NR) has progressed swiftly since the third generation partnership project (3GPP) standardized the first NR version (Release 15) in mid-2018. Nowadays, the world’s leading carriers are competing to provide various commercial services over 5G networks. Looking ahead to 2025 and beyond, it is expected that over 6.5 million 5G base stations will be installed to offer services to over 58% of the world’s population via over 100 billion 5G connections. Following the rapid development of 5G, an increasing number of commercialization use cases will drive the 5G network to continuously improve performance and expand capabilities. Hence, it is the right time to consider a well-defined framework and standardization for 5G NR evolution (5G-Advanced) to support commercialization between 2025 and 2030. First, this study addresses the key driving forces, requirements, usage scenarios, and capabilities of 5G-Advanced; then, it highlights the main technological challenges and introduces the top 10 promising technological directions in detail. Finally, other fascinating technological directions in 5G-Advanced are shortly mentioned.

Unmanned aerial vehicles (UAVs) are anticipated to be a potential data collection solution for wireless sensor networks (WSNs). The main challenges of integrating UAVs in WSNs are security threats and UAV’s onboard energy limitation. To cope with these two challenges, this article examines the secrecy energy efficiency (SEE) maximization problem in UAV-enabled WSN. Specifically, a full-duplex (FD) UAV gathers confidential information from ground sensor nodes (SNs) in the uplink while sending jamming signals to confound a ground eavesdropper (Eve) in the downlink. Considering a passive eavesdropping scenario lacking Eve’s instantaneous channel state information (CSI), the resulting problem is subject to the constraints of connection outage probability (COP), secrecy outage probability (SOP), securely collected bits, and flight trajectory. To tackle the intractable nonconvex problem, we first derive the optimal codeword rate and redundancy rate in closed-form expressions and then develop a low-complexity algorithm using the block coordinate descent (BCD) approach to alternatively optimize the SN scheduling, SN transmit power, UAV transmit power, and UAV trajectory. Simulation results verify the performance gains of the proposed scheme compared with the benchmark schemes. In particular, the proposed scheme achieves nearly the same secrecy rate gains at a lower UAV’s energy consumption cost than the sum secrecy rate maximization (SSRM) baseline. Moreover, it is revealed that trajectory optimization of the proposed scheme plays a crucial role in improving SEE performance compared with the circle trajectory (CT) baseline.

The sensitive information of vehicles which is closely related to the safety of transportation makes the privacy problems in the vehicular networks a popular concern. The development of artificial intelligence (AI) has led the Internet of Vehicles (IoV) to the next phase of intelligence, the Social IoV (SIoV). For social purposes, vehicles may upload captured images with more sensitive information. As a result, the privacy problem is even more serious in SIoV. The exited studies of privacy-preserving methods in vehicular networks mainly consider the spontaneous privacy disclosure. However, the main privacy leakage in real life comes from peer disclosure rather than spontaneous privacy disclosure, which is usually ignored. Therefore, this article innovatively presents a decentralized scheme for solving the peer disclosure issues in SIoV, which, to the best of our knowledge, is the first research in SIoV peer disclosure discussion. A directed acyclic graph (DAG)-based mutual supervision (Dmsv) algorithm is designed for mutually distrustful vehicles. It is verified that our proposed algorithm reduces at least 72% multidimentional privacy loss of image entropy leakage probability when compared with nonpeer disclosure prevention. The decentralized scheme also achieves a relatively low delay of around 24 s from the transaction generation to a network-wide consensus. This article provides a feasibility of applying DAG in a mobile system and provides guidance on how the transportation situation influences the confirmation delay and how to adjust the incentive mechanism according to the transportation situation to maintain robustness.

Internet of Things (IoT) is vulnerable to various cyber attacks due to the massive deployment of IoT devices and the openness of wireless environments. In this article, taking IoT devices as the network resources competed between an attacker and a defender, we study the modeling and analysis of network resource competition in an attack-defense game. The attacker and defender inject different competition strength in each IoT device as their strategies. As a result, the security state of each IoT device will change, which is captured by differential equations. To study the interaction between the attacker and defender and the evolution of the system security states, a zero-sum differential game is formulated by modeling the competition of IoT devices. To achieve the equilibrium of the formulated differential game, optimal control theory is employed to solve the optimization problems of players. Further, a Gauss–Seidel-like implicit finite-difference method is utilized to obtain the saddle point strategy. Finally, numerical results are provided to demonstrate the evolution of network resource competition between the attacker and defender. The results show that our formulated model can effectively and accurately characterize the evolution of the system security states with strategic interactions between the attacker and defender.

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.

With the continuous popularization and deepening of the Internet-of-Things (IoT) technologies, trillions of IoT Devices (IoTD) are connected to the network. The huge growth of wireless communication traffic and the surge of energy consumption make it a great challenge to support various requirements of IoTDs, such as ultrareliable and low latency. The 6th-generation (6G) network has put forward new goals and visions for green communication, network flexibility and intelligence, which are expected to solve these key challenges. In this article, we propose a cross-layer optimization scheme to achieve the trade-off between energy efficiency (EE) and spectral efficiency (SE) of the 6G enabled IoT networks, where the ultrareliable and low-latency applications are considered. Flexible self-organization of three parameters is realized, namely, transmission time interval (TTI), packet duplication (PD), and resource block (RB) allocation. The key technology of flexible TTI scheduling guarantees the reduction of latency, and the PD transmission can effectively improve the reliability. Furthermore, based on machine learning (ML) method, we propose the transfer asynchronous advantage actor–critic (TA3C) algorithm to realize parameter configuration and resource allocation. The simulation results show that the EE and SE tradeoff performance of our proposed flexible scheme is improved by at least 39.29% compared with the fixed parameter configuration. In addition, the TA3C algorithm has better convergence performance and reduces the algorithm complexity by up to 91.23% compared with other ML algorithms.

In recent years, the privacy issue in Vehicular Edge Computing (VEC) has gained a lot of concern. The privacy problem is even more severe in autonomous driving business than the other businesses in VEC such as ordinary navigation. Federated learning (FL), which is a privacy-preserved strategy proposed by Google, has become a hot trend to solve the privacy problem in many fields including VEC. Therefore, we introduce FL into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model parameter only with the help of MEC server. Moreover, different from the common assumption of honest MEC server and honest vehicle in former studies, we take the malicious MEC servers and malicious vehicles into account. First, we consider honest-but-curious MEC server and malicious vehicles and propose a traceable identity-based privacy preserving scheme to protect the vehicular message privacy where improved Dijk-Gentry-Halevi-Vaikutanathan (DGHV) algorithm is proposed and a blockchain-based Reputation-based Incentive Autonomous Driving Mechanism (RIADM) is adopted. Further, when the case comes to the non-credibility of both parties where semi-honest MEC server and malicious vehicles are considered, we propose an anonymous identity-based privacy preserving scheme to protect the identity privacy of vehicles with Zero-Knowledge Proof (ZKP). Based on the simulation of virtual autonomous driving based on real-world road images, it is verified that our proposes scheme can reduce 73.7 % training loss of autonomous driving, increase the accuracy to around 5.55 % while keeps effective privacy of message and identity under the threat of dishonest MEC server and vehicles.

Despite its advantages of flexility and low-cost networking, unmanned aerial vehicle (UAV) communications face various attacks such as eavesdropping. Existing studies on secure UAV communications assume fixed-location eavesdroppers and rarely consider interactions between legitimate nodes and eavesdroppers. In this paper, we investigate eavesdropping and anti-eavesdropping interaction between a UAV-enabled eavesdropper (UAV-E) and a UAV-enabled base station (UAV-BS) in a downlink wiretap system. The UAV-E aims to wiretap downlink signals by adaptively adjusting its trajectory while the UAV-BS aims to maximize secrecy-sum-rate with minimum power consumption by jointly optimizing user scheduling, power control, and trajectory. Dynamic differential equations are formulated to characterize motions of UAVs, following which a zero-sum differential game is formulated to model the “pursuit-evasion” interaction between the UAV-BS and the UAV-E. Definition and existence of Nash equilibrium (NE) are provided. To obtain the NE, Pontryagins minimum principle is leveraged to solve the trajectory design problem. Further, Gauss-Seidel-like implicit finite-difference method is leveraged to obtain saddle-point strategies at NE. Finally, numerical results are provided to verify the effectiveness of the proposed game model. It is revealed that the differential game can well-characterize the strategy interactions between UAVs. Moreover, results show that the initial positions and weights of UAVs, the energy consumption factor, and the user scheduling have key impacts on motion interactions between the UAV-BS and the UAV-E and further on UAV-BS’s power control.

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.

With the booming growth of Internet of Things (IoT), ensuring the secure access of the IoT terminals is paramount importance. Machine learning (ML) based physical layer authentication (PLA) has been proposed as a promising solution for preventing unauthorized access of terminals due to its robust security. However, suffered from limited battery resources, IoT terminals are difficult to bear the ML tasks, which restricts the generality of ML-based PLA. In this paper, we propose a collaborative PLA scheme based on horizontal federated learning (HFL) to release computational pressure on resource-constrained IoT terminals. Particularly, we model the ML-based PLA as a training issue of the classifier, in which the training task is to obtain the weight parameter of the neural network. Then, we design a distributed authentication framework to assign the ML task of authentication to the trusted collaborators. Finally, all the local parameters trained by collaborators are aggregated at the center IoT terminal to obtain a global classifier used for PLA. Simulations show that both miss detection rate and false alarm rate are less than 1%, confirming the effectiveness of the proposed PLA scheme.

In this paper, we investigate the time minimization in the downlink (DL) wireless powered transmission network (WPCN) consisting of one base station (BS) and two energy harvesting (EH) users. The hybrid non orthogonal multiple access (H-NOMA) and hybrid EH scheme are adopted, in which each user can harvest energy from both the dedicated energy signal and the information signal for the other user. We aim to minimize the total time consumption under the data rate and energy consumption constraints. To solve the formulated non-convex problem, we propose a two-layer iterative algorithm. The problem in the inner layer is transformed to convex and the problem in the outer layer focused on the power allocation coefficient can be solved by binary search. Simulation results show that H-NOMA can achieve the best performance compared with time-division multiple access (TDMA) and NOMA, and NOMA also outperforms TDMA under the zero circuit power and little decoding power.

Due to energy- and computation-efficiency, physical layer authentication has been acknowledged as a powerful approach in verifying the identity of mobile terminals, especially in the massive machine type communication (mMTC) scenario with resource-constraint terminals. In existing literatures, most of works are mainly focused on point-to-point verification, where only one terminal can be authenticated at a time. In this paper, we propose a novel physical layer group authentication mechanism exploiting the benefits of non-orthogonal multiple-access (NOMA) and the irreversibility of hash operation. The proposed mechanism is especially suitable for group authentication of multiple terminals in the mMTC networks with massive connections to one access point. In the authentication procedure, challenge-response signals are multiplied with upper layer keys with hash operations and then exchanged at the physical layer. Binary hypothesis test is employed to verify multiple terminals. Missing rate and false alarm rate are investigated to evaluate the performance of the proposed authentication mechanism. Signal to noise ratio(SNR) is the ratio of the power of total transmission signal and noise. The proposed scheme can achieve a missing rate of 0.8% with the false alarm rate below 1% under the SNR of 25dB.

As climate and energy issues are increasingly serious, energy efficiency in fifth generation (5G) communication networks has attracted tremendous attention. With the development of heterogeneous ultra-dense networks (H-UDNs), the growing number of base stations (BSs) will lead to more energy consumption. In this paper, we propose a BS sleeping strategy based on device-to-device (D2D) clustering communication. First, we obtain the optimal D2D cluster Head (CH) density by maximizing the total number of users which are served by the D2D CHs. Then, we consider the Received Signal Strength (RSS) and the Signal to Interference plus Noise Ratio (SINR) of the user to select the optimal D2D CHs. Furthermore, we adjust the load of the lightly loaded BSs and switch them into sleeping mode to save the energy of the network. The simulation results show that the strategy not only reduces energy consumption but also improves the user coverage probability of the network.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

The integration of mobile-edge computing (MEC) and energy harvesting (EH) can potentially improve the network performances and prolong the battery life of the device. In this article, we study the resource management problem in the device-to-device (D2D)-aided wireless powered MEC networks where one device can forward or execute computation data for other devices with its resources. Our problem seeks to optimize the computation offloading strategy, transmission power, energy transmit power, as well as CPU speed to maximize the long-term utility energy efficiency (UEE). UEE is defined as the achieved computation data per unit energy. Since the formulated problem is in fractional form and hard to solve, we employ the Dinkelbach algorithm to transform the problem into a parametric subtractive form. Furthermore, considering that the formulated problem is time varying and stochastic due to the dynamic task arrival rate and battery level, we transform the long-term problem into deterministic drift-plus-penalty subproblems for each time slot by introducing virtual queues and adopting the Lyapunov optimization theory. The proposed scheme can balance the optimal UEE and stable data queue by introducing the control parameter$V$. Theoretically, we reveal the tradeoff between the UEE and stable queue length for wireless powered MEC systems as$[O(1/V), O(V)]$. Finally, the simulations illustrate the efficiency of the proposed scheme compared with the existed work in terms of the UEE, stable queue length, and battery level.

Cooperative caching can unify storage across edge clouds and provide efficient delivery of popular contents under effective content placement. However, the placement and delivery are non-trivial in cooperative caching due to the decentralized property of edge clouds, as well as the temporal and spatial correlation of the placement. We propose a new distributed online learning approach to jointly optimize content placement and delivery without the a-priori knowledge on file popularity and link availability. Content placement and delivery can be asymptotically optimized in real-time by running distributed online learning at individual edge servers by exploiting stochastic gradient descent (SGD). The proposed approach can allow operations at different timescales by integrating mini-batch learning for farsighted content placement. The optimality loss, stemming from the different timescales, can asymptotically reduce, as the SGD stepsize declines. Simulations confirm that the proposed approach outperforms existing techniques in terms of cache hit ratio and cost effectiveness. Insights are shed on the optimal placement of popular contents.

The millimeter wave (mmWave) band has been considered as an effective way to meet the increasing traffic demand by sufficient bandwidth and high spatial reuse. The harsh propagation experienced at such high frequencies requires a dense base station deployment, which is not feasible to provide wired backhaul due to the unavailability of fiber drops. To address this issue, the integrated access and backhaul (IAB) architecture is proposed by the third generation partnership project (3GPP). In this paper, we investigate the spectrum and power allocation for IAB mmWave enabled cellular network to maximize the network capacity with considering the data rate requirements. A novel resource allocation scheme based on the sequential convex programming approach (RASCPA) is proposed. The simulation results show that our proposed scheme is superior to other schemes. The proposed scheme can increase the network capacity while satisfying the user data rate requirements.

With the support of massive multiple input multiple output (MIMO), millimeter wave (mmWave) communication technology has emerged as a promising solution to meet the increasing demand on data traffic. This paper addresses the joint optimization of beam selection and power allocation to maximize the spectral efficiency of multiple users across multiple base stations (BSs), where the number of available beams has spatial differences (i.e., nonuniform cardinalities) for different BSs. A mixed-integer programming problem is formulated under the considerations of complex interference environments (i.e., including intra-BS interference and inter-BS interference) and nonuniform cardinality, which is transformed into the convex optimization problem by relaxing the binary indicator of beam selection and utilizing the logarithmic approximation of spectral efficiency. The Lagrangian dual method and the gradient descent method are then exploited to solve the transformed optimization problem. Corroborated by simulation results, the proposed algorithm can be converged iteratively and is superior to the state of the art in terms of spectral efficiency.

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.

In recent years, vehicle-to-vehicle (V2V) communication underlaying cellular networks has attracted more and more attention both in academy and industry. In this paper, guaranteeing the latency and reliability of vehicle users (VUEs), we study the resource management problem to maximize the energy efficiency of VUEs while considering the QoS requirement of cellular users (CUEs). Based on the Lyapunov optimization theory, a novel two-layer power control and resource allocation scheme is proposed by exploiting the properties of fractional programming. Simulation results show that the proposed scheme can achieves remarkable improvements in terms of EE and ensure the latency and reliability requirements of V2V communication.

Vehicular edge computing (VEC) is a promising paradigm to satisfy the ever-growing computing demands by offloading computation tasks to vehicles equipped with computing servers. One of the major challenges in VEC system is the highly dynamic and uncertain moving route of vehicular servers. In order to address this challenge, a particular kind of vehicles (i.e., buses) is adopted as moving servers with the pre-designated route and timetable. On this basis, a fluctuation-aware learningbased computation offloading (FALCO) algorithm based on multi-armed bandit (MAB) theory is proposed. Specifically, base stations (BSs) are regarded as agents to learn the state of moving server so as to construct a stable observation set in the dynamic vehicular environment. In addition, the softmax function is applied to indicate the probability for each decision, which provides more flexible policies for obtaining better results. Simulation results demonstrate that our proposed FALCO algorithm can improve delay performance compared with the other existing learning algorithms.

In this paper, spectrum resource allocation in vehicle-to-vehicle (V2V) communications network is studied. Multiple V2V links can share one resource block (RB) for maximizing con-current links with the satisfaction of quality of service (QoS). The resource allocation problem is transformed into an inferential problem on a factor graph model. To solve this problem, the Belief Propagation based on Real-time Update of Messages (BPRUM) algorithm is proposed. In this method, the information matrix is updated after each message passing procedure is completed rather than after once iteration. From the simulation results, the proposed method achieves a significant increment in spectrum utilization compared to the existing algorithms.

Current physical layer authentication (PLA) mechanisms are mostly designed for static communications, and the accuracy degrades significantly when used in dynamic scenarios, where the network environments and wireless channels change frequently. To improve the authentication performance, it is necessary to update the hypothesis test models and parameters in time, which however brings high computational complexity and authentication delay. In this paper, we propose a lightweight cross-layer authentication scheme for dynamic communication scenarios. We use multiple characteristics based PLA to guarantee the reliability and accuracy of authentication, and propose an upper layer assisted method to ensure the performance stability. Specifically, upper layer authentication (ULA) helps to update the PLA models and parameters. By properly choosing the period of triggering ULA, a balance between complexity and performance can be easily obtained. Simulation results show that our scheme can achieve pretty good authentication performance with reduced complexity.

The ultra-dense deployment of small cells has been applied to the 5th-generation (5G) mobile networks. A large number of base stations (BSs) will lead to a dramatic increase in energy consumption, and network resources will be more difficult to fully utilize. In this paper, we propose the dynamic load adjustments (DLA) algorithm for small cells in heterogeneous ultra-dense networks. The proposed algorithm applies Q-learning to learn effective offloading policies which could combine the energy-saving function and the load balancing function. Based on the DLA algorithm, the heterogeneous ultra-dense networks could adjust the traffic load to turn off some redundant BSs or balance the load between heavily loaded BSs and lightly loaded BSs. The simulation results show that the algorithm not only improves the network energy efficiency when the average load of the networks is light, but also improves the network throughput when the average load of the networks is heavy.

Facing the dramatic increase of mobile devices and the scarcity of spectrum resources, grant-free nonorthogonal multiple access (NOMA) emerges as an enabling technology for massive access, which also reduces signaling overhead and access latency effectively. However, in grant-free NOMA systems, the collisions resulting from uncoordinated resource selection can cause severe interference and reduce system throughput. In this article, we apply deep reinforcement learning (DRL) in the decision making for grant-free NOMA systems, to mitigate collisions and improve the system throughput in an unknown network environment. To reduce collisions in the frequency domain and the computational complexity of DRL, subchannel and device clustering are first designed, where a cluster of devices compete for a cluster of subchannels following grant-free NOMA. Furthermore, discrete uplink power control is proposed to reduce intracluster collisions. Then, the long-term cluster throughput maximization problem is formulated as a partially observable Markov decision process (POMDP). To address the POMDP, a DRL-based grant-free NOMA algorithm is proposed to learn about the network contention status and output subchannel and received power-level selection with less collisions. The numerical results verify the effectiveness of the proposed algorithm and reveal that DRL-based grant-free NOMA outperforms slotted ALOHA NOMA with 32.9% and 156% performance gain on the system throughput when the number of devices is twice and five times that of the subchannels, respectively. When the number of devices is five times that of the subchannels, the success access probability of DRL-based grant-free NOMA is above 85%, compared to 33% in the slotted ALOHA NOMA system.

Non-orthogonal multiple access (NOMA) has been proposed to raise the spectral efficiency, which also brought new security threats. This letter studies the channel gain feedback falsification (CGFF) attacks of malicious users in NOMA. Since a slight falsification on channel gain feedback can still seriously damage the efficiency and security of NOMA, the detection of malicious users in NOMA is an important problem that is difficult to be solved by the traditional methods. Specific to the sensitivity of the eigenvalues to the sparse and slight anomaly, this letter analyzes the empirical spectral distribution (e.s.d.) of eigenvalues of channel gains feedback with and without CGFF attacks based on random matrix theory. On this basis, two lightweight detection schemes are proposed to detect CGFF attacks. The simulations prove the effectiveness of our proposed methods.

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.

As the versatile applications emerge, traffic offloading is an urgent issue to improve the performance for the fifth generation (5G) and beyond networks. We focus on the scenario where a device is enabled to transmit to more than one device simultaneously. The device-to-device (D2D) enabled traffic offloading scheme is studied by employing non-orthogonal multiple access (NOMA) and unlicensed access technologies. Our target is to maximize the capacity of the D2D network by optimizing subchannel assignment and power control while guaranteeing the capacity of NOMA-based cellular links and the WiFi system. The formulated problem is a non-convex mixed integer programming problem, which is hard to solve within a rational time. The problem is decomposed into subchannel assignment and power control subproblems. A matching based licensed subchannel allocation algorithm and an unlicensed subchannel access mechanism are proposed. Furthermore, we propose a centralized power control algorithm and a distributed power control algorithm based on global and local information, respectively. Besides, the unlicensed resource management scheme based on Stackelberg game is proposed to achieve the near-optimal utility of both D2D links and the WiFi system. The simulations illustrate that the proposed scheme can increase the throughput of D2D networks efficiently compared with other works.

Providing reliable and efficient coverage for cell-edge mobile users (MUs) is a key issue in wireless communication networks. With non-uniform structure and heterogeneity of network topology in the 5G/B5G networks, performance improvement of cell-edge MUs becomes even more challenging. Unmanned aerial vehicle (UAV) exhibits a comparable advantage in enhancing cell edge performance due to its flexible mobility and line-of-sight air-to-ground links. In this paper, we study UAV-assisted cell-edge MU offloading in the non-uniform heterogeneous cellular networks. A base station (BS) coordination and ground-to-air offloading scheme is proposed to enhance the cell-edge MUs' performance, whereby cell-edge MUs are periodically scheduled between coordinated ground BSs and a flying UAV. Furthermore, a theoretical framework is developed to analyze the average spectral efficiency (SE) and average network throughput. Specifically, closed-form expressions for the average SE are derived for MUs associated with the ground BSs. Upper and lower bounds for the average SE are also obtained when the MU is offloaded to the flying UAV. Finally, numerical and simulation results are provided to validate the theoretical analysis and investigate the impact of key system parameters on the system performance, which also demonstrate the advantages of the UAV-assisted offloading scheme, compared with benchmark solutions.

Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique (p, k)-Angelization provided a novel solution, in this regard, where one-to-one correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as “fingerprint correlation fcorr attack.” Mitigating that in this paper, we propose an improved solution “ c,k -anonymization” algorithm. The proposed solution thwarts the fcorr attack using some privacy measures and improves the one-to-one correspondence to one-to-many correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the real-world datasets prove the outperformance of the proposed solution as compared to its counterpart.

Pilot spoofing attack happens in the physical layer during the uplink pilot training stage. A malicious adversary sends identical pilot signals as a legitimate user, which can disrupt legitimate communications and lead to severe information leakage. In this paper, we propose a novel method using location verification to detect pilot spoofing attack. Specifically, we adopt several auxiliary devices in different locations to measure received signal strength (RSS), based on which, we then detect the spoofing attack by comparing the path-loss-based location and the algorithm-estimated location. Compared to the existing methods, our scheme does not need an uplink-downlink two-stage training, or alter the pilot structure. Simulation results show that our proposed method can provide excellent detection performance.

In this paper, we propose an area oriented authentication framework. We first derive the missing detection probability and successful spoofing rate of the attackers, and the false alarm probability and successful transmission rate of the legitimate nodes in closed forms. With these results, we can easily evaluate the risk of being attacked for legitimate nodes, and define three different areas: the no-risk area where the spoofers do not attack, the danger area where the spoofers are more likely to attack and can achieve a pretty good spoofing performance, and the warning area where the legitimate nodes should not get in. Moreover, we study the fuzzy area where it is hard to distinguish the legitimate nodes and attackers, and improved security measures should be added. These results provide useful insights for network operators. Simulations are finally given to verify our analytical results.

This paper studies the secrecy energy efficiency (SEE) maximization problem in a full-duplex (FD) bidirectional system, where an FD base station (BS) communicates with an FD user equipment (UE) with the existence of an external user who harvests energy from the ambient radio frequency (RF) signal with SWIPT and acts as a potential eavesdropper. To balance the secrecy rate and the energy efficiency, an SEE maximization problem is formulated subject to the minimum secrecy rate, the maximum transmitted power and the minimum harvested energy. The formulated non-convex problem is solved with a two-layer optimization algorithm, where Dinkelbach method is employed to deal with the fractional programming of the outer problem and a convex approximation method based on Taylor expansion is applied to transfer the inner problem into a convex iterative program. Numerical results demonstrate the effectiveness of the proposed algorithm and the advantages of FD operation in improving SEE.

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.

Vehicular edge computing (VEC) is a potential solution to meet delay-sensitive and computation-intensive vehicular applications demands. However, the nature of mobility brings a significant challenge, i.e., when and where to offload task can capture the optimal delay performance for VEC. To address this challenge, firstly, we characterizes the temporal-spatial correlation for VEC system. With the aid of this, we formulate the energy-constrainted delay minimization problem as a mixed integer nonlinear programming (MINLP). Due to the fact that it is hard to tackle the features about non-convex and coupling of designed MINLP problem, this paper transforms the original problem into task placement sub-problem and delayed offloading sub-problem. Specifically, the former is solved by two-stage decision tree algorithm and the latter is obtained by dynamic programming technique. On this basis, a temporal-spatial computation offloading scheme is proposed. Simulations demonstrate that our proposed scheme achieves superior delay performance and provides useful guides for choosing suitable schemes in different conditions.

Blockchain has been considered as a promising technology to improve the efficiency of data sharing in Vehicular Edge Computing (VEC) by data mining among RSUs and vehicles. In order to obtain more mining reward, RSUs and vehicles will compete to accomplish the data sharing mining task. However, it is easier for RSUs equipped with stronger computing capabilities to win the mining reward. In this condition, some vehicles provide their own computing resources to form a shared resource pool for the competition with RSUs. Meanwhile, the competition among the vehicles sharing the resource pool is non-negligible since some selfish vehicles divide the group reward but without providing resources. In this way, we provide a multilayer game model that involving a deformed N Iterated Prisoners Dilemma (NIPD) among vehicles and a bargain game between resource pool and RSUs. Further, we use multi-agent Deep Q Network (DQN) to achieve the equilibrium between vehicles and RSUs in mining. Finally, numerical results show the optimal strategy can attain a stable data sharing mining system in VEC network.

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.

In the era of Internet of Everything, massive connectivity and various demands of latency for Internet of Things (IoT) devices will be supported by the massive machine type communication (mMTC). Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have the advantages of improving network capacity, reducing MTC devices' (MTCDs) latency and enhancing quality of service. Exploiting these benefits, we focus on the energy efficient secure computation offloading in NOMA-based mMTC networks for IoT, where the relay equipped with an MEC server and a passive malicious eavesdropper are presented. We optimize the joint computation and communication resource allocation to maximize the secrecy energy efficiency of computation offloading while guaranteeing the delay requirements of MTCDs. Furthermore, we model the subchannels allocation problem as MTCD-to-subchannel matching. Exploiting difference of convex programming and successive convex approximation, we formulate the Dinkelbach-based SEE optimization algorithm and obtain the closed-form expression of power allocation for MTCDs' on each subchannel. Based on the communication resources allocation schemes, we propose the Knapsack algorithm to solve the problem of computation resource allocation. Furthermore, we formulate the joint computation and communication resource allocation algorithm for secure computation offloading. Simulation results demonstrate the effectiveness of proposed algorithm for supporting IoT devices energy efficient secure computation offloading.

In the mobile edge computing (MEC) network, the applications of devices can be offloaded to the MEC server via the wireless link and then processed through the computation resource, to satisfy the computation and latency demand. Thus, a two-stage tandem queue is formed in the MEC network, consisting of the transmission queue and computation processing queue. However, the fluctuating wireless channel environment not only leads to the stochasticity of service in the first transmission queue, but also brings random computation task arrival in the second computation processing queue, which makes it difficult to guarantee the end-to-end quality of service (QoS) requirement. In this paper, we firstly derive the effective capacity of MEC with the two-stage tandem queue. Further, we formulate the joint bandwidth and computation resource allocation problem under the statistical QoS guarantee, to maximize the total revenue of network. This problem is proven to be NP-hard by the reduction to the two-dimensional knapsack problem. Then we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) to reduce the computation complexity, where the complicated problem can be decomposed and transformed into some convex subproblems. Simulation results reveal the inherent relationship between the required bandwidth and computation resource in terms of the supported arrival rate and end-to-end delay, and also demonstrate the proposed scheme can achieve better performance than other schemes.

In this paper, we study the secrecy performance in a hybrid wiretapping wireless system, where the half-duplex (HD) or full-duplex (FD) eavesdroppers may wiretap the confidential signal and/or transmit a jamming signal. To evaluate the secrecy performance, we derive the approximate closed-form results for the secrecy outage probability and mean secrecy rate by means of the random matrix theory (RMT). The RMT method can greatly simplify the complicated mathematical analysis with high accuracy, and can provide a useful analytical framework for other researches. The Monte Carlo simulations and numerical results are provided to validate the theoretical analysis and demonstrate the impacts of the system parameters. From the perspective of BS transmission, increasing BS transmission power can greatly improve the secrecy performance in the low transmit power region, but the secrecy performance is constant in the high BS transmit power region. Moreover, in terms of adversaries, FD eavesdroppers have better wiretapping performance than HD eavesdroppers when the jamming power is relatively low; otherwise, this result is reversed.

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.

Indoor fingerprint location is widely used in passenger flow analysis, location based service, and security monitoring. RSS is usually used as signal fingerprint. However, in these applications the acquisition process relies on the uplink data transmission of the target device. These signals are often non-normally distributed and fluctuate greatly when the target moves. In order to cope with the above difficulties, this paper proposes a hidden markov model (HMM) framework based on targets maximum speed. It combines the speed parameters with path constraints to improve the calculation of state transition matrix. The kernel density estimation method based on the Wiener process is also used to solve the confusion matrix of HMM, which improves the accuracy of estimating the RSS distribution of the uplink signal. The performance of the algorithm are compared with the traditional HMM and NB methods in an actual indoor scenario. The influence of the target moving speed and the length of the observation sequence on the positioning accuracy is also analyzed.

Moving relay (MR) has been widely studied to extended the coverage of base stations (BS). In this paper, we investigated a distance-based hybrid-duplex scheme for MR and BS system. The key idea is that UE can access to MR or BS based on its received RSRP, and select its working mode based on the transmit distance. By setting appropriate switching distance between two modes, UE can have a probability working in full-duplex to increase throughput within rather low outage. The simulation results show good agreements with our analyze and verify the proposed scheme can keep rather low outage.

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.

Massive Multi-Input Multi-Output (MIMO) is one of the key technologies for the fifth generation (5G) cellular networks. As the number of antennas increases, pilot contamination (PC) is considered as one of the main limiting factors for the performance of massive MIMO systems. In this paper, a new dynamic pilot allocation scheme is proposed to mitigate the pilot contamination with low complexity. In this scheme, a pilot reuse indicator is introduced and each cell is divided into center zone and edge zone. When the pilots are allocated, the pilots for edge zone users are dynamically selected according to the pilot reuse indicator. So the scheme can mitigate the pilot contamination by reducing pilot repetition in the target cell and neighbor cells, improve the system capacity. Our simulation results show that the proposed scheme can significantly increase the uplink achievable rate and provide better stability in the scenario with nonuniform distributed users, especially with more users and less pilots.

To avoid the Radio Access Network overload caused by massive random access attempts from Machine-Type Communication (MTC), we propose a Quality of Service (QoS)-based Dynamic and Adaptive Mechanism (QDAM). Moreover, we propose a more practical algorithm based on QDAM to solve the difficulty in obtaining the total number of accessing devices in reality. Both of the proposed algorithms combine dynamic allocation of Random Access Channel (RACH) resource scheme and adaptive Access Class Barring (ACB) scheme. First, we give priority to the delay-sensitive devices when allocating preambles. Afterwards, based on the number of allocated preambles and the number of devices failed to access, we adaptively adjust the ACB factors of delay-sensitive devices and delay-tolerant devices. In addition, to minimize the delay of delay-sensitive devices and improve the resource efficiency, we derive the required number of preambles for them. Simulation results demonstrate that the proposed two algorithms outperform other reference algorithms in terms of access delay, resource efficiency and average throughput.

We consider the big data problems in the area of political polarization for USA biparty public view. In order to provide a mathematical insight, we model the big data structure as a zero mean random matrix with a deterministic perturbation matrix, analyze this model using random matrix theory (RMT), and simulate the real data to confirm this mathematical model. Then, we first propose an average capacity metric to numerically evaluate the polarization of two different data sources, namely US Democratic and Republic parties.With this metric, we derive the approximated capacity using the large dimension approach and free deconvolution approach in RMT. These two approaches show the same capacity changing trend that the Democrats and Republicans are now more ideologically divided than in the past.

In 5G ultra dense heterogeneous network, the scheme of Passive Reception Detection (PRD) can exploit more busy bands and enhance the small base station (SBS) access opportunity by identifying the distance between the active macro vehicle (MVE) and the macro base station (MBS). In this paper, with the assistance of the MBS, the MVE distribution area can be obtained by the SBS joint-PRD scheme. And the probability density function of the distance between the MVE and SBS is derived leveraging stochastic geometry. By combining the trajectory prediction of co-channel MVE location with levy flight mobile model, the opportunity of the SBS accessing the busy bands can be derived. It is useful to maximize the number of concurrent vehicle-to-vehicle transmissions. The simulation results show that by taking advantage of the proposed scheme in the mobile network, the overall system throughput can be improved more than 20% regardless of the co-channel MVE velocity.

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.

Device-to-device (D2D) communication and femtocell are introduced as underlays to the cellular systems by reusing the cellular resources, where Stackelberg game framework is well suited to jointly consider the utility maximization of the macrocell, D2D and femtocell. In this paper, we study transmit power allocation schemes for D2D transmitters and femtocell users to improve the performance of the system. In our scheme, macrocell system is considered as the leader while the D2D pair and femtocell system are considered as two followers who buy the same channel resource from the leader, forming a one-leader-two-followers Stackelberg game. After analysing the procedure, we first obtain the optimal price for the leader and the corresponding optimal transmit power for both followers. Then we propose an iterative power allocation algorithm(IPAA), which will finally construct a Stackelberg equilibrium. Finally, we perform computer simulations to study the performance of the IPAA. It is shown that the IPAA is effective in distributed power allocation and macrocell protection for the spectrum-sharing-based three-tier networks. To make it practical, we demonstrate the IPAA is also efficient in Log-Normal Shadowing Model.

Artificial noise (AN) assisted secure communication is a promising technique in the area of physical layer security. In this paper, we propose a new AN method to enhance the secrecy performance of a time reverse (TR) transmission system. Based on the proposed AN model, we first derive the closed-form secrecy rate, whose accuracy is verified through simulations. Then using it as an objective function, we further study the power allocation issue between useful information signals and the AN with the aid of maximizing the secrecy rate. The optimal power allocation is first derived in closed form and then analyzed in some special cases such as Rayleigh channel. Analytical and simulation results show that the secrecy performance can be considerably improved by over 10% even with a small amount of AN. And when there are less multi-paths in the channel or when the transmit power is small, more power for AN is required.

This paper addresses the cell association issue in the downlink of 3-dimension (3D) multiple input multiple output (MIMO) enabled heterogeneous networks (HetNets). In order to serve specific users more efficiently, the downtilts of cell-center users and cell-edge users are configured through dynamic vertical beamforming. Therefore, it is necessary to consider the factor of downtilt in cell association for 3D MIMO enabled HetNets, so that the users are more likely to access to an appropriate cell. In this paper, a novel joint processing algorithm based on Lagrange dual decomposition (JP-LDD) is proposed, where cell association and downtilts adjustment are jointly optimized. The objective of JP-LDD algorithm is to maximize the spectral efficiency of HetNets and NP-hard cell association problem is transformed into a linear format to facilitate the analysis, with access control principle considered as well. Finally, simulation results show the superiority of JP-LDD algorithm in both performance of spectral efficiency and offloading effects in HetNets. Furthermore, the feasibility of its application in massive MIMO systems is also verified.

Along with the increasing data demands in vehicular networks (VNs), how to satisfy large-amount data transmission under the requirements of low delay and high reliability has gained a lot of attentions. In this paper, we investigate the key performance metrics when vehicles download the required data by hybrid Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) patterns in multi-lane scenario. Analytical results are derived on the transmission time and the number of handovers. In addition, in order to realize the minimum number of handovers in V2V transmission process, we put forward a Maximum Single Download Time (MSDT) handover strategy. Finally, simulations and discussions are presented to validate the performance of the proposed strategy under different transmission models, and to prove its advantage by comparing with some other typical strategies.

Unmanned aerial vehicles (UAVs), featured by flexible configuration, robust deployment, and line-of-sight links, has a great potential to provide ubiquitous wireless coverage and high-speed transmission. In this paper, we aim to analyze the coverage performance of UAV-assisted terrestrial cellular networks, where partially energy-harvesting-powered caching UAVs are randomly deployed in the 3-D space with a minimum and maximum altitude, i.e., Hland Hh. A novel cooperative UAV clustering scheme is proposed to offload ground mobile terminals (GMTs) from ground cellular base stations to cooperative UAV clusters. A cooperative UAV cluster is developed within a cylinder with projection centered on a GMT, based on their energy states, the cached contents, and the cell loads. With tractable Poisson point process and Gamma approximation, explicit expressions for the successful transmission probabilities are obtained. A theoretical analysis reveals that the cooperative probability of a UAV and the offloading probability of a GMT have bell-shaped relation with respect to the radius of the cylinder and the cache hit probability (the matching probability of a content request and content cache). Numerical results are provided to demonstrate the impacts of the system parameters on the cooperative UAV cluster. The results also give the optimal average altitude (Hl+ Hh/2) and altitude difference (Hh-Hl) in maximizing the coverage performance with the proposed cooperative transmission scheme.

This paper proposes a novel quadrature space-frequency index modulation (QSF-IM) scheme as a promising energy-efficient radio-access technology for the fifth generation (5G) wireless systems. Motivated by the potential energy saving of spatial modulation (SM) with the part of information being carried through antenna indexes, the proposed scheme further leverages the benefits of SM by applying the idea across the spatial and frequency domains. Moreover, by deploying dual antenna constellation for in-phase and quadrature-phase transmission, the proposed scheme can enhance data rate at no extra cost of energy consumption, leading to further improvement in energy efficiency. Theoretical bit error rate and achievable sum-rate of the proposed scheme over frequency-selective correlated Rician and Rayleigh fading channels are derived and are shown to have good agreement with simulations. Furthermore, the effectiveness of the proposed scheme is analyzed through a comprehensive list of performance metrics, including spectral efficiency (SE), energy efficiency (EE), cost efficiency (CE), and economic efficiency. Performance trade-offs between these metrics are thoroughly investigated. Compared with other existing schemes, the proposed QSF-IM scheme is demonstrated to offer better EE-SE and EE-CE tradeoffs, and can therefore be considered as a potential candidate for energy-spectral efficient 5G systems.

In this paper, we study the performance of base station (BS) coordination in heterogeneous networks (HetNets) with cache-enabled and renewable energy-powered small cell BSs (SBSs). Macrocell base stations (MBSs) provide basic coverage, while the SBSs, powered by harvested energy, conduct content-aware coordinated transmission to provide high data rate and further improve the network coverage. Specifically, a joint transmission strategy is performed based on the knowledge of the energy states and the cached contents of SBSs, along with the awareness of the availability of channel resources and the average received signal strength (RSS) of the corresponding link. Stochastic geometry is applied to characterize the statistics of the cell load at MBSs and SBSs, as well as the aggregated information and interference signal strength. Then, the average user capacity for the joint transmission is obtained. Additionally, the coverage probability is derived with gamma approximation for the aggregated information and interference signal strength. Analytical results reveal that the average user capacity and coverage probability can be maximized with optimal cache size, energy harvesting rate and cooperative RSS threshold. Finally, extensive numerical and simulation results are provided.

The wiretap channel with shared key (WTC-K) refers to the classic wiretap channel with a secret key of arbitrary rate shared between the transmitter and the legitimate receiver. While the secrecy capacity of WTC-K has been obtained using random coding arguments, in this paper, we propose a low-complexity polar coding scheme achieving the secrecy capacity under the strong secrecy condition. Our construction consists of choosing the sets of information indices and using the shared key properly. Specifically, we utilize the structure of superposition coding in the proposed scheme.

Caching content by users constitutes a promising solution to decrease the costly transmissions with going through the base stations (BSs). To improve the performance of in‐network caching in device‐to‐device (D2D) communications, caching placement and content delivery should be jointly optimized. To this end, we jointly optimize caching decision and content discovery strategies by considering the successful content delivery in D2D links for maximizing the in‐network caching gain through D2D communications. Moreover, an in‐network caching placement problem is formulated as an integer nonlinear optimization problem. To obtain the optimal solution for the proposed problem, Lagrange dual decomposition is applied in order to reduce the complexity. Simulation results show that the proposed algorithm has a near‐optimal performance, approaching that of the exhaustive search method. Furthermore, the proposed scheme has a notable in‐network caching gain and an improvement in traffic offloading compared to that of other caching placement schemes.

Exponential rise in wireless data demands is already creating a significant burden on current mobile networks. Meanwhile, the upcoming 5G system is believed to consist of heterogeneous access networks. The user satisfaction and radio resource efficiency are the main challenges for the network convergence. Network virtualization technology is regarded as an effective solution to above challenges. Heterogeneous networks are virtualized to multiple logical slices to best serve specific services. Two fundamental questions are how to virtualize mobile resources among different slices, and how to shield the impact of time-varying wireless channel on slicing with Quality of Service (QoS) guaranteed. In order to solve these two problems, we propose a two-timescale Virtualized Radio Resource Pre-Allocation mechanism, consisting of the inter-slice pre-allocation and the intra-slice scheduling. Specifically, the subcarrier and transmission power are jointly optimized and the base station coordination is also supported. For different network deployment scenarios, both the centralized and distributed solutions are provided. Simulation results demonstrate that both solutions are able to guarantee the QoS with the minimum requirements of radio resource.

The unprecedented growth in the data demands requires the effective use of data offloading techniques in 5G networks. Machine type communication (MTC) foster this growing trend even further. This paper proposes a novel cooperative data offloading scheme with the help of multiple mobile relaying devices. The proposed scheme cooperatively aggregates data from surrounding MTC devices and transmits it to the base station. The paper also presents the framework for the selection of secured and trusted relays by using the concept of trust transitivity. The proposed relay scheme is compared with existing algorithms including mobile and stationary relay schemes. The simulation results show that our proposed scheme improves outage probability and communication delay by 33% and 25%, respectively as compared to fixed relay scheme.

We consider a hybrid eavesdropping wireless system, where the locations of the eavesdroppers are drawn from Poisson point process (PPP). The eavesdroppers work in a half-duplex mode with a certain probability to transfer from eavesdropping mode to jamming mode. Based on the stochastic geometry (SG) and the random matrix theory (RMT), we derive the analytic results for the secrecy outage probability (SOP) and mean secrecy rate (MSR), which are verified by Monte-carlo simulations. We further derive the closed-form results for a special case where some special system parameters are assumed. It is found that the secrecy outage probability increases fast with the increasing of eavesdroppers' density. To improve the secrecy performance, the transmit power should be optimally designed.

Caching the popular multimedia contents at the wireless edge such as mobile devices, has been identified as a key technology to unleash the ultimate potential of wireless networks. In addition to the physical link condition, social relationships are also of great importance for effectiveness enhancement of the D2D-based wireless caching network. In this paper, we analyze theoretically the performances of the cache-enabled D2D underlaid cellular network with the heterogeneous caching placement, while taking the role of social interaction into account. In this scenario, we firstly derive the achievable average ergodic rate that satisfies a successful content delivery by utilizing the social interaction statistics along with channel statistics with the aid of stochastic geometry. Then the successful transmission probability under the constraint of social interaction is further derived. Based on the expression, we reveal the impact of physical layer and content-related parameters on the network performance. Simulation results validate the accuracy of theoretical analysis and provide the condition when the network performance can benefit from D2D caching.

In this paper, we investigate the performance of artificial noise assisted secure communication in the presence of a full-duplex active eavesdropper who can simultaneously perform eavesdropping and jamming. An approximate closed-form expression for the secrecy rate is derived. With this result, we obtain a new result for the optimal power allocation factor maximizing the secrecy rate, and analyze the impact of self-interference coefficient and jamming power on it. We further consider a more practical scenario where the legitimate user aims to maintain a given target data rate and uses all remaining power for artificial noise (AN) to interfere with the eavesdropper. While the eavesdropper tries to compel the transmitter to reduce the AN by sending jamming signals to the legitimate receiver. To solve this conflict, we introduce a power cost parameter to describe the impact of jamming on the eavesdropper itself and then formulate a game-theoretic framework. We also derive the closed-form equilibrium solutions for both sides. These results show the optimal jamming strategy of the eavesdropper and provide insights into the low bound secrecy performance. Finally, simulation results are provided to verify our analytical results.

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.

To alleviate the burden on power grid in heterogeneous cellular networks, energy harvesting (EH) has inspired thorough research in both academia and industry. However, the mismatched distribution of energy storage and traffic load leads to a bad result that the renewable energy can not be utilized sufficiently. To tackle this issue, we propose an energy-biased-received-power (EBRP) scheme (i.e., users incline to associate with BSs at higher energy level) for energy-load balancing and on-grid energy saving. Taking into account the dynamic EH behavior, a performance analysis of coverage probability and energy efficiency is conducted in a two-tier heterogeneous network with EH enabled personal cells. Numerical and simulation results are provided along with the maximum-biased-received-power (MBRP) scheme. Numerical results show that the reduction of maximum transmit power at base stations is favorable for enhancing energy efficiency. Moreover, it is revealed that the proposed scheme shows superiority in energy-load balancing when the energy efficiency is comparable with that of the MBRP scheme.

UE-to-Network relay leverages the proximity communication between user equipments (UEs) and allows certain UEs to provide relay assistance for others, which can greatly improve system energy efficiency. In this paper, we consider the scenario where UEs suffering from bad channel condition and low battery level can communicate with the base station directly or via the help of other UEs in heterogeneous networks. The optimal power allocation and connectivity among UEs are studied, which aims at minimizing the system transmission energy while guaranteeing the minimum data rate requirement of each UE. An optimization framework is presented to formulate the system transmission energy minimization problem, which can be converted into a weighted one-to-one matching problem. And a practical joint transmission mode with relay selection and power allocation (JMRP) algorithm is developed to solve it. Simulation results show that the proposed algorithm outperforms the existing works in terms of system transmission energy and throughput.

Machine-type communications (MTC), considered as one of potential technologies in 5G, has attracted more and more attention in recent years. Massive MTC connection density but with small data rate leads to preamble deficiency and network congestion in the network. In order to solve this problem, we jointly utilize the back off strategy and the hierarchical access control method. We formulate a Quality of Experience (QoE)- oriented bandwidth resource allocation problem as a MINLP (Mixed Integer Non-linear problem) under the constraint of limited preambles. This problem is proven to be NP-hard and has extremely high complexity. To reduce the complexity, a two-step iterative algorithm is proposed with the aid of removal algorithm and convex optimization theory. Simulation result shows that out proposed method can effectively improve the QoE gain compared with typical algorithm.

As the future cellular networks tend to be randomly deployed and equipped with more antennas, we study the enhanced secrecy rate of multiple-input multiple-output (MIMO) systems with a stochastic geometry approach. To avoid intractable computational complexity and to have a deep insight into the security performance of such networks, we derive a closed-form lower bound on the ergodic secrecy rate, based on which the optimal power allocation parameter is derived. Analytic and Monte-Carlo simulation results show that the lower bound is quite tight. The density of base stations (BSs) and number of antennas both greatly infects the optimal power allocation parameter, e.g., positive secrecy rate may not achieved when the BSs density is small, otherwise the optimal power allocation will increase with the increase of the BSs density.

We investigate the problem of physical layer security in a wireless cooperative network, where communication is assisted by the untrusted relay. It is impossible for the amplify-and-forward protocol to convey a confidential message from the source to the destination without the help of other nodes, where only an untrusted relay is available. Nonetheless, our results are quite optimistic when there are multiple untrusted relays, which amplify the received signal and forward it to the destination. We treat the untrusted relay nodes as eavesdroppers, despite the fact that they are the enablers of communication. First of all, we prove that a positive secrecy rate can always be ensured regardless of the transmit power and the channel condition of the untrusted relays, as long as there exists a sufficient number of untrusted relays. Furthermore, a closed-form solution to the upper bound of secrecy rate is obtained. Finally, a practical approach is proposed to acquire a positive secrecy rate, which brings nearly no extra communication cost. Simulations are conducted to demonstrate the validity of the proposed approach.

This paper studies the performance of a Massive MIMO system with pilot contamination in the multi-cell multiuser network. RCI (Regularized Channel Inversion) precoding is considered due to its good performance. Instead of using a large system method which assumes the number of antennas and users tends to infinity with a fixed ratio, we analyze the system performance in a more general situation where the number of transmit antennas M and number of users K are finite. We first derive the closed-form expression of the optimal RCI factoramp;#945;opt,and then study the impact of pilot contamination on the system performance. Result shows that more factors will be considered in finite situation. It is shown that our analysis is more accurate especially in small values of M and K. And our derived amp;#945;opt enables much better performance than the traditional large system result.

This paper investigates the secrecy rate maximization problem for the multiple-input-single-output multiple-antenna-eavesdropper (MISOME) wiretap channel with multiple randomly located jammers. The multi-antenna base station (BS) transmits information signals along with artificial noise (AN) to disturb the eavesdropper. Moreover, the friendly jammers are properly selected to assist the legitimate link for better secure transmission with some payoffs. With this system model, we first formulate a Stackelberg game between the BS and the assisting jammers with full channel state information. Stackelberg equilibriums, including optimal fraction of transmit power for AN, optimal transmit power, and asking prices of assisting jammers, are first proved to exist and then analytically derived. A policy iterative algorithm is also proposed to obtain the optimal solutions. We then extend the Stackelberg game to the case of MISOME broadcast wiretap channel with channel distribution information of eavesdropper. Numerical results verify the accuracy of the derived results and the efficiency of the proposed algorithm. The results reveal that the proposed jammer-assisted secure transmission can greatly improve the secrecy performance and meanwhile save more energy for information signals, which is significant for future wireless communication.

Denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which results in a decline of user experience as well as heavy signaling overheads to the network. Therefore, the study on handover is of great importance especially in dense HetNets. In this paper, we focus on the analysis of cross-tier handover from the macro cell tier to the small cell tier, which shows the highest handover failure rate in current standards and industry studies. Using the stochastic geometry, we propose an analytical model for the cross-tier handover processes in the HetNet. Based on the derived analytical model, the closed-form expressions for the key handover performance metrics, including the handover rate, handover failure rate, and ping-pong rate, are deduced as functions of the base station density, time to trigger and user mobility. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical analyses, which provide guidance for the deployments of HetNets and corresponding handover strategies.

This paper investigates secrecy and connection performance for uplink transmission in a two-tier heterogeneous network with non-uniformly deployed low- power small base stations (BSs). All BSs and the eavesdropper are equipped with multiple antennas. We propose an aggregate interference approximation approach to characterize the statistics of interference generated by users associated with small BSs to facilitate our analysis. Then the secrecy outage probability and successful connection probability for a randomly located macro user are derived. In addition, we characterize them for a special case where macro BSs and eavesdropper are equipped with single antenna. Numerical results validate the effectiveness and accuracy of the aggregate interference approximation approach. Besides, the theoretical results fit well with the numerical results.

We consider the physical layer security for the downlink system, where the confidential messages can be wiretapped by both the intended users (IUs) and the external eavesdroppers (EEs). Regularized channel inversion (RCI) precoding is adopted for multiuser communication because it can control the mutual information leakage among users. And, to enhance the secrecy performance, artificial noise (AN) is introduced to disturb external eavesdroppers. Considering the large-system regime, by using stochastic geometry, the secrecy outage probability (SOP) and mean secrecy rate for the nearest EE wiretap and the strongest EE wiretap scenarios are derived, respectively. Analytical and simulation results show that i) when the AN is adopted, the SOP exponentially decays with the number of transmission antennas, ii) in most of the power allocation case, per-user secrecy rate can be improved significantly, e.g., there is an almost 2.7 times of improvement for the particular transmission antennas number.

Device to Device (D2D) communication has been proved to be an effective way to enhance cellular network capacity, which enables direct data exchange of localized traffic of users in proximity. Current D2D links are mainly based on WiFi Direct technology. In this paper, we propose a 4G LTE-assisted distributed D2D communication network. Information of user devices will be uploaded to a D2D server periodically via commercial 4G LTE network. The D2D server then will initialize a D2D network after collecting the device information. A Token sharing strategy is proposed to control the process of D2D networking, based on the information of SINR, location, battery power, as well as service QoS demand. Finally, our proposed system is demonstrated by Android smartphones.

Massive MIMO can significantly improve diversity/multiplexing gain, channel capacity and spectrum efficiency. An efficient signal detection algorithm plays a very important role for system performance. However, the BER, complexity and delay performance of present signal detection algorithms are far from optimal with the increasing number of antennas. This paper presents an improved mixed Gibbs sampling algorithm based on multiple random parallel Markov chains (MGS-MRPMC). It has lower complexity than traditional signal detection algorithms in massive MIMO systems. The simulation shows that the proposed algorithm can alleviate the stalling problem encountered in the mixed Gibbs sampling (MGS) algorithm with higher order QAM. Moreover, it can reduce the serious delay of the mixed Gibbs sampling with multiple restarts (MGS-MR) algorithm. The use of parallel strategy makes it suitable for hardware implementation in practice.

Coordinated MIMO (co-MIMO) can mitigate inter-ceil interference (ICI) and significantly improve spectral efficiency with accurate channel estimation. However, channel estimation error (CEE) is inevitable and may cause obvious performance degradation for coordinated scheduling algorithms due to the inaccurate information. In this paper, a novel coordinated scheduling algorithm is proposed based on an adaptive CEE model for the uplink co-MIMO system. Based on a new defined CEE sensitivity criterion, users are divided into different groups and applied to different scheduling approaches. Simulation results show that the proposed algorithm outperforms traditional coordinated scheduling algorithms in practical scenario with CEE. Furthermore, it can also achieve a better tradeoff between throughput and fairness compared to the robust user pairing algorithm.

Recently, device-to-device (D2D) communication has been considered as a promising technology to implement vehicle-to-vehicle (V2V) communication. To ensure road safety and traffic efficiency, V2V has more strict requirements for latency, reliability and access availability. In this paper, we consider the problem of resource block (RB) scheduling for maximizing the number of concurrent V2V transmissions instead of sum rate, allowing multiple vehicles to access to one RB. Firstly, the reliability requirement is transformed into a constraint of a matrix spectral radius to limit the accumulated interference. Secondly, utilizing spectral radius estimation theory, a Minimizing the Increment of Spectral Radius (MISR) resource block sharing algorithm is proposed to accommodate as many users as possible. Finally, simulation results show that the proposed MISR algorithm can improve spectrum efficiency by 150% and 96% in rush hour compared with some other existing methods.

With the development of high-speed railway, the future wireless communication system is facing the high-speed mobile environment, where rapid and significant variations will emerge in the instantaneous wireless channel conditions. The main challenge on the channel estimation is that plenty of pilots should be inserted to track the fast- varying channel. In this paper, we propose a method to estimate the fast-varying channel with the aid of Basis Expansion Model (BEM) and uplink Sounding Reference Signal (SRS). BEM is used to model the fast-varying channel with the combination of a finite number of basis functions, and the combination coefficients are slow-varying. Therefore, the sparse SRS can be used to estimate the channel, which will reduce the pilot number compared to the estimation with demodulation reference signals. Simulation results show that our proposed method provides better performance than the traditional one in the fast- varying channel, and can accommodate to different Doppler frequency shift with changing the BEM order.

This paper studies the physical layer security in a multi-user downlink, where a single user is selected for secret transmission during each time frame. Current works usually assume a worst case where all unselected users are curious and act as eavesdroppers, and conclude that no multiuser diversity is achievable for secrecy when the number of users is pretty large. However, the worst case may happen rarely in practice. A general scenario is that several (maybe all) of the unselected users act as eavesdroppers. In this case, selecting the user with the largest SNR (i.e., signal to noise ratio) does not necessarily achieve the maximum secrecy rate. For the general case, we derive the new closed-form expression of the secrecy outage probability, which increases with the number of curious users, and tends to converge in the high-SNR and large-user-number regime. When the number of curious users is supposed to be small, the secrecy outage probability could be any value smaller than one even in the large-user-number regime. These results provide additional insights into the system performance.

This paper investigates the coordinated multipoint transmission (CoMP) in heterogeneous cellular networks (HCNs) by analyzing a cooperative scheme based on the maximum long-term average received power (MLRP). Using stochastic geometry, the outage probability (OP) and energy efficiency (EE) of a two-tier HCN operating under this scheme are derived, based on which the trade-off between OP and EE is discovered. A novel index named revenue efficiency (RE) is then proposed as a complementary performance measure for the trade-off. Simulation results show the OP-EE trade-off. It also reveal that the suitable trade-off points between OP and EE that will deliver the maximum economic profitability can be found. Tendencies discovered in this paper may provide the operators with opportunities for further optimization in pursuit of economic profitability.

Abstract In the paper, we study the problem of secure connectivity for colluding eavesdroppers using relay selection in random wireless networks, where the relay nodes and eavesdroppers are all randomly distributed according to two independent Poisson point process. The decode‐and‐forward and randomize‐and‐forward two relay strategies are considered, and a new metric is defined for best relay selection and random relay selection. We derive closed‐form expressions for the secrecy outage probability for the two relay strategies. In particular, the effect of power allocation ratio and the maximum ratio combing at the destination node on the secrecy outage probability is demonstrated for the decode‐and‐forward relay strategy. Numerical results illustrate the secrecy performance gains with collaborative transmit diversity. © 2016 The Authors. Wireless Communications and Mobile Computing Published by John Wiley & Sons Ltd.

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.

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.

Multiple-input multiple-output (MIMO) technology has been widely used as an efficient way to improve the capacity of wireless channels. As an optimal detection for MIMOOFDM systems, the maximum likelihood (ML) algorithm whose complexity rises exponentially with the number of transmit and receive antennas is not practical for real-time detection. Instead, the sphere decoding (SD) algorithm was proposed to find the solution to the ML detection problem, however, the complexity is still large. The choice of the initial radius for SD detector has a significant impact on the complexity. In this paper, an improved depth-first- search sphere decoding (IDFS) algorithm based on the optimization of likelihood ascent search (LAS) predetection is proposed. The simulation results demonstrate that the proposed algorithm can not only achieve almost the same performance as ML detector but also efficiently decrease the complexity. For example, for about 49 visiting nodes for 16QAM and 30 visiting nodes for 4QAM, IDFS algorithm has about 4dB SNR gain than Schnorr-Euchner sphere decoding (SESD) algorithm as Fig.5 shows.

This paper presents a general analytical framework to evaluate the secrecy performance of transmit antenna selection (TAS) systems without requiring the identical average signal-to-noise ratios (SNRs) from different transmit antennas. Closed-form expressions of secrecy outage probability are derived in a multiple-input multiple-output (MIMO) wiretap channel. Moreover, a novel TAS scheme based on the limited feedback from both the receiver and the eavesdropper is proposed and analysed. With the additional consideration of the eavesdropper's channel, security is shown to be enhanced although the instantaneous channel state information (CSI) keeps unknown to the transmitter. Finally, numerical results substantiate the aforementioned analysis.

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.

Communication in 60-GHz frequency exhibits unique characteristics that are unavailable in traditional lower frequencies. Tremendous propagation loss together with oxygen absorption characteristics helps localize interference and encourages concurrent transmission. It is observed that in short-range, high-speed communication network, (Tx, Rx) pair tends to come close to each others. The network can be seen as naturally formed group of devices, which shared mutual interests. In Such networks, it is argued that throughput mainly depends on scheduling algorithms rather than transmission power control. In this paper, we proposed a concurrent scheduling algorithm based on vertex coloring technique. The proposed algorithm employs time and space division in scheduling in 60-GHz networks. Using vertex multi-coloring, we allow (Tx - Rx) communication pairs to span over more colors, enabling better time slot utilization. Since we use directional antennas, we extend distance based space division between two flows by considering angel between them. Since, data rate in 60-GHz mainly depends on transmission links, distance based relay selection algorithm is also proposed. We evaluate our scheduling algorithm in single-hop and multi-hop scenarios and discover that it outperforms traditional TDMA and greedy algorithm by significantly improving network throughput.

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.

Networks based on 60-GHz promise data rates in gigabits and show least tolerance towards signal blockage. In addition to severe propagation loss, signal blockage may result in an attenuation upto 40dB. Distributed relay selection scheme is proposed in this paper to cater for signal attenuation. By maintaining a table containing details about interference measurements and neighboring devices, a receiver device determines an optimal relay for its active transmission. Furthermore, outage probability analysis is provided and is utilized to compare our proposed scheme with direct transmission and fixed node relay scheme. Simulation results show that our proposed scheme significantly improves outage probability and system throughput.

In the paper, we study the problem of secure connectivity in general Multiple input and Multiple output (MIMO) stochastic networks, where legitimate users and eavesdroppers are equipped with multi antennas, and designed to distribute on a two- dimensional plane according to two independent Poisson point process(PPP)s. The non-colluding and colluding eavesdroppers scenarios are considered. Transmit-receive diversity (TRD) is explored to derive a closed-form expression of the upper bound of secrecy connection probability. For colluding eavesdroppers, we consider the worst cases where the received signals of eavesdroppers are Nakagami-m distributed with integer parameter m. Compared with MISO system, our analysis and simulation shows that a significant improvement of secrecy connection probability can be received by performing TRD in MIMO system.

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.

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.

With Device to Device (D2D) user equipment (DUE) sharing the same resources with traditional cellular user equipment (CUE), intra-cell interference is becoming a challenging issue nowadays in D2D underlaying network. However, due to the lack of convexity, globally optimal resource sharing scheme is generally unattainable. In this paper, we propose a genetic algorithm (GA) based user matching scheme (GAM) with optimal power allocation (OPA) to achieve the multi-dimension optimization. Firstly, we demonstrate that the optimal power allocation between DUE and CUE can be obtained by searching several finite solution sets in the feasible region boundries. Secondly, genetic algorithm is applied to obtain the near-optimal user matching in the whole network scope. Simulation results show that the GA based scheme can achieve 30Mbps system throughput gain comparing to the traditional greedy searching, while with a limited complexity increase.

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.

In current wireless cellular networks, interference has become the major obstacle to high spectral efficiency because of universal frequency reuse. Base station cooperation, also referred to as coordinated multi-point processing or transmission(CoMP), has emerged to mitigate it by estimating and avoiding interference from adjacent base stations(BSs). Through analyzing the scenario of CoMP, we formulate the scheduling problem as maximizing the weighted sum-rate considering both throughput and fairness. An iterative algorithm jointly handles resource allocation and power control is proposed to solve the problem. To reduce the complexity and accelerate convergence, three concepts, namely neighboring BS set, user filter and BS filter are introduced. Moreover, the performance gain and computational efficiency of our proposed algorithm are validated by system-level simulation.

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.

The world is witnessing a tremendous increase in data demands which is subject to the new emerging technologies, applications and services. 60 GHz communication network is one of such technology, claiming data rate in multi-gigabits. In this paper, we propose a scheduling algorithm for device-to-device 60 GHz network having directional antennas. The proposed algorithm utilizes the vertex coloring scheme and is optimized to improve system throughput. A threshold minimum distance between conflicting flows is used to keep the accumulative interference limited. Also, when there are conflicts among different flows, those with better data rate prospects will be scheduled priorly. Simulation results show that our scheme has brought significant improvement to system throughput almost by 19% and average flow number per slot is improved by 12%, as compared to other scheduling algorithms.

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.

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.

In this paper, we study the transport capacity of large multi-hop wireless CSMA networks. Different from previous studies that rely on the use of a centralized scheduling algorithm and/or a centralized routing algorithm to achieve the optimal capacity scaling law, we show that the optimal capacity scaling law can be achieved using entirely distributed routing and scheduling algorithms. Specifically, we consider a network with nodes Poissonly distributed with unit intensity on a$\sqrt{n}\times\sqrt{n}$square$B_{n}\subset\Re^{2}$. Furthermore, each node chooses its destination randomly and independently and transmits following a CSMA protocol. By resorting to the percolation theory and by carefully tuning the three controllable parameters in CSMA protocols, i.e., transmission power, carrier-sensing threshold, and countdown timer, we show that a throughput of$\Theta(1/\sqrt{n})$is achievable in distributed CSMA networks. Furthermore, we derive the pre-constant preceding the order of the transport capacity by giving an upper and a lower bound of the transport capacity. The tightness of the bounds is validated using simulations.

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.

With the growing concern over energy consumption, green communication is becoming more and more important. Lots of efforts have been put into investigating in energy-efficient wireless systems. Since Fractional Frequency Reuse (FFR) is a widely-used Inter-cell-interference coordination scheme in the OFDMA wireless network, we consider the energy-efficient power control in the FFR system. With demanded user rate constraints, we aim at minimizing the Joule consumed per transmission bit per-cell in the downlink. Based on Dinkelbach's theory, we transform the objective function into a parametric programming problem. By proving the problem is convex, we derive the optimal analytical solution by Karush-Kuhn-Tucker (KKT) conditions, along with an energy-efficient power control algorithm. Numerical results are given to verify our analysis, and demonstrate that the power control algorithm significantly improves the energy efficiency in FFR. The optimal frequency reuse factor is also given when considering the fairness of energy efficiency and power consumption.

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.

Based on the channel measurement in a typical indoor environment, a simple mode selection criterion is originally proposed for closed-loop multiplexing transmissions using LTE codebook. The fitting result of the capacity loss (CL) due to the limited size codebook is presented. High signal-to-noise ratio (SNR) analysis is also given to provide a deep insight for the addressed mode selection problem. Furthermore, the effect of the codebook design and dimension on the capacity loss is also investigated. Though based on a specific environment, the generality of the proposed algorithm is validated using ITU-R M.2135 channel model in four scenarios. The results are important for the adaptive technique development in modern communication systems.

In this paper, we investigate the beam squint problems in 60 GHz mm-wave wireless communication beamforming process. To minimize the degree of beam squint we formulate the objective task into a constrained optimization problem and derived a phase improvement scheme based on IEEE 802.15.3c codebook. For further improvement of system performance, main response axis (MRA) alignment scheme is proposed, the direction of MRA can be adjusted alignment exactly in different frequencies and can achieve higher array antenna gain with a lower side lobe level (SLL) and it is more effective than the first schemes, but requires more phase shifters. In order to reduce the complexity of MRA alignment scheme, we proposed MRA alignment simplify scheme, the scheme only require 1/2 phase shifters has a negligible performance loss compared with the second scheme. Extensive simulations have demonstrated that the proposed three schemes can suppress beam squint, improves system throughput and performs better than the existing scheme.

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.

Based on the channel measurement in a typical indoor environment, wideband multi-input multi-output (MIMO) channel capacity analysis result is reported in this paper. A series of non line-of-sight (NLOS) and LOS measurement positions are planned for capacity comparison in different propagation conditions. For fixed received signal-to-noise ratio (SNR), slight average capacity loss in LOS cases is observed compared to NLOS cases. However, the capacity in presence of LOS component appears more position-sensitive than that in NLOS cases. When considering the large scale path loss difference introduced by the receiver movement, the capacity in LOS cases greatly exceeds the NLOS cases. A key finding is that the measured maximum channel eigenvalue fits t Location-Scale distribution rather than Gamma distribution in indoor scenario. Finally, Ricean K-factor and spatial correlation results are presented.

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.

Frequency reuse (FR) has been widely deployed to achieve increased throughput and interference mitigation. Different FR schemes can prove to be efficient under different conditions and parameters. This paper formulates a generic mathematical model which can be used to obtain an FR scheme that can be tailored according to our requirements. The paper proposes a fuzzy set theory based generic mathematical model for deriving various Soft Fractional FR (SFFR) schemes. The derived schemes are evaluated using various parameters such as average throughput, spectral and power efficiency. To the best of our knowledge, the use of fuzzy set theory for deriving different SFFR schemes is the first effort that can be found in the literature. The analysis provided in the paper discovers an optimal SFFR scheme which provides higher spectral efficiency and throughput. Our proposed scheme, at FR =1.35, improves the power efficiency (8.68 Watts/(bps/Hz)) by staggering 21% and 57% as compared to FR=1 (11 Watts/(bps/Hz)) and FR=3 (20 Watts/(bps/Hz)), respectively.

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.

In this paper, we study the energy efficiency in the multiple access points (AP) coherent cooperative transmission (CCT) system with frequency-selective fading. Supposing the per antenna power constraint is sufficient, we derive the achievable optimal energy efficiency by distributing the transmit power among subchanels and cooperative APs, aiming at minimizing the Joule consumed per transmission bit of the CCT system. Based on Dinkelbach's theory, we solve our main problem with the associated parametric programming problem, and prove that the problem for the CCT can be transformed into an equivalent problem for the single AP transmission (SAT), which is much easier to be solved. The analytical solution is derived, along with an optimal power allocation algorithm. Numerical results are given to verify our analysis, and demonstrate that with the proposed scheme, the CCT outperforms the SAT in terms of the energy efficiency.

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.

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.

This paper considers the scheduling and resource allocation with adaptive modulation in downlink coordinated multi-point transmission (CoMP) systems, where multiple users are served via zero-forcing precoding simultaneously by several cooperative base stations (BSs). The joint scheduling and resource allocation to maximize the sum rate is formulated as a combinational optimization problem under constraints. As the searching space for this problem is extremely large, which prohibits an exhaustive search (ES), a genetic algorithm (GA) based solution is proposed. In particular, a two-dimension-binary chromosome coding scheme is designed to denote potential user selection and bit loading strategies across multiple subchannels. To handle per-BS power constraint, a penalty based fitness function is used, and in doing so GA can search for optimal solution in a larger region. To ensure convergence, a super individual named elite is added to each population. Simulation results indicate that the proposed algorithm leads to significant sum rate gain compared to existing schemes, and provides close to ES performance but with much lower computational complexity.

In this paper, we study the subchannel power allocation problem to maximize the total throughput of the downlink orthogonal frequency division multiplexing (OFDM) multiple access points (APs) systems with non-coherent cooperative transmission. Although the problem has been claimed as a non-convex optimization problem, we prove that the duality gap between the original problem and its dual optimization problem is nearly zero when the number of subchannel is large, which implies solving the problem in the dual domain. Then, the problem is solved with the standard Lagrange dual decomposition method with an acceptable deviation. In addition, in order to reduce the complexity of the dual decomposition based method, we propose a suboptimal low-complexity algorithm, at a minor cost of the total throughput. Numerical results are also given to verify the proposed schemes.

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.

The majority studies on resource allocation are based on continuous power allocation. However, the transmit power can be assumed as a finite set of discrete power levels only, especially for practical digital cellular systems. By simply rounding up or down, the conventional continuous power allocation algorithm will not suffice. In this paper, via transmitting signal on discrete power levels, multi-cell power allocation is modeled as a combinatorial optimization problem, which proved to be NP-hard. Ant colony optimization (ACO) is applied to get near-optimal solution of the problem. In particular, a multiple power level based searching graph is designed, and conventional ACO is improved that ant colonies in each cell cooperate to maximize system throughput. Simulation results indicate that with only 4 power levels, the proposed algorithm can achieve a significant rate gain over the existing schemes.

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.

In this paper, we study the subchannel (SC) power allocation for orthogonal frequency division multiplexing (OFDM) multiple access points (APs) systems with non-coherent cooperative transmission. The objective is to maximize the total capacity under per-AP power constraints. It can be proved that the optimal solution can be obtained by the combination of an optimal SC partition search and the power allocation across SCs for each feasible partition. Existing work exhaustively searched the optimal SC partition and used Lagrange dual method to compute the power allocation across SCs. Since the entire complexity increases exponentially with the number of SCs, the existing method is unsuitable for practical implementation. In this paper, we propose a novel optimal power allocation algorithm for non-coherent cooperative transmission with a much lower complexity. Firstly, a concept of “cut-off SC” is proposed for searching the optimal SC partition. Then, an efficient optimal power allocation algorithm across SCs is proposed for any given cut-off SC. Simulation results demonstrate that the proposed algorithm is optimal with a polynomial complexity, and ends within an acceptable number of iterations.

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.

The two-tier femtocell network is comprised of a central macrocell underlaid with shorter range femtocell hotspots. Due to the universal frequency reuse, this kind of new system architecture brings about urgent problems of the interference management and the resource allocation. Motivated by these problems, the following contributions are made in this paper: 1) a novel joint power and subchannel allocation problem for Orthogonal Frequency Division Multiple Access (OFDMA) downlink based femtocells is formulated on the premise of minimizing Femto BSs' radiating interference; 2) a pseudo-handover based scheduling information exchange method is proposed to avoid the collision interference; 3) an iterative scheme of subchannel allocation and power control is proposed to solve the formulated problem, which is an NP-complete problem. Through simulations and comparisons with three other schemes, the proposed scheme shows better performance in reducing interference and the Femto BS's transmit power, and improving the spectrum efficiency.

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.

Femtocells have been considered by the wireless industry as a cost-effective solution not only to improve indoor service providing, but also to unload traffic from already overburdened macro networks. Due to spectrum availability and network infrastructure considerations, a macro network may have to share spectrum with overlaid femtocells. In spectrum-sharing macro and femto networks, inter-cell interference caused by different transmission powers of macrocell base stations (MBSs) and femtocell access points (FAPs), in conjunction with potentially densely deployed femtocells, may create dead spots where reliable services cannot be guaranteed to either macro or femto users. In this paper, based on a thorough analysis of downlink (DL) outage probabilities (OPs) of collocated spectrum-sharing orthogonal frequency division multiple access (OFDMA) based macro and femto networks, we devise a decentralized strategy for an FAP to self-regulate its transmission power level and usage of radio resources depending on its distance from the closest MBS. Simulation results show that the derived closed-form lower bounds of DL OPs are tight, and the proposed decentralized femtocell self-regulation strategy is able to guarantee reliable DL services in targeted macro and femto service areas while providing superior spatial reuse, for even a large number of spectrum-sharing femtocells deployed per cell site.

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 (CoMP) transmission is considered in 3GPP LTE-Advanced as a key technique to improve the cell-edge performance. In order to support joint resource allocation among coordinate cells in CoMP systems, efficient frequency reuse schemes need to be designed. However, most of the existing frequency reuse schemes are not suitable for CoMP transmission due to not considering multi-cell joint transmission scenario in their frequency reuse rule. To solve this problem, a cooperative frequency reuse (CFR) scheme is proposed in this paper, which divides the cell-edge area of each cell into two types of zones, and defines a frequency reuse rule to support CoMP transmission for users in these zones. Compared with the conventional soft frequency reuse (SFR) scheme, simulation results demonstrate that the CFR scheme reduces the blocking probability by more than 50%, and improves the cell-edge throughput by 30~40%, with 5~9% additional cell-average throughput.

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.

The problem of resource allocation in multiuser OFDM system is a combinatorial optimization problem, difficult to solve in polynomial time. For the sake of reducing complexity, it can be solved either by relaxing constraints and making use of linear algorithms or by metaheuristic methods. In this paper, ant colony optimization, a typical algorithm of metaheuristic methods, is applied to solve the problem of resource allocation in multiuser OFDM system. The system model for the application of ACO on the problem, as well as two algorithms based on ACO, is proposed. Comparing to traditional strategies, it is indicated by numerical results that the proposed algorithms can significantly increase the throughput of the system and simultaneously guarantee fairness.

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.

This paper presents Customer Satisfaction (CS) based resource allocation strategy in orthogonal frequency-division multiplexing (OFDM) wireless system with multimedia traffic. The risk aversion utility functions are analyzed, based on which, the CS utility and the CS resource allocation strategy are proposed. Compared with the Proportional Fairness (PF) utility, the CS utility enables the system to adjust its resource allocation according to both the traffic requirements and the resource situation. Numerical results demonstrate that the CS resource allocation strategy outperforms the PF strategy in both real-time (RT) traffic and best effort (BE) traffic.

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.

The multiple-input-multiple-output (MIMO) technique, combined with multi-level quadrature amplitude modulation (M-QAM), has been considered a potential scheme for high data rate transmission in next generation mobile communication systems. In this paper, the symbol error rate (SER) of M-QAM MIMO system based on spatial multiplexing is analyzed and closed-form results are given under rich scattering Rayleigh fading channel for zero-forcing (ZF) receiver and zero-forcing perfect successive interference cancellation (ZF-PSIC) receiver. Furthermore, these two precise analytical SER expressions are taken as the upper bound and lower bound of zero-forcing successive interference cancellation (ZF-SIC) receiver without optimal ordering. Monte Carlo simulations validate the analytical results and prove the conclusions.

The IEEE 802.16 standard supports the multi-hop mesh mechanism, which focuses on high throughput, fast and cost-effective deployment. The standard defines the scheduling scheme in mesh mode, and many researchers have contributed greatly to this area. However, most of the existing research work is designed for single-radio single-channel environment and some heavy traffic nodes become the bottleneck of the network performance's improving. In this paper, we propose a flexible two-radio and single-radio combined deployment (TSCD) scheme for multi-channel transmission using interference free scheduling algorithm in IEEE 802.16 mesh networks. The simulation results show that the proposed scheme increases the overall thoughput and reduces delay of the network compared to all single-radio network. What's more, TSCD network gets almost the same performance compared to all two-radio network with less cost.

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.

This paper focuses on transmit pre-coding for multiuser multi-input multi-output (MU-MIMO) downlink channels. Multi-user interference (MUI) and noise are main factors that affect the performance of MU-MIMO system. Besides, the system performance will degrade as the receive power on different antennas gets unbalanced. The Block-diagonalization (BD) algorithm eliminates the MUI completely. However, the number of transmit and receive antennas are strictly limited, and the mitigation of noise is not considered. In this paper, two novel methods are proposed to solve these problems. One is antenna balancing in which we evaluate the receive power of each antenna and discard the unbalanced ones. This method could also relax the constraint on the number of antennas and enable more data streams transmission. The other one is noise suppression in which we design the pre-coding matrices using equivalent channels with noise compensation. Simulation results reveal that the noise suppression method can achieve better performance than BD, especially at lower SNR scale and the antenna balancing method can outperform BD in a wider SNR range. The methods provide significant gain in capacity without increasing computational complexity and greater performance improvement can be acquired when the two proposed methods are combined.

This paper presents a simplified V-BLAST (vertical bell lab layered spaced-time) detection algorithm from the hardware implement perspective. Simulation shows that the BER (Bit Error Rate) performance is close to Golden detection algorithm, but the complexity is greatly less. Then the paper provides an efficient hardware structure to implement this algorithm in FPGA (field programmable gate array), which can be used in the B3G TDD-MIMO-OFDM (Beyond 3G Time-Duplex-Division Multi-Input Multi-Output Orthogonal frequency division multiplexing) system. By applying bit-width reduction technique, the fabrication area it takes can be significantly reduced. In uplink, we adopted 4 transmit and 8 receive antennas. The implementation with Virtex square Pro Series FPGA was verified to be worked well in B3G system.

In this paper, the performance of discrete Fourier transform (DFT) based channel estimation (CE) for orthogonal frequency division multiplexing (OFDM) systems with transmit antenna diversity is analyzed. Conventional DFT-based CE suffers from the dispersive distortion of an estimated channel impulse response (CIR) due to the existence of virtual carriers (VCs). We analyze the mean squared error (MSE) performance and provide a concise expression which reveals the leakage effect due to VCs. Further, analytical expression for bit error rate (BER) with the effect of VCs is also derived. Simulation results illustrate the accuracy of the theoretical analysis.

This paper presents a high speed decoder architecture for irregular structured low density parity check (LDPC) codes and its field programmable gate array (FPGA) implementation. Algorithm transformation and architectural level optimizations are employed to reduce the critical path. The enhanced semi-parallel architecture is easily scalable and reconfigurable for larger block sizes and can be well suited for achieving high decoding throughput. Based on the proposed architecture, a (10240, 5120) irregular structured LDPC decoder is implemented on Xilinx FPGA Virtex-4 VLX80, the FPGA implementation results show that the irregular LDPC decoder can achieve a maximum (information data) decoding throughput of 223 Mbps at 18 iterations.

This paper proposes maximum utility principle slide handover strategy for multi-antenna cellular architecture. Based on generalized distributed cellular architecture-group cell, slide handover strategy is illustrated and its merits are presented. Slide antenna window is applied by slide handover in the handover process, which makes users always in the cell centre and eliminates cell-edge effect. But for traditional slide handover, the handover rules of adding new antenna elements and replacing or releasing existing antenna elements are only by the pilot strength of each antenna element. This will constrain the performance of slide handover. Therefore, the rules need to be enhanced. Maximum utility principle slide handover strategy, proposed by this paper, can effectively solve this problem. The utility function in the slide handover and steps for handover are described in this paper and system-level performance evaluation is provided with comparison of traditional slide handover to verify the merits of maximum utility principle slide handover strategy.

Although pseudo-orthogonal carrier interferometry (PO-CI) multi-carrier code division multiple access (MC-CDMA) could transmit doubled data streams, it highly depends on the modulation type. Through theoretical inter-code interference analysis, this paper puts forward that PO-CI/MC-CDMA systems could be de-spread ideally only with BPSK modulation. When complex modulation types are considered, PO-CI/MC-CDMA fails in de-spreading while simulation results of QPSK and QAM demonstrate this limitation. However, pulse amplitude modulations (PAMs) are found to be more suitable for the systems. The acceptable bit error rate (BER) performance of high rank PAMs is revealed by simulation too. Therefore, if the spectrum efficiency requisition is high, PAM is proposed to be the optimal modulation type for PO-CI/MC-CDMA systems.

To meet the challenges in sensing the spectrum in a reliable and timely manner, an approach of multi-antenna spectrum sensing based on spectral correlation property is proposed in this paper. Without any prior information of the licensed system, it extracts the frequency-domain channel information from the spectral correlation functions (SCF) of multiple antenna signals, and then generates the SCF of multi-antenna combining (SCF-MAC) over the frequency-cycle frequency plane. The expressions for the detection and false alarm probabilities of a decision based on SCF-MAC are derived for frequency selective channel, which indicate the proposed approach is capable of achieving full spatial diversity in spectrum sensing. Simulation results corroborate the theoretical analysis and show the significant performance improvement.

Wireless broadcast systems provide the users with high bandwidth while 3G cellular systems provide complementary service to support personality and interactivity. In this paper, we develop a novel scheduling algorithm for the integrated Wireless Broadcast/3G system. The proposed algorithm combines Analytic hierarchy process (AHP) and Grey relational analysis (GRA). Simulation results are presented to demonstrate that the proposed algorithm could effectively support data dissemination with low response time, request drop rate, and the unfairness of request drop.

In this contribution, an optimal inter-antenna and subblock shifting and inversion (IASSI) scheme exploiting additional degrees of freedom brought by multiple antennas and subblocks is proposed to alleviate peak-to-average power ratio (PAPR) problem of MIMO-OFDM systems. In order to reduce the complexity of proposed scheme, two suboptimal schemes based on sequential search and transformation are presented. Besides, combination of IASSI and matrix transform is proposed, which takes advantage of low autocorrelation characteristic of constant amplitude zero autocorrelation (CAZAC) sequence to further reduce PAPR. Simulation results show that IASSI improves PAPR reduction performance compared with existing PAPR reduction scheme. And when combined with orthogonal matrix transform utilizing CAZAC sequence, further gain can be obtained. All these proposed methods provide flexible tradeoff between satisfied PAPR reduction performance and cost of implementation.

Distributed cellular network (DCN) is regarded as one of the most promising architectures likely to be used in next generation mobile communication systems. Under this network architecture, co-channel interference among multi- users in adjacent cells could not be neglected. In this paper, based on detailed error analysis, several useful conclusions for the maximum Doppler shift estimation in multi-user environments are obtained and validated. Furthermore, in order to combat co-channel interference, a novel ACF-based algorithm with adaptive iteration (AI-ACF) is proposed for multi-user Doppler shift estimation. Simulation results indicate that, with moderate complexity, the proposed algorithm could reduce multi-user co-channel interference effectively and improve Doppler shift estimation performance significantly.

Power allocation problem in Decode and Forward (DF) wireless cooperative multiplexing communication systems is considered in this paper. We first derived an analytical bit error rate (BER) expression with BPSK modulation at the relays and Zero Forcing Successive Interference Cancellation (ZF-SIC) algorithm at the destination, which gives deep insight to how different power assignments among the source and the relays influence the BER performance. Based on this expression, a new power allocation algorithm is developed by minimizing BER under different power constraints, the effectiveness of which has been validated by both theoretical analysis and Monte Carlo simulations.

Amplify and forward (AF) is an effective mode for relaying systems with ease for implementation. In a conventional orthogonal frequency division multiplexing (OFDM) based AF system, relay stations simply amplifies the received signals and forward them to the destination station. Due to the fact that channel transfer function for the first hop (source-relay) and the second hop (relay-destination) may vary a lot, system capacity is limited. This contribution introduces a method named sub-carrier coupling, which couples sub-carriers for the two hops in a certain rule to improve system capacity. Firstly, system with a single relay station and its capacity is discussed, and the coupling scheme to maximize the system capacity is proposed. Then system with multiple relay stations is investigated, and two solutions are proposed. The solution with sub-carrier coupling can bring to much higher capacity since it determines the sub-carrier coupling scheme and assigns relay stations to forward signal for all sub-carriers jointly. Numerical simulation is carried out to show the efficiency of the proposed solutions.

The non-line-of-sight (NLOS) propagation has become a key obstacle to high accuracy location in urban environment. In this paper, a novel algorithm is proposed based on classical scattering models to mitigate the NLOS errors in location estimation. With distributed multi-antenna, the location of the scatterer that causes the NLOS errors is estimated, and the distance between the mobile station (MS) and the scatterer is also obtained. Then the estimated scatterers are used as virtual base stations (BSs) to locate MSs. Simulation results show that the proposed NLOS mitigation algorithm outperforms other algorithms, which indicates our NLOS mitigating method can be applied in NLOS environment effectively.

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.

Orthogonal frequency division multiplexing (OFDM) is a very promising transmission technology for the beyond 3G (B3G) wireless communication system with orthogonal frequency division multiple access (OFDMA) as its major multiple access technology. And in the multi-cell scenario, the management of inter-cell interference has significant impact on the performance of the wireless network. This paper proposes an optimal and a sub-optimal inter-cell scheduling strategy both of which coordinate the transmission of interfering cells. In addition, the coordinated scheduling strategies are proposed to be implemented by an distributed and coordinated network architecture such as the radio on fiber (RoF) system. The utility function is used to balance the efficiency and fairness of wireless resource allocation. Simulation results show that, the proposed inter-cell scheduling strategy provides significant gain in fairness over the single-cell scheduling strategy which doesn't involve multi-cell coordination of transmission.

In this paper, the simplified DSP implementation of training sequence-based on Least Square (LS) channel estimation in MIMO-OFDM system was demonstrated. A proper design of the training sequence not only simplifies channel estimation, but also still obtains the best estimation performance. For the purpose of getting channel estimation algorithm to work in real time, some main approaches are carried out to speed up operation, saving memory and improving accuracy. Statistics on resource occupation and running time of such a scheme in TMSC6416 chip was given. While the implementation is applicable in B3G MIMO-OFDM system, which adopted 4 transmit antennas and 8 receive ones.

This paper investigates a spatial multiplexing MIMO scheme with beamforming for downlink transmission. The proposed scheme combines spatial multiplexing MIMO technique with beamforming, which has the advantages of both techniques. The proposed scheme utilizes the uplink direction of arrival (DOA) to perform downlink beamforming. It is able to transmit parallel data streams as well as providing beamforming gain and improve the system performance significantly. The proposed scheme provides better BER performance than the traditional spatial multiplexing and beamforming techniques under the same simulation environment.

Generalized distributed antenna is one of the most promising architectures likely to be used in next generation mobile communication systems. In this paper, based on the architecture, multi-user co-channel interference in Doppler spread estimation is analyzed and several useful conclusions are obtained. Moreover, in order to combat this interference, a novel ACF-based multi-user joint Doppler spread estimation (M- JDSE) algorithm is proposed, taking advantage of centralized signal processing characteristic in generalized distributed antenna systems. Both theoretical analysis and simulation results demonstrate the significant performance improvement of proposed M-JDSE algorithm in multi-user environments.

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.

The Non-Line-of-Sight (NLOS) propagation effect has been considered as one of the most important issues in the location estimation especially in urban environment with serious blocking of direct paths. In this paper, we develop a new algorithm to mitigate the NLOS errors in location estimation by distributed multi-antenna. Utilizing the relativity between TOAs obtained from different antennas in a multi-antenna array, the positions of scatterers that cause the NLOS propagation, as well as the distances between mobile stations (MSs) and scatterers, are estimated. Then with appropriate location algorithm, the locations of MSs are obtained with information of scatterers. Simulation results show that this algorithm can mitigate NLOS errors effectively and improve the performance of TOA/TDOA based location algorithm in NLOS environment significantly.

For the Evolved UTRA (EUTRA), in OFDMA uplink, the Inter-cell interference cannot be predicted precisely, so scheduling becomes a puzzling problem and directly affects the uplink performance in OFDMA based system. Previous works (Galdata, et. al., 2003) did not concern such a problem or just consider single cell environment. In this paper, a novel scheme is proposed that could efficiently solve this problem in multi-cell circumstance. It predefines UEs distributed in the whole cell adopt fixed modulation and coding scheme (MCS) according to the distance from Node B. It employs initial average predictive interference to create the first-loop Inter-cell interference and perform scheduling according to the historical information in the following. Fast power control is adopted in order to compensate the imprecisely predictive inter-cell interference. Soft frequency reuse scheme is adopt in order to improve the cell-edge UE performance. To guarantee the UE fairness, proportional fairness (PF) scheduling is adopted in single input and single output (SISO) environment in this paper.

Abstract B3G–TDD uplink in China's FuTURE project is introduced in the paper. By taking advantage of MIMO–OFDM and special design for the TDD frame, peak data rate of 100 Mbps can be achieved. The system is required to support service in both indoor and outdoor environments, and guarantee high data rate communication even with mobility up to 250 kmph. Detail theoretical analysis was carried out to give the principle of designing frame structure for the system under different environments. In order to reduce the influence of Doppler Effect, adaptive frame structure is proposed for the uplink. By special design on the frame for different environments, the channel information can be tracked accurately with low overhead, which ensures the system performance. Through simulation based on the platform of Future Technologies for Universal Radio Enviroment (FuTURE) project, bit error rate (BER) can achieve lower than 10−6 when Eb/N0 is 3 dB for various mobility case in both indoor and outdoor environments, which satisfies the requirement of FuTURE project. By employing it into B3G–TDD uplink, robustness to Doppler Effect is strengthened greatly. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract With current research focusing on beyond 3G (B3G)/4G mobile systems, many advanced techniques are investigated by world‐wide research institutes and standard organization, such as multi input multi output (MIMO), orthogonal frequency division multiplex (OFDM), and multi‐antenna distributed cellular network architecture. Based on these novel techniques, the radio resource management (RRM) strategies, such as access control, also need to be developed. This paper proposes the maximum utility principle access control (MUPAC) basing on Dijkstra's Shortest Path Algorithm for multi‐antenna cellular network architectures. In the accessing process of the proposed algorithm, the shortest path in Dijkstra's Algorithm is replaced by the cost of accessing process, which is represented by utility function. Taking Generalized Distributed Cellular Architecture—Group Cell as an example, MUPAC is described in details with the utility function, maximum utility principle, flow chart of accessing process. Performance evaluation and analyses verify the merits of MUPAC algorithm in improving system capacity, accessing success probability, and efficiency of system resources usage. Copyright © 2007 John Wiley & Sons, Ltd.

Large progress has been made in the research and realization of the ldquoB3Grdquo (Beyond 3G) TDD mode demonstration system in ldquoB3Grdquo TDD group of Chinese FuTURE (Future Technologies for Universal Radio Environments) project, which is introduced in this paper. Since the ldquoB3Grdquo demonstration system aims at evaluating the radio transmit technologies such as MIMO, OFDM etc. to bear a high data rate up to 100 Mbps, the previous results presented in several literatures coming from the FuTURE project are focusing on the high data rate services transmission. However, services with low data rate such as voice services etc. should also be transmitted in the ldquoB3Grdquo system, which has a relatively smaller bandwidth. In this paper the technologies for voice service are introduced and the performance is analyzed in the whole-link demonstration system, since voice service is a basic service in the mobile communication systems.

The inter-cell interference of orthogonal frequency division multiple access (OFDMA) based multi-cell distributed network architecture is analyzed. Based on generalized distributed cellular architecture-group cell, the interference condition without power control and with power control is analyzed respectively, and the system outage probability compared to traditional cellular structure is evaluated. Analyses and simulation results indicate that the inter-cell interference of group cell architecture does not increase more than traditional cellular structure. Moreover, the system resources of group cell architecture are centralized scheduled and allocated by the access point (AP), which makes it flexible to apply centralized SRA power control algorithm to improve the system performance further.

According to the features of TD-SCDMA system, a novel multi-antenna pre-processing model and algorithm is proposed. Meanwhile, the more precise uplink channel estimation and downlink channel prediction are put forward for TD-SCDMA system to improve the performance of basic multi-antenna pre-processing algorithm. Simulation results show that: by using the proposed multi-antenna pre-processing technology, the capacity and performance of TD-SCDMA system is significantly improved. Meanwhile, the improved multi-antenna pre-processing algorithm performs better than the basic algorithm under fast varying channel condition.

This paper introduces a general framework of B3G (Beyond 3G) mobile communication system in China TDD (time division duplex) Special Work Group. System architecture is described that allows the integration of multiple antenna techniques, e.g., MIMO (multiple input multiple output), in physical layer and the novel cellular architecture, e.g., group cell, in network layer. MIMO techniques based on group cell architecture, including spaced MIMO, distributed MIMO and virtual MIMO, are presented. What's more, a multi-MIMO matrix detection algorithm for distributed MIMO structure in Group Cell B3G TDD system is also presented and analyzed with the corresponding mathematical model. Link-level and system-level simulation results show the performance and capacity of this group cell MIMO system respectively

In this contribution. we propose a novel space-frequency block coded adaptive modulated OFDM (SFBC-AOFDM) scheme to provide high data rate and high spectrum efficiency, this scheme is very effective to combat last lading anyway. Its robustness to frequency-selective fading could be adjusted by changing the SFBC coding size and adaptation unit. In this scheme, we extend the application of adaptive modulation into a MIMO-OFDM system in a simple way. Furthermore, a novel SFBC detection algorithm is proposed to reduce complexity for high order QAM employments. We simulate for SFBC-AOFDM with various MIMO settings and analyse the performance of both BCR and transmission efficiency.

For the third generation systems' low frequency efficiency and small system capacity, there should be greatly changed in physical techniques in fourth generation mobile communication systems while many advanced techniques suited for high bite rate, high frequency efficiency and large dynamic range, such as JT, STC, MIMO, OFDMA, distributed antenna, are taken into considerations. But besides physical techniques, basic network layer techniques, e.g. the constructions of cellular and the modes of handover, must be broken through in the fourth generation mobile communication systems. The group cell structure proposed in WTI is a novel cellular construction method, which is fit well for these new advanced physical techniques. And slide handover (intelligent group handover) in multicell infrastructure is also novel handover mode emphasized in this paper. According to the current research, the slide handover in multicell infrastructure could improve the system capacity dramatically.

For the TDD system, we propose a novel channel estimation method based on the third-order interpolation, the least-square approximation and the partition of the training sequence. We also put forward a novel time slot, based on which the interpolation of the channel estimation can be performed in every TDD time slot. By using the novel channel estimation method and the novel time slot, the channel estimation can be completed in every TDD time slot instead of across several continuous time slots. For the modulation modes such as QPSK and 16QAM, the simulation results show the novel channel estimation method can obtain performance gain compared with the conventional method.

Multi-antenna OFDM system calls the consideration of coding across antennas and OFDM tones. Derived from the design criteria for space-time block codes (STBC), the coding scheme termed space-frequency block codes (SFBC) is conceived to achieve the maximum diversity order for a given number of transmit and receive antennas. In this contribution, a practical space-frequency block coded OFDM scheme (SFBC-OFDM) is proposed for fast fading broadband channels. Besides, this novel scheme is rather flexible to combat the frequency-selective fading by decreasing the coding size. Simulation and analysis show that our scheme outperforms the STBC-OFDM scheme in the scenario with high vehicle speed and large delay spread when the number of subcarriers in the OFDM system is large.

Space-time block code (STBC) has a good BER performance. In 3GPP FDD specifications, space time transmit diversity (STTD) employs STBC to maintain orthogonality between the two antennas in order to avoid self-interference in fading channels. However, the capacity of multiuser STBC systems is still severely affected by multiple access interference (MAI) and interpath interference (IPI). Fortunately, nonlinear parallel interference cancellation (NPIC) can effectively reduce MAI and IPI with simple signal processing. And for the small spreading factor systems, weighted NPIC (WNPIC) technique provides a good solution. In order to alleviate the influence of the MAI and IPI of multiuser STBC systems under the frequency-selective Rayleigh fading channel, WNPIC based space-time block code (WNPIC based STBC) scheme is proposed firstly in this paper. And then the error probability of the WNPIC based STBC is analyzed theoretically. From our analyses, RAKE based STBC can be regarded as 0th stage WNPIC based STBC whose weighted factors are one. Later, to further evaluate the proposed scheme and verify our analyses, some simulation resulted are achieved. These simulation results show us the optimum weighted factors under different user environments and the multiuser BER performance of simple (RAKE) STBC systems, NPIC based STBC, WNPIC based STBC systems.

The joint transmission (JT) technique was mainly presented by Baier et al. (2000). JT used in the downlink channel does not require a training sequence in the downlink in theory. Moreover, the mobile station (MS) does not demand channel estimation. However, JT requires a huge dynamic range and good linearity of the power amplifier of the transmitter because of the high peak to average ratio of transmitted signals of JT. In this paper, a new scheme called pre-distortion based joint transmission is proposed. Two pre-distortion approaches are also presented. One is based on the pre-clip technique, the other on a pre-nonlinear power amplifier. Finally, simulation results show that the pre-distortion based JT scheme has better BER performance and the BER performance of pre-distortion based JT under nonideal power amplifier is better than that of JT under ideal power amplifier. This is to say, maybe it is not worth compensating the deep channel shading.

As an important space-time code, the layered space-time (LST) code has been studying widely since it was firstly proposed by Foschini in 1996. To exploit its potential, the Bell Lab Layered Space-Time (BLAST) structure of the LST was proposed by Bell Lab. There are two types of BLAST architectures: vertical BLAST (V-BLAST) and diagonally BLAST (D-BLAST). Detection algorithms of V-BLAST were proposed by Golden (see Electronics Letters, vol.35, no.1, 1999). His detection process uses linear combination nulling and successive symbol cancellation (SSC) based on inversing and ordering. However, inversing (Moore-Penrose pseudoinverse) and ordering operation for each iteration bring a huge computation complexity. To detect M transmit antenna signals, Golden's detection algorithm needs M inversing and M ordering operations. Aiming at this shortcoming, a new detection algorithm for layered space-time code is proposed. This new sub-optimal detection algorithm is based on Greville inversing process, with two inversing and one ordering process. Simulation results show us the proposed sub-optimal scheme still has a good BER performance.

Space-time code techniques have been widely studied in wireless communications, for they provide better signal qualities in fading channel environments and increase the system capacity. As an important space-time code, Bell Lab Layered Space-Time (BLAST) code has been paid more attention. However, the traditional detection algorithm of V-BLAST space-time code needs much time to perform linear combination nulling and successive symbol cancellation. The greater the number of transmit antennas, the greater the time delay. To overcome this disadvantage, a new scheme based on linear combination nulling and parallel symbol cancellation is proposed. The new scheme has a lower time delay. The simulation results of this new scheme are obtained by COSSAP simulation in flat Rayleigh channels. These simulation results are presented to verify the BER performance of the new scheme based on parallel symbol cancellation.

In 3GPP FDD specifications, space time transmit diversity (STTD) employs space-time block code (STBC) to maintain orthogonality between the two antennas in order to avoid self-interference in fading channels. However, the STTD technique is just open loop transmit diversity, and we investigate whether we can obtain further gain if closed loop STBC is used. A novel scheme based on closed loop STBC is proposed first. And then an optimized allocation scheme of a fixed transmitter power budget between two transmit antennas is presented. Finally, COSSAP simulation results show a significant performance improvement between closed loop STBC and traditional STBC. Moreover, these simulation results also show a good agreement with our theoretical analyses.