Ultra-Wide Bandwidth (UWB) localization based on time of arrival (TOA) and angle of arrival (AOA) has attracted increasing interest owing to its high accuracy and low cost. However, existing localization methods often fail to achieve satisfactory accuracy in realistic environments due to multipath effects and non-line-of-sight (NLOS) propagation. In this paper, we propose a passive anchor assisted localization (PAAL) scheme, where the active anchor obtains TOA/AOA measurements to the agent while the passive anchors capture the signals from the active anchor and agent. The proposed method fully exploits the time-difference-of-arrival (TDOA) information from the measurements at the passive anchors to complement single-anchor joint TOA/AOA localization. The performance limits of the PAAL system are derived as a benchmark via the information inequality. Moreover, we implement the PAAL system on a low-cost UWB platform, which can achieve 20 cm localization accuracy in NLOS environments.

Parallel arrays with coprime subarrays have shown its potential advantages for two dimensional direction of arrival (DOA) estimation. In this paper, by introducing two flexible coprime factors to enlarge the inter-element spacing of parallel uniform subarrays, we propose a generalized parallel coprime array (GPCA) geometry. The proposed geometry enjoys flexible array layouts by the coprime factors and enables to extend the array aperture to achieve great improvement of estimation performance. Meanwhile, we verify that GPCA always can obtain M² degrees of freedom (DOFs) in co-array domain via 2M sensors after optimization, which outperforms sparse parallel array geometries, such as parallel coprime array (PCA) and parallel augmented coprime array (PACA), and is the same as parallel nested array (PNA) with extended aperture. The superiority of GPCA geometry has been proved by numerical simulations with sparse representation methods.

As a fundamental problem in the field of the network science, the study of topological evolution model is of great significance for revealing the inherent dynamics and mechanisms of complex network evolution. In order to study the influence of different scales of preferential attachment on topological evolution, a topological evolution model based on the attraction of the motif vertex is proposed. From the perspective of network motif, this model proposes the concept of attraction of the motif vertex based on the degree of the motif, quantifies the influence of local structure on the node preferential attachment, and performs the preferential selection of the new link based on the Local World model. The simulation experiments show that the model has the small world characteristic apparently, and the clustering coefficient varies with the scale of the local world. The degree distribution of the model changes from power-law distribution to exponential distribution with the change of parameters. In some cases, the piecewise power-law distribution is presented. In addition, the proposed model can present a network with different matching patterns as the parameters change.

Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for location-based services continues to increase. Channel state information (CSI) can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers. However, the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data. In this paper, the Dynamic Fusion Feature (DFF) is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system, which combines the pre-processed amplitude and phase data. Then, the improved edit distance on real sequence (IEDR) is used as a similarity metric for fingerprint matching. Based on the above studies, we propose a new indoor fingerprint positioning method, named DFF-EDR, for improving positioning performance. During the experimental stage, data were collected and analyzed in two typical indoor environments. The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures, has good anti-noise capability, and effectively reduces the localization errors.

In this paper, a novel opportunistic scheduling (OS) scheme with antenna selection (AS) for the energy harvesting (EH) cooperative communication system where the relay can harvest energy from the source transmission is proposed. In this considered scheme, we take into both traditional mathematical analysis and reinforcement learning (RL) scenarios with the power splitting (PS) factor constraint. For the case of traditional mathematical analysis of a fixed-PS factor, we derive an exact closed-form expressions for the ergodic capacity and outage probability in general signal-to-noise ratio (SNR) regime. Then, we combine the optimal PS factor with performance metrics to achieve the optimal transmission performance. Subsequently, based on the optimized PS factor, a RL technique called as Q-learning (QL) algorithm is proposed to derive the optimal antenna selection strategy. To highlight the performance advantage of the proposed QL with training the received SNR at the destination, we also examine the scenario of QL scheme with training channel between the relay and the destination. The results illustrate that, the optimized scheme is always superior to the fixed-PS factor scheme. In addition, a better system parameter setting with QL significantly outperforms the traditional mathematical analysis scheme.

Benefiting from strong decoding capabilities, soft-decision decoding has been used to replace hard-decision decoding in various communication systems, and NAND flash memory systems are no exception. However, soft-decision decoding relies heavily on accurate soft information. Owing to the incremental step pulse programming (ISPP), program errors (PEs) in multi-level cell (MLC) NAND flash memory have different characteristics compared to other types of errors, which is very difficult to obtain such accurate soft information. Therefore, the characteristics of the log-likelihood ratio (LLR) of PEs are investigated first in this paper. Accordingly, a PE-aware statistical method is proposed to determine the usage of PE mitigation schemes. In order to reduce the PE estimating workload of the controller, an adaptive blind clipping (ABC) scheme is proposed subsequently to approximate the PEs contaminated LLR with different decoding trials. Finally, simulation results demonstrate that (1) the proposed PE-aware statistical method is effective in practice, and (2) ABC scheme is able to provide satisfactory bit error rate (BER) and frame error rate (FER) performance in a penalty of negligible increasing of decoding latency.

The M-BCJR algorithm based on the Ungerboeck observation model is a recent study to reduce the computational complexity for faster-than-Nyquist (FTN) signaling [1]. In this paper, we propose a method that can further reduce the complexity with the approximately same or better bit error rate (BER) performance compared to [1]. The information rate (IR) loss for the proposed method is less than 1% compared to the true achievable IR (AIR). The proposed improvement is mainly by introducing channel shortening (CS) before the M-BCJR equalizer. In our proposal, the Ungerboeck M-BCJR algorithm and CS can work together to defeat severe inter-symbol interference (ISI) introduced by FTN signaling. The ISI length for the M-BCJR algorithm with CS is optimized based on the criterion of the IR maximization. For the two cases τ = 0.5 and τ = 0.35, compared to Ungerboeck M-BCJR without CS benchmark [1], the computational complexities of Ungerboeck M-BCJR with CS are reduced by 75%. Moreover, for the case τ = 0.35, the BER performance of Ungerboeck M-BCJR with CS outperforms that of the conventional M-BCJR in [1] at the low signal to noise ratio region.

The open and broadcast nature of wireless channels leads to the inherent security problem of information leakage in wireless communication. We can utilize endogenous security functions to resolve this problem. The fundamental solution is channel-based mechanisms, like physical layer secret keys. Unfortunately, current investigations have not fully exploited the randomness of wireless channels, making secret key rates not high. Consequently, user data can be encrypted by reducing the data rate to match the secret key rate. Based on the analysis of the endogenous wireless security principle, we proposed that the channel-based endogenous secret key rate can nearly match the maximum data rate in the fast-fading environments. After that, we validated the proposition in an instantiation system with multiple phase shift keying (MPSK) inputs from the perspectives of both theoretical analysis and simulation experiments. The results indicate that it is possible to accomplish the one-time pad without decreasing the data rate via channel-based endogenous keys. Besides, we can realize high-speed endogenously secure transmission by introducing independent channels in the domains of frequency, space, or time. The conclusions derived provide a new idea for wireless security and promote the application of the endogenous security theory.

In vehicular fog computing (VFC), the resource transactions in the Internet of Vehicles (IoV) have become a novel resource management scheme that can improve system resource utilization and the quality of vehicle services. In this paper, in order to improve the security and fairness of resource transactions, we design a blockchain-based resource management scheme for VFC. First, we propose the concept of resource coin (RC) and develop a blockchain-based secure computing reource trading mechanism in terms of RC. As a node of the blockchain network, the roadside unit (RSU) participates in verifying the legitimacy of transactions and the creation of new blocks. Next, we propose a resource management scheme based on contract theory, encouraging parked vehicles to contribute computing resource so that RSU could complete proof of work (PoW) quickly, improve the success probability of block creation and get RC rewards. We use the gradient descent method to solve the computing resource utilization that can maximize the RC revenue of RSUs and vehicles during the block creation. Finally, the performance of this model is validated in simulation result and analysis.

This paper investigates a power control problem in a jamming system, where a separate smart jammer is deployed to ensure the communication security of the legal user. However, due to power leakage, the smart jammer may incur unintentional interference to legal users. The key is how to suppress illegal communication while limit the negative impact on legal user. A jamming counter measure Stackelberg game is formulated to model the jamming power control dynamic of the system. The smart jammer acts as a leader to sense and interfere illegal communications of the illegal user, while the illegal user acts as a follower. In the game, the impact of uncertain channel information is taken into account. According to whether illegal user considers the uncertain channel information, we investigate two scenarios, namely, illegal user can obtain statistical distribution and accurate information of interference channel gain and its own cost, respectively. This work not only proposes a jamming counter measure iterative algorithm to update parameters, but also gives two solutions to obtain the Stackelberg equilibrium (SE). The power convergence behaviours under two scenarios are analyzed and compared. Additionally, brute force is used to verify the accuracy of the SE value further.

Link scheduling has always been a fundamental problem in wireless networks for its direct impacts on the performance of wireless networks such as throughput capacity, transmission delay, lifetime, etc. Existing work is mainly established under graph-based models, which are not only impractical but also incorrect due to the essentially fading characteristics of signals. In this paper, we study the shortest link scheduling problem under two more realistic models, namely the signal to interference plus noise ratio (SINR) model and the Rayleigh fading model. We propose a centralized square-based scheduling algorithm (CSSA) with oblivious power control under the SINR model and prove its correctness under both the SINR model and the Rayleigh fading model. Furthermore, we extend CSSA and propose a distributed square-based scheduling algorithm (DSSA). Note that DSSA adopts CSMA/CA so that a wireless node can compete for the wireless channel before starting its communication. We also show theoretical analysis and conduct extensive simulations to exhibit the correctness and efficiency of our algorithms.

Network densification is a promising solution to fulfill network capacity requirement and transmission rate for beyond 5G and 6G wireless communications. Ultra-dense network (UDN) integrates heterogeneous network resources and coordinates technologies on quality of service controlling, to provide users with flexible service. However, dense deployment reduces coverage radius of the cell, resulting in an increase on handover frequency, which makes a serious impact on service continuity. In this paper, we propose a proactive selection method for dynamic access points grouping (DAPGing) in accordance with “user-centric” philosophy, which selects target Access Points (AP) and reduces handover times to ensure communication continuity. This method includes two criteria: 1) the user’s sojourn time, which is determined by analyzing the AP coverage area; 2) neighbor relationship between APs, which is determined by coverage area and signal strength characteristics between neighboring APs. Therefore, candidate APs become the proactive selected ones to update the AP group. Stochastic geometry is used to build system model and performance metrics are analyzed, including AP group coverage probability and average update frequency. Experimental analysis shows that the proposed proactive selection method brings similar coverage probability to traditional handover method, while average update frequency is reduced more than 20% selection criteria.

Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks. To improve the energy efficiency (EE) of the unlicensed secondary users (SU), a power allocation (PA) algorithm with polynomial complexity is investigated. We first establish the feasible range of power consumption ratio using Karush-Kuhn-Tucker optimality conditions to support each SU's minimum quality of service and the effectiveness of successive interference cancellation. Then, we formulate the EE optimization problem considering the total transmit power requirements which leads to a non-convex fractional programming problem. To efficiently solve the problem, we divide it into an inner-layer and outer-layer optimization sub-problems. The inner-layer optimization which is formulated to maximize the sub-carrier PA coefficients can be transformed into the difference of convex programming by using the first-order Taylor expansion. Based on the solution of the inner-layer optimization sub-problem, the concave-convex fractional programming problem of the outer-layer optimization sub-problem may be converted into the Lagrangian relaxation model employing the Dinkelbach algorithm. Simulation results demonstrate that the proposed algorithm has a faster convergence speed than the simulated annealing algorithm, while the average system EE loss is only less than 2%.

In industrial wireless scenarios, the impulsive noise (IN) incurred by machine running or operation causes a serious influence on the power-limited industrial wireless communications. It is challenging to ensure efficient and reliable transmission with quality of service (QoS) guarantee for machine-type communication devices (MTCDs). Considering the IN in the industrial process, this paper establishes the multiuser multiple-input single-output (MU-MISO) orthogonal frequency division multiplexing (OFDM) system model, which combines transmitter and receiver design. Two precoding schemes are designed to improve communication effectiveness at the transmitter. More specifically, the precoder design scheme which combines semi-definite relaxation (SDR) with difference-of-two-convex-function (D.C.) iterative algorithm, is developed by utilizing the Dinkelbach method to improve the system effectiveness. To decrease the computational complexity, we devise the quadratic-based fractional programming (QFP) algorithm, which decouples the variables by using a quadratic transform method. On this basis, the IN mitigation scheme is studied to reduce the system error rate (SER) at the receiver. With the goal of improving the reliability of industrial wireless communications, we propose a hybrid nonlinear IN mitigation (HNINM) scheme and then derive its closed-form expression of SER. The simulation results show that the proposed QFP algorithm achieves superior performance while the HNINM scheme decreases the SER of industrial wireless communications.

Cloud providers (e.g., Google, Alibaba, Amazon) own large-scale datacenter networks that comprise thousands of switches and links. A load-balancing mechanism is supposed to effectively utilize the bisection bandwidth. Both Equal-Cost Multi-Path (ECMP), the canonical solution in practice, and alternatives come with performance limitations or significant deployment challenges. In this work, we propose Closer, a scalable load balancing mechanism for cloud datacenters. Closer complies with the evaluation of technology including the deployment of Clos-based topologies, overlays for network virtualization, and virtual machine (VM) clusters. We decouple the system into centralized route calculation and distributed route decision to guarantee its flexibility and stability in large-scale networks. Leveraging In-band Network Telemetry (INT) to obtain precise link state information, a simple but efficient algorithm implements a weighted ECMP at the edge of fabric, which enables Closer to proactively map the flows to the appropriate path and avoid the excessive congestion of a single link. Closer achieves 2 to 7 times better flow completion time (FCT) at 70% network load than existing schemes that work with same hardware environment.

NDN is an important instance of Information-Centric Networking. When integrating NDN into MANET, exploring new routing is a necessary task for this research area. The LSAs flooding is a common method to obtain network topology during route establishment. However, the LSAs flooding often results in a broadcast storm in high-density MANET. Using the MPR set proposed in the OLSR can effectively reduce the number of LSAs in the process of route establishment and can further solve the broadcast storm. In this paper, an enhanced neighbor discovery protocol firstly is designed to establish a MPR set. The new protocol can effectively avoid the problem incurred by unidirectional links that impact the network performance in a wireless environment. And then a new and proactive routing NOLSR based on OLSR for NDN-MANET is proposed to support NDN in MANET. And another important work is that NOLSR is implemented on top of NDN Forwarding Daemon NFD. Finally, we make a comparative analysis between NOLSR and the two most relative schemes such as traditional LSA-flooding and the scheme [1] by emulation experiments in the NDN emulator mini-NDN.

The lack of communication infrastructure in the ocean inevitably leads to coverage blind zones. In addition to high-throughput marine satellites, unmanned aerial vehicles (UAVs) can be used to provide coverage for these blind zones along with onshore base stations. In this paper, we consider the use of UAV for maritime coverage enhancement. Particularly, to serve more ships on the vast oceanic area with limited spectrum resources, we employ non-orthogonal multiple access (NOMA). A joint power and transmission duration allocation problem is formulated to maximize the minimum ship throughput, with the constraints on onboard communication energy. Different from previous works, we only assume the slowly time-varying large-scale channel state information (CSI) to reduce the system cost, as the large-scale CSI is location-dependent and can be obtained according to a priori radio map. To solve the non-convex problem, we decompose it into two subproblems and solve them in an iterative way. Simulation results show the effectiveness of the proposed solution.