Satellite communications (SatCom) are envisioned as a critical enabler of 6G networks, enabling seamless global coverage by integrating terrestrial infrastructures with multi-layered satellite constellations. Among these, the integration between geostationary (GEO) and low Earth orbit (LEO) satellite networks is particularly attractive, as they combine the broad coverage of GEO satellites with the low latency and high capacity of LEO systems. Within this context, we address the resource allocation problem for LEO satellite through a joint design of beam size and transmit power, while accounting for GEO interference constraints, residual Doppler frequency offsets, and frequency reuse strategies. The objective is to maximize the spectral efficiency of LEO system operating in multi-beam GEO-LEO networks. Motivated by the limitations of classical deep reinforcement learning (RL) in such dynamic orbital settings and the potential of quantum RL for accelerated convergence, we propose a hybrid solution that exploits quantum acceleration during offline training and subsequently exports the learned policy into a classical representational format for onboard LEO satellite deployment. A fully quantum deep deterministic policy gradient framework with variational quantum circuit-based actor and critic is developed, along with a neural network-based policy translator for classical inference. To the best of our knowledge, this is the first deployment-ready quantum RL framework in SatCom, offering efficient offline training, reduced retraining latency, and practical deployment compatibility with existing LEO satellite hardware.

Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eaves-dropper (Eve) locations. Unlike conventional ISAC-based friendly jamming (FJ) approaches that require Eve’s CSI or precise angle-of-arrival (AoA) estimates, our method exploits radar echo feedback to guide directional jamming without explicit Eve’s information. To enhance robustness to radar sensing uncertainty, we propose a radar-aware neural network that jointly optimizes beamforming and jamming by integrating a novel nonparametric Fisher Information Matrix (FIM) estimator based on f-divergence. The jamming design satisfies the Cramér–Rao lower bound (CRLB) constraints even in the presence of noisy AoA. For efficient implementation, we introduce a quantized tensor train-based encoder that reduces the model size by more than 100 times with negligible performance loss. We also integrate a non-overlapping secure scheme into the proposed framework, in which specific sub-bands can be dedicated solely to communication. Extensive simulations demonstrate that the proposed solution achieves significant improvements in secrecy rate, reduced block error rate (BLER), and strong robustness against CSI uncertainty and angular estimation errors, under-scoring the effectiveness of the proposed deep learning–driven friendly jamming framework under practical ISAC impairments.

We present the first investigation into the transmission of multi-stream information from a base station equipped with reconfigurable holographic surfaces (RHS) to multiple users with the aid of multi-antenna arrays. Building upon this, we propose the joint design of RHS and baseband beamformers that enables multi-stream delivery at fair rates across all users. Specifically, we first introduce a max-min rate optimization approach, which aims for maximizing the minimum rate for all users through iterative solutions of quadratic problems. To reduce complexity, we then propose a surrogate-based optimization approach that offers a low-complexity design alternative relying on closed-form updates. Our simulations show that the surrogate-based approach achieves nearly the same minimum rate as max-min optimization, while delivering sum-rates comparable to those of sum-rate maximization, overcoming the rate-fairness deficiency typical of the latter.

Provisioning secrecy for all users, given the heterogeneity in their channel conditions, locations, and the unknown location of the attacker/eavesdropper, is challenging and not always feasible. The problem is even more difficult under finite blocklength constraints that are popular in ultra-reliable low latency communication (URLLC) and massive machine-type communications (mMTC). This work takes the first step to guarantee secrecy for all URLLC/mMTC users in the finite blocklength regime (FBR) where intelligent reflecting surfaces (IRS) are used to enhance legitimate users’ reception and thwart the potential eavesdropper (Eve) from intercepting. To that end, we aim to maximize the minimum secrecy rate (SR) among all users by jointly optimizing the transmitter’s beamforming and IRS’s passive reflective elements (PREs) under the FBR latency constraints. The resulting optimization problem is non-convex. To tackle it, we linearize the objective function, and decompose the problem into sequential subproblems. We prove that our proposed algorithm’s converges to a locally optimal solution with low computational complexity thanks to our closed-form linearization approach. This makes the solution scalable for large IRS deployments. Extensive simulations with practical settings show that our approach can ensure secure communication for all users while satisfying FBR constraints.

Anomaly detection aims to identify patterns and events that deviate from the norm. However, current methods struggle to achieve high detection accuracy due to data complexity, i.e., imbalance and particularly hidden features not directly observed in the raw data. To address this, we propose a novel neural network architecture/model, called Dual-Decoder Variational Auto-Encoder (DDVAE) which consists of an encoder and two decoders. The encoder maps the input data into the extended latent space, where the dimensionality is greater than that of the input data, providing more room to capture the relationship among features. The data in the extended latent space of DDVAE is modelled based on the distribution of the normal samples to generate stochastic latent variables before they are fed into the first decoder to reconstruct the input data. After that, reconstructed data at the output of the first decoder are used to reconstruct the extended latent space, in which anomalies produce higher reconstruction errors than normal samples when reconstructing both the input data and the extended latent space. The Area Under the ROC Curve (AUC) obtained by DDVAE is significantly greater than that of conventional non-parametric methods and generative models on eight benchmark anomaly datasets, by up to 3.9%. DDVAE also achieves an average miss detection rate of 16.9%. We observe that DDVAE achieves high AUC in cases of a low rate of anomalies compared to normal samples,

This paper introduces a novel joint user association and resource allocation framework to efficiently deal with eavesdropping attacks without requiring prior information about eavesdroppers. Specifically, the co-channel interference when reusing resource blocks is leveraged to disrupt the signal reception at eavesdroppers. To maximize the secure area, defined as the area where eavesdroppers cannot wiretap the channel due to co-channel interference, we first formulate the system by using the Markov decision process to capture the dynamics and uncertainty of mobile users and wireless communications. Then, a deep reinforcement learning (DRL)-based approach is proposed to obtain the joint optimal user association and resource allocation policy to utilize the co-channel interference and maximize the secure area. Extensive simulation results demonstrate that by intelligently associating users and allocating resource blocks to them, our proposed solution can help to effectively defeat eavesdropping attacks without requiring prior information of eavesdroppers which may not be readily available in practice. In addition, the proposed DRL-based algorithm can converge to the optimal policy quickly and achieve better performance compared to existing solutions.

The advancement of sixth-generation (6G) technology significantly enhances the Internet of Things (IoT) applications, especially in remote areas where traditional cellular infrastructure is not feasible. Satellite communication, a crucial component of 6G, extends IoT connectivity to these underserved regions. In this context, the growing interest in low Earth orbit (LEO) satellite communication stems from its recent advancements in offering high data rate services and minimizing service latency. Next-generation LEO satellite systems, with regenerative capabilities, allow for adaptability in bandwidth management and on-board data processing. However, the scarcity of satellite spectrum presents a barrier to the expansion of LEO satellite networks and the development of integrated terrestrial-space infrastructures. To address this challenge, we propose constructing a radio environment map (REM) aboard LEO satellites to opportunistically tap into the unused spectrum of geostationary (GEO) satellites within a cognitive GEO-LEO satellite IoT network. This solution facilitates REM construction through collaboration among neighboring LEO satellites while also considering the frequency reuse scheme of GEO satellites. Our REM construction approach leverages cyclostationary-based sensing at LEO satellites, serving the dual purpose of REM construction and Doppler shift estimation to track multiple GEO frequency signals. Following REM construction, LEO satellites utilize deep learning techniques to predict GEO spectrum occupancy without further sensing, thereby optimizing secondary spectrum utilization of the IoT network. We propose a deep learning neural network architecture based on a sequence-to-sequence model tailored for spectrum prediction at LEO satellites. Simulations demonstrate superior performance in detection probability of the proposed deep learning network compared to convolutional long short-term memory networks, achieving this with lower computational complexity.

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems.

As demand for ubiquitous connectivity grows, integrating satellite communications into sixth-generation (6G) networks has emerged as a crucial strategy to enhance global coverage, especially in remote and underserved regions. However, achieving the stringent performance, reliability, and spectral efficiency required for 6G presents significant challenges. Coexisting geostationary (GEO) and low Earth orbit (LEO) satellite networks offer a promising solution by enabling complementary coverage and enhanced service capabilities. Nonetheless, a critical challenge is managing intersystem interference from the LEO satellite system on the GEO system when sharing spectral resources, all while maintaining the performance of both systems. To address this, this paper introduces a fast fuzzy deep reinforcement learning (DRL)-based approach for joint beam design and power allocation in multi-beam GEO-LEO coexisting satellite networks. A robust design problem of LEO beam size and power allocation is formulated to maximize the spectral efficiency of the LEO system, considering tolerable interference on the GEO system, frequency reuse schemes employed by both GEO and LEO systems, and Doppler frequency offset induced by LEO satellite movement. A fast DRL algorithm, integrating fuzzy logic, post-decision state, and deep deterministic policy gradient, is proposed to solve this problem. Numerical results demonstrate a faster learning convergence rate for the proposed DRL algorithm compared to benchmark algorithms and confirm that the proposed method enhances LEO spectral efficiency while maintaining tolerable intersystem interference on the GEO system.

Intelligent reflective surfaces (IRS) allow us to actively customize the radio environment by manipulating the reflected signals upon them. Among their various applications, one notable use is enhancing user security and privacy. This is achieved by strategically reflecting signals from the transmitter to improve reception for authorized users while minimizing the signal quality for suspicious eavesdroppers. However, under multiuser settings, given the heterogeneity in users’ channels, locations, and the unknown location of the eavesdropper, it is challenging to avoid secrecy outage for all users. This paper takes the first step in investigating the multi-user secrecy rate (SR) maximization in IRS-aided systems. To this end, we aim to maximize the minimum user’s SR by optimizing the transmitter’s beamforming vector and the IRS’ passive reflective elements (PREs). The resulting problem is non-convex. To tackle this, we first linearize the objective function and decompose the problem into two sequential optimization problems. We then design an alternating optimization (AO) method to jointly optimize the transmitter’s beamforming vector and the IRS’ PREs. We prove that the proposed algorithm converges to a locally optimal solution of the above non-convex optimization problem. Numerical results demonstrate that the proposed Max-Min algorithm can provide secure communication for all the users.

In this work, we propose a novel machine learning empowered intrusion detection for Mobile Edge Computing (MEC) networks. Unlike most of the research works that focus on detecting attacks at the network layer, such as IP spoofing and Denial of Service (DoS) attacks, we aim to detect attacks/threats at the application layer, especially attacks caused by malicious codes embedded in offloaded computing tasks. This is an emerging issue in MEC networks as more and more MEC services allow MEC users to offload their computational tasks to the edge nodes to process. Yet, this is a very challenging problem in MEC, as data at the application layer is often complex and challenging to interpret, making anomaly detection difficult. Therefore, we first propose an effective solution to transfer data from the original offloading file to a new form, i.e., images, to make it more effective for the detection process. After that, a Convolutional Neural Network (CNN) and a collaborative learning process are proposed to learn information from training data (i.e., transformed images) and, at the same time, share the learned knowledge (i.e., trained models) together to improve the global accuracy in detecting attacks. Simulation results show that our approach can detect attacks with an accuracy of approximately 90%.

Outlier/anomaly detection plays a pivotal role in AI applications, e.g., in classification, and intrusion/threat detection in cybersecurity. However, most existing methods face challenges of heterogeneity amongst feature subsets posed by non-independent and identically distributed (non-IID) data. To address this, we propose a novel neural network model called Multiple-Input Auto-Encoder for Anomaly Detection (MIAEAD). MIAEAD assigns an anomaly score to each feature subset of a data sample to indicate its likelihood of being an anomaly. This is done by using the reconstruction error of its sub-encoder as the anomaly score. All sub-encoders are then simultaneously trained using unsupervised learning to determine the anomaly scores of feature subsets. The final Area Under the ROC Curve (AUC) of MIAEAD is determined by the maximum value among the feature subsets. Extensive experiments on eight real-world anomaly datasets from different domains, e.g., health, finance, cybersecurity, and satellite imaging, demonstrate the superior performance of MIAEAD over conventional methods and the state-of-the-art unsupervised models, by up to 4.3% in terms of AUC score. Furthermore, experimental results show that the AUC obtained by MIAEAD is mostly not impacted as the ratio of anomalies to normal samples in the dataset increases. In contrast, the fundamental anomaly detection method using Auto-Encoder (AE) experiences a significant decline in AUC as this ratio increases. We observe that MIAEAD has a high AUC when applied to feature subsets with low heterogeneity based on the coefficient of variation (CV) score. We also prove that the MIAEAD model uses fewer parameters than the AE model.

Web 3.0 is the third generation of the World Wide Web (WWW), concentrating on the critical concepts of decentralization, availability, and increasing client usability. Although Web 3.0 is undoubtedly an essential component of the future Internet, it currently faces critical challenges, including decentralized data collection and management. To overcome these challenges, blockchain has emerged as one of the core technologies for the future development of Web 3.0. In this paper, we propose a novel blockchain-based information management framework, namely Smart Blockchain-based Web (SBW), to manage information in Web 3.0 effectively, enhance the security and privacy of users’ data, bring additional profits, and incentivize users to contribute information to the websites. Particularly, SBW utilizes blockchain technology and smart contracts to manage the decentralized data collection process for Web 3.0 effectively. Moreover, in this framework, we develop an effective consensus mechanism based on Proof-of-Stake (PoS) to reward the user’s information contribution and conduct game theoretical analysis to analyze the user’s behavior in the considered system. Additionally, we conduct simulations to assess the performance of SBW and investigate the impact of critical parameters on information contribution. The findings confirm our theoretical analysis and demonstrate that our proposed consensus mechanism can incentivize the nodes and users to contribute more information to our systems.

Machine learning is a core component of many Intrusion Detection Systems (IDS) for IoT networks. However, developing a robust machine-learning model for IDSs on IoT networks is very challenging. This is due to the diversity of IoT devices resulting in the inconsistency and high dimensions of data collected from IoT networks. In other words, in IoT environments, the training data for IDSs on IoT networks is often heterogeneous since they are collected from multiple sources with different characteristics. To tackle these problems, this paper proposes a novel neural network architecture called Multiple-Input Auto-Encoder (MIAE). MIAE has multiple sub-encoders that can process multiple input sources with different dimensions. Moreover, the MIAE model is trained in an unsupervised learning mode, and it can transfer the heterogeneous inputs into lower-dimensional representation to facilitate classifiers to distinguish between the normal samples and types of attacks. The experimental results on the three most popular benchmark IDS datasets, i.e., NSLKDD, UNSW-NB15, and IDS2017, show the superior performance of the MIAE over three groups of methods including conventional classifiers, state-of-the-art dimensionality reduction models, and unsupervised representation learning methods for multiple inputs with different dimensions. MIAE combined with the Random Forest (RF) classifier also achieves 96.2% in terms of accuracy in detecting sophisticated attacks, e.g., Slowloris. In addition, the average running time for detecting an attack sample obtained by MIAE combined with RF classifier is only roughly 5E-7 seconds, whilst the model size is lower than 1 MB. This clearly shows the effectiveness of our proposed model when deployed in practice.

Federated Learning (FL) offers a privacy-preserving approach to training machine learning models from distributed data on resource-constrained devices. However, even for modern/high-end cars vehicular networks, onboard training with the whole of the raw data presents challenges due to limited computing capability and power consumption concerns. To address this conundrum, we propose a novel FL framework that leverages homomorphic encyption (HE) to allow vehicles to upload encrypted portions of their data to a cloud server. Thanks to the key feature of HE, the server can perform additional model updates directly on the encrypted data, alleviating the workload on vehicles while preserving privacy. Furthermore, model updates from vehicles are also HE-encrypted, guaranteeing end-to-end privacy protection. This approach reduces the computational burden on vehicles while maintaining the model quality and convergence performance of the FL framework. Additionally, it can mitigate biases stemming from heterogeneous data, resulting in more stable FL convergence. Extensive experiments demonstrate the effectiveness of our framework in reducing workload and improving learning stability in vehicular networks.

With the ever-increasing popularity of blockchain applications, securing blockchain networks plays a critical role in these cyber systems. In this paper, we first study cyberattacks (e.g., flooding of transactions, brute pass) in blockchain networks and then propose an efficient collaborative cyberattack detection model to protect blockchain networks. Specifically, we deploy a blockchain network in our laboratory to build a new dataset including both normal and attack traffic data. The main aim of this dataset is to generate actual attack data from different nodes in the blockchain network that can be used to train and test blockchain attack detection models. We then propose a realtime collaborative learning model that enables nodes in the network to share learning knowledge without disclosing their private data, thereby significantly enhancing system performance for the whole network. The extensive simulation and realtime experimental results show that our proposed detection model can detect attacks in the blockchain network with an accuracy of up to 97%.

Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. However, the IDSs for IoT devices face inherent challenges of IoT systems, including the heterogeneity of IoT data/devices, the high dimensionality of training data, and the imbalanced data. Moreover, the deployment of IDSs on IoT systems is challenging, and sometimes impossible, due to the limited resources, such as memory/storage and computing capability of typical IoT devices. To tackle these challenges, this article proposes a novel deep neural network/architecture called constrained twin variational auto-encoder (CTVAE) that can feed classifiers of IDSs with more separable/distinguishable and lower dimensional representation data. Additionally, in comparison to the state-of-the-art neural networks used in IDSs, CTVAE requires less memory/storage and computing power, hence making it more suitable for IoT IDS systems. Extensive experiments with the 11 most popular IoT botnet data sets show that CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods, whilst the running time for attack detection is lower than$2E{\mathrm{ -}}6$s and the model size is lower than 1 MB. We also further investigate various characteristics of CTVAE in the latent space and in the reconstruction representation to demonstrate its efficacy compared with current well-known methods.

This paper explores the significance of the timeliness of information in the context of fifth generation (5G) non-terrestrial networks (NTN). As 5G technology continues to evolve, its integration with non-terrestrial components such as satellites, high-altitude platforms, and unmanned aerial vehicles brings about new possibilities and challenges for ensuring the timely delivery of information. In this paper, we delve into the network structure of NTNs and emphasize the significance of timeliness in various applications, including 5G massive Internet of Things and enhanced Mobile Broadband. We conduct an in-depth review of the design technologies and methodologies that enhance the timeliness of information in these applications. These include network architecture design, resource allocation, protocol design, modulation design, trajectory planning, reconfigurable intelligent surfaces design, energy harvesting scheduling design, offloading strategy design, and caching strategy design. By exploring these technical aspects and solutions, we aim to provide valuable insights into ensuring timely information delivery in 5G NTN. Furthermore, we propose potential future research directions to further improve the timeliness of information in NTNs. Recognizing the importance of timeliness and addressing the related challenges will unlock the full potential of 5G NTN, enabling the successful deployment and operation of a wide range of applications and services that depend on real-time data exchange.

Scalable algorithms are conceived for obtaining the sum-rate capacity of the reconfigurable intelligent surface (RIS)-aided multiuser (MU) multiple-input multiple-output (MIMO) broadcast channel (BC), where a multi-antenna base station (BS) transmits signals to multi-antenna users with the help of an RIS equipped with a massive number of finite-resolution programmable reflecting elements (PREs). As a byproduct, scalable path-following algorithms emerge for determining the sum-rate capacity of the conventional MIMO BCs, closing a long-standing open problem of information theory. The paper also develops scalable algorithms for maximizing the minimum rate (max-min rate optimization) of the users achieved by the joint design of RIS’s PRE and transmit beamforming for such an RIS-aided BC. The simulations provided confirm the high performance achieved by the algorithms developed, despite their low computational complexity.

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in full-dimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users’ signal-to-leakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR max-min problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GM-SLNR and of the SLNR soft max-min problem. The simulations provided confirm the users’ improved-fairness ergodic rate distributions.

Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively.

Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security/privacy concerns. To address these issues, this paper proposes a novel sharding-based blockchain framework, namely MetaShard, for Metaverse applications. Particularly, we first develop an effective consensus mechanism, namely Proof-of-Engagement, that can incentivize MUs' data and computing resource contribution. Moreover, to improve the scalability of MetaShard, we propose an innovative sharding management scheme to maximize the network's throughput while protecting the shards from 51% attacks. Since the optimization problem is NP-complete, we develop a hybrid approach that decomposes the problem (using the binary search method) into sub-problems that can be solved effectively by the Lagrangian method. As a result, the proposed approach can obtain solutions in polynomial time, thereby enabling flexible shard reconfiguration and reducing the risk of corruption from the adversary. Extensive numerical experiments show that, compared to the state-of-the-art commercial solvers, our proposed approach can achieve up to 66.6% higher throughput in less than 1/30 running time. Moreover, the proposed approach can achieve global optimal solutions in most experiments.

By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), thus enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs' computing/communications resources to a given favorable set of users (e.g., closer to edge nodes) may block other devices from their services. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare or minimize the total energy consumption but do not consider the computing/battery status of each mobile device. This work develops an energy-based proportionally fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipments (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and continuous (resource allocation) variables. To tackle the NP-hard mixed integer optimization problem, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branch-and-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decisions and multiple subproblems (SPs) for resource allocation. To quickly eliminate inefficient offloading solutions, the MP is integrated with powerful Benders cuts exploiting the ENs' resource constraints. We then develop a dynamic branch-and-bound algorithm (DBB) to efficiently solve the MP considering the load balance among ENs. The SPs can either be solved for their closed-form solutions or be solved in parallel at ENs, thus reducing the complexity. The numerical results show that the DBBD returns the optimal solution in maximizing the proportional fairness among UEs. The DBBD has higher fairness indexes, i.e., Jain's index and min-max ratio, in comparison with the existing ones that minimize the total consumed energy.

Federated Learning (FL) plays a pivotal role in enabling artificial intelligence (AI)-based mobile applications in mobile edge computing (MEC). However, due to the resource heterogeneity among participating mobile users (MUs), delayed updates from slow MUs may deteriorate the learning speed of the MEC-based FL system, commonly referred to as the straggling problem. To tackle the problem, this work proposes a novel privacy-preserving FL framework that utilizes homomorphic encryption (HE) based solutions to enable MUs, particularly resource-constrained MUs, to securely offload part of their training tasks to the cloud server (CS) and mobile edge nodes (MENs). Our framework first develops an efficient method for packing batches of training data into HE ciphertexts to reduce the complexity of HE-encrypted training at the MENs/CS. On that basis, the mobile service provider (MSP) can incentivize straggling MUs to encrypt part of their local datasets that are uploaded to certain MENs or the CS for caching and remote training. However, caching a large amount of encrypted data at the MENs and CS for FL may not only overburden those nodes but also incur a prohibitive cost of remote training, which ultimately reduces the MSP’s overall profit. To optimize the portion of MUs’ data to be encrypted, cached, and trained at the MENs/CS, we formulate an MSP’s profit maximization problem, considering all MUs’ and MENs’ resource capabilities and data handling costs (including encryption, caching, and training) as well as the MSP’s incentive budget. We then show that the problem is convex and can be efficiently solved using an interior point method. Extensive simulations on a real-world human activity recognition dataset show that our proposed framework can achieve much higher model accuracy (improving up to 24.29%) and faster convergence rate (by 2.86 times) than those of the conventionalFedAvgapproach when the straggling probability varies between 20% and 80%. Moreover, the proposed framework can improve the MSP’s profit up to 2.84 times compared with other baseline FL approaches without MEN-assisted training.

This article aims to study intrusion attacks and then develop a novel cyberattack detection framework to detect cyberattacks at the network layer (e.g., brute password and flooding of transactions) of blockchain networks. Specifically, we first design and implement a blockchain network in our laboratory. This blockchain network will serve two purposes, i.e., to generate the real traffic data (including both normal data and attack data) for our learning models and to implement real-time experiments to evaluate the performance of our proposed intrusion detection framework. To the best of our knowledge, this is the first dataset that is synthesized in a laboratory for cyberattacks in a blockchain network. We then propose a novel collaborative learning model that allows efficient deployment in the blockchain network to detect attacks. The main idea of the proposed learning model is to enable blockchain nodes to actively collect data, learn the knowledge from data using the Deep Belief Network, and then share the knowledge learned from its data with other blockchain nodes in the network. In this way, we can not only leverage the knowledge from all the nodes in the network but also do not need to gather all raw data for training at a centralized node like conventional centralized learning solutions. Such a framework can also avoid the risk of exposing local data’s privacy as well as excessive network overhead/congestion. Both intensive simulations and real-time experiments clearly show that our proposed intrusion detection framework can achieve an accuracy of up to 98.6% in detecting attacks.

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios.

This article proposes a novel framework that utilizes an over-the-air Brain-Computer Interface (BCI) to learn Metaverse users’ expectations. By interpreting users’ brain activities, our framework can optimize physical resources and enhance Quality-of-Experience (QoE) for users. To achieve this, we leverage a Wireless Edge Server (WES) to process electroencephalography (EEG) signals via uplink wireless channels, thus eliminating the computational burden for Metaverse users’ devices. As a result, the WES can learn human behaviors, adapt system configurations, and allocate radio resources to tailor personalized user settings. Despite the potential of BCI, the inherent noisy wireless channels and uncertainty of the EEG signals make the related resource allocation and learning problems especially challenging. We formulate the joint learning and resource allocation problem as a mixed integer programming problem. Our solution involves two algorithms: a hybrid learning algorithm and a meta-learning algorithm. The hybrid learning algorithm can effectively find the solution for the formulated problem. Specifically, the meta-learning algorithm can further exploit the neurodiversity of the EEG signals across multiple users, leading to higher classification accuracy. Extensive simulation results with real-world BCI datasets show the effectiveness of our framework with low latency and high EEG signal classification accuracy.

A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convex-solver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput.

A network relying on a large antenna-array-aided base station is designed for delivering multiple information streams to multi-antenna users over high-frequency bands such as the millimeter-wave and sub-Terahertz bands. The state-of-the-art analog precoder (AP) dissipates excessive circuit power due to its reliance on a large number of phase shifters. To mitigate the power consumption, we propose a novel AP relying on a controlled number of phase shifters. Within this new AP framework, we design a hybrid precoder (HP) for maximizing the users’ minimum throughput, which poses a computationally challenging problem of large-scale, nonsmooth mixed discrete-continuous log-determinant optimization. To tackle this challenge, we develop an algorithm which iterates through solving convex problems to generate a sequence of HPs that converges to the max-min solution. We also introduce a new framework of smooth optimization termed soft max-min throughput optimization. Additionally, we develop another algorithm, which iterates by evaluating closed-form expressions to generate a sequence of HPs that converges to the soft max-min solution. Simulation results reveal that the HP soft max-min solution approaches the Pareto-optimal solution constructed for simultaneously optimizing both the minimum throughput and sum-throughput. Explicitly, it achieves a minimum throughput similar to directly maximizing the users’ minimum throughput and it also attains a sum-throughput similar to directly maximizing the sum-throughput.

In this paper, we propose a framework that uses Brain-Computer Interface (BCI) technology to create human-like avatars for user-driven Metaverse applications. This framework is designed to work efficiently with fast wireless connectivity and high computing demand, making it ideal for future infrastructures, e.g., 5G and beyond. The Metaverse system uses brain signals sent through wireless channels to create intelligent digital avatars that can provide helpful recommendations and assist in user-driven applications. To eliminate the computational burden on the user equipments, the computational tasks and resource allocation decisions are shifted to the centralized base station. As a result, our framework involves solving a mixed decision-making and classification problem. The goal is for the base station to efficiently allocate its computing and radio resources to users, as well as classify their brain signals. To this end, we develop a hybrid training algorithm that uses the latest advancements in deep reinforcement learning to solve the problem. Our algorithm involves three deep neural networks working together to handle both decision-making and classification tasks. Simulation results indicate that our framework can effectively manage system resources while accurately classifying users' brain signals.

Although Rate Splitting Multiple Access (RSMA) is a promising scheme to effectively manage interference and enhance data rate and spectral utilization, its applications for Virtual Reality (VR) streaming have not been well studied. In addition to the strict latency requirement as in conventional High-Definition streaming, VR streaming further requires more computing resources at the transmitter to promptly react to the dynamic of users' Field-of-View interests. Unfortunately, current conventional RSMA approaches could not effectively handle these problems since they are not intentionally developed to deal with the special features of VR streaming. To address these challenges, we first propose a novel hierarchical multicast technique to effectively integrate the RSMA and VR streaming by exploiting the Field-of-Views of VR users. Then, the VR streaming problem established based on RSMA is formulated as a joint computation and communication optimization problem which can not only guarantee VR streaming latency requirement but also effectively manage interferences among users. Finally, due to the dynamic and uncertainty of wireless channels and users' demands, we develop a deep reinforcement learning approach to find the optimal policy for the system. This learning solution allows us to find the optimal parameters for the system via trail-and-error learning process, and thus it is effective in dealing with the uncertainty and unknown information from surrounding environment. Simulation results demonstrate that our proposed solution can satisfy the VR requirement of millisecond latency that is much lower than those of the baselines.

Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve the user differential privacy while reducing the wireless resources. Specifically, an FL learning process can be fused with quantized Binomial mechanism-based updates contributed by multiple users to reduce the communication overhead/cost as well as to protect the privacy of participating users. However, the optimization of wireless transmission and quantization parameters (e.g., transmit power, bandwidth, and quantization bits) as well as the added noise while guaranteeing the privacy requirement and the performance of the learned FL model remains an open and challenging problem. In this paper, we aim to jointly optimize the level of quantization, parameters of the Binomial mechanism, and devices' transmit powers to minimize the training time under the constraints of the wireless networks. The resulting optimization turns out to be a Mixed Integer Non-linear Programming (MINLP) problem, which is known to be NP-hard. To tackle it, we transform this MINLP problem into a new problem whose solutions are proved to be the optimal solutions of the original one. We then propose an approximate algorithm that can solve the transformed problem with an arbitrary relative error guarantee. Intensive simulations show that for the same wireless resources the proposed approach achieves the highest accuracy, close to that of the conventional FL with no quantization and no noise added. This suggests the faster convergence/training time of the proposed wireless FL framework while optimally preserving users' privacy.

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

Unlike conventional anti-eavesdropping methods that always require additional energy or computing resources (e.g., in friendly jamming and cryptography-based solutions), this work proposes a novel anti-eavesdropping solution that comes with mostly no extra power nor computing resource requirement. This is achieved by leveraging the ambient backscatter technology in which secret information can be transmitted by backscattering it over ambient radio signals. Specifically, the original message at the transmitter is first encoded into two parts: (i) active transmit message and (ii) backscatter message. The active transmit message is then transmitted by using the conventional wireless transmission method while the backscatter message is transmitted by backscattering it on the active transmit signals via an ambient backscatter tag. As the backscatter tag does not generate any active RF signals, it is intractable for the eavesdropper to detect the backscatter message. Therefore, secret information, e.g., a secret key for decryption, can be carried by the backscattered message, making the adversary unable to decode the original message. Simulation results demonstrate that our proposed solution can significantly enhance security protection for communication systems.

Unmanned-aerial-vehicle (UAV)-assisted data collection has been emerging as a prominent application due to its flexibility, mobility, and low operational cost. However, under the dynamic and uncertainty of Internet of Things data collection and energy replenishment processes, optimizing the performance for UAV collectors is a very challenging task. Thus, this article introduces a novel framework that jointly optimizes the flying speed and energy replenishment for each UAV to significantly improve the overall system performance (e.g., data collection and energy usage efficiency). Specifically, we first develop a Markov decision process to help the UAV automatically and dynamically make optimal decisions under the dynamics and uncertainties of the environment. Although traditional reinforcement learning algorithms, such as$Q$-learning and deep$Q$-learning, can help the UAV to obtain the optimal policy, they often take a long time to converge and require high computational complexity. Therefore, it is impractical to deploy these conventional methods on UAVs with limited computing capacity and energy resource. To that end, we develop advanced transfer learning techniques that allow UAVs to “share” and “transfer” learning knowledge, thereby reducing the learning time as well as significantly improving learning quality. Extensive simulations demonstrate that our proposed solution can improve the average data collection performance of the system up to 200% and reduce the convergence time up to 50% compared with those of conventional methods.

Integrated communications and sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to autonomous vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a coherent processing interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this article develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.

Federated learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet of Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads, and enhance privacy for cyberattack detection systems. However, one of the biggest challenges for deploying FL in IoT networks is the unavailability of labeled data and dissimilarity of data features for training. In this article, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn “knowledge” from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated data sets of networks to have the same features, thus limiting the efficiency, flexibility, as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning (DL) models, even when their data sets have different features. Extensive experiments on recent real-world cybersecurity data sets show that the proposed framework can improve more than 40% as compared to the state-of-the-art DL-based approaches.

Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users’ demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes.

Intrusion detection (ID) on the cloud environment has received paramount interest over the last few years. Among the latest approaches, machine learning-based ID methods allow us to discover unknown attacks. However, due to the lack of malicious samples and the rapid evolution of diverse attacks, constructing a cloud ID system (IDS) that is robust to a wide range of unknown attacks remains challenging. In this article, we propose a novel solution to enable robust cloud IDSs using deep neural networks. Specifically, we develop two deep generative models to synthesize malicious samples on the cloud systems. The first model, conditional denoising adversarial autoencoder (CDAAE), is used to generate specific types of malicious samples. The second model (CDAEE-KNN) is a hybrid of CDAAE and the K -nearest neighbor algorithm to generate malicious borderline samples that further improve the accuracy of a cloud IDS. The synthesized samples are merged with the original samples to form the augmented datasets. Three machine learning algorithms are trained on the augmented datasets and their effectiveness is analyzed. The experiments conducted on four popular IDS datasets show that our proposed techniques significantly improve the accuracy of the cloud IDSs compared with the baseline technique and the state-of-the-art approaches. Moreover, our models also enhance the accuracy of machine learning algorithms in detecting some currently challenging distributed denial of service (DDoS) attacks, including low-rate DDoS attacks and application layer DDoS attacks.

Latent graph structure and stimulus of graph-structured data contain critical private information, such as brain disorders in functional magnetic resonance imaging data, and can be exploited to identify individuals. It is critical to perturb the latent information while maintaining the utility of the data, which, unfortunately, has never been addressed. This paper presents a novel approach to obfuscating the latent information and maximizing the utility. Specifically, we first analyze the graph Fourier transform (GFT) basis that captures the latent graph structures, and the latent stimuli that are the spectral-domain inputs to the latent graphs. Then, we formulate and decouple a new multi-objective problem to alternately obfuscate the GFT basis and stimuli. The difference-of-convex (DC) programming and Stiefel manifold gradient descent are orchestrated to obfuscate the GFT basis. The DC programming and gradient descent are employed to perturb the spectral-domain stimuli. Experiments conducted on an attention-deficit hyperactivity disorder dataset demonstrate that our approach can substantially outperform its differential privacy-based benchmark in the face of the latest graph inference attacks.

This paper focuses on a network scenario where a multi-antenna access point serves multiple single-antenna users in the presence of multiple eavesdroppers, with the aid of a reconfigurable intelligent surface (RIS). The RIS employs low-resolution programmable reflecting elements (PREs) for cost-effective implementation. In order to establish secure links for all users, we consider the joint design of the transmit beamformers and PREs to maximize either the geometric mean of secrecy rates or the worst user’s secrecy rate. Novel computational algorithms of low computational complexity are developed for the solution of these mixed discrete continuous optimization problems. Simulations show the merit of the proposed designs in in achieving fair secrecy rate distributions and ensuring secure links for all users.

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.

Most conventional Federated Learning (FL) models are using a star network topology where all users aggregate their local models at a single server (e.g., a cloud server). That causes significant overhead in terms of both communications and computing at the server, delaying the training process, especially for large scale FL systems with straggling nodes. This article proposes a novel edge network architecture that enables decentralizing the model aggregation process at the server, thereby significantly reducing the training delay for the whole FL network. Specifically, we design a highly-effective in-network computation framework (INC) consisting of a user scheduling mechanism, an in-network aggregation process (INA) which is designed for both primal- and primal-dual methods in distributed machine learning problems, and a network routing algorithm with theoretical performance bounds. The in-network aggregation process, which is implemented at edge nodes and cloud node, can adapt two typical methods to allow edge networks to effectively solve the distributed machine learning problems. Under the proposed INA, we then formulate a joint routing and resource optimization problem, aiming to minimize the aggregation latency. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Simulation results showed that the proposed algorithm can achieve more than 99$\%$of the optimal solution and reduce the FL training latency, up to 5.6 times w.r.t other baselines. The proposed INC framework can not only help reduce the FL training latency but also significantly decrease cloud's traffic and computing overhead. By embedding the computing/aggregation tasks at the edge nodes and leveraging the multi-layer edge-network architecture, the INC framework can liberate FL from the star topology to enable large-scale FL.

Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure. The collected data and learned knowledge can help the vehicular service provider (VSP) improve the global model accuracy, e.g., for road safety as well as better profits for both VSP and participating SVs. Nonetheless, there exist major challenges when implementing the FL in IoV networks, such as dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information history at each learning round. Then, each selected SV can collect on-road information and propose a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the VSP's limited payment budget and asymmetric information between the VSP and SVs. Through experimental results using real-world on-road datasets, we show that our framework can converge 57% faster (even with only 10% of active SVs in the network) and obtain much higher social welfare of the network (up to 27.2 times) compared with those of other baseline FL methods.

This work proposes a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, considering limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP. Particularly, the MAP first determines a set of the best MUs for the FL process based on MUs' provided information/features. Then, each selected MU can encrypt part of local data and upload the encrypted data to the MAP for an encrypted training process, in addition to the local training process. For that, the selected MU can propose a contract to the MAP according to its expected local and encrypted data. To find optimal contracts that can maximize utilities while maintaining high learning quality of the system, we develop a multi-principal one-agent contract-based problem considering the MUs' privacy cost, the MAP's limited computing resources, and asymmetric information between the MAP and MUs. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare up to 114% under the privacy cost consideration compared with those of baseline methods.

In this article, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain's security and performance.

Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system’s total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes’ actual performance is not known in advance. Moreover, the results have shown that CERA’s online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.

This paper considers hybrid beamforming consisting of analog beamforming (ABF) coupled with digital baseband beamforming (DBF) which is designed for multi-user (MU) multiple input multiple output (MIMO) millimeter-wave (mmWave) communications. ABF uses a limited number of radio frequency (RF) chains and finite-resolution phase-shifters to alleviate the power consumption at the base station (BS), while DBF uses either zero-forcing beamforming (ZFB) or regularized zero forcing beamforming (RZFB) to restrain MU interference. The joint design of ABF and DBF constitutes a computationally challenging mixed discrete continuous optimization problem. The paper develops efficient algorithms for its solution, which iterate scalable-complex expressions. Furthermore, we conceive a new class of MU RZFB for attaining higher rates. Simulations are provided to demonstrate the viability of the proposed algorithms and the advantages of the conceived RZFB.

Intrusion detection systems (IDSs) provide an ef-fective solution for protecting loT systems. However, due to the massive number of loT devices (in billions) and their heterogeneity, IDSs face challenges posed by the complexity of loT data such as correlation-based features, high dimensions, and imbalance. To address these problems, this paper proposes a novel neural network architecture, called Balanced Twin Auto-Encoder (BTAE) which consists of three components, i.e., an encoder, a hermaphrodite, and a decoder. The encoder of BTAE first aims to transfer the input data into the latent space before data samples (pre-images) are translated into this space by different translation vectors. In addition, the data of the skewed labels are also generated in the latent space to address the problem of imbalanced data in which the number of attack samples is often significantly lower than those of the benign samples. Second, the hermaphrodite component serves as a bridge to move the data from the encoder to the decoder. Third, the decoder tries to copy the distribution of the samples in the latent space. BTAE is trained by a supervised learning technique, and its data representation extracted from the decoder can well distinguish the attack from the normal data. The experiments on five loT botnet datasets show that BTAE outperforms three existing groups of methods, e.g., the typical supervised learning, the well-known sampling, and the state-of-the-art representation learning. In addition, the false alarm rate (FAR) of BTAE applied for loT intrusion detection is less than equal to 1.2%.

In this paper, we develop a novel privacy-aware framework to address straggling problem in a federated learning (FL)-based mobile edge network through maximizing profit for the mobile service provider (MSP). In particular, unlike the conventional FL process when participating mobile users (MUs) have to train their all data locally, we propose a highly-effective solution that allows MUs to encrypt parts of local data and upload/cache the encrypted data to nearby mobile edge nodes (MENs) and/or a cloud server (CS) to perform additional training processes. In this way, we can not only mitigate the straggling problem caused by limited computing/communications resources at MUs but also enhance the usage efficiency of learning data from all MUs in the FL process. To optimize portions of encrypted data cached and trained at MENs/CS given constraints from MUs and the MSP while considering data privacy and training costs, we first formulate the profit maximization problem for the MSP as an optimal in-network encrypted data caching and learning optimization. We then prove that the objective function is concave, and thus an interior-point method algorithm can be effectively adopted to quickly find the optimal solution. The numerical results demonstrate that our proposed framework can enhance the profit of the MSP up to 5.39 times compared with other FL methods.

By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs’ computing/communications resources to given favorable sets of users may block other devices from their service. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare (e.g., minimizing the total energy consumption) but not consider the computing/battery status of each mobile device. This work develops a proportional fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipment (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it NP-hard. To tackle it, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branchand-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decision and subproblems (SPs) for resource allocation. The SPs can either find their closed-form solutions or be solved in parallel at ENs, thus help reduce the complexity. The numerical results show that the DBBD returns the optimal solution of the problem maximizing the fairness between UEs. The DBBD has higher fairness indexes, i.e., Jain’s index and min-max ratio, in comparing with the existing ones that minimize the total consumed energy.

This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting. We consider a swarm of hovering UAVs that relays information from a terrestrial source to a distant mobile user and simultaneously generates jamming signals to obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate to harvest wireless energy, relay information, and jam the eavesdropper. To evaluate the secrecy rate, we derive the expressions of the secrecy outage probability (SOP) in the integral form for two popular detection techniques used by the eavesdropper, i.e., selection combining and maximum-ratio combining in high signal-to-noise ratio regime. Monte Carlo simulations validate the derived SOP and show that the proposed framework outperforms the conventional AF relaying system, in terms of SOP. The insights from SOP and analysis in this work sheds light on optimizing the energy harvesting time, the number of UAVs in the swarm as well as their placements, to achieve the required secrecy protection level.

Metaverse has recently attracted paramount attention due to its potential for future Internet. However, to fully realize such potential, Metaverse applications have to overcome various challenges such as massive resource demands, interoperability among applications, and security and privacy concerns. In this paper, we propose MetaChain, a novel blockchain-based framework to address emerging challenges for the development of Metaverse applications. In particular, by utilizing the smart contract mechanism, MetaChain can effectively manage and automate complex interactions among the Metaverse Service Provider (MSP) and the Metaverse users (MUs). In addition, to allow the MSP to efficiently allocate its resources for Metaverse applications and MUs’ demands, we design a novel sharding scheme to improve the underlying blockchain’s scalability. Moreover, to leverage MUs’ resources as well as to attract more MUs to support Metaverse operations, we develop an incentive mechanism using the Stackelberg game theory that rewards MUs’ contributions to the Metaverse. Through numerical experiments, we clearly show the impacts of the MUs’ behaviors and how the incentive mechanism can attract more MUs and resources to the Metaverse.

Intrusion detection systems (IDSs) play a pivotal role in defending IoT systems. However, developing a robust and efficient IDS is challenging due to the rapid and continuing evolving of various forms of cyber-attacks as well as a massive number of low-end IoT devices. In this paper, we introduce a novel deep learning architecture based on auto-encoders that allows to develop a robust intrusion detection system. Specifically, we propose a novel neural network architecture called Twin Variational Auto-Encoder (TVAE) for representation learning. TVAE includes a variational Auto-Encoder (VAE) and an Auto-Encoder (AE) that share a common stage where the decoder of the VAE is used as the encoder of the AE. The TVAE is trained in an unsupervised manner to effectively transform the original representation of data at the input of the VAE into a new representation at the output of the AE. In the new representation space, the difference between normal and attack data is more distinguishable. A variant of TVAE, namely Twin Sparse Variational Auto-Encoder (TSVAE) is also introduced by imposing a sparsity constraint on the representation units. The effectiveness of TVAE and TSVAE is evaluated using popular IDS and IoT botnet datasets. The simulation results show that the accuracy of TVAE and TSVAE can achieve the best results on six datasets, which is higher than those of state-of-the-art AE and VAE variants. We also investigate various characteristics of TVAE in the latent space as well as in the data extraction process. Besides applications on the IoT IDS, TVAE can also be applicable to all conventional network IDSs.

This paper aims to develop a highly-effective framework to significantly enhance the efficiency in using coded computing techniques for distributed computing tasks over heterogeneous wireless edge networks. In particular, we first formulate a joint coding and node selection optimization problem to minimize the expected total processing time for computing tasks, taking into account the heterogeneity in the nodes’ computing resources and communication links. The problem is shown to be NP-hard. To circumvent it, we leverage the unique characteristic of the problem to develop a linearization approach and a hybrid algorithm based on binary search and branch-and-bound (BB) algorithms. This hybrid algorithm can not only guarantee to find the optimal solution, but also significantly reduce the computational complexity of the BB algorithm. Simulations based on real-world datasets show that the proposed approach can reduce the total processing time up to 2.4 times compared with that of state-of-the-art approach, even without perfect knowledge regarding the node’s performance and their straggling parameters.

This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting (EH). In particular, we consider a swarm of hovering UAVs that relays information from a terrestrial base station to a distant mobile user and simultaneously generates friendly jamming signals to interfere/obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate in harvesting wireless energy, relay information, and jam the eavesdropper. To evaluate the performance, we derive the secrecy outage probability (SOP) for two popular detection techniques at the eavesdropper, i.e., selection combining and maximum-ratio combining. Monte Carlo simulations are then used to validate the theoretical SOP derivation. Using the derived SOP, one can obtain engineering insights to optimize the EH time and the number of UAVs in the swarm to achieve a given secrecy protection level. Furthermore, simulations show the effectiveness of the proposed framework in terms of SOP compared to the conventional AF relaying system. The analytical SOP derived in this work can also be helpful in future UAV secure-communication optimizations (e.g., trajectory and locations of UAVs). As an example, we present a case study to find the optimal corridor to locate the swarm so as to minimize the system SOP. Our proposed framework helps secure communications for various applications that require large coverage, e.g., industrial IoT, smart city, intelligent transportation systems, and critical IoT infrastructures, such as energy and water.

Accurate assessment of fish quality is difficult in practice due to the lack of trusted fish provenance and quality tracking information. Working with Sydney Fish Market (SFM), we develop a Blockchain-enabled fish provenance and quality tracking (BeFAQT) system. A multilayer Blockchain architecture based on attribute-based encryption (ABE) is proposed to tackle the privacy issue caused by applying Blockchain to secure supply chain data and achieve trusted and confidential data sharing among parties in fish supply chains. An Internet-of-Things (IoT) chain saves encrypted fish provenance and quality tracking data, and an ABE chain is specifically designed for the access control to the data in the IoT chain. Latest IoT and artificial intelligence (AI) technologies, including NarrowBand-IoT, image processing, and biosensing, are developed for fish origin proof, supply chain tracking, and objective fish quality assessment. As proven by field trials with SFM and a local fish supply chain, the BeFAQT is able to provide trusted and comprehensive fish provenance and quality tracking information in real time.

Unlike theoretical analysis of distributed learning (DL) in the literature, DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly dynamic wireless edge networks. This article addresses these problems by leveraging recent advances in coded computing and the deep dueling neural network architecture. By introducing coded structures/redundancy, a distributed learning task can be completed without waiting for straggling nodes. Unlike conventional coded computing that only optimizes the code structure, coded distributed learning over the wireless edge also requires to optimize the selection/scheduling of wireless edge nodes with heterogeneous connections, computing capability, and straggling effects. However, even neglecting the aforementioned dynamics/uncertainty, the resulting joint optimization of coding and scheduling to minimize the distributed learning time turns out to be NP-hard. To tackle this and to account for the dynamics and uncertainty of wireless connections and edge nodes, we reformulate the problem as a Markov Decision Process and design a novel deep reinforcement learning algorithm that employs the deep dueling neural network architecture to find the jointly optimal coding scheme and the best set of edge nodes for different learning tasks without explicit information about the wireless environment and edge nodes’ straggling parameters. Simulations show that the proposed framework reduces the average learning delay in wireless edge computing up to 66% compared with other DL approaches. The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.

With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods’ robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

Relay-aided multi-user OFDM is investigated under which multiple sources transmit their signals to a multi-antenna relay during the first relaying stage and then the relay amplifies and forwards the composite signal to all destinations during the second stage. The signal transmission of both stages experience frequency selectivity. The relay is powered both by an energy source through the wireless power transfer as well as by the energy recycled from its own self-interference during the second stage. Accordingly, we jointly design the power allocations both at the multiple source nodes and at a common relay node for maximizing the network’s sum-throughput, which poses a large-scale nonconvex problem, regardless whether proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS) is used for signal transmission to the relay. We develop new alternating descent procedures for solving our joint optimization problems, which are based on closed-forms and thus are of very low computational complexity even for large numbers of subcarriers. The results show the superiority of IGS over PGS in terms of both its sum-rate and individual user-rate. Another benefit of IGS over PGS is that the former promises fairer rate distribution across the subcarriers. Moreover, the recycled self-interference also provides a beneficial complementary energy source.

A multi-antenna aided base station (BS) supporting several multi-antenna downlink users with the aid of a reconfigurable intelligent surface (RIS) of programmable reflecting elements (PREs) is considered. Low-resolution PREs constrained by a set of sparse discrete values are used for reasons of cost-efficiency. Our challenging objective is to jointly design the beamformers at the BS and the RIS’s PREs for improving the throughput of all users by maximizing their geometric-mean, under a variety of different access schemes. This constitutes a computationally challenging problem of mixed continuous-discrete optimization, because each user’s throughput is a complicated function of both the continuous-valued beamformer weights and of the discrete-valued PREs. We develop low-complexity algorithms, which iterate by directly evaluating low-complexity closed-form expressions. Our simulation results show the advantages of non-orthogonal multiple access-aided signaling, which allows the users to decode a part of the multi-user interference for enhancing their throughput.

This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users’ rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs.

Internet of Things (IoT) has emerged as a cutting-edge technology that is changing human life. The rapid and widespread applications of IoT, however, make cyberspace more vulnerable, especially to IoT-based attacks in which IoT devices are used to launch attack on cyber-physical systems. Given a massive number of IoT devices (in order of billions), detecting and preventing these IoT-based attacks are critical. However, this task is very challenging due to the limited energy and computing capabilities of IoT devices and the continuous and fast evolution of attackers. Among IoT-based attacks, unknown ones are far more devastating as these attacks could surpass most of the current security systems and it takes time to detect them and "cure" the systems. To effectively detect new/unknown attacks, in this article, we propose a novel representation learning method to better predictively "describe" unknown attacks, facilitating supervised learning-based anomaly detection methods. Specifically, we develop three regularized versions of autoencoders (AEs) to learn a latent representation from the input data. The bottleneck layers of these regularized AEs trained in a supervised manner using normal data and known IoT attacks will then be used as the new input features for classification algorithms. We carry out extensive experiments on nine recent IoT datasets to evaluate the performance of the proposed models. The experimental results demonstrate that the new latent representation can significantly enhance the performance of supervised learning methods in detecting unknown IoT attacks. We also conduct experiments to investigate the characteristics of the proposed models and the influence of hyperparameters on their performance. The running time of these models is about 1.3 ms that is pragmatic for most applications.

Mobile service providers (MSPs) are particularly vulnerable to roaming frauds, especially ones that exploit the long delay in the data exchange process of the contemporary roaming management systems, causing multi-billion dollars loss each year. In this paper, we introduce BlockRoam, a novel blockchain-based roaming management system that provides an efficient data exchange platform among MSPs and mobile subscribers. Utilizing the Proof-of-Stake (PoS) consensus mechanism and smart contracts, BlockRoam can significantly shorten the information exchanging delay, thereby addressing the roaming fraud problems. Through intensive analysis, we show that the security and performance of such PoS-based blockchain network can be further enhanced by incentivizing more users (e.g., subscribers) to participate in the network. Moreover, users in such networks often join stake pools (e.g., formed by MSPs) to increase their profits. Therefore, we develop an economic model based on Stackelberg game to jointly maximize the profits of the network users and the stake pool, thereby encouraging user participation. We also propose an effective method to guarantee the uniqueness of this game's equilibrium. The performance evaluations show that the proposed economic model helps the MSPs to earn additional profits, attracts more investment to the blockchain network, and enhances the network's security and performance.

In this paper, we propose a novel economic-efficiency framework for an electric vehicle (EV) network to maximize the profits (i.e., the amount of money that can be earned) for charging stations (CSs). To that end, we first introduce an energy demand prediction method for CSs leveraging federated learning approaches, in which each CS can train its own energy transactions locally and exchange its learned model with other CSs to improve the learning quality while protecting the CS's information privacy. Based on the predicted energy demands, each CS can reserve energy from the smart grid provider (SGP) in advance to optimize its profit. Nonetheless, due to the competition among the CSs as well as unknown information from the SGP, i.e., the willingness to transfer energy, we develop a multi-principal one-agent (MPOA) contract-based method to address these issues. In particular, we formulate the CSs’ profit maximization as a non-collaborative energy contract problem under the SGP's unknown information and common constraints as well as other CSs’ contracts. To solve this problem, we transform it into an equivalent low-complexity optimization problem and develop an iterative algorithm to find the optimal contracts for the CSs. Through simulation results using a real CS dataset, we demonstrate that our proposed framework can enhance energy demand prediction accuracy up to 24.63 percent compared with other machine learning algorithms. Furthermore, our proposed framework can outperform other economic models by 48 and 36 percent in terms of the CSs’ utilities and social welfare (i.e., the total profits of all participating entities) of the network, respectively.

This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.

This paper considers a reconfigurable intelligent surface (RIS)-aided network, which relies on a multiple antenna array aided base station (BS) and an RIS for serving multiple single antenna downlink users. To provide reliable links to all users over the same bandwidth and same time-slot, the paper proposes the joint design of linear transmit beamformers and the programmable reflecting coefficients of an RIS to maximize the geometric mean (GM) of the users’ rates. A new computationally efficient alternating descent algorithm is developed, which is based on closed-forms only for generating improved feasible points of this nonconvex problem. We also consider the joint design of widely linear transmit beamformers and the programmable reflecting coefficients to further improve the GM of the users’ rates. Hence another alternating descent algorithm is developed for its solution, which is also based on closed forms only for generating improved feasible points. Numerical examples are provided to demonstrate the efficiency of the proposed approach.

This paper considers a network of a multi-antenna array base station (BS) and a reconfigurable intelligent surface (RIS) to deliver both information to information users (IUs) and power to energy users (EUs). The RIS links the connection between the IUs and the BS as there is no direct path between the former and the latter. The EUs are located nearby the BS in order to effectively harvest energy from the high-power signal from the BS, while the much weaker signal reflected from the RIS hardly contributes to the EUs’ harvested energy. To provide reliable links for all users over the same time-slot, we adopt the transmit time-switching (transmit-TS) approach, under which information and energy are delivered over different time-slot fractions. This allows us to rely on conjugate beamforming for energy links and zero-forcing/regularized zero-forcing beamforming (ZFB/RZFB) and on the programmable reflecting coefficients (PRCs) of the RIS for information links. We show that ZFB/RZFB and PRCs can be still separately optimized in their joint design, where PRC optimization is based on iterative closed-form expressions. We then develop a path-following algorithm for solving the max-min IU throughput optimization problem subject to a realistic constraint on the quality-of-energy-service in terms of the EUs’ harvested energy thresholds. We also propose a new RZFB for substantially improving the IUs’ throughput.

Major bottlenecks of large-scale Federated Learning (FL) networks are the high costs for communication and computation. This is due to the fact that most of current FL frameworks only consider a star network topology where all local trained models are aggregated at a single server (e.g., a cloud server). This causes significant overhead at the server when the number of users are huge and local models' sizes are large. This paper proposes a novel edge network architecture which decentralizes the model aggregation process at the server, thereby significantly reducing the aggregation latency of the whole network. In this architecture, we propose a highly-effective in-network computation protocol consisting of two components. First, an in-network aggregation process is designed so that the majority of aggregation computations can be offloaded from cloud server to edge nodes. Second, a joint routing and resource allocation optimization problem is formulated to minimize the aggregation latency for the whole system at every learning round. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Numerical results show that our proposed framework can dramatically reduce the network latency, up to 4.6 times. Furthermore, this framework can significantly decrease cloud's traffic and computing overhead by a factor of$K$/ M, where$K$is the number of users and$M$is the number of edge nodes, in comparison with conventional baselines.

This paper proposes a novel framework that can effectively address key challenges for the development of distributed learning over wireless edge networks. In particular, we first introduce a highly effective distributed learning model leveraging the most recent advanced coded distributed computing algorithm together with collaborative computing resources from wireless edge nodes to securely and effectively execute learning tasks. To minimize the average delay of learning tasks, the coding and scheduling policies must be jointly optimized. However, determining the optimal coding scheme together with the optimal edge nodes for different learning tasks is NP-hard due to the dynamics and uncertainty of the wireless environment and straggling problems at the computing nodes. Thus, we develop a highly effective approach utilizing advances of both reinforcement learning algorithms and the dueling network architecture to quickly find the optimal coding scheme together with the best edge nodes for different learning tasks without requiring completed information about the surrounding environment and straggling parameters in advance. Through extensive simulation results, we show that our proposed framework can reduce the average delay for the whole system up to 66% compared with other conventional learning and optimization approaches.

The Internet-of-Vehicles (IoV) can make driving safer and bring more services to smart vehicle (SV) users. Specif-ically, with IoV, the road service provider (RSP) can collaborate with SVs to provide high-accurate on-road information-based services by implementing federated learning (FL). Nonetheless, SVs' activities are very diverse in IoV networks, e.g., some SVs move frequently while other SVs are occasionally disconnected from the network. Consequently, obtaining information from all SVs for the learning process is costly and impractical. Furthermore, the quality-of-information (QoI) obtained by SVs also dramatically varies. That makes the learning process from all SVs simultaneously even worse when some SVs have low QoI. In this paper, we propose a novel selective FL approach for an IoV network to address these issues. Particularly, we first develop an SV selection method to determine a set of active SVs based on their location significance. In this case, we adopt a K-means algorithm to classify significant and insignificant areas where the SVs are located according to the areas' average annual daily flow of vehicles. From the set of SVs in the significant areas, we select the best SVs for the FL execution based on the SVs' QoI at each learning round. Through simulation results using a real-world on-road dataset, we observe that our proposed approach can converge to the FL results even with only 10% of active SVs in the network. Moreover, our results reveal that the RSP can optimize on-road services with faster convergence up to 63% compared with other baseline FL methods.

Federated learning (FL) has emerged as a highly-effective distributed learning framework for various AI-based mobile applications. However, in conventional FL, participating mobile users (MUs) may have limited computing resources to train their local data, which leads to learning quality degradation for the whole FL process. To address this problem, coded FL (codFL) has been recently introduced, allowing MUs to upload part of their coded data to a mobile application provider (MAP) before the learning process. As a result, codFL can not only deal with the MUs' limited computing resources, but also provide more benefits for the MUs to participate in the learning process. Nonetheless, in practice, the MAP and MUs often belong to different parties who unilaterally aim to maximize their individual utility functions. Thus, in this paper, we propose an effective mechanism for the codFL process to incentivize all the participating MUs while improving the learning quality of the MAP. Specifically, we first design a codFL contract optimization problem leveraging a multi-principal one-agent (MPOA) approach in contract theory, under limited computing resources at the MAP and MUs as well as information asymmetry between them. To find the optimal contracts for MUs, we develop an iterative contract algorithm which can produce maximum utilities for all MUs while satisfying all the constraints of the MAP. Numerical results show that our framework can enhance the utilities of MUs up to 113% and system performance in terms of social welfare up to 42% compared with the baseline method.

Conventional anti-jamming solutions like frequency hopping and rate adaptation that are more suitable for proactive jammers are not effective in dealing with reactive jammers. These advanced jammers with recent advances in signal detection can discern the activities of legitimate radios then attack them as soon as the transmission is detected. To combat this type of jammer, we develop an intelligent deception strategy in which the transmitter generates "fake" transmissions to attract the jammer. After that, the transmitter can either harvest energy from the jamming signals or backscatter the jamming signals to transmit data. As such, we can leverage jamming signals to improve the average throughput and reduce the packet loss. To effectively learn from and adapt to the dynamic and uncertainty of jamming attacks, we develop a Markov decision process (MDP) that can dynamically construct two decision epochs in each time slot to capture the special properties of our proposed deception mechanism. The Q-learning algorithm then can be adopted to find the optimal deception strategy for the transmitter. Nevertheless, due to very-slow convergence rates, conventional Q-learning algorithms may not be effective in dealing with smart jamming attacks. We thus develop an advanced deep reinforcement learning model based on deep dueling architecture to quickly obtain the optimal defense policy. Simulation results show that the proposed framework can improve the system throughput up to 173% and reduce the packet loss by 42% compared with other anti-jamming strategies that are not equipped with the proposed deception mechanism.

Unmanned Aerial Vehicles (UAVs) have been emerging as an effective solution for IoT data collection networks thanks to their outstanding flexibility, mobility, and low operation costs. However, due to the limited energy and uncertainty from the data collection process, speed control is one of the most important factors while optimizing the energy usage efficiency and performance for UAV collectors. This work aims to develop a novel autonomous speed control approach to address this issue. To that end, we first formulate the dynamic speed control task of a UAV as a Markov decision process taking into account its energy status and location. In this way, the Q-learning algorithm can be adopted to obtain the optimal speed control policy for the UAV. To further improve the system performance, we develop a highly-effective deep dueling double Q-learning algorithm utilizing outstanding features of the deep neural networks as well as advanced dueling architecture to quickly stabilize the learning process and obtain the optimal policy. Through simulations, we show that our proposed solution can achieve up to 40% greater performance, i.e., an average throughput of the system, compared with other conventional methods. Importantly, the simulation results also reveal significant impacts of UAV's energy and charging time on the system performance.

In this paper, we propose a novel blockchain-based platform for the coalition loyalty program management. The platform allows the customers to freely exchange loyalty points from different existing blockchain-based loyalty programs by utilizing the sidechain technology. Moreover, by adopting the Proof-of-Stake consensus mechanism, we can further increase customer engagement by allowing the customers to participate in the consensus process to earn additional tokens. However, this might lead to situations where the customers centralize all tokens to a single chain/loyalty program if the chain offers more rewards for consensus participation. Through security and performance analyses, we show that such centralization of stakes poses a threat to the security and performance of the platform. Therefore, we develop a non-cooperative game model to analyze the rational behavior of the users. We reveal that the consensus participation rewards govern the user behavior and the decentralization of the system. Numerical experiments confirm our analytical results and show that the ratios between the consensus rewards have a significant impact on the system's security and performance.

The paper is the first work that considers a constrained feedback control strategy to navigate an unmanned aerial vehicle (UAV) from a given starting point to a given terminal point while harvesting solar energy and providing a wireless communication service for ground users. Wireless communication channels are stochastic and cannot be known off-line, making the problem of off-line UAV path planning for wireless communication as considered in most existing works less meaningful. We consider the problem of navigating a solar-powered UAV from a starting point to a terminal point to harvest solar energy while serving the two-way communication between multiple pairs of ground users in a complex terrain. The objective is to jointly optimize the UAV’s flight time and its flight path by trading-off between the harvested energy and power consumption subject to the ground users’ minimum throughput requirement. We develop a new model predictive control (MPC) technique to address this problem. Namely, based on the well-known statistics of the air-to-ground (A2G) and ground-to-air (G2A) wireless channels, a predictive control model is proposed at each time-instant, which leads to an optimization problem over a receding horizon for the control design. This problem is non-convex due to the involvement of various optimization variables, which is then solved via novel convex iterations. Simulation results show the merits of the proposed algorithm. The results obtained by the proposed algorithm match with the benchmark non-MPC and offline-MPC approaches.

In intelligent transportation systems (ITS), vehicles are expected to feature with advanced applications and services which demand ultra-high data rates and low-latency communications. For that, the millimeter wave (mmWave) communication has been emerging as a very promising solution. However, incorporating the mmWave into ITS is particularly challenging due to the high mobility of vehicles and the inherent sensitivity of mmWave beams to dynamic blockages. This article addresses these problems by developing an optimal beam association framework for mmWave vehicular networks under high mobility. Specifically, we use the semi-Markov decision process to capture the dynamics and uncertainty of the environment. The Q-learning algorithm is then often used to find the optimal policy. However, Q-learning is notorious for its slow-convergence. Instead of adopting deep reinforcement learning structures (like most works in the literature), we leverage the fact that there are usually multiple vehicles on the road to speed up the learning process. To that end, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly obtain the optimal policy by simultaneously learning from various vehicles. Extensive simulations demonstrate that our proposed solution can increase the data rate by 47% and reduce the disconnection probability by 29% compared to other solutions.

We develop a joint offloading and resource allocation framework for a multi-layer cooperative fog computing network, aiming to minimize the total energy consumption of multiple mobile devices subject to their service delay requirements. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it an NP-hard and computationally intractable problem. To tackle it, we first propose an improved branch-and-bound algorithm (IBBA) that is implemented in a centralized manner. However, due to the large size of the cooperative fog computing network, the computational complexity of the proposed IBBA is relatively high. To speed up the optimal solution searching as well as to enable its distributed implementation, we then leverage the unique structure of the underlying problem and the parallel processing at fog nodes. To that end, we propose a distributed framework, namely feasibility finding Benders decomposition (FFBD), that decomposes the original problem into a master problem for the offloading decision and subproblems for resource allocation. The master problem (MP) is then equipped with powerful cutting-planes to exploit the fact of resource limitation at fog nodes. The subproblems (SP) for resource allocation can find their closed-form solutions using our fast solution detection method. These (simpler) subproblems can then be solved in parallel at fog nodes. The numerical results show that the FFBD always returns the optimal solution of the problem with significantly less computation time (e.g., compared with the centralized IBBA approach). The FFBD with the fast solution detection method, namely FFBD-F, can reduce up to 60% and 90% of computation time, respectively, compared with those of the conventional FFBD, namely FFBD-S, and IBBA.

A multi-user fog radio access network (F-RAN) is designed for supporting content-centric services. The requested contents are partitioned into sub-contents, which are then ‘beamformed’ by the remote radio heads (RRHs) for transmission to the users. Since a large number of beamformers must be designed, this poses a computational challenge. We tackle this challenge by proposing a new class of regularized zero forcing beamforming (RZFB) for directly mitigating the inter-content interferences, while the ‘intra-content interference’ is mitigated by successive interference cancellation at the user end. Thus each beamformer is decided by a single real variable (for proper Gaussian signaling) or by a pair of complex variables (for improper Gaussian signaling). Hence the total number of decision variables is substantially reduced to facilitate tractable computation. To address the problem of energy efficiency optimization subject to multiple constraints, such as individual user-rate requirement and the fronthauling constraint of the links between the RRHs and the centralized baseband signal processing unit, as well as the total transmit power budget, we develop low-complexity path-following algorithms. Finally, we confirm their performance by simulations.

Under both long (infinite) and short (finite) blocklength transmissions, this paper considers physical layer security for a wireless interference network of multiple transmitter-user pairs, which is overheard by multiple eavesdroppers (EVs). The EVs are assumed to have better channel conditions than the legitimate users (UEs), making the conventional transmission unsecured. The paper develops a novel time-fraction based transmission, under which the information is transmitted to the UEs within a fraction of the time slot and artificial noise (AN) is transmitted within the remaining fraction to counter the strong EVs' channels. Based on channel distribution information of UEs and EVs, the joint design of transmit beamforming, time fractions and AN power allocation to maximize the worst users' secrecy rate is formulated in terms of nonconvex problems. Path-following algorithms of low complexity and rapid convergence are proposed for their solution. Simulations are provided to demonstrate the viability of the proposed methodology.

Mobile edge caching/computing (MEC) has been emerging as a promising paradigm to provide ultra-high rate, ultra-reliable, and/or low-latency communications in future wireless networks. In this paper, we introduce a novel MEC network architecture that leverages the optimal joint caching-delivering with horizontal cooperation among mobile edge nodes (MENs). To that end, we first formulate the content-access delay minimization problem by jointly optimizing the content caching and delivering decisions under various network constraints (e.g., network topology, storage capacity and users' demands at each MEN). However, the strongly mutual dependency between the decisions makes the problem a nested dual optimization that is proved to be NP-hard. To deal with it, we propose a novel transformation method to transform the nested dual problem to an equivalent mixed-integer nonlinear programming (MINLP) optimization problem. Then, we design a centralized solution using an improved branch-and-bound algorithm with the interior-point method to find the joint caching and delivering policy which is within 1 percent of the optimal solution. Since the centralized solution requires the full network topology and information from all MENs, to make our solution scalable, we develop a distributed algorithm which allows each MEN to make its own decisions based on its local observations. Extensive simulations demonstrate that the proposed solutions can reduce the total average delay for the whole network up to 40 percent compared with other current caching policies. Furthermore, the proposed solutions also increase the cache hit ratio for the network up to 4 times, thereby dramatically reducing the traffic load on the backhaul network.

In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks. In particular, for a smart and reactive jamming attack, the jammer is able to sense the channel and attack the channel if it detects communications from the legitimate transmitter. To deal with such attacks, we propose an intelligent deception strategy which allows the legitimate transmitter to transmit “fake” signals to attract the jammer. Then, if the jammer attacks the channel, the transmitter can leverage the strong jamming signals to transmit data by using ambient backscatter communication technology or harvest energy from the strong jamming signals for future use. By doing so, we can not only undermine the attack ability of the jammer, but also utilize jamming signals to improve the system performance. To effectively learn from and adapt to the dynamic and uncertainty of jamming attacks, we develop a novel deep reinforcement learning algorithm using the deep dueling neural network architecture to obtain the optimal policy with thousand times faster than those of the conventional reinforcement algorithms. Extensive simulation results reveal that our proposed DeepFake framework is superior to other anti-jamming strategies in terms of throughput, packet loss, and learning rate.

This article studies the strategic interactions between an IoT service provider (IoTSP) which consists of heterogeneous IoT devices and its energy service provider (ESP). To that end, we propose an economic framework using the Stackelberg game to maximize the network throughput and energy efficiency of both the IoTSP and ESP. To obtain the Stackelberg equilibrium (SE), we apply a backward induction technique which first derives a closed-form solution for the ESP (follower). Then, to tackle the non-convex optimization problem for the IoTSP (leader), we leverage theblock coordinate descentandconvex-concave proceduretechniques to design two partitioning schemes (i.e., partial adjustment (PA) and joint adjustment (JA)) to find the optimal energy price and service time that constitute local SEs. Numerical results reveal that by jointly optimizing the energy trading and time allocation for IoT devices, one can achieve significant improvements in terms of the IoTSP’s profit compared with those of conventional transmission methods (up to 38.7 folds). Different tradeoffs between the ESP’s and IoTSP’s profits and complexities of the PA/JA schemes can also be numerically tuned. Simulations also show that the obtained local SEs approach the optimal social welfare when the benefit per transmitted bit exceeds a given threshold.

This paper develops a beam association framework for mm Wave vehicular networks to improve the system performance in terms of handover, disconnection time, and data rate under the high mobility of vehicles. In particular, we recruit the semi Markov decision process to capture the uncertainty and dynamic of the environment such as locations of beams, received signal strength indicator profiles, velocities, and blockages. Instead of adopting complex deep learning structures such as deep dueling and double deep Q-learning, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly derive the optimal beam association policy by simultaneously learning from various vehicles on the road. Through extensive simulation results, we demonstrate that the proposed framework can reduce the average disconnection time by 33% and increase the data rate by 60% compared to other solutions. We also observed that the proposed parallel Q-learning algorithm converges much faster to the optimal solution than state-of-the-art deep-learning based algorithms.

In this paper, an economic model is proposed to jointly optimize profits for participants in a heterogeneous IoT wireless-powered backscatter communication network. In the network under considerations, a power beacon and IoT devices (with various communication types and energy constraints) are assumed to belong to different service providers, i.e., energy service provider (ESP) and IoT service provider (ISP), respectively. To jointly maximize the utility for both service providers in terms of energy efficiency and network throughput, a Stackelberg game model is proposed to study the strategic interaction between the ISP and ESP. In particular, the ISP first evaluates its benefits from providing IoT services to its customers and then sends its requested price together with the service time to the ESP. Based on the request from the ISP, the ESP offers an optimized transmission power that maximizes its utility while meeting energy demands of the ISP. To study the Stackelberg equilibrium, we first obtain a closed-form solution for the ESP and propose a low-complexity iterative method based on block coordinate descent (BCD) to address the non-convex optimization problem for the ISP. Through simulation results, we show that our approach can significantly improve the profits for both providers compared with those of conventional transmission methods, e.g., bistatic backscatter and harvest-then-transmit communication methods.

The rapid adoption of electric or hybrid vehicles (EVs) has called for wide deployment of charging stations. These stations can be launched/owned by different owners, referred to as charging station providers (CSPs), which make energy contracts with a smart grid provider (SGP). However, there exists a shortage of mutual economic strategy between the SGP and CSPs in an energy request/transfer competition due to the selfish nature among them. In this paper, we propose an economic model leveraging a multi-principal single-agent (referred to as common agency) contract policy, aiming at maximizing the utilities of multiple CSPs while optimizing the utility of the SGP in an EV network. In particular, we first develop the common agency-based contract problem as a non-cooperative energy contract optimization problem, in which each CSP can maximize its utility given the common constraints from the SGP and the contracts of other CSPs. To deal with this problem, we develop an iterative energy contract algorithm to find an equilibrium contract solution where the contracts from the CSPs can produce maximum utilities of the CSPs and satisfy the constraints of the SGP. Through numerical results, we show that our proposed model can improve the social welfare of the EV network up to 54% and the utilities of CSPs up to 60% compared with the baseline method in which each CSP obtains the amount of energy that is proportional to its energy request.

Among all wireless jammers, dealing with reactive ones is most challenging. This kind of jammer attacks the channel whenever it detects transmission from legitimate radios. With recent advances in self-interference suppression or in-band full-duplex radios, a reactive jammer can jam and simultaneously sense/discern/detect the legitimate transmission. Such a jammer is referred to as a smart reactive jammer. However, all existing solutions, e.g., frequency hopping and rate adaptation, cannot effectively deal with this type of jammer. This is because a smart reactive jammer with sufficient power budget can theoretically jam most, if not all, frequency channels at sufficiently high power. This work proposes to augment the transmitter with an ambient backscatter tag. Specifically, when the jammer attacks the channel, the transmitter deceives it by continuing to transmit data to attract the jammer while the tag backscatters data based on both the jamming signals and active signals from the jammer and transmitter, respectively. However, backscattering signals from multiple radio sources results in a high bit error rate (BER). Thus, we propose to use multiple antennas at the receiver. The theoretical analysis and simulation results show that by using multiple antennas at the receiver, the BER and hence the throughput of the system can be significantly improved. More importantly, we demonstrate that with our proposed solutions, the average throughput increases and the BER decreases when the jammer attacks with higher power levels. We believe that this is the first anti-jamming solution that can cope effectively with a high- or even unlimited-power jammers.

Although the development of IoT Industry 4.0 has brought breakthrough achievements in many sectors, e.g., manufacturing, healthcare, and agriculture, it also raises many security issues to human beings due to a huge of emerging cybersecurity threats recently. In this paper, we propose a novel collaborative learning-based intrusion detection system which can be efficiently implemented in IoT Industry 4.0. In the system under consideration, we develop smart “filters” which can be deployed at the IoT gateways to promptly detect and prevent cyberattacks. In particular, each filter uses the collected data in its network to train its cyberattack detection model based on the deep learning algorithm. After that, the trained model will be shared with other IoT gateways to improve the accuracy in detecting intrusions in the whole system. In this way, not only the detection accuracy is improved, but our proposed system also can significantly reduce the information disclosure as well as network traffic in exchanging data among the IoT gateways. Through thorough simulations on real datasets, we show that the performance obtained by our proposed method can outperform those of the conventional machine learning methods.

Roaming fraud is one of the most significant financial losses for mobile service providers. The inefficiency of current exchanging data management methods among mobile service providers is the main obstacle for roaming fraud prevention. In this paper, we introduce a novel blockchain-based data exchange management system to address roaming fraud problems in mobile networks. This system provides a secure and automatic data exchange service among mobile service providers and mobile subscribers. In addition, we introduce an emerging Proof-of-Stake (PoS) consensus mechanism for the proposed blockchain-based roaming fraud prevention system, which can significantly reduce the delay in exchanging information as well as implementation costs for mobile service providers. To further enhance benefits and security efficiency for the proposed blockchain system, we develop an economic model based on Stackelberg game. This game model is very effective in maximizing profits for both the stakeholders and stake pool and useful in designing a robust blockchain-based mobile roaming management system. Through performance analysis and numerical results, we show that our proposed framework not only provides an effective solution to prevent mobile roaming fraud but also opens many business opportunities for future mobile networks.

Spectrum sharing has been gaining its popular adoption as a potential solution to improve spectrum utilization in future wireless systems. Both Federal Communications Commission (FCC) and European Telecommunications Standards Institute (ETSI) support dynamic spectrum access (DSA) as an enabling technology for spectrum sharing. To effectively realize DSA in practice, users (from both defense and commercial sectors) are required to share their (radio) operational information, which risks exposing their security, privacy, and business plan to unintended agents. Protecting users' operating information is hence the key to DSA's success. In this paper, taking the FCC's spectrum access system (SAS) as a study case, we investigate the operational privacy issue of Incumbent Users (IUs) and honest/dishonest Secondary Users (SUs). For the case of IUs and honest SUs, we propose a privacy-preserving scheme for DSA by leveraging encryption and obfuscation methods (PSEO). To implement PSEO, we introduce an interference calculation scheme that allows users to calculate an interference budget without revealing operational information (e.g., antenna height, transmit power, location...), referred to as the blind interference calculation scheme (BICS). BICS also reduces the computing overhead of PSEO, compared with FCC's SAS by moving interference budgeting tasks to local users and calculating it in an offline manner. To further save the overhead in calculating the interference map, we introduce a quantization method and optimize the grid sizes of the terrestrial area of interest. Additionally, for the case of IUs and dishonest SUs, we propose a “punishment and forgiveness” (PF) mechanism, which draws support from SUs' reputation scores (RSs) and reputation histories (RHs), to encourage SUs to provide truthful information. Theoretical analysis and extensive simulations show that our proposed PSEO and PF-PSEO schemes can better protect all users' operational privacy under various privacy attacks, yielding higher spectrum utilization with less online overhead, compared with state of the art approaches.

Full duplex (FD) wireless technology, which enables simultaneous transmission and reception on the same frequency, has shown its great potential for doubling the spectral efficiency as well as spectrum sensing while transmitting in cognitive radio networks (CRNs). However, the self interference (SI) suppression, the underlying technique of FD, is often imperfect, resulting in non-negligible residual SI that severely affects the test statistics of sensing methods. The residual SI thereby significantly deteriorates the spectrum sensing accuracy. In this work, we aim to address this issue by proposing a novel sensing approach in FD systems leveraging the Pilot-Tone (PT) structure of Orthogonal Frequency Division Modulation (OFDM) signals. In comparison with the conventional sensing methods in FD systems, the developed sensing approach holds the advantage in the robustness not only to residual SI but also the carrier frequency offset (CFO). Besides, the proposed sensing method is able to accomplish sensing tasks in low SNR conditions with much lower computational complexity. Numerical simulations results demonstrate that the probability of detection of our proposed approach can be improved up to 34.9%, compared with state- of-the-art sensing methods in FD systems, suffering from residual SI and CFO.

This paper investigates the coexistence of non- orthogonal multiple access (NOMA) and full-duplex (FD), where the NOMA successive interference cancellation technique is applied simultaneously to both uplink (UL) and downlink (DL) transmissions in the same time-frequency resource block. Specifically, we jointly optimize the user association (UA) and power control to maximize the overall sum rate, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize the UL users' decoding order and the DL users' clustering, which results in a mixed-integer non- convex problem. For solving this problem, we first relax the binary variables to be continuous, and then propose a low-complexity design based on the combination of the inner convex approximation framework and the penalty method. Numerical results show that the proposed algorithm significantly outperforms the conventional FD-based schemes, FD-NOMA and its half-duplex counterpart with random UA.

We propose a jointly optimal caching and routing framework (JOCAR) for a cooperative mobile edge caching network. This novel network architecture enables mobile edge servers/nodes (MENs) to collaborate in not only caching but also routing contents to users, in order to simultaneously minimize the total content-access delay for all mobile users and reduce the traffic on the backhaul network. To that end, we first formulate an access- delay minimization problem by jointly optimizing the content caching and routing decisions while accounting for various network configurations. Solving this problem requires us to deal with a nested dual optimization due to the strong mutual dependence between content caching and routing decisions. To tackle it, we first transform the nested dual problem to an equivalent mixed-integer nonlinear programming (MINLP) problem. Then, we design a branch-and-bound based algorithm with the interior-point method to find the near-optimal policy for the MINLP problem. Extensive simulations show that JOCAR can reduce the total average delay and increase the cache hit rate for the whole network by more than 40% and by four times, respectively, compared with other conventional policies.

In this paper, we propose novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These methods can learn and find the correlation of complex hidden features to improve the prediction accuracy. First, we propose an energy demand learning (EDL)-based prediction solution in which a charging station provider (CSP) gathers information from all charging stations (CSs) and then performs the EDL algorithm to predict the energy demand for the considered area. However, this approach requires frequent data sharing between the CSs and the CSP, thereby driving communication overhead and privacy issues for the EVs and CSs. To address this problem, we propose a federated energy demand learning (FEDL) approach which allows the CSs sharing their information without revealing real datasets. Specifically, the CSs only need to send their trained models to the CSP for processing. In this case, we can significantly reduce the communication overhead and effectively protect data privacy for the EV users. To further improve the effectiveness of the FEDL, we then introduce a novel clustering- based EDL approach for EV networks by grouping the CSs into clusters before applying the EDL algorithms. Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24.63% and decrease communication overhead by 83.4% compared with other baseline machine learning algorithms.

We investigate a joint offloading and resource allocation under a multi-layer cooperative fog and cloud computing architecture, aiming to minimize the total energy consumption of mobile devices while meeting users' QoS requirements, e.g., delay, security, and application compatibility. Due to the mutual coupling amongst offloading decision and resource allocation variables, the resulting optimization is a mixed integer non- linear programming problem that is NP-hard. Such problem often requires exponential time to find the optimal solution. In this work, we propose a distributed approach, namely feasibility-finding Benders decomposition (FFBD), that decomposes the original problem into a master problem for the offloading decision and subproblems for resource allocation. These (simpler) subproblems can be solved in parallel at fog nodes, thereby reducing both the complexity and the computational time. The numerical results show that the FFBD always returns the optimal solution of the problem with significantly less computation time (e.g., in comparing with the branch-and-bound method).

Practical and efficient network slicing often faces real-time dynamics of network resources and uncertain customer demands. This work provides an optimal and fast resource slicing solution under such dynamics by leveraging the latest advances in deep learning. Specifically, we first introduce a novel system model which allows the network provider to effectively allocate its combinatorial resources, i.e., spectrum, computing, and storage, to various classes of users. To allocate resources to users while taking into account the dynamic demands of users and resources constraints of the network provider, we employ a semi-Markov decision process framework. To obtain the optimal resource allocation policy for the network provider without requiring environment parameters, e.g., uncertain service time and resource demands, a Q-learning algorithm is adopted. Although this algorithm can maximize the revenue of the network provider, its convergence to the optimal policy is particularly slow, especially for problems with large state/action spaces. To overcome this challenge, we propose a novel approach using an advanced deep Q-learning technique, called deep dueling that can achieve the optimal policy at few thousand times faster than that of the conventional Q-learning algorithm. Simulation results show that our proposed framework can improve the long-term average return of the network provider up to 40% compared with other current approaches.

In this paper, we propose a novel approach to jointly address energy management and network throughput maximization problems for heterogeneous IoT low-power wireless communication networks. In particular, we consider a low-power communication network in which the IoT devices can harvest energy from a dedicated RF energy source to support their transmissions or backscatter the signals of the RF energy source to transmit information to the gateway. Different IoT devices may have dissimilar hardware configurations, and thus they may have various communications types and energy requirements. In addition, the RF energy source may have a limited energy supply source which needs to be minimized. Thus, to maximize the network throughput, we need to jointly optimize energy usage and operation time for the IoT devices under different energy demands and communication constraints. However, this optimization problem is non-convex due to the strong relation between energy supplied by the RF energy source and the IoT communication time, and thus obtaining the optimal solution is intractable. To address this problem, we study the relation between energy supply and communication time, and then transform the non-convex optimization problem to an equivalent convex-optimization problem which can achieve the optimal solution. Through simulation results, we show that our solution can achieve greater network throughputs (up to five times) than those of other conventional methods, e.g., TDMA. In addition, the simulation results also reveal some important information in controlling energy supply and managing low-power IoT devices in heterogeneous wireless communication networks.

We develop a novel deep learning model, Multidistributed Variational AutoEncoder (MVAE), for the network intrusion detection. To make the traffic more distinguishable, MVAE introduces the label information of data samples into the Kullback-Leibler (KL) term of the loss function of Variational AutoEncoder (VAE). This label information allows MVAEs to force/partition network data samples into different classes with different regions in the latent feature space. As a result, the network traffic samples are more distinguishable in the new representation space (i.e., the latent feature space of MVAE), thereby improving the accuracy in detecting intrusions. To evaluate the efficiency of the proposed solution, we carry out intensive experiments on two popular network intrusion datasets, i.e., NSL-KDD and UNSWNB15 under four conventional classifiers including Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The experimental results demonstrate that our proposed approach can significantly improve the accuracy of intrusion detection algorithms up to 24.6% compared to the original one (using area under the curve metric).

A design methodology using quasi-lumped elements for compact millimeter-wave on-chip bandpass filter (BPF) is presented in this work. To implement BPF using this approach, a novel inductor cell is presented first and then using this cell along with metal-insulator-metal (MIM) capacitors, two BPFs are designed. For the purpose of proof-of-concept, all three designs are implemented and fabricated in a standard 0.13-μm (Bi)-CMOS technology. The measurements show that the inductor cell generates a notch at 47 GHz with a chip size of 0.096 × 0.294 mm2without pads. Moreover, the 1st BPF has the center frequency at 27 GHz with an insertion loss of 2.5 dB and it has one transmission zero at 58 GHz with a peak attenuation of 23 dB. Unlike the 1st design, the 2nd design has two transmission zeros. The center frequency of this BPF is located at 29 GHz with a minimum insertion loss of 3.5 dB. Without the measurement pads, the chip sizes of the two BPFs are 0.076 × 0.296 mm2and 0.096 × 0.296 mm2, respectively.

To meet the capacity needs of the next generation wireless communications, U.S. Federal Communications Commission has recently introduced Spectrum Access System. Spectrum is shared between three tiers - Incumbents, Priority Access Licensees (PAL) and General Authorised Access (GAA) Licensees. When the incumbents are absent, PAL and GAA share the spectrum under the constraint that GAA ensure the aggregate interference to PAL is no more than -80 dBm within the PAL protection area. Currently GAA users are required to report their geolocations. However, geolocation is private information that GAA may not be willing to share. We propose a distributed GAA power allocation algorithm that does not require centralised coordination on sharing locations with other GAA users via SAS. We analytically proved the critical point of the interference along the PAL protection area to avoid calculating the interference on every points of the area. We proposed exclusion zone, transitional zone and open zone for GAA users to calculate the self-determined transmit power. Simulation results show that our method meets the interference requirement and achieve more than 90% of capacity approximation to the optimal centralised method, while completely masking the GAA locations.

Unmanned Aerial Vehicles (UAVs) are fast becoming a popular choice in a variety of applications in wireless communication systems. UAV-mounted base stations (UAV-BSs) are an effective and cost-efficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. We present a method of using UAV-BSs to provide coverage to mobile users in a fixed area. We propose an algorithm for predicting the user locations based on their mobility data and clustering the predicted locations, so that one UAV-BS would provide coverage to one user cluster. The proposed method, hence is similar to the UAV-BSs following the users to keep them under the coverage region. Simulation results show that the proposed method increases the user coverage by 47%-72% and increases the spectral efficiency by 43%-55% depending on the scenario and in addition, reduces the number of UAV-BSs required to provide coverage.

Network densification has been one of the principal causes of performance gain in cellular networks, and 5G networks will not be any different. As cell sizes shrink, handovers become more frequent incurring extra delays that bury all the prospective gains. Mobility in multi-tier dense cellular networks calls for a change in the way it has been traditionally handled in an always-on world, where users take universal data access for granted. Invisible to them, mobile network operators need to provision backhauling to include advanced interference mitigation techniques. In this paper, we propose a spectrum database-aided handover management technique that aims to mitigate the number of disconnections without overloading the backhaul unnecessarily. The proposed technique exploits a spectrum database that stores reception information along with geolocation data, commercially available on any handheld device. Moreover, we have benchmarked several state-of-the-art handover schemes for 5G networks against ours in a realistic urban environment with user mobility trace data. The results highlight that our method can deliver the same downstream traffic with 33% decrease in disconnections when compared to the conventional approach. At the same time, backhaul traffic is reduced up to 68% against our counterparts.

Military surveillance events and rescue activities are vital missions for the Internet-of-Things. To this end, foliage penetration for human detection plays an important role. However, although the feasibility of that mission has been validated, we observe that it still cannot perform promisingly under severe weather conditions, such as rainy, foggy, and snowy days. Therefore, in this paper, experiments are conducted under severe weather conditions based on a proposed deep learning approach. We present an auto-encoder/decoder (Auto-ED) deep neural network that can learn the deep representation and conduct classification task concurrently. Since the property of cost-effective, the device-free sensing techniques are used to address human detection in our case. As we pursue the signal-based mission, two components are involved in the proposed Auto-ED approach. First, an encoder is utilized that encode signal-based inputs into higher dimensional tensors by fractionally strided convolution operations. Then, a decoder is leveraged with convolution operations to extract deep representations and learn the classifier simultaneously. To verify the effectiveness of the proposed approach, we compare it with several machine learning approaches under different weather conditions. Also, a simulation experiment is conducted by adding additive white Gaussian noise to the original target signals with different signal to noise ratios. Experimental results demonstrate that the proposed approach can best tackle the challenge of human detection under severe weather conditions in the high-clutter foliage environment, which indicates its potential application values in the near future.

Effective network slicing requires an infrastructure/network provider to deal with the uncertain demands and real-time dynamics of the network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This paper develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demands from tenants. Specifically, we first propose a novel system model that enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case, in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling, that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with the real-time resource requests and the dynamic demands of the users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with the state-of-the-art network slicing approaches.

With conventional anti-jamming solutions like frequency hopping or spread spectrum, legitimate transceivers often tend to “escape” or “hide” themselves from jammers. These reactive anti-jamming approaches are constrained by the lack of timely knowledge of jamming attacks (especially from smart jammers). Bringing together the latest advances in neural network architectures and ambient backscattering communications, this work allows wireless nodes to effectively “face” the jammer (instead of escaping) by first learning its jamming strategy, then adapting the rate or transmitting information right on the jamming signals (i.e., backscattering modulated information on the jamming signals). Specifically, to deal with unknown jamming attacks (e.g., jamming strategies, jamming power levels, and jamming capability), existing work often relies on reinforcement learning algorithms, e.g., Q -learning. However, the Q -learning algorithm is notorious for its slow convergence to the optimal policy, especially when the system state and action spaces are large. This makes the Q -learning algorithm pragmatically inapplicable. To overcome this problem, we design a novel deep reinforcement learning algorithm using the recent dueling neural network architecture. Our proposed algorithm allows the transmitter to effectively learn about the jammer and attain the optimal countermeasures (e.g., adapt the transmission rate or backscatter or harvest energy or stay idle) thousand times faster than that of the conventional Q -learning algorithm. Through extensive simulation results, we show that our design (using ambient backscattering and the deep dueling neural network architecture) can improve the average throughput (under smart and reactive jamming attacks) by up to 426% and reduce the packet loss by 24%. By augmenting the ambient backscattering capability on devices and using our algorithm, it is interesting to observe that the (successful) transmission rate increases with the jamming power. Our proposed solution can find its applications in both civil (e.g., ultra-reliable and low-latency communications or URLLC) and military scenarios (to combat both inadvertent and deliberate jamming).

To enable machine learning at the edge of wireless networks (such as edge cloud), close to mobile users, is critical for future wireless networks, but challenging since the lower layers in edge cloud are substantially different from existing machine learning configurations in the cloud. In such geo-distributed computing environment, streaming data need to be evenly and cost-efficiently partitioned for different workers to produce an unbiased learning model with reduced parameter synchronization frequency. This paper presents a new online approach to optimally partitioning streaming data under time-varying network conditions. A new measure is proposed to quantify the evenness of data partitioning and restrain the optimization of data admission, partitioning, and processing. Stochastic gradient descent is applied to learn the optimal decisions online and asymptotically maximize the time-average utility of data partitioning. A new protocol is designed to further reduce the measurements of link costs, while preserving the asymptotic optimality, data evenness, and stability of the platform. Simulation results show that the proposed approach is superior to the state of the art in terms of throughput and cost efficiency, while only 24% of the links need to be measured to achieve the asymptotic optimality.

Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising technology to address the radio spectrum shortage. In particular, sensing of orthogonal frequency division multiplexing (OFDM) signals, a widely accepted multi-carrier transmission paradigm, has received paramount interest. Despite various efforts, noise uncertainty, timing delay and carrier frequency offset (CFO) still remain as challenging problems, significantly degrading the sensing performance. In this work, we develop two novel OFDM sensing frameworks utilizing the properties of deep learning networks. Specifically, we first propose a stacked autoencoder based spectrum sensing method (SAE-SS), in which a stacked autoencoder network is designed to extract the hidden features of OFDM signals for classifying the user’s activities. Compared to the conventional OFDM sensing methods, SAE-SS is significantly superior in the robustness to noise uncertainty, timing delay, and CFO. Moreover, SAE-SS requires neither any prior information of signals (e.g., signal structure, pilot tones, cyclic prefix) nor explicit feature extraction algorithms which however are essential for the conventional OFDM sensing methods. To further improve the sensing accuracy of SAE-SS, especially under low SNR conditions, we propose a stacked autoencoder based spectrum sensing method using time-frequency domain signals (SAE-TF). SAE-TF achieves higher sensing accuracy than SAE-SS using the features extracted from both time and frequency domains, at the cost of higher computational complexity. Through extensive simulation results, both SAE-SS and SAE-TF are shown to achieve notably higher sensing accuracy than that of state of the art approaches.

Ambient backscatter has been introduced with a wide range of applications for low power wireless communications. In this paper, we propose an optimal and low-complexity dynamic spectrum access framework for the RF-powered ambient backscatter system. In this system, the secondary transmitter not only harvests energy from ambient signals but also reflects these signals to transmit its modulated data to the receiver. Under the dynamics of the ambient signals, we first adopt the Markov decision process (MDP) framework to obtain the optimal policy for the secondary transmitter, aiming to maximize the system throughput. However, the MDP-based optimization requires complete knowledge of environment parameters, e.g., the probability of a channel to be idle and the probability of a successful packet transmission, that may not be practical to obtain. To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to “learn” from its decisions and then attain the optimal policy. Simulation results show that the proposed learning algorithm not only efficiently deals with the dynamics of the environment but also improves the average throughput up to 50% and reduces the blocking probability and delay up to 80% compared with conventional methods.

This paper investigates the coexistence of non-orthogonal multiple access (NOMA) and full-duplex (FD) to improve both spectral efficiency (SE) and user fairness. In such a scenario, NOMA based on the successive interference cancellation technique is simultaneously applied to both uplink (UL) and downlink (DL) transmissions in an FD system. We consider the problem of jointly optimizing user association (UA) and power control to maximize the overall SE, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize UL users’ decoding order and DL users’ clustering, which results in a mixed-integer non-convex problem. For practically appealing applications, we first relax the binary variables and then propose two low-complexity designs. In the first design, the continuous relaxation problem is solved using the inner convex approximation framework. Next, we additionally introduce the penalty method to further accelerate the performance of the former design. For a benchmark, we develop an optimal solution based on brute-force search (BFS) over all possible cases of UAs. It is demonstrated in numerical results that the proposed algorithms outperform the conventional FD-based schemes and its half-duplex counterpart, as well as yield data rates close to those obtained by BFS-based algorithm.

For an RF-powered cognitive radio network with ambient backscattering capability, while the primary channel is busy, the RF-powered secondary user (RSU) can either backscatter the primary signal to transmit its own data or harvest energy from the primary signal (and store in its battery). The harvested energy then can be used to transmit data when the primary channel becomes idle. To maximize the throughput for the secondary system, it is critical for the RSU to decide when to backscatter and when to harvest energy. This optimal decision has to account for the dynamics of the primary channel, energy storage capability, and data to be sent. To tackle that problem, we propose a Markov decision process (MDP)-based framework to optimize RSU's decisions based on its current states, e.g., energy, data as well as the primary channel state. As the state information may not be readily available at the RSU, we then design a low-complexity online reinforcement learning algorithm that guides the RSU to find the optimal solution without requiring prior-and complete-information from the environment. The extensive simulation results then clearly show that the proposed solution achieves higher throughputs, i.e., up to 50%, than that of conventional methods.

We propose a novel edge computing network architecture that enables edge nodes to cooperate in sharing computing and radio resources to minimize the total energy consumption of mobile users while meeting their delay requirements. To find the optimal task offloading decisions for mobile users, we first formulate the joint task offloading and resource allocation optimization problem as a mixed integer non-linear programming (MINLP). The optimization involves both binary (offloading decisions) and real variables (resource allocations), making it an NP-hard and computational intractable problem. To circumvent, we relax the binary decision variables to transform the MINLP to a relaxed optimization problem with real variables. After proving that the relaxed problem is a convex one, we propose two solutions namely ROP and IBBA. ROP is adopted from the interior point method and IBBA is developed from the branch and bound algorithm. Through the numerical results, we show that our proposed approaches allow minimizing the total energy consumption and meet all delay requirements for mobile users.

To effectively utilize/harvest short-lived whitespace that accounts for more than 30% of the cellular bands, it is critical to build a real-time radio environment map. Note that existing radio spectrum maps/databases (e.g., Google Spectrum Database) are updated on a daily or weekly basis. In this paper, we introduce a novel real-time crowdsourcing incentive solution that rewards mobile users who contribute their qualified spectrum sensing data to a radio environment map. First, we develop a feature-based model based on advanced machine learning techniques in order to estimate model parameters of the radio environment map. Based on the prediction model, we then propose a smart dynamic pricing strategy including prepaid and postpaid pricing schemes. The prepaid scheme is to guarantee the minimum payment for participants, and the postpaid scheme is to reward the participants according to their contributions. Importantly, in our model, the postpaid scheme will be adjusted iteratively in a real-time manner based on the contributions of participants to the spectrum map. After that we carry out real experiments through a mobile application and a cloud spectrum database. The experiment results show that our proposed solution can achieve not only better users' utilities, but also a lower overall system cost compared with those of some existing works.

Both Federal Communications Commission (FCC) and European Telecommunications Standards Institute (ETSI) support dynamic spectrum access (DSA) as an enabling technology for spectrum sharing. To effectively realize DSA in practice, users (from both defense and civil sectors) are required to share their (radio) operational information. That risks exposing their security, privacy, and business plan to unintended agents. In this paper, taking FCC's spectrum access system (SAS) as a study case, we propose a privacy-preserving scheme for DSA by leveraging encryption and obfuscation methods (PSEO). To implement PSEO, we propose an interference calculation scheme that allows users to calculate interference budget without revealing their operation information (e.g., antenna height, transmit power, location...), referred to as blind interference calculation method (BICM). BICM also reduces the computing overhead of PSEO, compared with FCC's SAS by moving interference budgeting tasks to local users and calculating it in an offline manner. Extensive detailed analysis and simulations show that our proposed PSEO is able to better protect all users' operational privacy, guaranteeing efficient spectrum utilization with less online overhead, compared with state of the art approaches.

Unmanned Aerial Vehicles (UAV) are gaining popularity in a range of areas and are already being used for a wide variety of purposes. While UAVs have many desirable features, limited battery lifetime is identified as a key restriction in UAV applications. Typical UAVs being electric devices, powered by on-board batteries, this constrain has limited their capabilities to a considerable extent. Thus planning UAV missions in an energy efficient manner is of utmost importance. To achieve this, for prediction of power consumption, it is necessary to have a reliable power consumption model. In this paper, we present a consistent and complete power consumption model for UAVs based on empirical studies of battery usage for various UAV activities. The power consumption model presented in this paper can be readily used for energy efficient UAV mission planning.

Unmanned Aerial Vehicles (UAV) are becoming popular in a range of areas. This has given rise to the concept of UAV swarms, where multiple UAVs act together to achieve a common task. With multiple UAVs flying in close proximity to each other, sharing the same airspace, the risk of inter-UAV collisions increases. It's important to avoid these collisions while having minimal impact on the UAV system. We propose a novel Potential Field Method (PFM) based algorithm for inter-UAV collision avoidance which considerably reduces the total time taken by the UAV system to achieve its goal. We control the collision avoidance actions of the UAVs by virtually relocating their targets. The positions of the virtual targets are calculated to minimize the collision probability, based on a probability function we introduced. The proposed algorithm reduces the total system time approximately by 20\% as opposed to the traditional PFM.

Spectrum Access System (SAS) is a three-tier spectrum sharing framework proposed for 3.5 GHz by Federal Communication Commission (FCC) in the United States. General Authorized Access (GAA) users in SAS do not have an assigned channel and can opportunistically access the Priority Access Licensee (PAL) channel satisfying the interference constraint proposed by FCC. Coexistence among GAA users in SAS is a key problem to be solved to enhance the system capacity to meet the increasing traffic demand. In this work, we propose a method for fair and efficient spectrum utilisation for GAA users. To achieve the fairness among GAA users equal interference budget allocation scheme is proposed for each set of GAA users that can hear each other. Our proposed method decide the optimal channel switching schedule that maximises the average capacity of GAA users while satisfying the interference constraint at PAL protection area. This work jointly considers the fairness between GAA users and the average capacity maximisation of GAA network. Simulation result justifies the performance of our proposed method for average capacity maximisation of GAA users and fairness between GAA users by comparing with existing works.

The optimal use of spectrum is a key focus for all regulatory bodies. Federal Communications Commission has introduced Spectrum Access System (SAS) to maximise the spectrum utilisation in the US 3.5 GHz band. SAS is a three-tier spectrum sharing framework where Citizen Broadband Radio Service (CBRS) devices can access the channel when it is not used by Incumbent Access users. CBRS consists of Priority Access Licensee (PAL) and General Authorized Access (GAA). In this paper, we consider the problem of optimum transmit power allocation for GAA users using a carrier sensing range i.e. maximum distance a user can be sensed while guaranteeing the interference to PAL from GAA users is below the threshold. We use carrier sensing range to find the sets of GAA users that cannot transmit at the same time and adjust the interference budget of transmitting GAA users. We present an algorithm for transmit power allocation for GAA users in the SAS. The proposed algorithm uses the transmission characteristics and location information provided by Citizen Broadband Radio Service Devices to SAS to maximise the peak capacity of GAA users ensuring the interference constraint to PAL. Simulation results show that the proposed algorithm significantly increases the peak capacity of GAA users by considering the carrier sensing range and adjusted interference budget.

The development of mobile cloud computing has brought many benefits to mobile users as well as cloud service providers. However, mobile cloud computing is facing some challenges, especially security-related problems due to the growing number of cyberattacks which can cause serious losses. In this paper, we propose a dynamic framework together with advanced risk management strategies to minimize losses caused by cyberattacks to a cloud service provider. In particular, this framework allows the cloud service provider to select appropriate security solutions, e.g., security software/hardware implementation and insurance policies, to deal with different types of attacks. Furthermore, the stochastic programming approach is adopted to minimize the expected total loss for the cloud service provider under its financial capability and uncertainty of attacks and their potential losses. Through numerical evaluation, we show that our approach is an effective tool in not only dealing with cyberattacks under uncertainty, but also minimizing the total loss for the cloud service provider given its available budget.

With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution.

The strongest-cell criterion has been extensively used for handover algorithms during the last cellular-network generations. When network topologies become multi-layered, it results in abrupt behaviors such as the ping-pong effect as a consequence of the power gap between tiers and their irregular deployment. This effect not only affects users' quality of experience but also introduces a significant network overhead. Therefore, we propose an original handover algorithm based on predicted incomplete channel states from a Radio Environment Map to reduce this effect. The proposed algorithm is user triggered, network assisted, and fully backward compatible with LTE-A. Moreover, we evaluate the performance of our proposed algorithm against LTE-A in a two-tier cellular network for different user speeds following the guidelines outlined by the 3GPP on diverse matters (channel, mobility, wrapping, etc.). When applying realistic timing, our results reveal a highly substantial improvement in the number of ping-pong handovers regardless of the handover policy adopted in comparison to LTE-A without sacrificing users' experience; for instance, we obtain at least an order of magnitude decrease in the ping-pong rate at the expense of losing less than 9 percent in spectral efficiency.

We propose a two dimension (2D) proactive uplink resource allocation (2D-PURA) algorithm that aims to reduce the delay/latency in event-based machine-type communications (MTC) applications. Specifically, when an event of interest occurs at a device, it tends to spread to the neighboring devices. Consequently, when a device has data to send to the base station (BS), its neighbors later are highly likely to transmit. Thus, we propose to cluster devices in the neighborhood around the event, also referred to as the disturbance region, into rings based on the distance from the original event. To reduce the uplink latency, we then proactively allocate resources for these rings. To evaluate the proposed algorithm, we analytically derive the mean uplink delay, the proportion of resource conservation due to successful allocations, and the proportion of uplink resource wastage due to unsuccessful allocations for 2D-PURA algorithm. Numerical results demonstrate that the proposed method can save over 16.5 and 27 percent of mean uplink delay, compared with the 1D algorithm and the standard method, respectively.

The well-known Internet of Things (IoT) is recently being considered for critical missions, such as search and rescue, surveillance, and border patrol. One of the most critical issues that these applications are currently facing is how to correctly distinguish between human and animal targets in a cost-effective way. In this paper, we present a relatively low-cost, but robust approach that uses a combination of device-free sensing (DFS) and machine-learning technologies to tackle this issue. In order to validate the feasibility of the presented approach, a variety of data is collected in a cornfield using impulse-radio ultra-wideband (IR-UWB) transceivers. These data are then used to investigate the influence of different statistical properties of the radio-frequency (RF) signal on the accuracy of human/animal target classification. Based on the probability density function of different statistical properties, two distinguishing features for target classification are found, namely, standard deviation and root mean spread delay spread. Using them, the impact on the classification accuracy due to different classifiers, number of training samples, and different values of signal-to-noise ratio is extensively verified. Even with the worst case, the classification accuracy of the system is still better than 91% in terms of distinguishing between human and animal targets (including goats and dogs), which indicates that the presented approach has a great potential to be deployed in the near future.

Foliage penetration (FOPEN) has been found to be a critical mission for a variety of applications, ranging from surveillance to military. Recently, an emerging technology, namely wireless sensor network (WSN)-based device-free sensing (DFS), has been introduced to the domain of FOPEN. This technology only utilizes radio-frequency signals for target detection and classification; thus, no additional hardware is required, just a wireless transceiver. Although the feasibility of using this technology for human detection indoors has been explored to some extent, it is questionable if the same technology can be transferred to outdoors. As far as FOPEN is concerned, the impact of seasonal variations on detection accuracy can be severe. To address this concern, in this paper, an experiment is conducted in four seasons, and how to ensure reasonable detection accuracy with seasonal variations is intensively investigated. To fully evaluate the potential of using the WSN-based DFS for FOPEN, an impulse-radio ultrawideband technology-based prototype is used to collect data samples in different seasons. Unlike the conventional approach based on a combination of statistical properties of received-signal strength and a support vector machine, this approach adopts two special measures for performance enhancement. One measure is to use a higher order cumulant (HOC) algorithm for feature extraction, so that the impact on detection accuracy due to unwanted clutters can be minimized. The other one is to determine the optimal parameters of the classifier by means of a flower pollination algorithm. Consequently, the adverse effects on detection accuracy due to variations of weather conditions in four seasons can be accommodated. According to the experimental result, it is shown that the average classification accuracy of the presented approach can be improved by at least 20% under all seasons with an ensured robustness.

Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or healthcare industry that require a rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in a fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a nondeterministic polynomial-time hard problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a tradeoff is maintained between the energy consumption and makespan. Specifically, this paper proposes a metaheuristic-based service allocation framework using three metaheuristic techniques, such as particle swarm optimization (PSO), binary PSO, and bat algorithm. These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic-based service allocation algorithms by conducting a set of rigorous evaluations.

Full duplex (FD) capability enables a "listen and talk" protocol for spectrum sensing that has been used as a new paradigm to increase the spectrum utilization in cognitive radio networks (CRNs). However, the spectrum sensing performance suffers from the imperfect self-interference suppression (SIS). This could significantly degrade the performance of FD systems in CRNs. In this paper, we investigate the issue of spectrum sensing with imperfect SIS in FD systems. By drawing support from a cyclic prefix (CP) of Orthogonal Frequency Division Modulation (OFDM) signals, we propose a novel spectrum sensing mechanism that is robust to self- interference. Comparing with other conventional sensing approaches in FD systems, the proposed method is independent of timing delay. That significantly improves the sensing performance, even without requiring a complex process for timing delay estimation. As a result, it also reduces the overhead of spectrum sensing. Extensive simulation results indicate that even with serious self-interference and timing delay, the presented approach is still able to achieve much higher performance than the conventional energy detection and waveform-based detection approaches.

Brain-computer interfaces (BCIs) are used to assist people, especially those with verbal or physical disabilities, communicate with the computer to indicate their selections, control a device or answer questions only by their mere thoughts. Due to the noisy nature of brain signals, the required time for each experimental session must be lengthened to reach satisfactory accuracy. This is the trade-off between the speed and the precision of a BCI system. In this paper, we propose a unified method which is the integration of ensemble classifier, dynamic stopping, and adaptive learning. We are able to both increase the accuracy, as well as to reduce the spelling time of the P300-Speller. Another merit of our study is that it does not require the training phase for any new subject, hence eliminates the extensively time-consuming process for learning purposes. Experimental results show that we achieve the averaged bit rate boost up of 182% on 15 subjects. Our best achieved accuracy is 95.95% by using 7.49 flashing iterations and our best achieved bit rate is 40.87 bits/min with 83.99% accuracy and 3.64 iterations. To the best of our knowledge, these results outperformed most of the related P300-based BCI studies.

The causality of gait freezing in patients with advanced Parkinson's disease is still not fully understood. Clinicians are interested in investigating the freezing of gait (FoG) histogram of patients in their daily life. To that end, one needs a real-time signal processing platform that can help record freezing information (e.g., timing and the duration of every gait freezing occurrences). Wearable wireless sensors have been proposed to monitor FoG epochs. Existing automated methods using accelerometers have been introduced with high accuracy performance only for subject-dependent settings (e.g., an individual offline training process). This is a troublesome for large scale out-of-lab deployment and time-consuming. In this work, we used spectral coherence analysis for accelerometer data to apply an anomaly detection approach. Conventional features such as energy and freezing index are introduced to help refine normal epochs while the anomaly scores from spectral coherence measures define FoG epochs. Using this new set of features, our new FoG detector for subject-independent settings achieves the mean ±SD sensitivity (specificity) of 89.2±0.3% (95.6 ± 0.3%). To our best knowledge, this is the best performance for automated subject-independent approaches in literature of freezing of gait detection.

Motivated by recent efforts in enabling economic models for spectrum sharing systems, in particular, for the Spectrum Access System in the US, we propose a game theoretic analysis of sublicensing between two types of access methods in such system - PAL and GAA. The aim of this paper is to illustrate how the operators' strategies affect their own payoffs and the overall utility in the Sublicenseing Game in a spectrum sharing system. We consider the problem of spectrum sharing among multiple operators who have to pay for a temporary PAL sublicense with the exclusive right to the PAL band or stay in the GAA band and share the spectrum for free with other GAA users. We first formulate this scenario as a noncooperative game, and then study the existence of a Nash equilibrium. Finally, to reduce the overall utility loss we let the spectrum sharing platform to coordinate individual operators by forming pair coalitions for them. According to our findings, when an operator has a large number of subscribers GAA band is the best response and a high PAL sublicense price holds back operators to access the PAL band. Additionally, making equal coalitions among operators can avoid overall utility loss.

Spectrum Access System (SAS) is a three tier spectrum sharing framework proposed by the FCC. In this framework the aggregate interference of tier-3 General Authorised Access (GAA) users should be below a predetermined threshold anywhere within the tier-2 Priority Access Licensee (PAL) exclusion zone. GAA are expected to use a diverse range of Radio Access Technologies (RATs) with different levels of loading. We propose an optimal transmit power and probability of spectrum utilisation allocation scheme for GAA users that meets the average aggregate interference constraint within the GAA network. Most of the capacity maximisation studies consider the instantaneous aggregated interference from secondary users. In this paper we present an average aggregated interference method to optimise the capacity of GAA users in a single channel. Simulation results suggest that we can significantly increase the capacity of the channel by considering the probability spectrum utilisation of GAA users.

This paper presents the design of a differential double-balanced Gilbert mixer in 0.13 um SiGe BiCMOS technology. A current-bleeding injection technique is adopted to increase the bias current at the driver stage without causing overvoltage headroom at the differential pair stage. This mechanism improves the performance in terms of conversion gain, linearity and noise figure. The proposed mixer achieves 10.7 dB conversion gain, 15 dB noise figure, -1.67 dBm 1-dB compression point, and 5 dBm IIP3. The designed double balanced Gilbert mixer with current bleeding is part of an integrated RF front-end for full duplex radio applications in the 2.4 GHz band and occupies an area of 0.1002 × 0.0748 mm2 excluding the pads.

To augment capacity of spectrum limited cellular systems, 3GPP proposed Licensed Assisted Access (LAA-LTE) while efforts are underway to standardize the standalone MulteFire (a small cell standalone version of LTE). LAA is expected to boost capacity of LTE via unlicensed spectrum (5GHz). On the other hand, recent advances in Self Interference Suppression (SIS) techniques allow radios to transmit and receive simultaneously on the same channel (i.e., in-band Full-Duplex, FD). As part of future wireless networks, Device-to-device (D2D) communications would find its great potential through this FD capability. However, due to high induced aggregate interference from FD and its impact on medium access probability, the rigorous and critical analysis is needed to find an optimum trade-off between performance efficiency and overheads. Using stochastic geometry and the random graph theory, in this article, we analyze the impact of different LTE network paradigms with HD/FD D2D devices. Moreover, the impact of state- of-the-art coexistence techniques (discontinuous transmission and listen-before-talk) recommended for LTE in unlicensed spectrum over HD/FD D2D network is also discussed. The analysis is supported with extensive simulation results that reveal insights of the coexistence mechanism efficiency employed by LTE, the impact of SIS and the cost of FD operation in D2D.

Design of high-performance continuous- time filter (CTF) for analogue signal processing is presented in this paper. To demonstrate of using a novel voltage-mode multiple-loop feedback (MLF) approach for CTF design, a 5th-order elliptic lowpass filter (LPF) is implemented in a standard 0.18-μm CMOS technology. The LPF is based on an inverse-follow-the-leader feedback structure with an input distribution network to generate the required transmission zeros. The LPF consumes 35 mA from a single 1.8 V power supply and it has a cut-off frequency of 30 MHz with less than 0.7 dB passband ripple and more than 60 dB stopband attenuation. In addition, a 65 dB dynamic range is achieved.

Spectrum Access System (SAS) allows incumbent military systems to share spectrum in a hierarchical manner with tier-2 Priority Access License (PAL) users and tier-3 General Authorized Access (GAA) users. FCC has recently allowed PAL owners to sublicense their channels. Therefore, when GAA channels are congested they can request a sublicense to access the PAL channel on a coordinated basis, which provides interference protection from other GAA users. In this paper, we propose a grid map to measure and monitor the secondary spectrum market for the purpose of spectrum trading with QoS guarantee. This work provides the subsequent spectrum trading models with a reasonable and dedicated interference graph for further optimization of spectrum allocation. Compared with traditional longterm spectrum licensing policy, short-term licensing makes the spectrum allocated effectively. We find the optimal resolution of the discrete grid map that maximizes the profit from sublicensing. Simulation results are provided to demonstrate how fine to grid the region and let the PAL owner achieve monetary benefit, in a given number of sensors.

In this letter, the possibility of using device-free sensing (DFS) technology for personnel detection in a foliage environment is investigated. Although the conventional algorithm that based on statistical properties of the received-signal strength (RSS) for target detection at indoor or open-field environment has come a long way in recent years, it is still questionable if this algorithm is fully functional at outdoor with the changing atmosphere and ground conditions, such as a foliage environment. To answer this question, a variety of the measured data have been taken using different targets in a foliage environment. Applying these data along with support vector machine, the impact on detection accuracy due to different classification algorithms is studied. An algorithm that based on the extraction of the high-order cumulant (HOC) of the signals is presented, while the conventional RSS-based one is used as a benchmark. The measurement results show that the classification accuracy of the HOC-based algorithm is better than the RSS-based one by at least 17%. Moreover, to ensure the reliability of the HOC-based approach, the impact on classification accuracy due to different numbers of training samples and different values of signal-to-noise ratio is extensively verified using experimentally recorded samples. To the best of our knowledge, this is the first time that a DFS-based sensing approach is demonstrated to have a potential to distinguish between human and small-animal targets in a foliage environment.

In Multi-User Multiple Input Multiple Output (MU-MIMO) Wireless Local Area Networks (WLANs), the optimal-solution such as Dirty Paper Coding (DPC) or the sub-optimal solution Zeroforcing Beamforming (ZFB) with perfect Channel State Information (CSI), is practically limited due to the complexity and the non-availability of perfect CSI at the Access Points (APs)/transmitters. In such a context, ZFB based on channel quantization available at the APs (ZFQ) is the obvious choice for the Multi-User transmission strategy. However, since the quantized CSI is used instead of the perfect CSI at the APs, the quantization error and its impact on the average rate for ZFQ have to be quantified in MU-MIMO WLAN settings. In this paper, we derive a closed-form expression for the upper bound of the channel quantization error and the average rate reduction due to the quantization error with respect to the perfect CSI at the APs. In MU-MIMO WLAN settings, our analytical and numerical studies show that, with an increasing number of antennas at the clients, both the quantization error bound and the average rate reduction increase for ZFQ, in comparison to the ZFB with the perfect CSI.

A critical task in forced oscillation technique (FOT), a promising lung function test, is to remove respiratory artefacts. Manual removal by specialists is widely used but time- consuming and subjective. Most existing automated techniques have involved simple thresholding methods in an unsupervised manner. Breath cycles can be classified by a binary classification model (classes: artefactual and accepted). While attempting to use off-the-shelf sorting algorithms (e.g., one-class support vector machine, knearest neighbours, and adaptive boosting ensemble), we noticed their poor detection performance. This may result from the dependence of samples as found in physiological studies of the lung function that challenges the learning process. Specifically, statistics of breaths that we recorded may change from one to another patient and even within the same recording of a patient. We introduce an additional feature engineering step that is an intermediate module to decorrelate samples, called feature learning (using Wilcoxon signed rank tests). To that end, we collected FOT recordings from various groups of patients (paediatric and adult including healthy and asthmatics). Artefacts in this work were recorded naturally and processed in a complete-breath approach. Performance metrics include evaluations on preservation of "accepted" breaths in the filtered output (including F1- score, throughput, and approval rate). Our experiment found that our feature engineering steps significantly improve the artefact removal performance of all implemented classifiers especially with feature inputs selected by mutual information criterion.

Compressed sensing based noncoherent UWB systems have been proved to be feasible with a sub-Nyquist sampling rate. As a kind of noncoherent UWB systems, code-shifted differential chaos shift keying (CS- DCSK) UWB system has drawn much attention recently. However, its receiver cannot directly be combined with compressed sensing to reduce the sampling rate. With this motivation, in this paper, we redesign the receiver of the CS-DCSK UWB system and further design two compressed sensing based receivers where the measurement matrix is redesigned. Bit error rate (BER) expression is derived over UWB channel. It is shown that the simulation results are in good agreement with the theoretical ones.

Joint application of polar channel coding combined with vector quantization lossy source coding is considered in this paper. The existing index assignment schemes in the literature cannot be used with polar codes due to their unique crossover probabilities. We elaborate on this problem and locally optimize index assignments. In addition, we propose an algorithm that jointly optimizes the number of quantization levels and the rate of the polar code in order to achieve minimum end-to-end distortion. It finds the optimal tradeoff between the distortion caused by channel errors and the quantization distortion. We also derive estimates for the crossover probabilities of the polar code which are required in the analysis. Simulation results confirm the effectiveness of the proposed algorithms and the accuracy of the crossover probabilities.

Random linear coding (RLC) can improve the performance of multicast transmissions in terms of throughput and energy efficiency. However, RLC and linear codes in general cannot necessarily attain the optimal performance in arbitrary networks. In this regard, partial packet recovery can be considered as a nonlinear strategy to complement such approaches for more general networks. In this paper, we propose a partial packet recovery scheme that benefits from the sparsity of bit errors in partially corrupted RLC packets. As opposed to many previous schemes, it performs without introducing preliminary checksums or preambles, demanding physical layer soft information, or requesting post-redundancy from the transmitter. It relies only on algebraic coding and data processing techniques, the existing knowledge at the receiver, and the conventional acknowledgment messages in RLC. By reconstructing and utilizing the partially corrupted packets that are usually discarded, it can reduce the average number of transmitted RLC packets required for successful decoding by typically 50%, which improves throughput and energy efficiency at the transmitter. We formulate our partial packet recovery in the form of a sparse recovery problem, present its different solutions using compressive sensing theory, discuss their complexity, and present and evaluate a Markov chain model for its performance.

To reproduce a band-limited continuous-time signal with optimal fidelity, usually it is sampled at the Nyquist sampling rate and then the sample values are quantized. In nonuniform sampling, the total number of samples are reduced in expense of adding some reconstruction complexity and assuming prior information about the signal. In this paper we propose nonuniform sampling-time with nonuniform bit resolution per sample (bit-depth) to reduce the total required bit budget (i.e. the number of samples timed the bit-depth) even further. This idea is based on the fact that the maximum local variation of band-limited signals is bounded in a given time horizon. Therefore, it is not necessary to allocate a fixed bit-depth proportional to the signal's dynamic range to each sample. Instead, we try to allocate adaptively only the needed number of bits to represent the next sample considering the physical characteristics of the signal. Both sampling and reconstruction entities can generate the next sampling-times and bit-depths locally by observing the current and previous samples only. We propose different techniques in our generalized sampling framework that share a common sampling architecture. Here we only consider ECG signals for evaluation purpose. Based on the simulation results, the total number of required bits to reconstruct the signal with negligible distortion can be reduced up to 88% without imposing any form of transform coding compression.

Energy efficiency is one of the most critical parameters in ultra-wideband (UWB) based wireless body area networks (WBANs). In this paper, the energy efficiency optimization problem is investigated for cooperative transmission with a single relay in UWB based WBANs. Two practical onbody transmission scenarios are taken into account, namely, along-torso scenario and around-torso scenario. With a proposed single-relay WBAN model, a joint optimal scheme for the energy efficiency optimization is developed, which not only derives the optimal power allocation but also seeks the corresponding optimal relay location for each scenario. Simulation results show that the utilization of a relay node is necessary for the energy efficient transmission in particular for the around-torso scenario and the relay location is an important parameter. With the joint optimal relay location and power allocation, the proposed scheme is able to achieve up to 30 times improvement compared to direct transmission in terms of the energy efficiency when the battery of the sensor node is very limited, which indicates that it is an effective way to prolong the network lifetime in WBANs.

We propose a novel scheme based on compressive sensing and sparse recovery to boost the performance of cross-packet random linear coding (RLC) by incorporating the partial packets in the decoding algorithm. In conventional RLC schemes, to successfully decode the packets the receiver needs to collect a certain number of correct innovative encoded packets. During this process, there are usually a lot of partially correct packets that are discarded. Our objective is to recover the errors in the partial packets to decrease the total transmitted packets to improve the performance in terms of throughput and energy efficiency. Assuming a systematic RLC, we first formulate this problem in form of a standard sparse recovery problem where the channel errors are sparsely distributed within the packets. Then we show that to correct a certain number of errors at the receiver, the minimum required number of transmitted packets is lower-bounded by the number of partial packets. We show that by correcting and exploiting the partial packets, the required number of RLC transmit packets to successfully deliver a given generation is reduced by typically 57% in comparison with the conventional scheme.

A novel joint source-channel (JSC) coding scheme that combines transform source coding with polar channel codes is proposed with performance gains at low SNRs in terms of bandwidth and energy efficiency. Binary wavelet transform source coding is first applied to decompose the binary source data into a set of components with uneven distributions, particularly, components that are zero with high probability. Normally, a polar code of rate K/N appends N − K redundant bits to the message which can decrease the throughput and introduce an extra energy consumption. By exploiting the binary wavelet transform, the proposed scheme transforms the original signal to the polar code without adding redundant zeros. Hence, the resulting JSC code is of rate one. Since no floating-point arithmetic is required and all operations are performed in GF(2), complexity is vastly reduced which can eventually alleviate the total hardware cost as well as the energy consumption.

Sensitivity analysis plays an important role in a variety of statistical methodologies, design procedures and model selection. For development of in-body wireless communications, it is essential to evaluate the designed system performance prior to conducting any practical procedures. Localization of a capsule endoscope inside the gastrointestinal tract is one of the areas that needs to be precisely addressed in wireless body area networks. A highly accurate location estimation of a capsule endoscope in the range of several millimeters is a challenging task. This is mainly because the Radio-Frequency signals encounter a high loss and a highly dynamic channel propagation environment. Therefore, investigation of an accurate path-loss model is required for the development of localization algorithms. Since practical experiments on the real human body are quite infeasible, various human phantom models have been developed for this purpose. This study provides a detailed sensitivity analysis for two different anatomical human phantom models and shows how much the adjustment of the voxeling and the number of cells in phantom models are crucial to reduce the measurement errors in path loss and improve system performance.

In this paper, we investigate a distributed beamforming problem to optimize energy efficiency (EE) in ultra-wideband (UWB) based implant body area networks (IBANs). To evaluate the impact of relay location on the EE, a relay location based cooperative network model is proposed, where multiple on-body relays are employed to assist an implant node to communicate with a BAN coordinator. With the proposed model, the EE optimization problem is mathematically formulated as a non-convex optimization problem. Sequential quadratic programming (SQP) combined with scatter search are applied to find the corresponding optimal solution. Simulation results illustrate that the proposed beamforming scheme outperforms other transmission schemes. A remarkable improvement can be achieved not only in EE but also in spectral efficiency (SE) compared to direct transmission. Moreover, numerical examples show that the relay location has a significant impact on the EE performance.

A distributed antenna system (DAS) architecture is a key enabler for Cloud Radio Access Networks (CRAN) where geographically separated base stations are connected to a centralized processing and decision making unit. Many schemes have been proposed to leverage Fractional Frequency Reuse (FFR) and co- ordinated joint transmission between base stations to improve cell-edge performance for static network deployments. In this paper, we investigate dynamic decision making that whether co-ordinated joint transmission should be selected in the downlink of a FFR-aided DAS. We derive the transmitting Signal-to- Noise-Ratio (SNR) threshold for making these decisions and we show that this SNR plays a key role in the FFR- aided DAS analysis and can be used as a guide in the evaluation of DAS performance.

Licensed Shared Access is a European standardisation effort which promotes repository based quasi-static hierarchical spectrum sharing. In this scheme the sharing time base is in the order of months if not years. For widespread use of Licensed Shared Access, shrinking the sharing time base is crucial. In this paper we propose a scheme to reduce the sharing time base to seconds or minutes scale. We present a new technique named lightweight Radio Environment Map based on a Kalman Filter derived from geo-location aware spectrum measurements, which can be run at the shared access licensee end. Our objective is to determine the active area of a static or slowly moving incumbent. We consider a challenging scenario where a large fraction of measurements is missing and the available measurements are highly distorted. Performance of our incumbent active area detection approach is evaluated by simulating a low power incumbent in an urban cellular environment. Simulation results show a substantial improvement of missed detection area in comparison to the counterpart that does not use our lightweight Radio Environment Map.

The explosion of different types of wireless communications is leading to an impending spectrum famine. As a result, spectrum sharing has gained increasing interest from governments, industry and regulators, such as FCC in US and CEPT in Europe. Licensed Shared Access (LSA), developed by CEPT and ETSI, is a concept for an efficient use of current spectrum resources to enable keeping pace with increasing mobile data usage demands. In this paper, we present a truthful auction mechanism for spectrum sharing based on the LSA concept. This proposal is to allocate Incumbents' idle spectrum to Licensee Access Points from different operators for the purpose of commerce. We give insights into spectrum allocation methods based on auction mechanisms to obtain high revenue to attract Incumbents to join in the LSA architecture and operators to offload data from primary spectrum band. The proposed LSA Auction (LSAA) mechanism combines independent set selection by bidding and an elaborately designed group bid called Rank-bid, which further improves the revenue compared to related allocation methods. Our simulation results show that LSAA results in enhanced performance for Incumbent revenue and Licensee satisfaction.

Efficient discovery and effective sharing of spectrum opportunities are the most challenging issues in a multi-channel cognitive radio network (CRN) with multiple secondary users (SUs). In this paper, we propose a novel spectrum sensing and access protocol in CRNs where SUs are allowed to sequentially sense two channels in a single time slot and are coordinated to access the potentially unused channels. The proposed protocol is formulated as a channel selection problem coupled with a channel assignment problem. We subsequently investigate the myopic sensing policy and propose a Markov chain-based greedy channel assignment scheme (MCGA) to maximize the expected total SU throughput. Moreover, we extend our proposal to the scenario with imperfect spectrum sensing and obtain the optimal sensing time to enhance the performance improvement by considering spectrum sensing and spectrum access in a joint manner. Finally, we evaluate the performance of our proposal in a saturated network. Numerical and simulation results demonstrate that compared to the existing work, our approach can achieve significant performance improvements in SU throughput.

In this paper we address the problem of rate scheduling in the Impulse Radio (IR) ultra-wideband (UWB) wireless body area networks (WBANs) and the minimum energy required to stabilize the queuing system. Targeting low complexity WBAN applications, we assume noncoherent receivers based on energy detection and autocorrelation for all nodes. The coordinating node can minimize the average energy consumption of the system and achieve the queue backlog stability of the sensor nodes by controlling the number of pulses per symbol. We first illustrate the necessary and sufficient conditions of network stability for a multi-mode UWB system and then propose a feasible rate scheduling algorithm based on the Lyapunov optimization theory. The scheduling algorithm uses the instantaneous channel state information and the length of the local queue of all sensor nodes and can approach the optimal energy-delay tradeoff of the network. We apply our theoretical framework to the IR-UWB physical layer of the IEEE 802.15.6 standard and extract the optimal physical layer modes that can achieve the desired energy-delay tradeoff.

A distributed antenna system (DAS) architecture is believed to be able to enhance capacity performance of Cloud Radio Access Networks (C-RAN), especially for users near the cell boundary who experience low Signal-Noise-Ratio (SNR). However, the problem of finding the analytical bounds on the capacity of DAS with the rising number of antennas in low SNR rigime has not been fully studied. In this paper, we investigate a case in C-RAN of multiple transmitting base stations and a single receiving user under low SNR conditions. We derive closed-form upper and lower bounds in efficiently computable expressions for differential capacity (DCAP) using the moment generating function (MGF) of SNR. Bounds accuracy is evaluated and compared to results in current literature. Numerical results corroborate our analysis and the analytic bounds on DCAP is tight in the low SNR regime. Furthermore, The upper bound approximates better compared with the one obtained in [1] under two different channel models. These lower and upper bounds provide more accurate capacity measures which can be used in the evaluation of DAS performance and C-RAN design.

Location estimation of a capsule endoscope in the gastrointestinal (GI) tract is a challenging task, as radio frequency signals encounter a high loss and highly dynamic channel propagation environment. In this paper, the possibility of using a radar system for capsule localization is investigated at frequencies of 2.4 GHz, 3.4 GHz, 4.8 GHz and 5.8 GHz respectively. Based on our theoretical analysis, the system at 2.4 GHz exhibits better performance. Therefore, in the next step a detailed analysis of the RF propagation in the human abdomen is presented with the aid of numerical simulations using Finite-Difference Time-Domain (FDTD) method. Our simulation results show that a radar system at 2.4 GHz can be considered for tracking the capsule if a high sensitivity receiver is deployed. In addition, we provide an in-depth study investigating the effect of antenna position and polarization on the received signal strength. The studies show that vertical polarization of the antenna outperforms other linear polarization because it provides better signal strength in the center of the human abdomen.

In this paper, spectral efficiency (SE) is investigated for cooperative ultra-wideband (UWB) based on-body area networks (OBANs). To optimize SE for single-relay cooperation, an equivalent generic cooperative model in UWB based OBANs is established first. With the proposed model, joint optimal relay location and power allocation for cooperation is then derived to solve the SE maximization problem. Simulation results show that direct transmission is preferable for UWB based OBANs when the transmitter and receiver are located on the same side of the human body. However, the joint optimal cooperative transmission scheme can achieve a significant improvement on SE compared with direct transmission when the transmitter and receiver are located on the different sides of the human body, which indicates that cooperation is more feasible to be applied in this case due to its robustness to the significant path loss.

Spectrum cartography is the process of constructing a map showing Radio Frequency signal strength over a finite geographical area. In our previous work we formulated spectrum cartography as a compressive sensing problem and we illustrated how cartography can be used in the context of discovering spectrum holes in space that can be exploited locally in cognitive radio networks. This paper investigates the performance of compressive sensing based approach to cartography in a fading environment where realtime channel estimation is not feasible. To accommodate for lack of channel information we take an iterative approach. We extend the well-known iteratively reweighted ℓ1minimisation approach by exploiting spatial correlation between two points in space. We evaluate the performance in an urban environment where Rayleigh fading is prominent. Our numerical results show a significant improvement in the probability of accurately making a spectrum sensing decision, in comparison to the well-known weighted approach and the traditional compressive sensing based method.

Denoising recovery algorithms are very important for the development of compressed sensing (CS) theory and its applications. Considering the noise present in both the original sparse signal x and the compressive measurements y, we propose a novel denoising recovery algorithm, named Regularized Subspace Pursuit (RSP). Firstly, by introducing a data pre-processing operation, the proposed algorithm alleviates the noise-folding effect caused by the noise added to x. Then, the indices of the nonzero elements in x are identified by regularizing the chosen columns of the measurement matrix. Afterwards, the chosen indices are updated by retaining only the largest entries in the Minimum Mean Square Error (MMSE) estimated signal. Simulation results show that, compared with the traditional orthogonal matching pursuit (OMP) algorithm, the proposed RSP algorithm increases the successful recovery rate (and reduces the reconstruction error) by up to 50% and 86% (35% and 65%) in high noise level scenarios and inadequate measurements scenarios, respectively.

Quality of Service (QoS) routing is one of the key enabling techniques for multimedia wireless sensor networks (WSNs). However, the multi-constraints QoS routing problem is an NP-hard problem, and the computational complexity of an exhaustive search over all the paths is too high for large scale multimedia WSNs. In this paper, a novel parallel elite clonal quantum evolutionary algorithm is proposed to solve the multi-constraints QoS routing problem. The proposed algorithm minimizes the energy consumption, while guaranteeing QoS performance, including delay, bandwidth, delay jitter and packet loss rate, in multimedia WSNs. The algorithm is tested by extensive simulations and its performance is compared with the genetic algorithm and ant colony optimization. Simulation results demonstrate that the proposed algorithm achieves lower energy consumption at a faster convergence rate than the other two evolutionary algorithms.

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.

Licence-exempt secondary Long Term Evolution systems have been proposed recently, in attempt to meet the needs of rapidly growing wireless mobile applications. However, where the secondary network is spread over a large geographical area, traditional detect-and-avoid algorithms are less effective in providing interference protection to Primary Users while maximising the secondary throughput. Spectrum cartography is an emerging technique that can be used to discover spectrum holes in space. We propose a downlink power allocation algorithm using Kriging Spatial Interpolation and Compressive Sensing based spectrum cartography in an environment where large scale shadow fading is prominent. We evaluate the performance of our approach by simulating a secondary Urban Microcell network operating in TV White Space. Simulation results show a significant improvement in interference and throughput, in comparison to traditional detect-and-avoid algorithms.

Spectrum cartography is the process of constructing a map showing Radio Frequency signal strength over a finite geographical area. Multiple research groups have recently proposed to use spectrum cartography in the context of discovering spectrum holes in space that can be exploited locally in cognitive radio networks. In our novel approach, we exploit the sparsity of primary users in space to formulate the cartography process as a compressive sensing problem. Further, we present a novel algorithm for solving the cartography problem that builds on the well-known Orthogonal Matching Pursuit algorithm. We evaluate the performance of our approach by simulating a cognitive radio network where primary users are low power wireless microphones. Our simulation results show a significant improvement in reconstruction error, in comparison to two existing compressive sensing based methods.

Recently, the recovery performance of analog Compressed Sensing (CS) has been significantly improved by representing multiband signals with the modulated and merged Slepian basis (MM-Slepian dictionary), which avoids the frequency leakage effect of the Discrete Fourier Transform (DFT) basis. However, the MM-Slepian dictionary has a very large scale and corresponds to a large-scale measurement matrix, which leads to high recovery computational complexity. This paper resolves the above problem by modulating and band-limiting the multiband signal rather than modulating the Slepian basis. Specifically, instead of using the MM-Slepian dictionary to represent the whole multiband signal, we propose to use the non-modulated Slepian basis to represent the modulated and band-limited version of the multiband signal based on the recently proposed Modulated Wideband Converter (MWC). Furthermore, based on the analytical derivation with the non-modulated Slepian basis, we propose an Interpolation Recovery (IR) algorithm to take full advantage of the Slepian basis, whereas the Direct Recovery (DR) algorithm using the Moore-Penrose pseudo-inverse cannot achieve this. Simulation results verify that, with low recovery computational load, the non-modulated Slepian basis combined with the IR algorithm improves the recovery SNR by up to 35 dB compared with the DFT basis in noise-free environment.

Cooperative spectrum sensing has been proposed to significantly improve spectrum sensing accuracy by taking advantage of the cooperation among multiple secondary users (SUs). Most of existing work assumes that all SUs have the same SNR values of primary users' signal while the difference of SNR values among SUs, although it is very common in practice, is largely ignored. In this paper, we investigate two cooperative spectrum sensing scenarios where multiple geographically diverse SUs may have different SNR values. In the first scenario a cognitive radio network (CRN) with a single primary channel is considered. We aim to optimize the individual thresholds of SUs to maximize SU throughput subject to the constraint of missed detection probability. In the second scenario where there are multiple channels in a CRN, we jointly optimize the allocation of SUs to sense different channels and the individual detection thresholds of SUs. Our objective is to maximize total SU throughput over multiple channels while guaranteeing the missed detection probability below a given threshold. Simulation results demonstrate that our proposed schemes achieve significant improvements in SU throughput over the existing schemes.

Efficient data transmission in cognitive radio networks (CRNs) is critical for cognitive radio (CR) users to communicate with each other in an opportunistic manner. Even with successful access to required channels, the transmission could still suffer from failures due to channel fading. In this paper, we propose a random linear coded scheme for efficient data transmission in multi-channel CRNs under practical fading channel conditions. We develop theoretical analysis and derive general form solutions for the batch delay associated with the proposed scheme. We also use our theoretical model to analyze the performances of two multi-channel automatic repeat request (ARQ) based schemes. Simulation results validate the analysis and show that the coded scheme outperforms the ARQ based schemes in terms of batch transmission delay. Additionally, the coded scheme is less dependent on feedback channels than the other schemes.

In ad hoc networks, each node is required to forward packets for others. However, based on energy consumption considerations, a node may reject other nodes' forwarding requests to save the limited battery power for its own data transmission. Therefore, a lot of incentive schemes have been proposed to promote the cooperation of the nodes. Most of the existing research work assumes that all the nodes in the network should provide full cooperation in order to optimize the transmission efficiency. However, this assumption is too strict because the activities of the nodes in ad hoc networks have some inherent uncertainty. In this paper, we propose a power control mechanism in ad hoc networks under a dynamic repeated game-theoretic framework. A notion of nodes' evaluation levels for the future experiences is defined to take account of the non-cooperation due to the inherent uncertainty in the ad hoc network nodes' activities. Our scheme does not require all the nodes in the ad hoc network to absolutely cooperate with each other. The simulation results show that, compared with the existing schemes, our power control mechanism considering non-cooperative packet forwarding improves the average transmission efficiency by ∼25% and has good scalability.

Recently, distributed data storage (DDS) for Wireless Sensor Networks (WSNs) has attracted great attention, especially in catastrophic scenarios. Since power consumption is one of the most critical factors that affect the lifetime of WSNs, the energy efficiency of DDS in WSNs is investigated in this paper. Based on Compressed Sensing (CS) and network coding theories, we propose a Compressed Network Coding based Distributed data Storage (CNCDS) scheme by exploiting the correlation of sensor readings. The CNCDS scheme achieves high energy efficiency by reducing the total number of transmissions Nttot and receptions Nrtot during the data dissemination process. Theoretical analysis proves that the CNCDS scheme guarantees good CS recovery performance. In order to theoretically verify the efficiency of the CNCDS scheme, the expressions for Nttotand Nrtotare derived based on random geometric graphs (RGG) theory. Furthermore, based on the derived expressions, an adaptive CNCDS scheme is proposed to further reduce Nttot and Nrtot. Simulation results validate that, compared with the conventional ICStorage scheme, the proposed CNCDS scheme reduces Nttot, Nrtot, and the CS recovery mean squared error (MSE) by up to 55%, 74%, and 76% respectively. In addition, compared with the CNCDS scheme, the adaptive CNCDS scheme further reduces Nttotand Nrtotby up to 63% and 32% respectively.

This paper presents a novel downlink resource allocation scheme for OFDMA-based next generation wireless networks subject to inter-cell interference (ICI). The scheme consists of radio resource and power allocations, which are implemented separately. Low-complexity heuristic algorithms are first proposed to achieve the radio resource allocation, where graph-based framework and fine physical resource block (PRB) assignment are performed to mitigate major ICI and hence improve the network performance. Given the solution of radio resource allocation, a novel distributed power allocation is then performed to optimize the performance of cell-edge users under the condition that desirable performance for cell-center users must be maintained. The power optimization is formulated as an iterative barrier-constrained water-filling problem and solved by using the Lagrange method. Simulation results indicate that our proposed scheme can achieve significantly balanced performance improvement between cell-edge and cell-center users in multi-cell networks compared with other schemes, and therefore realize the goal of future wireless networks in terms of providing high performance to anyone from anywhere.

Cooperative spectrum sensing has been proposed to significantly improve spectrum sensing accuracy by taking advantage of the cooperation among secondary users (SUs), but also this incurs some sensing cost. In this paper, we present a cooperative spectrum sensing model with consideration to spectrum sensing cost in distributed cognitive radio networks where each SU aims to maximize its utility. Under the scenario with selfish SUs, we formulate cooperative spectrum sensing as a non-cooperative game and obtain the mixed strategy Nash equilibrium of the formulated spectrum sensing game by deriving the sensing probabilities of SUs. Under the scenario with limited collaboration of SUs, we formulate cooperative spectrum sensing as a nonlinear optimization problem and derive the optimal sensing strategy of SUs by using our proposed Newton-Raphson based algorithm. Numerical results demonstrate that SUs with limited collaboration are able to achieve much better performance than the outcome of the Nash equilibrium and by choosing the optimal sensing strategy SUs are able to maximize their utility, which is an effective tradeoff between SU throughput and sensing cost.

In this paper we present a novel dynamic spectrum sensing and access model in cognitive radio networks. This model allows secondary users (SUs) to sequentially sense two channels in a single time slot and provides coordinated access of multiple SUs to the available channels. The presented access model is formulated as a channel assignment optimization problem which is shown to be NP-hard. We subsequently propose and analyze a Markov chain based greedy channel assignment scheme (MCGA) which allows for sequential sensing of two channels with a priority order per time slot. Finally, we analyze and evaluate the performance of our approach in a saturated network. Our analytical results, validated by simulation, indicate that compared to the existing work, our approach can achieve significant improvements in terms of SU throughput and MAC delay.

A Wireless Body Area Network (WBAN) comprises a number of tiny devices implanted in/on the body that sample physiological signals of the human body and send them to a coordinator node for medical or other purposes. As these miniature devices run on built-in batteries, energy is the most valuable resource in WBANs. This makes signal interference between neighboring WBANs a serious threat because it causes energy waste in these systems. To mitigate this internetwork interference, we propose a dynamic power control mechanism in WBANs which employs reinforcement learning (RL) to learn from experience and improve its performance. This paper presents guidelines in designing efficient RL power controllers in WBANs and provides an analysis of the effect of the reward function, discount factor, learning rate and eligibility trace parameter where the main performance criteria used are convergence and solution optimality in terms of throughput and energy consumption per bit.

This paper presents an improved positioning approach for cellular-network based mobile tracking in severe non-line-of-sight (NLOS) propagation environments. The proposed approach consists of two stages: the smoothing stage to suppress the NLOS errors in the distance measurements; and the position tracking stage. An improved distance smoothing method is proposed to significantly reduce the NLOS errors. It applies online distance mean and variance estimates to identify LOS and NLOS propagations. The online LOS and NLOS identification results, the distance mean and variance estimates are employed to update the Kalman filter (KF) for smoothing distance measurements. A data fusion technique is developed to combine distance measurements, mobile velocity and heading angle estimates provided by motion sensors through the extended KF. Simulation results demonstrate that the proposed two-stage approach significantly improves position accuracy compared to the existing NLOS mitigation algorithms, at the cost of increased computational complexity.

In this paper we propose a novel resource allocation scheme to achieve a balanced performance improvement for all users in a LTE network subject to inter-cell interference. In the proposed scheme the resource allocation process is implemented in two steps. In the first step interference coordination and scheduling are first conducted in a global manner to prevent cell-edge users from mutual interference. In the second step, optimal power allocation is conducted to maximize performance of cell-edge users while maintaining high performance of cell-center users. The optimal power allocation problem is solved using the Lagrange decomposition method. Simulation results show that our proposed scheme can significantly improve performance for all users in a multi-cell network and achieve a better performance balance between cell-edge and cell-center users.

This paper presents positioning algorithms for cellular network-based vehicle tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the complex propagation environment. A one-step position estimation method and another two-step method are proposed and developed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error so that the NLOS effect is significantly reduced. Vehicle velocity and heading direction measurements are exploited in the algorithm development, which may be obtained using a speedometer and a heading sensor, respectively. The developed algorithms are practical so that they are suitable for implementation in practice for vehicle applications. It is observed through simulation that in severe NLOS propagation scenarios, the proposed positioning methods outperform the existing cellular network-based positioning algorithms significantly. Further, when the distance measurement error is modeled as the sum of an exponential bias variable and a Gaussian noise variable, the exact expressions of the CRLB are derived to benchmark the performance of the positioning algorithms.

In the above-cited article, which appeared in the IEEE Transactions on Mobile Computing, vol. 11, no. 2, pp. 254-263, February 2012, the authors wish to clarify that the authors listed in reference [10] are incorrect.

Recent studies reveal that the main cause of the well-known unfairness problem in wireless networks is the ineffective coordination of CSMA-based random access due to topology asymmetry. In this paper, we take a modeling-based approach to understand and solve the unfairness problem. Compared to existing works, we advance the state of the art in two important ways. First, we propose an analytical model called the G-Model, which accurately characterizes the ineffective coordination of medium access in asymmetrical topologies. The G-Model can estimate network performance under arbitrary parameter configurations. Second, while previous works decompose a wireless network into embedded basic asymmetric topologies and study each basic topology separately, we go beyond the basic asymmetrical topology and design a model-driven optimization method called Flow Level Adjusting (FLA) to solve the unfairness problem for larger wireless networks. Through extensive simulations, we validate the proposed G-Model and show that FLA can greatly improve the overall fairness of wireless networks in which basic asymmetric topologies are embedded.

One major problem of cross-layer control algorithms in multi-hop wireless networks is that they lead to large end-to-end delays. Recently there have been many studies devoted to solving the problem to guarantee order-optimal per-flow delay. However, these approaches also bring the adverse effect of sacrificing a lot of network utility. In this paper, we solve the large-delay problem without sacrificing network utility. We take a fundamentally different approach of delay differentiation, which is based on the observation that flows in a network usually have different requirements for end-to-end delay. We propose a novel joint rate control, routing and scheduling algorithm called CLC_DD, which ensures that the flow delays are proportional to certain pre-specified delay priority parameters. By adjusting delay priority parameters, the end-to-end delays of preferential flows achieved by CLC_DD can be as small as those achieved by delay-order-optimal algorithms. In contrast to high network utility loss in previous approaches, we prove that our approach achieves maximum network utility. Furthermore, we incorporate opportunistic routing into the cross-layer design framework to improve network performance under the environment of dynamic wireless channels.

The decode-and-forward relay technique has been introduced in next generation wireless networks (such as LTE) to extend coverage and improve performance, although it may generate additional inter-cell interference. Soft frequency reuse (SFR) has been proposed as the most promising frequency planning strategy to mitigate inter-cell interference in LTE systems. In this paper we propose an effective combination of relay networks and SFR, with a dedicated relay topology and the SFR-based resource allocation scheme. In the proposed relay network, each relay station (RS) can jointly serve cell-edge users from adjacent cells to increase efficiency. The proposed resource allocation scheme, on the other hand, is able to achieve throughput improvement for both cell-edge users and cell-center users by minimizing the interference impact in the system. The benefit of relay networks with SFR-based resource allocation in terms of 30% overall performance improvement over conventional non-relay networks can be realized as demonstrated by the simulation results.

This paper proposes a novel load distribution aware soft frequency reuse (LDA-SFR) scheme for inter-cell interference mitigation and performance optimization in next generation wireless networks. Our proposed scheme aims to provide a solution to effectively achieve inter-cell interference mitigation while maintaining high spectrum efficiency to all users in the cell. The proposed scheme consists of two novel algorithms: edge bandwidth reuse and centre bandwidth compensation. Using the edge bandwidth reuse algorithm, cell-edge users can take advantage of uneven traffic load and user distributions within each cell to expand their resource allocations. The center bandwidth compensation algorithm, on the other hand, provides a protection mechanism for cell-center users to avoid exhaustive edge bandwidth extension. Applying LDA-SFR to an LTE network and comparing its performance against that of existing soft frequency reuse (SFR) and adaptive soft frequency reuse (ASFR) schemes indicates that LDA-SFR is superior as it achieves fairness between cell-edge users and cell-center users in terms of average throughput improvement.

An efficient hardware-optimized Physical Random Access Channel (PRACH) baseband signal generation algorithm and its ASIC implementation in the LTE user equipment (UE) transmitter are presented in this paper. A simplified DFT of the Zadoff-Chu (ZC) sequence as well as a phase computation are applied to the prime size DFT of the PRACH preamble and the large size IDFT is accomplished by groups of smaller size IFFTs. The optimized algorithm achieves significantly lower computational complexity compared with the original algorithm in the LTE specification and better performance compared to another publication. The ASIC architecture is also designed to reduce the memory size and logic complexity, which achieves a low hardware cost in terms of the cell area. The proposed design was implemented in 65nm CMOS and it was demonstrated that this design can satisfy the timing requirements of the LTE specification.

Based on energy consumption considerations, an ad hoc network node may reject other nodes' forwarding requests to save the limited battery power for its own data transmission. Therefore, a lot of incentive schemes have been proposed to promote the cooperation of the nodes. The utilization of the incentive schemes makes the nodes willing to cooperate with each other, because their non-cooperation can be punished in the future. However, the activities of the nodes in ad hoc networks have some inherent uncertainty. For example, the batteries of some nodes are exhausted or some nodes move to other regions. Under these situations, the existing incentive schemes are no longer effective and the nodes have to terminate their cooperation and stop forwarding packets for others. In this paper, we propose a power control mechanism in ad hoc networks under a dynamic repeated game-theoretic framework. A notion of nodes' evaluation levels for the future experiences is defined to take account of the non-cooperation due to the inherent uncertainty in the ad hoc network nodes' activities. The nodes achieve their optimal transmission efficiency by using a two-step power control mechanism. The simulation results show that compared with the existing schemes our power control mechanism considering non-cooperative packet forwarding improves the average transmission efficiency by approximately 25%.

A Wireless Body Area Network (WBAN) is made up of multiple tiny physiological sensors implanted in/on the human body with each sensor equipped with a wireless transceiver that communicates to a coordinator in a star topology. Energy is the scarcest resource in WBANs. Power control mechanisms to achieve a certain level of utility while using as little power for transmission as possible can play an important role in reducing energy consumption in such very energy-constrained networks. In this paper, we propose a novel power controller to mitigate internetwork interference in WBANs and increase the maximum achievable throughput with the minimum energy consumption. The proposed power controller employs reinforcement learning with approximation to learn from the environment and improve its performance. We compare the performance of the proposed controller to two other power controllers, one based on game theory and the other one based on fuzzy logic. Simulation results show that compared to the other two approaches, RLPC provides a substantial saving in energy consumption per bit, with a substantial increase in network lifetime.

Wireless Body Area Networks (WBANs) are an emerging technology for short-range wireless communication inside, on or around the human body, mainly for medical applications. A WBAN's scarcest resource is power. Due to the mobility of WBANs as well as the limited number of available channels, signals of neighboring WBANs can cause interference that may severely degrade the reliability and performance of the system and lead to more power consumption. In this paper, we propose a fast converging fuzzy power controller (FPC) with feedback whose inputs are the current interference power level, Signal-to-Interference-and-Noise (SINR) and the current transmission power level to provide interference mitigation in WBANs. We utilize a genetic algorithm to design and optimize the FPC to simultaneously maximize capacity, minimize power consumption and minimize convergence time. We compare the performance of the proposed approach with two game-theory power control approaches. Our simulation results show that compared to these other approaches, the proposed FPC provides a substantial saving in power consumption as well as quick convergence that is independent of the number of nodes in the system, while sacrificing only a small amount of capacity.

This paper studies optimal channel reservation in cooperative cognitive radio networks (CRNs) where secondary users (SUs) have access to the combined spectrum pool of cooperating CRNs. Motivated by SU high forced termination in cooperative CRNs, we propose two channel reservation schemes, Fixed Channel Placement Reservation (FCPR) and Dynamic Channel Placement Reservation (DCPR), and theoretically analyze their performances using the Markov chain approach. Our numerical results, validated by simulation, indicate that for a given number of reserved channels, the DCPR algorithm achieves better user experience by reducing the forced termination probability. Based on this analysis, we propose two enhanced reservation algorithms: Algorithm A maximizes the overall capacity of CRNs to enable network operators to increase their revenue; Algorithm B minimizes the user experience cost function to provide better services.

In multi-hop cognitive radio networks (CRNs), there can be dramatic increase in end-to-end delay when a traffic flow switches between a number of channels along its path. We propose a new Maximum Flow-Segment (MFS) based scheme to channel assignment in CRN by minimizing the number of times the channel is switched along a flow. Our MFS based scheme has been efficiently integrated into the AODV on-demand routing protocol. We demonstrate that our MFS based scheme reduces the number of channel switches for the traffic flows and reduces the end-to-end delay by 50%. Our scheme also minimizes the routing overhead, and achieves a higher and more stable throughput than the link based approach.

In this paper we consider the inter-network interference problem in Wireless Body Area Networks (WBANs). We propose a distributed inter-network interference aware power control algorithm motivated by game theory. A power control game is formulated considering both interference between nearby networks and energy efficiency of WBANs. We derive a distributed power control algorithm called ProActive Power Update (PAPU), which can efficiently find the Nash Equilibrium representing the best tradeoff between energy and network utility. A realistic power control procedure is proposed assuming limited cooperation between WBANs. We compare our algorithm with the ADP algorithm where users are punished for interfering with others and we show that our solution can utilize energy much more efficiently by only sacrificing a small amount of network utility. In addition, we show that by adjusting the energy price, PAPU provides a methodology for application scenarios where WBANs have different energy constraints and quality of service requirements.

This paper presents positioning algorithms for cellular network-based mobile tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the hostile environment. Two positioning methods with NLOS mitigation are proposed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error. Mobile velocity and heading angle information is exploited to greatly enhance position accuracy. It is observed through simulation that the proposed methods significantly outperform other cellular network based positioning algorithms. Further, the exact expressions of the CRLB are derived when the distance measurement error is the sum of an exponential and a Gaussian variable.

MIPv6 is one of the dominating protocols that enable a mobile node to maintain its connectivity to the Internet when moving from one access router to another. However, it suffers from long handoff latency and routing inefficiency. In this paper, we present a novel distributed mobility management scheme, ADA (Asymmetric Double-agents Architecture), which introduces two mobility agents to serve one end-to-end communication. One mobility agent is located close to the MN to limit the amount of MIPv6 signaling traffic outside the local domain. The other mobility agent is located close to the CN to minimize routing overheads. Quantitative analysis shows that ADA significantly outperforms the existing mobility management protocols.

Content distribution networks (CDNs) improve the performance of content delivery by replicating the popular content on surrogate servers deployed at the edge of the Internet. The CDN-P2P architecture, which combines the complementary advantages of both CDN and P2P networks, can improve the quality of service (QoS). In this paper, we propose a k-coordinated decentralized replica placement algorithm (DRPA) based on a gain formulation of the replica placement problem. Although the gain formulation is designed for different types of the CDN-P2P architecture, we focus on the robust ring-based architecture in this study. In our approach, each surrogate server makes the replica placement in terms of the content replicas on k closer surrogate servers, which enhances the system scalability compared to the centralized replica placement heuristics. In addition, according to the simulation results, the proposed algorithm is able to reduce the backbone traffic between the servers and the requesting peers compared to the traditional replica placement algorithms for the pure CDN.

In recent years, structured P2P communications is being widely used for its features of self-organization as well as good scalability and flexibility. To make sure that every node can participate in such a network whether it is behind a NAT or not, we must solve the NAT traversal problem. However, existing NAT traversal methods all need the support of a centralized server which will destroy the distributive characteristic of structured P2P. In this paper, we propose a distributed NAT traversal mechanism called SMBR (Selective-Message Buddy Relaying) for structured P2P. SMBR has two main advantages. The first one is that it does not need the support of a server and thus can maintain the characteristics of structured P2P. Secondly, SMBR uses different mechanisms for the control messages and data according to their size. For control messages, it uses the method of buddy's relay while for data direct connections can be built with the help of the buddy. Using this mechanism, SMBR can achieve a balance between the traversal time and the buddies' load.

P4P (Provider Portal for Applications) integrated P2P (peer-to-peer) network is one of the main trends of P2P networks. While P4P brings advantages to P2P, it also brings new challenges in solving trust problems in P2P networks. In this paper, we consider the P4P's characteristics, domain partition and strategy matrix guidance, and propose a novel domain-based trust model for P2P networks. In our model, we distinguish peer's intra-domain behavior and inter-domain behavior. Peer's intra-domain good behavior does not mean that the peer also behaves well when interacting with other domain's peers, due to P4P's inter-domain traffic control. In addition, we give each domain a trust value, using them to modify the strategy matrix provided by P4P. Simulation results show that our model can effectively distinguish good peers and malicious peers. Even when a peer behaves well in its own domain, our method can make other domains cooperate together to evaluate the peer's bad inter-domain behavior. Lastly, using the domain's trust value to modify the P4P's strategy matrix can reduce the impact of malicious peers' cheating behavior on forming the strategy matrix.

In ad hoc networks, reducing energy consumption and improving throughput are both important for high network performance. This paper presents a joint power and rate control adaptive algorithm to optimize the trade-off between power consumption and throughput in ad hoc networks. Each node chooses its own transmission power and rate based on limited environment information in order to achieve optimal transmission efficiency. In a fictitious game framework with strategy space transformation, our joint power and rate control adaptive algorithm can be viewed as a supermodular game. By interpreting the supermodular game using myopic best response updates, this algorithm can converge to the unique optimal transmission efficiency. Finally, the simulation results show that this supermodular game approach improves the average transmission efficiency by about 33%.

The throughput of conventional multicast transmission in wireless systems is limited by the user with the worst channel quality in the multicast service group. The subcarrier allocation for multicast services in multicarrier systems is a feasible solution to overcome the capacity limitation by exploiting the frequency diversity among subcarriers. However, most of the current subcarrier allocation algorithms are limited to unicast services. In this paper, we propose an optimal subcarrier allocation algorithm for multicast services with Quality of Services (QoS) guarantees. A low-complexity suboptimal algorithm is also proposed, which includes three steps: Conservative Allocation, Greedy Step and Iterative Enhancement. Simulation results show that the proposed algorithms significantly outperform the conventional multicast transmission scheme while at the same time guaranteeing the minimum data rates of all users. Moreover, simulation results also show that the performance difference between the optimal and suboptimal algorithms is small.

This paper focuses on wireless capsule endoscope magnetic field based localization by using a linear algorithm, an unconstrained optimization method and a constrained optimization method. Eight sensor populations are employed for performance evaluation. For each of five sensor populations, four different sensor configurations are investigated, which represent potential sensor placements in practice. Accuracy is evaluated over a range of noise standard deviations and the position area is set on a solid cylinder which well represents the realistic scenario of the human body. It is observed that the optimization method greatly outperforms the linear algorithm that should not be used alone in general. The constrained optimization approach outperforms the unconstrained optimization method in presence of large noise. Simulation results show that best position accuracy is achieved when the sensors are uniformly deployed on a 2D plane with some sensors on the boundary of the position area. For the sensor populations considered, when increasing sensor population by one, the accuracy improves by about 0.45 divided by the sensor population. The results provide useful information for the design of wireless endoscope localization systems.

With its new features such as multi-homing and multi-streaming the Stream Control Transmission Protocol (SCTP) has become a promising candidate as a general-purpose transport layer protocol. Multi-homing in an SCTP association can make concurrent multi-path transfer an appealing candidate to satisfy the ever increasing user demands for bandwidth. Multiple streams provide an aggregation mechanism to accommodate heterogeneous objects, which belong to the same application but may require different QoS from the network. However, the current approach lacks an internal mechanism to support preferential treatment among its streams for concurrent multi-path transfer. In this paper, we introduce WM2-SCTP (Wireless Multi-path Multi-flow - Stream Control Transmission Protocol), a transport layer solution for concurrent multi-path transfer with parallel subflows. WM2-SCTP aims at exploiting SCTP's multi-homing and multi-streaming capability by grouping SCTP streams into subflows based on their required QoS and selecting best paths for each subflow to improve data transfer rates. The results show that under different scenarios WM2-SCTP, can effectively enhance transmission efficiency.

Voice service is vulnerable to a number of attacks that can compromise the confidentiality, integrity and authenticity of voice communication. This paper describes the design of communication protocols for securing SIP based VOIP communication. It presents the architectural principles involved and the overall security solution comprising the design of secure extensions to SIP messages. Finally it evaluates the performance of the proposed scheme and presents some results.

In this paper, a novel radio admission control (RAC) scheme is proposed for handling multiclass services in Long Term Evolution (LTE) systems. An objective function of maximizing the number of admitted users is proposed to evaluate the system capacity. To solve the optimization problem, we present a combined complete sharing (CS) and virtual partitioning (VP) resource allocation model and develop a service degradation scheme in case of resource limitations in the proposed RAC scheme. Call blocking probability, system resource utilization and system capacity are used as performance metrics and are evaluated by using a K-dimensional Markov Chain model. Numerical results show that an optimal proportion of resource deployment for different service groups can be identified to maximize system capacity while at the same time maintaining quality of service (QoS) constraints of all admitted users.

With the development of heterogeneous networks, multimode terminals are becoming more and more popular. However, when there are several different kinds of sessions requiring transmission simultaneously, how to distribute these sessions among the available access networks according to the different features of the flows and the current link conditions of the candidate networks is a new challenge. In this paper, we propose a new solution to the flow distribution problem for multimode terminals. Our proposal, automatic flow distribution (AFD), includes a network selection algorithm located at the terminals and an admission control algorithm located at the access points of the networks. Consequently, the terminals and the networks can cooperate with each other and realize automatic flow distribution among the different available access networks. We utilize the notion of priority, ensuring that the more important sessions have preferential use of the network resources. In addition, in order not to excessively deteriorate the transmission performance of the lower priority flows, a probabilistic suspension scheme is introduced. Finally, the AFD method utilizes the concept of "entropy" to automatically compute the weights of different attributes which influence the flow distribution decision making, thus avoiding the users' difficulty to specify the weights manually.

In multiuser MIMO systems, the Channel Inversion Regularization (CIR) precoding outperforms Zero-Forcing (ZF) in the case of a small number of users and low SNR. However, unlike the zero-interference ZF, the optimal power allocation issue using CIR is a nonconvex optimization problem which will become more intractable with nonconvex QoS constraints. In this paper we focus on the challenging QoS-aware optimal power allocation problem, aiming to maximize the system sum rate and guarantee the users' minimum data rates. As a result, an "Iterative Geometric Programming" (IGP) strategy is proposed which transforms the underlying problem to a series of tractable Geometric Programming (GP) problems through an iterative convex approximation. Extensive simulations have been conducted and the results indicate that IGP is quite suitable to tackle the problem, which can achieve a good balance between the system sum rate and the individual QoS requirements.

Significant throughput gains and system fairness can be obtained by employing scheduling schemes based on precoding techniques. However, QoS guarantee requirements are seldom taken into account. In this paper, we propose a downlink scheduling algorithm for QoS-guaranteed services in multi-user multiple-input multiple-output (MIMO) systems with limited feedback. The proposed algorithm combines stream selection with multi-user packet scheduling. To maximize the overall capacity and reduce co-channel interference, streams are selected in accordance with preceding matrices. In multi-user packet scheduling, the base station first determines the SDMA region size for the primary streams. In addition, packet scheduling for the secondary streams is performed to completely exploit spatial multiplexing gains. Numerical results show that, at the cost of slightly lower system fairness, the proposed algorithm can achieve higher spectrum efficiency, and have a noticeable improvement in guaranteeing QoS requirements in terms of data rates and delay.

The concept of Logical Service Provision Number (LSPN) is proposed to evaluate the service provision capacity in multicast and unicast integrated multiclass service cellular networks. The tradeoff between call blocking probability and LSPN is investigated and an objective function of maximizing LSPN is proposed. To solve the optimal problem, the system is modeled as a 2K-dimensional Markov process with following features. The multiclass service system can provide multiple service classes using either multicast traffic or unicast traffic and each service class has call level QoS requirements. A static channel allocation model is adopted as the multicast traffic integration scheme. By solving the objective function, an optimal proportion of multicast traffic to unicast traffic can be identified to maximize the service provision capacity while satisfying QoS constraints.

In the IEEE 802.16e OFDMA systems, the data mapping algorithm maps the data to the appropriate rectangular regions in the two-dimensional matrix of time and frequency domain. Each region is described by an Information Element (IE) which is used for signaling and occupies a slot. The IEs as well as vacant slots in the allocated rectangular region result in a substantial amount of overhead. In order to minimize the overhead so as to increase system throughput, the paper proposes a "Mapping with Appropriate Truncation and Sort" (MATS) algorithm. Extensive simulations are conducted in terms of mapping efficiency, mapping cost and system throughput to evaluate the performance of MATS. The results show that compared with Raster, MATS can increase the mapping efficiency by up to 2.4% and reduce the mapping cost by up to 80% and 37% for constant bit rate traffic and variable bit rate traffic, respectively. Moreover, system throughput is increased by more than 3% in the 10 MHz bandwidth network. Consequently, MATS can substantially reduce the overhead and achieve high system throughput.

In this paper, a low-complexity SDMA-based greedy resource allocation (SGRA) algorithm is proposed for the uplink of IEEE 802.16 MIMO-OFDMA systems taking into account co-channel interference. The objective of SGRA is to allocate resources in the space-time-frequency domain in order to maximize system throughput while guaranteeing QoS requirements of real time services. By performing efficient interference management, SGRA can be carried out in two phases. In the first phase, greedy resource allocation, primarily involving uplink scheduling and subchannel allocation across the MAC and PHY layers, is performed in the time-frequency domain. In the second phase, the resource allocation is extended to the space-time-frequency domain. Simulation results show that SGRA can improve system throughput while at the same time guaranteeing the delay and minimum data rate requirements of users.

As a potentially competitive technique, erasure coding has been employed in wireless sensor networks (WSNs) to enhance transmission reliability. In this paper, to design a practical and efficient redundancy mechanism in WSNs, we firstly provide a theoretical study of packet delivery probability and average energy consumption for retransmission and redundancy mechanisms. The theoretical results indicate that in WSNs, the adaptive redundancy coding mechanism is more energy efficient than retransmission while keeping the same level of reliability in most scenarios. Then based on the mapping table and two basic design principles obtained from the theoretical analysis, we propose a reliable transport protocol ARRTP which combines the adaptive redundancy mechanism and partial retransmission. The simulation and trace-driven results both verify that when the loss probability varies from low to high, our protocol provides reliability with comparably lower energy consumption. Furthermore, compared with fixed redundancy degrees, the robustness of the adaptive mechanism are also evaluated.

Cooperative Multi-In Multi-Out (MIMO) schemes can reduce both transmission energy and latency in distributed wireless sensor networks (WSNs). When circuit energy is considered in such networks, the total energy consumed as the number of cooperating nodes increases becomes of particular interest. In addition, most of the previous works focused only on Space-Time-Block-Code (STBC) schemes and ignore other MIMO schemes. In this paper we present a comparison study of three cooperative MIMO schemes: Beamforming (BF), STBC and Spatial Multiplexing (SM) where both the transmission and circuit energies are considered. We consider a wireless sensor network operating in quasi-static Rayleigh fading channels with M cooperating transmit nodes and N cooperating receive nodes. We show that the Single-In-Single-Out (SISO) scheme is more energy-efficient and has lower packet latency at higher regions of transmission power while the three cooperative MIMO schemes are more energy-efficient and outperform the SISO scheme at the lower regions. From our analysis, we can conclude that, Beamforming outperforms both the SM and STBC schemes in term of energy-efficiency and lower packet latency. Also we suggest that the Beamforming scheme utilising two transmit nodes results in an efficient cooperative system.

Single frequency networks (SFN) broadcast is an efficient method to provide broadcast services in cellular networks. How to select broadcast services by the SFN operation to trade off between the occupied bandwidth and the SFN performance including spectrum efficiency and broadcast service continuity is a new problem. To the best of our knowledge no solutions have been proposed to solve this problem so far in the literature. We define the problem of SFN broadcast services selection as a knapsack problem and solve it to minimize the occupied bandwidth while at the same time guaranteeing the SFN performance. Based on the solution, several SFN broadcast services selection algorithms are proposed which vary in the reselection policy. Numerical results show that our proposed algorithms are applicable to different cases with different system requirements and in particular, that the semi-dynamic-SFN broadcast services selection algorithm is an efficient solution in general.

In heterogeneous wireless networks, handoff can be separated into two parts: horizontal handoff (HHO) and vertical handoff (VHO). VHO plays an important role to fulfill seamless data transfer when mobile nodes cross wireless access networks with different link layer technologies. Current VHO algorithms mainly focus on when to trigger VHO, but neglect the problem of how to synthetically consider all currently available networks (homogeneous or heterogeneous) and choose the optimal network for HHO or VHO from all the available candidates. In this paper, we present an analytical framework to evaluate VHO algorithms. Subsequently, we extend the traditional hysteresis based and dwelling-timer based algorithms to support both VHO and HHO decisions and apply them to complex heterogeneous wireless environments. We refer to these enhanced algorithms as E-HY and E-DW, respectively. Based on the proposed analytical model, we provide a formalization definition of the handoff conditions in E-HY and E-DW and analyze their performance. Subsequently, we propose a novel general handoff decision algorithm, GHO, to trigger HHO and VHO in heterogeneous wireless networks. Analysis shows that GHO can achieve better performance than E-HY and E-DW. Simulations validate the analytical results and verify that GHO outperforms traditional algorithms in terms of the matching ratio, TCP throughput and UDP throughput.

In this paper we address a new problem that has not been addressed in the past: how to improve energy efficiency for both unicast and multicast services without violating QoS requirements of mobile stations in 802.16e wireless networks. We propose a scheduling set based integrated scheduling (SSBIS) algorithm to solve the problem. SSBIS partitions all the mobile stations into multicast Scheduling Sets and a unicast Scheduling Set on the principle of minimizing mobile stations' energy consumptions by making use of the multicast transmission scheme and it adopts different scheduling policies based on the attributes of the Scheduling Sets to improve energy efficiency of the whole system. Numerical results show that SSBIS can result in a significant overall energy saving while at the same time guaranteeing the minimum data rates of mobile stations.

This paper proposes a new cross layer scheme (Fast RSVP) to reserve resources in mobile IPv6 networks. Through the cooperation of mobile IP and RSVP modules, Fast RSVP includes a number of mechanisms such as advanced resource reservation on neighbor tunnels, resource reservation on optimized routes, resource reservation for handover sessions, path merge etc. Network simulation results show that our scheme, compared with other traditional ways to reserve resources in mobile environments, has the following advantages: (1) it allows a mobile node to realize fast handover with QoS guarantees; (2) it avoids resource wasting caused by triangular routes and duplicate reservations; (3) it distinguishes different types of reservation requests, greatly reducing the handover session forced termination rate while maintaining high performance of the network.

This paper proposes a new cross layer scheme (fast RSVP) to reserve resources for mobile IPv6. Through the cooperation of mobile IP and RSVP modules, fast RSVP includes a number of mechanisms such as advance resource reservation on neighbor tunnels, resource reservation on optimized routes, resource reservation for handover sessions (guard channel) etc. Network simulation results show that our scheme, compared with other traditional ways to reserve resources in mobile environments, has the following advantages: (1) it allows a mobile node to realize fast handover with QoS guarantees; (2) it avoids resource wasting caused by triangular routes and duplicate reservations; (3) it distinguishes different types of reservation requests, greatly reducing the handover session forced termination rate while maintaining high performance of the network.

In this paper, we propose a practically efficient Subcarrier Allocation scheme based on Lagrangian relaxation to solve the problem of subcarrier allocation in OFDMA wireless channels. The problem of subcarrier allocation is formulated into an Integer Programming (IP) problem, which is relaxed by replacing complicating constraints with Lagrange multipliers using Lagrangian Relaxation. A subgradient method is used to optimize the Lagrangian dual function and a heuristic is designed to obtain the feasible solution. Lagrangian Relaxation Subcarrier Allocation (LRSA) is proven to be of polynomial complexity and it provides bounds on the value of channel efficiency. Numerical results show that compared with other algorithms proposed in the literature, LRSA can result in a significant improvement in channel efficiency, while at the same time guaranteeing minimum data rates of users.

The next generation wireless networking (4G) is envisioned as a convergence of different wireless access technologies with diverse levels of performance. Vertical handoff (VHO) is the basic requirement for convergence of different access technologies and has received tremendous attention from the academia and industry all over the world. During the VHO procedure, handoff decision is the most important step that affects the normal working of communication. In this paper, we propose a novel vertical handoff decision algorithm, self- adaptive VHO algorithm (SAVA), and compare its performance with conventional algorithms. SAVA synthetically considers the long term movement region and short term movement trend of mobile hosts, and achieves a good integrative handoff performance.

In recent years, many research works have focused on vertical handoff (VHO) decision algorithms. However, evaluation scenarios in different papers are often quite different and there is no consensus on how to evaluate performance of VHO algorithms. In this paper, we address this important issue by proposing an approach for systematic and thorough performance evaluation of VHO algorithms. Firstly we define the evaluation criteria for VHO with two metrics: matching ratio and average ping-pong number. Subsequently we analyze the general movement characteristics of mobile hosts and identify a set of novel performance evaluation models for VHO algorithms. Equipped with these models and evaluation criteria, we evaluate and analyze two types of decision algorithms: hysteresis based and dwelling-timer based algorithms. The results show a good match between simulation and analytical results.

In this paper, we tackle the packet scheduling problem in IEEE 802.16e wireless metropolitan area network (WMAN), where the Sleep Mode is applied to save energy of mobile stations (MSs). Our objective is to design an energy efficient scheduling policy which works closely with the sleep mode mechanism so as to maximize battery lifetime in MSs. To the best of our knowledge no power saving scheduling algorithms based on sleep mode defined in IEEE 802.16e have been proposed so far in the literature. We propose a longest virtual burst first (LVBF) scheduling algorithm which schedules packets of MSs in a virtual burst mode where there is one primary MS and multiple secondary MSs sharing the wireless link resource. LVBF prolongs MSs' lifetime by reducing the average time when MSs stay in the idle state and the number of state transitions between the awake and sleep states. Simulation results show that, in comparison with the round robin scheduling scheme, LVBF can produce significant overall energy saving, while guaranteeing the QoS requirements of MSs in terms of their minimum data rates.

In this paper, we expand the idea of spreading the transmitted symbols in OFDM systems by unitary spreading matrices based on the rotated Hadamard or rotated Discrete Fourier Transform (DFT) matrices proposed in the literature to apply to Space-Time Coded Multiple-Input Multiple- Output OFDM (STC-MIMO-OFDM) systems. We refer the resulting systems to as STC-MIMO Block Spread OFDM (STC-MIMO-BOFDM) systems. In the proposed systems, a multi-dimensional diversity, including time, frequency, space and modulation diversities, can be used, resulting in better bit error performance in frequency selective Rayleigh fading channels compared to the conventional OFDM systems with or without STCs. Simulations carried out with the Alamouti code confirm the advantage of the proposed STC-MIMO-BOFDM systems.

In this paper, the problem of scheduling multiple users sharing a time varying wireless channel is studied, in networks such as in 3G CDMA and IEEE 802.16. We propose a new generic wireless packet scheduling framework (WPSF), which takes into account not only the quality of service (QoS) requirements but also the wireless resource consumed. The framework is generic in the sense that it can be used with different resource constraints and QoS requirements depending on the traffic flow types. Subsequently, based on this framework a minimum rate and channel aware (MRCA) scheduling algorithm is presented. MRCA attempts to greedily enhance wireless channel efficiency by making use of multi-user channel quality diversity, while providing acceptable QoS in term of users' minimum rate constraints. Simulation results show the desirable properties identified in the algorithm.

Handoff is one of the key elements in ensuring quality of service (QoS) in mobile wireless networks. Handoff connections generally have higher priority than new connections. Traditional reservation policies that reserve some channels for handoff connections are not adaptive to traffic load changes. This paper proposes a new dynamic guard channel scheme (DGCS), which 1) adapts to various traffic loads; 2) combines differentiated QoS service model and priority handoff mechanism; 3) provides fairness for differentiated QoS services; 4) does not need to exchange state information among different cells, so it is easy to be implemented and is simple enough to be used in real time environments; 5) and utilizes network resources efficiently and puts a bound on each service blocking probability. The simulation results show that the ratios among different QoS service probabilities are guaranteed to be predefined values and system utilization is improved greatly.

This paper presents a new routing strategy for mobile ad hoc networks, called dynamic zone-based topology routing protocol (DZTR). We introduce new strategies to maintain up-to-date intrazone and interzone topology information at each node. We also propose a GPS-based location tracking mechanism, which reduces route discovery area and the number of nodes queried to find the required destination. Our routing strategy has been designed to work with a dynamic zone, which contains a set of member nodes. Every node outside a zone is called a single-state node. We perform theoretical performance analysis, which shows that our network topology creation process has significantly fewer overheads than flooding approaches.

Mobile ad hoc networking offers the promise of connecting mobile nodes without the need for a fixed infrastructure. Performance limitations of these networks under real network and traffic conditions are still largely unknown. In this paper we present a simulation study which investigates the impact of transmit range of nodes on resulting throughput performance. Although this aspect has previously been studied analytically in stationary packet radio networks, those studies were based on a number of simplifying assumptions. Our aim is to find out the optimum transmit range to maximise data throughput in mobile ad hoc networks which use the channel contention of the 802.11 MAC layer. Contrary to the available analytical results, we find that under a wide set of network and load conditions multi-hop networks have lower performance than single hop networks. Data throughput is maximised when all nodes are in range of each other. Moreover, the addition of relay only nodes does not significantly improve throughput performance of multi-hop networks.

We address issues with the performance of IEEE 802.11, when used in the adhoc mode, in the presence of hidden terminals. We present results illustrating the strong dependence of channel capture behavior on the SNR observed on contending hidden connections. Experimental work has illustrated that in a hidden terminal scenario, the connection having the strongest SNR is able to capture the channel, despite the use of the RTS-CTS-DATA-ACK 4-way handshake designed to alleviate this problem. Our results indicate that the near-far SNR problem may have a significant effect on the performance of an adhoc 802.11 network.

Integrated services IP networks are expected to provide a variety of services with differentiated QoS. This requires the implementation of mechanisms that can discriminate service classes in terms of QoS. The IETF has recently proposed a differentiated services (Diffserv) framework for provision of QoS. In this paper we analyse the performance of two Diffserv mechanisms: threshold dropping and priority scheduling in terms of packet loss and mean packet delay. A comparison of the two mechanisms is carried out with the requirement that both mechanisms provide the same level of packet loss for the preferred flow. This comparison extends the results reported in the literature for these two mechanisms. In particular, in this paper we determine the impact of buffer threshold and buffer size on packet loss and mean packet delay in these mechanisms.

This paper investigates the use of fixed bandwidth reservation for multi-service mobile networks. In particular it extends previous fixed multi-service network results to the mobile scenario and presents analytical and simulation results for new and handover call blocking probabilities. It also investigates the performance sensitivity to different traffic load ratios as well as the cell dwell time distribution.

Multi-player network games (also known as multi-player online games) are rapidly becoming a significant contributor to overall Internet traffic. However, the characteristics of the traffic generated by games are poorly understood, hence making it difficult to assess their impact (particularly on a large scale) on networks. This paper presents a study on the characteristics of multi-player games traffic, focusing on how the distributions of payload sizes and inter-arrival times vary with the number of players, thereby partially filling the aforementioned knowledge gap. We have found that these distributions are dependent on the game, but in some cases are independent of the number of players.

Wireless ATM (WATM) will be used to support broadband applications for future generation mobile services. To achieve this, the existing ATM protocol must be augmented with mobility management capabilities. One of the important components of mobility management is the ability of the network to seamlessly re-route existing connections to different parts of the network. To date, a number of schemes have been proposed for broadband wireless networks. These schemes can potentially be used in WATM networks. We compare the performance of these schemes in terms of their complexity, handover latency, communication disruption period and buffer and bandwidth requirements.