Existing community search methods heavily rely on labeled data or predefined structures, thus fail to capture obscure and dynamic community boundaries in open-world heterogeneous networks, leading to poor adaptability. They also ignore modeling behavioral patterns, resulting in poor search performance. To solve the above issues, this work formally defines the unsupervised behavior-driven community search problem for heterogeneous graphs and designs dual-view Contrastive Learning-based Unsupervised framework for Heterogeneous graph Community Search (CLUHCS). CLUHCS designs a relation view to encode local community cohesion and a meta-path view to capture global behavior semantics. By using PathSim averaging strategy to generate positive samples and self-supervised signals, we can completely eliminate label dependency. Then, contrastive training is leveraged to automatically learn community representations and solve the open community boundary ambiguity challenge. Furthermore, by capturing behavior patterns, the meta-path behavior modeling flexibly characterizes the formation mechanism of heterogeneous communities. Experiments on three datasets verify the effectiveness and efficiency of CLUHCS. CLUHCS significantly improves F1-score by 52.7% over the supervised baseline FCS-HGNN and by 41.5% over the unsupervised method TransZero.

Aiming at service-oriented design requirements for the Integrated Satellite Terrestrial network (ISTN), this paper proposes a cross-layer analysis framework and a distributed Cognitive Space Service Network architecture. These address challenges in traditional single-layer research, including un-quantified cross-layer deviations, incomplete link analysis, and a lack of multi-dimensional evaluation. A distributed on-orbit architecture for LEO satellites is constructed to enable cross-layer cooperation across physical-layer access, network-layer routing, and application-layer service matching. Beyond channel-fading-based analysis, a multi-link model incorporating node-induced interference is established. It derives uplink access success rate expressions, quantifies impacts of user density, link distance, and carrier bands on connectivity, and verifies interference-attenuation coupling via simulations. By integrating mutual information and entropy theory, a cross-layer deviation framework is built, using confluent hypergeometric distribution to model service matching uncertainty. This achieves quantitative modeling of “physical-network layer” cooperation gains and “network-application layer” adaptation deviations. The results provide theoretical tools for optimizing space-based intelligent networks. The architecture and methods directly support enhancing large-scale satellite network quality and constructing objective functions, providing a key technical path for service-oriented future space-ground integration systems.

This paper investigates a pinching-antenna (PA)-enabled cognitive radio network, where both the primary transmitter (PT) and secondary transmitter (ST) are equipped with a single waveguide and multiple PAs to facilitate simultaneous spectrum sharing. Under a general Ricean fading channel model, a closed-form analytical expression for the average spectral efficiency (SE) achieved by PAs is first derived. Based on this, a sum- SE maximization problem is formulated to jointly optimize the primary and secondary pinching beamforming, subject to system constraints on the transmission power budgets, minimum antenna separation requirements, and feasible PA deployment regions. To address this non-convex problem, a two-stage optimization algorithm is developed, in which stage 1 designs the PT/ST pinching beamforming and stage 2 updates the ST transmit power. For the PT and ST pinching beamforming optimization, the coarse positions of PA are first determined at the waveguide-level. Then, wavelength-level refinements achieve constructive signal combination at the intended user and destructive superposition at the unintended user. For the ST power control, a closed-form solution is derived. Simulation results demonstrate that i) PAs can achieve significant SE improvements over conventional fixed-position antennas; ii) the proposed pinching beamforming design achieves effective interference suppression and superior performance for both even and odd numbers of PAs; and iii) the developed two-stage optimization algorithm enables nearly orthogonal transmission between the primary and secondary networks.

This paper investigates an Intelligent Reflecting Surface (IRS)-enhanced vehicle-to-infrastructure (V2I) multiple input multiple output (MIMO) backscatter communication network. In this network, multiple IRSs send roadside information to a multi-antenna reader using the backscatter technique, while the inevitable self-interference at the reader is taken into account. To maximize the introduced system’s weighted sum rate, we propose an optimization scheme based on minimum mean square error with successive interference cancellation (MMSE-SIC). This scheme jointly optimizes the reader’s detection matrix, beamforming vector, and IRS reflection coefficients. The formulated non-convex problem is tackled using a block coordinate descent (BCD) algorithm combined with successive convex approximation (SCA) and semi-definite relaxation (SDR) methods. The proposed framework is compared with other schemes including the linear detection technique, highlighting the trade-off between performance and computational complexity. The study provides insights into the impact of key parameters, such as the reader’s transmit power and the number of IRS elements, on system performance. Furthermore, our findings lay the groundwork for future exploration of more effective transmission approaches in IRS-enhanced V2I backscatter communication networks.

This paper proposes a new architecture for the low-earth orbit (LEO) satellite ground station aided by movable antenna (MA) array. Unlike conventional fixed-position antenna (FPA), the MA array can flexibly adjust antenna positions to reconfigure array geometry, for more effectively mitigating interference and improving communication performance in ultra-dense LEO satellite networks. To reduce movement overhead, we configure antenna positions at the antenna initialization stage, which remain unchanged during the whole communication period of the ground station. To this end, an optimization problem is formulated to maximize the average achievable rate of the ground station by jointly optimizing its antenna position vector (APV) and time-varying beamforming weights, i.e., antenna weight vectors (AWVs). To solve the resulting non-convex optimization problem, we adopt the Lagrangian dual transformation and quadratic transformation to reformulate the objective function into a more tractable form. Then, we develop an efficient block coordinate descent-based iterative algorithm that alternately optimizes the APV and AWVs until convergence is reached. Simulation results demonstrate that our proposed MA scheme significantly outperforms traditional FPA by increasing the achievable rate at ground stations under various system setups, thus providing an efficient solution for interference mitigation in future ultra-dense LEO satellite communication networks.

Security analysis of continuous variable quantum key distribution (CV-QKD) remains a topic of extensive academic investigation. In this work, we propose a coherent-state CV-QKD protocol integrating a displacement-based quantum receiver and post-selection strategies. We establish a framework of the protocol under both individual and collective attacks, incorporating practical constraints such as noises and device imperfections. To enhance performance of the protocol, the postselection strategy and the transmitted signal power are optimized for different channel attenuation levels. Numerical results offer insights into the strategy of employing displacement receivers in CV-QKD protocols, revealing that larger transmitted signal power is required under low channel attenuation or severe receiver-side imperfections and noises to achieve higher the secret key rate.

The advancement of low-Earth orbit satellite networks has increasingly drawn attention to the integration of smart grids with these satellites. This paper focuses on a lowEarth orbit satellite and drone-assisted smart grid network architecture. First, the communication relationship between multiple users and drone relays is analyzed. Then, the concept of ‘fault nodes,’ a category of users that necessitate the occupation of fixed resources for stable communication, is introduced. Moreover, this paper investigates the throughput optimization problem. We transform it into a resource-matching problem and propose a Q-learning approach based on improved reward function to efficiently solve it. Simulation results verify that our proposed scheme yields superior throughput when the number of faults is small, and can effectively avoid resources occupied by fault nodes when the number of faults is high.

Visible light communication (VLC) is an important research direction for 6th-generation mobile networks (6 G). When the image sensor realizes the multi-light source diffuse reflection VLC based on code division multiple access (CDMA), the spread spectrum sequence used cannot be too long due to the limitation of sensor hardware. The increase in the light sources will produce serious cross-correlation interference, which affects the communication performance of the system. This paper proposes a pilot-aided multi-light source VLC scheme. Firstly, at the optical transmitter, a light source's emission waveform combination is designed, and the orthogonal and non-orthogonal sequences are used as the spread spectrum sequences of the communication and the pilot light sources. The pilot light source provides the synchronous function for the communication light sources, effectively solving the problem of cross-correlation interference and signal synchronization between the communication light sources. Secondly, at the optical receiver, this paper proposes a demodulation algorithm for the diffuse reflection superimposed signal corresponding to the emission waveform combination, aimed at accurately recovering the information transmitted by the communication light sources. The simulation results show that the proposed scheme can achieve good lighting effect and meet the basic communication requirements.

Satellite communication systems, particularly those using Low Earth Orbit (LEO) satellites, have gained significant attention due to their low-latency, high-throughput capabilities, and potential to provide global coverage, especially in underserved areas. However, a key challenge in LEO satellite communication is the Doppler shift effect, caused by the relative motion between the satellite and the ground terminal. This phenomenon leads to frequency shifts, which can result in synchronization errors, signal degradation, and communication inefficiencies. While various Doppler shift compensation methods have been proposed, such as coarse compensation using satellite ephemeris data and fine compensation using pilot-assisted estimation, existing solutions still face limitations, particularly in dynamic and high-speed satellite communication environments. In this paper, we propose an innovative two-stage Doppler compensation scheme that combines both coarse compensation based on ephemeris data and fine compensation using pilot-assisted estimation, with a focus on LEO satellite constellations. The key innovation of our approach lies in the integration of adaptive compensation techniques that dynamically adjust based on the relative motion of the satellite constellation and the ground terminal. This enables real-time compensation that not only mitigates Doppler shifts but also improves the quality of service in satellite networks. By addressing the limitations of existing solutions and offering a more flexible, real-time, and adaptive compensation mechanism, simulation results show that our proposed method significantly improves the reliability and throughput of LEO satellite networks, paving the way for more efficient and robust global satellite communication systems.

Since intelligent reflecting surface (IRS) typically consists of a large number of elements, near-field propagation becomes dominant in wireless communications. In this paper, we investigate the design for a secure IRS-aided near-field wireless powered communication network (WPCN). We aim to maximize the secrecy rate by jointly optimizing the energy precoding matrix at a power station (PS), the IRS coefficient matrix, and the transmission allocation time factor. To handle the optimization problem, we propose an effective iterative algorithm where the energy precoding matrix and the IRS coefficient matrix are obtained in closed and semi-closed forms, respectively. Numerical results reveal that compared with the far-field beamforming scheme, the proposed near-field beamforming significantly improves the secrecy rate. Moreover, results also show the necessity of properly allocating the transmission time of different phases in WPCN.

The rapid development of the aviation Internet of Things (IoT) has positioned in-flight connectivity (IFC) as one of its critical applications. Space-air–ground integrated networks (SAGINs) are essential for ensuring the performance of IFC by enabling seamless and reliable connectivity. However, most existing research treats satellites merely as transparent forwarding nodes and overlooks their potential caching capabilities to enhance IFC data rates. In this article, we explore an IFC-oriented SAGIN where satellites and ground stations (GSs) work together to transmit content to airborne passengers, thereby facilitating airborne communication. By categorizing files into cached (instantly accessible via satellites) and noncached files (available only through GSs), this article pioneers the integration of multiple intersatellite links (ISLs) into the IFC framework, thus innovating the content delivery process for both types of files. To minimize the average delay of content delivery, we formulate the corresponding optimization problems: 1) for cached files, we propose an exact penalty-based method to determine the satellite association scheme and 2) for noncached files, we present an efficient algorithm based on alternating optimization to jointly optimize satellite association and GS bandwidth allocation. Our proposed framework is low in complexity, paving the way for high-speed Internet connectivity for aviation passengers. Finally, simulation results are provided to demonstrate the effectiveness of our proposed IFC framework for SAGIN.

Integrated satellite-terrestrial networks (ISTNs) are increasingly recognized for their global communication. However, the existing research mainly focuses on simplified ISTNs, where cooperative strategies between satellites and base stations (BSs) are not easily applicable to real-world scenarios. There is a pressing need to investigate more realistic and complex ISTNs to address this gap. To address this gap, we investigate a more realistic and complex ISTN configuration, characterized by a large number of BSs, each divided into interference and service areas. Based on two cooperative modes, i.e., overlay and underlay spectrum sharing, two multiple access schemes are proposed for complex ISTNs using promising rate-splitting technology. These schemes consider multiple constraints simultaneously, such as communication delay, information rate, and power limit. Furthermore, a delay-rate adaptive user grouping strategy is proposed according to communication delay and information rate. For these schemes, the corresponding weighted sum rate problems are formulated, and an improved alternating optimization (AO) method is designed to solve the nonconvex challenges in two spectrum sharing modes. Moreover, a satellite-terrestrial coordinated iteration strategy based on AO is proposed to reduce the computational complexity in underlay spectrum sharing. Simulation outcomes confirm the advantages of our proposed schemes compared to various standard schemes.

In this paper, a robust transmission design is investigated for an intelligent reflecting surface (IRS)-assisted secure wireless powered communication network (WPCN) in the presence of hardware impairments at the transceiver. An IRS is deployed to enhance the efficiency of downlink (DL) wireless energy transfer (WET) and the security of uplink (UL) wireless information transfer (WIT). To maximize the secrecy throughput, the DL and UL time allocation, the transmit beamforming vector at the power station (PS), the receive beamforming vector at the access point (AP), and phase-shifts of IRS in DL and UL are jointly optimized. To handle the resulting non-convex optimization problem, an efficient algorithm based on block coordinate descent (BCD) is developed to iteratively update the optimization variables. Specifically, the closed-form solution of the receive beamforming vector is derived and the transmit beamforming vector can be obtained by the successive convex approximation (SCA) method. Then, the semidefinite relaxation (SDR) method is used to solve the corresponding IRS phase-shifts optimization sub-problems in DL and UL. Simulation results unveil that simultaneously optimizing the IRS phase-shifts in DL and UL can largely compensate for the hardware impairments compared to the schemes that optimize either DL/UL alone. Furthermore, the proposed robust transmission design scheme achieves higher performance improvement compared to the non-robust design which ignores the impact of hardware impairments.

To analyze the massive social networks for providing personalized services, community search is widely studied to find the densely connected subgraph that can reflect the network properties for a given query. The existing community search methods adopt single community model to make structural constraints on communities, which can only describe single interaction mode. Since they fail to capture the semantics of the network with multiple interaction modes, they struggle to find the representative communities. To solve this issue, we design a novel community model called ( τ, ρ )-camp to flexibly capture complex network semantics in any level of granularity. We propose the unified support maximized community search problem to find the communities with the densest network semantics, which is proven a NP-hard problem. By constructing a hierarchical index structure, we propose an approximate community search algorithm with approximation ratio of 2 and linear time complexity of the query size. Extensive experiments are conducted on two public datasets and two crawled datasets. The experimental results prove the effectiveness and efficiency of our method.

With the continuous advancement of onboard digital payload technology, high-throughput satellites (HTSs) now possess the capability to dynamically reconfigure their onboard resources in response to user traffic demands. This flexibility is primarily enabled by the efficient design of the satellite’s frequency plan and the beam power allocation scheme. However, the joint optimization of frequency planning and power allocation is a typical mixed-integer nonlinear programming (MINLP) problem, which can not be directly solved using traditional optimization methods. To address this issue, we propose a two-stage optimization framework, aimed at bridging the gap between the beam capacity and the user traffic request, while simultaneously avoiding co-channel interference among different beams. In the first stage, we propose a multi-agent deep reinforcement learning framework based on proximal policy optimization. By modeling each beam as an agent, each beam can select its reuse group, starting frequency, bandwidth, and transmit power based on the current system state. To mitigate the impact of neural network randomness on the results, we further introduce a second-stage optimization algorithm using the difference of convex (DC) programming to refine the beam power allocation. Simulation results demonstrate that the proposed algorithm significantly reduces execution time compared to benchmark algorithms, such as genetic algorithms and particle swarm optimization, while also demonstrating considerable performance improvements.

With the deployment of the QUIC protocol, website fingerprinting attacks targeting QUIC traffic are becoming a growing concern. Since the deployment is incremental, attackers must continuously crawl the QUIC traffic of new QUIC-enabled websites to update their attack models. For the latency caused by data crawling and classifier training, existing few-shot website fingerprinting (FSWF) attacks rely on representation learning to mitigate data dependency. To further achieve zero-latency identification, TCP traffic can be applied to construct the attack model before QUIC deployment. However, the different protocol semantics of TCP and QUIC lead to differences in the latent features. As representation learning models cannot eliminate the website feature differences, classifiers trained on TCP-based features are difficult to adapt to QUIC traffic. To address the issue, we propose a novel cross-protocol FSWF attack method to fuse cross-protocol website features. The proposed method forces TCP features and QUIC features to be in the same feature space by sharing model parameters, and reduces cross-protocol website feature differences through inter-protocol adversarial representation learning. Meanwhile, it utilizes a non-linear classifier to fit the fused features. The proposed method enables zero-latency identification for QUIC traffic based on a few TCP traffic. We conducted comprehensive evaluation experiments on public datasets from both closed-world and open-world settings. The proposed method outperforms state-of-the-art methods in zero-latency identification.

Satellite communication technology has rapidly developed to provide global information services, where low-Earth-orbit (LEO) satellites are the most popular due to lower transmission delay and easier deployment compared to geosynchronous-orbit (GEO) satellites. However, LEO satellites provide information services for a short time due to rapid movement relative to the Earth. When there are no visible LEO satellites, communication will be interrupted, and users must frequently detect accessible satellites, which wastes transmission power and decreases communication quality of service (QoS). To provide continuous information services and reduce detection frequency, we propose a GEO and LEO satellite joint service scheme to improve the communication QoS during the handover process between LEO satellites. Considering the access flexibility and spectrum efficiency, we further introduce a rate-splitting multiple access for the proposed multi-orbit satellite joint service scheme. We establish corresponding optimization problems for different communication scenarios using the weighted sum rate and max-min information rate as measurement indicators. To solve these non-convex optimization problems, we propose different alternating optimization algorithms to transform the initial problems into alternating convex problems. The simulation results show that our design handover schemes improve the communication QoS and reduce detection frequency, indirectly improving energy and spectrum efficiency.

The integrated satellite-terrestrial network (ISTN) has attracted much interest due to its global information serviceability. Recently, rate-splitting multiple access (RSMA) has been widely investigated to achieve highly efficient access. Motivated by this, we design the joint RSMA scheme based on spectrum sharing for the downlink ISTN, where part data of terminals are shared by satellite and base station. Furthermore, the max-min rate (MMR) maximization problem is formulated, and an alternating optimization algorithm based on weighted minimum mean square error is introduced to solve the non-convex problem. Simulations show that the joint RSMA scheme has a higher MMR than baseline schemes.

This paper focuses on the RF index optimization of orthogonal time-frequency space (OTFS). Firstly, an optimization problem is designed to cover the three elements of EVM/DD domain filtering /PAPR. The analysis of this problem shows that an iterative optimization scheme can be designed to solve this problem. Like many traditional researches, the proposed problem can be solved by optimization methods like ADMM. However, traditional methods are opaque and difficult to control which leads to difficulties in deployment, this paper proposes a joint adjustment method of EVM/DD-domain-filtering/PAPR with adjustable joint parameters for practical use, which can adjust the indexes adaptively according to the demand. The simulation results show the system’s effectiveness using parameter adjustment to achieve index modification.

Space-air-ground integrated networks (SAGIN) are pivotal for achieving uninterrupted in-flight connectivity (IFC). Most existing studies, however, merely treat satellites as transparent forwarding nodes, and overlook their caching capabilities in enhancing the IFC data rate. In this paper, we consider an IFC-oriented SAGIN, where the satellites collaboratively deliver the content to airborne passengers to facilitate airborne communication. Considering the cached files instantaneously accessible via satellites, this work pioneers the integration of multiple inter-satellite links (ISLs) into the IFC framework, thereby innovating the content delivery process. To minimize the average delay of content delivery, we formulate an optimization problem and propose an exact penalty-based method to derive the satellite association scheme. Our proposed framework has a low complexity and thus paves the way for high-speed Internet connectivity to aviation passengers. Finally, simulation results are presented to demonstrate the effectiveness of our proposed IFC framework for SAGIN.

The multibeam satellite system is crucial for the next generation communication, providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within limited spectrum resources presents formidable challenges. In response, rate-splitting multiple access (RSMA) has emerged, leveraging non-orthogonal transmission and precoding strategies concurrently. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using RSMA. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose a novel centralized frequency reuse strategy, exhibiting commendable performance alongside reduced computational complexity. Furthermore, we maximize the weighted sum rate (WSR) and introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problem into tractable convex counterpart. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications.

Multi-layer dense satellite networks (MDSNs) burgeons recently, and inter-satellite task offloading influenced by time-varying dynamic topology is an essential topic in MDSNs owing to appropriate scheduling scheme can significantly reduce the delay to improve the quality of service in MDSNs. However, the existing scheduling strategies primarily consider single-path transmission for each task in a time slot, which is unsuitable for large data amounts of tasks collected by earth observation satellites (OSs) in the system that pursues timeliness. In this paper, to effectively increase the QoS and utilize dense satellite resources, we propose the joint task-splitting and multiple path choosing (JTMPC) scheme related to transmitted links and combine OSs and communication satellites (CSs) resources to achieve tasks offloading. The constructed paths start with OSs, then go through multiple CSs simultaneously, and finally to ground stations for each task. Furthermore, we formulate the issue as a mixed integer nonlinear programming problem to minimize the end-to-end (E2E) delay by jointly considering path choosing, task-splitting ratio, queuing delay expenses, various topologies, and delay tolerance. Extensive analysis and numerical results corroborate that our proposed JTMPC scheme and the delay-oriented benders decomposition algorithm can achieve superior performance in E2E delay.

Orthogonal time frequency space (OTFS) modulation can provide a stable signal in a highly dynamic environment with high speed. In this paper, the PAPR characteristics and peak-to-average ratio (PAPR) reduction methods of OTFS signal with superimposed pilot are studied, and a two-stage PAPR reduction scheme combining distributed superimposed pilot and precoding is proposed. Pilot dispersion is used in the first stage, and partial precoding is used in the second stage to optimize PAPR performance. Simulation results show that this method can reduce the PAPR of superimposed pilot OTFS signal. In addition, in order to make OTFS applicable to vehicle communication, the resolution of OTFS is also analyzed.

Due to the cache’s capacity of forwarding information, Named Data Networking (NDN) has become a promising networking architecture. Since distributed caching is susceptible to cache pollution attacks (CPAs), researchers pay more attention to CPAs detection and defense. The current detection schemes seriously rely on an assumption that the content popularity remains stable over time. However, the change in interests of legitimate users in the network is unavoidable, which makes content popularity change dynamically. Thus, it is difficult to detect CPAs based on a static content popularity distribution. To address this issue, we propose a novel scheme to detect CPAs by analysing latency instead of popularity. The proposed scheme constructs the probability transfer matrix based on the Markov process of contents transfer and detects CPAs by the convergence states of the matrix. Once a CPA is detected, the affected router recognizes the attack type and adopts a specific defense method according to the attack type. This defense method can improve the network Quality of Service (QoS) by leveraging particular methods for different routers rather than the broadcasted global method. Extensive simulations in ndnSIM show that our scheme can effectively detect CPAs with higher detection ratio and defense CPAs with acceptable impacts on the overall network in network scenarios with dynamically changing content popularity.

This article focuses on the MIMO-OTFS system paradigm and analyzes its three-dimensional clustering sparse characteristics based on the convolution process of signals and dual dispersion fast time-varying channels. A iterative parameter estimation algorithm based on Wiener-Hopf optimal filtering was designed in the Delay-Doppler-Angle (DDA) domain, which is the Segmented Orthogonal Matching Tracking Scheme with the assistance of minimum mean square error iteration (MAI StOMP). The performance of the proposed algorithm was compared with that of the previous proposed algorithm, and numerical simulations were conducted. The results showed that the algorithm proposed in this paper has good Normalized Mean Square Error (NMSE) performance.

High performance and low complexity channel decoding algorithms have always been one of the core technologies in 6G mobile communication. Polar codes are the first type of constructive coding with achievable capacity, and are the forefront direction of channel coding research. For the traditional SC (successful cancellation) decoding algorithm, when the Polar code length is short, there is a problem of incomplete channel polarization that affects the accuracy of decoding, which cannot satisfy the requirements of ultra-high data rate, ultra-low latency, and ultra-low power consumption for communication in the 6G era. Deep learning methods have become a research hotspot due to their outstanding performance in decoding performance. Through the data-driven approach, neural networks were applied to the decoding of Polar codes, and the impact of different dataset construction methods on the decoding performance of the network was compared. Using the concept of sequence classification, a (16,8) short Polar code decoder was proposed innovatively based on the cascaded residual neural network (Residual Network) and gate recurrent unit (GRU). Under the same signal-to-noise ratio, the Polar code decoder based on Res-GRU has lower bit error rate and frame error rate compared to traditional SC decoding algorithm, and has lower decoding delay.

Multibeam satellite system (MSS) has become the trend because of its ability to provide seamless information services. However, co-frequency interference between beams significantly deteriorates communication performance. Existing interference management schemes regard MSS as multi-antenna systems and introduce precoding technology to eliminate interference, ignoring the characteristics of beam gain and the limited computing resources of satellites. Motivated by this, we design an adaptive grouping access scheme based on the promising ratesplitting multiple access to mitigate inter-beam interference. We initiate our discussion by formulating the weighted sum rate (WSR) maximization problems and deploying an enhanced alternating optimization strategy to navigate through these intricate non-convex issues. The efficacy and reduced complexity of our suggested approach are validated through simulation outcomes.

The widespread adoption of deep learning models has inspired an urgent need for their generalization capabilities. Despite their impressive performance on training data, achieving high accuracy on deployed untouched data remains a daunting challenge. To address this problem, improving the model’s adaptability to new samples is imperative. In this paper, we delve into the metrics of deep learning models, pointing out that the upper bound of the generalization error is a quantitative measure of their generalization ability. Outlining methods to enhance this ability, we subsequently improve the LSTM model for modulation recognition using the identified upper bound on the generalization error and the outlined enhancement strategy, significantly improving the accuracy. Finally, an outlook on future research is provided.

In recent years, social media has become one of the most popular ways of news dissemination. There is a phenomenon that numerous fake news are spreading rampantly on public social media platforms, posing a serious threat to the credibility of social media. Moreover, more and more social media news posts carry multimodal contexts, i.e., utilize not only text but also abundant images to describe the news. However, existing methods only involve the first image along with text in multimodal fake news detection. It severely hampers the extraction of global image semantic information and consequently damages the effectiveness of multimodal fake news detection. To address this issue, we propose a Cross-Image Semantic Fusion based multi-modal fake news detection method (CISF for short). The method uses an adaptive attention diffusion module to model semantic correlations among different images, fully leveraging the contextual dependencies between different images to achieve semantic interaction and fusion among images. On the basis, a global image semantic representation is generated to represent the entire image modality. Finally, the fake news detection is performed based on the multimodal fusion of the text and the global image semantic representation. We conduct experiments on two real-world datasets and demonstrate the effectiveness of the proposed method.

Communities are prevalent in large graphs such as social networks, protein networks, etc. Community search aims to find a cohesive subgraph that contains the query nodes. Existing community search algorithms often adopt community models to find target communities, and k-truss model is a popularly used one that provides structural constraints. However, the structural constraints presented by k-truss is so tight that the searching algorithm often can not find the target communities. There always exist some subgraphs that may not conform to k-truss structure but do have cohesive characteristics to meet users' personalized requirements. Moreover, the k-truss based community search algorithms can not meet users' real-time demands on large graphs. To address the above problems, this paper proposes the relaxed k-truss community search problem for the first time. Then we construct a relaxed k-truss index, which can help to find cohesive communities in linear time and provide flexible searching for nested communities. We also design an index maintenance algorithm to dynamically update the index. Furthermore, a community search algorithm based on the relaxed k-truss index is presented. Extensive experimental results on real datasets prove the effectiveness and efficiency of our model and algorithms.

Orthogonal time-frequency space(OTFS) is an emerging waveform design, but it suffers from PAPR problem. This paper discusses the reasons for the PAPR increase of OTFS signal in practical applications, and designs a new OTFS frame structure that flexibly adjusts the resource domain size, and analyzes the applicability of this frame structure in IoT devices and miniature low-speed devices. Finally, the simulation results show the effectiveness of the proposed frame structure in reducing PAPR and increasing energy efficiency.

Continuous variable quantum key distribution (CV-QKD) protocol has drawn much attention due to its compatibility with existing optical communication systems. In this paper, we propose a quaternary modulated CV-QKD protocol using displacement receivers and adopt the post-selection scheme to overcome the ‘3dB limit’. We first establish the model of displacement receiver for discriminating quaternary modulated coherent signals in a realistic situation. The performance of non-adaptive displacement receiver and multi-stage feedforward receiver are both investigated under different noises and device imperfections. To improve the receiver performance, we numerically optimize the displacement operation and check the quantum advantage of the displacement receivers over the classical homodyne detection. Then we analyze the security of the proposed CV-QKD protocol. The secret key rate is derived for both types of displacement receivers under the collective beam splitting attack. We also optimize the transmitted signal photons for different channel transmission efficiencies under practical system constraints. Numerical results shed light on the practical application of displacement receivers in CV-QKD protocols. This includes evaluating the necessity of optimizing the displacement and incorporating the feedforward structure in a displacement receiver according to different practical system limitations. Moreover, under higher channel transmission efficiency and increased receiver noise level, a larger coherent amplitude is required for transmitting signals to attain the maximum secret key rate.

Multibeam satellite system (MSS) plays an increasingly important role in the future communication system because of the ability to provide seamless information services. However, multibeam technology will cause serious inter-beam co-frequency interference (IBCFI), significantly deteriorating communication performance. Existing IBCFI management schemes mainly depend on precoding technologies, which regard MSS as multi-antenna systems and ignore characteristics of satellite beam gain and the limited computational resources. Meanwhile, terrestrial channels tend to be independent while satellite channels have a high correlation, which is rarely considered by existing work. On the other hand, rate-splitting multiple access (RSMA) has recently emerged due to the advantages of flexible multiple access and robust interference management. Therefore, we design a two-step adaptive grouping access scheme based on the promising RSMA to handle these challenges, where the first step takes the characteristics of satellite beam gain and computational resources into consideration, and the second step optimizes the channel correlation. Building on the two-step adaptive grouping access scheme, we formulate different weighted sum rate (WSR) maximization problems for different user groups. Furthermore, we introduce an improved alternating optimization algorithm to solve these non-convex problems. Finally, simulation results verify the effectiveness of our proposed scheme in WSR and computational complexity.

This paper proposes to integrate reconfigurable intelligent surface with backscatter communication (RIS-BackCom) for downlink non-orthogonal multiple access (NOMA) networks, where a RIS serves as a backscatter device to transmit the modulated signals to multiple single-antenna target users. Building upon the established system architecture, the weighted sum rate (WSR) is maximized for all the users under the constraints of total transmit power, RIS phase shift, rate fairness, and successive interference cancellation decoding rate. By employing the techniques of Lagrangian dual transform, quadratic transform and alternative optimization strategies, the original optimization problem is decomposed into three tractable sub-problems. Then, these sub-problems are effectively addressed using successive convex approximation and semidefinite relaxation methodologies. Experimental results demonstrate the feasibility and superiority of the proposed RIS-BackCom aided NOMA system.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a promising technology to reconfigure the radio propagation environment in the full space. Prior works on STAR-RISs have mostly considered the energy splitting operation protocol, which has high hardware complexity in practice. Moreover, the full and instantaneous channel state information (CSI) is always assumed available for designing the STAR-RIS nearly passive beamforming, which, however, is practically difficult to obtain due to the large number of STAR-RIS elements. To address these issues, we study the mode switching design in STAR-RIS aided non-orthogonal multiple access (NOMA) communication systems. Moreover, two efficient two-timescale (TTS) transmission protocols are proposed for different channel setups to maximize the respective average achievable sum-rate. Specifically, 1) for the case of line-of-sight (LoS) dominant channels, we propose the beamforming-then-estimate (BTE) protocol, where the long-term STAR-RIS transmission and reflection coefficients are optimized based on the statistical CSI only, while the short-term power allocation at the base station (BS) is designed based on the estimated effective fading channels of all the users; 2) for the case of rich scattering environments, we propose an alternative partition-then-estimate (PTE) protocol, where the BS first determines the long-term STAR-RIS surface-partition strategy based on the path-loss information only, with each subsurface being assigned to one user; and then the BS estimates the instantaneous subsurface channels associated with the users and designs its power allocation and STAR-RIS phase-shifts accordingly. For the two proposed transmission protocols, we further propose efficient algorithms to solve the respective long-term and short-term optimization problems. Moreover, we show that both proposed transmission protocols substantially reduce the channel estimation overhead as compared to the existing schemes based on full instantaneous CSI. Last, simulation results validate the superiority of our proposed transmission protocols as compared to various benchmarks. It is shown that the BTE protocol outperforms the PTE protocol when the number of STAR-RIS elements is large and/or the LoS channel components are dominant, and vice versa.

Few-shot website fingerprinting (FSWF) attacks attempt to identify whether the users have access to specific websites based on a few training data. Existing FSWF attack methods focus on adapting to variable network conditions in real scenarios. They use various techniques to transfer the model to adapt to test data which has a different distribution from training data. However, recent methods ignore the impact of pre-training data diversity on adaptability. The poor data diversity caused by the user-specific data crawl limits representation ability, and further hinders rapid adaptation to new network conditions. Due to the extreme Non-IId between multiple attackers’ datasets, it is not feasible to mix multiple datasets or perform traditional federated learning methods to improve representation ability. To address the issue, we propose a novel method based on a joint learning framework to achieve the collusion FSWF attacks. The proposed method fuses the feature spaces of multiple user-side attackers to enhance the representation ability of the local model, and constructs a virtual fusion center to mitigate the impact of Non-IID. It improves the adaptability under variable network conditions for the local attacker. This paper conducts comprehensive experiments to evaluate the performance of the proposed method in both closed-world and open-world settings. Compared with the state-of-the-art method, the proposed method improves the accuracy by up to 13.02% in the closed-world setting and the AUC by up to 0.085 in the open-world setting, respectively.

Anchor link prediction enhances the effectiveness of digital forensics through the identification of multiple social network users. The current methods based on deep learning are characterized by both the exaggerated similarity between adjacent nodes in the same latent space and the variation in the feature spaces caused by semantics. A novel approach is developed to fuse the semantic features of different networks in this paper. The proposed method is divided into two stages. Firstly, representation learning pays more attention to the influence of uncertainty on the equivalence of node network structure, and introduces the difference between adjacent nodes from the latent space. Secondly, a joint representation learning framework trains and exchanges the parameters depending on known anchor links. The joint representation learning framework injects fused features into the representation learning processes of different networks. The combination of enhanced discrimination and cross-network feature fusion reduces the feature space differences caused by the semantics of different social networks. This paper conducts comprehensive experiments on social networks in the real world. The outcome shows that the proposed approach is more efficient and robust compared to the existing state-of-theart methods.

Range query is an important operation on big multi-dimensional data. This paper studies the problem of multi-dimensional range query filtering for speeding up the range query processing by avoiding reading the useless data. To solve the problem, a novel multi-dimensional range filter is proposed to filter the multi-dimensional range queries, while the existing one-dimensional range filters can not provide efficient filtering. Based on the multi-dimensional range filter, an efficient range query processing algorithm is presented. It can directly return the locations of the I/O units that contain the data in the query result without any access to the input dataset. The time complexity of the algorithm is$O(3^{m}h)$, where$h$is the number of I/O units partially overlapping with a range query, and$m$is the dimension number. Since$m$is usually$o(\sqrt{\log n})$, it is a sublinear time algorithm if$V=O(n)$, where$n$is the size of the input dataset,$V=\prod _{i=1}^{m}d_{i}$, and$d_{i}$is the number of distinct values on the$i$-th dimension of the dataset for$1\leq i\leq m$. Experimental results show that the multi-dimensional range filter has low false positive rate and good filtering efficiency. The proposed range query processing algorithm achieves at least 3$\sim$7 times improvement compared to the one-dimensional filter based algorithms on different datasets.

To provide seamless communication service, the integrated satellite-terrestrial network (ISTN) has attracted lots of interest, where the promising dynamic spectrum sharing technology is widely used to improve spectrum efficiency. Meanwhile, rate-splitting multiple access (RSMA) has recently emerged due to the advantages of flexible multiple access and robust interference management. Based on the two promising technologies, we design joint satellite-terrestrial RSMA schemes in overlay and underlay spectrum sharing modes for ISTN, which considers both cases with and without inter-beam interference. Furthermore, we propose two adaptive RSMA schemes based on the hybrid spectrum sharing mode for adapting dynamic ISTN, where the number of terminals and spectrum resources available to satellite are unevenly distributed and time-varying due to the broad communication coverage. Finally, we consider the quality of service rate requirements and formulate joint optimization problems to maximize the weighted sum rate. To solve these non-convex optimization problems, we introduce an improved alternating optimization algorithm based on weighted minimum mean square error. Simulation results verify that the proposed schemes have significant performance gains compared with SDMA and NOMA schemes and can better adapt to the dynamic changes in the number of terminals and spectrum resources.

The environment observations and learning experiences shared by the cooperative learning agents accelerate multi-agent reinforcement learning (MARL) with partial observations for mobile applications but the performance degrades due to the redundant and outdated observations under severe channel fading in wireless networks. In this paper, we propose an efficient communication scheme in MARL for mobile applications that enables each learning agent to optimize the cooperative agents and the learning parameters to integrate the shared information. The cooperative agents are chosen according to the learning environment observations, the channel states, and the task similarity with neighboring agents. The learning parameters are chosen based on the attention mechanism that exploits the correlation with the local observation to enhance the agent receptive field for efficient policy exploration. Neural networks with weights updated based on the learning factors determined by the task similarity are designed to further improve the learning efficiency. The performance bounds including the information gain from the learning agent cooperation, the communication cost and the utility are provided based on the Nash equilibrium of the cooperative MARL communication game. The proposed scheme is implemented in the anti-jamming video transmission of the unmanned aerial vehicle swarms to optimize the transmit channel and power and experimental results verify the performance gain over the benchmark.

Rate-Splitting Multiple Access (RSMA) is an emerging and powerful multiple access scheme that relies on splitting and encoding user messages encoded into common and private streams, so as to partially decode multi-user interference and partially treat it as noise. In this paper, the secrecy rate constraint of each user is taken into consideration and a RSMA-based secure beamforming approach is proposed to maximize the weighted sum-rate (WSR). A generalized receiver model is considered where each user is also a potential eavesdropper wiretapping confidential messages for other users after decoding its own message. To solve the intractable non-convexity caused by security constraints in the formulated problem, a novel joint weighted minimum mean square error and successive convex approximation based alternate optimization algorithm is proposed and extended to maximize the instantaneous WSR with perfect channel state information at the transmitter (CSIT) and the weighted ergodic sum-rate with imperfect CSIT. Numerical results validate the effectiveness of the proposed design, which significantly improve the sum-rate performance and robustness to channel errors while guaranteeing message confidentiality and also unveil a better trade-off between message confidentiality and sum-rate performance thanks to its powerful interference management capability.

To provide seamless communication service, the integrated satellite-terrestrial network (ISTN) has become the trend of communication development, where terrestrial terminals and satellite terminals share the same spectrum resources. Meanwhile, rate-splitting multiple access (RSMA) has recently emerged for flexible multiple access and robust interference management. Based on the two promising technologies, spectrum sharing and RSMA, we design two coordinated multiple access schemes in overlay and underlay spectrum sharing modes for ISTN. Furthermore, we consider the quality of service rate requirements and formulate joint optimization problems to maximize the weighted sum rate. To solve the non-convex optimization problems, an improved alternating optimization algorithm based on weighted minimum mean square error is proposed. Simulation results verify that the proposed schemes have significant performance gains compared with various baseline schemes.

Multi-agent reinforcement learning (RL) utilizes the observations and learning experiences shared among the agents to accelerate learning speed under partial observations and the resulting learning efficiency depends on the cooperative agent selection and the RL task state formulation. In this paper, we propose an efficient communication scheme for multi-agent RL that enables each learning agent to optimize the cooperative agent selection and the task state formulation to improve the learning performance and the quality of service for RL-based applications in wireless networks. Based on the local observation, the radio channel states, the similarity of RL task with neighboring agents and previous communication cost, this scheme formulates a communication state, which is input to a neural network to estimate the communication policy distribution. The RL task state of the learning agent, which consists of the local observation such as channel states and previous task performance, as well as the correlation between the shared and the local observation extracted based on the attention mechanism, is formulated to enhance the agent receptive field. In addition, the shared learning information is also exploited to update the local learning parameters such as the task Q-values and neural network weights and further improve the RL task policy exploration. As a case study, the proposed communication scheme is implemented in the multi-agent deep Q-network based anti-jamming unmanned aerial vehicle swarm communications and the performance gain over the benchmark is verified via simulation results.

Federated learning is designed to train models in a distributed scheme while keeping the clients' data stored locally. The aggregation server only receives local models from clients and does not require clients to upload their local data, in which way it protects the clients' privacy. However, federated learning is vulnerable. The federated learning models are sensitive to poisoning attacks. Existing data poisoning attack methods assume that the attacker and the client have the same data distribution and data volume, which is unpractical. In this paper, we first propose a privacy inference-based poisoning data generation method, FLPDG. FLPDG changes the relationship between data and labels, and uses the data of benign clients to launch poisoning attacks. This method relies on the global model of an iterative update to obtain the data and labels of benign clients. Second, a privacy inference-based data poisoning attack model Poi_PDG is proposed. This model uses the FLPDG method to launch a data poisoning attack under conditions of insufficient original data volume of the attacker. Meanwhile, a defense method PDG_DF is proposed for Poi_PDG. It splits the image data into variance regions and utilizes GANs to hide the visual features of each image region. It controls the degree of feature hiding by setting different thresholds to keep the classification features of the image while ensuring the accuracy of the training model. Finally, several experiments are conducted to evaluate the proposed attack and defense methods, and the experimental results indicate the effectiveness of the methods.

This paper proposes an all-optical switching space network architecture in response to the electronic bottleneck problem faced by the future demand for massive data communication in space, based on the current status of single-wavelength transmission in space laser links. The objective is to optimize the wavelength mapping of the network link, enabling the transmission and exchange of data between any nodes in the space network using a single wavelength.One of the challenges in constructing the current all-optical switching network onboard is the immaturity of all-optical wavelength conversion devices, which cannot be effectively applied in space. Additionally, the wireless transmission of single-wavelength laser links in space necessitates the differentiation of transmitting and receiving wavelengths to avoid crosstalk.In this paper, we address the immaturity of wavelength conversion devices by proposing a satellite switching network for single-wavelength transmission, focusing on satellite topology. This approach ensures efficient transmission of the entire network’s signal without the need for wavelength conversion.The feasibility of the proposed method and architecture is demonstrated, offering a viable solution for an all-optical switching space network architecture that is not constrained by wavelength conversion devices.

With the rapid development of communication, the future Internet of Things will greatly expand its coverage to form the space–air–ground–ocean-integrated network (SAGOI-Net) and provide globally ubiquitous applications and services. In SAGOI-Net, the existing research work focused on system design and neglected the theoretical analysis of system performance. Furthermore, the Nakagami-$m$fading model can fit the experimental data better than the Rayleigh and Rice fading models. Also, the Nakagami-$m$fading model can adapt to the SAGOI-Net environment by changing the value of the shaping parameter$m$. However, there is no work to analyze the performance of SAGOI-Net using Nakatomi-$m$fading. Therefore, in this article, the synchronous downlink system is theoretically analyzed using the Nakagami-$m$fading model. However, Then, the statistical characteristics of multiple access interference (MAI) and MAI-plus noise are theoretically derived. Furthermore, the accurate expression of the bit-error rate (BER) for the fading model is also derived. After that, we establish the relationship between the number of users, BER, and the information transmission rate to optimize energy efficiency. Finally, simulation results verify that the theoretical derivation results are reliable and effective.

Space-air-ground integrated networks (SAGIN) have become a research hotspot facing the next generation of communications. The theoretical analysis for non-terrestrial networks (NTN) is significant before applying them in practical scenarios, but the existing works failed to provide a general analysis approach for NTN. Against this background, multiple satellites and civil aircrafts (CAs) are modeled as 3-D binomial point processes (BPPs) in the given finite space in this paper, and we desire to investigate the coverage performance of downlink CA augmented-SAGIN (CAA-SAGIN). Considering the sectorized beam pattern of platforms, we provide a detailed analysis of the different distributions of the serving and interfering platforms and derive the Laplace transform of the interference under shadowed-Rician fading channels. Then, the exact and closed-form expressions are obtained for the general cases with interference and the particular cases without interference via stochastic geometry. The approximations and boundary values are derived by adopting the existing mathematical theories. We analyze the effects of different parameters on the coverage probability of satellite and CA networks, and prove the validity of the derived analytical expressions, approximations, and bounds. Moreover, this work paves the way from the system level to exploit the generic coverage performance of NTN.

In this paper, the performance of optimally displaced threshold detection (ODTD) for discriminating ternary coherent signals is theoretically investigated. We first establish the receiver model for ternary phase shift keying optical quantum communication system using Kennedy receiver with ODTD in a realistic situation. Then we analytically study the error probability for ODTD and formulate a joint optimization problem to minimize this error probability of detection, which is a challenging mixed-integer programming (MIP) problem due to the discreteness of the detection threshold. We then propose an efficient algorithm to tackle this MIP problem and design the displacement and the threshold on both detection branches. We also discuss the receiving scheme using ODTD in the case of unequal prior probabilities and optimize the signal power distribution between the two branches. Numerical results show that for the discrimination of ternary coherent signals using Kennedy-type receiver, ODTD can mitigate the influence of thermal noise and dark count noise and is robust to imperfect quantum efficiency. Besides, we find that the error probability performance can be improved by optimizing the decoding order of the ternary signals and the reflectance of the beam splitter.

The backscatter communications (BackCom) and physical layer security are respected to realize extremely low-power secure communications in the imminent sixth generation (6G). In a BackCom system, the backscatter device without radio frequency components sends messages to users by reflecting the external signals. However, the double-fading effect limits BackCom’s performance and the commonly used broadcast mode is vulnerable to eavesdropping. Two promising technologies, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV), show excellent potential in handling these problems. In this paper, we propose a UAV-empowered IRS-BackCom network, where an IRS acts as the backscatter device and uses the received signals from a UAV for BackCom. We aim to guarantee secure transmission and maximize the broadcast secrecy rate by jointly optimizing the UAV’s beamformer and trajectory and the IRS’s reflection coefficient. To tackle the non-convex problem, we leverage the block coordinate descent method to decompose it into three subproblems. Specifically, the UAV’s beamformer and trajectory and the IRS’s reflection matrix are optimized alternatively. Further, we adopt reinforcement learning to facilitate the intractable UAV’s trajectory optimization. Simulation results verify the feasibility and effectiveness of the proposed system model and the optimization scheme.

To have a good understanding of attackers’ behaviour and seek effective defensive measures, an optimal strategy of false data injection (FDI) and denial-of-service (DoS) attacks is proposed to intrude sensors and transmission channels of cyber-physical systems (CPSs) from the perspective of an attacker. Specifically, all sensors of CPSs make measurements of the state and send them to a fusion center for state estimation. Intruders attempt to modify the measurements and block innovations’ transmission by respectively launching FDI and DoS attacks to maliciously degrade the system estimation performance. A cost function is introduced to quantify the estimation performance. In order to maximize the cost function, it is important for the attacker to decide which sensors and transmission channels to intrude due to energy constraint. A convex optimization problem is formed, by solving which the optimal attack strategy is obtained. Simulations demonstrate the optimality of our proposed attack strategy.

MIMO system makes full use of the space dimension, in the era of increasingly tense spectrum resources, which greatly improves the spectrum efficiency and is one of the future communication support technologies. At the same time, considering the high cost of direct communication between the two parties in a long distance, the relay communication mode has been paid more and more attention. In relay communication network, each node connected by relay has different security levels. In order to forward the information of all nodes, the relay node has the lowest security permission level. Therefore, it is meaningful to study the physical layer security problem in MIMO two-way relay system with relay as the eavesdropper. In view of the above situation, this paper proposes the physical layer security model of MIMO two-way relay cooperative communication network, designs a communication matching grouping algorithm with low complexity and a two-step carrier allocation optimization algorithm, which improves the total security capacity of the system. At the same time, theoretical analysis and simulation verify the effectiveness of the proposed algorithm.

This paper first proposes an ambient wireless energy harvesting intelligent reflecting surface (IRS)-based backscatter communication system, where an active transmission from an access point (AP) to a primary user (PU) and IRS-based backscatter communication coexist in a certain area. Specifically, the system operation consists of two phases. In the first one, the AP communicates with the PU, while the signal energy reaching the IRS can be fed into the energy harvester of IRS. In the second one, the signal from the AP is modulated into new signal for backscatter communication. By jointly optimizing the beamformers at the AP and the IRS as well as the time scheduling of two-phase process, the throughput of backscatter communication is maximized. Simulation results verify that the proposed scheme achieves a substantially enhanced communication performance.

Successive cancellation list (SCL) decoder has high decoding performance but also require great resource consumption. In this paper, we combine the SCL decoder and the critical set to form a new decoder, which can stably reduce the number of operations by 65%$\sim$70% and maintain similar decoding performance to SCL decoder. The reduction in resource consumption depends only on the arrangement of unfrozen bit sequences, not on the quality of the channel. We also propose to combine this new decoder with adaptive algorithms. In this way, the decoding performance of the decoder can be improved beyond SCL decoders without increasing resource consumption.

Internet of Things(IoT) is developed rapidly and has permeated through many communication industries. With the increasing development of Low-Earth-Orbit(LEO) satellite industry and its advantage of border coverage, it is feasible to implement global loT with LEO satellites. Chirp Spread Spectrum(CSS), as a kind of spread spectrum technology, has never been used in LEO satellite communication systems for loT, while it has been already used in LoRa modulation. This paper is on the research of multi-terminal access protocol based on CSS modulation for LEO satellite loT. And Chirp Spread Spectrum Aloha (CSSA) as a new kind of spread spectrum aloha protocol is proposed in this paper, which can improve the system throughput effectively.

The next generation communication system puts forward new requirements in user capacity, communication rate and coverage performance. Non-Terrestrial Network (NTN) communication system, namely satellite network, occupies a very important position. Random Access (RA) process is an important part of communication system, which needs to be completed by random access preambles. Considering the large delay and carrier offset of satellite communication, a sequence design based on coverage sequence is proposed to improve the user capacity. The pruned DFT -Spread filter bank multicarrier (FBMC) is used for signal processing, which can compensate and process the large normalized frequency offset. At the same time, the frame structure is designed to improve the disadvantage that the pruning DFT -Spread FBMC method has no Cyclic Prefix (CP), and a suitable structure is provided for frequency offset compensation.

Polar codes have been proved to be a coding method that reach the limit of Shannon channel capacity. In the past ten years, polar codes have attracted wide attention in academia and industry, such that the 5th generation wireless systems (5G) standardization process of the 3rd generation partnership project (3GPP) chose polar codes as a channel coding scheme.However, in the practical communication system, the channel conditions change from time to time, which leads to the uncertainty of the transmitted data. In order to improve the transmission rate and reliability, how to construct the polar code with rate compatible coding has become an urgent problem. In this papar, based on the discussion of the construction methods of 2/3 polar codes, we select the shortening scheme as the rate compatible coding rate and achieve the encoding and decoding based on LabVIEW .2/3 polar codes will be widely used in ground network and non-ground network in control channel.

As investigations on physical layer security evolve from point-to-point systems to multi-user scenarios, multi-user interference (MUI) is introduced and becomes an unavoidable issue. Different from treating MUI totally as noise in conventional secure communications, in this paper, we propose a rate-splitting multiple access (RSMA)-based secure beamforming design, where user messages are split and encoded into common and private streams. Each user not only decodes the common stream and the intended private stream, but also tries to eavesdrop the private streams of other users. We formulate a weighted sum-rate (WSR) maximization problem subject to the secrecy rate requirements of all users. To tackle the non-convexity of the formulated problem, a successive convex approximation (SCA)-based approach is adopted to convert the original non-convex and intractable problem into a low-complexity suboptimal iterative algorithm. Numerical results demonstrate that the proposed secure beamforming scheme outperforms the conventional multi-user linear precoding (MULP) technique in terms of the WSR performance while ensuring user secrecy rate requirements.

Although 5G networks have enabled mobile users to get a better experience, task scheduling remains challenging for massive Internet of Things (IoT) devices in remote areas. This paper investigates the task scheduling problem for delay-aware IoT applications in civil aircraft-augmented space-air-ground integrated networks (CAA-SAGIN), where the normalized sky access platforms (SAPs) can collect and forward the terrestrial tasks. Specifically, we first propose an access control scheme for a non-preemptive priority queuing system and a transmission control scheme with cross-layer optimization. Secondly, considering the uncertain distribution of the transmission numbers and generated data, we formulate a robust two-stage stochastic optimization problem of delay minimization. With the proposed robust task scheduling with risk aversion (RTS-RA) algorithm, the original problem can be decomposed into two subproblems, which can be further transformed into tractable semi-definite program (SDP) problems respectively. Simulation results show that the cross-layer optimization scheme can achieve a good tradeoff between delay and throughput. Also, the RTS-RA algorithm outperforms the exiting offloading schemes in terms of end-to-end delay, transmitted data, and energy consumption with lower computational complexity.

In this paper, we focus on antieavesdropping design in a multicell multiuser interference channel coexisting with a multiantenna eavesdropper, in which multiuser interference arises as a nonneglectable factor in securing communication. Supposing the eavesdropper is equipped with an arbitrary number of antennas, we jointly exploit the role of inherent multiuser interference and artificial noise (AN) to enhance security, and propose a noniterative secure transceiver design under a multiple input multiple output (MIMO) framework. The quantity relationship of system parameters is then analyzed to ensure feasibility. And the achievable secrecy rate is then derived without any knowledge of the eavesdropper. Finally, to balance the power allocated to AN and secrecy data, a power allocation strategy aiming at maximizing the achievable secrecy rate is designed, while guaranteeing legitimate users the required quality of service. With the adopted design, both the multiuser interference and AN are leveraged to facilitate communication security such that the proposed secure transceiver design can adapt to changes in eavesdropping antennas. Extensive numerical results have verified our analysis and demonstrated that the proposed power allocation strategy outperforms the baseline algorithms in terms of the achievable secrecy rate.

Correlation between the main and eavesdropping channels damages secrecy, the achievable secrecy rate under high correlation is small not to satisfy some high-rate applications. This paper optimizes an artificial noise (AN)-aided beamformer by employing a novel quality-of-service criterion, namely signal-to-interference-plus-noise-ratio outage-probability (SINR-OP), and seeks to maximize the target outage SINR under transmit power and SINR-OP constraints. Under Gaussian channel state information uncertainties, a robust ellipsoid-bounding-based approach is employed to approximate the probabilistic constraints; then, a suboptimal solution is obtained. The simulation results verify the proposed AN-aided beamforming achieves the high target outage SINR at the destination.

Ultra-dense network (UDN) is one of the most important techniques to support massive number of different types of user equipments (UEs). With the increasing density of small cell base stations (SBSs), the interferences among adjacent cells increase accordingly, and using joint transmission (JT), a specific technique of coordinated multi-point transmission, can effectively reduce or even eliminate the severe inter-cell interferences. This paper focuses on the resource allocation problem in UDNs when considering the application of JT. As area spectral efficiency (ASE) is a primary performance indicator in UDNs, we take ASE as an objective in the resource-allocation problem. If only ASE is considered as the objective, the resources tend to be allocated to UEs with better channel conditions, and thus some UEs may be allocated no resources. In order to cope with this problem, a second objective, i.e., fairness among UEs, is also considered. Thus, a multi-objective optimization problem is constructed. Then, both alternating direction method of multipliers (ADMM) and particle swarm optimization (PSO) algorithms are adopted to solve the optimization problem.

This paper studies a scheme of joint beamforming design combined with reconfigurable intelligent surface (RIS) selection to maximize the received signal-to-noise ratio (SNR) at the user. Specifically, we consider a communication system, in which multiple RISs are employed to assist the transmission from the base station (BS) to UE. To enhance the communication quality more effectively, the optimal RIS is selected based on the received SNR at UE. In this process, the joint beamforming at the BS and any RIS is optimized separately to maximize the received SNR at UE. Since the line of sight (LOS) propagation may be obstructed or suffer from severe power attenuation, this paper considers two scenarios that the direct link exists and is blocked. Simulation results show that the proposed RIS selection scheme is able to improve the received SNR more significantly than the counterparts without RIS selection.

With the development of mobile communication and information technology, many complex scenes that are difficult for front-line personnel to work have been improved with the support of new technologies. Sensors with more complete functions provide richer communication data, and technological developments such as heterogeneous networks also provide a better communication environment for the field and command center. In a more complex communication scenario such as fire, more abundant communication resources are used to conduct situational awareness on the scene. This paper proposes a fire situation information perception algorithm based on fuzzy neural network. The algorithm normalizes situation information data matrix and trains it as the input of fuzzy neural network. Finally, the fuzzy logic system theory and BP neural network are combined to obtain a fuzzy neural network with good perception of on-site situation information, which provides support for the subsequent decision-making of the command center and front-line personnel.

The generalized Kennedy receiver can be used to improve the secret key rate of continuous variable quantum key distribution protocol and thus attracts much attention recently. In this letter, we analytically study the performance of the post-selection based generalized Kennedy receiver in the presence of thermal noise. To improve the performance of the generalized Kennedy receiver, we optimize the displacement operation for different post-selection parameter and different thermal noises. Numerical results show that the thermal noise can greatly degrade the performance of the post-selection based generalized Kennedy receiver. Besides, the resistance of the generalized Kennedy receiver to the thermal noise can be improved by choosing an appropriate post-selection parameter.

Physical layer security technology is a wireless security technology that does not rely on traditional cryptography and relies on computational complexity. The opportunistic beamforming technology is to make use of the difference caused by channel fluctuation, reasonably precoding, and other means to obtain the maximum system capacity. The purpose of this paper is to explore the security rate of correlated channels in opportunistic beamforming. By constructing a multi-user related channel model, beamforming technology is used to seek the maximum signal-to-interference-noise ratio of legitimate users and illegitimate users, thereby obtaining the security and privacy rate of the related channel model. The simulation results show that if there is a correlation channel, the positive secure transmission rate can be obtained as long as the difference exists, and it decreases with the increase of correlation.

Enabling multi-giga bps rates, millimeter wave (mmWave) communications have become a promising technology in future vehicle-to-everything (V2X) networks. Massive multiple-input-multiple-output (MIMO) can also enhance the system performance through improving the spectral efficiency. In order to explore the performance of large antenna arrays in mmWave V2X cellular networks, the asymptotic spectral efficiency of the system is derived. Through combining the sparse scattering channel and hybrid analog and digital precoders, multiuser interference and noise can be suppressed as the number of antennas approaches infinity. The power scaling strategy of the system is also analyzed. Moreover, derivation results reveal that channel estimation error can also be suppressed by large number of antennas.

Because of the potential applications in quantum information processing tasks, discrimination of binary coherent states using generalized Kennedy receiver with maximum a posteriori probability (MAP) detection has attracted increasing attentions in recent years. In this paper, we analytically study the performance of the generalized Kennedy receiver having optimally displaced threshold detection (ODTD) in a realistic situation with noises and imperfect devices. We first prove that the MAP detection for a generalized Kennedy receiver is equivalent to a threshold detection in this realistic situation. Then we analyze the properties of the optimum threshold and the optimum displacement for ODTD, and propose a heuristic greedy search algorithm to design these parameters. We prove that the ODTD asymptotically approaches the Kennedy receiver with threshold detection when the signal power is large, and we also clarify the connection between the generalized Kennedy receiver with threshold detection and the one-port homodyne detection. Numerical results show that the proposed heuristic greedy search algorithm can obtain a lower and smoother error probability than the existing methods.

An artificial noise (AN)-aided beamformer specific to correlated main and wiretap channels is designed in this paper. We consider slow-fading multiple-input-single-output wiretap channels with multiple passive single-antenna eavesdroppers in which an independent transmitter side and correlated receiver side are assumed. Additionally, the source has accurate main channel information and statistical wiretap channel information. To reduce the secrecy loss due to receiver-side correlation, this paper proposes a channel-correlation-enabled transmission optimization scheme. In particular, the correlation is viewed as a resource to acquire more knowledge about wiretap channels. Based on this, the statistical distribution of wiretap channels is described more precisely, and an elaborate channel-correlation-enabled AN-aided beamformer is designed. Then, the achievable secrecy rate under transmit power and secrecy outage constraints is derived. Finally, the study is also extended to a specific scenario of multiple-antenna eavesdroppers. Simulation results show that the secrecy rate under transmit power and secrecy outage constraints can be improved under high correlation.

With the arrival of fifth-generation (5G) cellular networks, various industries have increasingly higher requirements for mobile Internet. The police mobile communication network is also seeking the interconnection between Police Digital Trunking (PDT) narrowband network and Broadband Trunking Communication (B-TrunC) network in China. In the future, the private mobile communication network will inevitably evolve towards the direction of large bandwidth and high rate to support more abundant mobile private network applications. Vertical handover technology is an essential key technology in hybrid networks composed of wide and narrow band networks. This paper proposes a vertical handover algorithm based on velocity pre-decision and fuzzy logic (VPD-FL). Firstly the velocity predecision algorithm can reduce the calculation of handover decisions, and then the fuzzy logic method is used to evaluate network performance indicators. Simulation and numerical results show that the VPD-FL algorithm has better network performance than the traditional vertical handover algorithm.

As one of the basic components of digital signal processing, digital finite impulse response (FIR) filters are widely used in image processing, speech recognition, and many other fields. This paper proposes an improved distributed algorithm (DA) to implement high-order digital FIR filters with less logical delay and hardware utilization. Firstly, the parallel DA is designed and then improved by look-up-table (LUT) decomposition. Secondly, the improved DA FIR filters are implemented on the Xilinx kintex-7 FPGA chip and used in high-speed ground penetrating radar (GPR) system to process radar signals. Finally, the performance of the DA filters with different order and structures are analyzed and compared, taking logical delay and hardware utilization as the key indicators. It comes to a conclusion that the parallel DA with LUT decomposition can implement high-order filter more effectively than traditional structures.

In daily life, speech is the main medium of human communication, and interpersonal communication is emotional. People hope that the computer can give a response based on the emotions contained in the voice. In this paper, we build a Wechat program of speech emotion recognition system, which is based on a random forest classifier. Firstly, the system preprocesses the collected speech signals in order to reduce noise. Secondly, 16 acoustic features are extracted from the pre-processed speech signals. The system obtains the emotional features of speech by applying 12 statistical functions to the original acoustic features. The emotional classification of Berlin Speech Emotion Database uses two classifiers: the Random Forest Classifier and the Support Vector Machine. The recognition accuracy of the SVM classifier is 83%. The accuracy of the random forest classifier is 89%. Finally, the random forest classifier is used to build the speech emotion recognition system.

Earthquake is a frequent natural disaster, and the rescue after earthquake is more difficult than those after other disasters because earthquake destroys the ground as well as the communication facilities on it. In this case, unmanned aerial vehicles (UAVs) are a good choice for survivals searching, and Ad hoc networks are a possible means for communication. As a combination, UAV Ad hoc networks play an important role in emergency rescue and communication system. However, UAV networks are unstable and resource limited, including energy resources and communication resources. The cluster heads in a clustered UAV network are more fragile to these disadvantages. In order to balance the cluster head selecting ratio and decline the collision probability, this paper proposes a dynamic cluster head selecting algorithm based on energy, mobility, distance, and node correlation degree. The algorithm calculates the weights of the UAVs according to the above-mentioned four parameters, and selects the best cluster heads. According to simulation results, the proposed algorithm declines the cluster head changing ratio and improves the packet delivery ratio.

This paper studies cooperative-jamming (CJ)-aided beamforming design under Gaussian channel uncertainties to reduce the secrecy loss due to reception correlation. In light of the difficulty of maximizing the outage-probability-constrained secrecy rate, a novel quality-of-service (QoS)-based optimization is considered. By employing the Bernstein-type inequality to approximate the probabilistic constraints, we seek to maximize the target outage signal-to-noise ratio (SNR) at the destination under transmit power and SNR outage constraints. Simulation results verify the designed CJ-aided beamforming substantially enhances the secrecy from a QoS perspective.

The increasing demand for the internet of vehicle (IoV) services has prompted the prosperity of cellular-based vehicle-to-everything (C-V2X). This paper investigates the resource allocation in an uplink SCMA-enabled C-V2X network. At the aim to maximize the sum rate of cellular links in condition of guaranteeing the reliability of V2X links, we propose a Two-Stage space code multiple access (SCMA) codebook allocation algorithm. The complicated resource matching problem is transformed into a 2-D matching problem by using V2X grouping algorithm at first. Then a Matching-Auction-based codebook allocation algorithm is adopted to address the issue of matching conflict. Finally, numerical simulation results indicate that the proposed scheme outperforms in terms of the sum data transmission rate of the cellular links and the reliability of V2X links.

Ultra-dense network (UDN) can effectively improve the network throughput by increasing the deployment density of base stations. However, due to the randomness of the large number of base station deployments, UDN will lead to huge computational complexity and signaling overhead. The existing clustering algorithms cannot obtain effective clustering results in extremely dense scenarios. In this paper, we propose a minimum separation clustering (MSC) algorithm, which selects the split base stations (SBSs) to connect the multiple dense cluster base stations (CBSs). SBSs can reduce the interference between CBS clusters by using different radio resources from CBSs, because it has higher priority in the resource allocation procedure. Furthermore, the traditional clustering evaluation indexes such as the sum of square error are not applicable to UDN where the base stations are deployed randomly, and hence we design the separation degree function, which evaluates the clustering effect from the compactness within a cluster and the dispersion between different clusters. Simulation results show that the proposed algorithm can not only reduce the proportion of SBSs so as to improve the spectral efficiency, but also reduce the inter-cluster interference and network scale.

Due to the explosive growth of mobile data traffic, the Ultra-Dense Networks (UDN) becomes one of the hot research technologies in the 5thGeneration mobile communication system (5G). With the multi-layer network structure, UDN brings spectrum multiplexing gain and system capacity improvement by densely deployed small cells in hot spot area. In this paper, in order to improve system Energy Efficiency (EE) and Spectrum Efficiency (SE), we propose a joint optimization algorithm considering both EE and SE for small cells in UDN with the constraints of interference threshold and transmission data rate requirement. We first formulate a compromised objective function considering both EE and SE, and then the optimization problem is transformed from the fractional form to subtractive form to simplify the solution procedure, and finally the results of resource allocation and power allocation are obtained by the iteration of Lagrangian dual decomposition method. Simulation results show that both EE and SE performance of the proposed algorithm is much better than the algorithms used in conventional communication systems, and compared with the algorithm to maximize SE in UDN, the EE can be significantly improved with very slightly reduced SE performance.

Sparse code multiple access (SCMA) is a novel type of non-orthogonal multiple access technology that combines the concepts of CDMA and OFDMA. The advantages of SCMA include high capacity, low time delay, and high date rate. In this paper, a power allocation algorithm is proposed for SCMA downlink systems where each tone is taken by more than one user to maximize the system's sum capacity. In SCMA systems, users are divided into different user groups. Thus, our proposed algorithm includes three-level power allocation. Since the power allocation problem is non-convex, the complexity of finding the optimal solutions is prohibitive. The Lagrange dual decomposition method is employed to efficiently solve the non-convex optimization problem. Results show that the optimized algorithm can significantly improve the sum capacity.

This paper proposes to employ multiple cooperative jammers to reduce the secrecy loss due to the correlation between main and wiretap channels. We consider slow-fading multiple-input single-output (MISO) wiretap channels with a passive single-antenna eavesdropper, in which an independent transmitter side and correlated receiver side are assumed. Considering that signal processing techniques as well as the blind growth of power at the source play a limited role in reducing the secrecy loss due to the receiver-side correlation, multiple cooperative jammers equipped with multiple antennas are introduced into the system. Owing to spatial diversity, the channel correlation from the different jammers to the destination and the eavesdropper varies. To interfere with the reception at the eavesdropper efficiently and consequently enhance security, some jammers in favorable channel conditions are selected to emit artificial noise (AN) with power optimization. Based on this, the secrecy outage probability is analyzed. The simulation results verify that the proposed scheme of multiple cooperative jammers provides substantial gains in terms of secrecy.

With large available bandwidth, millimeter wave (mm-wave) communications have attracted considerable research interests because of their potential to achieve multi-giga bps rates. However, one of the main challenges for mm-wave is high pathloss. To address this problem, full-duplex (FD) relaying can be used to increase the effective transmission distance and the spectral efficiency. Thus, studying the application of FD relaying in mm-wave communications will be of value. However, one of the main challenges in FD mm-wave relaying is the residual self-interference (SI), which includes line-of-sight (LOS) and non-LOS parts. To eliminate the SI and improve the spectral efficiency, we propose an orthogonal matching pursuit-based SI-cancellation precoding algorithm. Then, we propose an energy consumption model and analyze the energy efficiency performance. We formulate the joint spectral efficiency and energy efficiency optimization problem, which can be transformed into a convex problem. The numerical results show that the FD precoding scheme can effectively eliminate the residual SI and achieve approximately twice the spectral efficiency of the conventional half-duplex system. We also show that in low-spectral-efficiency regions, the optimal energy efficiency can be achieved, but the achievable energy efficiency will decrease in high-spectral-efficiency regions.

In PHY-security, high correlation between main and wiretap channels, which are frequently observed, can cause a significant loss of secrecy. Unfortunately, signal processing techniques at the transmitter (Alice), such as precoding and artificial noise (AN) techniques, are ineffective. Under this circumstance, this paper focuses on a slowly fading and reciprocal channel scenario wherein Alice sends a confidential message to an authorised receiver (Bob) with the transmission overheard by a passive unauthorised receiver (Eve), and all of them are equipped with multiple antennas. To prevent interception and ensure secrecy, we redesign a novel scheme of discriminatory channel estimation (DCE), in which training procedures are developed to limit the channel estimation performance at Eve while producing little effect on Alice and Bob. As a result, Eve's ability to obtain the channel information would deteriorate, thereby effectively increase the difference in decoding the message between Bob and Eve. Simulation results demonstrate the proposed scheme could provide substantial gains with respect to secrecy.

With the development of wireless technology, smart-phones and wireless terminal units have been used extensively. Hence, how to protect the secrecy data transmitted through the wireless environment has been an assignable problem. In our research, the problem we try to solve in this paper is to find an algorithm to enhance the system security by beamforming. We assume that the channel matrices are fixed and known to the sender and legal receiver, but the eavesdropper could not get the precise channel matrices. On the basis of the above assumptions, two wiretap channel models in different users conditions are proposed and algorithms under the models are researched. Numerical results of secrecy capacity and secrecy sum rate under the two models are also provided.

Sparse code multiple access(SCMA) is a novel kind of non-orthogonal multiple access technology which has the advantages of supporting more connections, low time delay and overcomes the near-far effect in CDMA system. In this paper we propose power allocation algorithms for SCMA downlink systems to maximize the energy efficiency. Since the resource allocation problem is non-convex, we employ the Lagrange dual decomposition method and Dinkelbach theory to solve the optimization problem. Our results shows that energy efficiency can be improved significantly by adopting optimal power allocation method. The SCMA maximum energy efficiency system save more energy in the condition of satisfying the user QoS requirement.

Achieving large confidential capacity under the wiretap channel model is a challenge due to the narrow modulation bandwidth and total transmission power constraints. The confidential capacity of a system can be improved through a non‐orthogonal multiple access technique that can obtain the highest transmission power in a downlink network. A clustering method is applied to network users who require data with similar contents. Based on the channel gain of each user, cluster heads are selected as agents for the corresponding clusters; then, the total transmission power is shared among the cluster heads. Before the power allocation process, the signal‐to‐interference‐plus‐noise ratio of the cluster heads is derived by considering clipping noise to ensure fairness. On this basis, an optimal power allocation scheme is proposed using Lagrangian dual theory. A case is presented to validate the performance of the proposed power allocation scheme. The comparison of the numerical results with those of other schemes shows that the proposed method achieves better performance regarding both secrecy sum capacity and outage probability.

This paper presents a new indoor localization approach by employing just single access point to get an accurate location of user's terminal. To achieve this goal, we first collect the Channel State Information (CSI) by using a commodity Wi-Fi device that supports the 802.11n protocol. Second, we propose a new algorithm to eliminate linear errors in CSI and make the phase of CSI closer to the real phase information in transmission data. Third, we improve the traditional Multiple Signal Classification (MUSIC) algorithm to make it more suitable for indoor localization environment and more accurately distinguish the Angle-of-Arrival (AOA) and Time-of-Arrival (TOA). Finally, we calculate the location of a user based on the transmission time and incoming direction. We deploy a test system in an actual environment, the results show that our approach can achieve the positioning average error less than 1m.

Sparse code multiple access (SCMA) is a novel air-interface technology proposed for the fifth generation (5G) mobile communication system. SCMA aims for energy saving, low latency and massive connectivity to satisfy 5G demand. SCMA grant-free transmission has been proposed to ensure low latency and massive connectivity. In this paper, the connection and packets drop performance of SCMA and OFDMA through a pre-existing resource scheduling scheme for uplink grant-free transmission are analyzed. When UEs are erratically required to transmit a great number of packets continuously, the packet loss rate is too high, which is a problem of the pre-existing scheme. Hence, a resource scheduling scheme based on feed-back for uplink SCMA grant-free transmission is proposed to solve this problem. The simulation results demonstrate that SCMA has a lower packet loss rate than OFDMA with the same resources and UEs. In the heavy traffic scenario, the proposed resource scheduling scheme based on feed-back has a better packet drop performance than the pre-existing scheme.

Sparse code multiple access (SCMA) is a novel kind of non-orthogonal multiple access technology in which combines ideas of CDMA and OFDMA. It performs well in terms of large-capacity, low time-delay and high data rate and overcomes the deficiency, near-far effect, in CDMA. Coming after low density signature (LDS) technology, SCMA increases extra coding gain though the design of specific codebooks. On the other hand, full-duplex technology utilizes same-time and same-frequency transmission to realize “non-duplex” communication. However, it is crucial to minimize relay self-interference to render full duplex feasible. This paper innovatively combines SCMA with full-duplex multiple-input multiple-output (MIMO) relay, and illustrates the system model and self-interference mitigation scheme: time-domain cancellation and space-domain suppression. The theoretical derivation and simulation results demonstrate that the integration of the two kinds of technology can elevate system capacity and spectral efficiency.

In spectrum sharing networks with relay cooperation, primary users can benefit from the assistance of secondary users while secondary users accessing the primary bandwidth to transmit their signals. Reasonable relay selection strategy can improve the performances of both primary and secondary users in terms of higher transmission data rate and lower outage probability, and at the same time, frequent relay switching can be avoided. In this paper, we propose a relay selection strategy considering both relay mobility and the required transmission data rate of primary user. The metrics of relay selection combine the relay activation duration and the transmission data rate of the secondary users through the mobility prediction of relay nodes. Simulation results show that the proposed strategy can select the best relay with lower primary outage probability and longer relay activation duration compared with existing schemes, and the transmission data rate of secondary user can also be improved so that the total number of transmit information bits can be increased.

This paper investigates a dynamic bandwidth allocation strategy with secrecy constraints, where big data can be viewed as a resource instead of a burden from the traditional perspective. Unlike usual cases, we take into account big data and security issues along with bandwidth allocation. It is reasonable to assume that big data derived from mobile network, by a series of processing, can generate a binary set S consisting of pairs of users. According to S, a metric closeness can be redefined to describe whether the same confidential content can be shared between two users. On this basis, data driven clusters can be formed. Then two bandwidth allocation algorithms, aiming at increasing secrecy sum capacity and individual secrecy capacity by sharing content in clusters, are proposed. The fairness among users and computation complexity are considered in the first algorithm, while the objective of the second algorithm is to maximize the secrecy sum capacity. In order to validate our proposed schemes, a concise case is presented and numerical results show that a significant performance gain over both secrecy sum capacity and individual secrecy capacity is achieved.

With the rapid growth of data traffic, the increasing data solutions are more and more difficult to cover, as the energy consumption become the urgent problem. In this paper, considering the shadow-fading effect, we model three traditional transmission model in the wireless vehicle communication environment. The three vehicle transmit model is under the cellular, including the direct transmission model, the transmission model in vehicular cell with fixed relay nodes (FRN) and mobile relay nodes (MRN). After the expression about the relationship between the transmission power and the shadow fading effect with the vehicular penetration loss (VPL) is determined, we deduce the average transmit energy band under the outage probability (OP) set. With considering the effect of the VPL and the shadow-fading, simulation and numerical results indicate that the MRN transmission model is to do better than the other two transmission model in the same OP.

In the hybrid network of Device-to-Device (D2D) and cellular communications, reducing the co-channel interference is always hotpot problem, in which the interference involves either the co-channel among D2D terminals or between cellular and D2D links. Lower co-channel interference can improve the link spectrum efficiency effectively. This paper focus on discussing the D2D communications under the dedicated model, which is defined for no interference existing between cellular and D2D links. We depict a D2D-paring scheme to confront the influence of co-channel interference in D2D communications by introducing the Voronoi diagram. According to the location of D2D terminals, the Voronoi diagram can be constructed and rule the D2D-paring criterion that D2D terminals establish communications link only between adjacent polygons. The interference Management algorithm is proposed, which shows that small communications range causes lower co-channel interference, especially, when the channel inversion principle is considered. More intuitive result is obtained in the simulation part. Compared the D2D-paring scheme with some introduced classic literature, both the coverage probability and link spectrum efficiency can all be enhanced obviously.

In recent years, D2D communication technology has been widely used in cellular networks. By introducing D2D users, cellular networks can improve the efficiency of using resources, which can improve the quality of communication. In the system where both D2D users and cellular networks users exist, we studied the method how every user is introduced into the cellular networks to improve the resource utilization. The distributed resource allocation for D2D communications underlying cellular networks is considered. By the game theory, the problem can be considered as a non-cooperative game. Firstly, Nash Equilibrium's existence is proved. Then a distributed algorithm is designed to solve the power allocation problem. Finally, Nash Equilibrium uniqueness and the global convergence of asynchronous distributed algorithm are proved. Simulation results show that the algorithm is convergent,which can get optimal power allocation by iteration method.

Based on data fusion technology, multi-sensor fusion positioning merges several positioning sources together to achieve an optimal positioning result by making full use of all the homogeneous or heterogeneous information from different fusion sensors. However, there has not been much research carried out about the effectiveness evaluation of multi-sensor fusion positioning system. In this paper, a self-feedback effectiveness evaluation algorithm is proposed which can not only evaluate multi-sensor fusion positioning systems, but also improve positioning performance by adopting feedback information. Besides traditional evaluation parameters, confidence level and plug and play capability are proposed as new evaluation parameters to estimate effectiveness of multi-sensor fusion positioning system. Simulations verify the efficiency of proposed evaluation parameters and self-feedback effectiveness evaluation algorithm.

This paper studies a transmission scheme for in-band full-duplex communication system, which is established on the base of complementary codes. Having ideal correlation properties, complementary codes are capable of providing effective mitigation of self-interference (SI) introduced by co-frequency co-time full-duplex (CCFD) communications, an efficient multiple access for multiple users as well as high-performance resistance against multi-path interference under fading channels. In this paper, we briefly investigate the basic structure and correlation properties of complementary codes. As the core part, complementary coded full-duplex multiuser communication system is set up, and more details of transmission signals and channels are presented. Then the mathematical analysis of bit error rate (BER) and outage probability (OP) verifies its strong capability of various interference suppression. Finally Monte-Carlo simulations are implemented to confirm theoretical derivations revealing that the system transmission performance will hardly worsen with the increasing intensity of SI in a certain range.

Owing to the rapid development of underwater applications, the research on underwater wireless sensor networks (UWSNs) is becoming more and more significant. Low bandwidth, high latency, limited energy and node float mobility are basic challenges for underwater sensor networks. Most of the theories for terrestrial sensor networks can not be applied to underwater sensor networks. In this paper, we focus on the localization and routing issues for dynamic UWSNs. Location based routing protocols can suit the dynamic situation of UWSNs well. However, most research assumes that the nodes know their locations, which may be not appropriate for some applications. This paper combines these two aspects together to design a localization based routing protocol for dynamic UWSNs. The simulation results show our method can effectively route data from the source nodes to the sink nodes in a dynamic environment.

Sparse code multiple access (SCMA) is a novel kind of air interface technology which can dramatically improve spectral efficiency of wireless radio access. Different from conventional CDMA and OFDMA, SCMA achieves the non-orthogonal multiple access of frequency domain. On the other hand, Multiple-Input Multiple-Output (MIMO) can make full use of spatial-domain resource to improve system performance without a corresponding increase in spectrum resource. This paper firstly combines SCMA and illustrates the system model and scheme. Particularly, Vertical Bell Labs Layered Space Time (V-BLAST) Coding and Space Time Block Coding (STBC) are applied in uplink and downlink respectively. The theoretical derivation and simulation results demonstrate that the integration of the two kinds of technology can achieve better performance.

Sparse code multiple access (SCMA) is a novel non- orthogonal access technique for future multiple access, which increases the spectral efficiency by directly mapping data streams of multiple users to sparse codewords in different SCMA layers, and these SCMA layers are imposed on the same SCMA block composed of limited resource elements (REs). As the sparsity of codewords, message passing algorithm (MPA) can be employed to detect multiple users' data with near optimal performance. But the complexity of MPA is still high, especially in the case of overload, where the downlink user receiver needs to implement the whole iterative process of MPA to get its data although the rest users' data is unnecessary for it. In this paper, we propose a novel multi-stage MPA algorithm, which sorts the level of signal to noise ratio (SNR) of imposed users to decide the detection order and iterations of each stage. Further by reconstructing the factor graph, it can realize downlink user detection without processing the whole MPA, which reduces the complexity by decreasing iterations. Combined with multi-stage MPA, power allocation should be reconsidered according to user's channel condition, which can further improve the performance of the proposed algorithm. The simulation results demonstrate that the performance of proposed detection algorithm is close to MPA at the low SNR region, and gains about 1dB over max-log-MPA, while the complexity is decreased around a half than MPA.

Multi-source fusion localization is an effective approach when a single source is unavailable or the positioning accuracy is unsatisfied, and it can take advantages of different location sources to achieve a better result. Data fusion is a process of merging different solutions and techniques with disparate types of information. In order to provide users with better location based services, this paper proposes a factor graph based multi-source data fusion algorithm for wireless localization. Different fusion sources are divided into multi-levels by adopting confidence level estimate algorithm. By using sum-product algorithm, the soft-information is calculated to complete the fusion process. Through some simulations, we can see that the proposed algorithm can improve the positioning accuracy greatly. At the same time, it has low complexity and a plug and play capability.

The fingerprinting location method is commonly used in WLAN indoor positioning system. Device diversity (DD) which leads to Received Signal Strength (RSS) value difference between the users' device and the reference device is becoming an increasingly important factor impacting the positioning accuracy. Thus, the device diversity is a key problem gained more and more attention in fingerprinting location system recently, which introduces many uncertainties to the positioning result. Traditionally, the Euclidean distance is widely adopted in fingerprinting method. However, when encountering with RSS value difference caused by device diversity, the localization performance is degraded significantly. Due to this problem, our paper proposes a method employing cosine similarity instead of the Euclidean distance to improve the positioning accuracy about 13.15% higher within 2 meters when device diversity exists in the positioning. The experiment results show that the proposed method presents a good performance without the expenses of computation caused by calibration method which is employed in many previous works.

Abstract Wireless local area network fingerprint‐based indoor location system is a hot topic these years because it needs no extra hardware and is very easy to deploy. However, it demands a database containing the distribution of received signal strength (RSS) of the area of interest,called radio map. Conventionally, we need to grid the area densely and manually measure RSS values on intersections, which will consume a lot of time and human resources. What is worse, change of the environment may render this database totally useless. Our consideration is to measure RSS on a small amount of these intersections and use them to build a radio propagation model. Then, this model can be deployed to predict RSS values of other intersections and reconstruct the radio map. In other words, we only need to collect a very small part the radio map and utilize the radio propagation model to recover the whole one. So far, many models have been proposed, among which the one suggested by Seidel, named floor attenuation factor propagation model, achieves great balance between computational request and accuracy. But it is not compatible with environments in some scenarios. So as to compensate for this deficiency, we take into account the angles formed by signal and surfaces of obstacles, and the results show better compatibility. The proposed model has four parameters that are related to the environments, and our second contribution in this paper is to propose a method to determine them. In fact, after collecting a small part of the radio map, we can estimate these parameters with least square method. Then, these parameters can be used to predict the signal strength at any other points in the same environment, and the whole radio map is rebuilt. According to practical experiments, performance of the radio map built by the proposed model is not as good as the manually collected one, but 80% of collecting labor is saved. Copyright © 2015 John Wiley & Sons, Ltd.

In this article, we present a comprehensive survey of existing literature in the area of complementary coded CDMA (CC-CDMA) technique for wireless communications to provide an introduction and overview to the field. According to the kinds of independent sub-channels, we divide the existing CC-CDMA solutions into two categories: time division multiplex (TDM) and frequency division multiplex (FDM) CC-CDMA systems. Then we compared them in terms of resistance of multiuser interference and multi-path interference, implementation complexity and spread and spectrum efficiency.

The Global Navigation Satellite System (GNSS) has been widely used by militaries as well as civilians. Generally, the locating accuracy is high, but the system is lack of immunity against spoofing attack, which may deceive the receiver into error positioning. In order to deal with the problem, a maximum particle weight monitoring scheme based on particle filter (PF) is proposed for spoofing detection in this paper. The scheme exploits the relation between spoofing and particle weight, and it can detect spoofing by catching the abnormal maximum particle weight. After detection process, an improved robust estimation method is applied in the spoofing suppression, thereby eliminating the impairing. Both the theoretical analyses and the simulation results verify the effectiveness of the spoofing detection and suppression schemes.

Seamless positioning is now the key technology to meet the increasing requirements of Location Based Service (LBS). The handoff algorithm plays an important role in the seamless positioning. Current handoff algorithms are mainly based on the Received Signal Strength (RSS), and it is not reliable in complex scenarios. This paper presents a Euclidean distance based handoff algorithm. The proposed handoff algorithm works reliably in complex scenarios. By using the proposed handoff algorithm, Wireless Local Area Network (WLAN) based fingerprint positioning system can be combined with any positioning system. Moreover, a dual-threshold scheme is proposed to solve the ping-pong problem and improve the accuracy of the proposed algorithm. Experiments are carried out to validate the effectiveness of proposed algorithm. The error rate of the handoff decision is only 2.39%.

To provide services for other applications, promoting the availability of Global Positioning System (GPS)-is very important. While in the urban environments or with the high-power malicious interference, GPS signal is shaded by buildings or overwhelmed by interference. Hence the receivers may not acquire correct pseudo random noise (PRN) codes. Assisted GPS (A-GPS) technologies can solve the shaded problem, and antenna array with adaptive digital beam forming (ADBF) or spacetime adaptive processing (STAP) has been proved the effective methodology for interference suppressing. However, A-GPS may be not available, and the radio-frequency (RF) front end is too costly and cumbersome for a single customer. This paper investigates the collaborative navigation technology, utilizing GPS group users with dynamic Ad-hoc network and internal range measurements. Decentralized and distributed network architectures are presented for shading and interference presences, based on which user assistance and ambiguity removing for ADBF are proposed. Simulation results verify the performance improvement of the collaborative navigation based on dynamic Ad-hoc networking for different presences.