Integrated Sensing and Communication (ISAC) enables simultaneous data transmission and sensing, yet dense vehicular scenarios exacerbate the rate–sensing tradeoff under strong interference and sensing waveform constraints. We propose a Rate-Splitting Multiple Access (RSMA) enabled hybrid linear–nonlinear precoding (HLNP) design that coordinates a beampattern-aware common-stream multicast beamformer with constructive-interference based private-stream symbol-level precoding (SLP). The common layer is optimized via a WMMSE-reformulated SOCP under sidelobe-mask constraints to ensure sensing compliance, while the private layer exploits symbol-wise interference under mild per-antenna limits for instantaneous waveform shaping. An ε-constraint Pareto procedure is developed to characterize the efficient tradeoff boundary. Simulations on vehicular channels demonstrate an enlarged achievable communication– sensing region and consistent gains over RSMA-MMSE and SDMA-MMSE at the same beampattern compliance across both relaxed and stringent sensing regimes.

Internet of Things (IoT) devices often deliver sensitive sensing and control data over wireless links that are inherently broadcast, making passive over-the-air eavesdropping a persistent threat even for simple point-to-point transmissions. Many physical-layer security (PLS) techniques, however, rely on accurate channel knowledge, additional spatial degrees of freedom, or strong secrecy assumptions that are often hard to guarantee in practical IoT links. In this paper, we propose an anti-eavesdropping secure transmission scheme by deliberately embedding controllable artificial interference into a conventional BPSK waveform on the same carrier, thereby forming an interference-embedded waveform transmitted over an AWGN channel. To exploit the common architectural asymmetry in IoT systems, the legitimate receiver located at an IoT gateway/edge node employs an Anti-Eavesdropping Waveform Demodulator (AEWD), a ResNet–self-attention–BiLSTM neural demodulator trained end-to-end to recover bits directly from raw I/Q samples without explicit interference parameter estimation. Under identical channel conditions, conventional model-based receivers that perform deterministic interference suppression followed by demodulation incur substantial BER degradation and frequently exhibit interference-limited error floors. Extensive simulations across a wide range of SNR and SIR demonstrate that AEWD consistently outperforms classical baselines under both single-tone and multi-tone nonstationary interference. The results suggest that waveform-level interference embedding combined with a dedicated neural demodulator can create a practical receiver performance gap to strengthen confidentiality for IoT communications.

Accurate long-range acoustic ranging is a critical enabling technology for Internet of Underwater Things (IoUT), particularly in deep-sea environments where electromagnetic and optical signals fail to propagate efficiently. In this regard, we propose an Acoustic Ranging algorithm based on BELLHOP ranging model and Dynamic ranging-ratio model (ARBD). Specifically, in response to the limitations of traditional ranging algorithms such as the shape of the Sound Speed Profiles (SSP), lack of consideration for reflection effects, and complex nonlinear root-finding methods, we transform the BELLHOP acoustic toolbox into a BELLHOP ranging model. A novel ARBD algorithm is developed by combining the fast convergence solution strategy of the Dynamic Ranging-ratio Model (DRM) with ray tracing. Furthermore, the Cramér-Rao lower bound (CRLB) is derived to establish the theoretical performance limits. The results demonstrate that the proposed algorithm reduces ranging errors compared to traditional algorithms, while maintaining fast computational efficiency and adaptability, particularly in long-range distance measurements in deep-sea environments.

Satellite IoT has emerged as a promising solution to extend connectivity beyond terrestrial networks, particularly in remote and underserved regions. However, grant-free random access (GF-RA) in satellite networks faces two key challenges: severe interference from massive uncoordinated terminals and limited computational resources on satellite platforms. In contrast to terrestrial multipath channels, the dominant line-of-sight propagation in satellite-terrestrial links induces deterministic spatial correlations between terminal positions and angles-of-arrival (AoAs). To leverage this property, a hierarchical satellite-terrestrial cooperative processing architecture is proposed, where computationally intensive terminal positioning tasks are offloaded to the terrestrial center, and satellites focus on real-time joint activity detection and channel estimation (JADCE). In this framework, we propose a location-prior-aided JADCE algorithm that incorporates terminal position information into the message-passing process to prune low-probability propagation paths and reduce computational complexity. The algorithm further integrates an expectation-maximization procedure to refine AoA estimates and optimize model hyperparameters. Furthermore, a region-based location management scheme is introduced to enable efficient terminal location estimation and storage. Based on this scheme, region affiliation probabilities are computed by evaluating the expectation of the posterior distribution conditioned on AoA estimates. Simulation results demonstrate significant improvements over benchmark schemes in terms of terminal activity detection and channel estimation accuracy.

Recent studies have shown that the stacked intelligent metasurfaces (SIM) can exploit inter-layer electromagnetic (EM) wave transmission for wave-domain signal processing. This enables over-the-air computing with characteristics such as high-speed operation, low energy consumption, and support for multitasking parallel processing. By leveraging these advantages, SIM show great potential in substituting or augmenting customized communication functions in conventional wireless systems. To support various transmitter-customized communication functions, we design a novel multiple-input multiple-output (MIMO) transmitter architecture based on SIM and develop corresponding detection algorithms. Initially, we present a SIM-enabled MIMO transceiver model. Unlike conventional wireless communication, the SIM-enabled transmitter automatically performs customized functions, such as precoding and channel encoding, as the EM waves propagate within the layers. At the receiver side, minimum mean square error (MMSE) and maximum likelihood (ML) detectors are adopted as benchmarks, and their theoretical performance bounds are derived accordingly. Furthermore, we propose a data-enhanced deep unfolding algorithm, namely orthogonal approximate message passing causal dilated convolutional transformer (OAMP-CDT), to demonstrate the performance of customized functions based on SIM. Simulation results verify that the proposed SIM-enabled MIMO transmitter supports customized functions via over-the-air signal processing, and the proposed OAMP-CDT algorithm outperforms the same series of algorithms, such as orthogonal approximate message passing (OAMP) and OAMP-Net2.

The advent of multi-layer reconfigurable intelligent surfaces (RIS) has introduced new possibilities for integrating communication, sensing, and computation within the wave domain. Existing models, however, often rely on simplified mathematical abstractions, failing to accurately represent the physical behavior of multi-layer RIS-assisted systems. To bridge this gap, we present a physically consistent model for multi-layer RIS-enabled multi-user communications. The model is based on electromagnetic (EM) theory and Chu’s antenna principles, incorporating mutual coupling (MC) effects through an impedance-parameterized multi-port circuit framework. Contrary to the conventional view of MC as a performance-limiting factor, our analysis demonstrates that, when properly managed, MC can enhance system efficiency. To this end, we design an optimization algorithm that iteratively updates the RIS phase control matrix and transmits power using gradient ascent combined with successive convex approximation (SCA), ensuring that physical constraints and MC effects are fully integrated. Simulation results demonstrate the proposed model’s ability to accurately predict system behavior and highlight the potential performance gains achievable through MC-aware design, offering a practical foundation for optimizing multi-layer RIS in advanced communication systems.

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.

Low Earth orbit (LEO) satellite communications play a critical role in achieving global connectivity, yet they face significant challenges due to high satellite mobility and incomplete channel state information (CSI). Moreover, the integration of reconfigurable intelligent surfaces (RIS) in certain scenarios introduces additional complexities. In this paper, we propose a novel MIMO channel prediction framework tailored for LEO satellite communications involving unmanned aerial vehicle-mounted RIS (UAV-RIS), employing a spatiotemporal-attention (ST-attention) mechanism to capture both the spatial correlations among antennas and the temporal dynamics of rapidly varying channels. Furthermore, we leverage masked pretraining to enhance the model’s robustness under scenarios of severe CSI incompleteness, enabling effective reconstruction of missing channel information. Comprehensive simulations demonstrate that our approach outperforms traditional model-based predictors, whether historical CSI is fully available or only partially observed.

Integrated sensing and communication (ISAC) opens new opportunities for next-generation systems by supporting simultaneous communication and sensing within a unified framework. In this paper, we investigate the waveform design and parameter estimation problem for orthogonal time-frequency-space (OTFS)-based ISAC in monostatic multiuser scenarios under dynamic multipath and cluttered environments. A key challenge lies in developing an OTFS-ISAC waveform to establish a structured input-output relationship, which is critical for enabling high-accuracy parameter estimation. To overcome this, we construct a periodic convolution sum spectrum (CSS) by designing practical transceiver filters, yielding a tractable received signal model. The corresponding optimization problem is solved efficiently within the alternating direction method of multipliers (ADMM) framework, where closed-form solutions are derived to avoid reliance on high-complexity numerical solvers. Furthermore, strong clutter in the environment makes it difficult for the base station (BS) to detect non-line-of-sight (NLoS) echoes, hindering full communication channel reconstruction. To address this issue, we adopt a hierarchical estimation scheme and embed pilot sequences (PSs) within a tailored delay-Doppler (DD) pattern. In addition, a weighted integrated sidelobe level (WISL)-based PS design is introduced, which leverages coordinate descent (CD) and discrete Fourier transform (DFT) techniques to support short sequence lengths. Building on the proposed OTFS-ISAC waveform, which integrates transceiver filters, DD pattern, and optimized PS, we propose a successive parameter estimation (SPE) algorithm with a dual-direction estimation strategy. By exploiting inter-Doppler interference (IDI) dispersion, the algorithm derives closed-form channel parameter estimates, applicable at both the BS and user equipments (UEs). Simulation results demonstrate that the proposed PS design method exhibits near-optimal correlation properties, while the estimation algorithm achieves competitive performance with significantly reduced complexity.

Reconfigurable Intelligent Surfaces (RIS) can directly modulate information into electromagnetic waves by using a data source to control the electromagnetic responses of the RIS units. With far fewer RF chains and modest area requirements, RIS is a promising candidate for antenna arrays in future holographic MIMO and extra-large MIMO systems. However, the physical structure of the RIS unit aperture causes diffraction, a phenomenon that was ignored in existing studies, making the adopted propagation models no longer applicable, especially in high-frequency or large-scale systems. In this paper, based on electromagnetic information theory (EIT), we present vector diffraction equations to accurately model the electromagnetic propagation characteristics of a RIS-MIMO transmitter. To accommodate near-field and far-field applications, we propose approximate propagation models respectively. This approach also serves as a theoretical method to determine the boundaries between different fields, helping researchers in selecting the appropriate propagation model based on phase precision requirements and computational complexity tolerance. Using these models, the mutual information of the RIS-MIMO transmitter is derived to reveal the upper bound of its information transmission capacity based on random field theory.

As wireless networks evolve, Vehicle-to-Everything (V2X) communications, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P), have gradually become more sophisticated, enabling information exchange between vehicles and their surrounding environment, thereby enhancing road safety and traffic efficiency. However, ensuring service quality when vehicles are moving at high speeds remains a significant challenge that cannot be overlooked.Due to the rapid changes in channels caused by the high mobility of vehicles, this paper models resource allocation as a multi-agent deep reinforcement learning problem. It analyzes multiple V2V links and V2I links and proposes a resource allocation algorithm that considers V2V communication efficiency based on the Deep Deterministic Policy Gradient (DDPG).Each agent interacts with the V2X network environment to obtain a common reward function and aggregates actions from other agents for training the critic network collectively. By designing the reward function, a balance between communication efficiency and power control can be achieved, thereby effectively increasing the transmission rate of V2V links.

As a promising technology for 6G networks, ultra-massive multiple-input multiple-output (UM-MIMO) faces substantial problems such as wavefront mismatch, high computational complexity, and reliance on unrealistic assumptions in hybrid field channel estimation. To address these issues, we propose a novel subarray-based measurement optimization framework. Initially, we adopt a wavefront decomposition model based on subarray. By decomposing the spherical wavefront into localized planar wavefront approximations, this model effectively resolves the wavefront mismatch and eliminates the dependence on the ratio factor while still ensuring high accuracy. Subsequently, a noise-robust measurement optimization algorithm is proposed to achieve a dual reduction in complexity. Specifically, a dynamic signal-to-noise ratio (SNR)-aware measurement optimization approach, utilizing heap sorting, prioritizes high-confidence measurements. Meanwhile, the two-dimensional inverse fast Fourier transform (2D-IFFT) estimation method reduces the unit computational load through parallel subarray processing. Simulation results demonstrate that our proposed algorithm consistently outperforms existing approaches across diverse SNR levels and ratio factors, with significant advantages in low-SNR scenarios. Additionally, in 2D array deployments, it shows an improvement of 3 dB in channel estimation error performance, highlighting its robustness and scalability in both configurations.

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 deployment of low-Earth orbit (LEO) constellations, combined with inter-satellite link (ISL) technology, will drive the development of future sixth-generation (6G) and satellite Internet of Things (SIoT). However, the deployment of a large number of LEO satellites and corresponding ISLs intensifies the spectrum resource scarcity in space, which may result in severe ISL interference (ISLI). Therefore, this paper proposes a theoretical framework for optimal design of LEO satellite constellations to mitigate ISLI. Specifically, the system average signal-to-interference-plus-noise ratio (SA-SINR) metric is introduced to depict ISLI, and deterministic analysis expression for SA-SINR is derived. Meanwhile, an integral merging algorithm (IMA) is proposed to accurately calculate the ground coverage area of satellite networks, considering the need to cover ground users in a wide distribution range. Then, the optimal constellation design issue is formulated into a mathematical multi-objective optimization problem, and an improved particle swarm optimization (PSO) scheme is designed to solve it. Simulation results show that the constellation designed based on the proposed approach outperforms the existing Starlink and OneWeb constellations in terms of ISLI and coverage performances, validating the effectiveness of our proposal.

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.

Internet of Underwater Things (IoUT) has emerged as a critical infrastructure for marine exploration and resource utilization, yet faces inherent challenges in data collection due to the limitations of multi-hop transmission. Multiple Autonomous Underwater Vehicles (AUVs) systems present a promising solution in data collection; however, conventional multi-AUV data collection schemes struggle with formation collaboration awareness, high-dimensional observation spaces, and environmental non-stationarity in underwater scenarios. To address these limitations, this paper proposes a Distributed Switching Multi-Agent Attention Actor-Critic (DS-MAAC) framework for energy-efficient underwater data collection. Specifically, we consider a scenario where the location of the sensor nodes are unknown. In this context, multi-AUV systems initially adopt a leader-follower formation for stable communication and obstacle avoidance, transitioning to a distributed system at appropriate timings to balance the effectiveness and efficiency of exploration. The fuzzy perception map is proposed to assist in distributed switching and progressively reduce environmental uncertainty. To improve the ability of AUVs to extract critical information from high-dimensional observation spaces, we introduce an attention mechanism to further enhance the collaborative collection performance. The simulation results demonstrate that the proposed DS-MAAC algorithm outperforms other baseline methods across multiple performance metrics, including reward, collection path, collection rate, collection efficiency, energy consumption, delay rate, and value of information, with performance improvements ranging from 11.07% to 56.30%. Sensitivity analyses are conducted to verify the robustness of the DS-MAAC algorithm and provide practical guidelines for parameter selection.

Reconfigurable holographic surface (RHS) exhibits low energy consumption and low cost, which attracts extensive attention. Since unmanned aerial vehicle (UAV) is limited by power and volume, holographic beam is suitable for UAV Internet of things (IoT) systems. However, UAV will encounter complex airflow, causing jitter, which indicates necessary suppression method for holographic beam disturbance. To address the challenge, we explore the problem of robust rotary holographic beam, consisting of angle controlling and hybrid holographic beamforming. The problem is formulated as minimizing the sum mean squared error (SMSE) under the beam disturbance. Resolving such non-convex issues poses considerable difficulty in achieving optimality. A heuristic scheme is proposed, which decouples the angle controlling and hybrid holographic beamforming. Specifically, angle controlling aims to maximize the sum of the mathematical expectations of the disturbed holographic beamforming matrix to mitigate disturbances. Meanwhile, hybrid holographic beamforming minimizes SMSE under beam disturbances to manage multi-user interference. The suppression gain and computational complexity are analyzed theoretically to validate the effectiveness of the proposed scheme. Furthermore, simulation results show that our scheme performs better than benchmark schemes in sum-rate and bit error rate (BER).

The advantages of Low Earth Orbit (LEO) satellite navigation in addressing the increasing demand for high-precision and universally accessible positioning have garnered widespread attention. Our work builds upon our previous development of the Zadoff-Chu non-orthogonal multiple access (ZC-NOMA) waveform for integrated communication and navigation (ICAN). Expanding on this foundation, we conceive the ICAN-oriented multi-satellite asynchronous transmission (ICAN-MSAT) framework, which allows users to concurrently receive communication and navigation signals from multiple satellites without synchronization, thus increasing communication flexibility and leveraging the geometric distribution and robust signals of LEO satellite constellations to enhance navigation performance. Within the ICAN-MSAT framework, we propose a novel multi-satellite asynchronous transmission oriented subcarrier and power allocation (MSASP) algorithm to manage mutual interference between communication and navigation components and inter-satellite interference (INSI) caused by asynchronous transmission, with the latter often overlooked in most existing work. Simulation results demonstrate that the proposed algorithm meets the performance requirements of both navigation and communication, achieving higher communication rates under the same navigation accuracy constraints compared to current benchmarks.

Due to the advantages of flexible deployment and strong environmental adaptability of an unmanned aerial vehicle (UAV), UAVs serve as aerial base stations (BSs) to meet Quality of Services (QoSs) of ground users in internet of things (IoT) networks. NOMA (Non-Orthogonal Multiple Access) is a potential technique in wireless communications area, which can significantly improve sum spectrum efficiency (SE) of systems. To avoid the limitation of perfect CSI, opportunistic beamforming (OBF) is proposed, where a set of randomly generated weights is used to preprocess transmitted signals. Due to multiuser diversity gain introduced by OBF, OBF-NOMA systems can achieve approximate sum SE to conventional NOMA systems. Additionally, reconfigurable intelligent surfaces (RISs) are involved to overcome obstruction and obtain further improvements of SE. Therefore, this paper proposes a RIS-aid OBF-NOMA system in UAV-IoT networks, where random weights and opportunistic phase matrix are respectively applied in a UAV and RIS. Statistical characteristics of equivalent channels are derived in Nakagami-m (m≥1) fading channels. Theoretical asymptotic analyses of SE and bit error rate (BER) are then presented. Furthermore, a non-convex optimization problem is formulated to maximize SE. To obtain the optimal solution, we divide the problem into two sub-optimization problems and apply a joint iterative algorithm. Numerical results show that the proposed method achieves a satisfactory SE without complex channel estimation and perfect CSI.

Underwater positioning is a crucial technology for Autonomous Underwater Vehicles (AUVs) to effectively carry out a range of underwater tasks. With an underwater environment that is complex and constantly changing, it can be challenging for a single positioning technology to keep up with long-range, multi-target, and high-precision applications. Solutions in the field of underwater positioning are continually evolving and improving. A multi-sensor fusion positioning scheme, which integrates multiple sensors including the Inertial Navigation System (INS), Long Baseline (LBL), Doppler Velocity Log (DVL), Conductivity Temperature Depth (CTD) and Depth Gauge (DG), can provide more accurate and reliable positioning. In this paper, a Bayesian inversion based Multi-sensor Fusion Localization (BIMFL) algorithm is proposed to enhance the performance of underwater fusion positioning. The proposed algorithm combines the strengths of multiple sensors and provides more precise location information. BIMFL uses the residual values of the measured data from various sensors as observations. Bayesian inversion effectively integrates prior information with observational information, both of which have their covariance matrices considered, hence determining the appropriate parameters of the fusing positioning system. Additionally, an update iterative process based on the Extended Kalman Filter (EKF) is adopted in BIMFL to refine positioning results. Simulation and experimental results have shown that BIMFL surpasses traditional single-sensor and EKF-based filtering methods.

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.

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.

To achieve truly seamless global intelligent connectivity, non-terrestrial networks (NTN) mainly composed of low earth orbit (LEO) satellites and drones are recognized as important components of the future 6G network architecture. Meanwhile, the rapid advancement of the Internet of Things (IoT) has led to the proliferation of numerous applications with stringent requirements for timely information delivery. The Age of Information (AoI), a critical performance metric for assessing the freshness of data in information update systems, has gained significant importance in this context. However, existing modeling and analysis work on AoI mainly focuses on terrestrial networks, and the distribution characteristics of ground nodes and the high dynamics of satellites have not been fully considered, which poses challenges for more accurate evaluation. Against this background, we model the ground nodes as a hybrid distribution of Poisson point process (PPP) and Poisson cluster process (PCP) to capture the impact of ground node distribution on the AoI of status update packet transmission supported by UAVs and satellites in NTN, and the visibility and cross-traffic characteristics of satellites are additionally considered. We derived the average AoI for the system in these two different situations and examined the impact of various network parameters on AoI performance.The simulation results verified the effectiveness of the proposed modeling and analysis method.

Wireless networks are evolving from a communication-only network to one with integrated sensing and communication (ISAC) capabilities. In such cases, the cooperation of multiple base stations (BSs) can be exploited to achieve precise sensing for multiple user equipments (UEs). However, the identities of the UEs are not contained in the echoes, making it difficult for the sensing receiver to associate UEs with their echoes when monostatic and bistatic sensing modes coexist. This leads to a loss of cooperative gain and larger echo interference between BSs, thereby degrading communication and sensing performances. To overcome this challenge and achieve multi-directional sensing of UEs, this paper develops a novel approach for parameter estimation and user-echo association using ISAC signals under doubly dispersive channels. In particular, we establish a model for multiple BSs cooperative sensing of extended UEs utilizing the orthogonal time frequency space (OTFS) signal. Meanwhile, the bistatic angles are introduced as indicators to characterize the correlation between the physical scattering structures of the UE from different directions, simplifying the complex association process. Additionally, we design a parallel off-grid sparse Bayesian learning (SBL) algorithm to estimate unknown parameters iteratively. Simulation results demonstrate that the proposed scheme achieves better NMSE and BER performance.

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.

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.

With application of a novel user-centric architecture, cell-free massive MIMO (CF-mMIMO) significantly improves the user experienced rate, thereby addressing the issue of constrained interference in dense cell networks. In this technology, high-precision joint over-the-air (OTA) calibration plays an essential role in ensuring the performance of CF-mMIMO networks. However, the large number of remote antenna units (RAUs) in CF-mMIMO, coupled with their spatial independence, introduces considerable complexity for scalable calibration solutions. To address this challenge, this paper proposes a dynamic RAUs clustering and self-healing calibration topology construction algorithm, designed to operate within the constraints of the minimum required signal-to-noise ratio (SNR) and coherence time. Furthermore, to minimize the time overhead of calibration, a calibration timing mechanism for both intra-cluster and inter-cluster of RAUs is devised. Finally, a RIS-aided and scalable OTA calibration algorithm is developed, which can achieve phase alignment for an arbitrary number of RAUs. Simulation results show that the proposed calibration algorithm exhibits low calibration errors and demonstrates excellent performance, resulting in a notable 9.5% increase in spectral efficiency.

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.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

This paper investigates an uplink grant-free massive access (GF-MA) system, where a large number of IoT devices collaborate to achieve complex applications. For this scenario, device activity identification is a challenging problem due to the interference from massive devices and the limitation in the number of pilot sequences. By utilizing the inherent correlation features in multi-device collaborative scenarios, in this work, we present a detection approach that aims to enhance the accuracy of both activity detection and channel estimation. Specifically, we first propose a task-driven activity (TDA) model to capture the active probability in multi-device collaborative scenarios. Subsequently, considering the TDA model, we propose a message-passing-based algorithm named TDA-JDE for device activity detection and channel estimation. The proposed algorithm jointly processes messages containing channel impulse response (CIR), device activity, and task status information to leverage device activity correlation information. Finally, to obtain the parameters in the TDA model, we propose a parameter estimation algorithm based on the expectation-maximization framework with relaxation and reconstruction strategy (EM-RR). Extensive numerical results show that the proposed algorithm can achieve higher detection accuracy when compared with three benchmark schemes in multi-device collaborative massive access (MA) scenarios.

Due to high flexible deployment and enhanced transmission capabilities, unmanned aerial vehicle (UAV) has attracted significant attention in recent years. UAV can serve as base station to provide communication coverage for emerging internet of things (IoT) in hot-spot areas. Stable topology plays an important role in improving connection and efficiency of UAV-IoT networks. However, when UAV-IoT nodes fail, network topology is destoryed and reconstruction becomes a formidable challenge, particularly in extremely harsh scenarios. Traditional topology reconstruction schemes predominantly rely on node movement to restore network connectivity, which neglect the performances of UAV-IoT networks. To remain stability and promote performances simultaneously, this paper proposes a distributed resource scheduling topology reconstruction (DRSTR) scheme, where both connectivity and throuphput of UAV nodes are jointly considered. Then, an optimization problem combining topology reconstruction and resource allocation is presented under the constraints of UAV-IoT nodes’ quality of service (QoS) requirements. Since the difficulty in solving the problem, the original problem is divided into three sub-optimal issues and an iterative approach is introduced to approximate the global optimal solution. Numerical results show that the proposed shceme achieves higher performances in UAV-IoT networks, compared to the traditional topology reconstruction shcemes.

Autonomous underwater vehicle (AUV) has been widely used in underwater missions. Cooperative localization (CL) is a key technology especially for multi-AUVs collaborative operations. With great demands for accurate and real-time localization, the error dispersion in nonlinear fusion and information transmission difficulties caused by underwater environment limitations become challenges in multi-AUVs CL. In this article, a federated learning (FL) framework for multi-AUVs CL is designed, based on which a novel CL algorithm combining the FL and extended Kalman filter (EKF) is proposed. The proposed FL-EKF algorithm can fuse the advantages of EKF and FL adequately to realize high-precision real-time underwater CL in long-duration operations. Simulations and experiments are conducted to verify the performance of the proposed algorithm.

Supporting multiuser communication and localization is a typical scenario in Integrated sensing and communications (ISAC). However, the problem of multi-echo induced by multipath and multiuser makes it hard to determine the relationship between user equipments (UEs) and these echoes. Thus, applying traditional estimation algorithms at the radar receiver inevitably leads to weak communication and localization performances due to the mismatch between echoes and UEs. In this paper, aiming to achieve multiuser association and localization under doubly dispersive multipath channels, we construct an ISAC unified waveform based on the orthogonal delay-Doppler division multiplexing (ODDM) principle and develop an off-grid cluster sparse Bayesian learning estimation (OG-CSBL) algorithm. Particularly, we focus on the mono-static setup, where the base station (BS) expects to communicate with multiuser while sensing their locations. We utilize the high-resolution range profile (HRRP) to characterize the physical features of UEs and establish associations with their echoes by exploiting the inherent cluster structure. To estimate parameters, we design a hybrid Dirichlet process (DP)-Gaussian hierarchical prior distribution and propose a variational Bayesian inference (VBI)-EM strategy. Additionally, we develop a backtrack echo identification scheme to facilitate precise UE localization. Simulation results demonstrate that the proposed scheme achieves superior NMSE performance, offers meter-level localization accuracy, and obtains better BER performance in the complex multiuser coexistence scenario.

Low earth orbit (LEO) satellites are expected to play an important role in enhancing terrestrial Internet of Things. This paper focuses on the challenge of joint terminal activity detection (TAD) and channel estimation (CE) in grant-free non-orthogonal random access (GF-NORA)-enabled LEO communication networks. To leverage the dominant line-of-sight (LoS) path and the limited angular spread characteristic of terrestrial-satellite links, we propose a two-stage joint TAD and CE (TS-JDE) detection scheme aimed to achieve lower computational overhead. This scheme utilizes a high-accuracy algorithm for LoS path estimation and leverages the channel correlations for low-complexity NLoS paths estimation. Specifically, in LoS estimation stage, to mitigate the inaccuracies caused by Taylor expansion errors, we propose an enhanced message-passing algorithm, termed TaMP-LoS. This algorithm exploits the structured sparsity in the delay-Doppler domain and incorporates the Lagrange remainder to evaluate the expansion error, thereby enhancing estimation accuracy. In the NLoS estimation stage, we develop a sparse Bayesian learning-based algorithm, named SBL-NLoS, designed to estimate NLoS channels characterized by the limited residuals of delay and Doppler. Simulation results show that our proposed algorithm can achieve better detection performance than the existing approaches for GF-NORA-enabled LEO networks.

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.

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.

Reconfigurable intelligent surface (RIS) is a programmable metasurface composed of sub-wavelength meta-atoms and a controller. Recent research works have shown that multi-layer RISs can form over-the-air neural networks. This enables support for a variety of tasks, including image recognition, mobile communication coding-decoding, and real-time multi-beam focusing. In this work, we present a RISs-based over-the-air joint source channel coding (JSCC) scheme, named as AirJSCC, where multi-layer RISs are used to realize wave-based complex-valued neural networks (CVNNs). AirJSCC transforms the computation process inherited in JSCC into the transmission of wireless signals through RISs, and thus many benefits, such as light-speed computation and high parallel processing capability, can be achieved. To the best of our knowledge, this is the first deep JSCC scheme that incorporates RISs and CVNNs. Simulation results demonstrate that AirJSCC achieves better image reconstruction performance compared with the baseline scheme, even under low signal-to-noise ratio (SNR) and limited bandwidth, and exhibits robustness against varying channel conditions.

The full-duplex technique can effectively increase the system throughput, which is beneficial for the application of IoT. However, the residual self-interference can not be ignored in the full-duplex relay system. In this paper, by considering the equivalent multipath signal with a long delay, the amplify-and-forward (AF) full-duplex relay system with zero-setting OFDM is proposed where an additional processing unit is added at the relay. The function of the processing unit is to change the amplification factor periodically. Theoretical analysis demonstrates that the proposed scheme can eliminate the equivalent inter-symbol interference (ISI) component of the residual self-interference. Furthermore, the closed-form expression of SINR gain is given by comparing the SINR of the proposed system and the traditional relay system. Meanwhile, the optimal amplification factor is also obtained using the gradient descent method.

With the advance of various applications of unmanned aerial vehicles (UAVs), employing UAVs as mobile aerial base stations is a potential technology to improve wireless communication quality. However, due to limited load capacity, UAVs are hard to handle complex computing tasks. In this paper, a computation time minimization problem is investigated under a multi-UAV connected mobile edge computation (MEC) system, where both computation offloading and energy constrain are considered. The formulated problem is a mixed integer nonconvex optimization problem and is hard to solve. To simplify problem, we propose an efficient iterative algorithm by successive convex optimization techniques. Specific, we maximize the computation offloading under given tolerable delay. Bisection search is applied to find the optimal task completed time. For speed up algorithm convergence, a low-complexity association scheme is proposed based on greedy algorithm. Simulation result shows that the proposed algorithm can reduce time overhead and make full use of the gain brought by multiple UAVs.

MOTIVATION: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data. RESULTS: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed. AVAILABILITY: The data and source code can be found at https://github.com/1axin/JSNDCMI.

Satellites and civil aircrafts (CAs) with computing ability are valuable access platforms, making it possible for Internet of Things (IoT) devices to offload their computation-intensive tasks in remote areas without network infrastructures. Unlike existing works mainly focused on the static scenarios or the interaction between any two types of local, edge and cloud nodes, we propose an innovative multi-tier hybrid parallel computation architecture in CA-augmented space-air-ground integrated networks (CAA-SAGIN). Specifically, devices perform local computing, CAs and satellites act as edge servers, and ground stations of satellite networks operate cloud computing. Aiming to minimize the weighted sum of end-to-end (E2E) delay and energy consumption, we formulate a partial computation offloading problem by jointly considering access strategy, transmit power, computing resource allocation, offloading ratio and delay tolerance. The platform selection exists both within and between layers, and there are inner- and inter-coupling relationships between communication and computing resources. The issue is solved by the proposed multi-tier partial task offloading (MPTO) algorithm. The original problem is firstly decomposed into primal and master subproblems by generalized benders decomposition (GBD) method, and parallel successive convex approximation (SCA) theory is utilized to transform the multi-variable NP-hard master problem into a convex one. Simulation results demonstrate the convergence and optimality of the MPTO algorithm and the advantages of this multi-tier hybrid computation offloading system. Also, the optimal tradeoff between E2E delay and energy consumption can be achieved by the MPTO algorithm.

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.

Supporting IoV is an important application scenario of 5G. For the lack of 5G terrestrial infrastructure, LEO satellite network has become the focus of research. LEO mega-constellations can provide high-speed, secure and full-time global network services for M2M and IoT in 5G network, with its short delay, low path loss and large number of satellites. With the application of mmWave and Massive MIMO, mega-constellation puts forward higher requirements for the accuracy of beam pointing. Frequent beam switching, or channel estimation, will produce greater overhead. According to the angle information for the terminal initiates the access request, and the estimation of the satellite trajectory, the beam direction of the satellite is predicted. By continuously adjusting the beam, the frequency of beam switching is reduced, and the overhead of angle estimation is reduced.

Green and energy-saving heterogeneous cellular networks are the trend of future mobile communication networks, and there are also a series of problems such as resource allocation, load balancing, and energy-saving design. As an important component of energy consumption in cellular networks, base stations have the characteristics of regularity and periodicity of services, so the introduction of base station dormancy technology can effectively save energy while ensuring the quality of service for users. Aiming at the sleep problem of base stations in heterogeneous cellular networks in urban areas, this paper analyzes the sleep probability and power consumption model of distance-aware micro base stations in sleep mode. Aiming at minimizing system power consumption, a sleep scheme in which macro base station and micro base station cooperate horizontally is designed. For the multi-constraint problem of user transfer in horizontal cooperation, a heuristic algorithm is proposed. The results show that the proposed cooperative scheme can save a lot of energy when the user SINR loss is small .

The advance of 5G has brought great convenience and opportunities for the application of visual indoor positioning in the Internet of Things, augmented reality, emergency rescue and other important fields, but its positioning performance still needs to be improved. In order to increase the accuracy and efficiency of the vision-based indoor localization system, we propose hierarchical image retrieval algorithm based on Bag-of-Visual-Word and eight-point algorithm with feature clouds for position estimation which can effectively refine the efficiency and accuracy of image retrieval and location estimation. The experimental results show that the proposed method can dramatically improve the real-time performance and accuracy of the visual positioning system.

Massive Internet of Things (MIoT) devices in the areas without cellular networks have difficulties transmitting data to the network. Since the vehicles have sufficient energy resources and the number of vehicles is high, vehicles passing through these areas can collect MIoT data and relay data to the cellular networks. In this paper, considering the limited energy resources of MIoT devices, we formulate an Energy Efficiency of Packet Uploading (EEPU) Maximization strategy to help MIoT devices upload more packets with less energy consumption to the vehicle. Also, considering the uncertainty of the vehicle arrival, we control the packet forwarding rate between devices to achieve the goal of MIoT device queue stability. The above optimization problem can be solved by the proposed EEPU Maximization Algorithm. Numerical results show that the energy efficiency of the proposed strategy is superior to other strategies, and our proposed strategy can allow a higher packet forwarding rate on the premise of ensuring queue stability.

Although 5G networks have been gradually commercialized, many scenarios like emergency areas and remote regions still exist with vast communication problems. In this paper, we provide capacity analysis for the novel network architecture called civil-aircraft augmented space-air-ground integrated networks (CAA-SAGIN). First, we discuss the influence of the spatial distribution of civil aircraft (CA) and satellites on the Rician factor. Then, based on the derived moment generating function related to small-fading variables, we deduce the closed-form expressions of ergodic capacity under nearest association strategies. The numerical results demonstrate that CA networks can provide significant ergodic capacity with the multi-platform association strategy. The benefits of CA networks are proved quantitatively, and our works can provide a reference for the design of future SAGIN.

Nowadays, massive Internet of Things (MIoT) devices play an essential role due to their easy deployment. In remote areas, although MIoT devices have difficulties accessing cellular networks directly, they can upload their collected information via the passing mobile carriers like vehicles. Considering the limited transmission range of MIoT devices and significant signaling overhead, it is necessary to preprocess these data before sending them to the vehicles. In this paper, we first formulate an energy-minimization problem to determine the optimal number of cluster heads and the optimal size of files while guaranteeing the transmission delay. After selecting cluster heads, a routing strategy is devised to enhance the link reliability further. The above optimization problems can be solved by the proposed Multi-Layer Clustering-Routing (MLCR) Algorithm. Numerical results prove the efficiency of the proposed MLCR Algorithm in MIoT data preprocessing and show a tradeoff problem between energy consumption and route reliability.

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.

Massive multiple-input-multiple-output (MIMO) and millimeter-wave (mmWave) have been adopted as the enabling technologies for the 5G and beyond 5G (B5G) systems. However, due to the large number of antennas, it is hard to obtain the channel state information (CSI) which is essential for obtaining desirable beamforming gains. Off-grid error is one of the main limiting factors of the channel estimation (CE) performance, which presents when the true angle does not lie on the discretized angle grid of mmWave channel. To address this problem, we propose a joint algorithm named off-grid approximate message passing (OG-AMP) to achieve both angular domain CE and off-grid errors eliminationin in this paper. Specially, we formulate CE issue as a Bayesian inference problem to compute the posterior of the channel coefficients and adopt the Gaussian approximation to simplify the sum-product algorithm. Our simulation results show that the proposed algorithm shows the superiority over a state-of-art benchmark method.

5G technology offers a solution to achieve huge capacity and low latency communication between vehicles and road infrastructure in vehicular networks. However, the coverage of 5G signal is significantly reduced due to high frequency band used, which means the original base stations (BSs) deployment cannot complete the task of regional seamless coverage well, especially in suburban areas with few BSs. To overcome this issue, we propose a piggyback mobile Internet of things (PMIoT) scheme to enhance 5G coverage in suburban areas, which the data is connected to 5G BSs through vehicle piggyback information. In addition, we define a new evaluation metric to measure the quality of coverage. The proposed work is simulated in real road network, and the results show that the scheme can significantly enhance 5G coverage in suburban areas with weak BSs deployment.

In order to meet mobile users’ unprecedented communication demands and the goal of global seamless communication, space–air–ground-integrated networks (SAGINs) have attracted lots of attention in recent years. The existing works related on air segment mainly discussed unmanned aerial vehicles (UAVs), airships, and balloons near the space. However, they neglected many other valuable resources, such as civil aircrafts (CAs). Moreover, communication problems for remote areas and emergency scenarios (such as disasters and hot-spot areas) have not been solved thoroughly. Motivated by these facts, we introduce CAs to enhance the current SAGIN and present a novel architecture called “CAs augmented space–air–ground-integrated vehicular networks” (CAA-SAGIVNs). The proposed network architecture makes breakthrough in three main aspects: 1) a normal network architecture; 2) collaboration with multiple sky access platforms (SAPs); and 3) service-oriented fair allocation. Although CAA-SAGIVN can bring out many benefits, it also faces more challenges due to its high mobility and cross-layer characteristics. Therefore, we provide an exhaustive review of state-of-the-art works on modeling, mobility management, solutions of service-oriented allocation in SAGIN. On the basis of the preliminary investigation and discussion, some open issues are identified as possible future research directions.

Multiple-input–multiple-output (MIMO) is a promising technique in Internet of Things (IoT) Networks, which can effectively multiplex more IoT users and improve the spectrum efficiencies (SEs) of the users. However, when the number of IoT users is large, the complexity becomes enormous. Moreover, perfect channel state information (CSI) is a basic assumption in MIMO systems, which is impractical with a large number of IoT users. In order to reduce the complexity and break the CSI limitation, an opportunistic-beamforming (OBF)-based IoT network is proposed, which can achieve high SE with low complexity and feedback under a large number of IoT users’ conditions. Then, to solve the unfairness problem and further improve SE, a downlink multiple-weight and multiple receive antenna OBF (MW-OBF-MRA) system with the proportional fairness (PF) strategy is proposed, where the transmitter and receiver are jointly designed. Moreover, a joint resource allocation scheme is provided to achieve the maximum SE with PF constraints. Numerical results show that our proposed system can achieve fairness with the minimum SE loss and provide better SE than the other beamforming schemes with the limited feedback information.

Unlike the conventional facial expressions, micro-expressions are involuntary and transient facial expressions capable of revealing the genuine emotions that people attempt to hide. Therefore, they can provide important information in a broad range of applications such as lie detection, criminal detection, etc. Since micro-expressions are transient and of low intensity, however, their detection and recognition is difficult and relies heavily on expert experiences. Due to its intrinsic particularity and complexity, video-based micro-expression analysis is attractive but challenging, and has recently become an active area of research. Although there have been numerous developments in this area, thus far there has been no comprehensive survey that provides researchers with a systematic overview of these developments with a unified evaluation. Accordingly, in this survey paper, we first highlight the key differences between macro- and micro-expressions, then use these differences to guide our research survey of video-based micro-expression analysis in a cascaded structure, encompassing the neuropsychological basis, datasets, features, spotting algorithms, recognition algorithms, applications and evaluation of state-of-the-art approaches. For each aspect, the basic techniques, advanced developments and major challenges are addressed and discussed. Furthermore, after considering the limitations of existing micro-expression datasets, we present and release a new dataset — calledmicro-and-macro expression warehouse(MMEW) — containing more video samples and more labeled emotion types. We then perform a unified comparison of representative methods on CAS(ME)$^2$for spotting, and on MMEW and SAMM for recognition, respectively. Finally, some potential future research directions are explored and outlined.

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.

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.

For the 6th generation (6G) communications, the interference exploitation generated from a growing number of intelligent factors with ultra scale is great challenge for the low complexity detection algorithms in the uplink multiuser massive multiple input and multiple output (MIMO) systems, especially for detecting high order quadrature amplitude modulation (QAM) signals. The deep learning technology is one of the key technical solutions for 6G. In this paper, a deep neural network based semidefinite relaxation (DNNSR) detection algorithm is proposed on the basis of the graphical detection model for uplink multiuser massive MIMO systems. Compared with the counterpart detection algorithms, along with a low polynomial average per symbol computational complexity, the proposed DNNSR detection algorithm requires lower average received signal to noise ratios to obtain better bit error rate performance as well as achieve theoretical spectral efficiency for graphical high order QAM signals in the uplink multiuser massive MIMO systems.

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.

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.

In this article, we present an unmanned aerial vehicle (UAV)-aided ocean monitoring network for remote oceanic data collection, in which monitoring data are transmitted first from battery-powered underwater sensor nodes (USNs) to sea surface sink nodes (SNs) in a data collection cycle using underwater acoustic communication, and then a UAV hovering in air collects all the data from SNs and relays them to a ground base station via wireless communication links. Aiming at maximizing network lifetime, we model the resource allocation, USN-to-SN access, and SN-to-UAV access issues as a mixed-integer nonconvex optimization problem. To efficiently solve it, we decompose the optimization into two stages. The first stage is to minimize time consumption in an SN-to-UAV nonorthogonal multiple access process and we solve it by designing a UAV deployment scheme, a subchannel matching scheme, and a joint power and time allocation scheme, based on which, the second stage is to maximize the residual energies of USNs in USN-to-SN transmissions under a modified frequency-division multiple access strategy in each collection cycle. The second-stage optimization is further decomposed into some similar subproblems, and each of them is considered as a bipartite graph matching problem between USNs and underwater acoustic channels. For each subproblem, we propose improved weight-based matching and bisection-based searching algorithms. Finally, we design a low-complexity iteration algorithm to approximate the optimal solution of the original problem by solving these subproblems. The simulation results validate the effectiveness of our proposals.

Ultra-dense network (UDN) is one of the most important techniques to support massive number of different types of user equipments (UEs). It is essential to understand the impact of densification on various system performance metrics in future cellular networks. Two contradictory conclusions were made from the existing research works. Some works indicated that the network densification has reached its upper limit and deploying more UEs may only reduce UDN performance; whereas the others claimed that more UEs can still be supported by deploying more small cell base stations (SBSs). The existence of above different conclusions is due mainly to the difficulties to find an effective means to take into account different path loss models in UDNs. In this paper, we aim to propose a unified analytical approach, which is effective to simplify UDN performance analysis based on different path loss models with a low computation complexity. Using the proposed approach with idle mode of SBSs enabled, we can identify the impact of the densities of SBSs and UEs on various performance metrics, i.e., coverage probability and average area spectral efficiency (ASE). The conclusions will be supported by the analytical and simulation results verifying the effectiveness of the proposed unified analytical approach.

Millimeter-wave cell-free and user-centric massive multiple input multiple output (MIMO) systems are promising, due to the reliable ultra-high data rate services. Thus, it is a significant issue to design low-complexity hybrid precoders. In this paper, a simplified sum-mean-square-error (SMSE) is used to design hybrid precoders, based on the array gain and spatial macro-diversity. Then, a heuristic hybrid pre-coding scheme is proposed, with two analog design algorithms that are with or without the knowledge of statistical information. The influence of the imperfect state information of beams is theoretically analyzed, from the instantaneous and ergodic aspects, verifying the robustness of the proposed scheme. Simulation results show that our proposed low-complexity hybrid precoder can be easily realized with acceptable bit error rate (BER) and sum-rate performances, especially when the total transmit power is small.

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.

In this paper, we propose a secure computationoffloading scheme (SCOS) in intelligently connected vehicle (ICV) networks, aiming to minimize overall latency of computing via offloading part of computational tasks to nearby servers in small cell base stations (SBSs), while securing the information delivered during offloading and feedback phases via physical layer security. Existing computation offloading schemes usually neglected time-varying characteristics of channels and their corresponding secrecy rates, resulting in an inappropriate task partition ratio and a large secrecy outage probability. To address these issues, we utilize an ergodic secrecy rate to determine how many tasks are offloaded to the edge, where ergodic secrecy rate represents the average secrecy rate over all realizations in a time-varying wireless channel. Adaptive wiretap code rates are proposed with a secrecy outage constraint to match time-varying wireless channels. In addition, the proposed secure beamforming and artificial noise (AN) schemes can improve the ergodic secrecy rates of uplink and downlink channels even without eavesdropper channel state information (CSI). Numerical results demonstrate that the proposed schemes have a shorter system delay than the strategies neglecting time-varying characteristics.

In recent years, deep learning has been used for Wi‐Fi fingerprint‐based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location‐based service (LBS). In this paper, we propose a Wi‐Fi fingerprint‐based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high‐level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE‐RNN for mobile user localization can be reduced to 1.60 m.

Energy efficiency (EE) performance of millimeter-wave (mmWave) large-array systems attracts a lot of attention. And insertion loss is an inherent characteristic of hybrid precoding systems, which greatly reduces the system performance. EE analysis considering the insertion loss based on the dynamic array architecture is still an open issue. This paper researches the ergodic EE of mmWave hybrid pre-coding system with the insertion loss, based on the adaptive overlapped subarray (OSA) architecture. The ergodic EE is a more valuable indicator than the instantaneous EE for the system under dynamic architecture. However, it is difficult to directly calculate the precise ergodic EE due to the intractable relationship between EE and the channel matrix. Instead, we simplify the precise ergodic EE to get a lower bound, and analyze the ergodic EE performance based upon the lower bound. By this way, we analyze the ergodic EE performance of two typical hybrid pre-coding schemes taking the dynamic architectures. Simulation results verify the effectiveness of analyses.

Sparse code multiple access (SCMA) is an appealing non-orthogonal multiple access technique, which transmits superimposed signals through various resources, such as frequency and time. It is known that multiple-input multiple-output (MIMO) has been widely used in wireless communications to improve the system performances by taking full advantages of fading channels. Regarding appealing feature of MIMO, this paper focuses on the SCMA system that exploits antennas (or space) as parallel resources to transmit superimposed signals, and this type is named as antenna-resource-based SCMA (AR-SCMA) systems. Unlike the classical frequency-resource-based SCMA (FR-SCMA), the received signals consist of both the superimposed multiple users' signals and interference from other antenna resources. To eliminate the antenna interference and separate users, we propose a joint zero-forcing (ZF-) and/or minimum mean square error (MMSE-) message passing algorithm (MPA). Simulation results show that our proposed AR-SCMA can improve the bit error rate (BER) performances, and achieve a higher channel capacity than the classical FR-SCMA system.

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.

A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods because these methods can learn only one projection matrix, and thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the coupled bilinear discriminant projection (CBDP). Specifically, the CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs' spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, the CBDP can learn the optimal matrix subspace where the GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, the CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of the CBDP are both monotonically increasing, so the convergence of the CBDP is demonstrated. In the terms of accuracy, the comparative experiments on the CASIA (B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.

Ultra-dense network (UDN) is one of the enabling technologies in the fifth generation (5G) wireless communications, and the application of joint transmission (JT) is extremely important to deal with severe inter-cell interferences in UDNs. However, most of the current works done in performance analysis on JT schemes in the literature were based largely on simulation results due to the difficulties in quantitatively identifying the number of desirable and interfering transmitters in a UDN setup. In this work, we are motivated to propose an analytical approach to investigate the performance of JT schemes with a unified approach based on stochastic geometry, which is in particular useful for studying different JT schemes as well as conventional transmission schemes without JT. Using the proposed approach, we can unveil the statistical characteristics (i.e., expectation, moment generation function, and variance) of desirable signal and interference powers of a given user equipment (UE), and thus the system performances, such as average signal-to-interference-plus-noise ratio (SINR) and area spectral efficiency, can be evaluated analytically. The simulation results are illustrated to verify the effectiveness of the proposed approach.

In large-array millimeter-wave (mmWave) systems, hybrid pre-coding is one of the most attractive research topics. This paper first presents a flexible adaptive overlapped subarray (OSA) architecture for the analog precoder, whose architecture can be adjusted by a connection network. An objective function is formulated to maximize the energy efficiency (EE) in consideration of the insertion loss for the proposed adaptive OSA based hybrid precoder. The optimal scheme is intractable to achieve, so that we present a heuristic hybrid pre-coding scheme, where the digital precoder is designed based on the zero-forcing (ZF) rule and the analog precoder is designed based on simplified EE optimization in consideration of the insertion loss. We discuss the effect of non-ideal factors on the EE performance, such as quantized phases, imperfect channel state information (CSI), and faulty switches. Simulation results show that, when the initial transmit power is large, our proposed scheme offers a better EE performance than the fully digital pre-coding scheme and many other popular hybrid pre-coding schemes. Moreover, it is found that faulty switches make the double-edged effect on the EE performance when the numbers of activated phase shifters and combiners are pre-defined.

To fully explore the benefits of developing device- to-device (D2D) communications in cellular systems, enabling relay-assisted (RA) D2D transmissions is a promising way. However, involving RA D2D mode will make the design of scheduling scheme at the base station (BS) side even more challenging. This work focuses on design of a scheduling scheme involving RA D2D mode for BS, which jointly considers power coordination, relay selection, mode selection, and resource allocation. Aiming to maximize the cell- wise throughput, we formulate such a scheduling issue into a mathematical optimization problem. We show how to decompose the formulated problem into two subproblems and solve them separately by using exiting algorithm and corresponding mathematical optimization theories. Particularly, the integer programming problem on mode and channel assignments is transformed into a linear programming problem to improve the solving efficiency. Simulation results validate the performance of the joint scheduling scheme in terms of cell-wise system capacity.

When natural disasters strike, the coverage for Internet of Things (IoT) may be severely destroyed, due to the damaged communications infrastructure. Unmanned aerial vehicles (UAVs) can be exploited as flying base stations to provide emergency coverage for IoT, due to its mobility and flexibility. In this paper, we propose multiantenna transceiver design and multihop device-to-device (D2D) communication to guarantee the reliable transmission and extend the UAV coverage for IoT in disasters. First, multihop D2D links are established to extend the coverage of UAV emergency networks due to the constrained transmit power of the UAV. In particular, a shortest-path-routing algorithm is proposed to establish the D2D links rapidly with minimum nodes. The closed-form solutions for the number of hops and the outage probability are derived for the uplink and downlink. Second, the transceiver designs for the UAV uplink and downlink are studied to optimize the performance of UAV transmission. Due to the nonconvexity of the problem, they are first transformed into convex ones and then, low-complexity algorithms are proposed to solve them efficiently. Simulation results show the performance improvement in the throughput and outage probability by the proposed schemes for UAV wireless coverage of IoT in disasters.

Hybrid pre-coding design is a promising research direction in large antenna array millimeter wave (mmWave) systems. The insertion loss is an inherent and significant feature of hybrid pre-coding, resulting in lower energy efficiency and inferior bit error rate (BER) performance. This paper takes the minimum sum-mean-square-error (Min-SMSE) considering the insertion loss as the optimization objective function, which aims at increasing the sum-rate and improving the BER performance. Then a hybrid pre-coding is designed based on this criterion with an adaptive overlapped subarray (OSA) architecture. It is proved that the optimization problem is non-convex. Thus, we decompose the optimization problem into two sub-optimum ones. One is to design the digital pre-coding based on the Min-SMSE criterion under the equivalent channel condition. The other one is to design the analog pre-coding based on the simplified Min-SMSE with the insertion loss objective function. Theoretical analyses of the proposed scheme are conducted, including the upper bound of the average BER, the lower bound of the average sum-rate, and the computational complexity. Simulation results show that our proposed scheme outperforms three representative hybrid pre-coding schemes with different architectures in both BER and sum-rate, when the numbers of the phase shifters and combiners are relatively small.

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.

Gait recognition has attracted growing attention in recent years as the gait of humans has a strong discriminative ability even under low resolution at a distance. Unfortunately, the performance of gait recognition can be largely affected by view change. To address this problem, we propose a Coupled Patch Alignment (CPA) algorithm that effectively matches a pair of gaits across different views. To realize CPA, we first build a certain amount of patches, and each of them is made up of a sample as well as its intra-class and inter-class nearest-neighbors. Then we design an objective function for each patch to balance the cross-view intra-class compactness and the cross-view inter-class separability. Finally, all the local independent patches are combined to render a unified objective function. Theoretically, we show that the proposed CPA has a close relationship with Canonical Correlation Analysis (CCA). Algorithmically, we extend CPA to "Multi-dimensional Patch Alignment" (MPA) that can handle an arbitrary number of views. Comprehensive experiments on CASIA(B), USF and OU-ISIR gait databases firmly demonstrate the effectiveness of our methods over other existing popular methods in terms of cross-view gait recognition.

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.

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.

In vehicle networks, how to implement V2V (vehicle-to-vehicle) communications is always one hot issue, because of the structure which is similar to D2D communications proposed by cellular networks, we try to achieve an efficient V2V communication model through D2D techniques. In this paper, we propose a double-tiered D2D communication mode with the ratio coefficient of interference distance. The Tier-I of mode accomplishes the pairing among D2D terminals based on Voronoi diagram, and the Tier-II of mode achieves the shared channel selections as many as possible. According to the description of hypergraph geometry structure to the double-tiered D2D pairing algorithm, and the algorithm is split into two sub-problems, which refer to both bipartite graph and Voronoi diagram match under the constraint of SINR. According to the convergence of match between ratio factors introduced in the double-tiered mode, the double-tiered mode can be proved to be an optimal mode of channel reusing. Finally, we perform computer simulations to verify the performance based on the double-tiered D2D communication mode, and results indicate that the proposed mode possesses better performance compared with the spectrum sharing D2D communication mode.

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.

In a traditional complementary coded CDMA (CC-CDMA) system, signals are spread using the direct sequence (DS) technique to suppress multipath interference (MI) and multiple access interference (MAI). However, DS spreading suffers from very poor bandwidth efficiency. This paper proposes a scrambling-based CC-CDMA system, namely complementary coded scrambling multiple access (CCSMA). A CCSMA system offers a unified platform to integrate CC-CDMA, multiple-input multiple-output (MIMO), and scrambling technique seamlessly. Utilizing complementary codes in scrambling modulation, a CCSMA system is able to suppress MI and MAI effectively. With the help of space domain scrambling, the CCSMA system offers much higher bandwidth efficiency than a CC DS-CDMA system. Bit error rate and system capacity are evaluated explicitly in this paper, verifying that CCSMA is a viable multiple access technique for providing high capacity in downlink MIMO channels.

(First paragraph) In the first edition of the special issue titled “Wireless Sensor Networks for Smart Communications”, a total of 22 manuscripts were received and 6 of these were accepted. This issue demonstrated that network congestion, user mobility, and adjacent spectrum interference are the main reasons for the degradation ofcommunication quality inWireless Sensor Networks (WSNs).

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.

The research on detection algorithms for uplink multiuser Massive multi input multi output (MIMO) system is a hot-spot for 5G. The recent detection algorithms are constrained by the assumed condition that the receiving antennas' number should be equal to or larger than the transmitting antennas' number. For the overloaded case that the total number of transmitting antennas of users in a cell is larger than that of receiving antennas at base station(BS), they fail to detect with a bad performance. Thus, this paper presents a compressed sensing based semidefinite relaxation (CSR) detection algorithm for this case, which is based on a sparse overloaded detection model where users select to access randomly and autonomously and the accessed state is unknown at BS. Simulation results shows its efficiency. Along with low polynomial computational complexity of O(N2.5K) per symbol, the proposed CSRD obtains an approximate optimum bits error rate performance of 10−5and a high correct detection rate of the users' accessed state at medium low average received signal to noise ratio for combined 4-quadrature amplitude modulation (QAM) and 16QAM signal without known users' accessed state at BS in the overloaded case of Nk> Nr, where NKand Nrdenote the users' and receiving antennas' numbers, respectively.

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.

Sparse code multiple access (SCMA) is a competitive non-orthogonal multiple access technique for the fifth generation (5G) wireless communications. The SCMA codebook design is a very essential problem. This paper presents a constellation-rotation-based method for designing codebooks for downlink SCMA system. The basic idea is to make the minimum Euclidean distance of the main-constellation as larger as possible, so as to achieve good BER (bit error rate) performance. By constructing proper sub-constellations and Latin matrix, it is able to achieve a good shaping gain. Simulation results show that the proposed SCMA codebooks provides good BER performance in both additive white Gaussian noise (AWGN) and flat fading channels.

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.

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.

Recent studies have shown that existing detection algorithms are not suitable for high‐order quadrature amplitude modulation (QAM) in massive MIMO system. In this paper, with an equivalent objective function by QR decomposition and further relaxation on the constrains, the authors develop an improved semidefinite further relaxation detector (SFRD), which is proved to be convex and has solutions within polynomial complexity time. Using the detection result from the proposed SFRD as the initial vector, they propose a novel semidefinite further relaxation on the likelihood ascent search (SFRLAS) detection algorithm. It has been shown through their studies that the proposed SFRLAS scheme can effectively approach the optimum bit error rate from the maximum‐likelihood detection algorithm for systems with high‐order QAM and large‐scale antennas, however, with a lower computational complexity. The spectral efficiency converges to the theoretical value at a much lower required average received signal‐to‐noise ratio. It is an effective method for high‐order QAM signal detection in massive MIMO system.

This paper proposes a new frequency-domain pre-coding algorithm for the downlink multi-user MIMO system in a frequency-selective fading channel, where each user is equipped with multiple antennas. The proposed algorithm is based on the minimum sum-mean-square-error (Min-SMSE) criterion, which is able to eliminate both the multi-user and multi-antenna interferences, resulting in an improved bit error rate (BER) performance. Compared with the traditional optimal Min-SMSE algorithm, the proposed algorithm can reduce the computational complexity through simplifying the iterative and power allocation processes. Furthermore, the theoretical BER and ergodic sum-rate are derived in detail. In addition, we define and analyze the complexity of the pre-coding algorithms. Simulation results show that the proposed algorithm compares favorably with the optimal Min-SMSE and traditional block diagonalization (BD) algorithms in the sense of the ergodic sum-rate due to the approximately equal power allocation. The BER performance of the proposed algorithm is close to that of the optimal one and much better than that of the BD algorithm in both the uncorrelated and correlated channels. Moreover, the computational complexity of the sub-optimal Min-SMSE algorithm is significantly less than that of its optimal counterpart, especially in the low SNR region.

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.

This paper presents Multiple Radio Access Technologies(M-RATs) cooperative model for Device- to-Device (D2D) communications, which depends on both the licensed and unlicensed spectrum. The notion of cooperation factor is introduced to capture and analyze the threshold constraint in cooperation scenes. This paper also derives the coverage probability and link spectrum efficiency in typical RAT, which takes into consideration of cooperation factor, density of D2D users and pathloss exponent. Moreover, the metric of cooperation gain is proposed to depict the differences between cooperation with M-RATs and aggregation of multiple spectrum, which can be proved for a decreasing function of cooperation factor. Simulation and numerical results indicate that cooperation can bring higher link spectrum efficiency than that of the non-cooperation, according with the theoretical analysis perfectly.

Multiuser multiplexing is an attractive research topic for wireless communication system, since it can increase the sum channel capacity dramatically. The multiuser multiplexing is realized based on the independent channel influences of different users. However, the multiplexing gain will be reduced, when the channels are correlated among different users, due to the destroyed independent statistics. This paper proposes a scheme that employs random beamforming (RBF), which is the evolution of opportunistic beamforming (OBF), to achieve multiuser multiplexing for correlated Rician channel. According to the feedback of the channel state information (CSI), the user with the largest signal-to-interference and noise ratio (SINR) is selected to transmit data, similarly to that of the traditional OBF system. Numerical analysis shows that the RBF technology is capable of boosting the performance of system capacity for correlated Rician channel, when the number of users is large enough.

As one of the candidate wireless access techniques of 5G system, non-orthogonal multiple access (NOMA) is a power domain non-orthogonal multiple-access technique, which can greatly enhance spectral efficiency and system capacity. This paper mainly focus on user pairing and access theme of NOMA system, and respectively presents channel state sorting pairing algorithm (CSS-PA) as user pairing (UP) theme and user difference selecting access (UDSA) algorithm as new user access theme. The two new algorithms are proposed out of considerations on the features of NOMA system with a successive interference cancellation (SIC) receiver, and take the users' channel conditions into account, which is pairing the users with the most distinctive channel conditions. Analytical and simulation results demonstrate that the new algorithms can improve system capacity greatly compared to the existing algorithms while guarantee the user fairness. Furthermore, we discuss to apply SIC with interference rejection combining (IRC) receiver into the NOMA/MIMO system, which is a possible extension of NOMA system combined with multi-antennas, and simulation result shows that the performance gain of IRC-SIC over MRC-SIC can achieve about 2dB in high signal-to-noise ratio situation.

Fingerprint positioning is considered to be one of the most promising indoor positioning technique. The most significant technological bottleneck of fingerprint positioning technology is the high labor cost of the radio map establishing process. Many radio map fast establishing algorithms were proposed to solve this problem. There is no evaluation norm for these algorithms but positioning accuracy, which is supposed to be unreliable. This paper presents a radio map establishing algorithm evaluation criterion. To avoid the influence of other factors, the proposed evaluation criterion focuses on the quality of the radio map only. The Euclidean distance between radio maps was calculated to indicate the radio map quality. Simulation results show that the evaluation criterion can effectively measure the quality of radio maps. Some typical radio map establishing algorithms are also compared in this paper based on the proposed evaluation criterion.

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.

In this paper, we propose an ant colony-based direct communication model routing (AntDMR) algorithm aimed at wireless emergency communication system. When network infrastructure is destroyed, direct communication mode is employed for guaranteeing system availability. Accordingly, the selection of effective transmission path is a key problem. Considering delay, energy consumption and pheromone track model, the next hop node selection probability function is redefined. Furthermore, the optimal route is achieved. Finally, by comparing with traditional routing algorithm, the performance is impoved more in AntDMR from time-delay and energy-efficiency perspective.

Fingerprint localization algorithm is widely used in indoor scenario positioning. Received Signal Strength (RSS) is commonly used as the signal feature of fingerprint algorithm. But RSS could be changed due to the temperature or environment reason, which makes it an unreliable signal feature. This paper proposes a novel positioning algorithm based on the formal K Nearest Neighbor (KNN) algorithm. The proposed algorithm enhances the robustness of positioning system take example by the Receiver Autonomous Integrity Monitoring (RAIM) receive technique of Global Navigation Satellite System (GNSS) technologies. Simulation shows the novel algorithm is helpful to avoid the influence of RSS dramatic changes.

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.

A distributed antenna system (DAS) is very attractive, since it can reduce transmit power by shortening the distance between antenna elements (AEs) and the mobile station. This paper investigates the channel capacity of the DAS over a multi-path frequency-selective fading channel. We first show that the optimal power allocation solution to approaching the channel capacity is unrealistic for the practical DAS. Then, a near-optimal scheme is given through maximally tightening the upper bound of the channel capacity. Approximate analytical results are obtained through the use of the central-limit theorem. Both simulation and theoretical results are presented to show that the channel capacity can be greatly enhanced by the proposed power allocation schemes. Moreover, the bit error rate performance is analyzed for the proposed scheme.

Smart metering utility network (SUN) is an emerging wireless technology for achieving intelligent control and information transfer in smart grid applications. However, the operation of SUNs on unlicensed bands is very sensitive to external interferences generated from other networks working on the same spectrum bands, such as wireless local area networks (WLANs). This paper aims to analyze packet error rate (PER) performance of a victim SUN receiver under the interferences of one or more WLAN transmitting nodes. To deal with a scenario leveraged by multiple random variables, such as offset between desired SUN data packet and WLAN packet, duration of WLAN data packet, and time interval between two consecutive WLAN packets, we propose a segmented packet collision model with the help of the probability theory. We first divide all the packets into small segments and then calculate the collision probability of each segment to acquire a more accurate bit error rate (BER). Then, based on different BERs in distinct segments of the desired SUN packet, average PER of the victim SUN receiver is obtained. Finally, the proposed analytical model is validated by simulations as an effort to evaluate PER performance of a SUN receiver under the WLAN interferences.

This paper aims to analyze coexistence performance and study the impact of WLAN interferences on SUNs. To quantitatively reveal packet error rate (PER) performance of SUNs under multiple WLAN interferers, a segmentation scheme for desired data packet with multiple interferers is developed, based on which a packet collision model in a multi-interferer environment is proposed. The new packet collision model captures interference scenarios in every time segment of a desired packet using the proposed segmentation scheme. It paves a way to make a precise estimation of the PER of SUNs under multiple WLAN interferers. Furthermore, we design a channel agility scheme for coexistence of SUNs and WLANs via improving the existing ones, and analyze its performance in interference identification and avoidance. The proposed scheme performs interference detection for every data packet, and thus, an SUN is able to avoid WLANs interferences in a timely manner.

As a hot-spot of 5G, the research on detection algorithms for massive multiple input multiple output (MIMO) system is significant but difficult. The traditional MIMO detection algorithms or their improvements are not appropriate for large scaled antennas. In this paper, we propose artificial bee colony (ABC) detection algorithm for massive MIMO system. As one advanced technology of swarm intelligence, ABC algorithm is most efficient for large scaled constrained numerical combinatorial optimization problem. Therefore, we employ it to search the optimum solution vector in the modulation alphabet with linear detection result as initial. Simulation and data analysis prove the correctness and efficiency. Versus the scale of massive MIMO systems from 64 × 64 to 1024 × 1024 with uncoded four-quadrature-amplitude-modulation signals, the proposed ABC detection algorithm obtains bit error rate of 10 − 5 at low average received signal-to-noise-ratio of 12 dB with rapid convergence rate, which approximates the optimum bit error rate performance of the maximum likelihood and achieves the theoretical optimum spectral efficiency with low required average received signal-to-noise-ratio of 10 dB in similar increasing regularity, over finite time of low polynomial computational complexity of per symbol, where NT denotes the transmitting antennas' number. The proposed ABC detection algorithm is efficient for massive MIMO system. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Visible light communication (VLC) is a newly emerging technology, which integrates communications and lighting purposes, and has become a very active research topic in the areas of wireless communications. It is expected to become an important part of the next generation wireless communications because of its unique features in using unlicensed spectrum, support of high data rate, and its resistance to electromagnetic interferences. In this survey paper, we begin with a review on the basis of photometry, which is used to establish channel models of VLC systems. Then, we will continue to address various issues on the fundamental characteristic features of VLC systems, the impact of indoor environments on system performance, and the analysis and discussions of five different types of typical VLC channel models and other related parameters in VLC channel models. Finally, in terms of the future works, we will show the possible follow‐up research focuses and directions as an effort to identify some new research topics on VLCs. Copyright © 2016 John Wiley & Sons, Ltd.

Currently, WiFi indoor positioning system based on IEEE 802.11 is widely attractive for its free infrastructure and high localization performance. However, due to working on-demand strategy in green WiFi scenario, the access points are not always available for mobile when radio map is built in the offline phase. Radio map with unknown received signal strength is not valid for positioning and usually be replaced by the minimum value, which leads to poor positioning performance. In This paper we propose a radio map recovery method based on inexact augmented Lagrange multiplier (IALM) algorithm, which achieves to precisely recover the missing received signal strength in the radio map for those access points unavailable in the offline. By solving the nuclear norm minimization, the IALM algorithm could not only recover the missing received signal strength, but also reduce the noise effectively. We have implemented the proposed method in our lab and evaluated its performances. The experiment results indicate the proposed method could precisely recover the radio map and achieve good positioning performance.

In spectrum aggregation (SA), two or more component carriers (CCs) of different bandwidths in different bands can be aggregated to support wider transmission bandwidth. CC selection is the new radio resource management (RRM) functionality introduced in LTE- Advanced. The current CC selection schemes do not consider the cognitive radio (CR) condition and are not suitable for CC selection in CR. Consequently, the authors propose a novel CC selection scheme in CR, termed as cognitive radio based least load (CR- LL) scheme. Under a dynamic traffic model, an equivalent throughput of the CCs based on the knowledge of primary users (PUs) is given. On this basis, the CR users data transmission time of each CC is equal in CR-LL. The simulation results show that CR-LL has the better performance than the current CC selection schemes in the CR condition. Meanwhile, CR-LL has the same performance as the current CC selection schemes when there are no PUs in the CCs.

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.

Due to the ideal correlation properties of complementary codes, a code division multiple access (CDMA) communication system using complementary codes as its signature codes can offer multiple access and multi-path interferences resistant performance. Compared to the other complementary codes, I/Q column-wise complementary (IQCC) codes can support much more users with a given flock size, and at the same time it can still maintain perfect correlation properties via quadrature phase-shift keying (QPSK) spreading scheme. In this paper, we analyze the downlink bit error rate (BER) performance of an IQCC-based CDMA communication system. Utilizing the derived BER expression, we demonstrate the multiple access and multi-path interferences resistant performance of the system under both flat and frequency-selective fading channels.

With the popularity of smart phones and the development of mobile computing, indoor positioning services have triggered large scale of technology innovation and commercial cooperation. In the field of Wi-Fi based fingerprinting positioning system, for one thing, we deploy space division method based on Random Forest for dividing the fingerprinting radio map into sub regions freely and classifying candidate points accurately. For another thing, we propose a dimension reduction method, which integrates Maximum Likelihood Estimation for estimating intrinsic dimensionality and Kernel Principal Component Analysis for feature extraction, to tremendously reduce the size of a radio map, thereby saving terminal storage and alleviating error margin. Compared with linear feature extraction methods and manifold learning techniques, the proposed method shows a better performance in low dimension. The experimental results demonstrate that the proposed indoor positioning system, which is based on the given space division and dimension reduction techniques, could achieve 98% space division accuracy, 85% confidence probability with 2m positioning error and reduce 74% size of the radio map in addition to its noise suppression ability.

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.

The physical-layer network coding (PNC) is very attractive, since it takes full advantages of interference signals at the relay node, therefore improves the channel capacity significantly. However, the flat fading channel would destroy the sum signal received at relay node, leading to detection error and capacity damage. In this paper, a pre-coding scheme for the multi-user PNC (MU PNC) is proposed, to make the modulated signal from different source nodes holding the same phase rotation when received by the relay node, so that to eliminate the mutual interferences. The theoretical and simulation analysis indicate that the pre-coding can improve the symbol error rate (SER) performance as well as capacity, meanwhile it also simplifies the structure of receiver at relay node.

Error correction is capable of enhancing the accuracy and validity of positioning, thus is attractive to utilize it for sensor positioning network. Considering the high accuracy feature of error correction, this paper provides a novel sensor positioning algorithm named ECL (An extended centroid localization algorithm based on error correction), which greatly improves the positioning accuracy. By analyzing the probability distribution of measurement error, the mathematical expression of measurement error based on two-point positioning scheme is given. Then the high-accuracy positioning is completed until the estimated coordinates based on three-point positioning have been corrected. In addition, the proposed ECL algorithm uses some sensors, which have known locations, participating in the second localization, and further improving centroid localization algorithm for reliability and practicality. Simulation results demonstrate that the accuracy and validity of the proposed algorithm all outperform conventional methods with acceptable complexity.

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.

The Distributed Iterative Localization algorithm (DILOC) is an attractive algorithm for wireless sensor network (WSN), which is used to locate sensors (with unknown locations) in m dimensional space. DILOC algorithm needs a minimal number of m +1 anchors (with known their exact locations). However, the convex hull test requires that sensors must be inside of the convex hull, so it will require relatively large communication radius. Considering this defect of DILOC, this paper proposes an improved algorithm that called distributed joint iterative localization (DJIL), through employing loop iteration and weighted average, which is capable of reducing communication radius and enhancing the precision of localization. The DJIL algorithm improves the convex hull test, and uses some sensors, which have known locations, participating in the second localization, and further improving centroid localization algorithm for reliability and practicality. Simulation results show that the communication radius is significantly reduced and error is also decreased for the DJIL algorithm. Compared with DILOC algorithm, if fewer anchors are distributed in region unequally, and the region contains more sensors, DJIL algorithm can reduce the communication radius of 30% and increase location accuracy of 21%. So DJIL algorithm is very flexible for scenarios of anchors uneven distribution.

The physical-layer network coding (PNC) has become a hot research topic, since it can potentially reduce the transmission time slots and increase the channel capacity. However, current studies are mainly concentrated on three nodes (two users and one relay) network. This paper considers a specific multiuser network that includes M (M>2) users with one relay, and illustrates the transmission scheme and evaluates its bit error rate (BER), information theoretical capacity and anti-noise performance under AWGN channel. Moreover, 2FSK is considered in the proposed network, since it does not need accurate phase tracking. Simulation results show that its information theoretical capacity would decrease significantly with the number of users in this network increase, however the capacity of the proposed scheme is still better than that of the IEEE802.11 networks.

The current and future communication and sensing systems such as cognitive radio, radar, or sensor network include a network of multiple communication modules/devices or sensors. The network of communication modules/devices or sensors should operate with multiple goals managed by an intelligent platform network that can manage the dynamics of each device/sensor to meet the common goals of the platform, rather than each device/sensor to operate as an independent system. Therefore, it is significant to perform cooperative communication and sensing within and between platforms of sensors and their communication modules/devices. This special issue contains 14 papers selected from submissions to the 6th International Conference on Communications and Networking in China (CHINACOM 2011), with further extension and satisfactory external peer reviews. These papers highlight some of the current research interests and achievements in the area of cooperative communications and sensing. The topics include cognitive radio, spectrum sensing, MIMO system, radar, compressive sensing, and localization. The paper by Huang et al. analyzed the performance bonds of a wireless relay system that consists of several relays stations working on both amplifier-and-forward (AF) and decode-and-forward (DF) algorithms. The outage probability behavior of the proposed mixed AF and DF relay systems under independent Nakagami-m fading channels was studied. The trade-off between the complexity and performance issues was also discussed. The paper by Sun et al. presented a review on evolution from symbol-level space-time coded MIMO to chip-level space-time coded MIMO. It discussed the characteristics and limitations of traditional symbol-level space-time coding schemes. The proposed chip-level space-time coded MIMO works well even in a fast fading channel in addition to its flexibility to achieve diversity and multiplexing gains simultaneously in varying channel environments. The paper by Liu et al. studied cognitive radio and proposed a double cooperative detection scheme based on weighed combination in order to improve the global detected performance of the cooperative detection. The paper by Peng and Adachi analyzed the performance of a single-carrier frequency domain adaptive antenna array (SC-FDAAA) in a distributed antenna network (DAN) system. In order to make it clear whether the performance of SC-FDAAA can benefit from the distributed nature of DAN system or not, they made a comparison between DAN system and the traditional cellular system, that is, central antenna network system. The paper by Li et al. proposed a conception on the terahertz communication system for plasma sheath penetration. An idea based on the capability of radiation from the terahertz communication system to penetrate the plasma sheath was proposed to provide the theoretical basis for real-time communication with the nearcraft. The paper by Zhang et al. studied single-carrier frequency-domain equalization (SC-FDE). The feasibility of building SC-FDE system was explored by comparing performance with orthogonal frequency division multiplexing (OFDM). It studied on a dual-mode (DD-LMS+MCMA) blind equalization algorithm applied in a single-carrier system. The simulation model of image transmission in SC-FDE system was built with better result through the comparative analysis of image. The paper by Wu et al. proposed a new compressive sensing-based compression and recovery ultra-wideband (UWB) communication system. Real-world UWB signal was collected and processed to evaluate the performance of our proposed system. Simulation results showed that the data could be perfectly recovered if the compression ratio does not exceed 2.5:1 when Bernoulli matrix is chosen as the sensing matrix. The paper by Zou et al. focused on improving the system capacity of 60GHz wireless personal area networks and put forward an effective timeslot allocation scheme for spatial reuse, which combines the existing exclusive-region-based scheduling algorithm with simple power control. The paper by Huang and Pi proposed a GPS-based positioning and navigation with aid of wireless network positioning scheme, which utilizes the existing resources of the wireless communication network to provide the cheaper and more accurate urban positioning service with the higher survivability. The paper by Arianpoo et al. presented a survey on applications of network coding to improve TCP performance over wireless mesh networks (WMNs). It provided a thorough survey of TCP performance issues over WMNs and the available solutions to address these issues. Among the existing methods, network coding and the ways that TCP interacts with network-coded systems were focused. Some open research areas in this field were suggested. The paper by Yu et al. proposed a combined code reuse scheme with two-dimensional orthogonal variable spreading factor codes assignment algorithm for uplink multi-user/multi-rate block spread multi-cellular code division multiple access (CDMA). It was confirmed by the computer simulation that the proposed code assignment algorithm improves the code reuse efficiency while achieving lower blocking probability than traditional CDMA. The paper by Yang et al. proposed a relatively complete and robust optimization model under the scenario where multi-secondary users cooperatively sense multi-channels. The objective of this model was to maximize the system throughput, while aiming to jointly optimize the parameters including the sensing time and the weight coefficients of the sampling results. The results showed the performance advantage of the proposed model on improving the system throughput by comparing with other state-of-the-art optimization models. The paper by Shi et al. proposed a new linear fractional shift invariant system structure associated with the fractional Fourier transform, and the linear time-invariant systems were noted as special cases. The eigenfunctions and eigenvalues of the new LFSI systems were presented. The paper by Wu et al. studied channel estimation algorithms for cooperative spectrum sensing in AF cooperative system. The influence of channel estimation error to system performance was firstly deduced, and then linear minimum mean-square error (LMMSE) channel estimation algorithm with delay time-domain windowing technique (LMMSE-DTW) and modified singular value decomposition based LMMSE algorithm (SVD-LMMSE) were proposed to improve the channel estimation performance for CDMA system and OFDM system, respectively. We would like to thank all authors for contributing papers to the special issue. We appreciate the staff of Wireless Communications and Mobile Computing for their support in editing this special issue. Qilian Liang is a Professor at the Department of Electrical Engineering, University of Texas at Arlington (UTA). He received the BS degree from Wuhan University in 1993, the MS degree from Beijing University of Posts and Telecommunications in 1996, and the PhD degree from University of Southern California (USC) in May 2000, all in Electrical Engineering. Prior to joining the faculty of the University of Texas at Arlington in August 2002, he was a Member of Technical Staff in Hughes Network Systems Inc. at San Diego, California. His research interests include wireless sensor networks, wireless communications, compressive sensing, smart grid, signal processing for communications, fuzzy logic systems and applications, and so on. Dr Liang has published more than 250 journal and conference papers and seven book chapters. He received the 2002 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, the 2003 US Office of Naval Research (ONR) Young Investigator Award, the 2005 UTA College of Engineering Outstanding Young Faculty Award, the 2007, 2009, 2010 US Air Force Summer Faculty Fellowship Program Award, the 2012 UTA College of Engineering Excellence in Research Award, and the 2013 UTA Outstanding Research Achievement Award. Victor C. M. Leung received the BASc (Hons) degree in electrical engineering from the University of British Columbia (UBC) in 1977 and was awarded the APEBC Gold Medal as the head of the graduating class in the Faculty of Applied Science. He attended graduate school at UBC on a Natural Sciences and Engineering Research Council Postgraduate Scholarship and completed the PhD degree in electrical engineering in 1981. From 1981 to 1987, Dr Leung was a Senior Member of Technical Staff and satellite system specialist at MPR Teltech Ltd., Canada. In 1988, he was a Lecturer in the Department of Electronics at the Chinese University of Hong Kong. He returned to UBC as a faculty member in 1989 and currently holds the positions of Professor and TELUS Mobility Research Chair in Advanced Telecommunications Engineering in the Department of Electrical and Computer Engineering. Dr Leung has co-authored more than 700 technical papers in international journals and conference proceedings, 29 book chapters, and co-edited eight book titles. Several of his papers had been selected for best paper awards. His research interests are in the areas wireless networks and mobile systems. Dr Leung is a registered professional engineer in the Province of British Columbia, Canada. He is a Fellow of IEEE, the Royal Society of Canada, the Engineering Institute of Canada, and the Canadian Academy of Engineering. He was a Distinguished Lecturer of the IEEE Communications Society. He is a member of the editorial boards of the IEEE Wireless Communications Letters, Computer Communications, and several other journals and has previously served on the editorial boards of the IEEE Journal on Selected Areas in Communications—Wireless Communications Series, IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, IEEE Transactions on Computers, and Journal of Communications and Networks. He has guest-edited many journal special issues and contributed to the organizing committees and technical program committees of numerous conferences and workshops. He was a recipient of the IEEE Vancouver Section Centennial Award and 2012 UBC Killam Research Prize. Weixiao Meng received the BEng, MEng, and PhD degrees from Harbin Institute of Technology (HIT), Harbin, China, in 1990, 1995, and 2000, respectively. From 1998 to 1999, he worked at NTT DoCoMo on adaptive array antenna and dynamic resource allocation for beyond 3G as a senior visiting researcher. He is now a full professor at the School of Electronics and Information Engineering of HIT. His research interests include broadband wireless communications and networking, MIMO, GNSS receiver, and wireless localization technologies. He has published three books and over 190 papers on journals and international conferences. He is the Chair of IEEE Communications Society Harbin Chapter, a senior member of the IEEE ComSoc, IET, the China Institute of Electronics, and the China Institute of Communication. He has been an editorial board member for Wiley's WCMC Journal since 2010 and the area editor for PHYCOM journal since 2014. He acted as leading TPC co-chair of ChinaCom2011, leading Services and Applications track co-chair of IEEE WCNC2013, Awards co-chair of IEEE ICC2015, and Wireless Networking Symposia co-Chair. In 2005, he was honored provincial excellent returnee and selected into New Century Excellent Talents (NCET) plan by Ministry of Education, China, in 2008. Fumiyuki Adachi received the BS and DrEng degrees in electrical engineering from Tohoku University, Sendai, Japan, in 1973 and 1984, respectively. In April 1973, he joined the Electrical Communications Laboratories of Nippon Telegraph & Telephone Corporation (now NTT) and conducted various types of research related to digital cellular mobile communications. From July 1992 to December 1999, he was with NTT Mobile Communications Network, Inc. (now NTT DoCoMo, Inc.), where he led a research group on wideband/broadband CDMA wireless access for IMT-2000 and beyond. Since January 2000, he has been with Tohoku University, Sendai, Japan, where he is a distinguished Professor of Communications Engineering at the Graduate School of Engineering. His research interest is in the areas of wireless signal processing and networking including broadband wireless access, equalization, transmit/receive antenna diversity, MIMO, adaptive transmission, channel coding, and so on. From October 1984 to September 1985, he was a United Kingdom SERC Visiting Research Fellow in the Department of Electrical Engineering and Electronics at Liverpool University. Dr Adachi is an IEEE Fellow and a VTS Distinguished Lecturer for 2011 to 2013. He is a co-recipient of the IEEE Vehicular Technology Transactions Best Paper of the Year Award 1980 and again 1990 and also a recipient of Avant Garde award 2000. He is a Fellow of Institute of Electronics, Information and Communication Engineers of Japan (IEICE) and is a recipient of IEICE Achievement Award 2002 and a co-recipient of the IEICE Transactions Best Paper of the Year Award 1996, 1998, and again 2009. He is a recipient of Thomson Scientific Research Front Award 2004, Ericsson Telecommunications Award 2008, Telecom System Technology Award 2009, Prime Minister Invention Prize 2010, British Royal Academy of Engineering Distinguished Visiting Fellowship 2011, and KDDI Foundation Research Award 2012.

ABSTRACT Recently, zero‐forcing beamforming has been widely used for multiple‐input and multiple‐output broadcast channels, because it could provide suboptimal capacity with low complexity. To increase the sum data rate, a good user selection algorithm is attractive. In this paper, a new user selection algorithm named maximum product of effective channel gains is presented. The proposed algorithm is described mathematically, and the lower bound of the sum capacity is demonstrated to be proportional to the product of effective channel gains. Our simulation results show that maximum product of effective channel gains can achieve higher sum rate compared with the classical algorithm, semi‐orthogonal user selection, with the same complexity order, especially when the signal‐to‐noise ratio is high; in addition, the complexity of the proposed algorithm is superior to minimum of the Frobenius norm of the pseudo‐inverse algorithm. Meanwhile, the number of users that can be served simultaneously is equal to the number of transmission antennas, which is the maximum performance that can be achieved.Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Space–time coded multiple‐input multiple‐output (MIMO) technology is an important technique that improves the performance of wireless communication systems significantly without consuming bandwidth resource. This paper first discusses the characteristics and limitations of traditional symbol‐level space–time coding schemes, which work largely on the basis of an assumption that signals are sent to a block‐fading channel. Therefore, the symbol‐level space–time coding schemes rely on symbol‐level signal processing. Taking advantage of orthogonal complementary codes, we propose a novel MIMO scheme, in this paper, based on chip‐level space–time coding that is different from the traditional symbol‐level space–time coding. With the help of space–time–frequency complementary coding and multicarrier modem, the proposed scheme is able to achieve multipath interference‐free and multiuser interference‐free communications with simple a correlator detector. The proposed chip‐level space–time coded MIMO works well even in a fast fading channel in addition to its flexibility to achieve diversity and multiplexing gains simultaneously in varying channel environments. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT The two‐dimensional (2D) block spread code division multiple access (CDMA) can avoid the uplink multiple‐access interference with low‐complexity single‐user detection in a slow fading channel and, therefore, is very attractive. In the 2D spreading, orthogonal variable spreading factor (OVSF) is used for spreading; an important problem is how to efficiently assign the limited resource of OVSF codes to users with different data rates, while meeting the requirement of quality of service in a multi‐cell environment. In this paper, it is shown that the code reuse can improve the code reuse efficiency and the proposed code reuse scheme combined with code assignment algorithm can allow flexible multi‐rate uplink transmission. The computer simulation confirms that the proposed code assignment algorithm improves the code reuse efficiency while achieving lower blocking probability than traditional CDMA. Copyright © 2012 John Wiley & Sons, Ltd.

Owing to ideal correlation properties of complementary codes (CCs), a complementary coded code division multiple access (CC-CDMA) system outperforms traditional CDMA systems in terms of its interference resistance in both synchronous and asynchronous transmissions. This paper shows that a normal CC-CDMA system loses its interference-free feature over uplink frequency selective fading channels due to the fact that it is incapable to reconstruct ideal complementary cross-correlation at a receiver. This work proposes a uplink pre-equalized CC-CDMA system to resist multi-user interference and near-far effect over asynchronous frequency selective fading channels with perfect channel state information (CSI). Simulation results show the superiority of the proposed scheme in terms of its interference and near-far effect resistance. In addition, the impact of imperfect CSI is also studied in this work.

Cooperative communication can significantly improve the performance of wireless network through the help of relay nodes. Choosing a good relay is very important in cooperation. However, for user mobility, both inter-user channel and channel between relay and destination will vary quickly. So relay will not be used all the time. It will be changed when it is no longer suitable to be a relay. A relay handoff algorithm based on energy gain and direction information is proposed in this paper. In this algorithm, handoff is performed when the energy gain of the working relay falls below the threshold and the energy gain of the best relay exceeds the energy gain of the working relay by a variable hysteresis level assisted by direction information. The simulation and analytical results show that this algorithm can reduce the average number of handoffs while keeping the energy gain at a good level .

Trunking communications has been widely applied in dedicated areas for many years, owning to its powerful functionality of scheduling and immediate group communication. Evolution of such services over the cellular mobile network attracts more and more interests due to its wider coverage and cheaper establishment and maintenance cost, especially with the coming era of Long Term Evolution (LTE). In this paper, a survey work is proposed to analyze the application prospects and requirements of the trunking communications over LTE. Taking the push-to-talk over cellular (PoC) as a candidate, the implementation framework is presented. Additionally, the key issues to guarantee quality-of-service (QoS) of the LTE-based PoC service are well analyzed in this paper.

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%.

In this paper a low complexity network coding scheme in TWRC is proposed. This scheme is based on soft information and employs soft-input hard-output (SIHO) channel decoding and designs detailed structures in source node, relay node and destination node. In design of SIHO decoder, Lloyd-Max quantizer and equal probability quantizer are compared. Furthermore, theoretical end-to-end BER bounds of proposed scheme through both AWGN and Rayleigh channels are given. Simulation results show that compared with existing network coding schemes in TWRC, proposed scheme has better BER performance and lower complexity.

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.

As the most competitive and cost-efficient localization technology, the map-aided fingerprinting-assisted positioning has drawn a large body of attentions during the past decade. There are normally two phases, the off-line and on-line phases, and the construction of radio map in the off-line phase will significantly influence the location accuracy in the on-line phase. However, the radio map which describes the mapping relationship between the physical positions of the reference points (RPs) and the recorded radio signal strength (RSS) or signal to noise ratio (SNR) always involves the cumbersome collection work. Further, thousands of Wi-Fi access points (APs) remaining idle will also bring serious concerns of the power consumption in the future. Therefore, in response to these compelling problems, we present a preliminary analysis towards the probabilistic location methods, develop the green global-greedy position estimation (3GPE) and introduce entropy deduction as a new metric for performance evaluation. Finally, our analytical expressions and simulation results with respect to the simple circle model and ideal indoor Wi-Fi regular environment demonstrate these issues and challenges.

Network coding, which works in the network layer, is an effective technology to improve the throughput of two-way relay networks. In this paper, we compare the existing network coding schemes: traditional network coding (NC) scheme, physical-layer network coding (PNC) scheme and soft network coding (SNC) scheme, and propose a new physical layer scheme which can be seen as the implementation of network coding in two-way relay networks. Relay nodes combine the received signals from two source nodes before demodulation and channel decoding. The computational complexity and BER performance of the proposed scheme are analyzed compared with the previous schemes. Theoretical analysis show that the proposed scheme can save 50% computational cost of demodulation and channel decoding processes compared with NC scheme, meanwhile increase the power efficiency of the relay nodes in two-way relay networks significantly. Future works with the proposed scheme are also discussed.

Indoor positioning in wireless local area network (WLAN) has been attracting increasing attentions for its cost effectiveness and reasonable positioning accuracy. Existing positioning methods all pay attention to establish more accurate relationship between received signal strength (RSS) and physical locations. However, the deployment of access points (APs) is ignored. This paper proposes an optimized APs deployment method for improving WLAN positioning accuracy. The objective of APs deployment is to maximize the RSS Euclidean distance between physical locations. We investigate and demonstrate the importance of RSS Euclidean distance to WLAN positioning. Simulation studies and experimental results show that the proposed APs deployment method can improve positioning accuracy significantly.

Long Term Evolution (LTE) has been proposed as a promising radio access technology to bring higher peak data rates and better spectral efficiency. However, scheduling and resource allocation in LTE still face huge design challenges due to their complexity. This paper divides the complex problem into three sub-problems: scheduling pattern, scheduling priority and quantity of scheduled data. Based on analysis of three sub-problems, a configurable dual-mode (CD) algorithm is proposed. CD algorithm is able to guarantee queuing delay with low loss of resource utilization and fairness by employing dual-mode scheduling mechanism. And it can be configured by three parameters catering to different performance requirements. By utilizing QoS Class Identifier (QCI) and Channel Quality Indicator (CQI) defined by LTE, low computation is realized in CD scheduler. Finally, performance evaluation of the proposed scheduler is presented. The results and correlative analysis testify effectiveness of CD algorithm.

Location estimation using received signal strength (RSS) in pervasively available Wi-Fi infrastructures has been considered as a popular indoor positioning solution. However, accuracy deterioration due to uncertainty of RSS and offline manual calibration cost limit the deployment of Wi-Fi positioning systems. This paper proposes a signal perturbation technique to enhance existing support vector regression (SVR) based Wi-Fi positioning. By signal perturbation, more RSS training samples are generated, thus enhancing the generalization ability of SVR. In addition, access point (AP) selection method is applied to reduce the input dimension by discarding the redundant APs. The proposed method is compared with previous classical methods in a real wireless indoor environment. Experimental results show that the proposed method improves accuracy while reducing calibration cost.

Combating time and frequency selectivity in wireless channels is one of the most challenging tasks in next generation wireless networks. In this paper, we propose an adaptive estimation algorithm to estimate Doppler shifts in a direct sequence code division multiple access (DS-CDMA) radio system with multiple Doppler subpaths. By modeling doubly selective channels using a basis expansion model (BEM), an expectation-maximization (EM) algorithm based adaptive estimation method is developed to extract accurate Doppler shift information. The Cramer-Rao lower bound (CRLB) analysis is conducted to study the performance bound of the proposed estimation algorithm. Based on the estimated Doppler shift results, a frequency domain equalizer (FDE) based receiver architecture is developed to exploit Doppler diversity in the frequency domain. Our analysis and simulation results demonstrate that this receiver architecture features a low complexity while still achieving a good performance compared with traditional CDMA receivers.

A critical challenge for mobile ad hoc networks is the design of efficient routing protocols that are able to provide high bandwidth utilization and desired fairness in mobile wireless environment without any fixed communication establishments. While extensive efforts have already been devoted to providing optimization based, distributed congestion elusion schemes for efficient bandwidth utilization and fair allocation in both wired and wireless networks, a common assumption therein is fixed link capacities, which will unfortunately limit the application scope in mobile ad hoc networks where channels are ever changing. In this paper, an effective congestion elusion scheme is presented explicitly based on ant colony algorithm for mobile ad hoc networks, which will explore the optimal route between two nodes promptly, meanwhile forecast congestion state of the link. Accordingly, a new path will be found rapidly to have the flow spread around to relieve the congestion state. Compare with OLSR, the scheme proposed here will greatly reduce the packet loss ratio and the average end-to-end delay at the same time, which illustrate that it will make use of networking resource effectively.

Applications for future broadband wireless network should work effectively under highly dynamic wireless channel environment, where strong frequency-selective and time-selective fading multi-path channels will exist. Though some advanced techniques, such as 2-dimensional (2D) block spread, frequency domain equalization (FDE) and antenna diversity techniques, can be applied to the code division multiple access (CDMA) networks to improve the bit error rate (BER) performance, they all require an accurate estimation of the channel. Conventional channel estimation method such as the minimum mean square errors (MMSE) based channel estimation scheme degrades significantly when the channel dynamics become severe. Therefore, in this paper, we propose an Unscented Kalman Filter (UKF) based iterative channel estimation method, which is jointly used with the MMSE channel estimation scheme, to track the channel dynamics in both time domain and frequency domain. Furthermore, we utilize the cyclic iteration to ensure UKF converge to the stable result accurately and quickly, and the iteration count can be adjusted according to the Doppler effects. Our performance analysis demonstrates that the proposed joint estimation method can achieve good BER performance under both low and high dynamic channel conditions.

Multi-access interference (MAI) free uplink transmission can be achieved by 2-dimensional (2D) block spread code division multi access (CDMA), and low-complexity single-user frequency-domain equalization (FDE) is performed at base station to obtain the frequency diversity gain. Cyclic delay transmit diversity (CDTD) can further improve the transmission performance by obtaining a larger frequency diversity gain in the frequency selective fading channel. However, accurate estimation of the channel transfer function is the premise for the use of 2D block spread CDMA with CDTD. Unscented Kalman filter (UKF) is an attractive technology and suitable for the Rayleigh channel estimation in time domain, which can be used to assist the minimum mean square error (MMSE) criterion based channel estimation (CE) to improve the BER performance. This paper proposes a joint UKF and MMSE criterion CE method. The achievable uplink BER performance is evaluated by computer simulation for 2D block spread multi-carrier (MC) CDMA, which shows that the joint CE can improve the BER performance about 0.8dB compared with MMSE-CE when BER=10-4.

Location estimation based on received signal strength (RSS) in WLAN environment is an attractive method for indoor positioning system. Unfortunately, due to the explicit nonlinearity and uncertainty of RSS signal, the traditional approaches always fail to deliver good location accuracy. This paper presents a novel positioning algorithm with kernel direct discriminant analysis (KDDA). We deploy the KDDA to map the original RSS vectors into a kernel feature space for feature extraction. The experimental results show that the proposed algorithm leads to higher location accuracy over the traditional algorithms including weighted k-nearest neighbor, maximum likelihood and kernel method. The performance improvement can be attributed to that the nonlinear discriminative location information can be efficiently extracted, while the redundant location information is considered as noise and discarded adaptively.

This paper proposes the optimal K nearest neighbors (KNN) positioning algorithm via theoretical accuracy criterion (TAC) in wireless LAN (WLAN) indoor environment. As far as we know, although the KNN algorithm is widely utilized as one of the typical distance dependent positioning algorithms, the optimal selection of neighboring reference points (RPs) involved in KNN has not been significantly analyzed. Therefore, in order to fill this gap, the optimal KNN positioning algorithm based on the best TAC is introduced. And this algorithm is beneficial to construct the reliable WLAN indoor positioning system and provide the efficient location based services (LBSs). The relationship among theoretical expectation accuracy, unit interval of neighboring RPs and dimensions of target location region is also revealed. Furthermore, the feasibility and effectiveness of optimal KNN positioning algorithm are verified based on the experimental comparisons respectively in the regular office room, straight corridors, static positioning and dynamic tracking situations.

Cooperative communications exploit the spatial diversity inherent in multiuser system by allowing cooperation among users having a wide range of channel qualities. How to search the cluster-head and its member relay nodes to cooperate is important. Since nodes are coming into or out of the clusters time to time, the network topology is perturbed and the reconfiguration of the network is unavoidable. In this paper, we propose a new algorithm called stability weighted clustering algorithm (SWCA) to solve this problem. We define the stability by using a new metric of mobility. In the traditional WCA, the speed of every separate node is considered. However, our proposed algorithm introduces a new weight factor, which uses the relative speed of two nodes to represent the mobility of the network. Simulation results show that the SWCA achieves better stability than the traditional WCA while it performs almost the same performance as the WCA with respect to the dominant set update rate and the load balancing factor (LBF).

The presence of the multi-access interference (MAI) limits the uplink performance of code division multiple access (CDMA). 2-dimensional (2D) block spread CDMA makes it possible to achieve MAI free uplink transmission while obtaining the frequency diversity gain by using low-complexity single-user frequency-domain equalization (FDE). To further improve the uplink transmission performance, the transmit diversity technique can be used. Cyclic delay transmit diversity (CDTD) can strengthen the frequency-selectivity of the channel. In this paper, we apply CDTD to 2D block spread CDMA with MMSE-FDE. The achievable uplink BER performance is evaluated by computer simulation for both direct sequence CDMA and multi-carrier CDMA. It is shown that CDTD can offer improved uplink BER performance in a frequency-selective uplink channel.