In real-world time-series modelling, graph structures are widely adopted because they explicitly encode node topology and capture complex network dynamics. In practice, however, a complete graph is often partitioned across multiple parties; each party can access only its local sub-graph and, owing to privacy regulations, cannot share topology or data, creating pervasive data silos. Federated Graph Learning (FGL) offers a privacy-preserving collaborative-learning paradigm, yet current methods still face two key challenges: (1) the graph topology itself contains sensitive structural information, which can lead to privacy leakage if directly shared during FGL; (2) cross-party edges are crucial for accurate modeling, yet exploiting them without compromising privacy remains a significant challenge. To overcome these obstacles, we propose FedSkeleton, a privacy-preserving framework for time-series prediction that comprises a Skeleton Construction Module and a Dual-stream Forecasting Module, enabling global dependency capture without revealing the topology. Extensive experiments show that FedSkeleton consistently outperforms existing baselines and even surpasses models trained in a centralized setting with full-graph access in certain cases. In addition, we conduct comprehensive security analysis, communication-cost evaluation and scalability experiments, demonstrating that FedSkeleton effectively resists common attacks, keeps communication overhead manageable, and remains robust with respect to key hyper-parameters and the number of participating parties.

Post-Training Quantization enables efficient Vision Transformer (ViTs) deployment with a small calibration data, and its prevalent use of uniform quantization harnesses AI accelerator matrix cores for high-speed inference. However, the application of uniform quantization is fundamentally challenged by the extreme non-uniformity of activation distributions.Specifically, the power-law nature of post-Softmax attention scores and the significant inter-channel variance in post-GELU activations create a dilemma for conventional quantization, as it struggles to preserve critical high-magnitude values without sacrificing overall precision. To resolve this core conflict, we introduce UQ-ViT (Uniform Quantization for Vision Transformers), a novel uniform quantization framework designed to reconcile high precision with hardware efficiency. Central to UQ-ViT are two operators: Dynamic Elimination of Maximum (DeMax) and Normalization Quantization (NormQuant). DeMax is a quantization operator for post-Softmax attention scores that utilizes uniform quantization. It dynamically eliminates and preserves dominant values, effectively mitigating quantization loss from the extreme values in the power-law distribution. NormQuant utilizes a per-channel quantization strategy during quantization and reverts to a per-tensor format for dequantization, achieving both high accuracy and computational efficiency. Crucially, it is applicable to any linear layer, enabling effective quantization of post-GELU activations in ViTs. Through extensive experiments on various ViTs and vision tasks, including image classification, object detection, and instance segmentation, we demonstrate that our proposed approach outperforms existing methods, achieving superior accuracy while ensuring hardware friendliness.

As an important component of the Internet of Things (IoTs), underground localization based on magnetic induction (MI) communication with the received signal strength indicator (RSSI) has been a topic of considerable interest in wireless underground sensor networks (WUSNs). Existing works have widely exploited the active device localization based on wireless communications in underground space, where the transmission media remains air. However, achieving device-free localization (DFL) in an underground endogenous environment presents relatively large gaps. In this paper, we propose a novel MI-based communication method to achieve the DFL accuracy in complex underground endogenous environment. First, we establish an end-to-end MI link model to precisely characterize the RSSI and explicitly correlate MI antenna configurations with magnetic field attenuation in the underground endogenous environment. Subsequently, based on the magnetic-field propagation characteristics, we analyze the shadowing loss caused by the passive target in the MI link. A MI-DFL method is then formulated by incorporating the shadowing model. Finally, extensive numerical simulations across various environmental settings and target types demonstrate the effectiveness and robustness of the proposed method, indicating that MI-DFL attains high localization accuracy within the underground endogenous environments.

Wireless Powered Underground Sensor Networks (WPUSNs) extend the lifetime of underground Internet of Things (IoT) through Radio Frequency (RF) harvesting technology. However, severe transmission attenuation and dynamic Quality of Service (QoS) requirements often lead to energy imbalance and waste issues in WPUSNs. To address the challenges, we introduce a recycling mechanism that enables nodes to harvest energy from each other, thereby proposing the recycling WPUSN. Under this framework, the time allocation strategy is key to balancing energy harvesting and transmission performance. We take time slot as the optimization variable and formulate an energy efficiency maximization problem under heterogeneous QoS constraints. To solve this nonconcave fractional programming problem, we develop a Quadratic Transform-based Projected (QTP) Algorithm, which iteratively applies Karush–Kuhn–Tucker (KKT) conditions and projection operations to approximate the optimal solution. The simulation results demonstrate that the proposed method improves energy efficiency performance by 17.454% and converges 70 times faster compared with the commercial optimization solvers. The proposed recycling WPUSN opens up new prospects for sustainable underground environmental monitoring.

Few-shot semantic segmentation (FSS) often struggles with the intra-class diversity issue between query and support images, caused by the category-biased information provided by limited annotated support images for matching objects. While increasing the number of annotated support images could mitigate this bias, it is impractical within the few-shot learning framework. Therefore, our proposed Unlabeled Query Integration Few-Shot Segmentation (UQI-FSS) tackles this challenge by incorporating unlabeled query images into the learning paradigm. This approach aims to achieve a more comprehensive category representation, which is essential to enhance segmentation accuracy in various scenarios. However, integrating unlabeled query images directly requires careful management to prevent the dilution of vital information from the annotated support set. To address this issue, we present an Unlabeled Query Integration Network (UQINet), which adaptively extracts beneficial and suppresses detrimental information from the unlabeled query images. Specifically, we first introduce an Information Bridging Module to close the gap between support and unlabeled query features, generating a pseudo-support set enriched with additional category data. Next, we introduce a Query Fusion Module to incorporate query information from both prototype and pixel levels into the pseudo-support features, thus improving their adaptability to the query. Finally, we propose an Adaptive Selection Module to select effective category information and combine the pseudo-support features, thereby activating target objects for precise segmentation prediction. Experimental results show considerable performance improvements over previous methods on various FSS benchmarks. The application of this method to four challenging scenarios further underscores its versatility and practical value.

In this paper, the joint active terminal identification (ATI) and channel estimation (CE) problem is investigated for the asynchronous grant-free random access system in the context of low-earth orbit (LEO) satellite Internet of Things (IoT) network. An asynchronous grant-free model is established to characterize the implications of delay and Doppler caused by LEO satellites. Concurrently, a generalized approximate message passing-aided structured joint detection (SJD) scheme with Rician parameters learning (RPL) is proposed for joint ATI and CE. The prior mean and variance of the Rician channel are treated as hyperparameters and updated via the expectation-maximization algorithm. Furthermore, to alleviate the modeling mismatch, we develop an off-grid model following Taylor expansion, accompanied by a mismatch error parameter learning (MPL) framework to boost the accuracy of joint detection. Simulation results demonstrate that the proposed RPL-SJD scheme outperforms existing approaches in terms of normalized mean square error with 4dB and activity detection error rate with 7dB.

In this paper, an affine frequency division multiplexing aided grant-free random access (AFDM-GF-RA) system is designed for low Earth orbit (LEO) satellite Internet of Things (SIoT) networks, where users employ the GF-RA to access the satellite concurrently by configuring the AFDM modulation. In order to evaluate the system’s performance, average bit error probability (ABEP) bounds are first derived by Cholesky decomposition, which are verified by simulation results. Furthermore, a block sparse prior expectation propagation detection (BSPEP) is developed for joint active users detection (AUD) and data detection (DD). User activity probability is considered as a prior for joint detection. Concurrently, the accuracy of AUD is further improved by combining the block sparsity characteristics of the user transmission signals. Simulation results show that the proposed detector outperforms the classical oracle least squares (OLS) benchmark and approaches the theoretical ABEP bound at high signal-to-noise ratio (SNR).

Gradient leakage attacks pose significant privacy risks in federated learning by exploiting transmitted gradients to reconstruct sensitive data. While existing defense mechanisms typically apply uniform perturbation across all gradients, we identify a critical oversight: privacy information in gradients exhibits inherent layer-wise heterogeneity. Through systematic analysis, we establish that different neural network layers contain varying amounts of reconstructable private information due to differential accumulation of nonlinear effects during gradient formation. This fundamental discovery enables our key innovation—Layer-Specific Gradient Protection (LSGP)—which pioneers surgical defense mechanisms that adapt protection intensity to each layer’s inherent privacy exposure level. Experimental results validate that LSGP achieves superior defense efficacy with comparable model utility compared to uniform protection baselines, establishing a new paradigm for efficient privacy-preserving machine learning through principled vulnerability analysis of gradient formation mechanics.

Low-altitude economy networking (LAENet) aims to deploy various aerial vehicles to support diverse services, where data collection from edge devices via unmanned aerial vehicles (UAVs) is a critical task. The key challenge lies in jointly optimizing energy consumption and data freshness in spectrum-constrained and eavesdropping-prone low-altitude environments during the data collection process. Although deep reinforcement learning (DRL) has become a viable solution for UAV-assisted data collection, the RL agent still has limited ability to obtain and utilize informative feedback from complex low-altitude environments. In this paper, we propose a large language model (LLM)-enhanced DRL framework for secure data collection in the LAENet, where we leverage an LLM to process environmental feedback for the RL agent. Specifically, we employ the LLM as (i) a state processor to transform basic environmental observations into task-aligned representations, (ii) a reward designer to generate enriched reward signals that guide the agent's actions toward the optimization objective, and (iii) a simulator to construct a virtual LAENet environment for evaluating enhanced state-reward pairs before policy training. Theoretical analysis and numerical results demonstrate that the proposed LLM-enhanced DRL framework achieves faster convergence, improved training stability, and superior performance compared with state-of-the-art baselines.

To achieve an efficient compression and reconstruction of vibrotactile signals in the Tactile Internet, an end-to-end neural vibrotactile codec, PC-NSVC, is proposed. By integrating a residual product quantizer (RPQ) within a deep autoencoder, PC-NSVC effectively reduces coding latency through joint training and inference of the entire framework, while simultaneously enhancing the quality of the reconstructed signals. The RPQ allows for control over transmission bitrates by adjusting quantizer parameters, enabling scalable codec across various network environments and bandwidths. Additionally, PC-NSVC incorporates psychohaptic model to account for the influence of human perception, further improving the perceptual fidelity of the reconstructed signals. A remote vibrotactile sharing prototype, TouchShare, was developed to conduct transmission and material classification tests. Simulation and transmission results demonstrate that the PC-NSVC scheme significantly improves the quality of reconstructed signals at different compression ratios and supports accurate material classification, outperforming existing schemes.

In this paper, we investigate the problem of task offloading and resource allocation in non-orthogonal multiple access (NOMA) based multi-beam satellite mobile edge computing (SMEC) systems, aiming to minimize the average task completion latency over a finite number of time steps. Such a problem is formulated as a sequential mixed-integer programming problem, which cannot be solved by standard optimization techniques. Thus, we convert the problem into a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) with hybrid action space and propose a deep reinforcement learning-based solution. In particular, a multi-agent hybrid Proximal Policy Optimization algorithm (MAHPPO) is designed to address the hybrid action space. Simulation results show that the proposed scheme outperforms several benchmark schemes.

The increasing parameters and expansive dataset of large lan- guage models (LLMs) highlight the urgent demand for a technical solution to audit the underlying privacy risks and copyright issues associated with LLMs. Existing studies have partially addressed this need through an exploration of the pre-training data detection problem, which is an instance of a membership inference attack (MIA). This problem involves determining whether a given piece of text has been used during the pre-training phase of the target LLM. Although existing methods have designed various sophisticated MIA score functions to achieve considerable detection performance in pre-trained LLMs, how to achieve high-confidence detection and how to perform MIA on aligned LLMs remain challenging. In this paper, we propose MIA-Tuner, a novel instruction-based MIA method, which instructs LLMs themselves to serve as a more precise pre-training data detector internally, rather than design an external MIA score function. Furthermore, we design two instruction-based safeguards to respectively mitigate the privacy risks brought by the existing methods and MIA-Tuner. To comprehensively evaluate the most recent state-of-the-art LLMs, we collect a more up-to-date MIA benchmark dataset, named WIKIMIA-24, to replace the widely adopted benchmark WIKIMIA. We conduct extensive experiments across various aligned and unaligned LLMs over the two benchmark datasets. The results demonstrate that MIA-Tuner increases the AUC of MIAs from 0.7 to a significantly high level of 0.9.

Satellite communication environments, characterized by dynamic links, exacerbate packet reordering, which leads to fundamental limitations in QUIC’s loss detection mechanism. A fixed packet threshold may misinterpret reordered packets as lost, leading to spurious retransmissions and throughput degradation. To address the challenge, we propose GRU-QUIC, a machine learning-enhanced QUIC protocol with packet reordering resilience for satellite networks. First, we model the relationship between the probability of spurious packet loss due to reordering and the throughput of QUIC, providing a theoretical basis for performance optimization. Second, we design a gated recurrent unit (GRU)-based prediction module on the receiver side to predict the next slot packet reordering displacement by learning the temporal correlation of packet reordering sequences. The sender then uses the reordering displacement indicated by the GRU to dynamically adjust the packet threshold of loss detection, effectively alleviating the problem of packet loss misjudgment due to packet reordering. Experiments demonstrate that GRU-QUIC significantly enhances the performance of QUIC transmission while maintaining protocol compatibility. In a simulated satellite network, GRU-QUIC effectively reduces unnecessary retransmissions and shortens file download completion time compared to standard QUIC.

Metasurface-aided near-field radio imaging is emerging as an essential part of the future mmWave communication systems. Adjusting the metasurface phase shift to generate multiple measurements can significantly increase the system imaging aperture and enhance the resolution. However, this imaging technique relies heavily on precise measurements, and high-precision sampling leads to expensive hardware costs and memory burdens. To address this issue, this paper draws inspiration from binary compressive sensing and proposes a method for imaging under one-bit quantization. First, we propose a dynamic metasurface-aided mmWave imaging system with one-bit sampling, which simplifies the received signal acquisition process and significantly reduces the hardware and storage requirements. Then, a fused binary compressive sensing model is developed with an additional total-variation norm penalty to promote target continuity and suppress artifacts in mmWave images. Subsequently, we employ the proximal binary iterative hard thresholding algorithm to optimize the joint sparsity and the total variation (TV) constraints. In addition, the hybrid ℓ1-TV constraint is introduced to solve the problem of unknown a priori sparsity of image, and the alternating direction multiplication method is designed for effective reconstruction. Finally, the simulation results show that the proposed algorithms can utilize the target features under binary measurements and achieve better imaging accuracy and focusing performance than the sparsity constraint-only methods.

This paper addresses co-design control strategies for continuous-time Markov jump systems where subsets of transition rate matrices are fixed a priori, challenging conventional co-design methodologies. A synchronously mode-dependent parametric framework is developed to address partial transition rate optimization alongside output feedback controller synthesis. Novel criterion is derived to guarantee mean-square stability by reconstructing adjustable switching parameters while preserving fixed system transitions. Stability analysis and controller design are unified through hybrid control principles. A numerical case studies validate the proposed approach, demonstrating enhanced feasibility compared to existing methods.

Blockchain-enabled Policy Decision Point (PDP) has been a promising solution to the centralization concern in practical deployment of Attribute-Based Access Control (ABAC). However, existing blockchain systems cannot support PDP adequately since PDP functionalities introduce extra latency to blockchain’s execution process and limits system throughput. This paper proposes an efficient PDP Blockchain (PDPB) by exploiting a minimum-redundancy execution paradigm. Concretely, we design a novel Echo-Based Execution Conclude (EBEC) mechanism to enable minimum redundancy request evaluation while ensure blockchain safety and liveness. Two optimization techniques, Echo Compacting (EC) and Load Balancing (LB), are proposed to reduce the communication and computation overhead of PDPB and further enhance its performance. We implement a prototype of PDPB and evaluate it on Amazon Web Services (AWS) servers. The results show that PDPB achieves more than 35.6% performance improvement over existing methods.

The proliferation of intelligent Internet of Things (IoT) applications has led to an increase in the complexity of tasks generated by IoT devices putting pressure on the timely execution of these tasks. Mobile edge computing (MEC) has emerged as a promising paradigm to deliver low-latency computing services, enabled by task offloading from users to MEC servers. Meanwhile, as the IoT applications become increasingly diversified, the demand for communication and computing resources significantly varies over different tasks, highlighting the importance of efficient task offloading and resource allocation strategies in supporting low-latency task processing. Considering the heterogeneity of tasks, this article investigates the problem of task offloading and resource allocation strategies in the MEC system with heterogeneous tasks and propose a deep reinforcement learning (DRL)-based solution. Specifically, we consider task offloading strategies across various combinations of different task types and focus on optimizing channel allocation to minimize task completion delay. The effectiveness of proposed approach in reducing task completion latency is demonstrated through simulation results.

Few-shot segmentation (FSS) aims to accurately segment target objects in a query image using only a limited number of annotated support images. Existing approaches typically follow a paradigm that directly leverages category information from the support set to identify target objects in the query. However, these methods often ignore the category information gap between query and support images, leading to suboptimal performance when faced with images containing objects exhibiting significant intra-class diversity. To address this issue, we propose a novel framework that introduces intermediate prototypes to capture both deterministic information from the support images and adaptive knowledge from the query at multiple scales. Our framework, named the K-shot Multi-scale Intermediate Prototype Mining Transformer (KMIPMT), is based on the Transformer architecture and learns intermediate prototypes in an iterative manner, where each KMIPMT layer propagates category information from both K-shot support features and multi-scale query features to intermediate prototypes. This information is then utilized to activate the query feature map. Through repeated iterations, both intermediate prototypes and the query feature are progressively enhanced, and the final refined query feature is used for generating precise segmentation predictions. Despite its simplicity, our method achieves remarkable performance gains on standard benchmarks, including PASCAL-$5^{i}$5i, COCO-$20^{i}$20i, and FSS-1000, setting new state-of-the-art results. Furthermore, we explore several practical and challenging extensions of our method, including 3D point cloud FSS, zero-shot segmentation, weak-label FSS, and cross-domain FSS. These extensions showcase the versatility and effectiveness of our proposed KMIPMT framework across different domains and scenarios.

We study the problem of Non-line-of-sight (NLOS) mmWave imaging under inaccurate knowledge of multiangle relay surfaces. To this end, we propose a novel double sparse structure-enhanced Bayesian compressive sensing framework with dictionary parameters updating. First, the hierarchical probabilistic model with a parametric multipath dictionary is constructed, where the angles of multiple relay surface are considered as an unknown parameter. Then, a double sparse spike-and-slab (DS-SS) prior is introduced to model the intra-group and inter-group sparsity of multipath image, where the expectation propagation method is employed for posterior inference (dubbed as DS-SSEP). Moreover, the maximum likelihood solutions of the dictionary parameters are estimated iteratively by coupling the expectation maximization framework with DS-SSEP. Several experiments demonstrate the superiority of our proposed method, which significantly reduces image reconstruction errors in imprecise layout scenarios.

Utilizing device mobility to form virtual large-scale antenna arrays can provide accurate angle-of-arrival (AoA) information for robots. However, existing wireless localization systems that exploit device mobility are designed based on far-field channel assumptions and cannot directly provide range estimates. To address this problem, in this paper, we develop a novel near-field localization architecture for mobile robots by fusing the robot’s motion trajectory and channel state information (CSI) of a single antenna. Specifically, we first utilize channel reciprocity to multiply the uplink CSI and downlink CSI to eliminate the phase offset. Second, we further propose a two-stage localization algorithm that separates the line-of-sight (LoS) path from the multipath, and a multi-scale iterative scheme is employed to refine the estimation of AoA and distance of the LoS path. In addition, the range and AoA profiles for different trajectory shapes and the Cramer-Rao bounds for localization accuracy under squared channels are derived. Finally, the effectiveness of the proposed system is verified in a real environment. The simulation and experimental test results show that the proposed near-field localization system can operate in complex channel environments, and its localization accuracy outperforms the existing schemes.

Digital identity is fundamental for accessing mobile applications and managing user attributes. However, existing centralized identity management solutions rely on third-party operators, posing privacy risks and limiting user control. The decentralized solutions seek to address the issues but often fall short in privacy preservation, efficiency, and cross-application compatibility. In this paper, we propose PPUgIM, a user-governed identity management scheme with universally composable security, emphasizing privacy and data sovereignty in distributed mobile applications. PPUgIM introduces a DID-like account equipped with multi-attribute credentials, enabling users to autonomously manage and selectively disclose various identities without revealing sensitive information. An enhanced authenticated data structure is designed based on vector commitments, supporting short and constant-size proofs for efficient batch authentication of attribute credentials. Furthermore, a formal security analysis of PPUgIM is conducted, and a prototype implementation is developed for performance evaluation. Results show that credential generation takes 500 ms, verification 110 ms, with a constant size proof of 0.15 KB. Proof overhead for identities is reduced by 38.1% compared with existing schemes, demonstrating PPUgIM’s practicality in real-world distributed mobile applications.

In the past few years, infectious diseases like COVID-19 have caused serious distress to the global society and the economy. To prevent its spread, the early detection and assessment of infectious diseases based on molecular tests or antigen testing of bodily have led to countless labor and material costs. Fortunately, with the rapid development of mobile localization and web techniques, the collected massive mobile trajectory data provide a promising solution for detecting positive cases. However, existing mobility data-driven infection case detection methods are limited in terms of modeling the complicated epidemic spreading processes and preserving user privacy of the mobility data. In this article, we propose a novel graph convolutional networks (GCN) model for detecting high-risk infection cases, where we incorporate a spatio-temporal hypergraph to model the complex interaction of individuals. Then, we elaborately design a privacy-preserving framework tightly coupled with the structure of the spatio-temporal hypergraph, which includes a mobility data obfuscation module to protect privacy and an accompanying confidence-aware mechanism to mitigate the consequent performance decline. Moreover, we introduce a causal propagation mechanism to further guarantee the temporal dependency and causal effect of the feature propagation in our spatio-temporal hypergraph, which introduces both the causal transform of node features and the causal gathering of edge features. Finally, extensive experiments on a large mobility dataset collected from location-based services (LBS) show that the proposed model improves the performance of infection case detection by at least 12.47% when compared with several widely adopted baselines. Besides, our code and datasets are available at the link ( https://github.com/wjfu99/EPI-HGNN ).

In order to prevent the re-emergence of an epidemic, predicting its trend while gaining insight into the intrinsic factors affecting it is a key issue in urban governance. Traditional SIR-like compartment models provide insight into the explanatory parameters of an outbreak, and the vast majority of existing deep learning models can predict the course of an outbreak well, but neither performs well in the other’s domain. Simultaneously, studying the commonalities and diversities in the causes of outbreaks among different countrywide regions is also a way to interrupt outbreaks. To address the issues of outbreak intrinsic relationships and prediction, we propose the Neural Compartmental Ordinary Differential Equations (NeuralCODE) model to study the relationship between population movements and outbreak development in different regions. Furthermore, to incorporate the commonalities and diversities in causes among different regions into the prediction and intrinsic inquiry problem, we propose an AutoML framework. Our results found that simply using the NeuralCODE algorithm could obtain better prediction and insight capabilities within different regions. With the introduction of AutoML, it became possible to explore the factors inherent in the epidemic’s development across regions and further improve the original algorithm’s predictive performance.

In this paper, we propose the IFresher framework to improve the timeliness of multi-agent mobile augmented reality (MAR) systems. Existing works have made strides in accuracy-latency trade-offs, but fail to directly address realtime task responsiveness and multi-agent contention challenges. To bridge this gap, we introduce the concept of the age of analytics information (AoAI), which quantifies the combined impact of video analytics (VA) accuracy, transmission delay, and computational efficiency. By deriving a closed-form expression for AoAI, IFresher establishes a central control mechanism that jointly optimizes bandwidth allocation and video configuration to minimize AoAI while ensuring accuracy. Due to the mixed-integer nonlinear characteristics of the problem and the fact that each agent only has local observations, the problem is reformulated into a decentralized partially observable Markov decision process (Dec-POMDP). We propose a multi-agent reinforcement learning (MARL) algorithm, named convex-embedded transformer QMIX (CTQMIX), using the centralized training and decentralized execution (CTDE) framework for agent collaboration. Specifically, the convex optimization ensures optimal bandwidth distribution, and the transformer captures temporal dependencies between observations and actions across time steps to improve decisionmaking in dynamic environments. Evaluations with real-world experiments show that the CTQMIX outperforms state-of-theart (SOTA) algorithms.

This paper presents an edge-assisted massive mobile live streaming (MMLS) framework named LOCA, integrating chunk-level analysis and long-term optimization to design resource allocation, bitrate adaptation, and source selection strategies. The proposed method ensures sustained real-time video delivery while minimizing latency and communication costs. Firstly, a chunk-level analysis of the entire process of video streaming is introduced, aiming at modeling fetch queue waiting time and rebuffering duration in each time slot. By embedding this mathamatical model into consideration, a long-term optimization is formulated to minimize rebuffering and communication overhead while maintaining high video qualities for massive users. Leveraging Lyapunov optimization, we transform this problem into a computationally tractable form. Further simplification via linearization achieves near-optimal solutions by adopting the mixed-integer linear programming method with enhanced computational efficiency. Simulation results demonstrate superior stability and long-term performance compared to the state-of-theart and baseline methods, validating the framework's efficacy in MMLS scenarios

Due to the intensive computing resource requirements, real-time video analytics applications typically need to transmit video to a server. However, the transmission inevitably suffers from network bandwidth limitations and fluctuations, making it challenging to guarantee video analytics performance. In this paper, we aim to maximize video analytics accuracy while maintaining low latency and frame loss rate, and propose a joint segment and frame bitrate adaptation (JSFBA) framework for real-time video analytics, which incorporates two reinforcement learning-based algorithms to adapt to bandwidth at both the segment and frame levels. Initially, considering the effect of video encoding on video analytics, we employ a bitrate control method to design a segment-level bitrate adaptation (SLBA) algorithm with a unique reward function. Based on the historical information of the video segments, SLBA selects the appropriate bitrate for each segment. Subsequently, by leveraging the ability to generate multiple bitrates in scalable video coding (SVC), we design a frame-level bitrate adaptation (FLBA) algorithm, which adapts to bandwidth in a more fine-grained manner by determining the number of layers sent for each frame. Extensive experiments on large-scale network traces reveal that JSFBA effectively balances various video analytics performance metrics and achieves maximum utility compared to state-of-the-art solutions.

In this paper, a novel joint source-channel coding (JSCC) scheme based on the residual vector quantized diffusion model is proposed for task-oriented agent communication. To make the agent communication compatible with existing digital communication systems, the feature vectors encoded by the parameterized encoder are quantized using multiple learnable codebooks, forming the quantized features. Furthermore, to improve the reliability of quantized feature transmission, a discrete channel denoising diffusion model (DCDDM) is designed. The DCDDM can learn the distribution of the sender agent’s quantized features, and then utilize this learned knowledge to correct the receiver agent’s received quantized features. Based on the corrected feature vectors, the parameterized decoder recovers task-related information for the receiver agent’s subsequent decision-making. Finally, the model parameters of the proposed scheme are trained within the reinforcement learning framework. In addition, we design a task-oriented agent communication proof-of-concept prototype. Simulation and experiment results show that the proposed scheme has better generalization capability than the existing schemes. That is, with the proposed scheme, the transmission of task-related information is more reliable, thus the multi-agent systems can collaborate better under different channel conditions.

Federated learning (FL) is a distributed artificial intelligence (AI) paradigm that enables clients to exchange local updates and builds the global AI model on the central server. However, the performance of traditional FL is easily affected by poisoning attacks because the central server cannot check the validity of local updates. Moreover, traditional FL lacks an effective incentive mechanism to sufficiently motivate clients to update local models actively. To address the above problems, we first propose a blockchain-based FL (BFL) framework to defend against the poisoning attack in a decentralized manner while ensuring the high performance of the global model. Then we design an incentive mechanism based on the static game with incomplete information to encourage legitimate nodes to participate in model training and remove attackers from the BFL. Moreover, we find the Nash equilibrium where legitimate nodes can always defend against poisoning attacks and provide high-quality models. Security analysis and simulation results show the security and efficiency of the proposed schemes.

With the rapid development of the Fifth Generation Mobile Communication Technology (5G) networks, network planning and optimization have become increasingly crucial. Generating high-fidelity network traffic data can preemptively estimate the network demands of mobile users, which holds potential for network operators to improve network performance. However, the data required by existing generation methods is predominantly inaccessible to the public, resulting in a lack of reproducibility for the models and high deployment costs in practice. In this article, we propose an Open data-based Diffusion model for mobile traffic generation (OpenDiff), where a multi-positive contrastive learning algorithm is designed to construct conditional information for the diffusion model using entirely publicly available satellite remote sensing images, Point of Interest (POI), and population data. The conditional information contains relevant human activities in geographical areas, which can effectively guide the generation of network traffic data. We further design an attention-based fusion mechanism to capture the implicit correlations between network traffic and human activity features, enhancing the model's controllable generation capability. We conduct evaluations on three different cities with varying scales, where experimental results verify that our proposed model outperforms existing methods by 14.36% and 13.05% in terms of generation fidelity and controllability. To further validate the effectiveness of the model, we leverage the generated traffic data to assist the operators with network planning on a real-world network optimization platform of China Mobile Communications Corporation. The source code is available online:https://github.com/impchai/OpenDiff-diffusion-model-with-open-data.

Satellite networks are crucial for future 6G communications, offering global coverage. However, traditional Transmission Control Protocol (TCP) struggles with poor stability in dynamic satellite networks. In contrast, QUIC is capable of providing stability in such environments thanks to features like seamless connection migration. Nevertheless, the loss detection and recovery strategy of QUIC needs improvement in satellite networks with relatively high transmission errors. To address this, we propose Polar-QUIC, an adaptive packet-level polar coding-assisted QUIC scheme. By leveraging the robust error correction capabilities of polar codes, we design a packet-level encoder/decoder, loss detection tuning module, and adaptive redundancy module that combines packet loss rate and congestion window size. Experimental results demonstrate that Polar- QUIC effectively reduces the average Download Completion Time (DCT) of QUIC in satellite networks. Specifically, Polar-QUIC minimizes the average DCT by up to 35.8% and 74.5%, respectively, compared to general forward error correction schemes such as Reed-Solomon Codes and Random Linear Codes.

Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Existing MIAs designed for large language models (LLMs) can be bifurcated into two types: reference-free and reference-based attacks. Although reference-based attacks appear promising performance by calibrating the probability measured on the target model with reference models, this illusion of privacy risk heavily depends on a reference dataset that closely resembles the training set. Both two types of attacks are predicated on the hypothesis that training records consistently maintain a higher probability of being sampled. However, this hypothesis heavily relies on the overfitting of target models, which will be mitigated by multiple regularization methods and the generalization of LLMs. Thus, these reasons lead to high false-positive rates of MIAs in practical scenarios. We propose a Membership Inference Attack based on Self-calibrated Probabilistic Variation (SPV-MIA). Specifically, we introduce a self-prompt approach, which constructs the dataset to fine-tune the reference model by prompting the target LLM itself. In this manner, the adversary can collect a dataset with a similar distribution from public APIs. Furthermore, we introduce probabilistic variation, a more reliable membership signal based on LLM memorization rather than overfitting, from which we rediscover the neighbour attack with theoretical grounding. Comprehensive evaluation conducted on three datasets and four exemplary LLMs shows that SPV-MIA raises the AUC of MIAs from 0.7 to a significantly high level of 0.9. Our code and dataset are available at: https://github.com/tsinghua-fib-lab/NeurIPS2024_SPV-MIA

This article presents a multichannel cryptosystem based on the proposed shared buffer techniques which enhance security for several Internet of Things (IoT) sensors and devices by using block cipher with feedback operation modes. Information security and its performance are both important characteristics for modern networking devices. In cryptography, the block cipher, for example, advanced encryption standard (AES), using feedback operation modes can enhance the security level but, unfortunately, the encryption cannot be performed in parallel owing to data dependency. Therefore, the pipeline and unrolling techniques are not applicable to increase the throughput of hardware designs. Under this situation, the round-based architecture is popular and regarded as an area-efficient solution. However, this method inherently limits its throughput. The proposed cryptosystem aims to provide many resource-constrained IoT devices with high-speed centralized encryption service to enhance their security levels, which are applicable for various scenarios, such as vehicular network, home network, and network function virtualization (NFV)/software-defined networking (SDN) IoT. Beyond that, the proposed design introduces the shared buffer technique based on linked lists and presents a novel queuing structure to enhance the memory utilization so that it can reduce 72.9% memory requirement of the naïve implementation while achieving the same speedup. According to the implementation result, an aggregate throughput of 130.91 Gb/s for encrypting ten IoT devices in cipher block chaining (CBC), cipher feedback (CFB), and output feedback (OFB) modes can be achieved on TSMC 40 nm. The area efficiency of this work significantly outperforms the state-of-the-art works.

Thanks to the great potential to alleviate the intercell interference issue, cell-free wireless network is regarded as one of the most promising networking architectures in the future. In the meantime, the dramatic increase in the number of wireless devices and their diversified communication rate requirements pose new challenges for cell-free networks. In this article, we integrate the new symbiotic radio (SR) transmission technique into cell-free networks. In particular, spectral- and energy-efficient passive backscatter communication in SR is achieved by passive reflective beamforming over multiple reconfigurable intelligent surfaces (RISs). On the one hand, distributed access points (APs) in cell-free network collaboratively perform active communication through direct links, and on the other hand, RISs reuse the spectrum and energy of active communication to passively backscatter its own information-bearing signal. Considering the coexistence of active and passive communication demands, we define the rate region of the RIS-based cell-free SR system as the union of all rate pairs achieved by the active and passive communication devices. To characterize the achievable rate region, we formulate an optimization problem to maximize the passive communication rate given a minimum active rate constraint, by jointly optimizing the active transmit beamforming and passive reflective beamforming. An efficient alternating algorithm is proposed to solve the formulated problem. Finally, simulation results are presented to show the rate region of RIS-based cell-free SR systems and demonstrate the effectiveness of the proposed joint beamforming algorithm.

Due to the huge file size of virtual reality (VR) videos, providing high-quality and low-latency live VR videos to multiple users using existing network architectures becomes exceptionally challenging. Although some tile-based multicast schemes can reduce the amount of transmitted data, users with differentiated channel conditions and interaction behaviors still need to be effectively grouped. In this paper, we first model VR video multicast scheduling in the case of orthogonal multiple access (OMA), where the objective of maximizing the sum of user experience qualities forms an NP-hard integer nonlinear programming problem. By analyzing the time complexity source of the exhaustion-based global optimal algorithm and the impact of two factors on grouping, we propose a scheduling algorithm with very low complexity based on dynamic programming in the OMA case and a field-of-view ratio sorting and grouping method for non-orthogonal multiple access (NOMA) scenarios. In extensive actual viewport experiments, the proposed algorithm outperforms the state-of-the-art multicast scheme adopting OMA by 17.86% with negligible time complexity, making it suitable for deployment in realistic environments. The proposed sorting method increases the system utility of NOMA’s optimal solution by 13.13%.

5G industrial cyber–physical systems (5G-ICPSs) have attracted substantial research interests due to their capability in the interconnection of everything. However, integrating the 5G network may expose systems to more potential risks. To reveal attack propagation, an attack path prediction approach based on dual reinforcement learning (RL) is proposed. First, a dual-network model is established, incorporating the security constraints for attacks against the 5G network into the attack graph. Second, employing RL,$Q $-value updating functions and reward mechanisms based on topology and vulnerability are designed. Finally, an optimal attack path prediction algorithm is developed. Unlike traditional methods, the proposed approach does not rely on the monotonicity assumption that a system component has only one vulnerability, enabling it to accurately predict the optimal attack paths. Our simulation results demonstrate that the proposed approach can identify possible attack sources and paths from a 5G-ICPS.

Non-line-of-sight (NLOS) sensing is essential for unmanned robots and intelligent transportation systems, as it enables the sensor to detect targets around street corners, reducing the collision risk. Existing NLOS millimeter-wave (mmWave) radar technologies are based on third-order bounce geometry and utilize a full specular reflection path on smooth relay surfaces to detect targets. However, these works primarily concentrate on ideal lab environments, which pose challenges in wild street scenarios with intricately rough-relay-surface (RRS), such as stone walls and rocks, where non-flat planar surfaces usually exist near a corner. In this article, we present an NLOS sensing system that employs a single commodity mmWave radar to recover a hidden target from multiple scattering paths caused by RRS. The core contribution of the NLOS system is a high-resolution hidden target recovery algorithm by leveraging the multiple scattering paths. Specifically, leveraging knowledge from stochastic geometry and electromagnetic roughness, a microfacets model is used to characterize the random RRS. To deduce the tensor signal model of NLOS mmWave radar sensing with multi-input–multi-output (MIMO) antennas, we first profile the nonlinear geometry relationship among the RRS scattering paths, then focus on the path reflected from each scattering point. Built upon the model, we design a novel stochastic geometry-aided three-stage recovery (SGTR) algorithm for NLOS sensing, which allows the use of estimated virtual ghost targets rather than considering them as a disturbance. We evaluate the effectiveness of the proposed NLOS sensing technique via both simulations and experimental tests.

Cross-domain collaboration under wireless communication scenarios has gained traction in Internet-of-Things (IoT) applications. Authentication is essential for ensuring the security of wireless IoT devices (IoTDs). However, the existing cryptographic and physical layer authentication schemes are unappealing in cross-domain scenarios due to the presence of resource-limited IoTDs, privacy concerns of cross-domain sharing, and the negative effect of malicious attackers. This paper proposes FedScatter, a lightweight cross-domain authentication scheme for securing wireless IoTDs using backscatter communication. First, device identity signatures are constructed by harnessing passive signal features generated from feather-light backscatter tags, incurring negligible overhead. Subsequently, a federated learning model is designed to aggregate device identity information across domains while respecting device heterogeneity and data privacy. A novel parameter aggregation algorithm is proposed to bolster authentication resilience and against malicious attacks to avoid model pollution by powerful attackers with substantial hardware resources. A FedScatter prototype is implemented and evaluated, demonstrating significant improvements over state-of-the-art works in both true positive rate and false positive rate under various attacks.

Mobile Edge Computing (MEC) deploys servers at cellular base stations to provide computing services for Industrial Internet of Things (IIoT) applications. However, in remote areas lacking cellular coverage, traditional MEC systems are ineffective. The advancement of Low Earth Orbit (LEO) satellite communication networks introduces satellite-based MEC, deploying servers on satellites, as a promising solution for remote areas. Yet, the limited resources for IIoT devices pose challenges for delivering low-latency computing services. In this paper, we investigate the problem of task partitioning and resource allocation in satellite-assisted MEC systems, aiming to minimize the task completion latency of IIoT devices. Tasks can be divided into multiple subtasks executed by local IIoT devices, MEC servers, or cloud servers. The subtasks can be independent of or dependent on each other. A proposed approach based on Deep Recurrent Q-learning Networks (DRQN) utilizes recurrent neural networks to learn temporal features resulting from satellite movement and optimize accordingly. Given the difficulty in obtaining global state information in large-scale networks, federated learning (FL) is integrated into the DRQN framework to enhance efficient decision-making by aggregating local models. The simulation validates the effectiveness of the proposed approach, demonstrating significant reductions in average latency.

We investigate the impact of network topology characteristics on flocking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network, including the network’s hop count features and information’s successful transmission probability (STP). Specifically, we first propose a distributed communication–calculation–execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system, where flocking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables. Then, we develop a fragmentation prediction model (FPM) to formulate the impact of hop count features on fragmentation for specific flocking scenarios. This model identifies the critical system and network features that are associated with fragmentation. Further considering general flocking scenarios affected by both hop count features and STP, we formulate the flocking fragmentation probability (FFP) by a data fitting model based on the back propagation neural network, whose input is extracted from the FPM. The FFP formulation quantifies the impact of key network topology characteristics on fragmentation phenomena. Simulation results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation, and several guidelines for constructing the multi-robot ad hoc network are concluded.

The spaceborne scatterometer is an active nonimaging radar system that is commonly used to measure the direction and speed of wind near the ocean surface. However, the typical resolution of the current spaceborne scatterometers is 25–50 km, which limits their applicability in scenarios requiring higher resolution requirement. In this article, an adaptive bilateral-total-variation regularization algorithm with Lorentzian norm (LABTV) is proposed using HaiYang-2 Scatterometer (HY2-SCAT) data. It introduces adaptive weight coefficients based on the bilateral total variation, which suppresses the noise effectively while maintaining the texture details of the images. Moreover, the Lorentzian norm further improves the performance of the reconstruction algorithm. To investigate its performance in resolution enhancement and the resulting accuracy, the proposed reconstruction algorithm is implemented using both simulated and actual HY2-SCAT measurements. Compared with some existing resolution enhancement algorithms, the proposed algorithm can achieve a comparable resolution enhancement after enhancing two times to an original-resolution pixel size of 25 km, with the root-mean-square error (RMSE) of 1.812 dB, the peak signal-to-noise ratio (PSNR) of 26.797 dB, the structural similarity (SSIM) of 0.974, and the coefficient of determination ($R^{2}$) of 0.967, for horizontally polarized transmitted and received (HH-pol) data, and a comparable resolution enhancement with RMSE of 1.788 dB, PSNR of 26.991 dB, SSIM of 0.976, and$R^{2}$of 0.962, for vertically polarized transmitted and received (VV-pol) data. Furthermore, HY2-SCAT images with a low-resolution pixel size of 25 km were enhanced four times to a high-resolution pixel size of 6.25 km. The technique is also validated using Scatterometer Satellite-1 (SCATSAT-1) data after four times enhancement to a high-resolution pixel size of 4.45 km.

In this study, we propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously calibrates depth and RGB features in the kernel and mask branches to generate instance-aware kernels and mask features. CalibNet consists of three simple modules, a dynamic interactive kernel (DIK) and a weight-sharing fusion (WSF), which work together to generate effective instance-aware kernels and integrate cross-modal features. To improve the quality of depth features, we incorporate a depth similarity assessment (DSA) module prior to DIK and WSF. In addition, we further contribute a new DSIS dataset, which contains 1,940 images with elaborate instance-level annotations. Extensive experiments on three challenging benchmarks show that CalibNet yields a promising result, i.e., 58.0% AP with 320×480 input size on the COME15K-E test set, which significantly surpasses the alternative frameworks. Our code and dataset will be publicly available at: https://github.com/PJLallen/CalibNet.

In this paper, we propose an edge selective sharing architecture (ESSA) for massive mobile video streaming (MMVS) and develop cross-layer optimizations for joint user scheduling and power allocation (JUSPA) in ESSA. This work aims to ensure users' high quality of experience (QoE) by efficiently using network resources over mobile edge computing (MEC)-assisted massive multiple-input and multiple-output (MIMO) networks. Initially, by taking advantage of the analytical and empirical characteristics of video streaming, ESSA is able to maintain MMVS by activating only a portion of facilities in wireless access networks (WANs) without sacrificing video quality. Following this, a cross-layer optimization problem is formulated for JUSPA. We simplify the problem into a generalized assignment (GA) problem by discussing the peculiarities of MMSV, whose approximate solution can be obtained in polynomial time. Moreover, load balancing based on user density is integrated to reformulate a capacitated facility location (CFL) problem solvable with low overall time complexity, effectively mitigating the increased overheads of MEC units and enhancing the overall performance of MMVS. Numerical results indicate that our ESSA with JUSPA schemes for MMVS achieves better streaming fluency and transmission efficiency than alternative methods.

The application of Wireless Sensor Networks (WSNs) in extreme environments is becoming increasingly widespread. Within these extreme environments, communication links between WSN nodes become more fragile. We refer to such WSNs as Wireless Weak-link Sensor Networks (WWSNs). The characteristics of WWSNs make them more vulnerable to internal attacks. During the data transmission process from source nodes to destination nodes, intermediary nodes could act as malicious entities capable of intercepting or manipulating data. Therefore, detecting malicious nodes is of utmost importance. This paper proposes a malicious node detection strategy based on dynamic trust management to address these challenges. The dynamic trust management algorithm integrates type-2 fuzzy logic and considers various trust factors to comprehensively evaluate node trust within WWSNs. Additionally, a dynamic trust value updating mechanism is proposed to accommodate the dynamic environmental changes inherent to WWSNs. Experimental results emphasize the effectiveness of the proposed approach in dynamically adapting to the network environment while achieving a high level of performance in detecting malicious nodes.

By providing an immersive experience with panoramic views, 360-degree video streaming has gained increasing popularity recently. In many cases, videos are transmitted to mobile users over cellular networks. However, due to the high bandwidth requirement of 360-degree videos and the growing number of users, it is challenging to provide high-quality live streaming services to all users with limited bandwidth. Improving spectral efficiency and reducing bandwidth consumption are two major approaches to address this issue, which can be achieved with non-orthogonal multiple access (NOMA) and scalable video coding (SVC), respectively. In this paper, we apply NOMA and SVC to 360-degree video streaming over a cellular network and propose a multicast scheme called SVCast, aiming to maximize the sum quality of experience of users served by a base station. Such a problem is formulated as an NP-hard problem, and we decompose it into two levels of subproblems. The lower-level subproblem is inter-group spectrum allocation, which is solved by a knapsack approach. The higher-level subproblem is intra-group multicast scheduling, and we propose a recursive algorithm to solve it. Simulation results demonstrate that SVCast improves the system utility by 31.9% on average. Furthermore, SVCast eliminates the need for viewport prediction by aggregating the contents from the viewports of multiple users.

Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage of the epidemic. However, there exists an inescapable risk of privacy leakage in the fine-grained user mobility trajectories required by individual-level infection prediction. In this article, we focus on developing a framework of privacy-preserving individual-level infection prediction based on federated learning (FL) and graph neural networks (GNN). We proposeFalcon, aFederated grAphLearning method for privacy-preserving individual-level infeCtion predictiON. It utilizes a novel hypergraph structure with spatio-temporal hyperedges to describe the complex interactions between individuals and locations in the contagion process. By organically combining the FL framework with hypergraph neural networks, the information propagation process of the graph machine learning is able to be divided into two stages distributed on the server and the clients, respectively, so as to effectively protect user privacy while transmitting high-level information. Furthermore, it elaborately designs a differential privacy perturbation mechanism as well as a plausible pseudo location generation approach to preserve user privacy in the graph structure. Besides, it introduces a cooperative coupling mechanism between the individual-level prediction model and an additional region-level model to mitigate the detrimental impacts caused by the injected obfuscation mechanisms. Extensive experimental results show that our methodology outperforms state-of-the-art algorithms and is able to protect user privacy against actual privacy attacks. Our code and datasets are available at the link: https://github.com/wjfu99/FL-epidemic .

In this paper, we propose an age of incorrect information (AoII)-aware data dissemination scheme for distributed multi-agent systems (MASs). In the proposed scheme, AoII is utilized to measure the importance of data in terms of timeliness and content. We formulate the joint optimization of time slot allocation and agent selection as a decentralized partially observable Markov decision process (Dec-POMDP), with the objective of minimizing the AoII. To solve the Dec-POMDP, a novel multi-agent reinforcement learning algorithm (DV-MAPPO) is proposed. In particular, to tackle challenges posed by the partial observability of global system information, each agent estimates the global system state using variational inference. Moreover, to improve the accuracy of global system state estimation, each agent is given an intrinsic reward that is dominated by the accuracy of estimates. The proposed data dissemination scheme is implemented and evaluated in various missions. Simulation results show that the proposed data dissemination scheme outperforms traditional data dissemination schemes in terms of AoII. Furthermore, in typical multi-agent collaborative tasks, the proposed scheme facilitates more efficient cooperation among multiple agents compared to the data distribution mechanisms that ignore the importance of data.

The vision of backscatter communication always incorporates compatibility with active radios to enable low-cost and easy deployment. However, recent innovations lack the flexibility to communicate with heterogeneous wireless devices directly. In this paper, we design and implement a flexible backscatter system, i.e., Flexcatter, which can support various modulation schemes in a software-defined way, to be compatible with different kinds of active radios. The key technique is signal emulation, where the tag can vary the reflection coefficient in the time domain to emulate desired baseband signals. We first carefully design a cost-effective impedance network, which employs two radio frequency (RF) switches to provide up to 16 reflection coefficients. Next, we establish and model the emulation mapping between desired baseband signals and available reflection coefficients. Besides, the emulation frameworks for different modulation schemes are presented. After that, to face the emulation distortion for orthogonal frequency division multiplexing (OFDM), we introduce the oversampling method in the baseband modulation process. We further build the prototype hardware, and experiment results show that Flexcatter can flexibly generate various kinds of backscatter signals, including Wi-Fi, BLE, and LoRa. Especially the OFDM transmission generated by Flexcatter can achieve a throughput of 25.1 Mbps.

Much exploration has been conducted to ensure the successful implementation of underground applications based on wireless underground sensor networks (WUSNs). However, most of them only focus on the communication aspect while neglecting location information. As a matter of fact, for scenarios such as earthquake and mine emergency rescue, it is necessary to achieve communication and positioning simultaneously. Nevertheless, accomplishing this goal is challenging, as the underground environment not only causes tremendous attenuation but also has limited hardware resources. Long range (LoRa) technology has high reception sensitivity and a simple structure, making it well suited for underground emergency rescue. Therefore, we propose a LoRa-based joint communication and localization system called LoRaAid. When a disaster occurs, the wearable LoRa device transmits sensing information to surrounding buried nodes. Through collaboration between nodes, diversity reception of signals and location acquisition of the target can be achieved simultaneously. Two different schemes are proposed to achieve correct demodulation, and the closed-form expression for bit error rate (BER) is also derived. While implementing communication, LoRaAid uses the mapping relationship between noise-reduced received signal strength indicator (RSSI) and distance to achieve trilateral localization. Numerous experimental results demonstrate that LoRaAid can enable long-range communication while achieving decimeter-level positioning accuracy.

User-centric cell-free massive MIMO (CFmMIMO), where only partial access points (APs) are selected to serve a specific user equipment (UE), is a scalable extension of CFmMIMO. Existing works have studied the spectral efficiency of large-scale fading decoding (LSFD) aided user-centric CFmMIMO that includes local combining at each AP and statistical channel state information (S-CSI) based fusion in the central processing unit (CPU). However, few efforts have so far been paid to analyze the error probability bound, and existing detectors fail to balance the error probability and fusion complexity. In this paper, we analyze the symbol error rate (SER) and design low-complexity near-optimal detectors for uplink user-centric CFmMIMO systems. Considering non-identical large-scale fading coefficients and local channel estimation errors, we first leverage the pairwise error probability to derive an SER upper bound for optimal linear fusion (OLF) in the CPU, which is suitable to different local combining methods at the APs. Then, by combining local normalization methods and S-CSI based UE grouping or error correction, we design improved detectors for local maximum-ratio and local minimum mean squared error combining successively. Simulation results verify the correctness of the derived SER bound, and show that the proposed detectors are capable of approaching the SER performance of conventional OLF based counterparts with reduced fusion complexity even in scenarios with pilot contamination.

Filter bank multicarrier (FBMC) is emerging as a promising approach to combat the orthogonal frequency division multiplexing (OFDM) sensitivity to synchronization errors for machine-type communication (MTC). However, related designs in FBMC-MTC fail to meet the requirements of transmitting diverse data between machines as well as eliminating the effects of FBMC’s inherent imaginary interference. To address this issue, in this paper, we propose an FBMC-MTC system with deep learning (DL) assistance. Specifically, we first construct a hybrid packet transmission architecture to meet the delay and throughput requirements of different packets. Second, we further present a DL-based receiver consists two modules driven by communication domain knowledge to enhance data reliability. The dense and convolutional layers are used in two different modules since the two types of packets have different pilot structures and propagation properties. Finally, we deploy the proposed FBMC-MTC system via universal software radio peripheral (USRP) and mobile robots and test the DL-based receiver over the air (OTA). Both simulation and OTA test results show that the proposed FBMC-MTC system can operate in various channel environments, and its receiver is better than the previously advanced OFDM and FBMC receivers.

For extremely large-scale array (XL-array) communications, the scatterers and/or user equipments (UEs) may be located in the near-field region and only visible to some portions of the XL-array. This paper studies the near-field spatial correlation function (S-CF) of multi-path XL-array communications with mixed line-of-sight (LoS) and non-LoS (NLoS) links. The generic near-field non-uniform spherical wave (NUSW) characteristic and the partial visibility property are considered. For the LoS link, a novel near-field S-CF is derived, which is in terms of the correlation of UE's visibility and location. It is found that the near-field S-CF depends on the UE's angle of arrival (AoA) and distance, which differs from the far-field result that only depends on the UE's AoA. For the NLoS links, we derive a novel integral expression for the near-field S-CF in terms of the correlation of the scatterers' visibility and location distribution. The near-field result is shown to depend on the scatterers' partial visibility and the power location spectrum (PLS) characterized by the AoAs and distances of scatterers, in contrast to the far-field model, which relies on the power angular spectrum (PAS). The result demonstrates that the near-field S-CF of the LoS/NLoS component no longer exhibits spatial wide-sense stationarity (SWSS) and is more generic than the far-field model. To gain further insights, we consider a specific scatterer's location distribution, namely the multi-ring scatterer model. Numerical results show the necessity of modeling near-field S-CF for XL-array communications with partial visibility.

Telemedicine systems have the potential to address the uneven distribution of healthcare resources and reduce the risk of doctor infection. Performance analysis of the telemedicine system under time-varying transmission delays is essential to use and optimize the system. However, existing performance analysis methods only consider the numerical deviation between signals but ignore the kinematic characteristics of the hardware devices, which causes signals unrealistic matching and inaccurate results. To this end, this paper proposes an accurate performance analysis algorithm by exploiting the kinematic characteristics of devices. Particularly, representative features such as displacement and inertia are first extracted from the devices motion. Then, the state criteria of the devices are formulated by the extracted features, which are also converted into additional constraints to improve the accuracy of signal matching in performance analysis. To verify the effectiveness of the proposed scheme, along with simulation, we also implement a prototype of the telemedicine system. The experimental results show that the proposed algorithm improves the hit rate to 95.68% and reduces the ambiguity rate by 73.5%, which is more accurate than the existing dynamic time warping algorithm.

In this paper, a novel intelligent transmission surface (ITS) assisted terahertz (THz) wideband massive multiple-input multiple-output (MIMO) non-terrestrial communication architecture is conceived, which is capable of reducing the hardware cost and power consumption remarkably compared to traditional architectures. To further address the beam squint impact in the THz wideband system, an angle-based hybrid beamformer is designed for the proposed architecture, which can effectively suppress the beam squint and maintain high spectral efficiency (SE) performance. Numerical results demonstrate that the proposed scheme is capable of approaching the optimal full-digital architecture in terms of the SE performance, and the proposed method can achieve significant energy efficiency performance gains over other existing architectures.

QUIC has drawn extensive attention in supporting low latency and secure Internet of Things (IoT) communications due to its efficient handshake and default end-to-end encryption. However, in Wi-Fi-enabled IoT communications with contentions for shared media, QUIC’s inherent acknowledgment (ACK) policy may induce non-negligible control overhead and limited data throughput. To address the problem, this article designs and implements an ACK frequency optimization scheme for QUIC by exploiting the tailored bandwidth-delay product (BDP) at the receiver, named QUIC-BDP. To accurately estimate real-time BDP, we design an “ACK-PING” strategy to compensate for the accuracy of round-trip timing estimation and utilize exponential averaging and sliding window filtering for stable bandwidth estimation. Experimental‘ results show that our proposed QUIC-BDP balances between the robustness and throughput performance while maintaining stable performance in lossy cases, with a reduced energy cost. Particularly, QUIC-BDP achieves up to a 67% gain in goodput compared to the original QUIC, and it improves goodput by up to 38% and 28% compared to existing solutions MSQUIC and QUIC-1:10, respectively. In addition, QUIC-BDP reduces energy cost by up to 50% compared to the original QUIC.

This research investigates the digital-to-analog converter (DAC) free architecture for the digital reconfigurable intelligent surface (RIS) system, where the transmission lines are implemented for reflection coefficient (RC) control to reduce power consumption. In the proposed architecture, the radio frequency (RF) switch based phase shifter is considered. By using a single-pole four-throw (SP4T) switch to simultaneously control the RCs of a group of elements, a 2-bit phase shifter is realized for passive beam steering. A novel modulation scheme is developed to explore the cost effectiveness, which approaches the performance of traditional quadrature amplitude modulation (QAM). Specifically, to overcome the limitation of the phase shift bits, joint frequency-shift and phase-rotation operations are applied to the constellation points. The simulation and experimental results demonstrate that the proposed architecture is capable of providing an ideal transmission performance. Moreover, 64- and 256-QAM modulation schemes could be implemented by expanding the elements and phase bits.

In this paper, theoretical performance of optimal maximum likelihood (ML) detector and near-optimal simulated detectors are designed for massive multiple-input multiple-output (MIMO) aided grant-free (MM-GF) systems. Specifically, the approximated average bit error probability (ABEP) bounds are firstly derived by exploiting the relationship between the Hamming distance (HD) and the pairwise error probability (PEP), which are confirmed by the simulation results. Moreover, an extended alphabet based expectation propagation (EA-EP) and an adaptive subspace matching pursuit (ASMP) algorithm are devised for signal detection of MM-GF without the prior information (PI) of active users. Simulation results show that the proposed detectors are able to outperform the classic oracle least squares (OLS) benchmark and are capable of approaching the theoretical ABEP bounds of ML.

Multiple-antenna backscatter is emerging as a promising approach to combat the cascaded backscatter channel fading to offer a high data rate for ambient backscatter communication (AmBC). However, related designs in AmBC fail to provide both diversity and multiplexing gains to backscatter tags. To address this issue, in this paper, we present a unified generalized space-time shift keying (GSTSK)-Backscatter architecture for AmBC to strike a flexible tradeoff between the attainable diversity and multiplexing gains in an efficient but low-complexity manner. Moreover, taking the non-identically distributed characteristics of the backscatter channel into account, we first derive asymptotic BER bounds for both the source and backscatter signals, which are verified by simulation results. Furthermore, we propose a residual- and threshold-based joint error correction mechanism (JECM) and design the near-optimal and low-complexity detector for GSTSK-Backscatter systems. Simulation results validate the analysis and show that our proposed JECM-based detector approaches the optimal detector and balances the BER and complexity performance.

In this paper, generalized space-time block-coded (GSTBC) spatial modulation (SM)-assisted reconfigurable intelligent surface (RIS) structures are designed for three communication scenarios, where RIS works as a transmitter, passive relay as well as active relay. Specifically, the information bits are first mapped to an appropriate GSTBC-SM symbol according to the required transmit rate and diversity order. Then, the mapped GSTBC-SM symbol is transmitted by the based station or the RIS elements based on the communication scenario. Both the signal detector and the upper bounds of average bit error probability are derived for the three cases, which are verified by the simulation results. Simulation results show that the proposed GSTBC-SM-RIS scheme is capable of providing a significant performance gain over the existing SM-RIS scheme, as well as the conventional GSTBC-SM counterpart.

Massive Multiple-Input Multiple-Output (MIMO), with its spatial multiplexing and channel hardening, has the potential to provide high capacity and reliability for massive video services. In spite of this, massive MIMO in the physical layer does not fully showcase its abilities in video applications unless it is specifically designed to do so. In this paper, we consider a cell-free massive MIMO system as an edge node for scheduling massive streams, and thus the standard server-to-client transmission is split into server-to-edge and edge-to-client. When the server-to-edge transmission is ideal, edge schedules massive streams to alleviate user conflicts in cell-free massive MIMO. Moreover, we propose a novel edge-to-client grouping algorithm for assigning the streams with severe interference to different time slots. The proposed algorithm achieves an innovative.7-approximation ratio while keeping the group sizes within a certain range. When the server-to-edge transmission is non-ideal, we design a special transmission framework called Aggressive, wherein the server sends the next video chunk when the current chunk reaches the edge rather than the client. Thus, the proposed framework saves considerable server-to-edge latency compared with the traditional framework. Simulation results show that the proposed user grouping algorithm improves the achievable rate by around 18% and the Aggressive framework improves the average bitrate.

Ambient LoRa backscatter enables battery-free wireless communication with long-range connectivity for the Internet of Things. However, current efforts mainly focus on symbol-level modulation, which trades considerable data rates for long backscatter ranges. To enhance the data rate, this paper presents and prototypes Pacim, which fully explores the potential of long-period LoRa chirps and conveys additional information by varying symbol lengths. Specifically, we propose the chirp interval modulation scheme that modulates multiple data bits in each time interval between two chirp-based anchor symbols. Moreover, we design a twin-chirp cancellation method at the receiver that eliminates the frequency discontinuity within anchor symbols, and propose a fine-grained detection algorithm to measure the arrival time of anchor symbols in the frequency domain. We further propose three reliable methods to improve transmission reliability and analyze the symbol error rate (SER) performance. We also build a hardware prototype and perform comprehensive evaluations. Our experiment results show that Pacim can achieve up to$8.6 \times $throughput gain while keeping long-range, compared with the state-of-the-art ambient LoRa backscatter design.

In this paper, we design and implement a flexible backscatter system, i.e., Flexcatter, which can support various baseband modulation schemes to be compatible with active radios. The key technique employed by Flexcatter is signal emulation, where the tag can vary the reflection coefficient in the time domain to emulate desired baseband signals. To meet the low-power requirements of Flexcatter, we first carefully design a cost-effective impedance network, which can provide up to 16 reflection coefficients for different signal amplitudes and phases. Moreover, we present an effective emulation strategy for desired baseband signals. Next, we introduce the oversampling method in the baseband modulation pipeline to mitigate the effect of emulation errors caused by hardware deficiency. We further build the prototype hardware, including an FPGA platform and the radio frequency (RF) module. Experiment results show that OFDM transmissions from Flexcatter can achieve a throughput of 21.1 Mbps at the backscatter range of 12 m. To the best of our knowledge, we are the first to generate OFDM transmission with 64 subcarriers using low-power RF switches. Our design has the potential to accept other types of existing active radios as the receiver to reduce the deployment cost significantly.

An Elastic Optical Network (EON) nowadays usually accommodates multiple Virtual Optical Networks (VON) by allocating orthogonal spectrum resources for them. A fixed resource allocation pattern may result in a low quality of service since data traffic on VONs may fluctuate with time. Spectrum trading (ST) among VONs could solve this problem by moving spectrum resources between them and blockchain-based ST has recently gained much interest since it avoids the traditional centralization concern. In this paper, we propose a smart contract based decentralized ST scheme for VONs. The system is built on a private blockchain which can be implemented with the off-shelf Ethereum platform at a low development cost. A frequency slot (FS) trading contract (FSTC) is designed to trade unoccupied spectrum resources among VONs. Compared with existing schemes, FSTC explicitly addresses the virtual link brokerage (VLB) problem by trading in the granularity of virtual links. Furthermore, FSTC ensures FS information authenticity and enables FS recyclability via mechanisms based on Ethereum smart contract features. We implement FSTC with Solidity and evaluate its costs which demonstrate the feasibility of the proposed solution.

In this paper, we propose a spatial modulation (SM) space time block code (STBC) aided orthogonal time frequency space modulation (SM-STBC-OTFS) system to achieve both high transmission rate and diversity gain in the context of high mobility communication scenarios. Specifically, we apportion the information bits into multiple groups depending on the number of subcarriers and time slots, where each group is modulated by the SM. Next, Alamouti code, orthogonal STBC (OSTBC) and quasi-orthogonal STBC (Q-OSTBC) structures are associated with SM systems. Then the SM-STBC symbols are invoked by OTFS modulation based on different transmission cycle. Finally, block message passing (BMP) detector is designed for the proposed SM-STBC-OTFS systems, which provides significant performance gain in multiple-input and multiple-output (MIMO) configurations. Simulation results show that the SM-STBC-OTFS system relying on the proposed detector is capable of providing considerable bit error rate (BER) performance gains over the OTFS and the MIMO-OTFS systems.

Access control plays a fundamental role in securing the Internet of Things (IoT) systems. Recently, blockchain-enabled secure and trusted access control has attracted remarkable attention since it alleviates the centralization concern in traditional frameworks. However, existing designs only deal with access request validation problems whereas overlooks content delivery issues. Considering that radio resources are increasingly stretched with the rapid growth of IoT devices in the 5G era, this may give rise to unacceptably high latencies of legal requests in peak hours. This article addresses this limitation by presenting JCRA, a Joint Content and Radio Access control framework for IoT applications. JCRA deploys access point contracts (APCs) and device contracts (DCs) to manage radio access points (APs) and IoT devices, respectively. The atom access contract (AAC) is responsible for conducting access control according to access policies in DCs as well as radio resource states in APCs. To enable flexible radio resource management that often involves solving optimization models in AAC, we introduce a task offloading scheme, which recruits specialized executor nodes to complete the computation-heavy part off-chain. A Proof-of-Solution-Quality rule is proposed for executor solution validation which ensures the off-chain calculation is trusted. We implement a prototype of JCRA to demonstrate its feasibility. The costs and performance of JCRA are also evaluated in detail.

Studying networked systems in a variety of domains, including biology, social science, and Internet of Things, has recently received a surge of attention. For a networked system, there are usually multiple types of interactions between its components, and such interaction-type information is crucial since it always associated with important features. However, some interaction types that actually exist in the network may not be observed in the metadata collected in practice. This article proposes an approach aiming to detect previously undiscovered interaction types (PUITs) in networked systems. The first step in our proposed PUIT detection approach is to answer the following fundamental question: is it possible to effectively detect PUITs without utilizing metadata other than the existing incomplete interaction-type information and the connection information of the system? Here, we first propose a temporal network model which can be used to mimic any real network and then discover that some special networks which fit the model shall a common topological property. Supported by this discovery, we finally develop a PUIT detection method for networks which fit the proposed model. Both analytical and numerical results show this detection method is more effective than the baseline method, demonstrating that effectively detecting PUITs in networks is achievable. More studies on PUIT detection are of significance and in great need since this approach should be as essential as the previously undiscovered node-type detection which has gained great success in the field of biology.

A long-standing vision of indoor localization is to eliminate infrastructure and deployment costs. Recent innovations make it possible to enable device-to-device (D2D) localization while requiring multiple antennas for the systems. We ask the following question: can we localize the more generally used single-antenna devices (e.g., IoT) using another single-antenna device (e.g., smartphone or smartwatch) in a smart environment where low-cost backscatter tags are widely deployed on walls or smart objects? In this article, we present TagLoc, a lightweight system that enables D2D localization without relying on large antenna arrays. Our observation is that the reflected signals from the ambient smart environment can be exploited to eliminate the requirement of bulky antenna arrays that are unachievable for the simple-designed IoT devices. Specifically, TagLoc creates multiple direction signatures using backscatter arrays in smart environments. Then, the receiver can accurately estimate the direction signatures from the transmitter to the arrays and then localize the target by cooperating all tag arrays. We prototype TagLoc using two single-antenna Intel NUCs with off-the-shelf Intel 5300 WiFi cards and customized backscatter tags. The results show TagLoc can achieve robust performance in a real indoor environment with a median localization error of 0.82 m.

Multiple-input–multiple-output (MIMO) is a promising enabler for massive connectivity of Internet of Things (IoT) devices by offering abundance of spatial degrees of freedom. One of the challenging issues is the excessive overhead induced by users’ channel state information (CSI) feedback, which impedes the gains of MIMO techniques. The popular solution to reduce the overhead is to adopt implicit feedback. However, existing implicit feedback methods mainly rely on either expensive hardware circuits or large antenna separation, making them unaffordable to low-cost and small-factor IoT devices. To circumvent this issue, in this article, a new channel feedback mechanism calledLazyBackis proposed, which jointly calibrates multiple users’ channels by removing the hardware diversity of different users. Furthermore, to ensure up-to-date CSI, LazyBack adopts a channel prediction algorithm to infer channel stability. When the channel varies quickly over time, LazyBack switches back to the explicit feedback mode to obtain the real-time downlink channel. A LazyBack prototype is implemented based on USRPs, and the experimental results show that it provides a$1.7 \times $and$3.1 \times $throughput improvement compared with the IEEE 802.11ac for$4 \times 4$and$8 \times 8$multiuser MIMO (MU-MIMO), respectively.

The Internet of Things (IoT) is envisioned to connect everything, spanning from terrestrial to nonterrestrial terminals, where reliable communication is expected to be allowed in both time-invariant and time-variant wireless channels. Since classic orthogonal frequency-division multiplexing (OFDM) modulation, which has been widely used in both the fourth-generation (4G) and the fifth-generation (5G) cellular systems, is sensitive to high Doppler effect, it is challenging to satisfy the ever-growing demands of future IoT. To circumvent this issue, the orthogonal time–frequency space (OTFS) scheme is proposed, which modulates the information bits in both the delay and the Doppler domains, and exhibits beneficial advantages in both static and high-mobility wireless channel scenarios. In this article, we present a comprehensive overview of OTFS for IoT, including the current transceiver design, the potential benefits, the challenge issues, as well as future design guidelines.

The sharing waveform-based integrated radar and communication (IRC) has great potential to apply in Internet-of-Things (IoT) devices due to its equipment miniaturizing and high efficiency of spectrum and energy, etc. However, the sharing waveform scheme demands a tradeoff between radar and communication performances. In this article, we focus on the design of multiple phase-shift keying (MPSK)-based linear frequency modulation (LFM) sharing waveform by exploring the tradeoff among energy leakage, peak-to-side lobe ratio (PSLR), and symbol rate. First, we derive the energy leakage and PSLR with respect to the symbol rate. Next, a novel LFM-MPSK waveform design is carried out by formulating a communication frame-based optimization, where the symbol rate is maximized with the constraints of the energy leakage and PSLR. Then, a branch-and-reduce algorithm is proposed to solve the optimization. Compared with the conventional one, the proposed LFM-MPSK waveform has a higher symbol rate with the same energy leakage and PSLR. Besides, the proposed waveform design alleviates the difficulty of the tradeoff among the energy leakage, PSLR, and symbol rate due to more Pareto solutions. Finally, all the analytical results are validated by simulations.

Due to its inherent ability in reducing hardware cost and power consumption while maintaining high system capacity, hybrid precoding is deemed as one of the key technologies in the upcoming 5G/6G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, it is challenging to design high performance hybrid precoders/combiners with low computational complexity. In this paper, based on the singular value decomposition (SVD) technique and the concept of equivalent channel, joint hybrid precoding strategies with high spectral-efficiency and low complexity are proposed for both single-user and multi-user massive MIMO systems. Specifically, for single-user massive MIMO scenarios, after transforming the design of hybrid beamforming into the problem of maximizing the square of sum eigenvalues for an equivalent channel, a two-stage successive method is conceived to design the analog precoder and combiner jointly, and the corresponding equivalent channel is constructed. Then, the digital precoding and combining operations are realized directly by applying the SVD technique to the matrix of equivalent channel. Meanwhile, the hybrid precoding strategy is extended to the multi-user scenario for achieving high performance resultant from multi-user diversity. Extensive simulations are conducted to verify the effectiveness of the precoding/combing schemes. The results show that our proposed schemes can achieve superior performance with lower complexity compared to the existing ones.

In this letter, different polarization characteristics of the sea clutter and target are considered to address the sea-surface weak target detection problem. We first construct a logarithmic generalized likelihood ratio test (log-GLRT) problem under the compound Gaussian model by considering multiple texture factors of sea clutter and four polarized channels. Based on the log-GLRT framework, a multivariable optimization problem is formulated to obtain the characteristic parameters of sea clutter and target. Then, under the block majorization-minimization (block-MM) framework, three parameters’ estimation strategies are designed to solve the optimization problem of the proposed parameters. Finally, a log-GLRT-based polarimetric detector is proposed by applying the estimated characteristic parameters. The experimental results on the Intelligent PIxel processing Xband (IPIX) data sets demonstrate that our proposed detector can achieve better detection performance than the several existed detectors.

In this paper, we propose a symbol encryption method based on the complex-valued symbol multiplication and design the encryption symbol placement for offset quadrature amplitude modulation based filter bank multicarrier (OQAM/FBMC) systems. Specifically, we firstly analyze the encryption and decryption effects of complex-valued symbol multiplication on the original OQAM symbols. Then, by utilizing the intrinsic interference characteristics of prototype filters, a symbol encryption method and the placement design are proposed to simultaneously achieve the encryption of all OQAM symbols and low complexity decryption. Finally, the symbol error rate (SER), average signal transmit power, computational overhead and power spectral density (PSD) are analyzed. Simulation results are consistent with the theoretical analysis and demonstrate that the proposed method outperforms the traditional method based on imaginary-valued encryption symbol insertion in terms of the SERs of both legitimate and illegitimate receivers.

In this paper, we propose a new framework for covert communication in hybrid active/passive networks, which explores the inherent uncertainty of the backscatter transmissions to achieve active and passive communication reciprocity in security. Under this framework, we first model the aggregate interference from the sporadic backscatter transmissions and derive the covert outage probability and the transmission success probability to capture the covertness and reliability. Then, we formulate a transmit power optimization problem to maximize the covert throughput subject to certain covertness and reliability requirements and derive a closed-form approximation of the maximum covert throughput. Particularly, we investigate the worst-case scenario of covert communication in which warden always uses the optimal detection thresholds. Finally, numerical results demonstrate that covert communication with the help of uninformed backscatters that are not coordinated with Alice in the hybrid network is feasible. Results also provide design guidelines for the optimal choice of interference parameters, which is capable of striking a balance between covertness and reliability.

In this paper, we investigate the two-hop active relaying for dynamic magnetic induction based underwater wireless sensor networks (MI-UWSNs). Specifically, we firstly propose the two-hop active relaying schemes with unidirectional (UD) and tri-directional (TD) active relays, respectively, by considering the angular misalignment in practical underwater MI environments. Then, the statistical properties of the received signal-to-noise ratio (SNR) for the proposed two-hop UD and TD active relaying schemes are rigorously analyzed and the corresponding closed-form expressions of the probability density functions are derived according to the distribution of the angular misalignment. Based on the statistical SNRs, we develop the analytical expressions of the ergodic achievable rate and the average bit-to-error rate for the two-hop UD and TD active relaying schemes employing the amplify-and-forward and decode-and-forward strategies. Extensive simulation results validate the effectiveness of our theoretical analyses and demonstrate that the proposed two-hop TD active relaying scheme performs the best among all comparative schemes.

Cloud-based cell-free massive multiple-input multiple-output (CFmMIMO) technology, which exploits a large number of distributed antennas to cooperatively serve multiple users, constitutes an appealing technique for B5G/6G wireless communications. However, the distributed nature of cloud-based CFmMIMO imposes great challenges in analyzing the error probability bounds, and very few efforts have so far been paid to optimize the detector design. In this paper, we try to add a stroke to this blank by analyzing the symbol error rate (SER) and design near-optimal detection algorithms. Specifically, considering non-identical large-scale coefficients and channel estimation errors, we first leverage the pairwise error probability to derive an asymptotic SER bound for uplink cloud-based CFmMIMO systems, which is verified by simulation results. Furthermore, motivated by the concepts of successive interference cancellation (SIC) and error correction mechanism (ECM), we design two distinct types of near-optimal detectors for cloud-based CFmMIMO systems and analyze their complexity and convergence performance. Finally, extensive simulation results show that our proposed SIC and ECM based detectors outperform conventional matched filtering (MF) and minimum mean squred error (MMSE) counterparts. In particular, the MMSE-SIC and MMSE-ECM detectors approach the derived asymptotic bound, and the MF-ECM detector strikes a balance between the SER and complexity in ultra CFmMIMO scenarios.

The low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks. Recent advances use an antenna array to extract fine-grained physical-layer signatures to identify the attackers, which adds burdens in terms of energy and hardware cost to IoT devices. In this paper, we present ShieldScatter, a lightweight system that attaches low-cost tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of tags. These signatures can be used to construct a sensitive profile to identify the location of the signals’ arrival, and thus detect the threat. In addition, we also design a tag-random scheme and a multiple receivers combination approach to detect a powerful attacker who has the strong priori knowledge of the legitimate user. We prototype ShieldScatter with USRPs and tags to evaluate our system in various environments. The results show that even when the powerful attacker is close to the legitimate device, ShieldScatter can mitigate 95 percent of attack attempts while triggering false alarms on just 7 percent of legitimate traffic.

A long-standing vision of backscatter networks is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatter networks toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further towards breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter networks. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. Furthermore, we devise a new metric to calculate log-likelihood ratio for accurate decoding, and present a stopping criterion of iterations to reduce decoding latency. We build a prototype PCB tag, and our experiments show that it achieves up to 11.5× throughput improvement over the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.

The advances in agile micro aerial vehicles (MAVs) have shown great potential in replacing humans for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight poses fundamental challenges in such dim or smoky environments. Current dominated computer-vision based solutions only work in well-lighted texture-rich environments. This paper addresses the challenge by proposing Marvel, an RF backscatter-based state estimation system with online initialization and calibration. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion with an automatic initialization module. Marvel is enabled by three new designs, a backscatter-based pose sensing module, an online initialization and calibration module, and a backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three concrete walls, with an accuracy of 34 cm for localization and 4.99for orientation estimation. We further demonstrate our online initialization and calibration by comparing to the perfect initial parameter measurements from burdensome manual operations.

Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobility-induced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent on-board sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensor-estimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of$1.24\times $and$1.28\times $compared with the bestknown RA algorithm for 2- and 3-antenna APs, respectively.

In this paper, we consider adaptive user equipments (UE) link selection and user association in millimeter-wave (mmWave) networks. We formulate a joint optimization of link selection, resource allocation, and user association, aiming to maximize the sum logarithmic rate of all UEs. The formulated problem is solved by decomposing it into two levels of subproblems. The lower-level subproblem is link selection and resource allocation with a given user association, which is solved by a three-stage process. In the first stage, we establish the D2D relaying architecture by assuming that all UEs are served via D2D relaying. Based on the relaying architecture, we derive the optimal resource allocation in the second stage. Finally, an adaptive link selection algorithm is proposed in the third stage to determine the set of UEs that switch from D2D relaying to multi-beam reflection. The high-level subproblem is user association, for which we solve it with a dual decomposition-based approach. Simulation results indicate that compared to benchmark schemes, the average data rate achieved by the proposed scheme is significantly higher than the benchmark schemes and is close to an upper bound. Besides, the proposed scheme achieves a good tradeoff between system performance and fairness.

The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities. Traditional video forensics approaches can localize forgery traces using spatial-temporal analysis on relatively long video clips, while falling short in real-time forgery detection. The recent work correlates time-series camera and wireless signals to detect looped videos but cannot realize fine-grained forgery localization. To overcome these limitations, we propose Secure-Pose, which exploits the pervasive coexistence of surveillance and Wi-Fi infrastructures to defend against video forgery attacks in a real-time and fine-grained manner. We observe that coexisting camera and Wi-Fi signals convey common human semantic information and forgery attacks on video streams will decouple such information correspondence. Particularly, retrievable human pose features are first extracted from concurrent video and Wi-Fi channel state information (CSI) streams. Then, a lightweight detection network is developed to accurately discover forgery attacks and an efficient localization algorithm is devised to seamlessly track forgery traces in video streams. We implement Secure-Pose using one Logitech camera and two Intel 5300 NICs and evaluate it in different environments. Secure-Pose achieves a high detection accuracy of 98.7% and localizes abnormal objects under playback and tampering attacks.

Hop count distribution (HCD), generally formulated as a discrete probability distribution of the hop count, constitutes an attractive tool for performance analysis and algorithm design. This paper devotes to deriving an analytical HCD expression for a finite ad hoc network under the minimum hop-count routing protocols. Formulating the node distribution with binomial point process, the network is provided as a bounded area with all nodes randomly and uniformly distributed. Considering an arbitrary pair of source node (SN) and destination node, an innovative and straightforward definition is presented for HCD. In order to derive HCD out, an original mathematical framework, named as the equivalent area replacement method (EARM), is proposed and verified. Under the EARM, HCD is derived by first considering the special case where SN locates at the network center and then extending to the general case where SN is randomly distributed. For each case, the accuracy of our HCD model is evaluated by simulation comparison. Results show that our model matches well with the simulation results over a wide range of parameters. Particularly, the derived HCD outperforms the existing formulations in terms of the Kullback Leibler divergence, especially when SN is randomly distributed.

The versatile nature of agile micro aerial vehicles (MAVs) poses fundamental challenges to the design of robust state estimation in various complex environments. Achieving high-quality performance in textureless scenes is one of the missing pieces in the puzzle. Previously proposed solutions either seek a remedy with visual loop closure or leverage RF localizability with inferior accuracy. None of them support accurate MAV state estimation in textureless scenes. This paper presents RFSift, a new state estimator that conquers the textureless challenge with RF-referenced monocular vision, achieving centimeter-level accuracy in textureless scenes. Our key observation is that RF and visual measurements are tied up with pose constraints. Mapping RF to feature quality and sift well-matched ones significantly improves accuracy. RFSift consists of 1) an RF-sifting algorithm that maps 3D UWB measurements to 2D visual features for sifting the best features; 2) an RF-visual-inertial sensor fusion algorithm that enables robust state estimation by leveraging multiple sensors with complementary advantages. We implement the prototype with off-the-shelf products and conduct large-scale experiments. The results demonstrate that RFSift is robust in textureless scenes, 10x more accurate than the state-of-the-art monocular vision system. The code of RFSift is available at https://github.com/weisgroup/RFSift.

The cybersecurity breaches render surveillance systems vulnerable to video forgery attacks, under which authentic live video streams are tampered to conceal illegal human activities under surveillance cameras. Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds. The recent work correlates time-series camera and wireless signals to recognize replayed surveillance videos using event-level timing information but it cannot realize fine-grained forgery detection and localization on each frame. To fill this gap, this paper proposes Secure-Pose, a novel cross-modal forgery detection and localization system for live surveillance videos using WiFi signals near the camera spot. We observe that coexisting camera and WiFi signals convey common human semantic information and the presence of forgery attacks on video frames will decouple such information correspondence. Secure-Pose extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame. We implement Secure-Pose using a commercial camera and two Intel 5300 NICs and evaluate it in real-world environments. Secure-Pose achieves a high detection accuracy of 95.1% and can effectively localize tampered objects under different forgery attacks.

In this paper, we focus our attention on the mitigation of beam squint for reconfigurable intelligent surface (RIS) aided wideband millimeter wave (mmWave) communications. Due to the intrinsic passive property, the phase shifts of all elements in RIS should be the same for all frequencies. However, in the wideband scenario, beam squint induced distinct path phases require designing different phase shifts for different frequencies. The above irreconcilable contradiction will dramatically affect the system performance, considering the RIS usually consists of enormous elements and the bandwidth of wideband mmWave communications may be up to several GHz. Therefore, we propose some novel phase shift design schemes for mitigating the effect of beam squint for both line-of-sight (LoS) and non-Los (NLoS) scenarios. Specifically, for the LoS scenario, we firstly derive the optimal phase shift for each frequency and obtain the common phase shift by maximizing the upper bound of achievable rate. Then, for the NLoS scenario, a mean channel covariance matrix (MCCM) based scheme is proposed by fully exploiting the correlations between both the paths and the subcarriers. Our extensive numerical experiments confirm the effectiveness of the proposed phase shift design schemes.

Abstract This work introduces an efficient neuron design for fixed‐point artificial neural networks with the rectified linear unit (ReLU) activation function for energy‐constrained wireless applications. The fixed‐point binary numbers and ReLU activation function are used in most application‐specific integrated circuit designs and artificial neural networks (ANN), respectively. It is well known that, owing to involved computation intensive tasks, the computational burden of ANNs is ultra heavy. Consequently, many practitioners and researchers are discovering the ways to reduce implementation complexity of ANNs, particularly for battery‐powered wireless applications. For this, a low‐complexity neuron to predict the sign bit of the input of the non‐linear activation function, ReLU, by employing the saturation characteristics of the activation function is proposed. According to our simulation results based on random data, computation overhead of a neuron using the proposed technique can be saved by a ratio of 29.6% compared to the conventional neuron using a word length of 8 bits without apparently increasing the prediction error. A comparison of the proposed algorithm with the popular 16‐bit fixed‐point format of the convolutional network, AlexNet, indicates that the computation can be saved by 48.58% as well.

Driven by the soaring traffic demand and the growing diversity of mobile services, wireless networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such large-scale and complicated wireless networks, optimal controlling is reaching unprecedented levels of complexity while its traditional solutions of handcrafted offline algorithms become inefficient due to high complexity, low robustness, and high overhead. Therefore, reinforcement learning (RL), which enables network entities to learn from their actions and consequences in the interactive network environment, attracts significant attention. In this article, we comprehensively review the applications of RL in wireless networks from a layering perspective. First, we present an overview of the principle, fundamentals, and several advanced models of RL. Then, we review the up-to-date applications of RL in various functionality blocks of different network layers, ranging from the low-level physical layer to the high-level application layer. Finally, we outline a broad spectrum of challenges, open issues, and future research directions of RL-empowered wireless networks.

Preamble resources are scarce and precious in random access (RA), which need to be efficiently utilized to support critical machine-type communication (MTC) with stringent access requirements. In this article, we study a grant-free RA scenario for critical MTC in the context of massive multiple-input–multiple-output (MIMO). To enhance the access reliability of delay-sensitive devices within a predefined latency budget, two dynamic preamble-resource partitioning (DPP) schemes are proposed. Particularly, by leveraging massive MIMO, we first analytically investigate the feasibility of DPP in the considered RA scenario and demonstrate its performance superiority to the conventional scheme. Based on the analytical results, we then propose a greedy DPP scheme that performs locally optimal preamble-resource partitioning in each RA slot. To find the globally optimal DPP solution, we further propose a reinforcement learning (RL)-based scheme by modeling the considered RA scenario as a Markov decision process. Simulation results show the practicality and effectiveness of the proposed DPP schemes. In particular, to achieve a target access failure rate of$1\times 10^{-2}$, the proposed RL-based scheme is able to enhance the RA traffic load by 42% and improve the preamble resource utilization by over 25% compared to the conventional baseline scheme.

In this article, we propose a novel waveform with low cyclic prefix (CP) overhead for narrow-band Internet-of-things (NB-IoT) systems. Compared with the classical orthogonal frequency-division multiplexing (OFDM), the proposed waveform employs the concept of symbol repetition on multiple successive OFDM symbols (SR-OFDMs) and only one CP is occupied by multiple SR-OFDM symbols; hence, the CP overhead is reduced significantly, improving the spectral and energy efficiencies in NB-IoT systems. It is proved that the proposed SR-OFDM can effectively fight against the multipath fading channels by a simple single-tap equalization, avoiding the mutual interference among symbols. In addition, it is shown that the proposed SR-OFDM and classical OFDM have the same size of the inverse discrete Fourier transform (IDFT) at the transmitter, which indicates the same subcarrier spacing. Especially, it is demonstrated that the signals of multiple symbols are separated at the receiver, and the demodulation of each symbol can be performed independently, which is helpful to reduce the complexity of symbol demodulation. To evaluate the proposed SR-OFDM, simulations have been done by considering the Stanford university interim (SUI) channels, which have large channel delay spread and exhibit high frequency selectivity.

This article analyzes the channel estimation performance of massive multiple-input-multiple-output (MIMO) Internet-of-Things (IoT) systems with Ricean fading. First, by utilizing the least squares (LSs) and minimum mean squared error (MMSE) estimation methods, we consider the relative channel estimation error (RCEE) between the IoT device and base-station, and provide the approximations of the expectation of RCEE ( Exprcee). Then, it is found that when the number of antennas M becomes infinite, pilot contamination (PC) exists in both cases. However, for MMSE case, Exprceescales down by the inverse of Ricean K-factor, and hence PC phenomenon disappears with a large Ricean K-factor. Moreover, as M→ ∞, the power scaling laws show that the pilot sequence power can be scaled down proportionally to 1/Mα( α > 0) with the MMSE case, where the performance is determined only by the Ricean K-factor. Next, the channel hardening and favorable propagation effects are examined via analyzing the approximations of the variance of RCEE ( Varrcee). Analysis implies that Varrceedecreases by 1/M when M→ ∞. For a large Ricean K-factor, Varrceeapproaches a nonzero constant for the LS case and scales down by the inverse of the square of Ricean K-factor for the MMSE case. Finally, all results are verified via Monte Carlo simulations.

Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgent to develop novel smart building energy management (SBEM) technologies for the advance of energy efficient and green buildings. However, it is a nontrivial task due to the following challenges. First, it is generally difficult to develop an explicit building thermal dynamics model that is both accurate and efficient enough for building control. Second, there are many uncertain system parameters (e.g., renewable generation output, outdoor temperature, and the number of occupants). Third, there are many spatially and temporally coupled operational constraints. Fourth, building energy optimization problems can not be solved in real time by traditional methods when they have extremely large solution spaces. Fifthly, traditional building energy management methods have respective applicable premises, which means that they have low versatility when confronted with varying building environments. With the rapid development of Internet of Things technology and computation capability, artificial intelligence technology find its significant competence in control and optimization. As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this article provides a comprehensive review of DRL for SBEM from the perspective of system scale. In particular, we identify the existing unresolved issues and point out possible future research directions.

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.

Wirelessly powered backscatter communication (WPBC) has been identified as a promising technology for low-power communication systems, which can reap the benefits of energy beamforming to improve energy transfer efficiency. However, existing studies on energy beamforming fail to simultaneously take energy supply and information transfer in WPBC into account. This paper takes the first step to fill this gap, by considering the restrictive relationship between the energy harvesting rate and achievable rate with estimated backscatter channel state information (BS-CSI). To ensure reliable communication and user fairness, we formulate the energy beamforming design as a max-min optimization problem by maximizing the minimum achievable rate for all backscatter tags subject to the energy constraint. We derive the closed-form expression of the energy harvesting rate, as well as the lower bound of the achievable rate for maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Our numerical results indicate that our scheme significantly outperforms state-of-the-art energy beamforming schemes. Additionally, the achievable rate of our scheme approaches more than 90% of the rate limit achieved via beamforming with perfect CSI for both receivers.

In distributed passive radar, optimization of the receiver placement attracts much attention recently to improve the performance. Whereas most research optimizes receiver placement for best coverage or localization performance, the effect of one performance optimization on the other is ignored. In this article, the theoretical analysis on the conflict between the coverage and localization performance optimization is presented. Then, a joint coverage and localization-based receiver placement optimization is formulated, in which the detection probability and the localization accuracy are chosen as the corresponding metrics. To solve this optimization problem, a multiobjective neighborhood search algorithm with multineighborhood structure is proposed, by which a set of Pareto solutions corresponding to the optimal receiver placements is obtained. Compared with the traditional neighborhood search algorithms, the proposed algorithm is able to provide better receiver placements approaching the global Pareto optimum, which is validated by the simulations.

Due to the openness of wireless medium, robotic networks that consist of many miniaturized robots are susceptible to Sybil attackers, who can fabricate myriads of fictitious robots. Such detrimental attacks can overturn the fundamental trust assumption in robotic collaboration and thus impede widespread deployments of robotic networks in many collaborative tasks. Existing solutions rely on bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, making them unaffordable to miniaturized robots. To overcome this limitation, we present ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots for Sybil attack mitigation. Instead of passively “observing” signatures, ScatterID actively “manipulates” multipath propagation by exploiting backscatter tags to intentionally create rich multipath signatures obtainable to single-antenna robots. Particularly, these signatures are used to carefully construct similarity vectors to thwart advanced Sybil attackers, who further trigger power-scaling and colluding attacks to generate dissimilar signatures. Then, a customized random forest model is developed to accurately infer the identity legitimacy of each robot. We implement ScatterID on the iRobot Create platform and evaluate it under various Sybil attacks in real-world environments. The experimental results show that ScatterID achieves a high AUROC of 0.987 and obtains an overall accuracy of 95.4% under basic and advanced Sybil attacks. Specifically, it can successfully detect 96.1% of fake robots while mistakenly rejecting just 5.7% of legitimate ones.

Electric vehicles (EVs) are being introduced by different manufacturers, thanks to their environment-friendly perspective to alleviate CO2pollution. In this paper, the proposed EV charging management scheme enables pre-empted charging service for heterogeneous EVs (depends on different charging capabilities, brands, etc.). Particularly, the anticipated EVs' charging reservations information, including their arrival time and expected charging time at charging stations (CSs), are brought for planning CS-selection (where to charge). Along with applying ubiquitous cellular network communication to deliver (delay tolerant) EVs' charging reservations, we further study the feasibility of applying opportunistic vehicle-to-vehicle (V2V) communication with delay/disruption tolerant networking (DTN) nature, due primarily to its flexibility and cost-efficiency in vehicular ad hoc networks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V-based charging reservation is promisingly cost-efficient in terms of communication overhead, while achieving a comparable charging performance to apply cellular network communication.

In this paper, we propose a distribution line fitting (DLF) based channel estimation method for offset quadrature amplitude modulation based filter-bank multicarrier (OQAM/FBMC) systems. Specifically, we firstly analyze the distribution characteristics of demodulated OQAM symbols at the receiver, and derive an intrinsic interference free relation between the real and imaginary parts of demodulated OQAM symbols. Then, based on the above derivation, we design a pilot structure of arbitrary columns of pilot symbols without guard symbols between pilot symbols and data symbols. Finally, the distribution lines can be fitted by utilizing the distribution characteristics of demodulated OQAM symbols and the channel frequency response can be estimated. Simulation results demonstrate that the proposed method with one column of pilot symbols outperforms the interference approximation method (IAM) in high signal-to-noise (SNR) regions and outperforms the pair of pilots (POP) method in all SNR regions in terms of bit error ratio (BER), and the proposed method with two columns of pilot symbols outperforms both IAM and POP in all SNR regions in terms of both BER and complexity.

Hybrid Transmit Precoding (TPC) is one of the most compelling solutions for millimeter wave (mmWave) multiple-input multiple output (MIMO) systems. However, most attention has been focused on narrow-band scenarios. Hence, we dedicate our efforts to the design of hybrid TPC for wideband mmWave MIMO systems, where the beam squint dramatically affects the system performance. We firstly show that the channel matrices of the different subcarriers possess distinct subspaces in case of high bandwidths, hence traditional hybrid TPC schemes suffer from an eroded performance. Therefore, we propose novel hybrid TPC schemes exploiting the full channel state information (CSI), which project all frequencies to the central frequency and construct the common analog TPC matrix for all subcarriers. Moreover, we propose several low-complexity array-vector based hybrid TPC schemes. The high-complexity manifold optimization based hybrid TPC method and the fully digital TPC operating with and without considering beam squint are provided as benchmarks. Our extensive numerical simulations show that the proposed hybrid TPC schemes are capable of achieving similar performance to the excessive-complexity fully digital TPC, when the bandwidth tends to 0.5 GHz and always outperform the traditional hybrid TPC schemes.

Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks. In this context, we propose device-to-device (D2D) cooperation based MEC to expedite the task execution of mobile user by leveraging proximity-aware task offloading. However, user mobility in such distributed architecture results in dynamic offloading decision that instigates mobility-aware task scheduling in our proposed framework. We jointly formulate task assignment and power allocation to minimize the total task execution latency by taking account of user mobility, distributed resources, tasks properties, and energy constraint of the user device. We first propose Genetic Algorithm (GA)-based evolutionary scheme to solve our formulated mixed-integer non-linear programming (MINLP) problem. Then we propose a heuristic named mobility-aware task scheduling (MATS) to obtain effective task assignment with low complexity. The extensive evaluation under realistic human mobility trajectories provides useful insights into the performance of our schemes and demonstrates that, both GA and MATS achieve better latency than other baseline schemes while satisfying the energy constraint of mobile device.

Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.

A long-standing vision of backscatter communications is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatters toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further toward breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter links. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. We build a prototype PCB tag and test it in various outdoor and indoor environments. Our experiments show that our prototype achieves up to 10× throughput gain, or extends the range limit by 1.8× compared with the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.

he advances in compact and agile micro aerial vehicles (MAVs) have shown great potential in replacing human for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight in such dim or smoky environments presents a conundrum: conventional GPS or computer vision based solutions only work in outdoors or well-lighted texture-rich environments. This paper takes the first step to overcome this hurdle by proposing Marvel, a lightweight RF backscatter-based state estimation system for MAVs in indoors. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion that does not require any infrastructure deployment, pre-trained signatures, or even without knowing the controller's location. The enabling techniques are a new backscatter-based pose sensing module and a novel backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial-off-the-shelf MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three brick walls, with an accuracy of 34 cm for localization and 4.99° for orientation estimation, outperforming commercial GPS-based approaches in outdoors.

We investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price), and temporally coupled operational constraints, it is very challenging to design an optimal energy management algorithm for scheduling heating, ventilation, and air conditioning systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management algorithm based on deep deterministic policy gradients. It is worth mentioning that the proposed algorithm does not require the prior knowledge of uncertain parameters and building the thermal dynamics model. The simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed algorithm.

In this article, we aim to reduce the pilot overhead of channel estimation in multiple input multiple output (MIMO) systems employing filter bank multicarrier (FBMC). The key idea is the employment of the concept of symbol repetition in frame structure. Firstly, we propose an MIMO-FBMC system based on block repetition, in which complex-valued symbols are transmitted instead of only real-valued symbols in classic MIMO-FBMC systems without block repetition. Then, we present two low-overhead channel estimation approaches in the block-repetition based MIMO-FBMC system and only two columns of symbols are required as the pilot overhead. However, the existence of the block repetition results in the increased sensitively to the channel coherence time. Then, the two channel estimation approaches are extended into classic MIMO-FBMC systems without block repetition. It is proved that, the proposed methods exhibit significant advantage in terms of pilot overhead, compared with the conventional interference approximation method (IAM) and frequency-division multiplexing (FDM) methods. To evaluate the performances of the proposed approaches, we also derive the Cramér-Rao Bound (CRB) bound, and carry out numerical summations with respects to both mean square error and bit error ratio metrics.

In this paper, we propose a generalized space-time block coded spatial modulation (GSTBC-SM) scheme for open-loop massive multiple-input and multiple-output (MIMO) downlink communication systems. Specifically, we firstly partition the information bits into multiple groups with each group modulated by the spatial modulation (SM), where the SM symbols are invoked for orthogonal STBC (OSTBC) and quasi-orthogonal STBC (Q-OSTBC) structures. Then, message passing (MP) and block minimum mean square equalization (B-MMSE) detectors are designed for our GSTBC-SM systems, to achieve near-optimal performance with significantly reduced complexity in massive MIMO configurations. Finally, we derive the theoretical average bit error probability (ABEP) of the proposed scheme. The main contribution is that the propose scheme achieves high transmission rate and diversity gain even with small number of radio frequency (RF) chains at the transmitter. Simulation results verify the theoretical derivations and show that the proposed GSTBC-SM scheme provides near 20 dB gain over the conventional GSTBC scheme under massive MIMO configurations.

Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized a broad class of civil and military applications. However, the designs of wireless technologies that enable real-time streaming of high-definition video between UAVs and ground clients present a conundrum. Most existing adaptive bitrate (ABR) algorithms are not optimized for the air-to-ground links, which usually fluctuate dramatically due to the dynamic flight states of the UAV. In this paper, we present SA-ABR, a new sensor-augmented system that generates ABR video streaming algorithms with the assistance of various kinds of inherent sensor data that are used to pilot UAVs. By incorporating the inherent sensor data with network observations, SA-ABR trains a deep reinforcement learning (DRL) model to extract salient features from the flight state information and automatically learn an ABR algorithm to adapt to the varying UAV channel capacity through the training process. SA-ABR does not rely on any assumptions or models about UAV's flight states or the environment, but instead, it makes decisions by exploiting temporal properties of past throughput through the long short-term memory (LSTM) to adapt itself to a wide range of highly dynamic environments. We have implemented SA-ABR in a commercial UAV and evaluated it in the wild. We compare SA-ABR with a variety of existing state-of-the-art ABR algorithms, and the results show that our system outperforms the best known existing ABR algorithm by 21.4% in terms of the average quality of experience (QoE) reward.

In scalable video coding (SVC) multicast, videos are encoded into several layers that represent multiple quality levels. Mobile users with different wireless channel conditions can obtain different numbers of layers and have different quality of experience (QoE). To enhance the QoE of the users that suffer from the worse channel quality, it is beneficial to stimulate users' cooperation in relaying enhancement layers. However, potential relays may be unwilling to truthfully cooperate with receivers, which results in the asymmetric information problem in relay selecting. In this paper, we model the video relaying selection as a market with multiple receivers (principals) and relays (agents), and solve the problem according to the contract theory. The proposed solution is divided into following two steps: first, contract design and item preselection, and second, matching between each principal and agent. We propose a contract parameter determination method termed as the Matching-Aware strategy. Different from traditional strategies, the proposed Matching-Aware strategy makes the contract competitive in principal-agent matching without knowing the probability distribution of relays' types. The matching step is undertaken by the base station with the purpose of maximizing the social welfare. Numerical results corroborate that the contract-based video relaying scheme can tackle the asymmetric information problem. Besides, compared with other two baseline strategies, the proposed Matching-Aware strategy achieves higher QoE.

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce up to 88% of the IoT's TX power when the AP sets 8× clock rate.

By adding users as a new dimension to connectivity, on-body Internet-of-Things (IoT) devices have gained considerable momentum in recent years, while raising serious privacy and safety issues. Existing approaches to authenticate these devices limit themselves to dedicated sensors or specified user motions, undermining their widespread acceptance. This paper overcomes these limitations with a general authentication solution by integrating wireless physical layer (PHY) signatures with upper-layer protocols. The key enabling techniques are constructing representative radio propagation profiles from received signals, and developing an adversarial multi-player neural network to accurately recognize underlying radio propagation patterns and facilitate on-body device authentication. Once hearing a suspicious transmission, our system triggers a PHY-based challenge-response protocol to defend in depth against active attacks. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances and environment changes. We build a prototype of our system using Universal Software Radio Peripheral (USRP) devices and conduct extensive experiments with various static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 91.6%, with a high area under the receiver operating characteristic curve (AUROC) of 0.96 and a better generalization performance compared with the conventional non-adversarial approach.

An important issue in an edge computing (EC) network is to increase the utilities of the end users concurrently accessing the computation resources. In this paper, we consider the task offloading in EC-enabled networks where the end users efficiently utilize the dispersed computation and communication resources in a multi-path multi-hop manner. We propose a binary optimization framework that generalizes multi-hop wireless EC task offloading as jointly making decisions of server selecting and traffic routing in networks of arbitrary topology (JoSRAT). We further develop an approximation algorithm JoSRAT that enables for a fully distributed implementation together with the worst-case performance guarantees. Interestingly, our proposed distributed algorithm achieves nearly optimal in the numerical evaluations, significantly outperforming the worst-case guarantees. The proposed algorithm also outperforms a widely-used heuristic, i.e., First Fit, in terms of computational time complexity, indicating the superior capability of the proposed framework.

In this paper, we firstly derive two approximations of the achievable uplink rate with the perfect/imperfect channel state information (CSI) in cell-free massive multi-input multi-output (MIMO) systems, and all these approximations are not only in the simple, but also converge into the classical bounds achieved in conventional massive MIMO systems where the base-station (BS) antennas are co-located. It is worth noting that the obtained two approximations with perfect CSI could be regarded as the special cases of the obtained two approximations with imperfect CSI when the pilot sequence power becomes infinite, respectively. Moreover, the theory analysis shows that all obtained approximations with perfect/imperfect CSI have an asymptotic lower bound α/2 log2L thanks to the extra distance diversity offered by massively distributed antennas, where L is the number of BS antennas and the path-loss factor α > 2, except for the free space environment. Obviously, these results indicate that the cell-free massive MIMO system has huge potential of spectral efficiency than the conventional massive MIMO system with the asymptotically tight bound log2L.

Delay diversity (DD) is a low-complexity and flexible transmit diversity technology for coded offset quadrature amplitude modulation based filter bank multicarrier (OQAM/FBMC) transmission systems. However, the introduction of delay parameters in DD-OQAM/FBMC systems leads to increased channel frequency selectivity, which makes the channel estimation problem more complicated. In this paper, we analyze and develop preamble-based channel estimation methods in DD-OQAM/FBMC systems from the perspective of frequency-domain, transform-domain, and time-domain aspects. Besides, channel estimators that can obtain the minimum channel estimation mean square error under least squares (LS) and linear minimum mean square error (LMMSE) criteria are derived according to different levels of channel knowledge. Specifically, the proposed frequency-domain method estimates each channel frequency response individually based on a simplified frequency-domain channel model. The transform-domain method first estimates the equivalent single-input-single-output channel observed at the receiver assuming subchannel flatness, and then uses a window function in the time domain to separate the channels of different transmit antennas. The time-domain method is constructed based on an accurate time-domain channel model to directly estimate the channel impulse response without making any assumption about the subchannel flatness. A series of simulation experiments is conducted to verify the effectivity of these channel estimation methods for DD-OQAM/FBMC systems.

Generalized index modulation (GIM) which implicitly conveys information by the activated indices is a promising technique for next-generation wireless networks. Due to the prohibitive challenge of bit-to-index combination (IC) mapping optimization, conventional GIM system obtains the bit-to-IC mapping table randomly, which may suffer from some performance loss. To circumvent this issue, we propose a low-complexity graph theory assisted bit-to-IC gray coding for GIM systems by minimizing the average hamming distance (HD) between any two ICs having one different value. Specifically, we decompose and transform the optimization problem into two subproblems using the graph theory, i.e., 1) Select an IC set whose corresponding graph has the minimum degree; 2) Design a bit-to-IC mapping principle to minimize the weight of the selected graph. Low-complexity algorithms are developed to solve the subproblems with a significant reduced complexity. Both simulation and theoretical results are shown that the GIM systems with our proposed mapping table are capable of providing significant performance gains over the conventional counterparts without the need for any additional feedback-link and without extra computational complexity. It is also shown that the proposed bit-to-IC mapping table is straightforward for any GIM systems over generalized fading channels.

Heterogeneous ultra dense network (UDN) emerges as one of the potential techniques to tackle the 1000x data challenge in 5G network. However, by cell densification, backhaul becomes one of the key issues in 5G heterogeneous UDN. In this paper, the cooperative wireless backhaul scheduling is concerned. First, a novel two-layers hierarchical model is proposed for the cooperative wireless backhaul, based on which energy efficiencies (EEs) of the cooperative orthogonal multiple access (C-OMA) and the cooperative non-orthogonal multiple access (C-NOMA) schemes are derived and analyzed. Moreover, based on the proposed model, a cooperative wireless backhaul optimization which maximizes EE is formulated. To solve this mixed integer non-linear optimization problem, the greedy algorithm-based C-OMA, C-NOMA and joint C-OMA and C-NOMA (JCC) schemes for cooperative wireless backhaul are proposed. Due to the benefits of both the C-OMA and C-NOMA, the proposed JCC scheme shows its superiority over C-OMA, C-NOMA and the traditional OMA schemes, which is validated by the simulation results.

Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such backscatter tags is crucial for IoT-based smart applications. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, which is laborious for deployment. To empower universal localization service, this paper presents Rover, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing measurements from backscattered WiFi signals and inertial sensors to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues including interference among multiple tags, real-time processing, as well as the data marginalization problem in dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.

Wirelessly powered backscatter communication (WPBC) has been identified as a promising technology for low-power communication systems, which can reap the benefits of energy beamforming to improve energy transfer efficiency. Existing studies on energy beamforming fail to simultaneously take energy supply and information transfer in WPBC into account. This paper takes the first step to fill this gap, by considering the trade-off between the energy harvesting rate and achievable rate using estimated backscatter channel state information (BS-CSI). To ensure reliable communication and user fairness, we formulate the energy beamforming design as a max- min optimization problem by maximizing the minimum achievable rate for all tags subject to the energy constraint. We derive the closed-form expression of the energy harvesting rate, as well as the lower bound of the ergodic achievable rate. Our numerical results indicate that our scheme can significantly outperform state-of-the-art energy beamforming schemes. Additionally, the proposed scheme achieves performance comparable to that obtained via beamforming with perfect CSI.

Multiple-antenna backscatter is emerging as a promising approach to offer high communication performance for the data-intensive applications of ambient backscatter communications (AmBC). Although much has been understood about multiple- antenna backscatter in conventional backscatter communications (CoBC), existing analytical models cannot be directly applied to AmBC due to the structural differences in RF source and tag circuit designs. This paper takes the first step to fill the gap, by exploring the use of spatial modulation (SM) in AmBC whenever tags are equipped with multiple antennas. Specifically, we present a practical multiple-antenna backscatter design for AmBC that exempts tags from the inter-antenna synchronization and mutual coupling problems while ensuring high spectral efficiency and ultra-low power consumption. We obtain an optimal detector for the joint detection of both backscatter signal and source signal based on the maximum likelihood principle. We also design a two-step algorithm to derive bounds on the bit error rate (BER) of both signals. Simulation results validate the analysis and show that the proposed scheme can significantly improve the throughput compared with traditional systems.

Recent years have witnessed the rapid proliferation of low- power backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such low-power backscatter tags is crucial for IoT-based smart services. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, increasing the deployment cost. To empower universal localization service, this paper presents Rover, an indoor localization system that simultaneously localizes multiple backscatter tags with zero start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing WiFi-based positioning measurements with inertial measurements to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues such as the interference among multiple tags and the real- time processing for solving the SLAM problem. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.

As the rapid proliferation of on-body Internet of Things (IoT) devices, their security vulnerabilities have raised serious privacy and safety issues. Traditional efforts to secure these devices against impersonation attacks mainly rely on either dedicated sensors or specified user motions, impeding their wide-scale adoption. This paper transcends these limitations with a general security solution by leveraging ubiquitous wireless chips available in IoT devices. Particularly, representative time and frequency features are first extracted from received signal strengths (RSSs) to characterize radio propagation profiles. Then, an adversarial multi-player network is developed to recognize underlying radio propagation patterns and facilitate on-body device authentication. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances. We build a prototype of our system using universal software radio peripheral (USRP) devices and conduct extensive experiments with both static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 90.4%, with a high area under the receiver operating characteristic curve (AUROC) of 0.958 and better generalization performance in comparison with the conventional non-adversarial-based approach.

In this paper, we propose a novel joint caching and massive multiple-input multiple-output (MIMO) transmission scheme, referred to as cache-aided massive MIMO, for advanced downlink cellular communications. In addition to reaping the conventional advantages of caching and massive MIMO, the proposed scheme also exploits the side information provided by cached files for interference cancellation at the receivers. This interference cancellation increases the degrees of freedom available for precoding design. In addition, the power freed by the cache-enabled offloading can benefit the transmissions to the users requesting non-cached files. The resulting performance gains are not possible if caching and massive MIMO are designed separately. We analyze the performance of cache-aided massive MIMO for cache-dependent maximum-ratio transmission (MRT), zero-forcing (ZF) precoding, and regularized zero-forcing (RZF) precoding. Lower bounds on the ergodic achievable rates are derived in closed form for MRT and ZF precoding. The ergodic achievable rate of RZF precoding is obtained for the case when the numbers of transmit antennas and users are large but their ratio is fixed. Compared to conventional massive MIMO, the proposed cache-aided massive MIMO scheme achieves a significantly higher ergodic rate especially when the number of users approaches the number of transmit antennas.

In this paper, channel tracking in fast-varying MIMO environments is considered. Firstly, the high-dimension channel vector of each user is decomposed into a semi-static factor load matrix (FLM) and a low-dimension factor coefficient vector (FCV). Thereby, channel tracking is replaced by the infrequent FLM tracking and low-complexity FCV tracking. Secondly, channel variations are represented by local polynomial modeling, based on which a recursive least square (RLS) algorithm with optimal forgetting factor (FF) is proposed. It is verified that the proposed algorithm has higher accuracy, better tracking ability and lower computational complexity than the existing methods.

This paper focuses on the design of low complexity hybrid analog/digital precoding and diversity combining in millimeter wave (mmWave) Internet of Things (IoT) systems. First, by exploiting the sparseness property of the mmWave in the angular domain, we propose a spatial lobes division (SLD) to group the total paths of the mmWave channel into several spatial lobes (SLs), where the paths in each SLs form a low-rank subchannel. Second, based on the SLD operation, we propose a low complexity hybrid precoding scheme, named hybrid precoding based on SLD (HYP-SLD). Specifically, for each low-rank subchannel, we formulate the hybrid precoding design as a sparse reconstruction problem and separately maximizes the spectral efficiency. Finally, we further propose a maximum ratio combining-based diversity combining scheme, named HYP-SLD-MRC, to improve the bit error rate (BER) performance of mmWave IoT systems. Simulation results demonstrate that, the proposed HYP-SLD scheme significantly reduces the complexity of the classic orthogonal matching pursuit scheme. Moreover, the proposed HYP-SLD-MRC scheme achieves great improvement in BER performance compared with the fully digital precoding scheme.

An efficient algorithm for the heartbeat detection in the Internet of Things (IoT) health-care system remains a challenging issue due to incurred random variations. The QRS complex reflects the electrical activity within the heart during the ventricular contraction. Although recently many QRS complex detection methods have been proposed with different features, their real-time implementations in low-cost portable platforms are still problems due to limited hardware resources. As a result, it is difficult to provide the accuracy level required for medical applications. By contrast, this paper focuses on developing a new method based on the Bayesian framework to provide a real-time and accurate QRS complex detector. More specifically, we propose a new algorithm with two stages, i.e., variance-based detection (VBD) and maximum-likelihood estimation (MLE), to detect QRS complexes. Furthermore, simulations with the benchmark MIT-BIH arrhythmia and QT databases verify the advantage of being easily portable to different databases using the proposed approach.

Massive multiple-input multiple-output (MIMO) opens up new avenues for enabling highly efficient random access (RA) by offering abundance of spatial degrees of freedom. In this paper, we investigate the grant-free RA with massive MIMO and derive the analytic expressions of success probability of the grant-free RA for conjugate beamforming and zero-forcing beamforming techniques. With the derived analytic expressions, we further shed light on the impact of system parameters on the success probability. Simulation results verify the accuracy of the analyses. It is confirmed that the grant-free RA with massive MIMO is an attractive RA technique with low signaling overhead that could simultaneously accommodate a number of RA users, which is multiple times the number of RA channels, with close-to-one success probability. In addition, when the number of antennas in massive MIMO is sufficiently large, we show that the number of orthogonal preambles would dominate the success probability.

In-network caching constitutes a promising approach to reduce traffic loads and alleviate congestion in both wired and wireless networks. In this article, we study the joint caching and routing problem in congestible networks of arbitrary topology (JoCRAT) as a generalization of previous efforts in this particular field. We show that JoCRAT extends many previous problems in the caching literature that are intractable even with specific topologies and/or assumed unlimited bandwidth of communications. To handle this significant but challenging problem, we develop a novel approximation algorithm with guaranteed performance bound based on a randomized rounding technique. Evaluation results demonstrate that our proposed algorithm achieves near-optimal performance over a broad array of synthetic and real networks, while significantly outperforming the state-of-the-art methods.

Connected devices in Internet-of-Things (IoT) continuously generate enormous amount of data, which is transient and would be requested by IoT application users, such as autonomous vehicles. Transmitting IoT data through wireless networks would lead to congestions and long delays, which can be tackled by caching IoT data at the network edge. However, it is challenging to jointly consider IoT data-transiency and dynamic context characteristics. In this paper, we advocate the use of deep reinforcement learning (DRL) to solve the problem of caching IoT data at the edge without knowing future IoT data popularity, user request pattern, and other context characteristics. By defining data freshness metrics, the aim of determining IoT data caching policy is to strike a balance between the communication cost and the loss of data freshness. Extensive simulation results corroborate that the proposed DRL-based IoT data caching policy outperforms other baseline policies.

In this letter, we consider the varying detection environments to address the problem of detecting small targets within sea clutter. We first extract three simple yet practically discriminative features from the returned signals in the time and frequency domains and then fuse them into a 3-D feature space. Based on the constructed space, we then adopt and elegantly modify the support vector machine to design a learning-based detector that enfolds the false alarm rate (FAR). Most importantly, our proposed detector can flexibly control the FAR by simply adjusting two introduced parameters, which facilitates to regulate detector's sensitivity to the outliers incurred by the sea spikes and to fairly evaluate the performance of different detection algorithms. Experimental results demonstrate that our proposed detector significantly improves the detection probability over several existing classical detectors in both low signal to clutter ratio (up to 58%) and low FAR (up to 40%) cases.

Aiming at accurate weak sea-surface target detection, this letter devotes to designing a learning-based detector that can work well even in varying detection environments. We first exploit the concept of the fractal theory to extract three representative features in the time and frequency domains and construct a three-dimensional feature space. We then combine the constructed feature space with the decision tree approach to design an environment-adaptive detector. Most importantly, we modify the decision tree based detector to an FAR-controllable detector to meet the requirements of different detection applications. Experimental results demonstrate that, compared with existing detectors, our proposed detector improves the detection probability in both low FAR (up to 35%) and low SCR cases (up to 55%).

In this paper, we propose two non-uniform quantization (NUQ) codebook-based hybrid precoding schemes for two main hybrid precoding implementations, i.e., the full-connected structure and the sub-connected structure, to reduce the feedback overhead in millimeter wave single user multiple-input multiple-output systems. Specifically, we firstly group the angles of the arrive/departures (AOAs/AODs) of the scattering paths into several spatial lobes by exploiting the sparseness property of the millimeter wave in the angular domain, which divides the total angular domain into effective spatial lobes' coverage angles and ineffective coverage angles. Then, we map the quantization bits non-uniformly to different coverage angles and construct NUQ codebooks, where high numbers of quantization bits are employed for the effective coverage angles to quantize AoAs/AoDs and zero quantization bit is employed for ineffective coverage angles. Finally, two low-complexity hybrid analog/digital precoding schemes are proposed, which utilize the NUQ codebooks. Simulation results demonstrate that the proposed two NUQ codebook-based hybrid precoding schemes achieve near-optimal spectral efficiencies and show the superiority in reducing the feedback overhead compared with the uniform quantization codebook-based works.

Hybrid precoding is essential for millimeter wave (mmWave) multiple-input multiple output (MIMO) systems due to its inherent advantage of a high gain, whilst alleviating the high cost of hardware. However, most of the existing literature considered either the narrowband or wideband single-user mmWave MIMO scenarios. Hence in this paper we focus our attention on the more challenging design of hybrid Transmit Precoding (TPC) for wideband multiuser mmWave MIMO systems by exploiting the long-term channel's covariance matrix and the angle of departure (AoD) information. Specifically, in the analog TPC designed, firstly, the analog TPC matrix having an infinite angular resolution is constructed based on the channel's covariance matrix. Then, we also propose a non-uniformly spaced quantization codebook based analog TPC having finite angular resolution. Furthermore, a phase compensation operation is carried out to alleviate the effect of beam squint. As for the design of the digital TPC, a two-stage scheme is proposed to cancel the inter-user interference and to attain multiplexing gains. We study the effects of various parameters on the achievable sum rate and demonstrate with the aid of our simulation results that the proposed hybrid TPC is capable of achieving a similar performance to the excessive-complexity fully digital TPC.

We study the optimal resource allocation in the MIMO-based wireless powered underground sensor network (WPUSN) for throughput maximization. Compared to existing WPUSNs that rely on single-antenna wireless power transfer techniques to transmit geological data in real time, the MIMO-based WPUSN can be adaptively replenished by beamforming. Note that, WPUSN has two remarkable features: the severe wireless path loss and diverse data traffic demands from different underground sensors. Further, by considering the quality of service (QoS) with respect to diverse data traffic demands and communication reliability, the throughput of the network is considered as a crucial measure, which suffers from a significant loss since it has to encounter a seriously unreasonable energy scarcity under the strong-heterogeneity underground environment. To this end, the MIMO is used to eliminate the unreasonable distribution of harvested energy resources and improve effective throughput for WPUSNs. In this paper, we formulate a non-convex optimization problem to maximize the system throughput in MIMO-based WPUSNs with QoS assurance. Specifically, we find there exists no mutual forbearance between the time allocation and the beamforming weight, and transform this non-convex problem to a solvable convex-constrained problem with convex sub-problems. Finally, we give a closed-form solution and show its advantages by simulations.

In this paper, a novel covariance-based channel feedback mechanism is investigated for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The concept capitalizes on the notion of user statistical separability which was hinted in several prior works in the massive antenna regime but has not fully exploited so far. We propose a hybrid statistical-instantaneous feedback mechanism where the users are separated into two classes of feedback design based on their channel covariance. Under the hybrid framework, each user either operates on a statistical feedback mode or quantized instantaneous channel feedback mode. The key challenge lies in the design of a covariance-aware classification algorithm which can handle the complex mutual interactions among all users. The classification is derived from rate bound principles and a precoding method is also devised under the mixed statistical and instantaneous feedback model. Simulations are performed to validate our analytical results and illustrate the sum rate advantages of the proposed feedback scheme under a global feedback overhead constraint.

In this paper, we consider the uplink channel parameter estimation problem in the presence of pilot contamination for massive multiple-input-multiple-output (MIMO) systems. We propose a parallel factor (PARAFAC)-based estimation scheme, which exploits the low-rank property of massive MIMO channels caused by the finite scattering in a physical environment. Specifically, we first parameterize the channel in terms of three parameters, i.e., fading coefficients, directions of arrival (DOAs), and delays; thereby, the channel is characterized via three equivalent PARAFAC models. Then, the proposed PARAFAC-based scheme is developed, which jointly estimates these three channel parameters using an alternating least squares (ALS) algorithm. Therein, we certify the identifiability of the three channel parameters of the PARAFAC models to mitigate the pilot contamination and state the convergence of the ALS algorithm, which guarantees that the three channel parameters can be uniquely determined with the proposed scheme. Moreover, to further reduce the computational complexity, two advanced schemes are proposed by antenna selection and reducing the estimation frequency of DOAs and delays, respectively. Simulation results show that the proposed schemes can achieve both low computational complexities and close to optimal Cramer-Rao Bound performance.

Grant-free random access (RA) with massive MIMO is a promising RA technique that provides significant benefits in increasing the channel reuse efficiency with low signaling overhead. Since user equipment (UE) detection and channel estimation in grant-free RA rely solely on the received preambles, preamble designs that enable high success rate of UE detection and channel estimation are very much in need to ensure the performance gain of grant-free RA with massive MIMO. In this paper, a super preamble consisting of multiple consecutive preambles is proposed for the high success rate of grant-free RA with massive MIMO. With the proposed approach, the success of UE detection and channel estimation for a UE depends on two conditions: 1) it is a solvable UE, where we define the UE whose super preamble is not a linear combination of the other UEs' super preambles as a solvable UE and 2) its super preamble is detected. Accordingly, we theoretically analyze the solvable rate of the UEs with multiple preambles and propose a reliable UE detection algorithm to obtain the super preambles of the UEs by exploiting the quasi-orthogonality characteristic of massive MIMO. The theoretical analysis and simulation results show that turning a preamble into a super preamble consisting of two or three shorter preambles, the success rate of UE detection and channel estimation could be significantly increased using the proposed approach.

Recent years have seen major innovations in developing energy-efficient wireless technologies, such as the Bluetooth low energy (BLE) for Internet of Things (IoT). Despite demonstrating significant benefits in providing low power transmission and massive connectivity, very few of these technologies directly connect to the Internet. Recent advances demonstrate the viability of direct communication among heterogeneous IoT devices with incompatible physical layers. These techniques, however, require modifications in transmission power or time, which may affect the media access control layer behaviors in legacy networks. In this paper, we argue that the frequency domain can serve as a free side channel with minimal interruptions to legacy networks. To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying demodulator and channel state information. The artificial Doppler shifts can be compensated for by the inherent frequency synchronization module and thus have a negligible impact on legacy communications. Our evaluation using commercial off-the-shelf BLE chips and 802.11-compliant testbeds has demonstrated that DopplerFi can achieve a throughput of up to 6.5 Kb/s at the cost of merely less than 0.8% throughput loss.

Information leakage rate is an intuitive metric that reflects the level of security in a wireless communication system, however, there are few studies taking it into consideration. Existing work on information leakage rate has two major limitations due to the complicated expression for the leakage rate: 1) the analytical and numerical results give few insights into the trade-off between system throughput and information leakage rate; 2) and the corresponding optimal designs of transmission rates are not analytically tractable. To overcome such limitations and obtain an in-depth understanding of information leakage rate in secure wireless communications, we propose an approximation for the average information leakage rate in the fixed-rate transmission scheme. Different from the complicated expression for information leakage rate in the literature, our proposed approximation has a low-complexity expression, and hence, it is easy for further analysis. Based on our approximation, the corresponding approximate optimal transmission rates are obtained for two transmission schemes with different design objectives. Through analytical and numerical results, we find that for the system maximizing throughput subject to information leakage rate constraint, the throughput is an upward convex non-decreasing function of the security constraint and much too loose security constraint does not contribute to higher throughput; while for the system minimizing information leakage rate subject to throughput constraint, the average information leakage rate is a lower convex increasing function of the throughput constraint.

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce the IoT's TX power by boosting the decoding capability at the receivers.

The lightweight protocols and low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks due to the lack of sophisticated security protocols. Recent advances advocate the use of an antenna array to extract fine-grained physical-layer signatures to mitigate these active attacks. However, it adds burdens in terms of energy consumption and hardware cost that IoT devices cannot afford. To overcome this predicament, we present ShieldScatter, a lightweight system that attaches battery-free backscatter tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of backscatter tags. These signatures can be used to construct a sensitive profile to identify the location of the signals' arrival, and thus detect the threat. We prototype ShieldScatter with USRPs and ambient backscatter tags to evaluate our system in various environments. The experimental results show that even when the attacker is located only 15 cm away from the legitimate device, ShieldScatter with merely three backscatter tags can mitigate 97% of spoofing attack attempts while at the same time trigger false alarms on just 7% of legitimate traffic.

We present WINS, a state estimator that fuses commodity Wi-Fi and an inertial sensor (IMU) to enable indoor autonomous flight of MAVs. It overcomes the lighting and environmental texture limitations of current vision-based approaches. WINS incorporates two modules: First, a real-time AoA estimation algorithm that outputs drift-free measurements up to 20 Hz to confine the drift of IMU. Second, a novel WiFi-inertial state estimator to let our highly nonlinear system work without the need of prior initializations and without knowing the position of Wi-Fi infrastructure (APs). The preliminary results show that WINS achieves a mean MAV's location accuracy of 61.7 cm with a maximum flying velocity of 1.27 m/s.

In this survey paper, we focus on the offset quadrature amplitude modulation-based orthogonal frequency division multiplexing (OQAM-OFDM)-based wireless communications in future Internet of Things (IoT). First, we present the OQAM-OFDM-based wireless communication system model for IoT and show its superiority compared with OFDM. Then, we give a survey and analyze key technologies including the prototype filter design, peak to average power ratio reduction, tail reduction, channel estimation, and multiple-input multipleoutput combination in OQAM-OFDM-based wireless communication systems for IoT. Extensive simulations are conducted to verify the spectral superiority of OQAM-OFDM and compare the corresponding performances of OQAM-OFDM with OFDM in the above key technology areas.

As a promising standard to realize low data-rate, low-power, and short-range communications among wireless devices, IEEE 802.15.4 has been widely applied in wireless sensor networks, and also been intensively investigated in vehicle-toinfrastructure (V2I) communications. In this paper, we analyze the effects of antenna height on IEEE 802.15.4 V2I communications in rural areas. In particular, we first propose a geometry-based piecewise model to quantify the variable foliage effects. Furthermore, we show the feasibility of extending foliage loss predictions from static to mobile cases with classical empirical methods. Simulation results verify the precision of our proposed channel model in rural areas by comparing it with other channel models and realistic measurements.

In this paper, we investigate the problem of minimizing the long-term total cost (i.e., the sum of energy cost and thermal discomfort cost) associated with a heating, ventilation, and air conditioning (HVAC) system of a multizone commercial building under smart grid environment. To be specific, we first formulate a stochastic program to minimize the time average expected total cost with the consideration of uncertainties in electricity price, outdoor temperature, the most comfortable temperature level, and external thermal disturbance. Due to the existence of temporally and spatially coupled constraints as well as unknown information about the future system parameters, it is very challenging to solve the formulated problem. To this end, we propose a real-time HVAC control algorithm based on the framework of Lyapunov optimization techniques without the need to predict any system parameters and know their stochastic information. The key idea of the proposed algorithm is to construct and stabilize virtual queues associated with indoor temperatures of all zones. Moreover, we provide a distributed implementation of the proposed real-time algorithm with the aim of protecting user privacy and enhancing algorithmic scalability. Extensive simulation results based on real-world traces show that the proposed algorithm could reduce energy cost effectively with small sacrifice in thermal comfort.

On-body devices are an intrinsic part of the Internet-of-Things (IoT) vision to provide human-centric services. These on-body IoT devices are largely embedded devices that lack a sophisticated user interface to facilitate traditional pre-shared key-based security protocols. Motivated by this real-world security vulnerability, this paper proposes SecureTag, a system designed to add defense in depth against active attacks by integrating physical layer (PHY) information with upper-layer protocols. The underpinning of SecureTag is a signal processing technique that extracts the peculiar propagation characteristics of creeping waves to discern on-body devices. Upon overhearing a suspicious transmission, SecureTag initiates a PHY-based challenge-response protocol to mitigate attacks. We implement our system on different commercial off-the-shelf wearables and a smartphone. Extensive experiments are conducted in a lab, apartments, malls, and outdoor areas, involving 12 volunteer subjects of different age groups, to demonstrate the robustness of our system. Results show that our system can mitigate 96.13 % of active attack attempts while triggering false alarms on merely 5.64 % of legitimate traffic.

In this paper, we investigate the problem of developing a geographical load balancing (GLB) scheme for distributed Internet data centers (IDCs) when they are price-makers in the deregulated electricity markets, i.e., GLB may impose impact on electricity prices in IDC locations due to the large power consumption of IDCs. Taking into account the dynamic characteristics (e.g., time-varying power demands and generation outputs) and actual physical constraints (e.g., active/reactive power flow balancing, transmission congestion, and network loss) of smart grids, it is difficult to obtain price impact models with analytical expressions accurately. As a result, it is challenging to design an efficient GLB scheme without requiring price impact models. To overcome the above challenge, we propose a price-sensitivity aware GLB scheme. The key idea of the proposed scheme is to impose proper limits on the workloads allocated to the IDC locations with high price-sensitivity coefficients by exploiting a number of interactive information between IDCs and main grids, so that the sudden increase in the total cost could be avoided. Here, the price-sensitivity coefficient is defined as the ratio of the percentage change in price to the percentage change in IDC power demand. Extensive simulation results show the effectiveness of the proposed GLB scheme.

Existing works have widely studied relay-aided underwater acoustic networks under some specialized relay distributions, e.g., equidistant and rectangular-grid. In this paper, we investigate two fundamental problems that under which conditions a relay should be deployed and where to deploy it if necessary in terms of the energy and delay performance in linear underwater acoustic networks. To address these two problems, we first accurately approximate the complicated effective bandwidth and transmit power in the logarithm domain to formulate an energy consumption minimization problem. By analyzing the formulation, we discover a critical transmission distance, defined as open distance, and explicitly show that a relay should not be deployed if the transmission distance is less than the open distance and should be otherwise. Most importantly, we derive a closed-form and easy-to-calculate expression for the open distance and also strictly prove that the optimal placing position is at the middle point of the link when a relay should be introduced. Moreover, although this paper considers a linear two-hop relay network as the first step, our derived results can be applied to construct energy-efficient and delay-friendly multi-hop networks. Simulation results validate our theoretical analysis and show that properly introducing a relay can dramatically reduce the network energy consumption almost without increasing the end-to-end delay.

In this paper, a non-orthogonal multiple access (NOMA)-enhanced scalable video coding (SVC) multicast scheme for cellular networks is proposed. This scheme combines the successive video-layer decoding in SVC with the successive interference cancellation (SIC) in NOMA, which enables a further reduction of the bottleneck effect imposed by cell-edge user equipments (UEs). Aiming at maximizing the overall video quality experienced by UEs in multiple multicast groups, the resource allocation for multiple groups and the scalable multicast scheduling within each group are formulated as a joint mixed-integer nonlinear programming problem. The formulated optimization problem is decoupled into a multi-group resource allocation (MRA) problem and multiple independent intra-group scalable multicast scheduling (IGSMS) subproblems. To solve IGSMS subproblems, we propose an optimal recursive algorithm, for which the optimal transmit power for each layer of the superposition coding needed in each iteration is derived in a closed form. The MRA problem is optimally solved via a knapsack approach. Extensive numerical results demonstrate the improved performance of the proposed NOMA-enhanced SVC multicast scheme over several baseline schemes.

To afford effective service of real-time monitoring and responses for smart cities, it is desired to provide ubiquitous network connections and high data rate services. However, the huge demands for ubiquitous high data rate wireless communications have caused a sharp increase in energy consumption and green house gas emission. In order to realize a sustainable smart city, it is critical to incorporate green communication technique into smart city developments. Device-to-device (D2D) communication has been recognized as one of the key technologies to improve data rate and reduce power consumption, which allows two physically nearby located user equipments to communicate directly with each other. In this paper, with the target of achieving green communications through D2D, we investigate the joint optimization of uplink subcarrier assignment (SA) and power allocation (PA) in D2D underlying cellular networks. Specifically, the problem formulation is to minimize the energy cost of all users in the system while guaranteeing the required data rate of both the D2D user equipments (DUEs) and cellular user equipments. Such an optimization problem is in general a mixed-integer nonlinear programming problem that is NP-hard. To make this problem tractable, we decompose it into the SA and PA subproblems. In particular, we design a heuristic algorithm to assign subcarrier by assuming that the transmit power is evenly allocated over all subcarriers. After that, we solve the PA subproblem by exploiting the difference between the concave function (D.C.) structure of the constraints and transform it into a convex optimization problem. Simulation results demonstrate the remarkable improvement in terms of power consumption by using our algorithms.

Multimedia Internet-of-Things (IoT) systems have been widely used in surveillance, automatic behavior analysis and event recognition, which integrate image processing, computer vision, and networking capabilities. In conventional multimedia IoT systems, videos captured by surveillance cameras are required to be delivered to remote IoT servers for video analysis. However, the long-distance transmission of a large volume of video chunks may cause congestions and delays due to limited network bandwidth. Nowadays, mobile devices, e.g., smart phones and tablets, are resource-abundant in computation and communication capabilities. Thus, these devices have the potential to extract features from videos for the remote IoT servers. By sending back only a few video features to the remote servers, the bandwidth starvation of delivering original video chunks can be avoided. In this paper, we propose an edge computing framework to enable cooperative processing on resource-abundant mobile devices for delay-sensitive multimedia IoT tasks. We identify that the key challenges in the proposed edge computing framework are to optimally form mobile devices into video processing groups and to dispatch video chunks to proper video processing groups. Based on the derived optimal matching theorem, we put forward a cooperative video processing scheme formed by two efficient algorithms to tackle above challenges, which achieves suboptimal performance on the human detection accuracy. The proposed scheme has been evaluated under diverse parameter settings. Extensive simulation confirms the superiority of the proposed scheme over other two baseline schemes.

Fifth-generation mobile communication systems aim to support massive access for future wireless applications. Unfortunately, wireless resource scarcity in random access (RA) is a fundamental bottleneck for enabling massive access. To address this problem, we propose a virtual carrier sensing (VCS)-based RA scheme in massive MIMO systems. The essence of the proposed scheme lies in exploiting wireless spatial resources of uplink channels occupied by assigned user equipments (UEs) to increase channel resources for RA. With the proposed scheme, RA UEs are able to exploit the spatial resources that are approximately orthogonal to those of assigned UEs, thus sharing the uplink channel resource with assigned UEs without causing significant interference to them. Specifically, to ensure RA UEs avoid serious interference with assigned UEs, a base station (BS) sends tailored virtual carriers to RA UEs on behalf of assigned UEs. The RA UEs then conduct VCS to determine whether or not the uplink channel resource is available for RA. The closed-form approximations for probability of channel availability and uplink achievable rate with the proposed scheme are derived. Theoretical analysis and simulation results show that the proposed scheme is able to significantly increase channel resources of RA for massive access.

In this paper, we investigate the problem of frame design, resource allocation, and user association in a massive multiple input multiple output (MIMO) heterogeneous network (HetNet) with wireless backhaul (WB) and linear processing. The objective is to maximize the sum downlink rate of all users, subject to constraints on data rates of WBs and fairness-aware constraints. Such a problem is formulated as an integer programming problem with both coupled variables and coupled constraints. We first develop a centralized scheme in which we decompose the original problem into two subproblems and iteratively solve them until convergence to achieve a near-optimal solution. We then propose a distributed scheme by formulating a repeated game among all users and prove that the game converges to a Nash Equilibrium. Simulation studies show that the proposed schemes are adaptive to different network scenarios and traffic patterns, and achieve considerable gains over several benchmark schemes.

In this paper, we propose a preamble optimization method for the frequency-division multiplexing (FDM)-structured preamble in multiple-input multiple-output systems employing offset quadrature amplitude modulation-based filter bank multicarrier. Specifically, we formulate an optimization problem based on the periodic preamble structure to minimize the mean square error (MSE) of the channel estimation. For two transmit antennas, we find the relationship between preambles and intrinsic interferences from neighboring symbols to achieve the minimum MSE, and derive the optimal closed-form solution. For more than two transmit antennas, we convert the original optimization problem into a quadratically constrained quadratic program and obtain the suboptimal solution by relaxing the nonconvex constraint. The simulation results demonstrate that, in terms of MSE and bit error rate performances, the proposed method outperforms the conventional FDM method at all signal-to-noise ratio (SNR) regimes and outperforms the interference approximation method-complex method at low-to-medium SNR regimes with lower preamble overhead.

Existing works have explored anchor deployment for autonomous underwater vehicles (AUVs) localization under the assumption that sound propagates straightly underwater at a constant speed. Considering that the underwater acoustic waves propagate along bent curves at varying speeds in practice, it becomes much more challenging to determine a proper anchor deployment configuration. In this paper, taking the practical variability of underwater sound speed into account, we investigate the anchor-AUV geometry problem in a 3-D time-of-flight-based underwater scenario from the perspective of localization accuracy. To address this problem, we first rigorously derive the Jacobian matrix of measurement errors to quantify the Cramer–Rao lower bound (CRLB) with a widely-adopted isogradient sound speed profile. We then formulate an optimization problem that minimizes the trace of the CRLB subject to the angle and range constraints to figure out the anchor-AUV geometry, which is multivariate and nonlinear, and thus generally hard to handle. For mathematical tractability, by adopting tools from the estimation theory, we interestingly find that this problem can be equivalently transformed into a more explicit univariate optimization problem. By this, we obtain an easy-to-implement anchor-AUV geometry that yields satisfactory localization performance, referred to as the uniform sea-surface circumference (USC) deployment. Extensive simulation results validate our theoretical analysis and show that our proposed USC scheme outperforms both the cube and the random deployment schemes in terms of localization accuracy under the same parameter settings.

In this paper, we investigate the problem of energy management for an HVAC (Heating, Ventilation, and Air Conditioning) system of a residential building in smart grid environment without violating user thermal comfort limits. Specifically, we intend to minimize the long-term total cost (i.e., the sum of energy cost and thermal discomfort cost) associated with the HVAC system by taking into account uncertainties of outdoor temperature and electricity price. Due to the time coupling incurred by indoor temperature dynamics, it is very challenging to solve the formulated minimization problem. To address the challenge, we propose an online HVAC control algorithm based on Lyapunov optimization techniques without requiring any parameter predictions. Simulation results based on real-world traces show that the proposed algorithm can reduce energy cost significantly with small sacrifice in thermal comfort.

Device to device (D2D) relaying and multi-beam reflection are two effective approaches to deal with the blockage problem in millimeter-wave (mmWave) communication, each with its own limitations when serving a large number of user equipments (UE). A combination of D2D relaying and multibeam reflection is expected to enhance the performance, but the selection of UEs to be served by each approache remains a challenge. In this paper, we consider adaptive mode selection between D2D relaying and multi-beam reflection in a time division duplex (TDD) mmWave network. We formulate a joint mode selection and resource sharing problem with the objective of maximizing the sum logarithm rate, and propose a two-stage solution algorithm. In the first stage, we derive the optimal resource sharing solution under the case that all UEs are served by D2D relaying. In the second stage, an adaptive algorithm is proposed to determine the set of UEs that switch from D2D relaying to multi-beam reflection. Simulation results demonstrate that the proposed scheme achieves considerable performance gain compared to several benchmark schemes.

Recently, many applications (e.g., wearable cognitive assistance) with the devices of Internet of Things (IoT) (e.g., Apple Watch and Google Glass) have been fast developed. However, the current Internet may not be suitable for the future IoT applications due to the limited capabilities of the data caching and content processing services with the existing Internet architecture. In this paper, we extend the named data networking and develop the object-oriented network (OON) as a novel Internet architecture to implement both the native data caching and content processing in the network layer. The datagrams with processable payloads as well as the cached contents are both referred to as the operable objects in OON for abstraction. With the proposed OON architecture, operable objects can be processed and transmitted by forwarding them to the subroutines of content processing programs and the interfaces of content deliveries, respectively, according to the proposed naming rules. For performance evaluation, we implement the dynamic adaptive multimedia streaming application atop the proposed OON architecture in ns-3. Our simulation results show that the proposed OON architecture can effectively increase the potential quality of experience for mobile users.

In this paper, we propose a relative channel estimation error (RCEE) metric, and derive closed-form expressions for its expectation Exprcee and the achievable uplink rate holding for any number of base station antennas M, with the least squares (LS) and minimum mean squared error (MMSE) methods. It is found that RCEE and Exprcee converge to the same constant value when M → ∞, which renders the pilot power allocation (PPA) substantially simplified and a PPA algorithm is proposed to minimize the average Exprcee per user under a total pilot power budget F in multi-cell massive multipleinput multiple-output systems. Numerical results show that the PPA algorithm brings considerable gains for the LS estimation compared with equal PPA (EPPA), while the gains are significant only with large frequency reuse factor (FRF) for the MMSE estimation. Moreover, for large FRF and large F, the performance of the LS approaches to that of the MMSE. Besides, a scheduling strategy is proposed to allocate pilot power in the whole system, which can approach the optimal performance. For the achievable uplink rate, the PPA scheme and improves the minimum achievable uplink rate compared with the EPPA scheme.

We investigate the problem of base station (BS) ON-OFF switching, user association, and power control in a heterogeneous network (HetNet) with massive multiple input multiple output (MIMO), aiming to turn OFF under-utilized BS's and maximize the system energy efficiency. With a mixed integer programming problem formulation, we first develop a centralized scheme to derive the near optimal BS ON-OFF switching, which is an iterative framework with proven convergence. We further propose two distributed schemes based on game theory, with a bidding game between users and BS's, and a pricing game between wireless service provider and users. Both games are proven to achieve a Nash Equilibrium. Simulation studies demonstrate the efficacy of the proposed schemes.

Filter bank multicarrier (FBMC) systems based on offset quadrature amplitude modulation (OQAM), namely FBMC-OQAM, have been criticized for their inefficiency in the use of spectral resources, because of the long ramp-up and ramp-down tails at the beginning and the end of each data packet, respectively. We propose a novel method for shortening these tails. By appending a set of virtual (i.e., none data carrying) symbols to the beginning and the end of each packet, and clever selection of these symbols, we show that the ramp-up and ramp-down tails in the FBMC-OQAM can be suppressed to the extent that they are deemed negligible and thus may be ignored. This shortens the length of signal burst in each FBMC-OQAM packet and improves its spectral efficiency, viz., the same data is transmitted over a shorter period of time. We develop an optimization method that allows computation of virtual symbols for each data packet. Simulation results show that, compared with the existing methods, the proposed tail-shortening approach leads to superior out-of-band emissions performance and a much lower error vector magnitude for the demodulated symbols.

In this paper, we investigate the fundamental energy- efficient power allocation problem in spectrum-sharing wireless networks. We introduce an energy-rate tradeoff index (EI) to characterize how efficiently the energy is consumed and formulate an optimization problem to maximize the EI subject to the power budget constraints. We first show that the EI maximization problem is intimately connected to the extensively investigated energy efficiency (EE) maximization problems. Due to the nonconvexity and NP-hardness of the formulation, we focus on designing cost-efficient and easy-implementation algorithms instead of finding globally optimal but exponential-complexity solutions. Leveraging the high signal-to-interference-plus-noise ratio (SINR) approximation method, we develop a simple fixed-point algorithm with fast convergence, fully distributed framework, and tuning-free properties. Simulation results exhibit the effectiveness of the proposed algorithm.

In this paper, we jointly consider mode selection, admission control, partner assignment, and power allocation to investigate the sum rate maximization problem in underlay SCMA device-to-device (D2D) networks. Due to its mixed combinatory, we first decouple the problem to devise efficient algorithms. In particular, we propose a channel gain based mode selection criterion and a greedy-style partner assignment scheme, and obtain closed-form solutions for both admission control and power allocation. To further reduce the computational cost, we also exploit the structure of the formulation to devise a much faster heuristic algorithm. Simulation results exhibit the superiority of the proposed algorithms against other schemes.

The combination of singular value decomposition (SVD)-based beamforming and filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) has not been successful to date. The difficulty of this combination is that, the beamformers may change dramatically between adjacent subchannels, therefore destroy the orthogonality of FBMC/OQAM real-valued symbols, under frequency selective channels. In this paper, we address this problem from two aspects: 1) an SVD-FBMC/OQAM architecture is proposed to support beamforming with finer granularity in frequency domain based on the frequency spreading FBMC (FS- FBMC) structure; 2) a method is proposed to smooth the beamformers from tone to tone (FS-FBMC tone). The proposed finer beamforming and smoothing greatly improve the smoothness of beamformers, therefore effectively suppress the leaked ICI/ISI from adjacent subchannels. Simulations are conducted under the scenario of IEEE 802.11n wireless LAN. Results show that the proposed SVD- FBMC/OQAM system shares very close BER performance with its orthogonal frequency division multiplexing (OFDM) counterpart under the frequency selective channels. The proposed finer beamforming architecture may also serve as a paradigm for the combinations of other multiple- antenna (MIMO) techniques and FBMC/OQAM.

The traffic of the base station can be offloaded by content sharing through device-to-device (D2D) communications if popular on-demand contents have been cached in user devices. In D2D content sharing, the receiving user gains benefit by obtaining contents while the transmitting user has to consume the transmission energy. However, users are selfish and have no obligation to help others. To motivate user involvement in D2D content sharing, we propose an energy-aware incentive mechanism where the key idea is that physically neighboring users can form a collaborative group. In a collaborative group, a user obtains contents from other users while consuming energy on providing contents to other users. We model the problem as a coalition formation game with non- transferable utility. To solve the problem, we also propose an algorithm which is proved to be of convergence and stability. Finally, simulation results show that our proposed mechanism has significant performance gains compared with two baseline schemes.

With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from distributed data centers. These data are so large-scale, high-dimension and widely distributed that we propose a distributed sparse online algorithm to handle them. Additionally, privacy-protection is an important point in social networks. We should not compromise the privacy of individuals in networks, while these social data are being learned for data mining. Thus we also consider the privacy problem in this article. Our simulations shows that the appropriate sparsity of data would enhance the performance of our algorithm and the privacy-preserving method does not significantly hurt the performance of the proposed algorithm.

In this paper, we investigate the problem of optimal base station (BS) ON-OFF switching and user association in a heterogeneous network (HetNet) with massive MIMO, with the objective to maximize the system energy efficiency (EE). The joint BS ON-OFF switching and user association problem is formulated as an integer programming problem. We first develop a centralized scheme, in which we relax the integer constraints and employ a series of Lagrangian dual methods that transform the original problem into a standard linear programming (LP) problem. Due to the special structure of the LP, we prove that the optimal solution to the relaxed LP is also feasible and optimal to the original problem. We then propose a distributed scheme by formulating a repeated bidding game for users and BS's, and prove that the game converges to a Nash Equilibrium (NE). Simulation studies demonstrate that the proposed schemes can achieve considerable gains in EE over several benchmark schemes in all the scenarios considered.

In this paper, we address an important problem in the wireless monitoring, i.e., how to choose channels with best (or worst) qualities timely and accurately. We consider both scenarios of one or more sniffers simultaneously monitoring multiple channels in the same area. Since the channel information is initially unknown to the sniffers, we shall adopt learning methods during the monitoring to predict the channel condition by a short time of observation. We formulate this problem as a novel branch of the classic multi-armed bandit (MAB) problem, named exploration bandit problem, to achieve a trade-off between monitoring time/resource budget and the channel selection accuracy. In the multiple sniffer cases, including partly-distributed (with limited communications) and fully-distributed (without any communications) scenarios, we take communication costs and interference costs into account, and analyze how these costs affect the accuracy of channel selection. Extensive simulations are conducted and the results show that the proposed algorithms could achieve higher channel selection accuracy than other exploration bandit approaches, hence it proves the advantages of the proposed algorithms.

Machine-to-machine (M2M) systems enable machines or devices to collect data, exchange information, and act on the environment without direct human intervention. A device in an M2M system not only collects data for its own usage but also shares the data with other devices automatically to achieve certain goals. Therefore, emerging M2M systems for civil transportation, electric power grid, medical treatment, industrial automation, etc., can be set up based on the networking of devices. In this survey paper, we first introduce the general architecture and communication networks for M2M systems. Then, we categorize emerging M2M systems according to the types of M2M context, M2M task, and M2M objective. We further survey recent solutions for M2M systems from both academia and industry with the insights of the M2M system categorization. Finally, we summarize challenges in developing M2M system solutions.

To avoid an additional large demand charge introduced by the adoption of electric vehicles (EVs), we investigate a joint energy management problem for geographically distributed data centers (DCs) and EVs of the employees in this paper. Specifically, we intend to minimize the long-term total cost of DCs and EVs by jointly scheduling DC workloads and EV charging demands, without violating heterogeneous EV charging requirements and the given power limits in all geographical locations. We first formulate a stochastic programming problem with the considerations of uncertainties in DC workloads, electricity prices, and EV energy demands. Since these uncertain system parameters are time-varying and the size of the formulated problem is large, we then propose a distributed online algorithm to solve the formulated problem based on Lyapunov optimization technique and a variant of alternating direction method of multipliers. Finally, extensive simulation results show the effectiveness of the proposed algorithm.

The transmission of simultaneous and latency-sensitive data puts forth a significant challenge for the smart grid communications. In this paper, we investigate the application of device-to-device (D2D) communications for the energy management in the electric distribution network. Specifically, we develop a D2D-assisted relaying framework to exploit the spatial diversity and the differentiated data rate requirements, which improves the spectral efficiency, especially for the scenarios that there are faults in the electric distribution network. We study the data transmission scheduling problem under the proposed D2D-assisted relaying framework, aiming to minimize the overall information loss rate, while taking into account the uncertainties in the communication latency. To this end, we first cast the data transmission scheduling problem as a two-stage stochastic programming problem and derive the solution. Then, we develop a real-time distributed data transmission scheduling scheme based on the sample path realizations. Extensive simulation results show significant performance improvement by using the proposed D2D-assisted relaying framework compared with two baseline frameworks for a variety of different cases.

Device-to-device (D2D) communications underlaying a cellular infrastructure have been recognized as an important network-organization architecture in 5G networks. In these scenarios, existing works have explored the impacts of one or several factors among mode selection, admission control, partner assignment, and power allocation on the network performance. In this paper, we put forward an optimization framework that considers all of these coupled factors to investigate the spectrum sharing problem in D2D networks. In particular, we introduce an objective that combines the access rate and the network sum rate and then maximize it subject to users' quality-of-service requirements and resource allocation constraints. Due to its mixed combination, we focus on designing cost-efficient and easy-to-implement algorithms instead of finding globally optimal but exponentially complex solutions. By decomposition, we first devise two novel mode selection criteria and an admission-prioritized partner assignment scheme and obtain closed-form solutions for both admission control and power allocation. Moreover, we present a simple but interesting geometric interpretation on the physical implication of admission conditions. To further reduce the computational cost, we also exploit the structure of the formulation to devise a much faster heuristic algorithm, which usually runs at an order of millisecond. Simulation results show the low computational complexity of the proposed algorithms and exhibit their superiority against other schemes in terms of the access rate and the sum rate.

To provide ubiquitous Internet access under the explosive increase of applications and data traffic, the current network architecture has become highly heterogeneous and complex, making network management a challenging task. To this end, software-defined networking (SDN) has been proposed as a promising solution. In the SDN architecture, the control plane and the data plane are decoupled, and the network infrastructures are abstracted and managed by a centralized controller. With SDN, efficient and flexible network control can be achieved, which potentially enhances network performance. To harvest the benefits of SDN in wireless networks, the software-defined wireless network (SDWN) architecture has been recently considered. In this paper, we first analyze the applications of SDN to different types of wireless networks. We then discuss several important technical aspects of performance enhancement in SDN-based wireless networks. Finally, we present possible future research directions of SDWN.

In this paper, we first propose a novel construction of 16-quadrature amplitude modulation (QAM) near-complementary sequences with low peak-to-mean envelope power ratio (PMEPR) in orthogonal frequency division multiplexing (OFDM) systems. The proposed 16-QAM near-complementary sequences can be constructed by utilizing novel nonlinear offsets, where the length of the sequences is n = 2m. The family size of the newly constructed 16-QAM near-complementary sequences is 8 × (m!/2) × 4m+1, and the PMEPR of these sequences is proven to satisfy PMEPR ≤ 2.4. Thus, the proposed construction can generate a number of 16-QAM near-complementary sequences with low PMEPR, resulting in the improvement of the code rate in OFDM systems. Furthermore, we also propose a novel construction of 64-QAM near-complementary sequences with low PMEPR, which is the first proven construction of 64-QAM near-complementary sequences. The PMEPRs of two types of the proposed 64-QAM near-complementary sequences are proven to satisfy that PMEPR ≤ 3.62 or PMEPR ≤ 2.48, respectively. The family size of the newly constructed 64-QAM near-complementary sequences is 64 × (m!/2) × 4m+1.

We study the optimal resource allocation in the wireless powered underground sensor network (WPUSN) for throughput maximization. The WPUSN is a new networking paradigm where underground sensors can be replenished by a radio frequency energy harvesting technique and transmit geological data to the nearby aboveground access point in real time. In this paradigm, the underground portion of the wireless communication link suffers from severe path loss. Moreover, different underground sensors may have diverse data traffic demands. In this paper, we formulate an optimization problem to maximize the throughput in WPUSNs with the quality of service (QoS) consideration in terms of communication reliability and diverse data traffic demands. Specifically, we map the QoS requirements to signal-to-noise ratio thresholds and transform our problem into a convex optimization problem with linear constraints. We then present a closed-form solution for the transformed problem through a problem decomposition of the Karush-Kuhn-Tucker conditions. Our closed-form solution uncovers the insights that how the wireless channel states, reliability requirements, and data traffic demands affect the optimal resource allocation in the WPUSN. Finally, we demonstrate the effectiveness of the proposed scheme by running simulations.

Recently, based on the homomorphic signatures, the authentication schemes, such as homomorphic subspace signature (HSS) and key predistribution-based tag encoding (KEPTE), have been proposed to resist against pollution attacks in network coding. In this paper, we show that there exists an efficient multi-generation pollution attack on HSS and KEPTE. In particular, we show that using packets and their signatures of different generations, the adversary can create invalid packets and their corresponding signatures that pass the verification of HSS and KEPTE at intermediate the nodes as well as at the destination nodes. After giving a more generic attack, we analyze the cause of the proposed attack. We then propose the improved key distribution schemes for HSS and KEPTE, respectively. Next, we show that the proposed key distribution schemes can combat against the proposed multi-generation pollution attacks. Finally, we analyze the computation and communication costs of the proposed key distribution schemes for HSS and KEPTE, and by implementing experiments, we demonstrate that the proposed schemes add acceptable burden on the system.

IEEE 802.15.4 is a promising standard that can provide efficient communication quality at low cost and low data rates. This standard has been studied for vehicle-to-infrastructure (V-I) communication in wireless sensor network (WSN)-based vehicular ad hoc network (VANET) applications. To adopt this standard for vehicular communications, link quality metrics must be investigated for topology design and network optimization. In this study, a series of measurements were made with IEEE 802.15.4 radios to identify wireless channel characteristics with regard to antenna height, vehicle velocity, and distance in various vehicular scenarios. The empirical behavior of link communication quality was also investigated in terms of received signal strength, packet error rate (PER), and packet loss distribution (PLD). The empirically measured and experimental results, which have been validated through appropriate analytical modeling, provide valuable insights, as well as guide design decisions and tradeoffs for WSN-based VANET applications.

To meet the explosive demand on delivering high-definition video steams over cellular networks, we design a Social-aware video multiCast (SoCast) system leveraging device-to-device (D2D) communications. One salient feature of SoCast is to stimulate effective cooperation among mobile users (clients), by making use of two types of important social ties, i.e., social trust and social reciprocity. By using SoCast, clients form groups to obtain missing packets from other clients and restore incomplete video frames, according to the unique video encoding structure. In return, the user perception of the mobile video quality can be substantially improved. Specifically, we first cast the problem of social ties based group formation among clients for cooperative video multicast as a coalitional game, and then devise a distributed algorithm to obtain the core solution (group formation) for the formulated coalitional game. Further, a resource allocation scheme is proposed for the base station to handle D2D radio resource requests from client groups. Extensive numerical studies using real video traces corroborate the significant gain using SoCast.

Multiple-input multiple-output (MIMO) is a promising way to increase link capacity and energy efficiency in the next generation communication systems. However, the benefits of such an approach depend on proper channel state information (CSI) availability at the transmitter. The CSI is usually estimated at the receiver and fed back to the transmitter through a band-limited channel. Thus, an efficient feedback scheme is needed. In this paper, a comprehensive Bayesian compressive sensing (BCS) based feedback mechanism is proposed for time-varying spatially and temporally correlated vector autoregression (VAR) wireless channel, and the feedback rate distortion function is derived in closed form in statistics. The proposed BCS feedback scheme utilizes the sparse CSI features and prior knowledge to significantly compress the dimensionality of the feedback CSI. Furthermore, the relationship between the feedback rate and downlink capacity is derived in closed form in statistics to guide rate-adaptive feedback in MIMO system. We find out that the ergodic downlink capacity of a user is determined only by its own feedback rate in the proposed feedback scheme. Theoretical and simulation results all show that the proposed feedback scheme can realize efficient, rate-adaptive feedback based on downlink capacity requirement, and the proposed feedback performance is superior to other related works.

In this paper, a staggered frame structure is proposed for single-cell massive multiple-input multiple-output (MIMO) systems, and the key idea is that different users transmit training pilots at nonoverlapped time. As a result, users do not have to be synchronized strictly to send pilots and orthogonal pilots are not required. Moreover, we also propose two interference suppressed channel estimation methods, i.e., the linear minimum mean square error (LMMSE)-based and orthogonal projection based least squares (OPLS) methods for the staggered frame structure. Specifically, the LMMSE-based method minimizes the mean square error of the estimation, whereas the OPLS method only estimates the part of the user's channel response that is orthogonal to the other users. All conducted simulation results demonstrate that the massive MIMO system with the both proposed methods can achieve high average achievable rate. Furthermore, when the conjugate beamforming is employed, the massive MIMO system could obtain even higher average achievable data rate with the proposed OPLS method than that of the system with perfect channel state information. Moreover, the computational complexity of the proposed OPLS method is very low.

In this paper, we address differential non-coherence detections in uplink massive multiple input multiple output (MIMO) systems. Utilizing the channel statistics information, a special 16 quadrature amplitude modulation (QAM) can be adopted to achieve differential detection in uplink massive MIMO systems, which avoids costs of pilots or channel estimation. In addition, a higher-order extension of this differential 16-QAM is also presented in this paper. Moreover, it is found that users in different cells can transmit symbols simultaneously without causing interference to each other by jointly processing signals from all base stations if the number of receive antennas goes to infinity. For comparison, we also provide the performances of the differential amplitude phase shift keying (DAPSK) and coherence detection with pilot contamination in uplink massive MIMO systems, respectively. Simulation results show that the proposed differential 16-QAM scheme exhibits significant advantage over 16-DAPSK, and exhibits a large performance gain compared with the coherence detection with pilot contamination.

In this paper, we consider the wire-tap channel model consisting of three users, namely, a transmitter, a legal receiver, and an eavesdropper, where the eavesdropper has possible unlimited centralized or distributed multiple antennas, while the legal user has just a single antenna. With this model, we study the realization of strict positive secrecy rate through Rayleigh flat fading channels using M-ary phase shift keying (PSK) and orthogonal space-time block code (OSTBC) based on channel reciprocal principle. We demonstrate that the information rate at the eavesdropper can be reduced to zero and a positive communication rate at the legal receiver can be realized if the transmitter employs a proper phase precoder based on the channel reciprocal, and uses OSTBC and M-ary PSK modulations for data transmissions. Application examples of space-time codes for positive secrecy rate are illustrated. As we have given the eavesdropper more capabilities than the legal receiver and thus captured a worst case scenario, any positive secrecy rate that is achieved serves as a lower bound on the secrecy capacity.

Cooperation in resource sharing among wireless users and network operators has been widely studied in wireless communication. However, because of the limited coordination capability or cheating strategies, each participant of the cooperation may cease its cooperative behavior or duties unilaterally during the resource sharing, resulting in unsatisfying quality of services (QoSs) for all other participants. In this paper, we model the resource sharing among participants as an iterated game. Specifically, we first define the participant who is responsible for maintaining the social welfare as an administrator of cooperation (AoC), and other selfish participants as the regular participants of cooperation (PoCs). Then we consider three scenarios, i.e., with two-player applying discrete strategy, two-player applying continuous strategy, and multi-player applying continuous strategy, Finally, we investigate the power control problem in each of scenarios, and apply the zero-determinant strategies for the AoC to find the maximum social welfare that the AoC can achieve with existence of PoCs. Simulation results show that the high and stable social welfare can be maintained by the the AoC with the proposed zero-determinant algorithm.

The hardware cost and power consumption of a massive multiple-input multiple-output (MIMO) system can be remarkably reduced by using a very low-resolution analog-to-digital converter (ADC) unit in each antenna. However, such a pure low-resolution ADC architecture complicates parameter estimation problems. These issues can be resolved and the potential of a pure low-resolution ADC architecture can be achieved by applying a mixed ADC architecture, whose antennas are equipped with low-precision ADCs, while few antennas are composed of high-precision ADCs. In this paper, a unified framework is presented to develop a family of detectors on a massive MIMO uplink system through probabilistic Bayesian inference. Our basic setup comprises an optimal detector, which is developed to provide a minimum mean-squared-error estimate on data symbols. Considering that highly nonlinear steps are involved in quantization, we also investigate the potential for complexity reduction on an optimal detector by postulating a common pseudo-quantization noise model. We provide asymptotic performance expressions, including mean squared error and bit error rate for optimal and suboptimal MIMO detectors. These expressions can be evaluated rapidly and efficiently. Thus, they can be used for system design optimization.

Designing efficient channel access schemes for wireless communications without any prior knowledge about the nature of environments has been a very challenging issue, in which the channel state distribution of all spectrum resources could be entirely or partially stochastic or adversarial at different times and locations. In this paper, we propose an online learning algorithm for adaptive channel access of wireless communications in unknown environments based on the theory of multiarmed bandits (MAB) problems. By automatically tuning two control parameters, i.e., learning rate and exploration probability, our algorithms could find the optimal channel access strategies and achieve the almost optimal learning performance over time in different scenarios. The quantitative performance studies indicate the superior throughput gain when compared with previous solutions and the flexibility of our algorithm in practice, which is resilient to both oblivious and adaptive jamming attacks with different intelligence and attacking strength that ranges from no-attack to the full-attack of all spectrum resources. We conduct extensive simulations to validate our theoretical analysis.

Abstract In this paper, we propose an interference mitigation method to suppress the downlink interference in multi‐macrocell/femtocell networks, and analytically evaluate the interference mitigation and average rate performances. Specifically, the proposed interference mitigation method consists of three steps: frequency partitioning, cell partitioning, and sub‐band allocation. In the frequency partitioning step, the whole downlink frequency band is divided into nine non‐overlapping sub‐bands. In the cell partitioning step, each macrocell is divided into four macrocell regions and three femtocell regions for macrocells' and femtocells' communications, respectively. In the sub‐band allocation step, each macrocell or femtocell region is allocated a sub‐band to guarantee that any two neighboring macrocell/femtocell regions use different sub‐bands. Conducted simulation results show that the proposed method is effective in mitigating the downlink interference and improving the average downlink per‐channel rate in multi‐macrocell/femtocell networks. In summary, the major contribution of the proposed interference mitigation method is that the downlink interference can be mitigated without cooperation between macrocells and femtocells, while the full frequency utilization of the macrocell is achieved. Copyright © 2016 John Wiley & Sons, Ltd.

Offset quadrature amplitude modulation-based orthogonal frequency division multiplexing (OFDM) systems cannot be directly combined with the Alamouti code because of the intrinsic imaginary interference. In this paper, we propose a block-wise space-frequency block coding (SFBC) scheme and a block-wise space-time block coding (STBC) scheme for offset quadrature amplitude modulation-based OFDM systems, which achieve bit error rate performances that are close to OFDM systems. The proposed schemes satisfy the orthogonality condition of the Alamouti code in the complex field with guard band/intervals. To improve the spectral efficiency of the block-wise SFBC scheme, we also consider the case without the guard band. It is observed that only the two innermost subcarriers do not satisfy the complex orthogonality condition when the guard band is removed. Then, a simple equalization scheme is proposed to independently equalize the two innermost subcarriers. Simulation results show that the block-wise SFBC scheme works well under channels with mild-to-moderate frequency selectivity, and the block-wise (STBC ) scheme suffers less than 1 dB loss under severe frequency selective channels at the bit error rate of 10 − 3, when only a simple one tap zero-forcing equalizer is employed. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In this paper, we propose a constellation amplitude modification (CAM) method to reduce the peak‐to‐average power ratio (PAPR) in orthogonal frequency division multiplexing systems. The key idea of the proposed CAM method is to modify the original constellation amplitude and choose the constellation points with the minimum PAPR as the transmitted constellation points. The CAM method formulates the PAPR reduction as a convex optimization problem, and then, it applies the customized interior‐point method to solve the optimization problem. Compared with the traditional methods, the CAM method does not increase the transmit power, and the receiver has no additional operations. Simulation results show that the CAM method converges fast and offers significant PAPR reduction. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In this paper, a massive multiple input multiple output downlink scenario is considered where the number of users varies in a large dynamic range. An adaptive joint precoding and pre‐equalization with reduced complexity is proposed. Specifically, the successive over‐relaxation method is employed in the pre‐equalization process to avoid the high‐dimensional channel matrix inversion, and a reduced‐length feedback filter is proposed to reduce the computational complexity of the precoding. Moreover, an adaptive transceiver structure is proposed to switch on/off the precoding process so that multiple users can be accommodated with the least cost of the computational complexity. Simulation results show that, compared with the traditional scheme, the proposed adaptive joint precoding and pre‐equalization can save about 90% of the computational complexity. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract We consider a cognitive radio network which coexists with multiple primary users (PUs) and secondary users (SUs) transmit over time‐varying channels. In this scenario, one problem of the existing work is the poor performances of throughput and fairness due to variances of SUs' channel conditions and PUs' traffic patterns. To solve this problem, we propose a novel prediction‐based MAC‐layer sensing algorithm. In the proposed algorithm, the SUs' channel quality information and the probability of the licensed channel being idle are predicted. Through the earlier predicted information, we schedule the SUs to sense and transmit on different licensed channels. Specifically, multiple significant factors, including network throughput and fairness, are jointly considered in the proposed algorithm. Then, we formulate the prediction‐based sensing scheduling problem as an optimization problem and solve it with the Hungarian algorithm in polynomial time. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs. Copyright © 2014 John Wiley & Sons, Ltd.

Device-to-device (D2D) communication is known as a promising way to cope with the growing mobile video traffic, which may suffer from the short duration caused by the limited energy supply. In this paper, we propose a practical D2D cooperation framework based on distributed optimization to extend the video transmission duration. In the multi-path multi-hop D2D communication scenario, we effectively schedule the routes and video traffic workloads to avoid low-battery D2D outages due to non-uniform energy consumption. Specifically, we formulate the D2D cooperation as a consensus problem among the network operator and cooperative devices, and then solve it in a flexible distributed fashion. Numerical results show that the proposed framework could reach the optimality quickly in time division duplex communication systems and significantly increase the duration of the cooperative video transmission.

In this paper, we propose a novel additive cancellation signal (ACS) method for sidelobe suppression in non-contiguous orthogonal frequency division multiplexing (NC-OFDM) based CR systems. The key idea of the proposed method is to dynamically add several additive cancellation symbols on both the primary user (PU) subcarriers and the secondary user (SU) subcarriers, to generate the additive cancellation signals for suppressing the sidelobe power of NC-OFDM signals. Moreover, the ACS method formulates the problem of sidelobe suppression as a quadratically constrained quadratic program (QCQP), and the optimal additive cancellation signal can be obtained by the standard interior-point method. Simulation results show that the proposed ACS method can provide significant sidelobe suppression performance.

In this paper, we investigate the problem of incorporating full-duplex (FD) transmission in cognitive femtocell networks (CFN) to achieve higher spectrum utilization. We aim to maximize the sum rate of a full-duplex cognitive femtocell network (FDCFN) as well as guaranteeing the quality of service (QoS) of users in the form of a required signal to interference plus noise ratios (SINR). We propose a duplex mode selection strategy based on stable roommate matching, as well as a greedy channel allocation algorithm with a proven performance bound. Numerical results show that the proposed schemes effectively improve the sum rate of the FDCFN.

To increase the number of accommodated users in the cellular heterogeneous network (HetNet), we propose an energy efficient load balancing strategy for device-to-device (D2D) based relay communications. In a HetNet, user could sent data to an adjacent uncongested femtocell through the D2D-based relay communications rather than wait for the response from the congested macrocell. Specifically, the proposed strategy manages resources by taking into account both the cross-tier and the co-tier interference, and solves the maximum transmission rate problem based on D2D communications in heterogeneous network. Simulation results show that, with the guarantee of the performance of the pre-existing users, the heterogeneous network can accommodate more users and eventually achieve a higher throughput and better energy efficiency, thus the performance is significantly improved.

Cognitive radio (CR) is a critical technology for improving spectrum utilization and solving the radio spectrum scarcity problem. In CR devices, spectrum sensing is important to implement opportunistic spectrum access. Many spectrum sensing schemes have been proposed, including uncooperative, cooperative, centralized, and distributed algorithms. However, they aimed to obtain a global consensus sensing result, which may not always be possible in large-scale cognitive radio networks (CRNs) due to heterogeneous spectrum availability in different areas. Hence, some new spectrum sensing schemes should be designed to discover idle heterogeneous spectrum in CRNs. In this paper, we propose an intelligent cooperative spectrum sensing algorithm based on a non-parametric Bayesian learning model, namely the hierarchical Dirichlet process, which groups spectrum sensing data without the need to know the number of hidden spectrum states, and discovers a common sparse spectrum within each group. Furthermore, a concisely distributed information exchange scheme is designed, where intra-cluster and inter-cluster spectrum information is shared for global spectrum cognition. Experimental results show that the proposed algorithm can exploit the spatial relationship among sensed data to achieve a better spectrum sensing performance in terms of detection probability and false alarm probability.

Abstract Recently, Liu and Wang proposed a homomorphic signature scheme for network coding, which was claimed to resist against pollution attacks. However, we show that in Liu and Wang's scheme, after several generations, the adversary is able to launch a successful forgery attack with a high probability. Therefore, Liu and Wang's scheme is not secure. After analyzing the cause of the attack, an improved scheme is given, which can combat against the proposed attack in an efficient way. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we investigate the problem of minimizing energy cost for distributed Internet data centers (IDCs) in smart microgrids while taking system dynamics into consideration. Specifically, IDC operators expect to minimize the long-term energy cost with the uncertainties in electricity price, workload, renewable energy generation, and power outage state. At first, we formulate the problem as a stochastic program that captures service request distribution, server provisioning, energy storage management, generator scheduling, power transactions between smart microgrids, and main grids. Second, we use the Lyapunov optimization technique to design an operation algorithm, which enables an explicit tradeoff between energy cost saving and battery investment cost. Finally, the effectiveness of the proposed algorithm is evaluated with practical data.

In this paper, we investigate the problem of minimizing the long-term energy cost for an Internet data center (IDC) in deregulated electricity markets. Specifically, IDC operators intend to minimize energy cost by scheduling workload and battery jointly, which can fully exploit the temporal diversity of electricity price. First, we formulate a stochastic optimization problem taking heterogeneous service delay guarantees and battery management into account. Then, we design an online operation algorithm to solve the problem based on Lyapunov optimization technique. Meanwhile, we analyze the feasibility and performance guarantee of the proposed algorithm. Finally, extensive simulation results based on real-world data show the effectiveness of the proposed algorithm.

The ever-increasing demands for local area services underlaying cellular networks benefit from direct device-to-device (D2D) communications, where an efficient scheme for resource allocation is needed to increase the system capacity as the result of interference caused by spectrum sharing. Current works mainly focus on maximizing the overall transmission capacity according to interference constraints of the physical domain. However, D2D users in the social domain form different social communities, and each social community is likely to improve its own group's data transmission cooperatively without considering other communities. Therefore, social relationships among mobile users influence the strategy of the resource allocations for the D2D communications. In this paper, we first introduce social relationships in the continuum space into the resource allocation for D2D communications, which consider the complex social connections in the social domain. Then a social group utility maximization game is formulated to maximize the social group utility of each D2D user, which quantitatively measures the joint performance of social and physical domains. We theoretically investigate the Nash Equilibrium of our proposed game and further propose a distributed algorithm based on the switch operations of the resource allocation vector. Numerical results demonstrate that our proposed solution increases the utility of overall social groups about 45% on average without loss of the fairness compared with other state-of-the-art schemes.

It is well known that conventional rate-compatible RC codes, such as Raptor codes, only perform well at long code lengths. However, we propose a class of RC codes with short code lengths in this paper. Particularly, we develop a computational approach to design online-generated RC low-density parity-check LDPC codes available on noisy channels. We first propose a diagonal-tailed encoding to generate Quasi-regular low-density generator matrix codes. Then, an optimal encoding profile for RC codes is achieved with a linear interpolation approach that is based on the fixed-rate quasi-regular LDPC codes. Finally, we evaluate the rateless and fixed-rate performances of the proposed RC codes by extensive simulation results on various code rates with different modulations. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we study a more practical cognitive radio (CR) scenario where the primary user (PU) operates under more than one transmit power levels. Different from the existing research where PU is assumed to have only one constant transmit power, the new consideration well matches the existing and ongoing standards, i.e., IEEE 802.11 Series, GSM, LTE, LTE-A, etc. The primary target in this new consideration is still to detect the presence of PU, while a secondary target to identify the PU's transmit power level could also be achieved. By doing this, the secondary user (SU) could realize more "cognition" compared to the conventional strategy where only the `on-off" status of PU is detected. To make the study complete, we derive closedform results for multiple thresholds as well as the performance analysis. Numerical examples are provided to corroborate the proposed studies.

In this paper, we propose SoCast — a cooperative video multicast framework to stimulate effective cooperation among mobile users (clients), by leveraging two types of important social ties, i.e., social trust and social reciprocity. By using SoCast, clients can form groups to restore incomplete video frames by obtaining missing packets from other clients, according to the unique video encoding structure. In return, the user perception video quality of mobile video multicast can be improved. Specifically, we first cast the problem of social ties based group formation among clients as a coalitional game, and then devise a distributed algorithm to obtain the core solution (group formation) for the formulated coalitional game. Further, a resource allocation mechanism is proposed for the base station to handle radio resource requests from client groups. Extensive numerical studies with real video traces corroborate the significant performance gain by using the SoCast.

In this paper, we consider the spectrum sensing for cognitive radio (CR) when the primary user (PU) has a set of discrete transmit powers. Different from the traditional sensing approach where the binary hypothesis testing was used, we here apply the the multiple hypotheses testing in order to not only check the ``on/off'' status of PU but also to identify its power level. To better illustrate our idea, we set the price of erroneous sensing in Bayes risk to be unit, such that maximum a posterior (MAP) detection is optimal in terms of minimizing the total error rate. The closed expression of the decision regions as well as the performance metrics are derived. Simulation results are provided to corroborate the proposed studies.

In this study, a novel phase offset‐based partial transmit sequence (PTS) scheme is proposed to reduce the peak‐to‐average power ratio (PAPR) in Alamouti coded multi‐input–multi‐output orthogonal frequency division multiplexing systems, and its key idea is that different phase rotation sequences are multiplied by their corresponding phase offsets at the transmitter. Moreover, a minimum Euclidean distance decoder is proposed to recover the phase rotation sequences at the receiver. The theoretical analysis and simulation results show that the proposed PTS scheme could offer good performances of both the bit error rate and the PAPR reduction without transmitting the side information, resulting in the increase of the data rate.

In this paper, we investigate the problem of minimizing carbon-aware energy cost for distributed Internet data centers (IDCs) in smart microgrids. Specifically, a socially responsible IDC operator intends to jointly minimize the long-term energy cost and carbon emission in IDC operations. Since the future system parameters (e.g., electricity price, workload, renewable energy generation, and carbon emission rate) are random, we formulate the above-mentioned problem as a stochastic program to minimize the time-averaged expectation of the weighted summation of energy cost and carbon emission with guaranteed quality of service for service requests. Then, we design an operation algorithm to solve the formulated problem based on Lyapunov optimization technique without requiring any knowledge about system statistics. Finally, evaluations based on real-life data show that the proposed operation algorithm can achieve lower energy cost and carbon emission simultaneously compared with the carbon-oblivious algorithm.

In this paper, we study the problem of achieving the optimal tradeoff between operation risk and expected energy cost for Internet data center (IDC) operators in deregulated electricity markets according to the risk preferences of IDC operators. To achieve the target above, we propose a risk-constrained stochastic programming decision framework. Then, we formulate a risk-constrained expected energy cost minimization problem with the uncertainties in spot price and workload. To solve the formulated problem, we use a decomposition-based cutting plane algorithm. Finally, extensive evaluations based on real-life data show the effectiveness of the proposed decision framework.

It is well known that broadband wireless communications (BWC) are necessary for high speed vehicles since passengers need many broadband wireless multimedia services. However, one design challenge is to identify sufficient spectrum resource to support BWC in high speed vehicles. Recently, cognitive radios (CRs) are being considered as a promising technology to solve scarcity problem of spectrum resource. Therefore, we investigate the spectrum resource allocation problem in the CR based high speed vehicle network (CR-HSVN) in this paper. Specifically, we propose a spectrum resource allocation framework, where high speed vehicles could effectively utilize the TV white spaces. Subsequently, we formulate the allocation of spectrum resource as an optimization problem to maximize the available spectrum resources (i.e., TV white spaces) utilized by the CR-HSVN, and investigate the property of the CR-HSVN to reduce the complexity by separation computing method without loss of optimality. Furthermore, we analyze the optimal solution based on branch and bound method, the suboptimal solutions based on single channel and linear programming, respectively. Simulation results show that the proposed framework could offer excellent performances of the spectrum utilization and fairness for the CR-HSVN. Meanwhile, the aggregated interference from vehicles to primary users is constrained.

In this paper, we introduce a new family of low-density parity-check (LDPC) codes, called as invertible subset LDPC (IS-LDPC) code, for peak-to-average power ratio (PAPR) reduction in OFDM systems with low complexity. An IS-LDPC code has a number of disjoint invertible subsets, and each invertible subset can be independently inverted to generate other valid codewords of the LDPC code. To construct IS-LDPC codes with good error-correcting performance, we propose a modified progressive edge-growth construction algorithm and verify its effectiveness by analyzing the constructed Tanner graphs. Both theoretical analysis and numerical results show that the IS-LDPC codes exhibit good error-correcting performance and the proposed PAPR reduction scheme based on IS-LDPC codes significantly reduces the PAPR. Compared with the existing coding-based candidate generation schemes, the proposed scheme has a much lower searching complexity when the codeword is transmitted by multiple OFDM symbols. With all mentioned advantages, the proposed PAPR reduction scheme based on IS-LDPC codes could serve as an attractive PAPR reduction solution for future multicarrier communication systems.

ABSTRACT This paper proposes a novel method to detect the number of two‐dimensional (2D) harmonics in additive colored noise based on the enhanced covariance matrix. We define an enhanced covariance matrix using the covariances of the observed signal. We get a special inherent relation between the number of 2D harmonics in additive colored noise and the eigenvalues of the enhanced covariance matrix, which can be used to detect the number of 2D harmonics in additive colored noise by analyzing the eigenvalues of the enhanced covariance matrix. The proposed method has a super resolution and does not need to assume the color and distribution of the additive noise. The effectiveness of the proposed method has been validated by both the theoretical analysis and extensive simulations. Copyright © 2012 John Wiley & Sons, Ltd.

For non-contiguous (NC) OFDM based cognitive radio (CR) systems, schemes have been developed in literature to acquire spectrum synchronization information (SSI) with perfect carrier frequency offset (CFO) synchronization. However, OFDM is extremely sensitive to the CFO in practice, which leads to inter-carrier interference (ICI), hence degrading the spectrum synchronization performance for existing schemes. An accurate CFO estimation is therefore required before setting up the SSI. In this paper, we present a novel scheme based on the maximum likelihood (ML) algorithm to estimate the CFO for the NC-OFDM receiver when the SSI is unknown. A corresponding Cramér-Rao lower bound (CRB) with the ideal SSI is derived to demonstrate the efficiency of the proposed scheme. Simulation results show that the proposed scheme is robust against interference and achieves a satisfactory accuracy of estimation, which is close to the relevant CRB.

This paper considers joint estimation with multiple sensors powered by energy harvesters in wireless sensor networks. In particular, we focus on a network with K sensor nodes, which communicate with a fusion center via K orthogonal channels and power themselves by harvesting energy from the environment. Assuming a deterministic energy-harvesting model under which the harvested energy profile is known prior to transmission, the worst-case mean-square error (MSE) minimization problem over a finite horizon of T estimation periods is investigated. We consider the cases that the sensors have either infinite or finite battery capacity, and develop efficient iterative algorithms to compute the optimal power allocation strategy, with numerical results presented to validate our analysis.

Femtocells are attractive candidates in the future cellular system such as long-term evolution (LTE). However, the interference introduced by femtocells may hinder the improved system performance in LTE networks. In this paper, we consider the challenging problem of interference management in LTE networks with femtocells, propose two frequency reuse patterns and a novel interference avoidance method with the frequency resources of the macrocell reused by femtocells. The key idea of the interference avoidance method is to identify the macrocell user equipments (MUEs) which are near femtocells with the help of spectrum sensing performed by femtocells and avoid MUEs and nearby femtocells using the same frequency resources through spectrum scheduling. Moreover, we conduct simulations to verify that our proposed method is successful in avoiding the potential interference and improving the network performance.

In this paper, we analyze the noise distribution and correlation characteristic on each subcarrier in OQAM-OFDM systems, and present a method based on the maximum likelihood (ML) criterion to improve the channel estimation. Then, the performance results are obtained by considering an IEEE 802.22 channel model and the proposed method is compared to the conventional frequency domain averaging (FDA) method. Simulation results show that the proposed method is an efficient method to improve the channel estimation.

Recently, the peer-to-peer (P2P) live streaming system has benefited from the advent of network coding. However, it was demonstrated that malicious nodes could significantly reduce the network throughput by launching pollution attacks or entropy attacks. In this paper, we propose an efficient symmetric-key based authentication scheme for P2P live streaming system with network coding, to provide in-network detection against pollution attacks and entropy attacks simultaneously. Since the nature of P2P live streaming requires that the detection scheme has high computation efficiency and small communication overheads, we firstly propose a homomorphic message authentication code (MAC), called as PMAC, which has small key size and low computation overhead. Then, the proposed PMAC and the delayed key disclosure technique are employed to make sure that the peers could not only detect the corrupted blocks, but also upload blocks in accordance with random linear network coding. Furthermore, the performance evaluation demonstrates that the proposed scheme has both low communication and computation overheads.

A frequency quadrupling scheme for optical mm-wave signal generation is proposed and demonstrated based on dual-electrode dual-parallel integrated Mach-Zehnder modulator (MZM) consisting of three dual-electrodes MZMs. The electrical filters or optical filters are not required in this scheme. The theoretical analysis is presented to configurate the phases and DC biases of the integrated MZM. The simulation and experiment verification are both conducted to evaluate the performance of the proposed scheme. With 9GHz RF drive signal, the optical sideband suppression ratio (OSSR) higher than 35dB is demonstrated experimentally. The 36GHz mm-wave is generated with the RF spurious suppression ratio (RFSSR) over 30dB.

In this letter, a novel phase offset selected mapping (SLM) scheme is proposed to reduce the peak-to-average power ratio (PAPR) in Alamouti coded multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, and its key idea is that different phase rotation sequences are multiplied by their corresponding phase offsets at the transmitter. Moreover, a minimum Euclidian distance (MED) decoder at the receiver is proposed to recover the phase rotation sequences. Therefore, the proposed SLM scheme does not need to reserve bits for the transmission of side information, resulting in the increase of the data rate. Theoretical analysis and simulation results show that the proposed SLM scheme could offer good performances of both the bit error rate and PAPR reduction.

Recently, homomorphic message authentication code (MAC) schemes have been proposed to resist against pollution attacks in network coding. However, existing methods face a common challenge: the generated MAC t belongs to a small finite field Fq, which means that an adversary could attack by randomly guessing the value of t, and succeed with probability 1/q. Since q is a predetermined system parameter which is typically set as 28, the resulted security 1/256 could be unsatisfactory in practice. In this paper, we propose an efficient homomorphic MAC for authentication in network coding. The proposed method achieves a reliable security parameter 1/qlusing only one key, where l could be chosen according to different security requirements. Compared with previous approaches that using multiple tags, the proposed homomorphic MAC has both low computation and communication overheads.

To achieve secure group communication, one-time session keys need to be shared among group members in a secure and authenticated manner. In this paper, we propose an improved authenticated key transfer protocol based on Shamir's secret sharing. The proposed protocol achieves key confidentiality due to security of Shamir's secret sharing and provides key authentication by broadcasting a single authentication message to all members. Furthermore, the proposed scheme resists against both insider and outsider attacks.

ABSTRACT Binary fountain codes such as Luby transform codes are a class of erasure codes which have demonstrated an asymptotic performance close to the Shannon limit when decoded with the belief propagation algorithm. When these codes are generalized toGF(q) forq > 2, their performance approaches the Shannon limit much faster than the usual binary fountain codes. In this paper, we extend binary fountain codes toGF(q). In particular, we generalize binary Luby transform codes toGF(q) to develop a low complexity maximum likelihood decoder. The proposed codes have numerous advantages, including low coding overhead, low encoding and decoding complexity, and good performance over various message block lengths, making them practical for real‐time applications. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Half‐duplex amplify‐and‐forward (AF) transmissions may result in insufficient use of degrees of freedom if they always use the cooperative mode regardless of the fading states. In this paper, we investigate the conditions under which cooperation offers better performance and the corresponding optimal power allocation during cooperation. Specifically, we first derive an expression of ergodic capacity and its upper bound for an AF cooperative communication system with n relay nodes. Secondly, we propose a novel quasi‐optimal power allocation (QOPA) scheme to maximize the upper bound of the derived ergodic capacity. For the QOPA scheme, the cooperative mode is only adopted when the channel gain of source‐to‐destination is worse than that of relay‐to‐destination. Moreover, we analyze the performance of the system with QOPA scheme when the relay moves, which is based on the random direction model, in a single‐relay wireless network. For a multi‐relay AF network, we compare the ergodic capacity and symbol error rate, corresponding to the proposed QOPA and equal power allocation schemes, respectively. Extensive simulations were conducted to validate analytical results, showing that both ergodic capacity and symbol error rate of the system with QOPA scheme are better than those of the system with equal power allocation scheme in a multi‐relay AF network. Copyright © 2011 John Wiley & Sons, Ltd.

The available spectrum in cognitive radio networks is usually discontinuous but wide, which provides abundant frequency domain diversity. In this paper, we identify the opportunity of leveraging the newly-emerged technique interference alignment to exploit such diversity to support concurrent transmission and improve the network throughput in secondary networks. To enable interference alignment, independent-fading subcarriers should be grouped together to provide sufficient dimensions for intended signals and non-intended interferences at the receiver side. We formulate the subcarrier grouping problem for interference alignment to maximize the number of concurrent transmissions, and propose a greedy-based algorithm to solve it, which is proved to be optimal. Simulation results show that using the proposed scheme, the total throughput in cognitive radio networks can be greatly improved.

For non-contiguous OFDM (NC-OFDM) based communication systems, especially NC-OFDM-based cognitive ratio (CR) systems, one of critical challenges is to establish spectrum synchronization between the transmitter and receiver before data transmission. In [5], an a posterior probability (APP) detection algorithm has been proposed for the receiver to detect the subchannels occupied by the transmitter (active subchannels). However, the encoding scheme adopted in [5] is a concatenation of a convolutional coder and a low rate repetition coder so that the system code rate is only 1/4 when half of the subcarriers are active. In this paper, we consider using low density parity-check (LDPC) codes to improve the system data transmission rate. Furthermore, with the channel model a of NC-OFDM-based communication system, we employ density evolution algorithm to obtain good degree distribution pairs and adopt a modified progressive edge-growth (PEG) algorithm to construct the parity-check matrix for the LDPC code. Simulation results show that our optimized LDPC code for NC-OFDM-based communication systems could obtain satisfactory error performance at a high data transmission rate (system code rate of 1/2).

In chain-type wireless sensor networks (CWSNs), sensor nodes closer to the base station (BS) need to receive and transmit more packets, resulting in more energy consumption and shorter lifetime. Due to the friability of the CWSN's topology, the short lifetime of nodes closer to the BS usually limits the CWSN lifetime. To enhance the CWSN lifetime, in this paper, we propose a novel sensor nodes deployment strategy. With a non-uniform deployment method and an alternative duty mode, the proposed strategy can balance the energy consumption of sensor nodes in CWSNs. Hence, the CWSN lifetime can be effectively prolonged. Numerical experiments have been conducted, showing better performance of the proposed strategy than existing methods.

Due to the high propagation delay and limited bandwidth of underwater acoustic channels, the design of media access control (MAC) protocols for underwater acoustic sensor networks (UASNs) faces great challenges and opportunities. In this paper, we propose a novel handshaking-based MAC protocol, called as sender and receiver concurrent reservation (SRCR) protocol, for multi-hop UASNs. The key idea of the proposed protocol is to adopt a concurrent reservation mechanism to allow the sender's neighbors and the receiver's neighbors to transmit packets during the communication between the sender and the receiver without collision. Simulation results have confirmed that the proposed protocol can offer good performance of both throughput and delay in multi-hop UASNs with high channel utilization.

The concept of femtocell that operates in licensed spectrum to provide home coverage has attracted interest in the wireless industry due to high spatial reuse, and extensive deployments of femtocells is expected in the future. In this paper, we consider the scenario that a femtocell service provider (FSP) expects to rent spectrum from the coexisting macrocell service provider (MSP) to serve its end users. In addition to the spectrum leasing payment, the FSP may allow hybrid access of macrocell users to improve the utilities of itself and MSP, which are defined as the sum of data traffic and payment/revenue. We propose the spectrum leasing framework taking hybrid access into consideration. The whole procedure is modeled as a three-stage Stackelberg game, where MSP and FSP determine the spectrum leasing ratio, spectrum leasing price and open access ratio sequentially to maximize their utilities, and the existence of the Nash Equilibrium of the sequential game is analyzed. We characterize the equilibrium, in terms of access price, spectrum acquisition of FSP, the open access ratio, and price of anarchy via simulation. Numerical results show that both MSP and FSP can benefit from spectrum leasing, and hybrid access of femtocell can further improve their utilities, which provide sufficient incentive for their cooperation.

In this paper, we propose a novel framework of cognitive radio assisted cooperation (CRAC) for downlink transmissions in orthogonal frequency-division multiple access (OFDMA) - based cellular networks. In the proposed CRAC framework, relay stations are deployed in each cell and have spectrum sensing capability. In turn, they can access unoccupied white space to opportunistically obtain additional sub-channels to assist relaying information for cellular users. One of promising novelties is that the proposed CRAC considers joint resource allocation which includes transmission mode selection, relay station allocation, and transmit power/sub-channel allocation, to cost-effectively provide services and applications. Specifically, we first formulate the joint resource allocation as a sum utility maximization problem with power constraints on the base station and relay stations, which is a mixed integer programming problem. Then, we leverage dual decomposition method and derive a centralized optimal solution. Extensive simulation results are presented and demonstrate that the proposed CRAC can achieve a significant performance improvement in terms of the downlink network throughput while maintaining comparable fairness among cellular users in contrast to the traditional relay-based cooperation approach.

This paper is motivated by optimal adaptive resource allocation policies at the base station in wireless communication systems, and we particularly study the maximization of the weighted rate sum in Gaussian broadcast channel. The problem of finding the optimal power allocation in Gaussian broadcast channel is non-convex and intractable to solve using traditional methods with low complexity. In this paper, we derive a closed-form optimal solution of the weighted rate sum maximization in Gaussian broadcast channel. The derived closed-form optimal solution can give the optimal power allocation for the weighted rate sum maximization one time and does not need to determine the water level and iterative process. Thus, it is very easy to be implemented with an adaptive power allocation in the real communication systems.

To reduce the long term electricity cost of smart appliances (SAs) with deferrable operation time in smart grid, we propose a novel energy buffering framework to intelligently schedule the distributed energy storage (DES) for the cost reduction of SAs in this paper. The proposed energy buffering framework determines the action policy (e.g., charging or discharging) and the power allocation policy of the DES to provide DES power to proper SAs at proper time with lower price than that of the utility grid, resulting in the reduction of the long term financial cost of SAs. Specifically, we first formulate the optimal decision problem in the energy buffering framework as a discounted cost Markov decision process (MDP) over infinite-horizon. Then, we propose an optimal scheme for the energy buffering framework to solve the discounted cost MDP based on online learning approach. Extensive simulation results show that the proposed optimal scheme for the energy buffering framework can significantly reduce the long term financial cost comparing with the baseline schemes and the myopic scheme.

In this paper, we focus on critical event monitoring in wireless sensor networks (WSNs), where only a small number of packets need to be transmitted most of the time. When a critical event occurs, an alarm message should be broadcast to the entire network as soon as possible. To prolong the network lifetime, some sleep scheduling methods are always employed in WSNs, resulting in significant broadcasting delay, especially in large scale WSNs. In this paper, we propose a novel sleep scheduling method to reduce the delay of alarm broadcasting from any sensor node in WSNs. Specifically, we design two determined traffic paths for the transmission of alarm message, and level-by-level offset based wake-up pattern according to the paths, respectively. When a critical event occurs, an alarm is quickly transmitted along one of the traffic paths to a center node, and then it is immediately broadcast by the center node along another path without collision. Therefore, two of the big contributions are that the broadcasting delay is independent of the density of nodes and its energy consumption is ultra low. Exactly, the upper bound of the broadcasting delay is only 3D+2L, where D is the maximum hop of nodes to the center node, L is the length of sleeping duty cycle, and the unit is the size of time slot. Extensive simulations are conducted to evaluate these notable performances of the proposed method compared with existing works.

ABSTRACT In this paper, we consider the narrowband interference problem for orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) systems, in which parts of the OFDM subcarriers and parts of the data frame can be seriously interfered, resulting in significant performance degradation. We propose a scheme of iterative noise plus interference estimation and decoding (IED) to mitigate the performance degradation caused by the narrowband interference, which is based on expectation maximization (EM) algorithm. To reduce the number of OFDM symbols for time domain averaging required in the proposed scheme, and adapt the proposed scheme to rapid changing narrowband interference conditions, we also propose an IED scheme with frequency domain partial averaging (IED‐FPA). Moreover, we derive the Cramér‐Rao lower bounds for unbiased noise plus interference variance estimations, and they can be achieved via the proposed IED schemes. Simulation results show that the proposed IED‐FPA scheme can effectively achieve the same performance as that of the optimal maximum likelihood decoder with full knowledge of the power plus interference variances, and the proposed IED‐FPA scheme is very robust with respect to the number of the interfered subcarriers and positive errors of the knowledge of the interfered subcarriers' number. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we propose a novel distributed navigation algorithm for people to escape from critical event region in wireless sensor networks (WSNs). Unlike existing works, the scenario discussed in the paper has no goal or exit as guidance, leading to a big challenge for the navigation problem. To solve it, our proposed navigation algorithm computes the convex hull of the event region just by some topological methods. With the reference of the convex hull, people can be easily navigated out of the event region. Both the computation complexity and communication overhead of the proposed algorithm are very low, as it only needs to flood two shortest path trees in a limited area around the event region with a distance [L/2π] + 1 to the event boundary, where L is the length of the boundary. Conducted simulations have verified the effectiveness and scalability of the proposed algorithm.

In this paper, a novel spectrum sharing scheme is proposed to improve the achievable traffic load of secondary users (SUs) under grade of service (GoS) constraints in cognitive wireless networks with heterogeneous traffic. The key idea of the proposed scheme is to introduce preemptive priority and buffering mechanism for real-time traffic and non-real-time traffic, respectively, according to their different delay characteristics. The proposed scheme can reduce the forced termination probability and the blocking probability for heterogeneous calls simultaneously. Numerical results show that the proposed scheme can effectively improve the achievable traffic load of SUs under GoS constraints.

Recently, cognitive radio has become a hot spot, in which secondary users could use the frequency bands when primary users are absent. Hence, the detection of primary users' presence plays an important role in cognitive radio networks. In this paper, a cognitive radio network with multiple licensed users is considered. Secondary users cooperatively detect the presence of primary users using energy detection and send the sensing results to the secondary base station. To minimize the probability of false alarm while keeping the probability of detection with respect to the secondary network, we should schedule secondary users to cooperation appropriately, which is formulated as an optimization problem. However, it is hard to solve this optimization problem, hence two suboptimal solutions are proposed. Simulation results show that the proposed schemes are able to greatly improve the sensing performance in cognitive radio networks.

In this paper, we construct a novel MAC-layer sensing framework for fast discovery of spectrum opportunities. We employ the sequential probability ratio test and a novel collaborative sensing scheme, in which the collaboration exists among multi-users and multi-slots. Then, we propose a novel scheme of adaptive collaboration sensing schedule for fast spectrum opportunities discovery and prove that the sensing order based on sorting is optimal with respect to fast discovery. Moreover, we formulate the adaptive collaboration as an optimization problem, which, however, is an NP-hard problem. Then, we give a suboptimal solution to the problem with low complexity. Simulation results show that the proposed scheme could fast discover the spectrum opportunities.

In this paper, we focus on the dynamic spectrum access of infrastructure-based cognitive radio networks, a primary network and multiple secondary networks, which are collocated with each other. To improve network performance of all networks, we propose a cooperative communication-aware spectrum leasing framework. In the proposed framework, the primary network leverages secondary APs as cooperative relays, and decides the optimal strategy on the relay selection and the price for spectrum leasing. Based on primary network's strategy, secondary networks determine the length of spectrum access time they purchase from the primary network. Finally, each network allocates the total spectrum access time of the network among its end users. The above sequential decision procedure is formulated as a Stackelberg game, with primary network acting as the leader and secondary networks as the followers, and a unique Nash Equilibrium (NE) point is achieved through backward induction analysis. At this NE point, all networks maximize their utilities in terms of transmission rate and revenue/payment. Simulation results show that the primary network and secondary networks achieve higher utilities by exploiting cooperative transmission under our proposed framework, which gives all networks incentive for cooperation.

In this paper, we propose a novel method to generate the cyclostationary signature for Orthogonal Frequency Division Multiplexing (OFDM) signals with a low overhead of signature (i.e. number of signature subcarriers) and a good performance of the signature detection under frequency-selective fading channel, for dynamic spectrum access (DSA) networks. Moreover, we also present how to employ the proposed method to detect the embedded signature. Simulations are conducted to make a comparison of the detection performance between the proposed method and the Sutton method. Relying on single hopping subcarrier to generate the signature, the overhead of the proposed method is only one fourth that of the Sutton method in our example. Nevertheless, the simulation results showed that the detection performance of the proposed method is no worse than that of the method proposed by Sutton.

For non-contiguous OFDM (NC-OFDM) based cognitive radio systems, one challenge is to establish the spectrum synchronization before the data transmission over an idle channel. In this paper, we propose two novel detection schemes, i.e., hard-decision-based detection (HDD) and soft-decision-based detection (SDD) schemes, to detect NC-OFDM symbols without the information of out-of-band spectrum synchronization. The key idea of the proposed schemes is to employ the received training symbol to calculate a posterior probability (APP) of each subchannel's being active. Specifically, for the proposed HDD scheme, the secondary receiver detects whether a subchannel is active or not based on its APP. When the APP of a subchannel is larger than a preset threshold, it will be employed for the data detection. Moreover, we derive the miss detection and false alarm probabilities of the hard decision based detection of active subchannels, respectively. For the proposed SDD scheme, all subchannels are employed for the data detection, in which as a priori information of the subchannels' being active, all APPs are taken into account in the bit metric calculation. Theoretical analysis and simulation results show that the proposed schemes can provide satisfactory performance.

Abstract Soft‐decision decoding of Reed—Solomon (RS) codes consists of three key steps: multiplicity calculation, bivariate interpolation, and factorization. In this work, we investigate the first step, with the aim of reducing the complexity of multiplicity calculation. It is observed that the objective value of multiplicity calculation in Koetter and Vardy's (KV) algorithm can be expressed as an increasing function of independent parameter. Based on this fact, we propose to use bisection or golden section methods in multiplicity calculation. Simulation study demonstrates that our proposed approach can significantly reduce the computational complexity of RS codes decoding. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract In this paper, we first propose a simplified tone reservation (STR) method with low computational complexity which is based on the Fourier series expansion. Then, we analyze how to combine the STR method with the cross antenna rotation and inversion method to reduce the peak‐to‐average power ratio (PAPR) for multi‐input multi‐output orthogonal frequency division multiplexing (MIMO‐OFDM) system. To validate the analytical results, extensive simulations are conducted and the numerical results show the efficiency of the proposed schemes including the PAPR reduction and low computational complexity for MIMO‐OFDM system. Copyright © 2010 John Wiley & Sons, Ltd.

For non-contiguous OFDM (NC-OFDM) based cognitive radio (CR) systems, one of huge challenges is to establish spectrum synchronization before data transmissions. In this paper, we propose a novel active subchannel detection scheme for the NC-OFDM receiver without out-of-band spectrum synchronization information. The key idea of the proposed scheme is to employ the received training symbol to calculate the a posterior probability (APP) of each subchannel being active, and then APPs are used to make a decision on which subchannels are active and to be processed in data detection. Furthermore, performance analysis of active subchannel detection is given. Both theoretical analysis and simulation results show that the proposed scheme can provide satisfactory performance of the symbol error rate (SER) when the interference from primary users is considered.

In this paper, we consider the weighted sum throughput maximization (WSTM) problem of multicell network with full spectral reuse. The WSTM problem is a nonconvex combinatorial problem, and difficult to tackle in practice. Thus, we present and discuss three low-complexity suboptimal algorithms, which solve the WSTM problem via joint user scheduling and power control (USPC) among a set of coordinating cells. Extensive system level simulations show that all the proposed algorithms have close weighted sum throughput (WST) performances to that of exhaustive search (ES). Furthermore, the proposed distributed iterative (DI) algorithm, which enjoys both distributed operation and fast convergence, is the most attractive one in practical systems.

In this paper, we propose a dynamic fractional frequency reuse (DFFR) scheme, which is based on a interference avoidance request (IAR) mechanism, to suppress the inter-cell interference (ICI) for downlink OFDMA cellular networks. The key idea of the proposed scheme is that base stations (BSs) exchange IAR messages among each other, and then dynamically control the transmit power according to their received IAR messages. For the elastic data traffic, we combine the IAR mechanism with proportional fairness (PF) scheduling algorithm, to achieve good performances of both total cell throughput and cell edge user (CEU) throughput. Extensive system level simulations show that the proposed DFFR scheme outperforms the reference frequency reuse schemes in terms of the total cell throughput and CEU throughput. Furthermore, the proposed DFFR scheme requires no centralized controller, and the exchanged information among BSs is very little.

In this paper, we propose an iterative data detection scheme to combat unknown narrowband interference for orthogonal frequency division multiplexing (OFDM) systems. The key idea of the proposed scheme is to joint channel estimation, noise plus interference power estimation and decoding together to enhance both the estimation accuracy and bit error ration (BER) performance. We derive the Cramér-Rao Bounds (CRB) for mean square errors of channel estimation and noise plus interference estimations, and the CRBs can be achieved via the proposed scheme with very few iteration numbers, hence, the complexity of the proposed scheme is very low. Moreover, simulation results show that the decoding performance of the proposed scheme substantially approaches to that of the maximum likelihood decoder with perfect channel estimation and full knowledge of interference distribution.

In this letter, we propose a novel scheme to construct a novel class of 2-D binary sequences with zero correlation zone, which is based on perfect arrays and orthogonal sequences. Compared with all existing 2-D binary sequences, the proposed construction can generate more sets of two-dimensional binary sequences with long zero correlation zone. Moreover, the constructed 2-D sequence sets could be applied directly, such as arrays of sound sources, position detection arrays, phased-array antennas, time-frequency coding, and spatial correlation.

In this letter, we propose a novel scheme to reduce the PAPR of OFDM signals, and its key idea is that the side information of partial transmit sequence is considered as a part of channel frequency response. Therefore, the proposed scheme does not need to reserve bits for submitting side information, resulting in an increase in the data rate. Furthermore, we propose a novel pilot arrangement, in which pilot tones are independently inserted into each subblock. Simulations show that the proposed scheme with the novel pilot arrangement could offer good performances of bit error rate and PAPR reduction.

Cooperative communication can obtain spatial diversity without using multiple antennas, and thus achieve more reliable transmission or consume less power. Accordingly, a new cooperative MAC mechanism in wireless networks, the DCMAC, is proposed in this paper. The DCMAC makes full use of the broadcasting characteristics of wireless channel to obtain channel information, chooses the most suitable cooperative nodes, and reserves wireless channel efficiently. Evaluation results show that DCMAC can choose the most suitable cooperative nodes to improve system performance.

Many communication signals exhibit cyclostation-arity with some cycle frequencies and the cycle frequencies are related to signal parameters such as symbol rates or carrier frequencies. Dandawate proposed an asymptotically optimal chi-square statistical test to check the presence of cyclostationarity. In the last few years, the statistical test has been applied to spectrum sensing for cognitive radios. We notice that, for most modulated communication signals like PSK, QAM, OFDM and GMSK, the cyclic autocorrelations for different delays at the same cycle frequency are deterministically related in average sense. And this property is not considered in Dandawate's test. So we propose a novel spectrum sensing algorithm in this paper, which improves the performance of cyclostationary feature detection by using the relationship among the cyclic autocorrelations for different delays. The simulation results demonstrate that the improved method has a better performance in an additive white gaussian noise (AWGN) channel and a multipath fading environment.

In the paper, we design a navigation system based on sensor network to guide a robot to walk out of event region. The navigation system does not require any exit or locations.

A theoretical analysis is presented to show that in orthogonal frequency division multiplexing systems, the peak-to-average power ratio (PAPR) can be reduced by performing a unitary matrix transformation on the input vector of N symbols. The authors also prove that this transformation does not degrade the bit error rate (BER) or power spectral density (PSD) performance. Based on this, the inverse discrete Fourier transform matrix is proposed as the unitary matrix to reduce the PAPR. The simulation results show that the proposed scheme can obtain significant PAPR reduction while maintaining good performance in the BER and the PSD. To further evaluate the performance of the proposed scheme, the authors compare it with some well known PAPR reduction techniques by simulations. It is demonstrated that the proposed scheme can offer better system performance and achieve a better compromise with regard to the PAPR reduction, BER, spectral efficiency and computational complexity.

Conventional linear multicast can be constructed on any acyclic network by increasing the order of the finite field to a sufficiently large amount over which the multicast is defined. In this paper, we first discuss the reciprocal theorem of the conventional linear multicast and design a linear multicast on any give acyclic network with constant finite field by extending the multicast dimension and relaxing the constraint on the information storage. In particular, we propose the binary linear multicast network coding and the linear multicast with binary coefficients. With the proposed method, the computation complexity for network coding at the intermediate nodes can be significantly reduced; therefore cheap network nodes can be deployed in a large scale due to their low cost for wireless communications. In addition, some applications of the proposed binary linear multicast network coding in wireless communication networks are illustrated and validated.

In most application scenarios of wireless sensor networks (WSN), sensor nodes are usually deployed randomly and do not have any knowledge about the network environment or even their ID's at the initial stage of their operations. In this paper, we address the clustering problems with a newly deployed multi-hop WSN where most existing clustering algorithms can hardly be used due to the absence of MAC link connections among the nodes. We propose an effective clustering algorithm based on a random contention model without the prior knowledge of the network and the ID's of nodes. Computer simulations have been used to show the effectiveness of the algorithm with a relatively low complexity if compared with existing schemes.

In this comment, we give a detailed proof to get the power allocation solution for an amplify-and-forward cooperative communication system with n relay nodes, which is based on analyzing. Our conclusions show that the solution with "+" sign is optimal although the solution with "-" sign is also valid for equations (41) and (42) in.

In cognitive radio networks, the secondary (unlicensed) users need to find idle channels via spectrum sensing for their transmission. Cooperative spectrum sensing (CSS) is a promising technology in spectrum sensing with an admirable performance. In CSS, multiple secondary users individually sense the idle channels and send their decisions to the network center, and then the center will do a final decision. In this paper, we focus on the optimal sensing settings of the CSS to maximize the channel throughput under some constraints on the interference to cognitive radio network. Particularly, we consider two scenarios in this paper. In the first scenario, only one channel is sensed at one time, and we aim at maximizing an individual channel capacity. Some simple algorithms are also derived to calculate the optimal solution for the first scenario. In the second scenario, multiple channels are jointly sensed simultaneously, and our objective is to maximize the overall channel capacity. We prove that the optimization problem in the second scenario can be converted into a convex-optimization problem, which can be solved efficiently and reliably. Simulation results show a significant improvement of the channel capacity by using the proposed optimal CSS in cognitive radio networks.

To enable coexistence between an OFDM based opportunistic spectrum access system and a primary user, we proposed two novel methods called EAIC (Extended Active Interference Cancellation) and EAIC-H (EAIC-Hybrid) for spectral sculpting of OFDM signal. In EAIC and EAIC-H, cancellation signals are added to OFDM signal to cancel interference in target spectrum band caused by data tones, so that interference perceived by primary user can be limited. The cancellation signal (EAIC tones) has longer time duration than that of OFDM symbol, which enables a better notching capability than that of most existing methods. Optimal weights of EAIC tones have been analyzed and given in this paper. Simulation results show that the proposed methods can obtain very deep spectral notches of about 80 dB. Although the EAIC tones cause certain interferences to OFDM data tones, the SNR degradation of OFDM system is very limited and it can be acceptable for high order modulation such as 64 QAM. Compared with EAIC, EAIC- H provides a better tradeoff between notching performance and SNR degradation for high order modulation.

It has been widely known that one of the key design parameters in orthogonal frequency division multiplexing (OFDM) systems is the distribution of peak-to-average power ratio (PAPR). Recently some theoretical approaches to determine the PAPR distribution have been proposed based on the assumption that all subcarriers are allocated with equal power. However, this assumption may not be valid due to the following facts. First, in all realistic OFDM systems, usually only a subset: of subcarriers are used to carry information (active subcarriers) and the rest (inactive subcarriers) are set to zero. Second, due to the efficiency concerns transmission power should be allocated to active subcarriers. Third, power allocation may vary depending on different constellations used by different active subcarriers and their signal-to-noise-ratios. In this paper, we propose a general approach to identify PAPR distribution in OFDM systems. Specifically, a more accurate analytical expression of the PAPR distribution is derived with the help of Extreme Value Theory for Chi-squared-2 process in OFDM systems with unequal power distribution strategy. To validate the analytical results, extensive simulations have been conducted, showing a very good match between the identified PAPR distribution and that of real OFDM systems.

Cognitive radio (CR) is a dynamic spectrum access technology as a solution to spectrum under-utilization problem in some licensed bands. Operating over an exceedingly wide spectrum, CR systems usually adopt multi-carrier modulation (MCM) to implement flexible channelization. Consequently, efficient channel allocation scheme becomes extremely important to an MCM based CR (MCM-CR) system. In this paper, a maximum likelihood detection model is developed to detect the presence and locations of licensed users (LUs) signals in the frequency domain. Performance of the detection model, including the optimal detection region, detection probability and false alarm probability, is analyzed. A one-order two-state Markovian chain model is proposed to predict channel status information. In particular, a novel subcarrier allocation scheme for MCM-CR systems is proposed, taking into account the confidence of channel estimation, quality of services (QoS) of rental users (RUs) and throughput. To validate the analytical results, simulations have been conducted to show effectiveness of the proposed scheme.

Abstract Long propagation delay is one of the most important characteristics in underwater wireless sensor networks (UWSNs) and poses a great challenge for medium access control (MAC) protocol design, especially for contention‐based MAC protocols due to intolerable delay caused by unpredictable retransmissions in UWSNs. Recently, some MAC protocols for UWSNs have been suggested in the literature, and most of them are based on random access with their capabilities to compensate propagation delay. However, two issues should still be resolved in these protocols: (a) they need to make measurements to realize duty cycle synchronization and (b) packet collisions exist, which not only reduce the throughput (thus increasing delays) but also waste energy. In this paper, we propose a novel MAC protocol explicitly designed for UWSNs, which makes the best use of the propagation delay to resolve collision problem and reduce overhead of control‐packet to save energy. The proposed MAC protocol can assure that the number of retransmissions is not more than one. To validate the analytical results, simulations have been conducted to show that the proposed MAC protocol can offer a low energy consumption while avoiding collisions in UWSNs. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we first discuss multicarrier modulation (MCM) based cognitive radio (CR) systems. A maximum likelihood ratio spectrum detection model is then presented to detect the occurrence and spectrum gap of licensed users (LUs) signals. Next, we theoretically study the proposed model by deducing an optimal decision region and the detection probability and false alarm probability. Simulation results validate the derived performances of the proposed model for MCM based CR systems.

In this paper, we provide the design criteria of the nonlinear companding transforms for reduction in peak-to-average power ratio (PAPR) of multi-carrier modulation (MCM) signals, which can enable the original MCM signals to be transformed into the desirable distribution. As examples, some novel nonlinear companding transforms have been proposed to transform the amplitude or power of the original MCM signals into uniform distribution, which can effectively reduce the PAPR for different modulation formats and subcarrier sizes without any complexity increase and bandwidth expansion. It has been shown by computer simulations that the proposed schemes can significantly improve the performance of MCM systems including bit-error-rate and PAPR reduction.

In this paper, we propose a novel nonlinear transform scheme to reduce the Peak-to-Average Power Ratio (PAPR) in multi-carrier modulation (MCM) systems. The key idea of the proposed scheme is to transform the original MCM signals such that the amplitude or the power of the companded signals follows uniform distribution. In this manner, the proposed scheme can effectively reduce the PAPR for different modulation formats and sub-carrier sizes without increasing the system complexity and signal bandwidth. Extensive simulation results show that the proposed schemes can significantly improve the performance of MCM systems.

This letter addresses the carrier frequency offsets and channel gains estimation problem for MIMO channels with multiple carrier frequency offsets that are not assumed to be equal for all transmit-receive antenna pairs. Training sequences are proposed for both flat fading and frequency selective fading MIMO channels. Under the proposed training sequences, sub-optimal performance of parameter estimation can be achieved by a subspace-based method with reasonable computational complexity. The method can also provide flexible tradeoff between complexity and performance

This paper investigates the optimal access point (AP) placement (OAPP) in simultaneous broadcast system using orthogonal frequency division multiplexing (OFDM) for high data rate indoor wireless LAN. We aim to determine the best combination of multiple AP placements that minimizes average bit error rate (BER) of terminals when in put information including an indoor configuration, a number APs, and total transmission power is given a priori. Since OAPP is categorized as a hard combinatorial problem with many variables and constraints, we applied a nonlinear optimization scheme called uniform design simulated annealing (UDSA) to search for an approximate optimal solution and analyzed its performance in detail. The simulation results demonstrate that the UDSA algorithm has been successfully applied to OAPP and its effectiveness was also confirmed in comparison with very fast simulated annealing (VFSA) and local search (LS) algorithms.