Ultra-Wideband (UWB) technology enables high-precision positioning in underground subway environments with poor GNSS signal coverage, but ranging measurements often suffer from data loss due to occlusions, multipath effects, and ranging failures. While recent studies have focused on imputing missing measurements using temporal modeling, they often overlook spatial factors such as anchor group vary and train motion patterns. To address this limitation, this paper proposes a novel approach for imputing missing UWB ranging data by jointly exploiting temporal dynamics and spatial constraints. A mask matrix is introduced to model data availability, and a spatio-temporal optimization framework is established to guide the imputation process. Based on this framework, a Dynamic Spatio-Temporal Missing Data Imputation Network (DSTMIN) is developed for robust localization in complex subway environments. In particular, DSTMIN leverages missing-aware attention and adaptive graph fusion, significantly enhancing its robustness to large missing blocks and non-random data loss. Simulation results demonstrate that DSTMIN outperforms state-of-the-art models, such as STGCN and GRIN, under various missing rates. Under a 40% ranging data missing ratio, DSTMIN reduces the root mean square error (RMSE) by 18.6% and 16.3% compared with STGCN and GRIN, respectively.

Intelligent radio maps (IRMs) have emerged as a critical enabler for next-generation wireless networks, offering comprehensive spatiotemporal awareness of the electromagnetic environment with limited sensing resources and low computational overhead. They play a crucial role in enhancing spectrum efficiency, enabling intelligent resource allocation, supporting anti-jamming communications, improving interference management, and facilitating environment-aware networking. This paper presents a systematic overview of how to construct high-quality IRMs. We first introduce six evaluation metrics aligned with practical deployment requirements and evolving wireless network demands. Guided by these metrics and recent advances in artificial intelligence (AI), we provide an in-depth review of spectrum sensing approaches and state-of-the-art methods for spectrum inference. We further explore the intrinsic connections between these two steps and propose an integrated sensing–inference construction scheme. Extensive experiments demonstrate that the integrated scheme achieves superior IRM construction performance under sparse sensing, validating its practical potential for future wireless networks.

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

In this paper, we study a UAV-Parking-Vehicles assisted Mobile Edge Computing (MEC) network for providing enhanced edge computing services, where a UAV serves to relay ground users’ tasks to parking vehicles having idle computing resources in the vicinity for processing.We formulate the problem of average task delay minimization in this case as a long-term discrete mixed integer programming problem. We transform this problem into two subproblems: Service deployment optimization problem at large time scale and task offloading optimization problem at small time scale. For the former, we define a system utility for measuring the effect of a service deployment profile at parking vehicles, formulate the system utility minimization problem for optimizing the service deployment at vehicles, and propose a Computing resource allocation and Genetic Algorithm based Service deployment Algorithm (CGSA) for obtaining optimized service deployment profiles at parking vehicles on a per cycle basis. For the latter, we propose a Bandwidth allocation, Task offloading, and Transmission scheduling Algorithm (BTTA) for determining optimized task offloading profiles for all users on a per time slot basis. Extensive simulation results show the high performance of our proposed algorithms compared with baseline algorithms.

Unmanned Aerial Vehicles (UAVs) enabled Mobile Edge Computing (MEC) has been an attractive paradigm for providing flexible and high-quality offloading services to ground users. In this paper, we study a hierarchical aerial MEC network architecture for improved quality of user experiences. Specifically, we study a bilevel UAV-enabled MEC network where a fixed-wing UAV (F-UAV) and multiple rotor UAVs (R-UAVs) are jointly deployed to provide continuous MEC services to ground users with dynamic demands. We formulate a long-term optimization problem for minimizing the utility of all users while considering the stability of task queue backlogs and energy consumption budgets at users and R-UAVs, where user utility measures the per-slot task processing performance at user side. We apply Lyapunov optimization technique to decompose the original problem into deterministic per-slot optimization subproblems. We derive the optimal offloading conditions at different types of edge nodes. We accordingly propose a Bilevel UAVs based Online Task processing and Resource allocation Algorithm (BOTRA) for determining the offloading and local processing profiles at users. Extensive simulation experiment results show the high performance of the proposed BOTRA algorithm compared with benchmark algorithms.

The spread of infectious diseases in networked populations poses significant challenges for public health intervention strategies. Traditional centrality-based and heuristic network dismantling approaches prioritize highly connected nodes but often fail to account for individual mortality risk, limiting their effectiveness in minimizing overall fatalities. While recent advances in machine learning have improved intervention strategies, existing models largely focus on reducing disease transmission rather than directly targeting mortality outcomes. To address this gap, we propose a reinforcement learning-based framework that integrates graph representation learning to identify and remove high-risk nodes, thereby maximizing network fragmentation while minimizing overall deaths. The framework is trained using synthetic networks and evaluated on five synthetic and four real-world datasets, benchmarking its performance against state-of-the-art network dismantling methods [graph dismantling with machine learning (GDM), generalized network dismantling (GND), and graph enhanced reinforcement learning (GERL)]. Experimental results demonstrate that the proposed method consistently outperforms baseline approaches, particularly in scale-free and community-structured networks, where targeted node removal significantly weakens network connectivity and suppresses epidemic spread. Moreover, in real-world networks, the method achieves lower cumulative death rates and higher epidemic thresholds, highlighting its robustness in controlling disease propagation. By incorporating mortality risk into network representation learning, the proposed framework offers a scalable, adaptive, and socially responsible approach to epidemic mitigation, misinformation control, and network resilience enhancement.

Accurate location information is an essential prerequisite for location-based service and operation safety in railways. Ultra wide band (UWB) fused with inertial measurement unit (IMU) has been thought as a promising localization technique for trains. However, due to trains’ high speed, ranging results from UWBs are seriously influenced by Doppler effect. Most existing work focused on Doppler effect mitigation in single round of communication, but ignored its negative impact on two-way time of flight (ToF) and fusion performance. In this paper, we propose an adaptive fusion model with ranging rectifications for high-speed trains. Firstly, two-way ToF ranging errors caused by Doppler effect are studied. Secondly, Doppler effect is mitigated effectively, with prior information of trains’ states in time series. Finally, an adaptive UWB/IMU fusion scheme is proposed, where residual errors of UWB observations after Doppler effect mitigation are modeled by a closed-form expression. Simulation results show that, regarding single time-slot localization, the proposed ranging rectification algorithm outperforms four typical algorithms, with gains of 78.42%, 60.22%, 58.09%, and 28.38%, in terms of RMSE. Regarding continuous localization with temporal correlation, the proposed adaptive fusion model achieves gains of 75.07%, 73.27%, 53.52% and 70.02%. Furthermore, its effectiveness is verified in practical platform.

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

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

Activity detection plays an important role in grantfree random access, a promising approach for handling a large number of users in use cases like massive machine type communication (mMTC). Existing activity detection algorithms cover various scenarios but overlook wideband distributed antenna systems, a practical configuration for next-generation wireless networks. When following conventional activity detection methods, sparse Bayesian learning (SBL) could be an option in this case. However, SBL-based methods for wideband systems lack the consistency of maximum likelihood estimation (MLE), resulting in unsatisfactory detection performance. This paper proposes a novel distributed activity detection framework for wideband cell-free multiple-input and multiple-output (MIMO). Specifically, we provide a novel uplink channel model for activity detection in wideband cell-free MIMO, accounting for asynchronous reception. Additionally, we present possible SBLbased methods, identifying their limitations, which motivates the development of a new approach for activity detection. Next, we propose a covariance-matching distributed activity detection framework that matches the sample covariance matrix to the estimated covariance matrix. Simulation results demonstrate the effectiveness of the proposed distributed algorithm.

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

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

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

Task assignment is a key issue in mobile crowdsensing (MCS). Most existing work in this area has focused on the task assignment for the single platform scenario, which can cause considerable waste of limited human resources or reduced task completion rate due to potential spatial mismatching between distributions of users and tasks. In this article, we study multiplatform cooperative task assignment. The design goal is to maximize the social welfare while ensuring cooperative and individual rationality. We formulate this problem, transform it to a maximum value flow problem, and prove its NP-hardness. We first propose a greedy-maximum-flow-based task matching (GMTA) mechanism for interplatform task matching. In GMTA, there are two phases in each time slot: 1) in the former phase, earliest-deadline-first-based intraplatform optimal task assignment is carried out at each individual platform and 2) in the second phase, greedy-maximum-flow-based task matching is carried out to perform interplatform cooperative task assignment for those overloaded tasks in the first phase. We then enhance GMTA by designing an iterative-maximum-flow-based task matching (IMTA) mechanism, which is to achieve enhanced social welfare at the cost of increased computational overhead. We deduce time complexities of both mechanisms, and prove that they satisfy cooperative and individual rationality. Extensive simulations are conducted and the simulation results demonstrate the effectiveness of our proposed mechanisms.

This paper presents a novel machine-learning-based SAR ADC designed for energy-constrained voice-activated Internet-of-Things (IoT) applications, such as portable voice assistants, wearable health monitors, and smart home devices. Voice interfaces in these devices require always-on operation while maintaining ultra-low power consumption to preserve battery life. The proposed ADC integrates a machine-learning (ML) model into the successive approximation register (SAR) ADC search process to significantly reduce comparator activity and digital-to-analog converter (DAC) switching energy. Our approach leverages ML to predict the first-order difference between an unknown sample and its previously digitized sample, dynamically adjusting the SAR ADC search range and minimizing unnecessary conversions. To further optimize energy efficiency, the proposed method incorporates a dual-stream strategy: one optimized for signals with smaller ML prediction errors, and the other for signals with larger ML prediction errors. A time comparator-based error detection mechanism is introduced to adaptively select the most power-efficient search stream. These enhancements lead to significant power savings while maintaining high conversion accuracy. Measurement results from a fabricated 10-bit SAR ADC chip demonstrate a 70.05% reduction in comparator activity compared to the conventional monotonic approach. With a 5.6 kHz sinusoidal input, the ADC achieves an SNDR of 57.78 dB and an SFDR of 67.06 dB. The total power dissipation remains below 0.54 lW, with an additional estimation of 0.22 lW for the digital part, making it an ideal solution to the always-on voice interfaces in battery-operated IoT devices.

Fast underwater localization serves as a critical application of Internet-of-Underwater-Things (IoUT), such as underwater search and rescue. Narrowband sinusoidal pulses are very commonly used in locator beacons installed on flight recorders. A mobile anchor (e.g., autonomous underwater vehicle (AUV)) has to keep receiving the signal and measuring Doppler shift for a long time, so that enough information can be collected for reliable localization. The need of a long observation window is deeply rooted in the fact that the Doppler shift measurements are highly correlated when they are taken at closely located spots. In this paper, we will show that by replacing the narrowband beacon signal with a wideband one, high-accuracy positioning can be achieved within a short period of time. The basic idea is to simultaneously measure Doppler shift and time of arrival (ToA) from wideband signals, and the errors spaces corresponding to these two measurements are complementary even when they are taken at the same position. The Cramér-Rao bound (CRB) will be derived for such a system. In low-signal-to-noise ratio (SNR) regime, the observation window has to be prolonged for effective information extraction, and we will see that the wideband signals still have an edge over the narrowband signals in positioning accuracy. Efficient algorithms are designed for positioning and the closed-form positioning error is derived. We also show that the localization accuracy will experience a significant drop when ToA is replaced by time difference of arrival (TDoA), because the perfect complementation no longer holds. The performance of the proposed algorithm, along with corresponding comparisons, is verified through simulations over various parameters such as SNR, number of measurements, and length of observation window, etc.

The rapid development of High-Speed Railway (HSR) puts higher requirements on comprehensive perception and reliable transmission in tunnel scenarios. To realize efficient and reliable perception information transmission of HSR in the tunnel, we propose a multisensor perception communication system, which consists of an Access Point (AP) deployed on each carriage for perception information transmission and self-powered wireless sensors. The AP remote transmits the perception information through the leaky cable deployed in the tunnel. We construct an optimization problem for minimizing the transmission time of the whole system’s perception information in the multi-network system and the adjacent area of the carriage. A Multi-Agent Cooperation-based Deep Reinforcement Learning (MA-CDRL) algorithm is proposed to get the optimal scheduling strategy for reducing the transmission time. We construct the CDRL neural network for the algorithm to introduce the states of other APs, resulting in the system making more efficient transmission strategies. In the simulations, the proposed algorithm gets a better performance than the comparison algorithms and is verified in various dynamic HSR scenarios, such as different travel speeds and sensor distributions.

Federated Learning (FL) enables collaborative model training across devices, but data exchanges pose privacy risks. Homomorphic Encryption (HE) is widely used to enhances privacy in FL but incurs significant communication and computation latency. Prior work reduced this latency using compressions, but sacrificed learning accuracy and overlooked the impact of the number of participating devices on latency. Over-the-air computation (AirComp) leverages wireless channels' superposition property to achieve high spectral efficiency and efficient aggregation irrespective of device number. In this paper, we propose HEAirFed, integrating AirComp with the state-ofthe-art HE scheme CKKS for efficient privacy-preserving FL. In HEAirFed, we develop a ciphertext-oriented wireless communication module to ensure homomorphic operations leverage AirComp's superposition property, enabling correct decryption. We further build a rigorous error analysis model, derive the worst-case upper bound of approximation error, and characterize this bound's impact on the convergence guarantee of HEAirFed, measured by the optimality gap with bounded approximation error. Then, we minimize this gap and derive a near-optimal solution in semi-closed form. Extensive experimental results on real-world datasets validate the ciphertext-oriented design's necessity, the error analysis's correctness, and demonstrate that HEAirFed achieves a substantial reduction in communication and aggregation latency compared to baseline, with minimal learning accuracy loss.

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.

Adaptive compressive spectrum sensing (ACSS) plays a vital role in cognitive radio networks due to its reduced sampling rate and power consumption. Most of the existing ACSS schemes focus on the improvement of spectrum sensing performance, but do not provide the deterministic assurance of sensing results. Hence, we propose an ACSS based on deterministic estimation model (ACSS-DEM) to provide the confidence level of the reconstructed signals. This is realized by deriving the closed-form expression of the cumulative distribution function (CDF) of reconstructed errors. Firstly, in each sensing interval of ACSS, we propose a novel signal reconstruction algorithm that incorporates the prior knowledge into ℓ2,1-norm minimization of block-sparse signals. Secondly, a prior knowledge refining strategy is designed through convex geometry theory to further improve the reconstruction accuracy. Finally, the CDF of reconstruction error is derived and regarded as a stopping criteria for observation sample collection. The experiment results demonstrate that the proposed ACSS-DEM delivers optimal spectrum-sensing performance, offering over a 12.2% improvement in detection performance at a false alarm probability of 0.1, compared with other typical ACSS methods, e.g., ACSS-JL, ACSS-SOC, and ACSS-CV.

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

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

Mobile crowdsensing is a sensing paradigm using mobile users’ smart devices to perform sensing tasks, which has attracted much attention due to its low system cost, high flexibility, and wide coverage. In this paper, we study the hybrid sensing online task allocation problem for maximizing the total quality of completed tasks under given budget constraint. We formulate this problem as a 0-1 integer programming. To address this problem, we propose an efficient hybrid sensing based online task assignment algorithm (HSTA), which consists of two major components: Expected task completion quality based opportunistic user recruitment and participatory user recruiting and path planning. We present the detailed algorithm design of HSTA and deduce its computational complexity. Simulation results demonstrate the effectiveness of the proposed HSTA algorithm.

Task assignment is a critical issue in mobile crowd-sensing, which is aimed to maximize the number of completed tasks subject to budget constraints. However, existing work in this aspect did not consider the correlation between the tasks submitted by the same task requester. That is, tasks in the same subset from the same task requester are often correlated such that they are considered completed only when all of them are completed, and partial completion of them are useless. This requirement largely affects the performance of existing algorithms for the assignment of such partially correlated tasks. In this paper, we formulate the problem of maximizing the total number of completed tasks subject to such correlation and also budget constraints as an integer programming problem. We propose two greedy algorithms, one is requester happiness utility based algorithm and the other is minimum task remaining subset first algorithm. We present design details of both algorithms and deduce their computational complexities. Numerical results demonstrate that these two algorithms can significantly outperform the existing work.

This paper proposes an enhanced resource selection (eInP-RS) scheme for efficient intra-platoon message delivery of cooperative awareness messages (CAMs) and decentralized environmental notification messages (DENMs). To achieve this goal, the eInP-RS scheme allows each vehicle to transmit CAM and DENM packets on a C-V2X channel (CH1) and an 802.11p channel (CH2), separately, and introduces four mechanisms to enhance the standardized sensing-based SPS scheme. A contention window (CW) size adjustment mechanism is introduced to enable a vehicle to adjust its CW size according to the information it collects on CH1 in order to avoid potential packet collisions on CH2; a resource partition mechanism is introduced to divide frequency-time resources in a selection window into two sets in order for vehicles moving in opposite directions to select different resources and thus avoid potential merging collisions on CH1; an intra-platoon cooperation mechanism is introduced to enable the PL of a platoon to know the resource and channel occupation information of the platoon's hidden nodes on CH1 and CH2; and a packet collision detection mechanism is used to enable a non-platoon vehicle to detect packet collisions occurring on both channels after a lane-changing maneuver to avoid potential merging collisions. Simulation results show that the proposed eInP-RS scheme outperforms the standardized sensing-based SPS scheme in terms of the intra-platoon CAM/DENM delivery ratio and the average intra-platoon DENM delivery delay.

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.

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

This paper presents a new approach to analog-to-digital converter (ADC) for low to medium-activity signals. We integrate the concept of reinforcement learning into the successive approximation register (SAR) ADC search methodology to reduce the comparator activity and switching energy of the digital-to-analog converter (DAC). This method is based on selecting the best solution among 8 available different solutions to digitize the first-order difference between an unknown sample and its previous digitized sample, in addition to the conventional method to digitize the sample amplitude itself. In this way, the number of comparisons needed can be smartly reduced. Our 10-bit SAR ADC simulation results show that the proposed method reduces comparator activity for low to medium-activity signals by up to 79.74%, while it operates the same as the conventional method for high-activity signals.

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.

Mobile Edge Computing (MEC) can provide low-latency and workload-intensive computing services to user equipments. Elastic scaling, service placement, and task scheduling are key techniques affecting the performance of an MEC system. Elastic scaling is to determine the set of active servers and also the amount of computation resources allocated for each service deployed at a server, service deployment is to determine the set of services/applications to be deployed at each server, and task scheduling is to determine how tasks are assigned among different servers. In this paper, study an MEC system where user demands fluctuate spatially and temporally. Our objective is to minimize the total power consumption and task response time. We accordingly formulate the joint optimization of elastic scaling, service placement, and task scheduling in this case as a Mixed-Integer Nonlinear Programming (MINLP). Due to the hardness of the problem, we propose an efficient joint elastic scaling, service placement, and task scheduling algorithm. Simulation results show that our proposed algorithm can effectively reduce the system cost as compared with baseline algorithms.

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

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

Mobile crowdsensing (MCS) is a cost-effective sensing paradigm by incentivizing mobile users to perform sensing tasks using their smartphones with rich embedded sensors. An important problem in MCS is how to achieve high task completion rate for location dependent tasks since some of them can be far away from potential users. Most existing work in this aspect assumes that there is only one service platform without consideration of existence of multiple platforms and also impact of their cooperation on task completion rate. In this paper, we design a task bundling based multi-platform cooperation mechanism (TBMCM) for completion of location dependent sensing tasks. The design objective is to maximize the system profit while improving the task completion rate. TBMCM works in a slot-by-slot manner. In each slot, each platform first assigns its tasks and task bundles to its registered users through reverse auctions and then submits information about its idle users and not-assigned-yet unpopular tasks to a cross-platform cooperation managing entity (CCM) for cross-platform cooperation. Then, the CCM entity releases the tasks and task bundles created using its collected tasks to the idle users. Meanwhile, each platform has the option to bundle its own tasks with the tasks provided by the CCM entity. A task bundling method is then designed to perform effective bundling between unpopular and popular tasks for improved task completion rate. We show the proposed mechanism satisfies individual and cooperative rationality. Extensive simulation results show the high performance of TBMCM in terms of task completion rate and system profit.

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.

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.

With the rapid growth of Internet of Things and proliferation of handheld smart devices, mobile crowdsensing has been regarded as an effective sensing paradigm due to its high scalability, low cost, and wide coverage. In this paper, we study hybrid task assignment where semi-opportunistic and participatory users co-exist for task executions while tasks are delay sensitive and have heterogeneous qualities. The design objective is to maximize the total quality of completed tasks subject to a total budget shared by both types of users. We formulate this problem as an integer programming problem. We propose an efficient hybrid users based task assignment algorithm (referred to as HU-TSA), which works in an iterative way as follows. It first selects the top n (initially, n = 1) semi-opportunistic users in terms of quality-cost ratio for task assignment. It then clusters the remaining tasks into different regions based on their closeness and then performs utility based optimized user-region binding and standardized task density based path planning for the participatory users. It repeats the above process over all possible values of n to seek an optimal budget splitting between the two types of users for improved performance. We present the detailed design description of HU-TSA and deduce its computational complexity. Extensive simulations are carried out and the results show the effectiveness of HU-TSA by comparing with existing algorithms.

Scalable consensus protocol is essential for providing high-throughput and secure blockchain services in wireless networks. In this article, we propose a scalable credible-committee-based blockchain consensus (SCBC) protocol for resource-limited multihop wireless networks, which contains the following key designs: 1) credit-based committee selection algorithm, which improves the system security by selecting credible committee members; 2) scalable credible-committee-based consensus algorithm, which supports efficient consensuses using small-sized committee and threshold signatures; and 3) criticality-based localized broadcast algorithm, which is designed to suppress broadcast redundancy and further improves the consensus efficiency. Thorough security analyses show that SCBC satisfies both safety and liveness properties, and can resist more attacks than traditional Byzantine fault-tolerant consensus protocols. We deduce the message complexity of SCBC. Extensive simulation results demonstrate that our proposed protocol SCBC outperforms existing work in terms of throughput and consensus latency.

Fingerprint-based indoor localization is one of the most promising solutions for various Intelligent Internet of Things (IIoT) systems. However, recent studies show that the key design challenges of current fingerprint-based localization techniques come from the following three aspects: 1) temporal variation caused by various patterns of IIoT device operations and stochastic fluctuation of wireless signals, 2) spatial unevenness of collected RSSI samples due to complex multi-floor environments, and 3) high feature sparsity of collected RSSI samples in large areas. To address these challenges, we present a localization architecture for multi-floor indoor localization in multi-building environment and accordingly propose a fingerprint-based localization method (referred to as GrowNetLoc) based on Gradient Boosting Neural Network (GrowNet) and Long Short-Term Memory (LSTM) network. Regarding building/floor identification, the gradient ensemble model GrowNet is utilized for extracting the mapping relationship between uneven RSSI samples and building/floor indices. Regarding location estimation, LSTM network is adopted as one layer of base learner to extract temporal features of RSSI samples, and a gradient boosting strategy is further used for overcoming the sample sparsity issue and improving the location estimation performance. Extensive experiments are conducted on real datasets and the results demonstrate that GrowNetLoc has superior localization accuracy and robustness performance compared with the existing methods.

This article proposes a cellular vehicle-to-everything (C-V2X) mode 4 and 802.11p-based resource selection (eInP-RS) scheme for efficient intraplatoon message delivery. The proposed eInP-RS scheme is intended to improve the delivery performance of cooperative awareness messages (CAMs) and decentralized environmental notification messages (DENMs) within a platoon. To achieve this goal, it allows each vehicle to transmit CAM and DENM packets on a C-V2X channel (CH1) and an 802.11p channel (CH2), separately, and introduces four mechanisms to enhance the standardized sensing-based semi-persistent scheduling (SPS) scheme. A contention window (CW) size adjustment mechanism is introduced to enable a vehicle to adjust its CW size according to the information it collects on CH1 in order to avoid potential packet collisions on CH2; a resource partition mechanism is introduced to divide frequency-time resources in a selection window into two sets in order for vehicles moving in opposite directions to select different resources and thus avoid potential merging collisions on CH1; an intraplatoon cooperation mechanism is introduced to enable a platoon leader to know the resource and channel occupation information of the platoon’s hidden nodes on CH1 and CH2; and a packet collision detection mechanism is used to enable a nonplatoon vehicle to detect packet collisions occurring on both channels after a lane-changing maneuver to avoid potential merging collisions. Simulation results show that the proposed eInP-RS scheme outperforms the standardized sensing-based SPS scheme in terms of the CAM/DENM delivery ratio and average DENM delivery delay of a platoon vehicle.

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.

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

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

Recently, underwater acoustic sensor networks can be widely applied to various aspects of ocean monitoring. In these applications, one of the critical issues is to improve the coverage efficiency of the monitoring events with minimum energy consumption. Here, we show an energy-efficient sensor deployment strategy to achieve the optimal coverage for events. Inspired by the foraging behavior of krill herd, the movement pattern of krill herd is improved to apply to the sensors with distributed communication to reduce energy consumption. Meanwhile, the coverage threshold is considered to improve the convergence and combined with the information about the surrounding sensors and events to make each sensor move to the optimal position to achieve the optimal coverage with minimum energy consumption. Furthermore, two important performance indexes: 1) coverage efficiency and 2) energy consumption are analyzed to better evaluate the deployment strategy. Simulation results confirm that the proposed deployment strategy performs good energy efficiency and coverage efficiency.

Mobile crowdsensing (MCS) has been a promising paradigm for gathering sensing data from surrounding environment by leveraging smart devices carried by mobile users and also their subjective initiatives. In this sensing paradigm, mobile users can make full use of such sensors-rich smart devices for task executions. Recently, social mobile crowdsensing (SMCS) has received a lot of attention and much work has been carried out. Many incentive mechanisms exploit the social relations among users/workers for improving the system performance. However, most existing work in this area focused on offline and socially-aware scenarios. In this paper, we study both online socially-aware and socially-unaware scenarios for maximizing the platform utility. We formulate the problem of worker selection for maximizing the platform utility and prove this problem is NP-hard. For the socially-aware scenario, we propose an incentive mechanism (called SA-WGRA), which adopts sociality and capability based clustering algorithm for Worker Group formation and uses Reverse Auction for worker selection. For the socially-unaware scenario, we propose an incentive mechanism (called SUA-CGRA), which adopts Coalitional Game combined with Reversed Auction for worker selection. We prove that both mechanisms achieve computational efficiency, individual rationality, and platform rationality. Moreover, for SUA-CGRA, we prove that its formed coalitions satisfy coalition rationality, and further each of its formed coalitions is convex and hence the Shapley value is in the core solutions for profit distribution in each formed coalition. Simulations results show that both SA-WGRA and SUA-CGRA can effectively improve the platform utility.

Disseminating continuous packet flows injected at multiple location-random source nodes to all network nodes, known as the multi-source dynamic global broadcast problem, is a fundamental building block for wireless multi-hop networks to run smoothly and efficiently. Previous studies on dynamic global broadcast all assume reliable communications. However, in realistic wireless networks, there exist unpredictable transmission failures caused by the randomized signal interference from uncorrelated wireless networks sharing the same spectrum or even malicious attackers. In this paper, by integrating the Signal-to-Interference-plus-Noise-Ratio (SINR) model, multi-channel communication mode, and randomized malicious channel jamming controlled by an adaptive adversary, we present an SINR-based adversarial channel jamming model to capture the unpredictable transmission failures in a wireless multi-hop network. We first propose a distributed Jamming-resilient Multi-source Static Broadcast (JMSB) algorithm based on random channel selection and message transmissions for multi-hop wireless networks under the above SINR-based adversarial channel jamming model. We then propose a distributed stable Jamming-resilient Multi-source Dynamic Broadcast (JMDB) algorithm which iterates JMSB repeatedly and efficiently in a two-stage manner. We derive the maximum supportable broadcast throughput of JMDB under the stability guarantee, i.e., the expected boundedness on the queue length of each network node and expected broadcast latency for each injected packet. Simulation results shows the stability and throughput efficiency of our proposed JMDB algorithm.

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.

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

Consensus protocol is a key technology enabling blockchain to provide secure and trustful services in wireless networks. However, most previous study on blockchain consensus protocols for wireless networks relies on reliable message transmissions and honest leaders. In practice, wireless blockchains inherently suffer from limited physical resources and unreliable wireless channels due to environmental noises and adversary attacks. This paper studies the design of Byzantine fault tolerant consensus protocol for blockchain in single-hop wireless networks subject to signal-to-noise constraint. For this purpose, we propose a low-latency and reliable Byzantine fault-tolerant consensus protocol LRBP, which incorporates the following three designs: 1) Randomized credit-based block proposer selection, which can prevent adversary corruption and improve the system throughput, 2) Enhanced threshold Boneh-Lynn-Shacham signature based voting mechanism, which can achieve communication-efficient block validity voting by using piggyback-based acknowledgment and criticality-based adaptive channel accessing probability adjustment, and 3) Random linear network coding based batch forwarding, which supports reliable block transmissions. We derive the consensus success probability and consensus time complexity of LRBP. We prove that LRBP simultaneously satisfies the properties of persistence and liveness. It is resistant to the 51% attack, Sybil attack, double-spending attack, and jamming attack. Simulation results show the high efficiency of LRBP as compared with existing work.

Mobile edge computing (MEC) is a promising computing paradigm and can effectively reduce the energy consumption and computing costs at mobile devices by offloading computation-intensive and latency-sensitive applications/tasks to edge servers. However, how to achieve cost-effective dependent task offloading and resource allocation subject to application completion time constraint and service configuration constraint at edge side in heterogeneous MEC environments remains a challenge. To address this challenge, in this paper, we study the multi-application dependent task offloading and resource allocation problem in heterogeneous MEC environments for jointly minimizing the energy consumption and computing cost. We first formulate this problem as a mixed integer nonlinear programming (MINLP) problem. We propose a two-stage alternating optimization algorithm. In the first stage, a genetic-based algorithm is proposed to determine an optimized task offloading profile for given transmit power matrix, a look ahead based task scheduling algorithm is designed to obtain an optimized task schedule for the profile. In the second stage, the transmit power allocation problem for a given offloading profile is solved using convex optimization techniques. Extensive simulation results show that the proposed algorithm can effectively reduce the total cost of task executions as compared with baseline algorithms.

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.

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.

For underwater localization, mobile anchors are generally preferred because they are easy to deploy and retrieve, such as the autonomous underwater vehicle (AUV). However, a big disadvantage is that we can only install small sonar arrays on AUVs due to their limited size. This leads to poor angular domain resolution. On the other hand, the mobility makes it possible for us to capture multiple snapshots of the acoustic signals when the AUV is located at different positions, and synthesize a virtual array, i.e., the Doppler effect. In this case we can equivalently obtain a large virtual uniform planar array (UPA), which gives much higher spatial resolution and positioning accuracy. Mathematically, we have not only the DoA (Direction of Arrival) measurements, but also the Doppler shift measurements for positioning. In this paper, we propose a low-complexity algorithm to jointly estimate Doppler shift and DoA. Based on these estimates, we can obtain a series of nonlinear equations concerning the target's position. To solve these equations, a two-phase localization algorithm of linear computational complexity will be presented. We also derive Cramer-Ran lower bound (CRLB) of the system as a benchmark. The theoretical analysis will be verified through extensive simulations.

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.

Mobile crowdsensing (MCS) has been an effective sensing paradigm by utilizing the smart devices carried by mobile users to complete sensing tasks at different locations. An important problem in MCS is how to achieve effective task assignment in the context of opportunistic sensing, where mobile users are selectively recruited to perform tasks in an opportunistic way. However, most existing work in this aspect suppose there are only one service platform and further the sensing qualities of users are known a priori. In this article, we study the task assignment when there are multiple service platforms and further the sensing qualities of users are unknown a priori. The design objective is to maximize the overall sensing qualities of finished tasks at all platforms. For this purpose, we build a multiplatform cooperation framework and formulate the task quality maximization problem in this case as a 0–1 integer linear programming (ILP) problem. We propose a multiplatform-cooperation-based task assignment mechanism (MCTA). MCTA includes two phases. The first phase establishes stable cooperation relationship among platforms while respecting their respective cooperation willingness, and for this phase, we propose a cross-platform cooperation relationship construction algorithm. The second phase performs effective online task assignment, and for this phase, we propose two online multiarmed bandit (MAB) with sleeping -arms-based user selection algorithms using local and global learning, respectively, based on whether cross-platform user-sensing-quality learning is allowed. We derive the regrets of the proposed algorithms and prove that MCTA has the properties of cooperation stability and computation efficiency. Extensive simulation results show the high performance of our proposed MCTA mechanism as compared with the existing work.

Efficient target localization is critical to many marine applications in the Internet of Underwater Things (IoUT). Doppler effect becomes more predominant in the underwater environment when the relative speed of the moving object, i.e., the observer, to the signal propagation speed in water is much larger than that in the air. This along with the observer-target geometry will bring in a notable impact on the localization performance. In this article, we derive the Cramér–Rao lower bound (CRLB) and formulate the A-optimality criterion-based observer trajectory optimization problem to improve the localization performance based on time delay and Doppler shift measurements. We show that in the worst case scenario, there will be a conflict between the nonaccessible zone constraint and the observer dynamics constraint, which will lead to an erroneous result. To address this problem, we propose a warning zone-based augmented Lagrange multiplier method (ALMM) where the nonaccessible zone constraint is relaxed to resolve the conflict and ensure the nonaccessible requirement of the targeted zone is maintained. Performance evaluations are conducted through extensive simulations for different scenarios, and the results are compared to other methods with or without trajectory optimization. We demonstrate that trajectory optimization using time delays and Doppler shifts can greatly improve the target localization accuracy in underwater networks.

Next generation cellular network is expected to provide the simultaneous high-accuracy localization and ultra-reliable communication services, even in high mobility scenarios. To that end, the novel orthogonal time frequency space (OTFS) modulation has been developed as a promising physical-layer transmission technique, evident by the outstanding performance in terms of robustness against time-frequency selective fading over the orthogonal frequency division multiplexing (OFDM) counterpart. However, when OTFS meets massive multiple-input multiple-output (MIMO), the specific conditions, under which the delay-Doppler (DD) domain channel model holds, are not identified. In addition, the channel estimation and localization performance in such system is rarely studied. In this work, we target at these new challenges, and conduct comprehensive modelling, performance analysis, and algorithm design for massive MIMO-OTFS based simultaneous localization and communications. Specifically, we derive new channel models for the massive MIMO-OTFS system, which captures both time-frequency dispersion and spatial wideband effects. The specific conditions, under which the new models hold has been unveiled as well. Based on the new models, we establish the theoretical foundations for channel estimation and localization, by deriving the Cramér-Rao lower bounds of channel parameter and location estimation errors. Such bounds have been achieved with the newly designed low-complexity channel estimation and localization algorithms. Numerical simulations of the proposed framework with prevailing pulse functions are also conducted and the results validate the proposed designs and analysis.

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.

Multi-source global broadcast is a fundamental problem in multi-hop wireless networks. The Static Multi-source Global Broadcast problem (SMGB), which considers static packet injection at all source nodes, has been extensively studied in recent years. However, packets are more likely to be continuously injected over time in realistic multi-hop wireless networks. In this paper, we focus on studying the Dynamic Multi-source Global Broadcast problem (DMGB), in which packets are continuously injected to$k$($k\geq 2$) source nodes in the network according to a widely-used dynamic packet injection model and the objective is to disseminate each injected packet across the whole network quickly. We solve this DMGB problem under the Signal-to-Interference-plus-Noise-Ratio (SINR) interference model. Specifically, we first present a distributed randomized algorithm for solving the SMGB problem. We then iterate this SMGB algorithm repeatedly to construct a distributed DMGB algorithm. We prove the proposed DMGB algorithm is stable, i.e., the expected number of packets in each node’s message queue is bounded at any time and further the expected global broadcast latency for each injected packet is bounded. Simulation results validate the effectiveness of the proposed DMGB algorithm.

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

Multi-access edge computing (MEC) has been an important and promising paradigm for offering computing services to mobile users with computation-intensive and latency-critical tasks. In this paper, we study a D2D collaboration based MEC system, where the service platform purchases resources from resource-rich collaborative D2D devices when the task arrival rate exceeds the platform’s capability for providing satisfactory QoS. The design objective is to maximize the platform profit while maximally satisfying the delay requirements of tasks. We define delay based utility functions for different participants and accordingly formulate the platform profit maximization problem as a Mixed Integer Non-Linear Programming (MINLP) problem. For the online case where future task arrivals are unknown in advance, we propose a reverse auction based task assignment and urgency-value based transmission scheduling algorithm (RAGM). We present the detailed algorithm design and deduce its computation complexity. We prove that RAGM satisfies individual rationality of all participants. We conduct extensive simulations and the results show the high performance of RAGM as compared with benchmark algorithms.

Opportunistic Mobile Crowdsensing (MCS) is an attractive and cost-effective sensing paradigm because it does not affect workers' daily routines. However, its opportunistic nature in task executions can lead to low task completion rate for deadline-sensitive tasks as compared with participatory sensing. Besides, existing work in opportunistic M CS lacks of study on how to effectively coordinate among multiple service platforms for idle worker sharing so as to improve the sensing performance. In this paper, we design a multi-platform cooperation based incentive mechanism (MPCIM) for deadline-sensitive task assignment in the context of opportunistic mobile crowdsensing. The design objective is to maximize the system profit while improving the task completion rate. In MPCIM, each platform first decides how many idle workers it can provide and also how many tasks it needs assistance at different locations; Then, a cross-platform managing entity is responsible for performing maximal matching between the idle workers and excessive tasks among different platforms to improve the task completion rate while respecting individual rationality and cooperative rationality. Extensive simulation results show that MPCIM can effectively improve the system profit and also task completion rate.

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.

Mobile crowdsensing has become a promising sensing paradigm with the popularization of mobile devices. In this paper, we focus on an opportunistic mobile crowdsensing scenario where there are multiple task requesters and users, who move in an opportunistic way in the target environment. When a task requester encounters a user, he can assign some of his held tasks to the user and receive corresponding task results when they re-encounter sometime later. In this paper, we study how to minimize the largest makespan of all requesters for the task result collections. To address this issue, we propose a cluster based largest makespan sensitive online task assignment (C-LOTA) algorithm. C-LOTA first performs two-phase clustering which clusters the users into different clusters, one for each task requester, based on their relativeness to the task requesters and also the task workloads at different requesters. C-LOTA then iteratively performs greedy intra-cluster task assignment such that largest task is firstly assigned and the first idle user always takes the task, until all tasks are assigned. We present the detailed algorithm design of C-LOTA. We deduce its computation complexity. Simulation results show that C-LOTA can achieve much better performance compared with existing work.

The explosive development of short video applications has brought severe pressure on radio resources at hotspot areas. The features of short video recommendations-and-pushing techniques provide us an opportunity to relieve the radio resource pressure via wireless multicast: An edge server can be deployed at the base station, which receives short video lists recommended by remote video server and then pushes such mobile video services to local users through wireless multicast. In this paper, we study how to reshuffle the video lists received from remote server so as to facilitate wireless multicast to maximally reduce the required wireless resource while considering the fact that a user client can only buffer one short video for watching based on off-the-shelf short video APPs. We formulate the problem of video list reshuffling for minimizing the total wireless resources consumption as an integer programming problem. We design a Minimum degree of Freedom based Maximum Filling video reshuffling algorithm (MFMF) to address this problem. MFMF moves videos from the original video lists into same sized but reshuffled video lists in a greedy manner, once for a video, whose moving can satisfy the most reshuffled video lists, and if multiple such choices exist, selects the one having the least position options. This process continues until all the videos are moved. We deduce the computation complexity of MFMF. Numerical results demonstrate the significantly high performance of MFMF.

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

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.

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.

Massive multiple-input-multiple-output (MIMO) and millimeter wave have been adopted as the enabling technologies for the 5G and beyond 5G (B5G) systems. A challenging problem introduced by the use of large antenna size and wide bandwidth is beam squint, i.e., spatial-wideband effect. Beam squint can significantly degrade the channel estimation performance for conventional channel estimators. Research effort on channel estimation under beam squint conditions has been very limited. For the few available work that attempts to address this problem, they require either all subcarriers or multiple symbols used as pilot for channel estimation, so large overhead becomes inevitable. Therefore, in this paper, we propose an efficient channel estimation method that only requires a small number of subcarriers. The channel estimation problem is formulated as a nonlinear least squares optimization problem. Initial parameter estimation is critical, which will affect the efficiency and convergence of the proposed algorithm. Using a densely-spaced antenna structure and consecutive subcarriers assignment approach, we can effectively avoid the aliasing effect and reduce the ambiguity during the initialization phase. A subcarrier assignment criterion is proposed to achieve the optimal performance. Closed-form expressions of the Cramér-Rao lower bound (CRLB) and the achievable rate are derived to evaluate the performance. Both simulation results and theoretical analysis show that even with a small number of subcarriers, the estimation error closely approaches the CRLB, and its effect is negligible compared with the noise when evaluating the signal-to-noise ratio with a simple linear detector. Furthermore, the number of pilot subcarriers has little impact on the achievable rate.

Low-activity signals, such as voice, electrocardiogram (ECG), and ultrasonic signals, in the Internet-of-Things applications have both posed unique challenges and offered special opportunities for modern analog-to-digital conversion. This article presents a new successive approximation register (SAR) analog-to-digital converter (ADC) search methodology, which is aimed at low-activity signals for reducing comparator activity and switching energy of digital-to-analog converter (DAC). By using statistical histogram information of the low-activity signals, two search solutions are proposed. The first solution is designed for some part of signal that has small difference between two adjacent samples, while the second solution is designed for that with large difference. To engage one suitable solution, the digital interval between two adjacent samples needs to be detected. In addition, a new DAC tactic is proposed to reduce the activity of DAC switches. Our simulated 10-bit SAR ADC for voice signals shows that by using our proposed method, the comparator activity is reduced by 62.09%, and the DAC switching energy is decreased by 85.90% compared to the monotonic method. In addition, the activity of DAC switches is further trimmed by 39.66% compared to the monotonic method.

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

How to effectively organize various heterogeneous clients for effective model training has been a critical issue in federated learning. Existing algorithms in this aspect are all for single model training and are not suitable for parallel multi-model training due to the inefficient utilization of resources at the powerful clients. In this paper, we study the issue of multi-model training in federated learning. The objective is to effectively utilize the heterogeneous resources at clients for parallel multi-model training and therefore maximize the overall training efficiency while ensuring a certain fairness among individual models. For this purpose, we introduce a logarithmic function to characterize the relationship between the model training accuracy and the number of clients involved in the training based on measurement results. We accordingly formulate the multi-model training as an optimization problem to find an assignment to maximize the overall training efficiency while ensuring a log fairness among individual models. We design a Logarithmic Fairness based Multi-model Balancing algorithm (LFMB), which iteratively replaces the already assigned models with a not-assigned model at each client for improving the training efficiency, until no such improvement can be found. Numerical results demonstrate the significantly high performance of LFMB in terms of overall training efficiency and fairness.

Drones (also known as unmanned aerial vehicles) has been widely utilized to enhance the performance of mobile crowdsensing (MCS) where the drones can greatly increase the service range by serving as mobile task executors. In this paper, we consider the joint use of truck and drones for effective task execution in the MCS model. In this model, drones are dispatched to depart from the truck, execute one or multiple tasks, and converge with the truck for data collection and battery replacement. In this process, the truck can serve as a mobile drone hub as well as a mobile task executor. The design objective is to minimize the overall cost for truck movement, drone flying, and driver payment in the whole MCS process. We formulate this problem as a mixed integer linear programming (MILP) problem. Due to the NP-hardness of this problem, we propose an efficient algorithm based on variable neighborhood search for efficient joint task assignment and path planning. We conduct numerical experiments and the result demonstrate the effectiveness of the proposed algorithm and the advantage of joint use of truck and drones for mobile crowdsensing.

Mobile crowdsensing is a new paradigm for data collection by utilizing the mobility of sensor-rich hand-held smart devices. One of the key challenges in mobile crowdsensing is how to effectively assign tasks to mobile users in an online manner. In this paper, we study the online task assignment problem in mobile crowdsensing where each task has specific time window for its sensor data collection. The objective is to maximize the total profit of the platform in whole sensing period. We first model the crowdsensing system and formulate the profit maximization problem under study. To address this problem, we propose two heuristic algorithms, one is bipartite-match-based algorithm (BMA) using Kuhn-Munkres algorithm and the other improves the first by using data offloading for data upload cost reduction, if applicable. We present detailed algorithm design for both algorithms and deduce their computational complexities. Finally, simulation results validate the effectiveness of our proposed algorithms.

In this paper, we study the design of opportunistic routing for efficient data offloading in vehicular opportunistic networks with the assistance of sparsely deployed static relays. The objective is to maximize the data offloading ratio and also reduce the average delivery delay while respecting the data’s delay requirement. For this purpose, we propose a sparse relay assisted opportunistic routing algorithm for efficient data offloading. We show how to efficiently utilize the encounters with static relays for improved data offloading performance based on vehicles’ trajectories. We further design a greedy algorithm for optimized relay deployment. We reduce the computational complexity of the data offloading algorithm and also the relay deployment algorithm, respectively. Simulation results demonstrate the high performance of our proposed algorithms.

Internet of underwater things (IoUT) attracts many interests in these years both in academia and industry, such as marine data collection, pollution monitoring, and offshore exploration. As a fundamental issue of IoUT, the underwater acoustic channel experiences long delay and temporal-spatial uncertainty compared with terrestrial communications and networks. It is difficult to capture full characteristics of the underwater acoustic channel by statistical models. In this paper, we investigate the properties of acoustic propagation in seawater and different underwater acoustic channel models. Moreover, we survey five underwater acoustic channel models, including ray-theoretical model, normal mode model, multipath expansion model, fast-field model, and parabolic equation model, which are the corresponding solutions of the wave equation. We conclude the paper with the characteristics of each model in terms of different aspects.

Wireless sensor network (WSN), consisting of several sensor nodes, is one of the most promising technologies emerged in the past decade. The positioning system for WSN is particularly meaningful and widely used in the military surveillance, air-sea rescue, traffic monitoring, and etc. However, the traditional positioning system always suffers from deployment and maintenance of anchors. In this paper, we propose a positioning system employing a Raspberry Pi platform attached to a DJI drone as a mobile anchor. The DJI drone can serve as multiple virtual anchors by moving and broadcasting its location information periodically. Thus, it is possible to localize sensor node by itself when the sensor node collects the drone's position. A Gauss-Newton method is applied to improve the accuracy of the proposed positioning system. We also elaborate the adaption of the Gauss-Newton method with the geodetic coordinates. The goal of the proposed positioning system is to achieve higher accuracy and higher coverage at lower cost.

As the data traffic in future wireless communications will explosively grow up to 1000-fold by the deployment of 5G, several technologies are emerging to satisfy this demand, including multiple-input multiple-output (MIMO), millimeter wave communications, Non-orthogonal Multiple Access (NOMA), etc. Millimeter wave communication is a promising solution since it can provide tens of GHz bandwidth by fundamentally exploring higher unoccupied spectrum resources. As the wavelength of higher frequency shrinks, it is possible to design more compact antenna array with large number of antennas with independent RF (Radio Frequency) chains, causing high cost and complexity. By exploring the spatial sparsity of the millimeter wave channels, lens antenna array has been investigated recently as a promising choice with limited RF chains and low complexity. In this paper, we investigate the outage probability for highway communication systems with lens antenna array, under overtaking scenario, where high mobility of users is expected. When a vehicle is trying to pass another one, the channels between these two vehicles and the RSU (Road Side Unit) are unresolvable, thus causing outage for at least tens of symbol durations. We apply power-domain NOMA in this scenario, where these two users are paired by a threshold derived with the QoS of each user, to alleviate this problem and achieve low outage probability.

Traffic light control is vital for the efficiency of urban transportation. Recently, the increasing of vehicles has brought great challenges to the traffic light control system. However, traditional traffic light controlling methods are inefficient due to the sophistications of traffic dynamics. In this paper, we propose a Graph Attention mechanism based Multi-Agent Reinforcement Learning method (GA-MARL) by extending the Actor-Critic framework to improve the efficiency of cooperation in traffic signal control. The proposed algorithm is based on hard-attention and soft-attention mechanism, which can help agent filter information effectively and calculate the importance of other agents. In addition, we complete our algorithm by adopting the framework of Centralized Training with Decentralized Execution (CTDE) to overcome the challenge of non-stationary non-Markovian environments. Simulation results prove that our proposed method outperforms the representative methods in the literature.

Mobile video traffic has experienced explosive growth in recent years due to the rapid development of mobile intelligent terminals and cellular communication technologies. The rapid growth of mobile video traffic has brought significant energy expenditure for mobile network operators. To reduce the energy expenditure, one promising solution is to exploit renewable energy harvested from surrounding environments for cellular traffic delivery. In this article, we investigate mobile video streaming in green cellular networks with hybrid energy sources, i.e., grid energy and ambient energy, to optimize both video quality and energy expenditure. Specifically, we formulate a stochastic optimization problem to maximize the long-term time-averaged network service utility, which is the difference of video quality and energy expenditure. The problem formulation takes the following factors into account: time-varying grid electricity price, energy harvesting process, and different time scales of rate adaptation (RA), resource management, and electricity price fluctuation. We exploit Lyapunov optimization framework to decompose the problem into three subproblems: 1) RA subproblem; 2) battery energy management subproblem; and 3) joint power control and subchannel assignment subproblem. We propose an efficient online green video streaming algorithm to solve these subproblems. We analyze the stability of the proposed algorithm with respect to lengths of energy queue and user request queues. Extensive simulations are conducted and the results validate the efficiency of the proposed algorithm.

Millimeter wave communications show great potentials in many applications, one of which is the high-speed railway(HSR) communication system. However, a major challenge is the Doppler effect caused by the relative-movement between the train and the base station (BS), which leads to fast channel variation. To compensate for the Doppler shift, an accurate channel model is indispensable, and the far-field channel model is generally employed, which assumes that the dimensions of the antenna arrays are negligible compared to the distance between transmitter and receiver. This model is widely used in Cellular systems, but the underlining assumption is not always true for railway communication systems. In this paper, the modeling of the Doppler effect for millimeter wave in HSR communications is conducted, and data-aided Doppler estimation and compensation algorithms are designed based on the new model. We show that the conventional far-field channel model is based on the first-order Taylor expansion of the actually channel, and the second-order component cannot be ignored for HSR communications. Extensive simulations are conducted to verify the validity of the new model and the effectiveness of the proposed algorithms.

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.

With the rapid development of mobile smart devices, mobile crowdsensing has become an attractive paradigm for sensor data collection. In a mobile crowdsensing system, the platform can publish a set of tasks and then recruit suitable mobile users to accomplish these tasks. In this paper, we study the budget-constrained task assignment problem for mobile crowdsensing. We assume users can choose to take different transportations for task execution, and different choices have different task coverages, travel expenses, and travel time. We model the crowdsensing system and formulate the budget-constrained task assignment problem under study. We prove this problem is NP-hard. To address this problem, we propose a Value/Reward Maximum First heuristic algorithm (VRMF). We present the detailed algorithm design and deduce its computational complexity. Simulation results validate the effectiveness of our proposed algorithm.

Incentive mechanism has been an important research direction in mobile crowdsensing. An effective incentive mechanism is critical to ensure the adequate number of participants/workers by providing them proper rewards. However, existing incentive mechanisms lack consideration on potential contributions of individual workers when recruiting new workers and retaining existing workers in the system. In this article, we propose a reverse auction-based incentive mechanism (RAIN), which considers participants' potential contributions when recruiting new workers, performing reverse auctions, and retaining existing workers. The design objective is to optimize the worker composition in the system while reducing the system cost. In RAIN, the potential contribution of a user to the system is measured as the degree at which the user's joining or staying in the system can remedy the inadequacy of workers for task auction/execution at the frequently visited locations of the user. We present design details of RAIN which includes selective worker recruitment, reverse auction based on biased bids, and selective retaining of auction losers, all based on individual users' potential contributions to the system. Extensive simulation results show that RAIN can effectively optimize the worker composition in a system and also effectively reduce the system cost.

The autonomous underwater vehicle(AUV)-aided localization techniques for underwater acoustic devices show promising applications in many scenarios, and most researches in this area are based on the time of arrival (ToA) or the time difference of arrival (TDoA) measurements. However, these measurements are not readily available. To develop a more universally applicable scheme, we investigate the possibility of employing the Doppler shift measurements for underwater localization of acoustic devices in this paper. To be specific, we employ a low-complexity algorithm for Doppler estimation, and prove that the estimation error can be well approximated by zero-mean Gaussian distribution. Based on the Doppler estimates, we can obtain a series of nonlinear equations. To solve them, we propose a two-phase linear algorithm to obtain high-accuracy position information of the target devices. Compared with the conventional iterative algorithms, the proposed one does not require initial estimate. Both the closed-form localization error and the Cramér-Rao lower bound are presented. They prove to be consistent for reasonably small Doppler estimation error. Besides, we conduct simulations to verify the theoretical analysis. Moreover, the complexity of the proposed algorithm only grows linearly with the number of Doppler shift measurements.

Underwater event/object detection is an enabling technique for many marine applications. for the surveillance of target water areas, the future underwater network can serve as a backbone system, and every sensor in this network is an agent. When the target moves into the target area or when an event happens, the agents will detect acoustic signals from the target or event. The acoustic waves arrive at different agents at different time. Based on the correlation of the received signals between two agents, the time difference of arrival (TDoA) can be estimated, which locks the target/event’s position on one branch of a hyperbola, represented by a nonlinear equation. With three or more agents, the target/event’s position can be uniquely decided. To make this system universally applicable, the average underwater acoustic velocity is also assumed to be unavailable, and a two-phase linear algorithm is proposed. A coarse estimation is obtained in Phase I, and the result is further refined in the Phase II. Extensive simulations are provided to verify the effectiveness of the proposed system.

Mobile crowdsensing is a novel paradigm by exploiting mobility, sensing, computation, and communication capability of smart devices. In this paper, we study data offloading problem for mobile crowdsensing in opportunistic social networks. In this scenario, mobile users can upload sensing data directly via cellular networks using various data plans. A mobile user can also resort to another user for data offloading by forwarding sensing data to that user using short-range communications (when they encounter). To minimize total data uploading cost while meeting given uploading deadlines, data plan assignment for users and data forwarding strategy when two users encounter should be elaborately designed. In this paper, we use Benders decomposition algorithm to solve offline data plan assignment problem. Then we propose two algorithms including progress- balanced algorithm and social-aware forwarding algorithm to solve online data forwarding problem. Simulation results show that data offloading between users can largely reduce the total data uploading cost. Simulation results also show that the performance of our proposed online algorithms is close to the offline optimal solution.

With the widespread of smart devices, mobile crowdsensing has become an attractive way to perceive and collect sensing data. In this paper, we focus on studying AP-assisted task assignment in mobile crowdsensing. The objective is to effectively reduce the average or worst-case makespan of tasks. We focus on a scenario that a task requester needs the assistance of mobile users for task accomplishment while they can meet directly or via APs in an opportunistic manner. We model the crowdsensing system and then formulate the problems under study. We then propose an AP-assisted average makespan sensitive online task assignment (AP-AOTA) algorithm and an AP-assisted largest makespan sensitive online task assignment (AP-LOTA) algorithm. In the proposed algorithms, task assignment at each step considers both the inter-encountering time between requester and each user and that between them while going through APs. We present design details of the proposed algorithms. We derive their computational complexities to be O(mn2), where m is the number of tasks and n is the number of users. Finally, trace-driven simulation results show that the proposed algorithms outperform existing work.

Mobile crowdsensing is a new sensing paradigm exploiting potential of crowds to collect data, which has various advantages over traditional sensor networks such as low cost, high coverage, and high mobility. Privacy preservation is a crucial issue in mobile crowdsensing because worker privacy might be exposed if workers share their location information to service platform or other workers. In this paper, we assume workers can determine their own privacy preservation levels and they do not need to upload their location information to the platform or share to other workers for sensing behavior coordination. Moreover, workers move to task locations to collect sensing data in a distributed manner. We accordingly propose a privacy-aware online task assignment framework to achieve high task coverage. In this framework, spatial task-application information in previous cycles is used to estimate worker density and an incentive pricing mechanism is designed to guide workers to collect sensing data in low-worker-density areas. We present detailed mechanism design. Extensive simulation results show that our proposed solution has much better performance than the baseline mechanism.

Incentive mechanism design is a critical issue in mobile crowdsensing and a lot of work has been carried out. However, existing mechanisms in this area generally lack of consideration of individual worker/candidate's (potential) contribution to the system when recruiting new workers or when detaining existing workers. In this paper, we design a reverse auction based incentive mechanism. The design objective is to maximally reduce the system maintenance cost (including auction cost and recruitment cost) by optimizing the composition of workers in the system. For this purpose, in the recruiting process, candidates are queried in the descending order of their potential contributions to the system, while in the detaining process, likelydropping-out workers are rewarded with inner lottery whose amount is adjusted based on their usefulness to the system. In the auction process, prices are calculated based on workers' bids and also their usefulness to the system. We present detailed mechanism design. Simulation results show that our mechanism outperforms existing work.

The deployment of a large-scale array of hydrophones in underwater acoustic (UWA) communications brings numerous benefits, in terms of high spectrum and energy efficiency, and the high data rate communications. However, along with the merits, large array signal processing is known to be computationally costly and long processing delay required. Even with linear detection methods such as minimum mean-square error (MMSE) based schemes, the computational complexity is still considerable as the matrix inversion operations are involved. With Gauss-Seidel method, the matrix inversion operations are avoided, while the iterative processing achieves comparable system performance to the MMSE-based schemes. However, Gauss-Seidel method introduces successive data detection, causing significant processing delay. Meanwhile, the successive detection structure is inefficient in hardware implementation. In this paper, we propose a block Gauss-Seidel method for large array signal detection in UWA communications. In the proposed scheme, Gauss-Seidel method is performed on a set of small size block matrices, and the processing on each block can be parallelized. As a result, the total processing delay can be greatly reduced. Moreover, the parallel processing structure is quite efficient for hardware implementation. We also utilize the UWA channel model developed in recent work to investigate the performance of the proposed scheme, and the results are promising.

It is known that the acquisition of the complete channel state information at receivers is difficult in multiple‐input multiple‐output (MIMO) systems. Channel estimation error is unavoidable in practical applications. Under imperfect channel conditions, the channel estimate is directly applied to the equalisation process in the conventional minimum mean‐square error (MMSE)‐based turbo equalisation scheme. A few studies treat the channel estimation error as an independent component from the channel estimate and slightly enhanced performance is achieved. Unlike the existing work, the authors derive the MMSE‐based iterative processing conditioned on channel estimate. Moreover, they note that in low‐density parity check coded systems, the parity‐check procedure is also involved. The pass in parity check indicates that the message bitstream is successfully recovered. This information can be utilised to reduce the overall computational complexity by degrading the MIMO size since the unknown parameters are reduced. By extending the analysis in a small‐scale MIMO system to a large‐scale one, they propose to utilise the normalised transmission power in the development. Numerical results show the proposed schemes outperform the existing schemes in terms of system bit error rate and computational complexity performance.

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

Pilot contamination has been known as one of the most challenging issues in massive multiple-input multiple-output (MIMO) systems. Every user will experience interferences from users in adjacent cells who employ the same pilot sequence. For cell-edge users, pilot contamination is particularly detrimental, because their signals might be overwhelmed by the interference. In this paper, we propose a pilot decontamination method based on a spatial filter, which exploits the spatial sparsity of massive MIMO channels. In massive MIMO systems, the communication protocols are generally divided into four phases: pilot transmission, processing, uplink data transmission, and downlink data transmission. In the first phase, the base station (BS) receives both the desired signal and the pilot contaminated signal. In the second phase, all users in the target cell stay silent for one symbol period, and the BS only receives interference from adjacent cells. The fast Fourier transform can then be employed to analyze the spatial spectrums of the received signals. The spatial sparsity of the massive MIMO channels makes it possible to identify the pilot contamination components by comparing the two spectrums on different spatial signatures (or angles of arrival). A spatial filter can then be constructed to eliminate pilot contamination. Both the theoretical analysis and simulation results demonstrate the effectiveness of the proposed method, whose complexity is comparable to that of the traditional matched filter-based channel estimator.

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

Massive MIMO has been viewed as one of the most promising techniques for 5G communications. However, its potential is highly confined by the so called pilot contamination issue. For cell-edge users, this problem is particularly critical, because their signals might be overwhelmed by their peers in adjacent cells. In this paper, we propose an innovative pilot decontamination method based on spatial filter, which can greatly improve the channel estimation accuracy for cell-edge users. There are two phases in the proposed method: pilot transmission phase and idle phase. During the first phase, users transmit pilot sequences to BS, and the BS employs matched filter to obtain channel estimation, which contains both desired signal and pilot contamination. In the second phase, all users in the target cell stay silent for one symbol period, and the BS receives signal from adjacent cells. Then, fast Fourier transform can be employed to analyze the spatial spectrums of received signals in these two phases. By comparing these two spectrums, pilot contamination components can be identified, and a spatial filter can be constructed to eliminate them. Both theoretical analysis and simulation results are presented to justify the efficacy of the proposed method.

Wireless local area network (WLAN) fingerprint-based indoor positioning system has a wide application prospect because it supplies good positioning performance without the requirement of additional hardware installations. However, huge labor and time are needed to establish the fingerprint database called radio map. To address this problem, we propose an efficient radio map building method using tensor completion. The cost of the proposed method is reduced by lessening the number of reference points (RPs) to be measured. Due to the strong correlation between data, estimating received signal strength (RSS) at unmeasured reference points can be formulated as a low rank tensor completion problem. And the issue can be transformed into a convex optimization problem by substituting rank operation with trace norm operation. We solve the problem by employing the alternating direction method of multipliers (ADMM) algorithm. The experiment results indicate that the proposed method can not only reduce the effort of radio map building remarkably, but also achieve high positioning accuracy.

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

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

For the purpose of localization and time synchronization of underwater sensor networks, buoys are generally distributed on the sea surface of the area of interest, serving as fixed anchors. However, this method is not economical and has poor scalability. An alternative is to employ an autonomous underwater vehicle (AUV) as a mobile anchor. By receiving the periodical broadcast signals from the AUV, any sensor in the communication range can measure time of arrival of received packets and obtain a series of nonlinear equations. In this letter, we proposed an efficient linear algorithm to solve the nonlinear equations, and gave closed-form positioning and synchronization error analysis. Besides, we show that the proposed method can approach the Cramér-Rao lower bound by both theoretical analysis and simulation.

Most analytical work for zero-forcing (ZF) receivers are conducted for small-scale multiple-input multiple-output (MIMO) systems in large signal-to-noise ratio (SNR) region and under small channel estimation error conditions. Using large receiving antennas, systems are expected to work in the low-SNR region and under large channel estimation error. In these conditions, we observe an obvious mismatch between the existing analytical results and the simulations. In this letter, we derive an accurate analytical bit error rate (BER) expression for ZF receivers under imperfect channel estimation. We show that our results match nicely with the simulations in small-scale and large-scale MIMO systems, even when large channel estimation error presents.

In this paper, we investigate low-complexity data detection scheme for massive multiple-input multiple-output (MIMO) uplink transmission. We propose to utilize the stair matrix, instead of diagonal matrix in existing proposals, for the development, and achieve near linear minimum mean-square error detection performance. We first demonstrate the applicability of the proposed method by showing that the probability (that the convergence conditions are met) approaches one as long as sufficiently large number of antennas are equipped at the base station. We then propose an iterative method to perform data detection and show that much improved performance can be achieved with the computational complexity remaining at the same level of existing iterative methods, where the diagonal matrix is adopted. Furthermore, we conduct numerical simulations, and the results validate the significant performance enhancement of using the stair matrix over the diagonal matrix in all performance aspects. Moreover, we apply the proposed scheme to a massive MIMO system, where the extended vehicular A channel data are generated. The performance improvement of the proposed scheme over existing proposals is also validated.

The explosive demand of online video watching brings huge bandwidth pressure to cellular networks. Efficient video caching is critical for providing high‐quality streaming Video‐on‐Demand (VoD) services to satisfy the rapid increasing demands of online video watching from mobile users. Traditional caching algorithms typically treat individual video files separately and they tend to keep the most popular video files in cache. However, in reality, one video typically corresponds to multiple different files (versions) with different sizes and also different video resolutions. Thus, caching of such files for one video leads to a lot of redundancy since one version of a video can be utilized to produce other versions of the video by using certain video coding techniques. Recently, fog computing pushes computing power to edge of network to reduce distance between service provider and users. In this paper, we take advantage of fog computing and deploy cache system at network edge. Specifically, we study transcoding based video caching in cellular networks where cache servers are deployed at the edge of cellular network for providing improved quality of online VoD services to mobile users. By using transcoding, a cached video can be used to convert to different low‐quality versions of the video as needed by different users in real time. We first formulate the transcoding based caching problem as integer linear programming problem. Then we propose a Transcoding based Caching Algorithm (TCA), which iteratively finds the placement leading to the maximal delay gain among all possible choices. We deduce the computational complexity of TCA. Simulation results demonstrate that TCA significantly outperforms traditional greedy caching algorithm with a decrease of up to 40% in terms of average delivery delay.

Smart devices with a rich set of low-cost sensors enable a new sensing paradigm called mobile crowdsensing. In mobile crowdsensing, tasks are distributed at a variety of locations. Mobile users travel through different task locations to perform different tasks. The diversity of task locations and user trajectories makes the optimal scheduling problem intractable. In this paper, we mathematically formulate the optimal task scheduling problem as a continuous path planning problem, which is known to be NP-hard. Then we propose two online heuristic algorithms to maximize the task quality improvement for each newly arriving user. These algorithms work in a hop by hop manner for task selection and adopt different measures and strategies including: (1) ratio of task quality increment and travel cost and (2) task spatial density. We present detailed algorithm design and deduce their computational complexity. Extensive simulation results show that our algorithms outperform existing work.

Vehicular ad hoc networks (VANET) have gained increasing interests due to the development of the intelligent transport systems (ITS), aiming to improving road safety, traffic efficiency, and providing in-vehicle entertainment. Meanwhile, the fast developing 5G cellular networks have brought innovative techniques to support the demand of ITS such as high rate communications, low latency and high energy efficiency. Massive multiple-input multiple-output (MIMO), as one of the key technologies in future 5G, is to deploy hundreds of antennas at base station, serving up to tens of users simultaneously in shared time-frequency resources. This technique, is attractive for the wireless vehicle-to-infrastructure (V2I) access for multiple vehicles on the road. However, in massive MIMO, the computational complexity is costly even with linear detection methods. The iterative methods, such as Gauss-seidel based signal detection method, are preferred as the computational complexity is low, and near-optimal system performance can be achieved. In this paper, we propose block Gauss- Seidel method based signal detection in V2I massive MIMO uplink transmission. The proposed scheme utilizes the properties of block diagonal matrix, and the Gauss-Seidel method is applied to each block. By doing that, the processing at each block can be paralleled, hence the new structure is much efficient for hardware implementation. In addition, we demonstrate that the system performance is quite close to the original Gauss-Seidel method but at low complexity and fast processing time.

WLAN indoor positioning system has a wide application prospect because it is entirely based on the network infrastructure and mobile terminals which are both prevalent in our daily life without the need of additional equipment. However, indoor multipath channel, signal randomly blocking, unstable transmission power would no doubt cause interference to the propagation of signal, which introduces noise to the radio map built in the offline phase, and further degrades the positioning accuracy. Therefore, in this paper, we propose a radio map noise reduction method by using Hankel matrix. Based on the special structure of Hankel matrix, we could effectively separate the noise from the signal. We perform the noise reduction separately on each Hankel matrix coming from different signal vectors but not the entire radio map. The experiment results indicate that the proposed method could achieve better noise reduction on the radio map and contribute good positioning performance.

In recent years, received signal strength (RSS) based indoor localization system using WLAN has attracted considerable attention. However, signal strength variations across diverse devices becomes a major problem in this system, especially in the crowdsourcing based localization system. In this paper, the linear regression algorithm is proposed to solve this problem automatically. First of all, the problem of device diversity and the adverse effects caused by this problem are analyzed. Then the intrinsic relationship between different RSS values collected by different devices is mined by the linear regression algorithm. The problem of device diversity will be handled by this algorithm. In crowdsourcing systems, when the major problem is eliminated, a unique radio-map can be created in the offline phase and the user's location can be estimated by a localization algorithm in the online phase. Experimental results show that the proposed method results in a higher reliability and localization accuracy.

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.

In massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection can achieve near-optimal performance. However, it suffers from high computational complexity due to the involvement of matrix inversion. This issue becomes severer when user number (U) and receive antenna number (S) increase. Existing approaches such as Neumann series expansion method, Gauss-Seidel and Jacobi methods, can partly address this issue by approaching the matrix inversion with matrix multiplications or solving linear equations with iterative methods, respectively. However, matrix multiplications and the initialization for iterative methods are still costly. In this paper, we propose a further improved Jacobi method based soft-output massive MIMO detection scheme. The contributions include the use of matrix-vector product and a new approach to compute the log likelihood ratio (LLR). By using the matrix-vector product, the overall computational complexity is reduced from O (B × U2) to O(B × U). The new approach uses the noise-plus-interference (NPI) from the MMSE estimation, instead of using that from the first iteration. We then propose an approximation method to obtain the covariance of the NPI from MMSE estimation. Finally, we demonstrate through numerical simulations that the proposed scheme outperforms the existing schemes in terms of computational complexity and system bit error rate performance.

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.

Compared with the traditional multi‐user MIMO (multiple‐input and multiple‐output), massive MIMO aims to serve tens of users with hundreds of antennas on each base station. All users can use the same time–frequency resources through space division multiple access, leading to vast improvement on spectral efficiency. However, to achieve the benefits, channel state information is usually required, and the acquisition is difficult in massive MIMO systems. Theoretically, each user should be assigned with orthogonal pilot sequences to avoid interference; however, due to the huge number of users (much more than available orthogonal pilot sequences) in service, pilot reuse in adjacent cells is inevitable, causing inter‐cell interference. This phenomenon is often referred to as pilot contamination (PC) and is believed to be the fundamental limit on system capacity of massive MIMO systems. To solve this problem, many methods have been proposed since 2010, when the concept of massive MIMO was first proposed. In this study, the authors reviewed these methods, categorised them into four groups and compared their advantages and limitations. Although a survey on PC has been conducted by Elijah et al ., where they tried to cover various aspects of the PC issue, their work focuses on the analysis of rationale and limitations of different contamination mitigation methods. Besides, performance evaluations are conducted and presented.

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 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.

Mobile data offloading benefits both end users and content providers for enhancing user experiences and more data cost effectiveness, thus attracted lots of researchers' efforts on studying new offloading opportunities and optimized solutions. However, it is still under-explored in subway environment and this comes more valuable as more users are taking subway as daily means of transport. Indeed, motivated by special data offloading opportunities found in a subway train environment for users, we designed a local data distribution model and a super node selection algorithm based on context information and node resources, by combining the characteristics of users' interests on various contents, users' behavior and resources availability. Simulation results clearly show the high efficiency of our data distribution model and super node selection algorithm for offloading cellular data by as high as 90%.

The opportunistic forwarding paradigm is an emerging technology that takes advantage of the broadcast nature of wireless communications to compensate the channel unreliability. In this paper, we propose an analytical model based on Markov Chain to evaluate the performance of opportunistic forwarding. In our model, the network state is described by the combination of the packet advancement progress and the schedule of opportunistic forwarding, it could provide better understanding of the multi-packet transmissions in a network. This paper contains two simulation studies based on the iterative estimation and the random walk to show the transmission cost of the batched opportunistic data forwarding.

Real-time video communications has been incorporated into many instant communication tools such as ichat, Skype, QQ, etc. Real-time video communications has low delivery delay requirement, which imposes great challenge to the provisioning of such services. To address this problem, in this paper, we propose a selective redundant transmission mechanism to support real-time streaming on multi-interface wireless terminals. This mechanism selectively duplicates some video frames according to the tightness of their lifetimes and further schedule their transmissions (or some of them) via neighbors' assistance. We build a model to select the optimal encoding rate and also the optimal per-frame copy number in order to maximize the peak signal noise ratio (PSNR) of video streaming service when maximal allowable total traffic rate is given. Numerical results show that the proposed mechanism can significantly improve the PSNR of real-time video streaming as compared with existing work.

Recently, research on wireless sensor networks with mobile sinks (mWSN) has attracted a lot of attention. The mobility of such sinks often results in unpredictable changes of network topology, and brings big challenge to the design of efficient routing protocols for such networks. In this paper, we focus on design of an energy efficient distributed routing protocol for mWSNs. For this purpose, we propose a lightweight ring-based bidirectional routing protocol, referred to as BI-LRRP. BI-LRRP does not need location information and it performs ring-based routing on multi-ring based network structure for packet delivery. To reduce the transmission cost and also prolong the network lifetime, BI-LRRP uses bidirectional search for finding a mobile sink before actual packet delivery. Simulations results show that the proposed protocol can achieve high performance as compared with existing work.

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.

Opportunistic data forwarding has proven to be a powerful technique to achieve a high throughput in wireless mesh networks. It proactively utilizes the link quality variation rather than fighting it. In this article, we propose a time-based coordination scheme of opportunistic forwarding, dubbed ExOR Compact, that uses network coding to provide a reliable data transfer service at the network layer. Computer simulation shows how the proposal stacks against the seminal work on opportunistic forwarding and traditional IP forwarding.

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.

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.

The emerging wireless energy transfer technology based on Radio Frequency (RF) is a promising technology for wireless rechargeable sensor networks (WRSN) as it can charge sensor nodes simultaneously. In this paper, we use a mobile charger to stay at some locations and stay for certain time at each location to charge all the nodes in the network. We first define a power-charging function for the whole network and then get a set of candidate stop locations for the mobile charger by analyzing the property of this function. After the set of candidate locations are determined, we formulate two optimization problems: one is to minimize total charging time and another is to maximize the charging efficiency, subject to a charged energy threshold at each sensor node. Simulation results show that our method for choosing stop locations can greatly reduce the total charging time and improve charging efficiency.

Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a while since its last discharging. Accordingly, the battery can work for a longer time when it takes some rest between consecutive discharging processes than when it works all the time. However, to the best of our knowledge, this impact has not been considered in the design of energy-efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm for wireless sensor networks. In this algorithm, each node in the network periodically decides to be in the dominating set or not. Nodes that have taken sleep in the preceding round are encouraged to involve in the dominating set in the current round while nodes that have worked in the preceding round are encouraged to sleep in the current round for battery recovery. Detailed design description is presented. The complexity of the proposed algorithm is deduced to be O(D2), where D represents node degree. Simulation results show that our algorithm can significantly improve the network lifetime performance as compared with existing work.

With the increasing popularity of WLAN infrastructure, WiFi fingerprint-based indoor positioning systems have received considerable attention recently. Much existing work in this aspect adopts classification techniques that match a vector of radio signal strengths (RSSs) reported by a mobile station (MS) to pretrained reference fingerprints sampled from different access points (APs) at different reference points (RPs) with known positions. However, in the calculation of signal distances between different RSS vectors, existing techniques fail to consider the fact that equal RSS differences at different RSS levels may not mean equal differences in geometrical distances in complex indoor environment. To address this issue, in this paper, we propose a feature-scaling-based k-nearest neighbor (FS-kNN) algorithm for achieving improved localization accuracy. In FS-kNN, we build a novel RSS-level-based FS model, which introduces RSS-level-based scaling weights in the computation of effective signal distances between signal vector reported by a MS and reference fingerprints in a radio map. Experimental results show that FS-kNN can achieve an average location error as low as 1.70 m, which is superior to existing work.

This paper presents a novel recovery algorithm based on sparsity adaptive matching pursuit (SaMP) and a new near-optimal pilot placement scheme, for compressed sensing (CS)-based sparse channel estimation in orthogonal frequency division multiplexing (OFDM) communication systems. Compared with other state-of-the-art recovery algorithms, the proposed algorithm possesses the feature of SaMP of not requiring a priori knowledge of the sparsity level, and moreover, adjusts the step size adaptively to approach the true sparsity level. Furthermore, we focus on the pilot pattern design in sparse channel estimation. Although a brute-force search guarantees the optimal pilot pattern, it is prohibitive to examine all possibilities due to high computational complexity. It is known that by minimizing the mutual coherence of the measurement matrix when the signal is sparse on the unitary discrete Fourier transform (DFT) matrix, the optimal set of pilot locations is a cyclic difference set (CDS). Based on this, we propose an efficient near-optimal pilot placement scheme in cases where CDS does not exist. Simulation results show that the proposed channel estimation algorithm, with the new pilot placement scheme, offers a better tradeoff between the performance-in terms of mean-squared-error (MSE) and bit-error-rate (BER)-and complexity, when compared to other estimation algorithms.

ABSTRACT Routing in a low duty‐cycled wireless sensor network (WSN) has attracted much attention recently because of the challenge that low duty‐cycled sleep scheduling brings to the design of efficient distributed routing protocols for such networks. In a low duty‐cycled WSN, a big problem is how to design an efficient distributed routing protocol, which uses only local network state information while achieving low end‐to‐end (E2E) packet delivery delay and also high packet delivery efficiency. In this paper, we study low duty‐cycled WSNs wherein sensor nodes adopt pseudorandom sleep scheduling for energy saving. The objective of this paper is to design an efficient distributed routing protocol with low overhead. For this purpose, we design a simple but efficient hop‐by‐hop routing protocol, which integrates the ideas of multipath routing and gradient‐based routing for improved routing performance. We conduct extensive simulations, and the results demonstrate the high performance of the proposed protocol in terms of E2E packet delivery latency and packet delivery efficiency as compared with existing protocols. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract In addition to the requirements of the terrestrial sensor network where performance metrics such as throughput and packet delivery delay are often emphasized, energy efficiency becomes an even more significant and challenging issue in underwater acoustic sensor networks, especially when long‐term deployment is required. In this paper, we tackle the problem of energy conservation in underwater acoustic sensor networks for long‐term marine monitoring applications. We propose an asynchronous wake‐up scheme based on combinatorial designs to minimize the working duty cycle of sensor nodes. We prove that network connectivity can be properly maintained using such a design even with a reduced duty cycle. We study the utilization ratio of the sink node and the scalability of the network using multiple sink nodes. Simulation results show that the proposed asynchronous wake‐up scheme can effectively reduce the energy consumption for idle listening and can outperform other cyclic difference set‐based wake‐up schemes. More significantly, high performance is achieved without sacrificing network connectivity. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Topology control is an efficient strategy for improving the performance of wireless ad hoc and sensor networks by building network topologies with desirable features. In this process, location information of nodes can be used to improve the performance of a topology control algorithm and also ease its operations. Many location‐based topology control algorithms have been proposed. In this paper, we propose two location‐assisted grid‐based topology control (GBP) algorithms. The design objective of our algorithm is to effectively reduce the number of active nodes required to keep global network connectivity. In grid‐based topology control, a network is divided into equally spaced squares (called grids). We accordingly design cross‐sectional topology control algorithm and diagonal topology control algorithm based on different network parameter settings. The key idea is to build near‐minimal connected dominating set for the network at the grid level. Analytical and simulation results demonstrate that our designed algorithms outperform existing work. Furthermore, the diagonal algorithm outperforms the cross‐sectional algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a certain period of time since its last discharging. Accordingly, the battery can work for a longer time when it takes some rests between consecutive discharging processes than when it works all the time. However, this effect has not been considered in the design of energy-efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm (BRE-CDS) for wireless sensor networks. In BRE-CDS, each network node periodically decides to join the connected dominating set or not. Nodes that have slept in the preceding round have priority to join the connected dominating set in the current round while nodes that have worked in the preceding round are encouraged to take sleep in the current round for battery recovery. Detailed algorithm design is presented. The computational complexity of BRE-CDS is deduced to be O(D2), where D is node degree. Simulation results show that BRE-CDS can significantly prolong the network lifetime as compared with existing work. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, a turbo equalization scheme for MIMO-OFDM systems under imperfect channel estimation based on soft-input soft-output (SISO) minimum mean-square error (MMSE) sorted QR decomposition (SQRD) is proposed. A turbo structure consists of a SISO detector and a SISO decoder where extrinsic information is exchanged between the two SISO modules. Turbo equalization schemes are preferable in practical communication systems due to their good performance and acceptable computational complexity. MMSE-SQRD based SISO detection derives from SISO MMSE detection, and successive interference cancellation (SIC) is performed using a posteriori information obtained from previous detected symbols. Compared to SISO MMSE detection, MMSE-SQRD based SISO detection is of low complexity but has significant bit error rate (BER) performance enhancement. However, the derivation of the MMSE-SQRD based SISO detection scheme is under perfect knowledge of channel information at receivers. When channel estimation errors are presented, it has been pointed out that the system performance will degrade. In this paper, we studied this practical issue, and proposed the SISO MMSE-SQRD based turbo equalization under imperfect channel estimation. We first model the channel estimation error as added random Gaussian noise over the channel estimation matrix; based on that, we rederive the SISO MMSE detection for the data, and then redefine the extended channel matrix and receive vector by taking into account of channel estimation errors; after that, the SQRD algorithm is adjusted in accordance; MMSE-SQRD based data detection algorithm is finally performed. Numerical simulation results show that the proposed SISO MMSE-SQRD based turbo equalization for MIMO-OFDM systems under imperfect channel estimation outperforms the traditional MMSE based SISO detection with imperfect channel estimation in terms of BER performance and computational complexity.

Much previous work had demonstrated the remarkable performance of backpressure based routing and scheduling algorithms in wireless sensor networks (WSNs). However, the absence of consideration on energy use efficiency in the design of existing backpressure based algorithms makes them difficult to be deployed in resource-limited WSNs. In this paper, we study how to improve the energy use efficiency of backpressure based algorithm. For this purpose, we propose an energy efficient backpressure routing and scheduling algorithm (EBP) for WSNs. In EBP, a new link weight calculation method is designed, based on which nodal energy status is considered when making decisions on backpressure based transmission scheduling. In EBP, packets are encouraged to be forwarded to nodes with more residual energy while the throughput-optimality of backpressure based algorithm is still preserved. Simulation results show that EBP can obtain significant performance improvements in terms of energy use efficiency, network throughput, and packet delivery ratio as compared with existing work.

In this paper, we study how to deploy new access points (AP) to achieve improved accuracy for WiFi- based indoor localization systems. Existing mechanisms in this aspect are typically simulation based and further they do not consider how to use pre-existing APs in target environment for achieving high localization performance. To overcome these issues, in this paper, we propose a measurement-based AP deployment mechanism (MAPD). MAPD takes advantage of those pre-existing APs to identify candidate positions with poor localization accuracy for deploying new APs. We then collect the fingerprints for all possible AP deployment layouts via over- deployment of APs, one at each candidate position. Finally, we present a greedy search algorithm to identify m positions out of the n candidate positions (mn) while minimizing the location error. Experimental results demonstrate that the localization errors can be largely reduced: Mean error distance can be reduced by 0.56 meter (26%) and 0.17 meter (10%) as compared with the case without deploying new APs and previous work, respectively; Moreover, the maximum location error can be reduced by 1.53 meter (27%) and 0.51 meter (11%), respectively.

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.

Recently, inter-session opportunistic network coding has been considered as a promising technology for improving the performance of a wireless multi-hop network (WMN). However, most existing work in this field did not consider the issue of how the wireless medium is accessed could largely affect the performance of localized network coding. In this paper, we theoretically analyze the throughput improvement obtained by combining network coding and transmission scheduling in a WMN. Then we formulate the optimal throughput problem as a minimum length scheduling problem subject to potential coding opportunities and coding based transmission conflict constraints. We further propose a distributed coding aware transmission scheduling mechanism for WMNs. Simulation results show that our proposed mechanism can remarkably improve the network throughput as compared with existing work.

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.

Opportunistic forwarding and network coding utilize the broadcasting nature of wireless transmission and fluctuation of link quality for enhanced performance in multi-hop wireless networks. However, TCP is not well supported because of the way they operate. The frequent occurrences of dropped packets and out-of-order arrival of them in opportunistic forwarding and decoding delay in network coding overthrow TCP's congestion control. We propose a mechanism, dubbed TCPFender, for TCP to function over the network layer that uses opportunistic data forwarding and network coding, to properly conduct congestion control in TCP and provide reliable data transport. Our experiment shows that TCPFender achieves significantly higher throughput compared to TCP over IP in a simulated wireless mesh.

In this paper, we study how to effectively reduce the average end-to-end (E2E) packet delay in backpressure based scheduling in wireless multi-hop networks. We accordingly propose a virtual gradient based back-pressure scheduling algorithm, referred to as VBR. In VBR, intentional virtual queue, whose length (called virtual gradient) depends on the distance to destination, is first built at nodes in a network in the network configuration phase. In this way, virtual gradient is established at nodes in the network. In the network operation phase, the scheduling decision at each node needs to jointly consider both real queue length and virtual queue length. Simulation results show that VBR can obtain significant performance improvement on back-pressure based routing and scheduling, in terms of packet delivery ratio and average E2E delay.

In this paper, we propose a new precoding scheme that can introduce correlation between the original transmitted symbols. The precoding process is based on the operations in Galois Field with size q = 2m(GF (q)). In the existing precoding schemes such as orthogonal space-time block code, the generated symbols carry the information of all coded symbols; this correlation can be utilized by the receiver to provide diversity in space and time domain. Similarly, the proposed precoding scheme that utilizes the operations defined in GF (q) can also introduce such correlation. We evaluate the mutual information of the system and conclude that mutual information is always no less than that in the system without the proposed precoding scheme regardless of the source distribution. In addition, when the source is uniformly distributed, the proposed GF (q) precoding scheme achieves the maximum mutual information of the channel, i.e. channel capacity. Hence, we can derive that the proposed precoding scheme in GF (q) can preserve source information during the transmission in the channel. Convinced by the potential benefit of the increased mutual information, the proposed GF (q) precoding scheme is promising in approaching channel capacity.

Underwater acoustic (UWA) channels are often characterized as time-varying systems which result in intercarrier interference (ICI) in the reception of orthogonal frequency division multiplexing (OFDM) signals. Recently, compressed sensing (CS) has gained a fast-growing interest by exploiting the sparse nature of UWA channels in OFDM communication networks. This paper studies selected characterizations of the UWA channels, and reviews several mathematical UWA channel models in the literature. Moreover, we present a CS-based sparse channel estimation based on a recently-established statistical channel model, which incorporates acoustic signal propagation laws and random local displacements. The sparse coefficients can be estimated using CS-based reconstruction algorithms.

In recent years, underwater acoustic sensor networks (UWSNs) are envisioned for different potential applications, ranging from long-term marine environmental monitoring, industrial instrumentation control, to military surveillance and security. Compared to wireless sensor networks (WSNs), energy-efficient data transmission becomes more critical in UWSNs due to non-rechargeable batteries of sensor nodes with limited amount of energies in long-term marine monitoring applications. Besides, in underwater acoustic communications, transmitting and receiving power levels dominate the energy consumption during the data transfer. Data packet retransmission caused by network deployment error increases the energy consumption and reduce the network lifetime. In this paper, we investigate a two-dimensional deployment strategy of UWSNs with a square grid topology. We present a mathematical model to study the deployment error of UWSNs. Based on this model, a parameter, i.e., α, is introduced to balance the network robustness and the energy consumption of sensor nodes. α is defined as the ratio of the transmission range of a sensor node to the distance between two closest adjacent sensor nodes. We report optimal values of α corresponding to this balance for different sizes of UWSNs.

This paper examines the problem of compressed sensing-based sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. In particular, we present an improved estimation algorithm based on the sparsity adaptive matching pursuit (SAMP), which is referred to as the adaptive step size SAMP (AS-SAMP), and compare it with the existing algorithms. Without requiring a priori knowledge of the sparsity, the proposed algorithm adjusts the step size adaptively to approach the true sparsity, thus improving the estimation accuracy. Simulation results show that the proposed algorithm provides a better trade-off between the mean squared error (MSE) performance and complexity when compared with conventional methods.

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.

Abstract In this paper, we study the issue of routing in a vehicular ad hoc network with the assistance of sparsely deployed auxiliary relay nodes at some road intersections in a city. In such a network, vehicles keep moving, and relay nodes are static. The purpose of introducing auxiliary relay nodes is to reduce the end‐to‐end packet delivery delay. We propose a sparsely deployed relay node assisted routing (SRR) algorithm, which differs from existing routing protocols on how routing decisions are made at road intersections where static relay nodes are available such that relay nodes can temporarily buffer a data packet if the packet is expected to meet a vehicle leading to a better route with high probability in certain time than the current vehicles. We further calculate the joint probability for such a case to happen on the basis of the local vehicle traffic distribution and also the turning probability at an intersection. The detailed procedure of the protocol is presented. The SRR protocol is easy to implement and requires little extra routing information. Simulation results show that SRR can achieve high performance in terms of end‐to‐end packet delivery latency and delivery ratio when compared with existing protocols. Copyright © 2013 John Wiley & Sons, Ltd.

With the increasing popularity of wireless local area network infrastructure, Wi-Fi fingerprint based indoor positioning systems have received considerable attention in recent years. In the literature, most existing work in this area focuses on techniques that match the vector of radio signal strength (RSS) values reported by a mobile device to the fingerprints collected at predetermined reference points (RPs) by comparing the similarity (measured based on RSS difference) between them. However, these existing techniques fail to consider the fact that equal RSS differences at different RSS levels may not mean equal distances in reality. To address this issue, in this paper, we propose a feature scaling based k-nearest neighbor algorithm (FS-kNN) for improved localization accuracy. In FS-kNN, we build a novel RSS-based feature scaling model, which introduces signal-level-scaled weights in the calculation of effective signal distance between signal vector reported by mobile device and existing fingerprints. Experimental results show that FS-kNN can achieve an average error distance as low as 1.93 meters, which is superior to previous work.

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.

In this paper, we study the issue of mobility scheduling for mobile data collector (MDC) in wireless sensor networks. Most existing work in this area focuses on geometric-based optimization without considering the spatial correlation among different locations. In this paper, we study the mobility scheduling problem from the informative perspective by using Gaussian process to capture the spatial correlation of real world phenomena. Based on the Gaussian process model and collected sensing data from a number of sensor nodes in the network, one can predict the sensing values at the remaining interesting locations and can further estimate the prediction accuracy. This approach can potentially shorten the length of data collection tour with small penalty in data accuracy. We use the mutual information maximization criteria to evaluate the quality of a data collection tour. We accordingly formulate the informative mobility scheduling problem which finds the data collection tour with the maximal mutual information under certain mobility constraint. The problem is shown to be NP-hard and we accordingly propose two efficient heuristic algorithms. We evaluate the performance of our algorithms by comparing them with geometric-based algorithms through extensive simulations and the results show that our algorithms can return much shorter tours while achieving the same level of data quality.

In this paper, we design a novel lightweight ring-based routing protocol (LRRP) for wireless sensor network with mobile sinks (mWSN). The design objective is to significantly reduce the protocol overhead for route discovery and management while preserving high routing performance. For this purpose, LRRP builds a base ring by finding a shortest cycled path surrounding an artificially created topological hole in the network and, based on the base ring, it builds a ring-based structure to cover remaining nodes in the network and further assigns them ring IDs and virtual angles to ease the packet forwarding. LRRP works in a hybrid way for routing updates and packet forwarding. Specifically, each mobile sink (MS) dynamically chooses agent nodes, one on each ring, to proactively update its reachability as it moves. Each data packet is forwarded using ring-based forwarding along a pre-selected ring until reaching an MS or an agent node with path to an MS, from which the packet will be directly forwarded towards the MS. LRRP further considers how to achieve a good tradeoff between energy balancing among different rings and data path lengths. Extensive simulation results show that LRRP can significantly reduce the protocol overhead and achieve prolonged network lifetime as compared with existing work while achieving a very high packet delivery ratio.

In this paper, we propose a location-based friend-assisted coding-aware routing protocol (LFCR) for wireless multihop networks. To achieve improved network throughout, LFCR performs inter-flow network coding based routing with the assistance of location information. Specifically, LFCR combines friend-assisted path discovery and coding-aware routing. Further, when making decision on next hop selection, LFCR takes into account both coding opportunities and forwarding progress in next hop selection and attempts to make a good tradeoff between them. Simulation results show that LFCR significantly outperforms existing work in terms of network throughput, packet delivery ratio, and coding frequency.

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.

Recently, much effort has been made for implementation of back-pressure scheduling in wireless networks. In this paper, we explore the implementation of back-pressure-based forwarding in wireless sensor networks. For this purpose, we propose Gradient-pressure, a practical Gradient-assisted anycast-based back-pressure framework for wireless sensor networks. Gradient-pressure introduces gradient information to assist transmission scheduling and realizes distributed anycast-based back-pressure scheduling on top of IEEE 802.11. Simulation results demonstrate that Gradient-pressure has high performance in terms of energy-use efficiency and goodput.

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.

As a novel fundamental platform for providing real time access to wireless network, vehicular communication is drawing more and more attentions in recent years. IEEE 802.11p is a main radio access technology which supports communication for high mobility terminals. Due to the limited coverage, it is usually deployed coupling with cellular network to achieve seamless mobility. In cellular/802.11p heterogeneous network, vehicular communication has the characteristics of short span of time associating with Road Side Unit (RSU). Moreover, the media access control (MAC) scheme of IEEE 802.11p decides that packet collision probability increasing followed by the increasing of user quantity, which leads to the decreasing of network throughput. In response to these compelling problems, we propose a fuzzy Q-learning based vertical handoff (FQVH) control strategy for supporting the mobility management. FQVH has online learning ability and can give optimal handoff decisions adaptively with no need for prior knowledge on handoff behavior. Simulation results verify that it can adjust handoff strategies to different traffic conditions, and keep users always connected to the best network.

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.

Localized topology control is attractive for generating reduced topologies with desirable features such as sparser connectivity and reduced transmit powers. In this paper, we propose an energy efficient localized topology control algorithm called X-LMST in order to achieve prolonged network lifetime. In X-LMST, each node is required to keep its one-hop neighborhood topology. Moreover, in X-LMST, a new metric is introduced for characterizing the energy criticality status of each link in the network. Each node independently constructs a local energy-efficient near-minimal spanning tree (MST) for finding a reduced neighbor set while maximally avoiding overusing energy-critical links in its one-hop neighborhood for future communications. Simulation results show that X-LMST significantly outperforms existing work in terms of network lifetime.

In this paper, we investigate multi-user interference cancellation (MUI) schemes for deployment at roadside unit in vehicular ad hoc networks. Generally, MUI schemes can be divided into linear and nonlinear groups. In linear MUI schemes, successive and parallel interference cancellation schemes are widely used. In successive interference cancellation (SIC) schemes, the receiver will detect the user's data on a per user base, and immediately cancel the interference of the detected user for the next detection. On the contrary, parallel interference cancellation (PIC) schemes detect a group of users' data simultaneously, then cancel the interference of all users in the next round of operation. There exists error propagation problem in both successive and parallel interference cancellation schemes. To address the problem, ordered successive interference cancellation scheme has been proposed to improve the bit error rate (BER) performance by the receiver detecting the user with the highest instantaneous signal-to-interference-plus-noise ratio (SINR) and canceling the interference of that user for another continuously. The process repeats until all users' data are detected. Besides, iterative processing techniques are also introduced to further improve the system BER performance. In this paper, we study and compare the interference cancellation schemes for uplink transmission in vehicular ad hoc networks. Simulation results show that the BER performance is much better in ordered successive interference cancellation schemes than both the SIC and PIC schemes, especially in high signal-to-noise ratio regions where the multi-user interference becomes dominant. It is also shown through the study that the ordering procedure can efficiently avoid the error propagation problem in vehicular networks.

ABSTRACT Achieving high data quality and efficient network resource utilization is two major design objectives of wireless sensor networks (WSNs). However, these two objectives are often conflictive. By allowing sensors to report sampled data at high rates, fine‐grained data quality can be obtained. However, the limited resources of a WSN make it difficult to support very high traffic rate. Therefore, the capability of adaptively adjusting sensor nodes' traffic‐generating rates on the basis of the availability of network resources and application requirements is critical. This issue has attracted much attention recently, and some work has been carried out. To achieve high data quality and improved utilization of network resources, in this paper, we propose rate‐based adaptive precision setting (RAPS) protocol, which works in a way that each sensor can adaptively adjust its traffic‐generating rate on the basis of the current network resources availability and application requirements. RAPS introduces the following two key factors into its design: application's precision requirement and packet arrival rate. Analytical and simulation results show that RAPS can achieve improved data quality while reducing packet delivery latency. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we propose an energy-efficient on-demand multicast routing protocol (EMP) for wireless ad hoc and sensor networks. The design objective is to prolong the network lifetime of such networks. For this purpose, EMP introduces the strategy of energy critical avoidance in the process of ondemand construction of multicast routing trees. That is, those energy-critical nodes in the network are discouraged from in-volving a multicasting task. EMP also incorporates the destination-driven feature in its tree construction process in order to reduce the tree cost. We present the detailed design description of EMP. Simulation results show that EMP can achieve high performance in terms of network lifetime.

Low duty cycled scheduling can largely prolong the lifetime of a wireless sensor network (WSN) but also brings longer end-to-end (E2E) delivery delay. In this paper, our design objective is to pursue the near minimum E2E energy consumption subject to a desired success ratio that the E2E delay is below a delay bound. Accordingly, we design a Markov decision process based geographic routing protocol such that each relay node currently holding a packet makes localized forwarding decision on continuing waiting or transmitting immediately to the so far best forwarder candidate based only on local network state information. Simulation results show that the designed protocol can achieve expected success ratio subject to given delay bound and also high energy use efficiency.

In this paper, we propose an efficient network coding based back-pressure algorithm (NBP). NBP introduces the interflow network coding to improve the performance of the backpressure algorithm (a famous throughput-optimal cross-layer scheduling algorithm) for scheduling the transmissions of packets and also higher transmission efficiency. We theoretically prove that NBP can stabilize such networks. Simulation results demonstrate that NBP significantly outperforms traditional back-pressure algorithm in terms of packet delivery delay and average forwarding queue length.

Underwater acoustic sensor network (UWSN) has many important future applications in environmental, natural resources development, and geological oceanography. The recent advancement in underwater acoustic networking technologies and autonomous underwater vehicle (AUV) technologies enables the new underwater networking paradigm of using the AUV as the mobile sink for data collection. In this paper, we propose a coordination scheme for data gathering in UWSN using AUV. The mobility of AUV makes communication between AUV and sensor nodes challenging and it is difficult to guarantee that sensor nodes will be able to come out of sleep mode for communication. With different energy consumption requirements and constraints, the operation of AUV includes sending beacon messages, channel detection, and data reception, whereas that for the underwater sensor nodes involves prolonged sleep mode with sporadic data transmission. Therefore, different cycle periods would be required for AUV and sensor nodes. A new scheme is proposed and the shortest wakeup time and optimal amount of sleeping time for sensor nodes are investigated. Moreover, we demonstrate the effectiveness of our scheme when time synchronization does not exist between AUV and sensor nodes. Furthermore, transmission power control by using received signal strength (RSS) is introduced in order to decrease energy consumption and increase communication reliability. Simulation results show that the proposed scheme with power control leads to relative low energy consumption during communication under the harsh underwater environment compared with that without power control.

In this paper, we measure and study the peer startup process in PPLive, a popular commercial P2P streaming system, and focus on a fundamental issue in this aspect: how a peer initializes its buffer when it joins a channel, i.e., initial offset placement of peers' buffers in the startup stage. We build a general model of peer startup process in chunk-based P2P streaming systems and present an initial offset placement scheme we inferred from the measurement results, i.e., proportional placement (PP) scheme. With FP scheme, the initial buffer offset is set to the offset of the reference neighbor peer plus an advance proportional to the reference neighbor peer's offset lag or buffer width. We evaluate the performance of PP scheme and find it is stable when the placement is based on offset lag, but will be unstable when it is based on buffer width if the chunk fetching strategy and neighbor peer selection mechanism are not properly designed. We finally report our detailed measurement results of the peer startup process and initial offset placement algorithms used in PPLive. Our models and measurement results could be useful for guiding the analysis and design of buffering protocols for a real P2P live streaming system.

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.

Underwater acoustic communications in the ocean is complicated as the acoustic signals may be attenuated, distorted and delayed. In this paper, we review the underwater acoustic signal propagation properties in terms of sound speed profile, spreading loss and absorption loss. We study and compare different approaches on the calculation of signal transmission loss in the water, more specifically, the ray theory model approach and the semi-empirical formula approach. Using the Acoustic Toolbox, we compare their performance under different environmental parameters, including the sound source depth, bathymetry data, and the horizontal distance between the sound source and receiver. Furthermore, in order to obtain how the acoustic propagation characteristics will affect the performance of medium access control (MAC) protocol, we adopt pure ALOHA protocol and use network simulator ns-2 to study the throughput performance under both shallow water and deep ocean conditions. Our results indicate that the transmission loss in the shallow water is close to the result of semi-empirical formula with transition region (k = 1.5), which is close to the result of semi-empirical formula with spherical spreading loss (k=2) in deep water.

Spectrum sensing and awareness constitute key functionalities of a cognitive radio (CR), and encompass signal detection, classification, and blind parameter estimation. This paper proposes a novel algorithm for the tone frequency spacing estimation and joint classification of M-ary frequency shift keying (M-FSK) signals in a fading environment. The proposed algorithm relies on the number and position of the first-order cycle frequencies (CFs), and requires neither recovery of the carrier and symbol timing, nor the estimation of channel parameters or signal and noise powers. Receive spatial diversity is exploited to enhance the classification and estimation performance of the algorithm. The simulation results for the algorithm performance confirm its effectiveness.

Opportunistic data forwarding draws more and more attention in the research community of wireless network after the initial work ExOR was published. However, as far as we know, all existing opportunistic data forwarding only use the nodes which are included in the forwarder list in the entire forwarding progress. In fact, even if a node is not a listed forwarder in the forwarder list, but it is on the direction from source node to destination node, and when it successfully overhears some packets by opportunity, the node actually can be utilized in the opportunistic data forwarding progress. In this paper, we propose the local cooperative relay for opportunistic data forwarding in mobile ad-hoc networks. In general, three contributions we have in this paper, 1) we open more node to participate in the opportunistic data forwarding even though the nodes are not included in the forwarder list, 2) we propose the procedure to select the best local relay node, namely the helper-node, from many candidates but require no inner communication between them, 3) the helper-node is selected just when it is needed, and the such real time selection can tolerate and bridge vulnerable links in mobile networks.

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.

The link quality variation of wireless channels has been a challenging issue in data communications until recent explicit exploration in utilizing this characteristic. The same broadcast transmission may be perceived significantly differently, and usually independently, by receivers at different geographic locations. Furthermore, even the same stationary receiver may experience drastic link quality fluctuation over time. The combination of link-quality variation with the broadcasting nature of wireless channels has revealed a direction in the research of wireless networking, namely, cooperative communication. Research on cooperative communication started to attract interests in the community at the physical layer but more recently its importance and usability have also been realized at upper layers of the network protocol stack. In this article, we tackle the problem of opportunistic data transfer in mobile ad hoc networks. Our solution is called Cooperative Opportunistic Routing in Mobile Ad hoc Networks (CORMAN). It is a pure network layer scheme that can be built atop off-the-shelf wireless networking equipment. Nodes in the network use a lightweight proactive source routing protocol to determine a list of intermediate nodes that the data packets should follow en route to the destination. Here, when a data packet is broadcast by an upstream node and has happened to be received by a downstream node further along the route, it continues its way from there and thus will arrive at the destination node sooner. This is achieved through cooperative data communication at the link and network layers. This work is a powerful extension to the pioneering work of ExOR. We test CORMAN and compare it to AODV, and observe significant performance improvement in varying mobile settings.

ABSTRACT In this paper, we focus on the energy efficiency of secure communication in wireless sensor networks (WSNs). Our research considers link layer security of WSNs, investigating both the ciphers and the cryptographic implementation schemes, including aspects such as the cipher mode of operation and the establishment of initialization vectors (IVs). We evaluate the computational energy efficiency of different symmetric key ciphers considering both the algorithm characteristics and the effect of channel quality on cipher synchronization. Results show that the computational energy cost of block ciphers is less than that of stream ciphers when data are encrypted and transmitted through a noisy channel. We further investigate different factors affecting the communication energy cost of link layer cryptographic schemes, such as the size of payload, the mode of operation applied to a cipher, the distribution of the IV, and the quality of the communication channel. A comprehensive performance comparison of different cryptographic schemes is undertaken by developing an energy analysis model of secure data transmission at the link layer. This model is constructed considering various factors affecting both the computational cost and communication cost, and its appropriateness is verified by simulation results. In conclusion, we recommend using a block cipher instead of a stream cipher to encrypt data for WSN applications and using a cipher feedback scheme for the cipher operation, thereby achieving energy efficiency without compromising the security in WSNs. Copyright © 2011 John Wiley & Sons, Ltd.

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.

ABSTRACT Node localization is essential to wireless sensor networks (WSN) and its applications. In this paper, we propose a particle swarm optimization (PSO) based localization algorithm (PLA) for WSNs with one or more mobile anchors. In PLA, each mobile anchor broadcasts beacons periodically, and sensor nodes locate themselves upon the receipt of multiple such messages. PLA does not require anchors to move along an optimized or a pre‐determined path. This property makes it suitable for WSN applications in which data‐collection and network management are undertaken by mobile data sinks with known locations. To the best of our knowledge, this is the first time that PSO is used in range‐free localization in a WSN with mobile anchors. We further derive the upper bound on the localization error using Centroid method and PLA. Simulation results show that PLA can achieve high performance in various scenarios. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract In‐network aggregation is crucial in the design of a wireless sensor network (WSN) due to the potential redundancy in the data collected by sensors. Based on the characteristics of sensor data and the requirements of WSN applications, data can be aggregated by using different functions. MAX—MIN aggregation is one such aggregation function that works to extract the maximum and minimum readings among all the sensors in the network or the sensors in a concerned region. MAX—MIN aggregation is a critical operation in many WSN applications. In this paper, we propose an effective mechanism for MAX—MIN aggregation in a WSN, which is called Sensor MAX—MIN Aggregation (SMMA). SMMA aggregates data in an energy‐efficient manner and outputs the accurate aggregate result. We build an analytical model to analyze the performance of SMMA as well as to optimize its parameter settings. Simulation results are used to validate our models and also evaluate the performance of SMMA. Copyright © 2010 John Wiley & Sons, Ltd.

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.

Innovative routing in mobile ad hoc networks is crucial for unleashing the full potential of such networks. In this paper, we propose a new Proactive Source Routing (PSR) protocol that has a very small communication overhead but provides nodes with more network structure information than distance-vector based protocols. The value of the source routing protocol includes: 1) better control of path selection by the source nodes for congestion avoidance, load and energy consumption balancing, and bypassing untrusted areas, 2) alleviation of IP forwarding at intermediate nodes, and 3) support for opportunistic data forwarding. PSR complements DSR as a proactive counterpart to provide responsive data transportation services in heavily loaded networks. Our simulation results show that PSR achieves performance similar to OLSR and DSDV, but with only a small fraction of the communication overhead.

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.

In this paper, we propose a new method, called Load-Aware Channel Assignment Exploiting Partially Overlapping Channels (Load-Aware CAEPO), which uses not only non-overlapping channels but also partially overlapping channels. We further improve our scheme by introducing the concept of node grouping to enhance the performance, and develop another new channel assignment algorithm called Load-Aware CAEPO-G. Simulation results demonstrate that our proposed schemes can significantly improve the aggregated network performance.

An accurate localization scheme is essential to many underwater sensor applications. However, due to the persistent existence of uncertainties and measurement errors, an accurate localization is very difficult to achieve. To mitigate this problem, multi-iteration measurement and least squares scheme are often adopted in terrestrial applications to find a good estimate. But, in underwater applications the multi-iteration scheme is not practical due to high communication cost. Meanwhile, it has been observed that the errors in distance measurement often follow a certain pattern, which can be utilized to further improve on localization accuracy. In the paper, we analyze and utilize the measurement error distributions to better improve localization accuracy. An analytical model is developed for performance evaluation, along with extensive simulations. Both uniform error distribution and normal error distribution are considered in our research. Our results indicate that our proposed probabilistic localization method can significantly improve the localization accuracy over the commonly adopted least squares estimate (LSE) scheme.

Time synchronization is critical in distributed wireless networks to achieve and maintain coordination among distributed network nodes. In this paper, we propose a routing based time synchronization protocol (RBTSP) for multi-hop wireless networks. Different from many existing time synchronization protocols, our objective aims at minimizing the number of timing packet exchange and reducing the non-deterministic delay. We conduct mathematical analysis and simulation experiments to demonstrate the working of the proposed time synchronization method. The results manifest that our scheme can achieve better performance on synchronization accuracy and power efficiency.

In this paper, we focus on secure communication in wireless sensor networks (WSNs). Specifically, we investigate different factors which affect the energy cost of link layer cryptographic security schemes, such as the payload size, the source of the initialization vector, and the channel quality. We propose an approach to evaluate the performance of cryptographic communication schemes by developing an analysis model considering these factors. The appropriateness of this model is supported by simulation results. In conclusion, we recommend cipher feedback (CFB) mode for the cipher operation, thereby achieving energy efficiency without compromising the security.

An accurate localization scheme is essential to many underwater sensor applications. However, due to the persistent existence of uncertainties and measurement errors, an accurate localization is very difficult to achieve. The communication cost is much higher in underwater networks compared to terrestrial networks and this calls for more accurate localization schemes even if they involve more computational burden. In the paper, a scheme based on minimum mean absolute error (MMAE) is introduced and extensive simulation results are presented to compare this and the commonly used minimum mean squared error (MMSE) method. Both uniform error distribution and normal error distribution are considered. Our results indicate that MMAE clearly result in better localization accuracy when compared to MMSE.

Abstract Development of communication channels for underwater sensor networks holds many unique challenges. Communication near the bottom of the ocean is no exception as the effects of reflection and refraction greatly affect how acoustic waves travel between a source and an intended receiver. Deployment and testing in the ocean are difficult and expensive; thus there is a strong reliance on models to aid in design and development of a potential network. Since each ocean region can present very unique challenges, it is of great value to model an environment based on real environmental parameters whenever available. A well prepared channel model will provide the ability to show channel capacity as it relates to node positions, as well as showing the performance of modulation techniques to an environment with propagation characteristics and path arrivals. This channel model will also be implementable into a simulation package to allow for high quality simulation of higher level protocols. The proposed method has proved to be a useful tool in modeling a particular environment and provides insight into underwater sensor node placement and modulation. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract This paper proposes a joint data aggregation and encryption scheme using Slepian‐Wolf coding for efficient and secured data transmission in clustered wireless sensor networks (WSNs). We first consider the optimal intra‐cluster rate allocation problem in using Slepian‐Wolf coding for data aggregation, which aims at finding a rate allocation subject to Slepian‐Wolf theorem such that the total energy consumed by all sensor nodes in a cluster for sending encoded data is minimized. Based on the properties of Slepian‐Wolf coding with optimal intra‐cluster rate allocation, a novel encryption mechanism, called spatially selective encryption, is then proposed for data encryption within a single cluster. This encryption mechanism only requires a cluster head to encrypt its data while allowing all its cluster members to send their data without performing any encryption. In this way, the data from all cluster members can be protected as long as the data of the cluster head (calledvirtual key) is protected. This can significantly reduce the energy consumption for performing data encryption. Furthermore, an energy‐efficient key establishment protocol is also proposed to securely and efficiently establish the key used for encrypting the data of a cluster head. Simulation results show that the joint data aggregation and encryption scheme can significantly improve energy efficiency in data transmission while providing a high level of data security. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Cognitive ultra‐wideband (C‐UWB) systems have recently received much attention because the huge bandwidth of ultra‐wideband (UWB) systems can better exploit the advantages of cognitive radio (CR) systems. Dynamic spectrum access (DSA) is a key technique in CR systems to implement dynamic spectrum change and can be easily implemented by changing the transmitted pulse shape in a C‐UWB communication system. In this paper, we propose an orthogonal expansion based pulse shaping method to implement DSA and to compensate for antenna distortion, which uses the orthogonal Hermite functions as the orthogonal basis. In order to eliminate the direct current (DC) component existing in even orthogonal Hermite functions and to reduce the computational complexity, two modified methods and a simplification procedure are also proposed. Our results indicate that the proposed orthogonal expansion based pulse shaping methods have a much lower computational complexity than the semi‐definite programming (SDP) method, while achieving a high power efficiency. Furthermore, we demonstrate that the distortion caused by the antenna effects can also be compensated during the pulse shaping process and a better signal‐to‐noise ratio (SNR) can thus be achieved. Therefore, the proposed method is very suitable for practical application in C‐UWB communications, in which the spectrum environment changes rapidly. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Recent development in wireless communications has lead to the problem of growing spectrum shortage. Cognitive radio, as a novel technology, has been proposed in recent years to solve this problem by dynamically accessing the spectrum so as to enhance the spectrum utilization. Security in cognitive radio networks has become a challenging issue because there are more chances open to attackers by cognitive radio technology, compared to those from conventional wireless networks. These weaknesses and vulnerable aspects, introduced by the nature of cognitive radio, may cause serious impact on the security and quality of service (QoS) provisioning for the entire network. Unfortunately, at present, there is no specific secure protocol designed for cognitive radio networks. Therefore, in this paper, we conduct a high‐level survey to review and reflect the state‐of‐the‐art work on the security issues in cognitive networks. We focus on analyzing the security system at the macroscopic level, where both protection and detection are considered to be the most essential parts to ensure security in the whole network. Furthermore, we investigate special characteristics of cognitive networks at different protocol layers, including physical layer, link layer, network layer, transport layer and application layer. We recognize both the challenges and experiences, and focus on constructing the secured cognitive wireless networks. Copyright © 2009 John Wiley & Sons, Ltd.

Underwater acoustic sensor networks are quite different from terrestrial wireless sensor networks. Localization for underwater applications is different due to the bandwidth limited acoustic communication, sparsely distributed network deployment, and more expensive and powerful sensor nodes. In this paper, we propose a new scheme to achieve better localization accuracy for underwater acoustic sensor networks. Instead of using the commonly adopted circle-based event detection and least squares algorithm based location estimation, the proposed scheme utilizes the hyperbola-based approach for event localization and a normal distribution for estimation error modeling and calibration. Our analysis and simulation results indicate that the performance of the proposed scheme is clearly better than those from the least squares location estimation based localization schemes.

Unlike most IEEE 802.11-based ad hoc networks, in which only a single channel is used, wireless mesh networks allow the simultaneous use of multiple channels to increase the aggregated capacity. Many efforts have been taken to better exploit multiple non-overlapped channels. Although the IEEE 802.11 b/g standards, which govern the unlicensed 2.4 GHz industrial, scientific and medical (ISM) band, provide 11 channels, only three of them, namely 1, 6 and 11 are nonoverlapped. In this paper, we propose a new channel assignment scheme named channel assignment exploiting partially overlapping channels (CAEPO). CAEPO can not only assign non-overlapped channels, but also exploit partially overlapping channels. In the proposed scheme, the traffic-aware interference between channels is considered to be the main factor, which is turned into a metric according to the overlapping degree between channels. In addition to that, packet loss ratio is another major consideration in the development of our proposed channel assignment scheme. From simulation results, we can see that using 11 channels, CAEPO effectively improves the network performance.

Time synchronization is important for wireless sensor networks because it facilitates cooperation among nodes and helps raise power efficiency. Time synchronization protocols like TPSN, RBS and FTSP have provided great schemes to fulfill fast synchronization with efficiency. In some applications, nodes might hope to sleep for a long time without timestamp exchanges with other nodes. In that case, accurate time drift prediction is quite necessary. For that purpose, firstly, we propose a time synchronization scheme, which fully utilizes the broadcast nature. The scheme achieves time synchronization with fewer timestamps compared with RBS and TPSN. Secondly, we introduce a method to find relative time drift rate on the fly. Thirdly, we introduce a scheme to predict time drift rates of the next few hours. We also analyze a few factors that deteriorate frequency drift or time drift rate. The diurnal periodical environment trend, instead of mathematical extrapolation, is used for time drift rates prediction of the next few hours.

Recent developments of wireless communication lead to the problem of growing spectrum shortage. Cognitive radio, as a novel technology, tends to solve this problem by dynamically utilizing the spectrum. Security in cognitive radio network becomes a challenging issue, since more chances are given to attackers by cognitive radio technology compared to general wireless network. These weaknesses are introduced by the nature of cognitive radio, and they may cause serious impact to the network quality of service. However, at present there are no specific secure protocols for cognitive radio networks. Motivated by this, the current state of art in security of cognitive radio network is reviewed in this paper. We focus on analyzing the security system at the macroscopic level, where both protection and detection are significant parts for ensuring security of the whole network. Special characteristics of cognitive radio network in different protocol layers are also investigated, such as physical layer, link layer, network layer, transport layer and application layer.

Abstract This paper proposes a cooperative fault‐detection mechanism for detecting cluster‐head failures in cluster‐based UnderWater Sensor Networks (UWSNs). The proposed detection mechanism aims to accurately and fast detect the failure of a cluster head in order to avoid unnecessary energy consumption caused by a mistaken detection. For this purpose, it allows each cluster member to independently detect the fault status of its cluster head and then employs a distributed agreement protocol to reach an agreement on the fault status of the cluster head among multiple cluster members. It runs concurrently with normal network operation by periodically performing a detection process at each cluster member. To reduce energy consumption, it uses a time division multiple access medium access control (TDMA MAC) protocol and makes use of the data periodically sent by a cluster head as the heartbeats for fault detection. A couple of forward and backward time‐division‐multiplexing (TDM) frames are specially structured for enabling multiple cluster members to reach an agreement within two frames in each detection process. Moreover, a schedule generation algorithm is also proposed for a cluster head to generate the transmission schedule in the forward and backward frames. Through simulation results, we show that the proposed detection mechanism can achieve high detection accuracy under high packet loss rates in the harsh underwater environment, and can detect a cluster‐head failure faster than a traditional fault‐detection mechanism within a delay bound of two TDM frames. Copyright © 2008 John Wiley & Sons, Ltd.

Underwater sensor network deployment can be quite difficult and expensive, thus much of the early development is limited to simulation work. There are some underwater physical channel models available in various simulation tools, but they are often overly generalized and rely on assumptions about the ocean as a whole. They are not appropriate for practical applications, as the ocean is such a diverse environment. In this paper, we present the development of an improved channel model based on the BELLHOP beam tracing program. The most significant advantage is that it allows the generation of a customized model built for a specific location. Furthermore, we conduct case studies using real environment data from Newfoundland in the Atlantic Region to demonstrate the differences between the proposed model and commonly used NS-2 models, which are of practical significance in real underwater sensor applications. Our results have shown promising applications of this new model for future underwater communications.

In order to achieve efficient dynamic spectrum access (DSA) in Cognitive Ultra-Wideband (C-UWB) systems, an orthogonal wavelets based dynamic pulse shaping method is proposed to obtain pulses which can adapt to any given spectral requirements. Using Meyer wavelet set as the orthogonal basis, pulses with high power efficiency can be achieved. The compact support property of Meyer wavelets enables us to efficiently reduce the computational complexity in dynamic pulse shaping calculation. Moreover, we demonstrate that the proposed method is easy to implement and can achieve a good balance between power efficiency and system complexity.

A Wireless Multimedia Sensor Network (WMSN) is an emerging networking paradigm that allows retrieving video and audio streams, still images, and generic sensing data. Different from conventional wireless sensor networks, a WMSN requires higher network bandwidth and throughput to deliver multimedia contents effectively using energy-constrained devices. In this paper, we propose a clustered on-demand multi-channel MAC protocol (COM-MAQ) to support energy-efficient, high- throughput, and reliable data transmission in WMSNs. The operation of proposed protocol consists of three sessions: request session, scheduling session, and data transmission session. For COM-MAC to achieve high energy efficiency, first, a scheduled multi-channel medium access is used within each cluster so that cluster members can operate in a contention-free manner within both time and frequency domains to avoid collision, idle listening and overhearing. Second, to maximize the network throughput, a traffic-adaptive and QoS-aware scheduling algorithm is executed to dynamically allocate time slots and channels for sensor nodes based on the current data traffic information and QoS requirements. Finally, to enhance transmission reliability, a spectrum-aware ARQ is incorporated to better exploit the unused spectrum for a balance between the reliability and retransmission. Simulation results indicate that COM-MAC can achieve increased network throughput at the cost of a small control and energy overhead.

Node clustering has been widely considered in underwater sensor networks (UWSNs) to improve energy efficiency and prolong network lifetime. Network survivability is a great concern in cluster-based UWSNs. In this paper, we propose a dependable clustering protocol to provide a survivable cluster hierarchy against cluster-head failures in such networks. The proposed clustering protocol attempts to select a primary cluster head and a backup cluster head during clustering so that the cluster members associated with the failed cluster head can quickly switch over to the backup cluster head in the event of a cluster-head failure. Meanwhile, it attempts to select a set of clusters with minimum total cost so that network lifetime can be prolonged to ensure long-term underwater environmental monitoring. Simulation results show that the protocol can effectively enhance network survivability and improve network capacity in the event of cluster-head failures.

Modern switches and routers require massive storage space to buffer packets. This becomes more significant as link speed increases and switch size grows. From the switch design and network traffic perspective, to minimize packet loss, the buffering resource allocated for each switch port is normally based on the worst case scenario, which is usually huge. However, under normal load conditions, the buffer utilization for such configuration is very low. Therefore, we propose a reconfigurable buffer sharing scheme, which is based on the hybrid SRAM/DRAM architecture and can flexibly adjust the buffering space for each port according to the traffic pattern and buffer saturation status. The target is to improve buffer utilization, while not posing much constraint on the buffer speed. In this paper, we study the performance of the proposed buffer sharing scheme using an iterative analytical model under uniform traffic. Performance under non-uniform traffic is studied through simulations. The simulation results fit well with those from the analytical model. Moreover, our results demonstrate that significant performance enhancement can be achieved by sharing the port buffering resources.

In this paper, we propose an efficient fault-prevention clustering protocol for improving the lifetime and robustness of underwater sensor networks (UWSNs). The proposed clustering protocol takes into account both the reliability and residual energy status of each sensor node during clustering, and attempts to select those healthy nodes as cluster heads through failure prediction, cost evaluation, and clustering optimization. The purpose of failure prediction is to predict the potential failure of an underwater sensor based on its lifetime distribution so that those unhealthy nodes are prevented from being selected as cluster heads. Cost evaluation is introduced to evaluate the cost caused by the failure of a cluster head. Clustering optimization aims to construct a cluster hierarchy that minimizes the overall cost of all selected clusters based on the cost evaluation of each sensor node. The simulation results show that the proposed clustering protocol can not only significantly prolong network lifetime, but also improves network robustness and capacity compared with existing clustering protocols.

In this paper, an innovative frequency domain joint estimation algorithm for synchronization parameter and channel impulse response (CIR) in direct sequence code division multiple access (DS-CDMA) systems is proposed. The algorithm is based on the expectation-maximization (EM) method. It can provide accurate estimation of channel state information and synchronization parameter for a DS-CDMA receiver even with a simple equalization module (e.g., an one-tap multiplier based frequency domain equalizer (FDE)), and a radio receiver with this approach performs better than a costly multi-tap multiplier based equalizer, such as the time domain equalizer (TDE). A generic receiver architecture based on the frequency domain equalization for a composite radio, which works in both CDMA and orthogonal frequency division multiplexing (OFDM) modes, is also proposed. The Cramer-Rao lower bound (CRLB) of the proposed estimator and its optimization scheme are derived. This architecture can be implemented with an iterative approach, and the results demonstrate that this adaptive receiver performs very well with a relatively low cost.

The ocean, which covers about two-third of the Earth surface, is a largely unexplored world that has fascinated humans since the beginning of human history. Over a long period of time, there is a great interest in exploring the ocean and other underwater environments (e.g., rivers, lakes, and reservoirs) for scientific, environmental, commercial, and military purposes. With the increasing demand for acquiring localized, precise and real-time knowledge of the harsh underwater environments, traditional underwater exploration technologies such as SONAR or other remote sensing technologies can no longer meet such demands. Underwater sensor networks are an emerging network paradigm which provides a promising solution to exploring the ocean and underwater environments. An underwater sensor network consists of a number of underwater sensor nodes with sensing, data processing, and communication capabilities, which are deployed in a region of interest and collaborate to accomplish a common task such as underwater environmental monitoring, mine reconnaissance, and military surveillance. Driven by a broad range of potential applications in both civilian and military areas as well as rapid technological advances in microelectronics, wireless communications, and embedded processing, underwater sensor networks have recently received much attention from both academia and industry. Distinct from terrestrial sensor networks, an underwater sensor network has some unique characteristics that need to be particularly addressed such as low communication bandwidth, large propagation delay, harsh geographical environment, and floating node mobility. These unique characteristics present many challenges in the design of underwater sensor networks, which have recently motivated a growing interest and a considerable amount of research activities in this emerging area. This special issue includes a collection of eight outstanding research papers, which cover a diversity of topics on the design of network architectures and protocols for underwater sensor networks. The issue begins with an invited paper, ‘Prospects and Problems of Wireless Communication for Underwater Sensor Networks,’ contributed by Jun-Hong Cui et al. This paper reviews the physical fundamentals and engineering implementations for efficient information exchange via wireless communications using physical waves as the carrier among nodes in an underwater sensor network. It also makes recommendations for the selection of the communication carrier for underwater sensor networks with engineering countermeasures that can possibly enhance the communication efficiency in specified underwater environments. In the second paper, ‘Coverage and Connectivity in Three-Dimensional Underwater Sensor Networks,’ Alam and Haas studied the node deployment problem in a 3D underwater sensor network and provided a solution to the coverage and connectivity problem with limited and full communication redundancy requirements. In the third paper, ‘Placement of Multiple Mobile Data Collectors in Underwater Acoustic Sensor Networks,’ Alsalih et al. studied the placement problem of mobile data collectors in underwater sensor networks and proposed two routing and placement schemes. One is delay-tolerant placement and routing (DTPR), which can maximize the network lifetime without any delay consideration. The other is delay-constrained placement and routing (DCPR), which can maximize the network lifetime with an upper bound on the maximum delay. The fourth paper, ‘Target Tracking Based on a Distributed Particle Filter in Underwater Sensor Networks,’ by Huang et al. proposes two algorithms for tracking mobile targets in cluster-based underwater sensor networks based on a distributed particle filter. One of them can achieve higher tracking accuracy while the other can significantly reduce the communication cost, energy cost, and tracking response time. In the fifth paper, ‘Utilizing Acoustic Propagation Delay to Design MAC Protocols for Underwater Wireless Sensor Networks,’ Guo et al. proposed an efficient MAC protocol for underwater sensor networks, which makes use of the propagation delay to avoid collisions, thus reducing control overhead and energy consumption. In the sixth paper, ‘Path Unaware Layered Routing Protocol (PULRP) With Non-Uniform Node Distribution for Underwater Sensor Networks,’ Gopi et al. proposed a PULRP for 2D underwater sensor networks with mobile nodes, which has been demonstrated to have better throughput and delay performance as compared to the underwater diffusion (UWD) algorithm. In the seventh paper, ‘PAS: Probability and Sub-Optimal Distance (SOD)-Based Lifetime Prolonging Strategy for Underwater Acoustic Sensor Networks,’ Dou et al. proposed a couple of lifetime prolonging strategies for underwater sensor networks: probability-based energy-balancing (PEB) strategy and SOD-based data transmission strategy. They showed through simulation results that both strategies can efficiently save energy consumption and thus prolong the network lifetime. In the last paper, ‘Development of Routing Protocols for the Solar-Powered Autonomous Underwater Vehicle (SAUV) Platform,’ Bartos et al. presented a summary of the experience obtained in the development, evaluation, and field testing of two routing protocols for the SAUV platform. Useful suggestions based on field experience are also presented for improving the design and evaluation of routing protocols for a harsh underwater environment. We thank all the authors who submitted their papers to this special issue. Owing to the limitation of space, we can include only eight papers in the issue. We are grateful to all the reviewers for their time and efforts in carefully reviewing all the papers and providing valuable review comments. We also thank the Editor-in-Chief, Mohsen Guizani, for his continuous support for this special issue, and all the publication staff for their support during the publishing process. It is our hope that the papers included in this special issue present a good snapshot of the latest research progress in the design of network architectures and protocols for underwater sensor networks and become an important reference for researchers and practitioners in the area. Finally, we hope that the readers will find this special issue timely and informative.

In this paper, an iterative EM-based frequency domain joint estimation algorithm of synchronization parameter and channel impulse response (CIR) in direct sequence code division multiple access (DS-CDMA) system is proposed. This algorithm is based on the expectation-maximization (EM) method. A complete Cramer-Rao lower bound (CRLB) analysis of the proposed algorithms is also given in this paper. Because this algorithm can provide accurate estimation of channel conditions and synchronization parameter for DS-CDMA receivers, even with a simple equalization module, for example, a one-tap multiplier based frequency domain equalizer (FDE), good performance can be achieved at a low cost.

In this paper, we propose an algorithm for frequency domain adaptive Doppler shift estimation in the presence of multiple Doppler subpaths in DS-CDMA systems. By modeling doubly selective channel with a basis expansion model (BEM), the proposed method works based on expectation-maximization (EM) algorithm and can accurately estimate Doppler shifts. A complete Cramer-Rao lower bound (CRLB) analysis of the estimation algorithms is also provided.

In this paper, we propose a combined data aggregation and encryption scheme using Slepain-Wolf coding for efficient and secured data transmission in wireless sensor networks (WSNs). We first study the optimal intra-cluster rate allocation problem in using Slepain-Wolf coding for data aggregation, which aims to find a rate allocation subject to Slepian-Wolf theorem such that the total energy consumed by all sensor nodes in the cluster for sending encoded data is minimized. Based on the properties of Slepain-Wolf coding with optimal intra-cluster rate allocation, we then propose a novel encryption mechanism, called spatially selective encryption, for data encryption within a single cluster. This encryption mechanism only requires the cluster head to encrypt its data while allowing all cluster members to send their data without performing any encryption. Using this mechanism, as long as the data of the cluster head (or thevirtualkey) is protected, the data from all cluster members can also be protected, which can significantly reduce the energy consumption for data encryption. Furthermore, an energy-efficient key establishment protocol is also proposed to securely and efficiently establish the key used for encrypting thevisualkey.

In this paper, we study the application of distributed lossy source coding for data aggregation in cluster-based wireless sensor networks (WSNs). We consider a clustered lossy coding (CLC) problem, which aims to select a set of disjoint clusters to cover the whole network such that the total rate of encoded data generated by all clusters or nodes in the network is minimized, given the spatial correlation structure of the network and a couple of total and individual distortion constraints. To solve this problem, we first prove that the overall optimization problem can be decoupled into two independent optimization problems: an optimal clustering problem and an optimal distortion allocation problem. The first problem aims at constructing a clustered hierarchy to minimize the global network entropy without considering distortion allocation, while the second problem aims to optimally allocate a distortion to each sensor node under the given distortion constraints without considering node clustering. We then present a distributed optimal-compression clustering protocol to solve the first problem and use Lagrange multipliers to solve the second problem.

In this paper, we propose an agreement-based fault detection mechanism for detecting cluster-head failures in clustered UnderWater Sensor Networks (UWSNs). The proposed detection mechanism aims to accurately detect the failure of a cluster head in order to avoid unnecessary energy consumption caused by a mistaken detection. For this purpose, it allows each cluster member to independently detect the fault status of its cluster head and at the same time employs a distributed agreement protocol to reach an agreement on the fault status of the cluster head among multiple cluster members. The detection mechanism is based a TDMA MAC protocol used in the network and runs concurrently with normal network operation by periodically performing a distributed detection process at each cluster member. To reduce energy consumption, it makes use of the data periodically sent by a cluster head as the heartbeats for fault detection. A couple of forward and backward TDM frames are specially structured for enabling multiple cluster members to reach an agreement within two frames in each detection process. Moreover, a schedule generation algorithm is also proposed for a cluster head to generate the transmission schedule of the forward and backward frames. Through simulation results, we show that the proposed detection mechanism can achieve high detection accuracy under high packet loss rates in the harsh underwater environment, and can faster detect a cluster-head failure than a traditional fault detection mechanism.

In this paper, an innovative frequency domain joint estimation algorithm of synchronization parameter and channel impulse response (CIR) in Direct Sequence Code Division Multiple Access (DS-CDMA) system is proposed. This algorithm is based on the expectation-maximization (EM) method. Because this algorithm can provide accurate estimation of channel conditions and synchronization parameter for DS-CDMA receivers, even with a simple equalization module, for example, a one-tap multiplier based frequency domain equalizer (FDE), a receiver architecture following this approach can still provide better performance than the costly multi-tap multiplier based equalizer, such as the time domain equalizer (TDE). Performance is well maintained under fast fading channel for mobile stations at high velocity. Moreover, a generic receiver architecture purely based on frequency domain equalization for a composite radio environment, that is CDMA and Orthogonal Frequency-Division Multiplexing (OFDM) systems, is also proposed in this paper. This architecture can be readily implemented by software defined radio (SDR) technology. Our results manifest that this adaptive receiver architecture is very promising and can achieve good performance with low cost.

In this paper, we propose a frequency domain adaptive estimation of Doppler shifts for multiple Doppler subpaths in direct sequence code division multiple access (DS-CDMA) systems. By modeling the doubly selective channel using a basis expansion model (BEM), the proposed expectation-maximization (EM) algorithm-based estimation method can obtain accurate information of Doppler shifts. Based on the estimated information of Doppler shifts, a FDE-based receiver architecture is developed to exploit Doppler diversity in frequency domain. Simulation results demonstrate that this receiver structure features a low computational complexity while achieving good performance compared with traditional receiver structures in DS-CDMA systems.

In this paper, we study the node clustering problem in underwater sensor networks (UWSNs). We formulate the problem into a cluster-centric cost-based optimization problem with an objective to improve the energy efficiency and prolong the lifetime of the network. For this purpose, a cost metric is defined for a potential cluster, which takes into account three important parameters that are relevant to the energy status of the cluster, including (1) the total energy consumption of the cluster members for sending data to the cluster head; (2) the residual energy of the cluster head and its cluster members; and (3) the relative location between the cluster head and the underwater sink (uw-sink). To solve the formulated problem, a novel distributed clustering protocol called minimum-cost clustering protocol (MCCP) is proposed, which selects a set of non-overlapping clusters from all potential clusters based on the cost metric assigned to each potential cluster and attempts to minimize the overall cost of the selected clusters. MCCP can adapt geographical cluster head distribution to the traffic pattern in the network and thus avoid the formation of hot spots around the uw-sink. It can also balance the traffic load between cluster heads and cluster members through periodical re-clustering the sensor nodes in the network. Simulation results show that MCCP significantly improves the energy efficiency and the lifetime of a UWSN as compared with the well-known HEED protocol.

Slepian-Wolf coding is a promising distributed source coding technique that can completely remove the data redundancy caused by the spatially correlated observations in wireless sensor networks (WSNs). In this paper, we study the major problems in applying Slepian-Wolf coding for data aggregation in cluster-based WSNs with an objective to optimize data compression so that the total amount of data in the whole network is minimized. We first consider the clustered Slepian-Wolf coding problem, which aims at selecting a set of disjoint potential clusters to cover the whole network such that the global compression gain of Slepian-Wolf coding is maximized. To solve this problem, a distributed optimal-compression clustering protocol (DOC2) is proposed. Under the optimal cluster hierarchy constructed by DOC2, we then consider the optimal intra-cluster rate allocation problem and present an approximation algorithm that can find an optimal rate allocation within each cluster to minimize the intra-cluster communication cost. With the optimal intra-cluster rate allocation found, the procedures to perform Slepian-Wolf coding within a cluster are also presented.

Clustering is an effective technique for improving the energy efficiency and prolonging the network lifetime of a Wireless Sensor Network (WSN). Although it has been widely investigated, most of the studies are based simplified channel conditions and simple computer simulations using tools such as NS-2 and OPNET. It is highly desired that the algorithms proposed for WSN are studied in real network environment and using practical devices so that the obtained results provide a better guideline for real applications. In this paper, we study the implementation of distributed clustering protocols in WSNs. The performance of two popular schemes, HEED and HIDCA protocols, are studied using an emulation approach. It is further verified through our experimental work using Mica2 sensor devices from the Crossbow Technology Inc. Our results demonstrate the working of clustering algorithms in practical small scale networked sensor systems. It also confirms the superior performance of the HEED algorithm over HIDCA in terms of power consumption and network lifetime.

A new design method to generate pulses for ultra-wideband (UWB) communication systems is presented. The systematic searching method is based on the linear combination of a number of Gaussian derivative pulses to form one single pulse whose power spectral density (PSD) not only conforms to the FCC spectral mask, but also effectively exploits the allowable bandwidth and power. In addition, the effect of pulse shaping on the mitigation of multiuser interference (MUI) is also taken into consideration in the proposed pulse shape design method. In particular, the authors develop a parameter called normalized mean-squared partial pulse correlation, which only has relation with the pulse shape and can directly affect the signal-to-noise ratio (SNR) of the receiver in a multi-user environment. By choosing a pulse which minimizes this parameter, the authors obtain the desired pulse shape. Compared with the widely used single Gaussian derivative pulses, the pulses they designed achieve better bit error rate performance in both single link and multi-user TH-PPM and TH-BPSK UWB communication systems.

Abstract — This paper presents the architecture design as well as the performance analysis of a new cell-based multicast switch for broadband communications. Using distributed control and a modular design, the Balanced Gamma (BG) switch features a high performance for unicast, multicast and combined traffic under both random and bursty conditions. Although it has buffers on input and output ports, the multicast BG switch follows predominantly an output-buffered architecture. The performance is studied under uniform and non-uniform multicast traffic in terms of cell loss ratio and cell delay. The results are compared with those from an ideal pure output-buffered multicast switch to demonstrate how close its performance is to that of the ideal but impractical switch. Comparisons with other published switches reveals the superior of the BG switch and the tradeoffs between complexity and performance in a packet switch design. It is shown that the multicast BG switch achieves a performance close to the ideal switch while keeping hardware complexity reasonable. Index Terms — Multicast, Balanced Gamma (BG) switch, performance analysis, multistage interconnection network (MIN),

This paper presents an analytical model for the performance analysis of a new cell-based multicast switch for broadband communications. Using distributed control and a modular design, the Balanced Gamma switch features a high performance for unicast, multicast and combined traffic under both random and bursty conditions. Although it has buffers on input and output ports, the multicast BG switch follows predominantly an output-buffered architecture. The analytical model follows the three phase switching operation. The performance is evaluated under multicast random traffic in terms of cell loss ratio and cell delay. Performance under bursty traffic is studied through simulation and the results are compared to those of an ideal pure output-buffered multicast switch.

Given a collection of points in the plane, one may draw a cell around each point in such a way that each point's cell is the portion of the plane consisting of all locations closer to that point than to any of the other points. The resulting geometric figure is called a Dirichlet tessellation. An algorithm for obtaining the boundaries of the cells given the points was derived by Green and Sibson in 1978. Here, methods are described for obtaining the locations of the points, given only the cell boundaries.

The paper presents the design and implementation of a new multicast switch for broadband communications. Using distributed control and modular design, the multicast balanced gamma (BG) switch features a scalable, high performance architecture for unicast, multicast and combined traffic under both uniform and non-uniform traffic conditions. The important design characteristic of the switch is that a distributed cell replication function for multicast cells is integrated into the functionality of the switching element (SE) with the self-routing and conflict contention functions. We discuss in detail the design issues associated with the multicast functionality of the switch. VLSI implementation results for the BG switch fabric using 0.18 /spl mu/m CMOS technology are presented. Scalability and performance properties of the multicast BG switch are also briefly discussed.