Reconfigurable antennas (RAs) can actively adjust their radiation patterns to meet the demands of the communication network. In this paper, we investigate precoding and radiation pattern design in a multi-user multi-input single-output (MUMISO) downlink system. To fully exploit RAs, we introduce pattern sample vectors corresponding to each user's sub channel to assess the impact of pattern reconfiguration across all scattering paths. Based on this, we formulate the design problem by adopting closed-form precoding to optimize the radiation patterns for maximizing the sum rate. Specifically, we propose two methods: a heuristic approach using singular value optimization (SVO) and a successive convex approximation (SCA)-based minimization strategy to achieve desired patterns. These methods effectively address the non-convexity in the objective function. Numerical results demonstrate that the reconfigurable radiation pattern can strategically manipulate the wireless channel, leading to significant enhancements in system performance.

WiFi-sensing systems that can accomplish multiple tasks simultaneously are attracting significant attention due to their potential for large-scale commercial applications. However, different WiFi sensing scenarios may often rely on various task-specific features, posing a challenge in balancing these different, or asymmetrical, characteristics across tasks. In this paper, we propose a system that aims to address the asymmetrical problems in the joint recognition of users’ locations and activities. First, we define activity recognition as a high-level task and location recognition as a low-level task based on their respective difficulty levels. Then, the proposed system employs cascading attention-based modules to transfer prior knowledge between different tasks. The key insight of the proposed architecture is to mimic skilled learners in similar situations, who often tackle easier problems first to enable them to solve more challenging problems later on. Based on this behavioral strategy, the proposed attention-based modules are designed to generate masks that select specific characteristics from the low-level task to help the high-level task learn respective features more effectively. Finally, extensive experimental results based on two open datasets demonstrate the superiority of our system in accuracy compared to other state-of-the-art methods.

In multicell cellular networks, coordinated multiuser scheduling (CMUS) based on block diagonalization precoding, which jointly selects the scheduled users among multiple base stations (BSs), is an efficient method to eliminate inter-user interference. However, most existing CMUS algorithms require global channel state information and many iterations, which are impractical in dynamic wireless networks due to its heavy information overhead and computational complexity. In this paper, we propose a distributed adaptive CMUS algorithm based on multi-agent reinforcement learning (MARL) to maximize the sum rate with less information overhead and computational complexity. In the proposed scheme, an adaptive multiple Deep Q-Network (DQN) architecture is deployed at each BS, aiming to reduce information overhead and computation complexity by identifying suitable CMUS policies with fewer DQNs. Each BS trains its own multi-DQNs and performs appropriate CMUS actions based on local information. Simulation results show that, by executing a subset of the DQNs, the proposed adaptive multi- DQN architecture reduces at least 50% of the information overhead and the computational complexity of the scheme without adaptive multi-DQN architecture. Additionally, compared to the centralized iterative approach, the proposed scheme costs 9.44% of the information overhead and 3.33%0 of the running time of the centralized iterative approach while achieving marginally superior performance.

Indoor location-based services (LBSs) are critical for enhancing social and commercial activities that require accurate and efficient localization techniques. Existing deep-learning-based indoor localization methods mainly focus on predefined global features to learn local discriminative representations, which increases learning difficulty and is not efficient or robust to scenarios with small variations. To address the above issues, we propose a novel fine-grained temporal features-based localization (FT-Loc) framework that utilizes multiple subsignal features to provide accurate location estimation, and each subsignal represents a piece of clue for a specific position. Specifically, the proposed framework takes multiple local signal sequences as input, and deep networks considering temporal correlations are designed for extracting features from the corresponding location clues, respectively. Then, a lightweight attention generation scheme is used to learn the importance of each temporal representation. Guided by the obtained attention values, we fuse multiple local features to generate more distinguishing ones for accurate localization. The experimental results show that FT-Loc significantly outperforms existing localization schemes with accuracy improvements of at least 43.36%.

WiFi-sensing systems that can accomplish multiple relative tasks simultaneously are attracting significant attention due to their potential for large-scale commercial applications. However, different WiFi sensing scenarios may often rely on various task-specific features, posing a challenge in balancing these different, or asymmetrical, characteristics across tasks. In this article, we propose a system called WiADN which aims to address the asymmetrical problems in the joint recognition of users’ locations and activities. Our system is composed of two critical parts: 1) an asymmetrical network architecture and 2) an adaptive weight loss (AWL) module employed during the training phase. First, we define activity recognition as a high-level task and location recognition as a low-level task based on their respective difficulty levels. Then, the proposed architecture leverages the cascading attention-based modules to transfer the prior knowledge between different tasks. The key insight of the proposed architecture is to mimic the skilled learners in similar situations, who often tackle easier problems first to enable them to solve more challenging problems later on. Based on this behavioral strategy, the proposed attention-based modules are designed to generate masks to select specific characteristics from the low-level task to help the high-level task to learn respective features more effectively. Additionally, the AWL module based on the task uncertainty theory is employed to balance two tasks’ asymmetry from the perspective of loss optimization. Furthermore, extensive experiment results based on two open data sets demonstrate the superiority of our system in the accuracy with other state-of-the-art methods. At last, the effectiveness and the robustness of our system are also verified through the comparative studies and ablation experiments. Our source codes are available athttps://github.com/jzhoujg/WiADN.

This paper proposes an indoor localization system by exploring the temporal and frequency features of complex Channel State Information (CSI) under the fifth-generation (5G) cellular network. In particular, we first acquire some successive raw CSIs from multiple base stations (BSs). Then, amplitude-based sequences are obtained by employing a sliding window moving over a consecutive time step on CSI amplitudes. Moreover, the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are adopted to learn robust time-frequency features from the constructed CSI sequences. To emphasize the contributions of the critical elements to final location estimations, we utilize an attention mechanism to assign the local learned features with different weights. We implement the proposed scheme and verify its performance with extensive experiments in some representative indoor scenes.

Automatic modulation recognition (AMR) is one of the most essential techniques in non-cooperative orthogonal time frequency space (OTFS) and orthogonal frequency division multiplexing (OFDM) communication systems. Since coexistence of OTFS and OFDM is a potential and practical solution in the future wireless communication scenarios, classification of the OTFS scheme and the OFDM scheme will be a challenging and meaningful task. In this paper, we propose a deep learning-based method, including multi-layer convolution neural networks (CNNs) and an attention-based residual Squeeze-and-Excitation Module (SE), to extract effective characteristics of OTFS and OFDM signals in multi-path Doppler spread fading channel. To obtain comparable and convincing results, the design of OTFS transmitters is on the basis of OFDM systems and contains six different sub-carrier modulation modes (BPSK, QPSK, 8PSK, 16QAM, 64QAM and 256QAM). Meanwhile, data structures of the signals are all well-deigned for fair comparisons. In addition, datasets include five modulation modes (OTFS, OFDM and other commonly-used modulation modes) and different Doppler spread values to verify our proposed method. The simulations show that our proposed SE-CNN model performs better than other baseline methods. Moreover, extensive experiment results demonstrate the robustness of our proposed method.

The physical layer (PHY) security technology based on radio frequency (RF) fingerprint can effectively solve the secure access problem of wireless devices. The hardware impairments of the devices can be used to generate the unique RF fingerprint to identify different wireless devices. Fingerprint extraction as a key step in the process of identification faces the challenges of ensuring the identification accuracy with reduced sample dimension and low testing and training time. To address the above problems, we propose a lightweight RF fingerprint extraction scheme to extract the physical layer attributes and effectively reduce the data dimension and time consumption. Based on the proposed RF fingerprint, the Bayesian classifier is used to identify the wireless devices. Furthermore, a joint judgment strategy is proposed to improve the identification accuracy by using multiple segments of one signal frame. The experimental result shows that, compared to the existing RF fingerprint identification schemes, the proposed RF fingerprint identification scheme obtains the best identification accuracy with lower time and data consumption.

Abstract Nowadays, parents attach importance to their children's primary education but often lack time and correct pedagogical principles to accompany their children's learning. Besides, existing learning systems cannot perceive children's emotional changes. They may also cause children's self‐control and cognitive problems due to smart devices such as mobile phones and tablets. To tackle these issues, we propose an intelligent companion learning system to accompany children in learning English words, namely theIntelligent Augmented Reality Educator (IARE). The IARE realizes the perception and feedback of children's engagement through the intelligent agent (IA) module, and presents the humanized interaction based on projective Augmented Reality (AR). Specifically, IA perceives the children's learning engagement change and spelling status in real‐time through our online lightweight temporal multiple instance attention module and character recognition module, based on which analyses the performance of the individual learning process and gives appropriate feedback and guidance. We allow children to interact with physical letters, thus avoiding the excessive interference of electronic devices. To test the efficacy of our system, we conduct a pilot study with 14 English learning children. The results show that our system can significantly improve children's intrinsic motivation and self‐efficacy.

In dense small cell networks, the coordinated multi-point noncoherent joint transmission (JT) is a key technique to mitigate inter-cell interference and enhance network capacity. However, the capacity-maximizing power control and beamforming problem subject to a total transmit power constraint at each individual small cell base station (BS) is inherently nonconvex and NP-hard. To solve this problem, most existing algorithms require global channel state information (CSI) and a lot of computations, which are infeasible and impractical in dynamic wireless networks with limited computing power and link capacity. In this paper, we characterize a low-dimensional solution structure for the power control of the sum-rate maximization problem in time division duplex (TDD) dense small cell networks. Taking advantage of this low-dimensional structure, a distributed noncoherent JT scheme based on multi-agent reinforcement learning (MARL) is proposed to maximize the sum-rate of the dense small cell networks with reduced information overhead. In the proposed scheme, each BS acts as an agent and makes decisions locally. It is proved that the optimal sum-rate can be achieved for single-transmit-antenna BSs by using the proposed scheme. Compared to the best method presently known, the proposed scheme achieves a similar sum-rate with considerably lower computational complexity and information overhead, which makes it more appealing for practical implementations.

The WiFi-based fingerprint indoor-positioning system has attracted increasing interest from industry and academia, benefiting from the widespread deployment of the wireless local area network (WLAN) infrastructure. However, with the expansion of application scenarios, this system suffers from labor-intensive work for received signal strength (RSS) fingerprint construction. In this article, we propose a crowdsourcing-based radio map construction method and trajectory matching algorithm to solve the time-consuming preliminary fingerprint data collection in the offline phase, where the tedious collection work is replaced by the massive crowdsourcing sensor data. First, the latent information of the map is extracted by using some image processing methods, and all possible routes are obtained simultaneously with the depth-first traversal method. Then, considering the strict restrictions on walking in indoor environments, the crowdsourcing trajectories are produced by matching the result of pedestrian dead reckoning (PDR) with the candidate routes based on the Shape Context algorithm. Further, a crowdsourcing trajectory can be represented by uniformly distributed reference points based on step detection, and each point corresponds to a unique RSS of access points (APs). Since such a fingerprint construction method can be performed while smartphone holders are moving and not aware of their actual positions, it can be referred to the dynamic construction method of the radio map. The real scenario experiments reveal that the proposed solution can significantly reduce the time and manpower consumption to build the radio map. Moreover, compared with traditional schemes, our crowdsourcing-based radio map construction method for the WiFi fingerprinting localization (CRCLoc) system can improve the accuracy and robustness of the indoor-positioning results.

In this paper, we study the secrecy performance of multiple-input single-output (MISO) wiretap channel with random beamforming (RB), where the eavesdropper is equipped with multiple antennas. In traditional RB schemes, the transmitter utilizes only one antenna to emit information signal and adopts the rest antennas to produce a random beamforming vector for security. To make full use of the power resource and improve the secrecy performance, we propose a power-minimizing and signal-splitting random beamforming (PM-SSRB) scheme, where the random beamforming vector is generalized with arbitrary number of transmit antennas based on power-minimizing. To evaluate the secrecy performance of the proposed scheme, we analyze the ergodic secrecy rate and derive the closed-form expression of the ergodic rate of the MISO wiretap channel. Simulation results show that, compared with the traditional hybrid artificial fast-fading scheme (AFF) and artificial noise (AN) scheme, the proposed SSRB scheme and PM-SSRB scheme perform much better in terms of the ergodic secrecy rate in all power regimes. More importantly, when the eavesdropper has more antennas than the transmitter, our schemes always outperform the AFF and AN schemes. The PM-SSRB scheme is also shown to be superior to the secret-key AFF scheme.

In this paper, we propose an advanced direction calibration method for the smartphone-based indoor localization system on the basis of map information and reinforcement learning (RL). Currently, the direction estimated by pedestrian dead reckoning (PDR) is biased due to the low-precision sensor in smartphone and magnetic field distortion in indoor environment. Thus, the direction calibration methods draw increasing attention. Since the movement of pedestrian is restricted by the indoor environment, the map information could be used to correct the heading of pedestrian and then improve the localization performance. Furthermore, since the tracking of pedestrian can be modeled as a Markov decision process. we propose a novel direction calibration algorithm based on deep Q-network (DQN). Different from the traditional direction calibration algorithms that usually rely on image processing, the proposed method use DQN to find an optimal policy to determine the moving direction. We conduct experiments in a realistic representative office environment to reveal the validity of the proposed direction calibration algorithm. The experiment results indicate that the proposed algorithm can remarkably alleviate the cumulative error, and improve the accuracy, stability and robustness of the indoor positioning system.

In this paper, a novel mobile tracking method based on pedestrian dead reckoning (PDR) and wireless local area network (WLAN) RSS fingerprint is proposed, which estimates the real-time location of pedestrian continuously using the improved particle filter. The existing PDR systems mostly focus on the condition that sensor axes are relatively fixed to user. However, the sensor axes may be changing during walking period in several motion patterns, for example that the smartphone is swinging with hand or kept in bag. Therefore, we propose a novel PDR algorithm for different handheld patterns, which detects the steps based on multimode finite-state machine (MFSM) with adaptive updating thresholds and estimates the heading direction with principal component analysis (PCA) and ambiguity resolution. On the other hand, for a long continuous walking process, localization error will accumulate and lead to the particle filter losing tracking of the target device. To deal with this problem, we design an improved particle filter with advanced resampling strategy for recovery when localization fails. We conduct experiments for four motion patterns in two realistic representative indoor environments: office building and shopping mall. Experiment results reveal the proposed localization system could achieve an average localization accuracy within 2m even in the toughest motion pattern.

Recently, the demand for indoor positioning has gradually increased. Considering that people walk indoors with a serious restriction, the map information is extremely significant, which can be used as an aid in indoor positioning. In order to exploit map information thoroughly and automatically, and obtain a high- precision positioning result, we propose a map- aided particle filter (PF) algorithm based on WiFi and Pedestrian Dead Reckoning (PDR) in this paper, which exploits WiFi RSS fingerprint, inertial sensors and indoor map information comprehensively. Before the online localization, some specific image processing methods which are Morphological operation, Skeleton extraction and Line detection, are introduced to extract the latent information of indoor map, such as the skeleton of passageway in buildings, the possible forwarding directions, etc. Using the extracted features of the floor plan, the particle filter can adjust the estimated heading direction from the PDR module based on the areas of particle distribution. The real scenario experiments reveal the validity of candidate direction matching. The results also indicate that the proposed algorithm can remarkably alleviate the cumulative error, and effectively solve the problem of trajectory drift and particle deactivation during indoor positioning. Thus, compared with traditional schemes, our proposed algorithm can improve the accuracy, stability and robustness of the indoor positioning system.

In this paper, a secure generalised precoding aided quadrature spatial modulation (SGPQSM) scheme is proposed to resist the passive eavesdropping that is unknown to the transmitter. With the help of artificial noise in the null space, the SGPQSM scheme can retain all the advantages of generalised precoding aided quadrature spatial modulation (GPQSM) at the legitimate receiver while producing interference to the eavesdropper. Signal space diversity (SSD) is also used to increase the "diversity order". Then the secrecy capacity of our proposed SGPQSM is analyzed and the optimal power allocation between signal and artificial noise is investigated. Simulation results demonstrate that our SGPQSM can significantly improve the secrecy performance. In multiple-input multiple-output systems, the proposed SGPQSM scheme performs better than the existing secure scheme.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) in relay interference channels (ICs), where energy-constrained relays harvest energy from sources' radio-frequency signals and use the harvested energy to forward the information to destinations. We adopt the power splitting (PS) relay protocol and the interference alignment (IA) technique to jointly realize energy transfer and interference management. We propose two novel transmission schemes for the SWIPT in relay IC networks, namely, one-stage PS IA scheme and two-stage PS IA scheme. For both schemes, we investigate the optimal PS ratios that maximize the network sum rate. We obtain the closed-form optimal PS ratios for the two-stage PS IA scheme and develop a distributed and iterative algorithm to derive the optimal PS ratios for the one-stage PS IA scheme. We further study the optimal design of the IA precoding and decoding matrices. Based on the Grassmann manifold approach, we present an algorithm to obtain the optimal IA matrices for both schemes. The optimality of the designs of both the PS ratios and the IA matrices is then verified by simulations. Our results show that the proposed schemes effectively improve the performance of the network and significantly outperform benchmark schemes.

This paper investigates the secrecy performance of amplified-and-forward (AF) relay wiretap model by using energy harvesting (EH) and signal alignment technique. All nodes equipped with multi antennas play different roles in terms of source, destination, relay, eavesdropper and jamming node. The jamming node designs artificial noise based on signal alignment principle, and then transmits it to the eavesdropper with harvested energy. Due to different power limitations, we propose power allocation schemes called Local Power Constraints scheme (LPCS) and Global Power Constraints scheme (GPCS), which maximize the secrecy rate of the system and obtain better secrecy performance when compared with the non-EH scheme and the partial nodes power allocation EH scheme. Furthermore, we also analyze the effect of the energy harvesting on the secure degrees of freedom (SDOF) in the high signal-to-noise (SNR) region, and simulate the secrecy performance with convex optimization method by proving the convexity of the optimization problem. Results show that the proposed schemes outperform the compared schemes on the secrecy performance, and achieve more secrecy rate when the destination and the relay have more antennas.

In this paper, we study a multi-user multi-relay interference-channel network, where energy- constrained relays harvest energy from sources' radio frequency (RF) signals and use the harvested energy to forward the information to destinations. We adopt the interference alignment (IA) technique to address the issue of interference, and propose a novel transmission scheme with the IA at sources and the power splitting (PS) at relays. A distributed and iterative algorithm to obtain the optimal PS ratios is further proposed, aiming at maximizing the sum rate of the network. The analysis is then validated by simulation results. Our results show that the proposed scheme with the optimal design significantly improves the performance of the network.

In three dimensional (3D) multiuser multi-input multi- output (MU-MIMO) systems with limited feedback, the high computing complexity and the large storage requirement at terminals are the significant challenges due to a large 3D codebook used with the massive antenna array setting in the base station (BS). In this paper, we study a limited feedback strategy based on the separate horizontal and vertical double codebook to reduce the storage space and the codeword search complexity. Especially, two user scheduling schemes based on the double codebook are proposed. In order to schedule more users, the first scheme under the relaxed condition distinguishes users only in horizontal dimension and the scheduled users may have the same quantified vertical channel vectors. In the second scheme with the strict condition, the scheduled users have not only the orthogonal horizontal channel vectors but also the different vertical channel vectors to suppress the inter-user interference as much as possible. The simulation results demonstrate that the first scheme can achieve better system total performance for more users selected to transmit simultaneously, while the second scheme can achieve better per-user performance due to the lower inter-user interference.

This paper studies privacy-preserving for downlink transmission in multiuser relay systems, where a source communicates with multiple users via a relay employing the amplify-and-forward (AF) protocol. At any scheduling unit, only one user (desired user) is selected to receive the source information, and the other users (undesired users) are viewed as potential eavesdroppers due to the broadcast nature of wireless medium. A cooperative physical-layer scheme is proposed to prevent information leakage. The key idea of this scheme is to schedule a cooperating user in addition to the desired user to deliver the artificial noise (AN). By exploiting the characteristics of channels, the cooperating user carefully designs the AN transmitted during two time slots such that the AN can be cancelled out at the desired user, but can not be removed at the undesired users. As a result, the detection performance of the desired user is free of interference, while that of undesired users is heavily degraded, thereby preserving the data confidentiality of the desired user. To maximize the secrecy rate of the system, a user scheduling policy is developed. Further, the lower bound of the ergodic secrecy rate (ESR) is derived, and its asymptotic behavior is analyzed via extreme value theory (EVT). Theoretical analysis and simulation results show that, thanks to the proposed cooperative AN injection mechanism, the system ESR grows with the increasing number of users, and much higher secrecy rate can be achieved compared to the existing schemes in literature.

In this paper, we study the degree of freedom(DoF) for the two-cell downlink multiple-input multiple-output(MIMO) interference multiple access channel(IMAC) with the delayed channel state information at the transmitters(CSIT). We propose a two-stage interference alignment scheme(TSIA) which makes full use of both the outdated and the current CSIT to align not only the interference from the adjacent cell onto the zero space, but also the intra-cell interference onto the subspace spanned by the previously received interference signals, respectively. Based on the TSIA method, we characterize the sum DoF of the network and the result shows the significant gain compared with the traditional TDMA-ZF scheme. Furthermore, the trend of the sum rate of the network well coincides with the analysis of DoF, which verifies our work.

In this paper, an amplify-and-forward two-way relay system with unknown carrier frequency offsets (CFOs) is considered. A double differential transmission scheme is proposed to achieve successful two-way relaying transmission without any CFOs information. The average symbol error rate (SER) performance of the proposed scheme is analyzed and a closed-form upper bound of the average SER is derived. Simulation results are provided to validate the proposed scheme.

This paper investigates a four-node wiretap network including one source (S), one relay (R), one legitimate receiver (D) and one eavesdropper (E) with cooperative jamming in a two-hop relay transmission. In the first phase, S transmits an useful signal to R while D transmits an interference signal with the purpose of confounding the eavesdropper. In the second phase, R broadcasts the mixture signal together to D. In this paper, based on large-scale fading, we propose two system models named One-link Wiretap Model and Three-link Wiretap Model to distinguish the receiving links at E. Then, a power allocation scheme is proposed to maximize the average secrecy capacity through two-dimension optimization. Meanwhile, we solve the optimization problem by two different methods called IBO and IAO. Simulation results show that the proposed power allocation strategy can achieve a higher average secrecy capacity than the reference scheme RJPA (Rate-optimal jamming power allocation) in the two models. Besides, a larger security range can be obtained by the proposed scheme.

Femtocells have attracted more and more attention in academia, industry and standardization forums. Besides, energy efficiency has been widely discussed in recent years. However, most of the existing works focus on the energy efficiency of macro base stations, the energy efficiency of femto base stations is neglected. In this paper, we study the maximization of energy efficiency of downlink OFDMA macro-femto networks. Both the transmit power constraint of femto base stations and the SINR thresholds of femto users and macro users are considered. We model the subchannel and power allocation problem as a non-cooperative game and a price function is introduced. To decrease the computational complexity, joint subchannel and power allocation are decomposed into two steps and a distributed resource allocation scheme is proposed to resolve the resource allocation problem. Simulation results show that the proposed algorithm has better performance in terms of energy efficiency compared with equal power allocation and an energy-efficient power control algorithm.

Harvesting energy from the radio-frequency (RF) signal is an exciting solution to replenish energy in energy-constrained wireless networks. In this paper, an amplify-and-forward (AF) based wireless relay network is considered, where the relay nodes need to harvest energy from the source's RF signal to forward information to the destination. To improve the performance of information transmission, we propose two distributed relay selection protocols, Maximum Harvested Energy (MHE) protocol and Maximum Signal-to-Noise Ratio (MSNR) protocol. Then, we derive the outage probabilities of the system with our proposed relay selection protocols and prove that the proposed selection protocols indeed can improve the system performances and the MSNR protocol outperforms the MHE protocol. Simulation results verify the analysis and theorems. In addition, the effects of key system parameters are also investigated via simulations.

Femtocell technology, which aims at improving indoor signal coverage and offloading traffic from macro base stations (MBSs), has attracted interest in wireless industry. Among all the access methods, hybrid access proves to be the most promising one. However, it is difficult to promote the hybrid access mode since all the femto holders (FHs) merely care about their own benefits. In this paper, we propose a price discount strategy for wireless service provider (WSP) to promote the hybrid access mode of femtocell in which WSP provides a price discount in exchange for the femto holders to share part of their resource to macro users. The problem is formulated and analyzed as a Stackelberg game where WSP acts as the leader and femto holders as the followers. The optimal resource allocation ratio for each FH is decided independently and the optimal price discount factor for WSP is also obtained. Furthermore, we analyze how the bandwidth allocation strategy will affect the utility of WSP and an optimal bandwidth allocation ratio is decided. Numerical results show that both WSP and femto holders can benefit from the price discount strategy.

Although Orthogonal Frequency Division Multiplexing (OFDM) is an effective way to combat frequency selective fading of the wireless channel, it is very sensitive to frequency shifts. Doppler spreading due to the mobility of communication terminals or reflectors destroys the orthogonality among the subcarriers, and results in intercarrier interference (ICI). In this paper, we propose a multiple Doppler frequency offsets (DFOs) estimation based scheme to diminish the ICI in an OFDM transceiver for communication between a base station (BS) and a high-speed train. The proposed scheme utilizes a particularly designed preamble to estimate the DFOs by single receiving antenna, and preprocesses the received (downlink) OFDM symbol based on the DFOs estimates so that the post signal-to-interference ratio (SIR) gain can be obtained. Numerical results demonstrate that the proposed scheme can effectively obtain the DFOs estimates and achieve an obvious SIR gain when there exists a strong line-of-sight (LoS) path between the BS and the high-speed train.

An amplify-and-forward relay network composed of a source (S), N relays and a destination (D) is considered, where the relays are untrusted in the sense that they may eavesdrop on the transmission from S to D, that is they may decode messages of the source. As a part of the system, these untrusted relays are willing to help the communication from S to D. To prevent the relays from decoding the source message, a secure spacetime code with full diversity is designed at the source node. In this paper, no secret information is exchanged between S and D in advance. Training symbols are transmitted by S and D respectively. Based on the assumption of channel reciprocity, S and D can obtain the equivalent channels between them, which are unavailable to the relays. By exploiting the equivalent channels and random source antenna selection, random phase rotation is designed for each space-time code block at S to prevent the relays from eavesdropping. Simulation results are presented to verify the performance of the proposed secure spacetime coding scheme.

Physical (PHY) layer security has recently become a hot issue in wireless communication. In this paper, an approach to a generalized anti-eavesdropping space-time network coding (GAE-STNC) for cooperative communications is proposed to achieve the physical layer security and overcome the problem of imperfect synchronization while still guaranteeing full diversity. Based on the assumption of channel reciprocity, the basic idea is to exploit the channel state information (CSI) between the legitimate transmitters and the receiver, which is used to generate the secret key. With this secret key, the transmitters introduce pseudo random interferences by adding an anti-eavesdropping matrix to the initial system. Since the eavesdropper's channel is typically independent of the legitimate channel, the channel between the legitimate transmitters-receiver pair, the signal received by the eavesdropper is interfered. While the receiver can effectively decode the signal by utilizing the global CSI, the eavesdroppers can not decode the received signal correctly. Then, two specific schemes derived from the GAE-STNC are proposed. Numerical analysis and simulation results are presented to illustrate the proposed GAE-STNC schemes.

Aerial LiDAR (Light Detection and Ranging) point clouds are gathered by a downward scanning laser on a low-flying aircraft. Due to the imaging process, vertical surface features such as building walls, and ground areas under tree canopies are totally or partially occluded, resulting in gaps and sparsely sampled areas. These gaps produce unwanted holes and uneven point distributions that often produce artifacts when visualized using point-based rendering (PBR) techniques. We show how to extend PBR by inferring the physical nature of LiDAR points for visual realism and added comprehension. More specifically, the class of object a point is related to augments the point cloud in pre-processing and/or adapts the online rendering, to produce visualizations that are more complete and realistic. We provide examples of point cloud augmentation for building walls and ground areas under tree canopies. We show how different types of procedurally generated geometry can be used to recover building walls. These methods are generic and can be applied to any aerial LiDAR data set with buildings and trees. Our work also incorporates an out-of-core strategy for hierarchical data management and GPU-accelerated PBR with extended deferred shading. The combined system provides interactive visually-complete rendering of virtually unlimited-size LiDAR point clouds. Experimental results show that our rendering approach adds only a slight overhead to PBR and provides comparable visual cues to visualizations generated by off-line pre-computation of 3D polygonal urban models.

We present a scalable out-of-core technique for mapping colors from aerial oblique imagery to large scale aerial LiDAR (Light Detection and Ranging) point cloud. Our method does not require meshing or intensive processing of points, only fast and effective augmentation is applied to fill occluded points on building walls and under tree canopies. The presented system applies a modified visibility pass of GPU splatting to map colors, where occluded points are filtered out by projecting all points as oriented surface splats into images. A weighting scheme is utilized to accumulate colors from all contributing images while leveraging image resolution and surface orientation. The effectiveness of color mapping is demonstrated through visualizations of colored points by a GPU splatting algorithm.

A new cooperative transmission protocol with network coding scheme for synchronized multi-source system are proposed. The users are organized into two groups. The users in different groups transmit in distinct time slots, while the users in the same group transmit with orthogonal waveforms. In each transmission, the user transmits his own information and relays other users' information at the same time. The proposed protocol is able to mimic the full-duplex transmission with half-duplex mode to achieve high data rate. The network code for this protocol can be designed using classical linear block codes, whose minimum Hamming distance of the network code determines the system diversity order. Hence, with well-designed network code, the proposed protocol can achieve high diversity order. Simulation results show that the proposed scheme outperforms previous work.

Due to the broadcast nature, wireless transmissions can be overheard by any receiver within the transmission range. A physical layer approach for secure wireless cooperative communications is proposed in this paper. Considering the asynchronous nature of cooperative communications, this paper proposes an eavesdropping-resistant space-time network coding (STNC) scheme for multinode cooperative communications to prevent eavesdropping and overcome the problem of imperfect synchronization. In the proposed scheme, training symbols are transmitted by the destination D instead of the user nodes. Therefore, each user node can obtain the channel state information (CSI) between itself and D, which is unavailable to the eavesdropper E. By exploiting such CSI and the transmit information symbols, weighting coefficient is designed at each user node to prevent E from interception and ensure successful decoding at D. Based on the pairwise error probability (PEP) analysis, STNC is designed to achieve full diversity at D and improve the transmission efficiency of the asynchronous cooperative system. Simulation results are presented to verify the performance of the proposed eavesdropping-resistant STNC scheme.

Due to the asynchronous nature of cooperative communications, simultaneous transmissions from two or more nodes are challenging in practice. The existing cooperative communications employing successive transmission from one user node to the other can avoid the synchronization problem but results in large transmission delay. In addition, channel estimation in multi-source cooperative communications is a challenging and costly task due to the amount of training, especially when the number of cooperative users is large. Considering these practical challenges in multi-source cooperative communications, this paper proposes a differential space-time network coding (DSTNC) scheme for narrowband multi-source cooperative communications to overcome the problems of imperfect synchronization and complex channel estimation without introducing large transmission delay. Each user in the network linearly combines the correctly decoded symbols via network coding and transmits its packet in time division multiple access (TDMA) mode. The pairwise error probability is analyzed and the design criteria of the DSTNC are derived to achieve full diversity. For broadband cooperative communications, distributed differential space-time-frequency network coding (DSTFNC), which is differentially encoded within each orthogonal frequency-division multiplexing (OFDM) block, is designed through mapping from the proposed DSTNC. When the statistical channel power-delay profile is known at the corresponding user node, each node can permutate its channel independently to improve the performance of the DSTFNC scheme. Simulation results are presented to verify the performance of the proposed schemes.

Due to the broadcast nature of wireless medium, wireless transmissions can be overheard by any undesired receivers with eavesdropping capability within source transmission range. A novel physical layer approach for secure wireless cooperative communications against eavesdropping is proposed in this paper. For an asynchronous cooperative communication network with a cluster of user nodes transmitting to a common destination, we propose an anti-eavesdropping space-time network coding (AE-STNC) scheme to prevent eavesdropping and overcome the problem of imperfect synchronization. In the proposed scheme, training symbols are first transmitted by the destination (D). Owing to channel reciprocity, each user node can obtain the channel state information (CSI) between itself and D, which is unavailable to the eavesdroppers. By exploiting such CSI, anti-eavesdropping encoding is designed for each user node to create high decoding error rate at the eavesdroppers and ensure successful decoding at D. Furthermore, the AE-STNC is designed to achieve full diversity at D. Power allocation subject to average power constraint is considered and the secure region against eavesdroppers is also investigated. Based on the proposed AE-STNC scheme, an anti-eavesdropping space-time-frequency coding (AE-STFNC) scheme is proposed for broadband asynchronous cooperative communications. Simulations are provided to verify the performance and security of the proposed transmission schemes.

In multinode cooperative communications, simultaneous transmissions from two or more nodes are challenging due to its asynchronous nature. In addition, channel estimation is a costly task due to the amount of training, especially when the number of cooperative users is large. Considering all these practical challenges in multinode cooperative communications, this paper propose a new transmission scheme, namely differential space-time network coding (DSTNC), to overcome the problems of imperfect synchronization and complicated channel estimation. Each user in the network linearly combines the correctly decoded symbols by a network coding vector, which is designed to achieve full diversity without introducing large time delay. The pairwise error probability (PEP) is analyzed and the design criteria of the DSTNC are derived based on the PEP. The proposed DSTNC scheme can be applied to any number of cooperative users. Simulation results are shown to verify the performance of the proposed transmission scheme.

Cooperative communications can be used to improve communication reliability. However, the transmissions from different relaying nodes result in a great challenge in practice. The use of TDMA for relaying transmissions causes large transmission delay. In contrast, FDMA and CDMA associate with the issue of imperfect frequency and timing synchronization due to the simultaneous transmissions from the relaying nodes. In this work, we propose a space-time network coding (STNC) scheme utilizing transform-based coding to improve communication reliability while maintaining a stable network throughput and overcoming the issue of imperfect frequency and timing synchronization. Based on TDMA, a node in the network forms a linearly-coded signal from the overheard symbols and transmits it to the destination in its dedicated time slot. The pairwise error probability (PEP) is analyzed and design criteria of the STNC are derived to ensure achieving full diversity. Simulations are conducted to verify the performance of the proposed scheme and to reveal its advantage over a distributed space-time block coding scheme under timing synchronization errors.

In cognitive radio systems, secondary users should continuously sense the licensed spectrum in case a primary user starts to transmit. Whether or not the secondary users can sense the spectrum during the secondary communication is an attractive problem. In this paper, we consider a particular wireless cooperative communication system acting as a secondary system, and demonstrate that by exploiting the cooperative property of the cooperative communication, the secondary system dose sensing while communicating. Compared with the conventional periodical sensing scheme, the proposed method achieves better sensing performance without any overhead. The theoretical analysis is given, and the sensing performance of the secondary system is investigated. Simulation results show that the sensing performance of the proposed method is improved significantly as opposed to that of the conventional spectrum sensing.

In wirelessad-hocnetworks, network coding can take advantage of the bi-directional traffic flows to increase the achievable throughput. In this letter, on the one hand, cooperative diversity based on distributed cyclic delay diversity is integrated into the physical network coding scheme to combat fading in wireless channel. On the other hand, to reduce the system overhead for the coherent detection at each destination terminal, differential modulation based on linear constellation precoding is proposed. Theoretical analysis and numerical results both confirm that our two-way relaying scheme can achieve full spatial diversity. Furthermore, the proposed scheme can significantly improve the performance over traditional one-way wireless relay scheme and the existing two-way wireless relay schemes due to its enhanced spectral efficiency and lower implementation complexity.