In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly uses two localization methods: 1) generative adversarial network (GAN) based positioning method and 2) time difference of arrival (TDOA) based positioning method. Since GAN-based method cannot defend against a strong jamming signal while TDOA-based method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to localize the target UAV. This problem is formulated as an optimization problem. The aim of this problem is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model based collaborative reinforcement learning (RL) method which enables the active UAV to optimize its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the UAVs movement and the unknown jamming attack pattern. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

In this paper, the problem of using active unmanned aerial vehicles (UAVs) and a base station (BS) to jointly localize a target UAV under global positioning system (GPS) spoofing attacks is studied. In the considered model, active UAVs transmit signals, which will be reflected by the target UAV and received by active UAVs. Based on the signal transmission time, active UAVs calculate the distance between the target UAV and active UAVs. Then, active UAVs transmit these distance measurement information and their GPS information to the BS for localizing the target UAV. During the localization process, the target UAV is equipped with a GPS jammer, which can interfere with GPS information of active UAVs. Since the localization accuracy depends on distance between the target UAV and active UAVs and GPS information accuracy of active UAVs, active UAVs must optimize their trajectories and determine whether to transmit measurement information to the BS for localizing the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error of the target UAV between the estimated and true positions of the target UAV by jointly optimizing the trajectories of active UAVs and determining distance information transmission scheme. To find the optimal solution, a historical observations and actions-based reinforcement learning (HOA-RL) method is proposed. Compared to traditional state-based reinforcement learning (RL) methods, the proposed method can capture the historical decision-making process of agents and optimally adjust trajectories of active UAVs and measurement information transmission scheme based on their observations. Simulation results show that the proposed method can achieve 44.4% and 70.4% gains in terms of reducing the positioning error of the target UAV compared to Qmix method and Qtran method, respectively.

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods.

This paper delves into the design of an energy efficient collaborative federated learning (CFL) methodology using which mobile devices exchange their FL model with a subset of their neighbors without reliance on a parameter server based on the distributed graph neural network (GNN) method. Each device is unable to send its FL model to every neighboring device due to device mobility and wireless resource limitations. To reduce the energy consumption of FL model transmission, each device must choose a subset of devices with which to share its FL model. This problem is formulated as an optimization problem to meet the constraints of delay and training loss while minimizing the energy consumption for model transmission. However, the formulated problem is difficult to solve since the device mobility patterns, and the relationship between the device connection scheme and CFL performance are unknown. To address this challenge, we analytically characterize the relationship between dynamic device connections and the performance of CFL methodology. Based on the analysis, a GNN based algorithm is proposed to enable each device to select a subset of its neighbors and the transmit power in a decentralized method. Compared to standard optimization methods that must determine device connections in a centralized manner, the GNN based method enables each device to use its neighboring devices' location and connection information to individually determine a subset of devices to transmit the local model. Given the device connections, the optimal transmit power of each device can be determined by convex optimization. Simulation results show that the proposed method can reduce the energy consumption for model transmission and training loss by up to 46% and 2%, respectively

In this paper, a passive low earth orbit (LEO) satellite localization framework is investigated. In our considered model, one active satellite and multiple passive satellites are selected to localize a target LEO satellite, where the active satellite transmits signals to the target satellite and passive satellites receive signals reflected by the target satellite. Based on the received signals, passive satellites calculate the transmission distances and send this distance information to the active satellite that will estimate the position of target satellite. Since LEO satellites are powered by the sun, the available energy that can be used for target satellite localization is limited and dynamic. Hence, the satellite selection scheme must be optimized for improving the localization accuracy under the energy consumption constraints. This problem is cast into an optimization setting with a goal of minimizing target satellite positioning error by jointly optimizing active/passive satellite selection and transmit power allocation. To solve this problem, a mixture Gaussian distribution-based reinforcement learning (MGD-RL) method is proposed. The proposed MGD-RL method enables each LEO satellite to determine whether to be an active or a passive satellite and optimize its transmit power under the energy constraints. Furthermore, the proposed MGD-RL method can approximate the probability distribution of value functions by using mixture Gaussian distributions, thus reducing the training complexity of the designed RL. Simulation results demonstrate that, compared to a value decomposition network method and independent RL method, the MGD-RL method can improve the positioning accuracy of the target LEO satellite by up to 26.8% and 48.9%.

This paper investigates a fully distributed federated learning (FL) problem, in which each device is restricted to only utilize its local dataset and the information received from its adjacent devices that are defined in a communication graph to update the local model weights for minimizing the global loss function. To incorporate the communication graph constraint into the joint posterior distribution, we exploit the fact that the model weights on each device is a function of its local likelihood and local prior and then, the connectivity between adjacent devices is modeled by a Dirichlet distribution. In this way, the joint distribution can be factorized naturally by a factor graph. Based on the Dirichlet-based factor graph, we propose a novel distributed approximate Bayesian inference algorithm that combines loopy belief propagation (LBP) and variational Bayesian inference (VBI) for distributed FL. Specifically, VBI is used to approximate the non-Gaussian marginal posterior as a Gaussian distribution in local training process and then, the global training process resembles Gaussian LBP where only the mean and variance are passed among adjacent devices. Furthermore, we propose a new damping factor design according to the communication graph topology to mitigate the potential divergence and achieve consensus convergence. Simulation results verify that the proposed solution achieves faster convergence speed with better performance than baselines.

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

This paper studies an end-to-end video semantic communication system for massive communication. In the considered system, the transmitter must continuously send the video to the receiver to facilitate character reconstruction in immersive applications, such as interactive video conference. However, transmitting the original video information with substantial amounts of data poses a challenge to the limited wireless resources. To address this issue, we reduce the amount of data transmitted by making the transmitter extract and send the semantic information from the video, which refines the major object and the correlation of time and space in the video. Specifically, we first develop a video semantic communication system based on major object extraction (MOE) and contextual video encoding (CVE) to achieve efficient video transmission. Then, we design the MOE and CVE modules with convolutional neural network based motion estimation, contextual extraction and entropy coding. Simulation results show that compared to the traditional coding schemes, the proposed method can reduce the amount of transmitted data by up to 25% while increasing the peak signal-to-noise ratio (PSNR) of the reconstructed video by up to 14%.

This paper studies the problem of the lightweight image semantic communication system that is deployed on Internet of Things (IoT) devices. In the considered system model, devices must use semantic communication techniques to support user behavior recognition in ultimate video service with high data transmission efficiency. However, it is computationally expensive for IoT devices to deploy semantic codecs due to the complex calculation processes of deep learning (DL) based codec training and inference. To make it affordable for IoT devices to deploy semantic communication systems, we propose an attention-based UNet enabled lightweight image semantic communication (LSSC) system, which achieves low computational complexity and small model size. In particular, we first let the LSSC system train the codec at the edge server to reduce the training computation load on IoT devices. Then, we introduce the convolutional block attention module (CBAM) to extract the image semantic features and decrease the number of downsampling layers thus reducing the floating-point operations (FLOPs). Finally, we experimentally adjust the structure of the codec and find out the optimal number of downsampling layers. Simulation results show that the proposed LSSC system can reduce the semantic codec FLOPs by 14%, and reduce the model size by 55%, with a sacrifice of 3% accuracy, compared to the baseline. Moreover, the proposed scheme can achieve a higher transmission accuracy than the traditional communication scheme in the low channel signal-to-noise (SNR) region.

In this paper, we investigate codebook-based near-field beam training for extremely large-scale intelligent reflecting surface (XL-IRS). Compared with the conventional far-field beam training method that only searches for the best beam direction, the near-field beam training is more challenging since it requires a beam search over both the angular and distance domains due to the spherical wavefront propagation model. To reduce the near-field beam-training overhead of two-dimensional exhaustive search, we propose a novel two-layer codebook-based near-field beam training scheme that decomposes the two-dimensional search into two sequential phases. Specifically, the layer-l codebook designed based on the omnidirectivity of random-phase beam pattern is firstly employed to estimate the user distance. Then, given the estimated user distance of the layer-1, a customized layer-2 codebook is employed to scan the candidate locations of the user. Numerical results demonstrate that the proposed scheme can achieve more accurate estimation of the user distance and angle, as well as higher data rate with smaller training overhead, compared with benchmarks.

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively.

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. The goal is to jointly determine the bitwidths employed for local FL model quantization and the set of devices participating in FL training at each iteration. We pose this as an optimization problem that aims to minimize the training loss of quantized FL under a per-iteration device sampling budget and delay requirement. However, the formulated problem is difficult to solve without (i) a concrete understanding of how quantization impacts global ML performance and (ii) the ability of the server to construct estimates of this process efficiently. To address the first challenge, we analytically characterize how limited wireless resources and induced quantization errors affect the performance of the proposed FL method. Our results quantify how the improvement of FL training loss between two consecutive iterations depends on the device selection and quantization scheme as well as on several parameters inherent to the model being learned. Then, to address the second challenge, we show that the FL training process can be described as a Markov decision process (MDP) and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Simulation results show that the proposed FL algorithm can reduce the convergence time by 29% and 63% compared to a model free RL method and the standard FL method, respectively.

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments.

In this paper, we investigate the semantic-aware efficient sampling policy for remote state estimation in a digital twin (DT) empowered smart factory with multiple wireless sensing devices and an edge server. In this setting, wireless sensing devices must continuously sample the factory states and transmit semantic-aware sensing data to the server. Using the received sensing data, the server builds a realtime DT mapping remotely that analyzes and predicts the events in the factory. Since the DT requires continuous data transmission, maintaining the DT inevitably consumes significant amounts of limited wireless resources. To address this issue, we reduce the required amount of data transmission by making wireless devices only send the semantic-aware sensing data that indicates the occurrence of events, otherwise stay idle. In particular, we first invoke the age of incorrect information (AoII) to measure the semantic of the sensing data, which represents the freshness of the concerned events. Next, we formulate an optimization problem that minimizes the long-term AoII of remote state estimation through the devices deciding whether to sample the factory states at each time slot. To solve this problem, we first transform the original problem into a state-wise constrained Markov decision programming (CMDP) and then propose a soft actor-critic (SAC) based algorithm to learn a sampling policy to take sample actions within the sampling rate constraint, while considering packet error. Simulation results show that, the proposed algorithm can reduce the number of samples by up to 44% compared to the error-based sampling scheme, with the same estimation accuracy.

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

In this paper, we consider the optimization of federated learning (FL) over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp). In such a system, MIMO devices transmit their locally trained FL models to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. AirComp enables efficient wireless model aggregation by the PS in bandwidth-limited settings. However, wireless channel fading can produce distortions in AirComp-based FL. To tackle this challenge, we develop a novel aggregation scheme that combines digital modulation with AirComp to mitigate wireless fading while ensuring communication efficiency. We formulate this as a joint transmit-receive beamforming design optimization problem which dynamically adjusts the beamforming matrices to minimize the FL training loss with transmission errors. To solve this problem based on limited information at the PS, we employ an artificial neural network (ANN) to estimate the local FL models of all devices. Then, we derive a closed-form optimal design of the transmit and receive beamforming matrices based on predicted FL models. Numerical evaluations validate the advantages of the proposed methodology in terms of model training performance compared with baselines.

In this paper, the problem of using several controlled unmanned aerial vehicles (UAVs) to localize a target UAV in real time is investigated. In the considered model, the controlled UAV consists of one active UAV and four passive UAVs. Each passive UAV receives signals transmitted from the active UAV and reflected by the target UAV, and then estimates the distance from the active UAV to the target UAV and then from the target UAV to the passive UAV. Each passive UAV then transmits this distance information to a base station (BS), which estimates the location of the target UAV. Since the target UAV will change its location according to its performed task, each controlled UAV must optimize its trajectory to continuously localize the target UAV. This trajectory design problem is formulated as an optimization problem whose goal is to jointly optimize the trajectories of active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards, thus finding better trajectories for controlled UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 58.3% and 84.8%, compared to VD-RL and independent DRL methods, respectively.

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding methods.

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

Time Sensitive Network (TSN) is a set of protocols working at the data link layer, which provides a delay guarantee for different services through various traffic shaping protocols. In TSN, to avoid timeouts caused by urgent services being blocked by large-bandwidth services, the IEEE 802.1 Qbu frame preemption protocol was proposed, which allows high-priority frames to reduce the delay by preempting low-priority frames. However, this mechanism will increase the delay of low-priority frames. With a unique storage structure, image and video can tolerate a certain degree of byte loss, such as color bytes, which will reduce the quality of the image but will not affect video fluency. Combined with this feature, in this paper, we propose a frame preemption enhancement mechanism assisted by frame truncation, which properly discards non-important bytes of the Ethernet frame payload field to ensure the deadline constraints. To minimize the information loss of service caused by frame truncation, we propose a frame structure based on the current Ethernet frame, which adjusts the bytes arrangement order according to their importance. In this case, when frame truncation is applied for satisfying the delay requirement, it can ensure that the discarded bytes are of the lowest importance. Simulation results show that, compared with the standard frame preemption mechanism, our proposed mechanism can send more payload bytes to the receiver without increasing the delay and jitter of other services, and the switch memory usage peak is also reduced by 12.41%.

Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) are expected to be key technologies in future wireless communication systems. With the ability of modifying the wireless channel, RIS has a great potential to improve the channel capacity and spectrum efficiency. However, the modification of wireless channel may change the optimal user pairing strategy of NOMA, which will cause large interference and decoding failure at the receiver. The joint optimization of user pairing and RIS beamforming in NOMA systems has not been studied yet. To address this issue, this paper considers a RIS assisted NOMA downlink transmission with one base station (BS), where the edge users are paired with the central users to share the same resource blocks, and RISs are deployed to serve the edge users. Our goal is to maximize the sum rate of edge users by jointly optimizing the user pairing, the passive beamforming at the RISs, the active beamforming and power allocation at the BS, subject to the users’ minimum rate and the total transmitting power. Since the formulated problem is non-convex and difficult to be solved, we propose an alternating iterative algorithm to obtain the suboptimal solution. Simulation results show that the proposed scheme can increase the sum rate of edge users by 5% and 7% compared to the other two baseline methods.

With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.

In this paper, the problem of audio based semantic communication is investigated over wireless networks. In the considered model, wireless edge devices must transmit large-sized audio data to a server using semantic communication techniques. The techniques enable the transmission of audio semantic information which captures the contextual features of audio signals. To extract the semantic information from audio signals, a wave to vector (wav2vec) architecture based autoencoder that consists of convolutional neural networks (CNNs) is proposed. The proposed autoencoder enables high-accuracy audio transmission with small amounts of data. To further improve the accuracy of semantic information extraction, federated learning (FL) is implemented over multiple devices and a server. Simulation results show that the proposed algorithm can converge effectively and can reduce the mean square error (MSE) between the recovered audio signals and the source audio signals by nearly 100 times, compared to a traditional coding scheme.

Wireless power transfer (WPT) can provide sustainable power supply to the distributed devices via electromagnetic (EM) waves. The narrow beams formed by millimeter wave (mmWave) beam can concentrate the transmission power and greatly improve the energy efficiency of WPT. However, the mmWave propagation is susceptible to blockage and suffers higher path-loss, resulting in low power intensity harvested by the devices in no line of sight (NLOS) state. In this paper, we propose a sectorized directional WPT scheme for wireless sensor network (WSN) assisted by the emerging intelligent reflecting surface (IRS), where power beacon (PB) transfers energy to the devices in selected charging sectors, and the IRS is deployed in each sector to achieve high passive beamforming gain and provide additional effective reflection paths to enhance WPT efficiency drastically. We aim to maximize the weighted sum-power received by devices via jointly optimizing the transmit precoders at the PB and reflect phase shifts at the IRS, subject to the individual energy harvesting constraints of each device. To solve this non-convex problem, an efficient algorithm to find a sub-optimal solution is proposed. Simulation results show that the proposed scheme can increase the weighted sum-power by 15% and 26% compared to the two baseline methods.

Abstract Network function virtualisation (NFV), software defined networks (SDNs), and mobile edge computing (MEC) are emerging as core technologies to satisfy increasing number of users' demands in 5G and beyond wireless networks. SDN provides clean separation of the control plane from the data plane while NFV enables the flexible and on‐the‐fly creation and placement of virtual network functions (VNFs) and are able to be executed within the various locations of a distributed system. In this paper, VNF placement and resource allocation (VNFPRA) problem is considered which involves placing VNFs optimally in distributed NFV‐enabled MEC nodes and assigning MEC resources efficiently to these VNFs to satisfy users' requests in the network. Current solutions to this problem are slow and cannot handle real‐time requests. To this end, an SDN‐NFV infrastructure is proposed to tackle the VNFPRA problem in wireless MEC networks. Our aim is to minimise the overall placement and resource cost and also to minimise the total number of VNF migrations. A genetic based heuristic algorithm is proposed. The superior performance of the proposed solution is confirmed in comparison with four existing algorithms, i.e. resource utilisation‐single objective evolutionary algorithm (RU‐SOEA), genetic non‐bandwidth link allocation algorithm (GA‐NBA), random‐fit placement algorithm (RFPA), and first‐fit placement algorithm (FFPA). The results demonstrate that a coordinated placement of VNFs in SDN/NFV enabled MEC networks can satisfy the objective of overall reduced cost. Simulation results also reveal that the proposed scheme approximates well with the optimal solution returned by Gurobi and also achieves reduction on overall cost compared to other methods.

In this article, a joint task, spectrum, and transmit power allocation problem is investigated for a wireless network in which the base stations (BSs) are equipped with mobile-edge computing (MEC) servers to jointly provide computational and communication services to users. Each user can request one computational task from three types of computational tasks. Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users. This problem is formulated as an optimization problem whose goal is to minimize the maximal computational and transmission delay among all users. A multistack reinforcement learning (RL) algorithm is developed to solve this problem. Using the proposed algorithm, each BS can record the historical resource allocation schemes and users’ information in its multiple stacks to avoid learning the same resource allocation scheme and users’ states, thus improving the convergence speed and learning efficiency. The simulation results illustrate that the proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard$Q$-learning algorithm.

In this article, the problem of minimizing energy and time consumption for task computation and transmission in mobile-edge computing-enabled balloon networks is investigated. In the considered network, high-altitude balloons (HABs), acting as flying wireless base stations, can use their powerful computational capabilities to process the computational tasks offloaded from their associated users. Since the data size of each user’s computational task varies over time, the HABs must dynamically adjust their resource allocation schemes to meet the users’ needs. This problem is posed as an optimization problem, whose goal is to minimize the energy and time consumption for task computation and transmission by adjusting the user association, service sequence, and task allocation schemes. To solve this problem, a support vector machine (SVM)-based federated learning (FL) algorithm is proposed to determine the user association proactively. The proposed SVM-based FL method enables HABs to cooperatively build an SVM model that can determine all user associations without any transmissions of either user historical associations or computational tasks to other HABs. Given the predictions of the optimal user association, the service sequence and task allocation of each user can be optimized so as to minimize the weighted sum of the energy and time consumption. Simulations with real-city cellular traffic data show that the proposed algorithm can reduce the weighted sum of the energy and time consumption of all users by up to 15.4% compared to a conventional centralized method.

The performance optimization of UAV communication systems requires the joint design of UAV trajectory and communication efficiently. To tackle the challenge of infinite design variables arising from the continuous-time UAV trajectory optimization, a commonly adopted approach in the existing literature is by approximating the UAV trajectory with piecewise-linear path segments connected via a finite number of waypoints in three-dimensional (3D) space. However, this approach may still incur prohibitive computational complexity in practice when the UAV flight period/distance becomes long, as the distance between consecutive waypoints needs to be kept sufficiently small to retain high approximation accuracy. To resolve this fundamental issue, we propose in this paper anewandgeneralframework for UAV trajectory and communication co-design with flexible number of waypoint optimization variables (calleddesignablewaypoints) or theirsub-pathrepresentations. First, we propose aflexible path discretizationscheme that optimizes only a number of selected waypoints (designable waypoints) along the UAV path for complexity reduction, while all the designable and non-designable waypoints are used in calculating the approximated communication utility along the UAV trajectory for ensuring high trajectory discretization accuracy. Next, we propose a novelpath compressionscheme, which treats the UAV trajectory as a signal and compresses its path representation based on the basis decomposition. Specifically, the UAV 3D path is first decomposed into three one-dimensional (1D) sub-paths and each sub-path is then approximated by superimposing a number of selected basis paths (which are generally less than the number of designable waypoints) weighted by their corresponding path coefficients, thus further reducing the path design complexity. Finally, we provide a case study on UAV trajectory design for aerial data harvesting from distributed sensors, and numerically show that the proposed flexible path discretization and path compression schemes can significantly reduce the UAV trajectory design complexity yet achieve favorable rate performance as compared to conventional path/time discretization schemes.

In this article, the problem of vertical handover in software-defined network (SDN) based heterogeneous networks (HetNets) is studied. In the studied model, HetNets are required to offer diverse services for mobile users. Using an SDN controller, HetNets have the capability of managing users' access and mobility issues but still have the problems of ping-pong effect and service interruption during vertical handover. To solve these problems, a mobility-aware seamless handover method based on multipath transmission control protocol (MPTCP) is proposed. The proposed handover method is executed in the controller of the software-defined HetNets (SDHetNets) and consists of three steps: location prediction, network selection, and handover execution. In particular, the method first predicts the user's location in the next moment with an echo state network (ESN). Given the predicted location, the SDHetNet controller can determine the candidate network set for the handover to pre-allocate network wireless resources. Second, the target network is selected through fuzzy analytic hierarchical process (FAHP) algorithm, jointly considering user preferences, service requirements, network attributes, and user mobility patterns. Then, seamless handover is realized through the proposed MPTCP-based handover mechanism. Simulations using real-world user trajectory data from Korea Advanced Institute of Science & Technology show that the proposed method can reduce the handover times by 10.85% to 29.12% compared with traditional methods. The proposed method also maintains at least one MPTCP subflow connected during the handover process and achieves a seamless handover.

In this article, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that generates a global FL model and sends the model back to the users. Since all training parameters are transmitted over wireless links, the quality of training is affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS needs to select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To seek the solution, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can improve the identification accuracy by up to 1.4%, 3.5% and 4.1%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation, 2) a standard FL algorithm with random user selection and resource allocation, and 3) a wireless optimization algorithm that minimizes the sum packet error rates of all users while being agnostic to the FL parameters.

In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied. In particular, each IoT device monitors a physical process that follows nonlinear dynamics. As the dynamic of the physical process varies over time, each device must sample the real-time status of the physical system and send the status information to a base station (BS) so as to monitor the physical process. The dynamics of the realistic physical process will influence the sampling frequency and status update scheme of each device. In particular, as the physical process varies rapidly, the sampling frequency of each device must be increased to capture these physical dynamics. Meanwhile, changes in the sampling frequency will also impact the energy usage of the device. Thus, it is necessary to determine a subset of devices to sample the physical process at each time slot so as to accurately monitor the dynamics of the physical process using minimum energy. This problem is formulated as an optimization problem whose goal is to minimize the weighted sum of AoI and total device energy consumption. To solve this problem, a machine learning framework based on the repeated update Q-learning (RUQL) algorithm is proposed. The proposed method enables the BS to overcome the biased action selection problem (e.g., an agent always takes a subset of actions while ignoring other actions), and hence, dynamically and quickly finding a device sampling and status update policy so as to minimize the sum of AoI and energy consumption of all devices. Simulations with real data of PM 2.5 pollution in Beijing from the Center for Statistical Science at Peking University show that the proposed algorithm can reduce the sum of AoI by up to 26.9% compared to the conventional Q-learning method.

In this paper, the problem of minimizing energy consumption for task computation and transmission in a cellular network with mobile edge computing (MEC) capabilities is studied. In the considered network, each user needs to process a computational task at each time slot. A part of the task can be transmitted to a base station (BS) that can use its powerful computational ability to process the tasks offloaded from its users. Since the data size of each user's computational task varies over time, the BSs must dynamically adjust the resource allocation scheme to meet the users' needs. This problem is posed as an optimization problem whose goal is to minimize the energy consumption for task computing and transmission via adjusting user association scheme as well as their task and power allocation scheme. To solve this problem, a support vector machine (SVM)-based federated learning (FL) is proposed to determine the user association proactively. Given the user association, the BS can collect the information related to the computational tasks of its associated users using which, the transmit power and task allocation of each user will be optimized and the energy consumption of each user is also minimized. The proposed SVM-based FL method enables the BS and users to cooperatively build a global SVM model that can determine all users' association without any transmission of users' historical association and computational task offloading. Simulations using real data on city cellular traffic from the OMNILab at Shanghai Jiao Tong University show that the proposed algorithm can reduce the users' energy consumption by up to 20.1% compared to the conventional centralized SVM method.

In this paper, the joint optimization of information transmission and energy utilization in energy harvesting wireless sensor networks (EH-WSNs) is studied. In EH-WSNs, the imbalance of energy harvesting and consumption affects the sensing and communication ability of the network. To solve this problem, a novel energy aware opportunistic routing protocol with long-short term memory (LSTM) based solar energy prediction is proposed. The protocol innovatively takes account of the nodes' current residual energy and the harvesting solar energy in a short term which is predicted by a LSTM neural network as key factors in forwarding candidates election process of the opportunistic routing. Additionally, in order to jointly optimize the energy consumption and information transmission, a new metric with combined energy factor and relay efficiency for each node is proposed to assist candidate node selection where the relay priority considers the residual energy and working history of nodes. Simulation results show that the proposed protocol increases the network throughput by 12% and reduces retransmission rate by 15% compared to opportunistic routing based on geography. Simultaneously, it has stronger ability to balance energy consumption among sensor nodes.

In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground users, while collecting energy from the charging stations located at the center of a user group. Meanwhile, to serve ground users and harvest energy, the UAV must be examined and repaired regularly. In consequence, it is necessary to optimize the trajectory design of the UAV while jointly considering the maintenance costs, the number of users that are served by the UAV, and the energy consumption and harvesting. To capture the relationship among these factors, we first model the completion of service and the harvested energy as reward, and the energy consumption during the deployment as cost. Then, the deployment profitability is defined as the reward to the cost of the UAV trajectory. Based on this definition, the trajectory design problem is formulated as an optimization problem whose goal is to maximize the deployment profitability of the UAV. To solve this problem, a foraging algorithm is proposed to find the optimal trajectory so as to maximize the deployment profitability. The proposed algorithm can find the optimal trajectory for the UAV with a polynomial time complexity. Fundamental analysis shows that the proposed algorithm can achieve the maximal deployment profitability. Simulation results show that the proposed algorithm can effectively reduce the operation time and achieve up to 25.6% gain in terms of the deployment profitability compared to Q-learning algorithm.

In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIP) that can detach the users from their virtual world. To measure the BIP for wireless VR users, a novel model that jointly considers the VR application type, transmission delay, VR video quality, and users' awareness of the virtual environment is proposed. In the developed model, base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to provide high data rates for the VR users, thus, reducing the number of BIP for each user. Since the body movements of a VR user may result in a blockage of its wireless link, the location and orientation of VR users must also be considered when minimizing BIP. The BIP minimization problem is formulated as an optimization problem which jointly considers the predictions of users' locations, orientations, and their BS association. To predict the orientation and locations of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' locations and orientations. Using these predictions, the user association policy that minimizes BIP is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIP by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users perform an FL algorithm that trains their local FL models using their own data and send the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL learning algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to 1) an optimal user selection algorithm with random resource allocation and 2) a random user selection and resource allocation algorithm.

In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIPs) that can detach the users from their virtual world. To measure the BIPs for wireless VR users, a novel model that jointly considers the VR applications, transmission delay, VR video quality, and users' awareness of the virtual environment is proposed. In the developed model, the base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to increase the data rate of VR users, thus, reducing the number of BIPs for each user. Therefore, the mobility and orientation of VR users must be considered when minimizing BIPs, since the body movements of a VR user may result in blockage of its wireless link. The BIP problem is formulated as an optimization problem which jointly considers the predictions of users' mobility patterns, orientations, and their BS association. To predict the orientation and mobility patterns of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' mobility patterns and orientations. Using these predictions, the user association policy that minimizes BIPs is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIPs by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.

In this paper, the problem of content caching and transmission is studied for a wireless virtual reality (VR) network in which cellular-connected unmanned aerial vehicles (UAVs) capture videos on live games or sceneries and transmit them to small base stations (SBSs) that service the VR users. To meet the VR delay requirements, the UAVs can extract specific visible content from the original 360° VR data and send this visible content to the users so as to reduce the traffic load over backhaul and radio access links. To further alleviate the UAV-SBS backhaul traffic, the SBSs can also cache the popular contents that users request. This joint content caching and transmission problem is formulated as an optimization problem whose goal is to maximize the users' reliability, defined as the probability that the content transmission delay of each user satisfies the instantaneous VR delay target. To address this problem, a distributed deep learning algorithm that brings together new neural network ideas from liquid state machine (LSM) and echo state networks (ESNs) is proposed. The proposed algorithm enables each SBS to predict the users' reliability so as to find the optimal contents to cache and content transmission format for each cellular-connected UAV. Simulation results show that the proposed algorithm yields 25.4% gain in terms of reliability compared to Q-learning.

In this paper, the problem of 360° image transmission is studied for a wireless network of virtual reality (VR) users that communicate with cellular base stations (BSs). The VR users will send their uplink tracking information to the BS and receive the VR images in the downlink. To satisfy VR users' delay target, the BSs can change the image transmission format for each image requested by users so as to reduce the downlink traffic load. Meanwhile, the VR users can directly rotate the already received VR image and use the rotated VR images at a later time to further reduce the downlink traffic load. This 360° image transmission and image rotation problem is then formulated as an optimization problem whose goal is to maximize the users' successful transmission probability which is defined as the probability that the delay of tracking information and image transmission for each VR user satisfies the VR delay requirement. A liquid state machine (LSM) based transfer learning algorithm is proposed to solve this optimization problem. The proposed LSM-baseda transfer learning algorithm enables each BS to transfer the already learned successful transmission to the new successful transmission that must be learned so as to increase the convergence speed. Simulation results show that the proposed algorithm achieves 14.9% gain in terms of successful transmission probability compared to Q-learning.

In this paper, the problem of content caching and transmission is studied for a wireless virtual reality (VR) network in which cellular-connected unmanned aerial vehicles (UAVs) capture videos on live games or sceneries and transmit them to small base stations (SBSs) that service the VR users. To meet the VR delay requirements, the UAVs can extract specific visible content (e.g., user field of view) from the original 360° VR data and send this visible content to the users so as to reduce the traffic load over backhaul and radio access links. The extracted visible content consists of 120° horizontal and 120° vertical images. To further alleviate the UAV-SBS backhaul traffic, the SBSs can also cache the popular contents that users request. This joint content caching and transmission problem are formulated as an optimization problem whose goal is to maximize the users' reliability defined as the probability that the content transmission delay of each user satisfies the instantaneous VR delay target. To address this problem, a distributed deep learning algorithm that brings together new neural network ideas from liquid state machine (LSM), and echo state networks (ESNs) is proposed. The proposed algorithm enables each SBS to predict the users' reliability so as to find the optimal contents to cache and content transmission format for each cellular-connected UAV. Analytical results are derived to expose the various network factors that impact content caching and content transmission format selection. Simulation results show that the proposed algorithm yields 25.4% and 14.7% gains, in terms of reliability compared to Q-learning and a random caching algorithm, respectively.

Providing seamless connectivity for wireless virtual reality (VR) users has emerged as a key challenge for future cloud-enabled cellular networks. In this paper, the problem of wireless VR resource management is investigated for a wireless VR network in which VR contents are sent by a cloud to cellular small base stations (SBSs). The SBSs will collect tracking data from the VR users, over the uplink, in order to generate the VR content and transmit it to the end-users using downlink cellular links. For this model, the data requested or transmitted by the users can exhibit correlation, since the VR users may engage in the same immersive virtual environment with different locations and orientations. As such, the proposed resource management framework can factor in such spatial data correlation, so as to better manage uplink and downlink traffic. This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both the uplink and downlink. In the downlink, the cloud can transmit 360° contents or specific visible contents (e.g., user field of view) that are extracted from the original 360° contents to the users according to the users' data correlation so as to reduce the backhaul traffic load. In the uplink, each SBS can associate with the users that have similar tracking information so as to reduce the tracking data size. This data correlation-aware resource management problem is formulated as an optimization problem whose goal is to maximize the users' successful transmission probability, defined as the probability that the content transmission delay of each user satisfies an instantaneous VR delay target. To solve this problem, a machine learning algorithm that uses echo state networks (ESNs) with transfer learning is introduced. By smartly transferring information on the SBS's utility, the proposed transfer-based ESN algorithm can quickly cope with changes in the wireless networking environment due to users' content requests and content request distributions. Simulation results demonstrate that the developed algorithm achieves up to 15.8% and 29.4% gains in terms of successful transmission probability compared to Q-learning with data correlation and Q-learning without data correlation, respectively.

In this paper, the problem of joint caching and resource allocation is investigated for a network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground users over the LTE licensed and unlicensed bands. The considered model focuses on users that can access both licensed and unlicensed bands while receiving contents from either the cache units at the UAVs directly or via content server-UAV-user links. This problem is formulated as an optimization problem, which jointly incorporates user association, spectrum allocation, and content caching. To solve this problem, a distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed. Using the proposed LSM algorithm, the cloud can predict the users' content request distribution while having only limited information on the network's and users' states. The proposed algorithm also enables the UAVs to autonomously choose the optimal resource allocation strategies that maximize the number of users with stable queues depending on the network states. Based on the users' association and content request distributions, the optimal contents that need to be cached at UAVs and the optimal resource allocation are derived. Simulation results using real datasets show that the proposed approach yields up to 17.8% and 57.1% gains, respectively, in terms of the number of users that have stable queues compared with two baseline algorithms: Q-learning with cache and Q-learning without cache. The results also show that the LSM significantly improves the convergence time of up to 20% compared with conventional learning algorithms such as Q-learning.

In order to meet the demands of data-intensive continuous monitoring in wireless body area network, we address a structured sparse signal recovery method to exploit both spatial and temporal correlations in data using compressive sensing (CS). Using a simultaneously low-rank and joint-sparse (L&S) signal model, we employ a Bayesian learning treatment by incorporating an L&S-inducing prior over the data and the appropriate hyperpriors over all hyperparameters, resulting in effective reconstruction of the L&S data. Simulation results suggest that the proposed L&S-bSBL is superior to the state-of-the-art recovery methods in terms of computation burden and runtime cost.

In this paper, the problem of resource management is studied for a network of wireless virtual reality (VR) users communicating using an unmanned aerial vehicle (UAV)- enabled LTE over unlicensed (LTE-U) network. In the studied model, {the UAVs act as VR control centers that collect tracking information from the VR users over the wireless uplink and, then, send the constructed VR images to the VR users over an LTE-U downlink.} Therefore, resource allocation in such a UAV-enabled LTE-U network must jointly consider the uplink and downlink links over both licensed and unlicensed bands. In such a VR setting, the UAVs can dynamically adjust the data size of each VR image by tuning its quality and format. By doing so, the UAVs can adjust the transmitted data size according to the spectrum allocated to each user so as to meet the delay requirement. Therefore, resource allocation must also take into account the image quality and format. This VR-centric resource allocation problem is formulated as a noncooperative game that enables a joint allocation of licensed and unlicensed spectrum bands, as well as a dynamic adaptation of VR image quality and format. To solve this game, a learning algorithm based on the machine learning tools of echo state networks (ESNs) with leaky integrator neurons is proposed. Unlike conventional ESN learning algorithms that are suitable for discrete-time systems, the proposed algorithm can dynamically adjust the update speed of the ESN's state and, hence, it can enable the UAVs to learn the continuous dynamics of their associated VR users. Simulation results show that the proposed algorithm achieves up to 14% and 27.1% gains in terms of total VR QoE for all users compared to Q-learning using LTE-U and Q-learning using LTE.

In this paper, the echo state network (ESN) memory capacity, which represents the amount of input data an ESN can store, is analyzed for a new type of deep ESNs. In particular, two deep ESN architectures are studied. First, a parallel deep ESN is proposed in which multiple reservoirs are connected in parallel allowing them to average outputs of multiple ESNs, thus decreasing the prediction error. Then, a series architecture ESN is proposed in which ESN reservoirs are placed in cascade that the output of each ESN is the input of the next ESN in the series. This series ESN architecture can capture more features between the input sequence and the output sequence thus improving the overall prediction accuracy. Fundamental analysis shows that the memory capacity of parallel ESNs is equivalent to that of a traditional shallow ESN, while the memory capacity of series ESNs is smaller than that of a traditional shallow ESN. In terms of normalized root mean square error, simulation results show that the parallel deep ESN achieves 38.5% reduction compared to the traditional shallow ESN while the series deep ESN achieves 16.8% reduction.

Non-orthogonal multiple access schemes with higher spectral efficiency and the capability of accessing more users than orthogonal multiple access (OMA) scheme is preferred for the fifth generation (5G) wireless networks. In this work, a non-orthogonal transmission scheme called pattern division multiple access (PDMA) is proposed for the 5G wireless communication systems. Two pattern matrices are proposed for 150% and 200% overloaded PDMA systems. Then, a low complexity symbolwise belief propagation iterative detection and decoding (BPIDD) algorithm for multiple user detection is proposed. Numerical results and analysis provided by utilizing extrinsic information transfer chart show that the proposed scheme outperforms the traditional OMA systems significantly, and the performance of the proposed scheme is better than that of the existing schemes up to 1 dB. Finally, simulation results show that the proposed scheme is superior to the existing schemes and the proposed BPIDD algorithm is better than that of the BP algorithm over 0.5 dB.

In this paper, the problem of resource management is studied for a network of wireless virtual reality (VR) users communicating over small cell networks (SCNs). In order to capture the VR users' quality-of-service (QoS) in SCNs, a novel VR model, based on multi-attribute utility theory, is proposed. This model jointly accounts for VR metrics, such as tracking accuracy, processing delay, and transmission delay. In this model, the small base stations (SBSs) act as the VR control centers that collect the tracking information from VR users over the cellular uplink. Once this information is collected, the SBSs will then send the 3-D images and accompanying audio to the VR users over the downlink. Therefore, the resource allocation problem in VR wireless networks must jointly consider both the uplink and downlink. This problem is then formulated as a noncooperative game and a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed to find the solution of this game. The proposed ESN algorithm enables the SBSs to predict the VR QoS of each SBS and is guaranteed to converge to mixed-strategy Nash equilibrium. The analytical result shows that each user's VR QoS jointly depends on both VR tracking accuracy and wireless resource allocation. Simulation results show that the proposed algorithm yields significant gains, in terms of VR QoS utility, that reach up to 22.2% and 37.5%, respectively, compared with Q-learning and a baseline proportional fair algorithm. The results also show that the proposed algorithm has a faster convergence time than Q-learning and can guarantee low delays for VR services.

The explosive growth of mobile data traffic and the scarcity of available licensed spectrum make resource allocation in heterogeneous networks a critical issue. A distributed resource allocation algorithm for software defined cellular networks for future 5G networks is proposed. The adoption of integrated femto-WiFi small cells is used to alleviate spectrum shortage, by permitting simultaneous access to both the licensed bands (via cellular interface) and unlicensed bands (via WiFi interface). A weighted utility maximization problem is formulated to optimize resource allocation, utilizing the software defined network controller’s global view. A fully distributed solution based on the weighted utility maximization optimizes resource allocation, keeping the interference from small cells to macrocells below predefined thresholds. The proposed algorithm considers the sDevices, which have both cellular and WiFi interfaces, and the wDevices which have WiFi-only interfaces. Numerical simulations substantiate the superiority of the proposed resource allocation algorithm, which increases significantly the average throughput and average utility of all devices, compared with the traditional and current methods. Throughput gains as large as 41.6% in spectral efficiency for the average of all sDevices and wDevices are achieved by the new designs.

In this paper, the problem of resource management is studied for a network of wireless virtual reality (VR) users communicating over small cell networks (SCNs). In order to capture the VR users' quality-of-service (QoS), a novel VR model, based on multi-attribute utility theory, is proposed. This model jointly accounts for VR metrics such as tracking accuracy, processing delay, and transmission delay. In this model, the small base stations (SBSs) act as the VR control centers that collect the tracking information from VR users over the cellular uplink. Once this information is collected, the SBSs will then send the three dimensional images and accompanying surround stereo audio to the VR users over the downlink. Therefore, the resource allocation problem in VR wireless networks must jointly consider both the uplink and downlink. This problem is then formulated as a noncooperative game and a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed to find the solution of this game. The proposed ESN algorithm enables the SBSs to predict the VR QoS of each SBS and guarantees the convergence to a mixed-strategy Nash equilibrium. Simulation results show that the proposed algorithm yields significant gains, in terms of total utility value of VR QoS, that reach up to 22% compared to Q-learning. The results also show that the proposed algorithm has a faster convergence time than Q- learning and can guarantee low delays for VR services.

In this paper, the problem of joint caching and resource allocation is investigated for a network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground users over the LTE licensed and unlicensed (LTE-U) bands. The considered model focuses on users that can access both licensed and unlicensed bands while receiving contents through UAV cache-user links and content server-UAV-user links. This problem is formulated as an optimization problem which jointly incorporates user association, spectrum allocation, and content caching. To solve this problem, a distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed. Using the proposed LSM algorithm, the cloud can predict the users' content request distribution while having only limited information on the network's and users' states. The proposed algorithm also enables the UAVs to autonomously choose the optimal resource allocation strategies depending on the network states. Simulation results using real datasets show that the proposed approach yields up to 33.3% and 50.3% gains, respectively, in terms of the number of users that have stable queues compared to two baseline algorithms: Q-learning with cache and Q-learning without cache. The results also show that LSM significantly improves the convergence time of up to 33.3% compared to Q-learning.

The main challenges for machine type communication (MTC) are not only supporting the random access of massive users, but also increasing the spectral efficiency in future 5G system. To address these challenges, we propose a novel compressed sensing (CS) based non-orthogonal multiple access (NOMA) multiple input multiple output (MIMO) scheme, called CS-NOMA MIMO scheme, for the downlink of MTC. In the proposed scheme, a version of low mutual coherence spreading signatures, named CS signatures, is introduced to enable system overloading. Furthermore, a novel CS-based tensor frame structure is proposed, in which symbols are spread over multiple CS signatures. In order to allow CS based multiuser detection (CS-MUD) to be deployed in the user receivers, we provide the theoretical analysis of restricted isometry property (RIP) for the effective channel matrix which guarantee the sparse signal recovery. Simulation results using two CS algorithms show that the proposed scheme achieves a relatively high system overload (up to 3) when the active users are relatively sparse with an activity ratio of 1%.

A cognitive device-to-device (D2D) network with D2D transmitters (DTs) that harvest radio-frequency (RF) energy from the primary transmitters (PTs) is investigated. A novel D2D transmitter-assisted cooperative (DTAC) protocol is proposed, in which a group of DTs that have no transmission opportunity act as potential relays to improve the communications of the primary network. The primary network outage probability is characterized and used to make comparisons between the direct link and the cooperative link which adopts different combining techniques at the primary receivers. The active probability of the DTs is derived, and the D2D network throughput is maximized by seeking an optimal transmission power for the PTs. Simulation results are provided to validate the theoretical analysis.

With growing needs to better understand our environments, the Internet-of-Things (IoT) is gaining importance among information and communication technologies. IoT will enable billions of intelligent devices and networks, such as wireless sensor networks, to be connected and integrated with computer networks. In order to support large scale networks, IETF has defined the routing protocol for low power and lossy networks (RPL) to facilitate the multihop connectivity. In this paper, we provide an in-depth review of current research activities. Specifically, the large scale simulation development and performance evaluation under various objective functions and routing metrics are pioneering works in RPL study. The results are expected to serve as a reference for evaluating the effectiveness of routing solutions in large scale IoT use cases.

In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network is studied. In the considered model, the network can leverage human-centric information, such as users' visited locations, requested contents, gender, job, and device type to predict the content request distribution, and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs' locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users' QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user's content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users' content request distribution and their mobility patterns, we derive the optimal locations of UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 33.3% and 59.6% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared with a benchmark algorithm without caching and a benchmark solution without UAVs.

Uplink-downlink decoupling in which users can be associated to different base stations in the uplink and downlink of heterogeneous small cell networks (SCNs) has attracted significant attention recently. However, most existing works focus on simple association mechanisms in LTE SCNs that operate only in the licensed band. In contrast, in this paper, the problem of resource allocation with uplink-downlink decoupling is studied for an SCN that incorporates LTE in the unlicensed band. Here, the users can access both licensed and unlicensed bands while being associated to different base stations. This problem is formulated as a noncooperative game that incorporates user association, spectrum allocation, and load balancing. To solve this problem, a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed. This proposed algorithm allows the small base stations to autonomously choose their optimal resource allocation strategies given only limited information on the network's and users' states. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution, which constitutes a mixed strategy Nash equilibrium for their studied game. Simulation results show that the proposed approach yields significant gain, in terms of the sum-rate of the 50th percentile of users, that reaches up to 167% compared with a Q-learning algorithm. The results also show that the ESN significantly provides a considerable reduction of information exchange for the wireless network.

In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user and determine which content to cache at remote radio heads and the BBUs. This problem is formulated as an optimization problem, which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks (ESNs) with sublinear algorithms is proposed. Using ESNs, the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to capture the maximum amount of user information needed for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared with two baseline algorithms: random caching with clustering and random caching without clustering.

In this paper, we consider the reconstruction of a high-dimensional seismic volume with randomly missing traces. Seismic data in the frequency-space domain are represented via a high-order tensor. Applying the parallel matrix factorization model to the underlying seismic tensor, we propose an iterative approximate message passing (AMP) approach to seismic data interpolation based on loopy belief propagation. In particular, we extend the bilinear generalized AMP (BiG-AMP) approach to incorporate parallel low-rank matrix factorizations by using a "turbo" framework, enabling iterative message passing between the subgraphs of the allmode unfoldings of the seismic tensor. The computational complexity of our algorithmic framework is low and scales linearly with the data size. Simulation results with synthetic seismic data suggest that the proposed algorithm yields better reconstruction performances relative to existing methods.

We study the connectivity of overlaid wireless networks where two users can communicate if the signal-to-interference ratio is larger than a threshold subject to an outage constraint. By using percolation theory, we first specify a 2-dimensional connectivity region defined as the set of density pairs-the density of secondary users and the density of primary users- within which the secondary network is percolated. Several interesting properties of this region are also revealed. Our work provides a new perspective for better understanding of the connectivity of large-scale overlaid networks.

Uplink-downlink decoupling in which users can be associated to different base stations in the uplink and downlink in heterogeneous small cell networks (SCNs) has attracted significant attention recently. However, most existing works focus on simple association mechanisms in LTE SCNs that operate only in the licensed band. In contrast, in this paper, the problem of resource allocation with uplink-downlink decoupling is studied for an SCN that incorporates LTE in the unlicensed band (LTE-U). Here, the users can access both licensed and unlicensed bands while being associated to different base stations. This problem is formulated as an optimization problem which jointly incorporates user association, spectrum allocation, and load balancing. To solve this problem, a distributed algorithm based on the machine learning framework of echo state networks is proposed using which the small base stations autonomously choose their optimal bands allocation strategies while having only limited information on the network's and users' states. Simulation results show that the proposed approach yields significant gains, in terms of total rate, that reach up to 41% and 54%, respectively, compared to Q-learning and nearest neighbor algorithms. The results also show that ESN significantly improves convergence time of up to 17% compared to Q-learning.

A key technology in the 5th generation (5G) wireless communications is non-orthogonal multiple access (NOMA) which can effectively support massive random access. Since in a 5G system the number of active users generally does not exceed 10% of the total number of users in practice, the main challenge for multi-user detection (MUD) is the joint detection of sparse active users and their data. To address this challenge, we propose a novel compressed sensing based NOMA (CS-NOMA) scheme which can be deployed in a CDMA or an OFDM system and does not have control signaling overhead and need no knowledge of the active state of users. In the proposed scheme, K users are multiplexed over N shared resources (K ≫ N). Then, a CS-based MUD is applied to the proposed CS-NOMA scheme. Simulation results show that, in the case that active users are sparse (e.g., 10%), the proposed scheme can achieve a superior performance even when the system loading is up to 300%.

In the practical heterogeneous cellular networks, a macro base station (MBS) is usually not located at the center of the macrocell and the distances from the MBS to its edges are also different. For this reason, the tri-sectoring method defined by 3GPP which divides three equal sectors and allocates each sector the same power may severely degrade the communication performance of the edge users. In this paper, we propose a novel tri-sectoring method and a MBS sector power allocation scheme for orthogonal frequency division multiple access based downlink of MBSs under co-channel deployment in heterogeneous cellular networks. We first evaluate the performance of 3GPP tri-sectoring method by using a hexagonal grid model and a Matern hard-core process (MHP) model, respectively, to demonstrate the inapplicability of 3GPP tri-sectoring method to the practical MHP model. Then, we devise a novel tri- sectoring method which considers load balancing and the directional radiation pattern of antennas. At last, a MBS sector power allocation scheme is proposed to control their downlink transmit power according to the downlink outage probability of edge macrocell users. Simulation results show that the proposed methods can improve the throughput of femtocell users and area spectral efficiency.

Connectivity is crucial to ensure information availability and survivability for wireless networks. In this paper, we propose a unified framework to study the connectivity of wireless networks under a general type of interference attack, which can address diverse applications including Cognitive Radio, Jamming attack and shadowing effect. By considering the primary users, jammers and deep fading as unified Interferers, we investigate a 3-dimensional connectivity region, defined as the set of key system parameters - the density of users, the density of Interferers and the interference range of Interferers - with which the network is connected. Further we study the impact of the Interferers' settings on node isolation probability, which is a fundamental local connectivity metric. Through percolation theory, the sufficient and necessary conditions for global connectivity are also derived. Our study is supported by simulation results.

In this paper, we investigate mode selection for coordinated multi-point (CoMP) transmission in downlink LTE-A networks, where inter-cluster interference exists. The proposed scheme selects the single-cell (SC) or coordinated beamforming (CB) transmission mode according to interference level to maximize the weighted sum rate with fairness consideration. To formulate the optimization problem, an approximated closed-form expression for the achievable rate is derived by modeling the probability distributions of signal and interference powers as Gamma distributions. Since the original optimization problem is a combinatorial and non-convex one with high complexity, a low-complexity and sub-optimal algorithm is proposed, which first performs coordinated cell selection and then transmission mode selection. Simulation results show that the closed-form approximation of the achievable rate is very tight and the proposed mode selection scheme can improve the average system throughput by 13% and the 10th percentile user throughput by 10% compared with the existing scheme.

The performance of wireless powered communication networks (WPCNs) with transmitters (TEs) self-sustained by opportunistically harvesting radio-frequency (RF) energy from nearby power beacons (PBs) is investigated. A ``harvest-then-transmit'' protocol is considered, under which a TE first harvest the wireless energy from its nearest PB and then send information to its associated receiver (RE) with the energy stored in the battery. A variable power transmission mode is proposed that the TE is allowed to transmit as long as its harvested energy is larger than a predefined transmission threshold. The transmission probability of the TEs is derived. The coverage probability with the TEs non-fully charged and fully charged, respectively, are characterized. By using numerical analysis, we study the tradeoff between energy harvesting and data transmission for different system parameters, such as energy harvest-ratio, transmission threshold, and PB density. Simulation results are provided to validate our theoretical analysis.

It is predicted that billions of intelligent devices and networks, such as wireless sensor networks (WSNs), will not be isolated but connected and integrated with computer networks in future Internet of Things (IoT). In order to well maintain those sensor devices, it is often necessary to evolve devices to function correctly by allowing device management (DM) entities to remotely monitor and control devices without consuming significant resources. In this paper, we propose a lightweight RESTful Web service (WS) approach to enable device management of wireless sensor devices. Specifically, motivated by the recent development of IPv6-based open standards for accessing wireless resource-constrained networks, we consider to implement IPv6 over low-power wireless personal area network (6LoWPAN)/routing protocol for low power and lossy network (RPL)/constrained application protocol (CoAP) protocols on sensor devices and propose a CoAP-based DM solution to allow easy access and management of IPv6 sensor devices. By developing a prototype cloud system, we successfully demonstrate the proposed solution in efficient and effective management of wireless sensor devices.

This paper studies the opportunistic spectrum access (OSA) of secondary users in large-scale overlay cognitive radio (CR) networks. Particularly, a two-phase carrier sensing based protocol, namely the primary receiver assisted carrier sensing (PRA-CS) protocol, is investigated. Under the PRA-CS protocol, a secondary transmitter (ST) is allowed to transmit only if it satisfies the interference constraint at all the active primary receivers (PRs) and has the minimum back-off timer among its secondary contenders. It is worth noting that under the PRA-CS protocol, due to the fact that the activation of STs relies on the spatial realizations of both the primary and secondary networks, even the first order moment measure (average density) of the point process formed by the active STs can not be exactly characterized. To tackle this new difficulty, approximations are made on the conditional distributions of the eligible STs as well as the active STs given a typical primary/secondary receiver (PR/SR) activated at the origin. Based on such approximations, the coverage (transmission non-outage) performance of the primary/secondary network under the proposed PRA-CS protocol is characterized. Simulations are provided to validate our analysis.

In this paper, we investigate multi-cell coordinated scheduling and power allocation in downlink long term evolution advanced (LTE-A) systems, where orthogonal frequency division multiple-access (OFDMA) is used. The proposed scheme performs joint scheduling, power allocation, and modulation and coding scheme (MCS) selection to maximize the overall weighted throughput with proportional fairness. Our scheme considers the practical constraints in LTE-A systems. Since the optimization problem is a combinatorial and non- convex one and is with high complexity, low- complexity and suboptimal algorithms are proposed, which separate the scheduling and power allocation into two subproblems. Simulation results show that the proposed scheme can improve the average system throughput by 10% and the 10th percentile throughput by 15% compared with the existing scheme.

This paper studies the opportunistic spectrum access (OSA) of the secondary users in a large-scale overlay cognitive radio (CR) network. Two threshold-based OSA schemes, namely the primary receiver assisted (PRA) protocol and the primary transmitter assisted (PTA) protocol, are investigated. Under the PRA/PTA protocols, a secondary transmitter (ST) is allowed to access the spectrum only when the maximum signal power of the received beacons/pilots sent from the active primary receivers/transmitters (PRs/PTs) is lower than a certain threshold. To measure the resulting transmission opportunity for the secondary users by the proposed OSA protocols, the concept of spatial opportunity, which is defined as the probability that an arbitrary location in the primary network is detected as a spatial spectrum hole, is introduced and then evaluated by applying tools from stochastic geometry. Based on spatial opportunity, the coverage (non-outage transmission) performance in the overlay CR network is analyzed. With the obtained results of spatial opportunity and coverage probability, we finally characterize the spatial throughput, which is defined as the average spatial density of successful transmissions in the primary/secondary network, under the PRA and PTA protocols, respectively.

This paper studies the opportunistic spectrum access (OSA) of secondary users in a large-scale overlay cognitive radio network. Particularly, a threshold-based protocol is investigated, where the secondary transmitter is allowed to access the spectrum only if the maximum signal power of the received beacons transmitted by the primary receivers is lower than a certain threshold. To measure the resulting transmission opportunity for the secondary users by the proposed OSA protocol, the concept of spatial opportunity is introduced and evaluated by applying tools from stochastic geometry. Due to the dependency between the realizations of the active primary and secondary users, an exact calculation of the coverage probabilities of the primary and secondary networks is infeasible. To tackle this difficulty, approximation is made on the conditional distribution of the active secondary transmitters given a typical primary/secondary receiver activated at the origin. Based on this approximation, the coverage performance of the primary/secondary network under the proposed OSA protocol is characterized. To our best knowledge, this paper is the first attempt of using stochastic geometry to evaluate the performance of the threshold-based opportunistic spectrum access in large-scale cognitive radio networks.

Consider a wireless network that has two tiers with different priorities: a primary tier vs. a secondary tier, which is an emerging network scenario with the advancement of cognitive radio technologies. The primary tier consists of randomly distributed legacy nodes of density n, which have an absolute priority to access the spectrum. The secondary tier consists of randomly distributed cognitive nodes of density m=nβwith β≥ 2, which can only access the spectrum opportunistically to limit the interference to the primary tier. Based on the assumption that the secondary tier is allowed to route the packets for the primary tier, we investigate the throughput and delay scaling laws of the two tiers in the following two scenarios: (i) the primary and secondary nodes are all static; (ii) the primary nodes are static while the secondary nodes are mobile. With the proposed protocols for the two tiers, we show that the primary tier can achieve a per-node throughput scaling of λp(n)=Θ(1/log n) in the above two scenarios. In the associated delay analysis for the first scenario, we show that the primary tier can achieve a delay scaling of Dp(n)=Θ(√(nβlog n λp(n))) with λp(n)=O(1/log n). In the second scenario, with two mobility models considered for the secondary nodes: an i.i.d. mobility model and a random walk model, we show that the primary tier can achieve delay scaling laws of Θ(1) and Θ(1/S), respectively, where S is the random walk step size. The throughput and delay scaling laws for the secondary tier are also established, which are the same as those for a stand-alone network.

In this paper, we study the expected density of progress for wireless ad hoc networks with Nakagami-m fading. The expected density of progress is defined as expectation of the product between the number of simultaneous successful transmission per unit area and the distance towards the destination. By considering three next hop receiver (RX) selection strategies, i.e., nearest RX selection strategy, random RX selection strategy and furthest RX selection strategy, we derive the closed-form expressions to the expected density of progress. Numerical results show that, when the terminal density is small, the expected density of progress with nearest RX selection strategy is nearly the same as that with furthest RX selection strategy, and the expected density of progress with random RX selection strategy is the lowest; when the terminal density is larger, the nearest RX selection strategy has the largest expected density of progress, and furthest RX selection strategy has the smallest expected density of progress.

In this paper, we propose a selection region based multihop routing protocol with directional antennas for wireless mobile ad hoc networks, where the selection region is defined by two parameters: a reference distance and the beamwidth of the directional antenna. At each hop, we choose the nearest node to the transmitter within the selection region as the next hop relay. By maximizing the expected density of progress, we present an upper bound for the optimum reference distance and derive the relationship between the optimum reference distance and the optimum transmission probability. Compared with the results with routing strategy using omnidirectional antennas in, we find interestingly that the optimum transmission probability is a constant independent of the beamwidth, the expected density of progress with the new routing strategy is increased significantly, and the computational complexity involved in the relay selection is also greatly reduced.

We propose a optimal power allocation algorithm for Distributed Sensing networks under the assumption of a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. Each node computes a local statistic and communicates it to the fusion center over rayleigh fading wireless channels. At the fusion, the linear minimum mean square error (LMMSE) is used. Sensor networks in which energy is a limited resource, our goal is to minimize the distortion under certain power constraints. In this paper, for a given power constraints, equal power transmission strategy and the optimal power allocation strategy is considered. In the later, we first discuss the problem with only a sum power constraint, and then discuss the general case with both sum and individual power constraints. Finally, we demonstrate the applicability of our results through numerical examples. Result shows that the optimal distributed estimation algorithm for homogeneous sensor networks achieves the power gain by turning off sensors that with bad channels and bad observation quality.

We study the scaling laws for the throughputs and delays of two coexisting wireless networks that operate in the same geographic region. The primary network consists of Poisson distributed legacy users of densityn, and the secondary network consists of Poisson distributed cognitive users of densitym, withm>n. The primary users have a higher priority to access the spectrum without particular considerations for the secondary users, while the secondary users have to act conservatively in order to limit the interference to the primary users. With a practical assumption that the secondary users only know the locations of the primary transmitters (not the primary receivers), we first show that both networks can achieve the same throughput scaling law as what Gupta and Kumar (IEEE Trans. Inf. Theory,vol. 46, no. 2, pp. 388-404, Mar. 2000) established for a standalone wireless network if proper transmission schemes are deployed, where a certain throughput is achievable for each individual secondary user (i.e., zero outage) with high probability. By using a fluid model, we also show that both networks can achieve the same delay-throughput tradeoff as the optimal one established by El Gamal (IEEE Trans. Inf. Theory, vol. 52, no. 6, pp. 2568-2592, Jun. 2006) for a standalone wireless network.

We study the transmission capacities of two coexisting wireless networks (a primary network vs. a secondary network) that operate in the same geographic region and share the same spectrum. We define transmission capacity as the product among the density of transmissions, the transmission rate, and the successful transmission probability (1 minus the outage probability). The primary (PR) network has a higher priority to access the spectrum without particular considerations for the secondary (SR) network, where the SR network limits its interference to the PR network by carefully controlling the density of its transmitters. Assuming that the nodes are distributed according to Poisson point processes and the two networks use different transmission ranges, we quantify the transmission capacities for both of these two networks and discuss their tradeoff based on asymptotic analysis. Our results show that if the PR network permits a small increase of its outage probability, the sum transmission capacity of the two networks (i.e., the overall spectrum efficiency per unit area) will be boosted significantly over that of a single network.

Consider a static wireless network that has two tiers with different priorities: a primary tier vs. a secondary tier. The primary tier consists of randomly distributed legacy nodes of density n, which have an absolute priority to access the spectrum. The secondary tier consists of randomly distributed cognitive nodes of density m = nbetawith beta ges 2, which can only access the spectrum opportunistically to limit the interference to the primary tier. By allowing the secondary tier to route the packets for the primary tier, we show that the primary tier can achieve a throughput scaling of lambdap(n) = Theta(1/log n) per node and a delay-throughput tradeoff of Dp(n) = Theta (radic(nbetalog nlambdap(n))) for lambdap(n) = O (1/log n), while the secondary tier still achieves the same optimal delay-throughput tradeoff as a stand-alone network.

We study the transmission capacities of two coexisting wireless networks (a primary network vs. a secondary network) that operate in the same geographic region and share the same spectrum. The primary (PR) network has a higher priority to access the spectrum without particular considerations for the secondary (SR) network, where the SR network limits its interference to the PR network by carefully controlling its node density. Considering general power-law wireless channels with path-loss exponent α ≫ 2 and small-scale Rayleigh fading, based on the stochastic geometry theory, we derive the transmission capacities for both of the two networks and quantify their tradeoff via asymptotic analysis. Our results show that if the PR network permits a small increase of its outage probability, the sum transmission capacity of the two networks (i.e., the overall spectrum efficiency per unit area) will be boosted significantly over that of a single network, which generalizes our previous result in [1] over a special case of deterministic power-law channel with α = 4.

We study the delay-throughput tradeoffs for two coexisting wireless networks that operate in the same geographic region. The primary network consists of Poisson distributed legacy users of density n, and the secondary network consists of Poisson distributed cognitive users of density m, with m > n. The primary users have a higher priority to access the spectrum without particular considerations for the secondary users, while the secondary users have to act conservatively in order to limit the interference to the primary users. With a practical assumption that the secondary users only know the locations of the primary transmitters (not the primary receivers), based on our previous work in, we show that both networks can achieve the same delay-throughput tradeoff as the optimal one established by El Gamal et al. for a stand-alone wireless network.

The indispensable prerequisites in characterizing information content of DNA molecules by computational methods are the numerical representations of symbolic DNA sequences. Current numerical representation methods for DNA sequences do not contain the genetic code context information, which may play an important role in defining protein coding regions. We propose a novel numerical representation of DNA sequences based on genetic code context within DNA sequences and explore the feasibility of applying this method to identify protein coding regions in genomes. Computational experiments indicate that incorporating genetic code information into numerical representations is a promising approach in which DNA sequences are uniquely represented and more information is represented so that digital processing tools can be applied to the periodicity analysis in DNA sequences effectively.

We study the scaling laws for the throughputs of two coexisting wireless networks that operate in the same geographic region. The primary network consists of Poisson distributed legacy users of density n, and the secondary network consists of Poisson distributed cognitive users of density m, with m > n. The primary users have a higher priority to access the spectrum without particular considerations for the secondary users, while the secondary users have to act conservatively in order to limit interference to the primary users. With a practical assumption that the secondary users only know the locations of the primary transmitters, we show that both networks can achieve the same throughput scaling law as a stand-alone wireless network if proper transmission schemes are deployed, where a finite throughput is achievable for each individual secondary user (i.e., zero outage) with high probability.

In this paper, we propose a multistage soft decision equalization (SDE) technique using max-log-MAP sphere decoder (MLMSD) for block transmission over frequency selective multi-input multi-output (MIMO) channels. Using the Toeplitz structure of the channel matrix, we convert the general signal model into a series of small-sized sub-signal models. We propose to use MLMSDs based on these sub-models to iteratively cancel interferences and get the estimations of the transmitted symbols. Simulation shows that when the block size is large, the proposed algorithm outperforms the Schnorr-Euchner sphere decoder and the probability data association SDE with a reasonable computational complexity in full-rank MIMO channels.

In this paper, we propose a multistage soft decision equalization (SDE) technique using max-log-MAP sphere decoder (MLMSD) for block transmission over frequency selective multi-input multi-output (MIMO) channels. Using the Toeplitz structure of the channel matrix, we convert the general signal model into a series of small-sized sub-signal models. We propose to use MLMSDs based on these sub-models to iteratively cancel interferences and get the estimations of the transmitted symbols. Simulation shows that when the block size is large, the proposed algorithm outperforms the Schnorr-Luchner sphere decoder and the probability data association SDL with a reasonable computational complexity in full-rank MIMO channels

Subcarrier allocation schemes for SISO-OFDM systems in multiuser downlink scenario is well-documented. In this paper, with the goal of maximizing channel capacity, we extend it to MIMO-OFDM systems and propose a novel resource scheduling scheme with dynamic antenna and subcarrier allocation. This is done by maximizing the number of equivalent parallel subchannels in space-frequency structure and corresponding channel fading coefficients in frequency domain. The results show that our proposed algorithm outperforms multiuser MIMO-OFDM systems with static time-division multiple access (TDMA) technique which employ fixed and predetermined time-slot allocation scheme

A novel parallel receiver is proposed for turbo-coded multi-antenna system, which combines detection and decoding, and fully utilizes the original information in terms of Shannon information theory. We modify the conventional maximum a posteriori (MAP) BCJR algorithm to decode multi-antenna interference signal directly. This algorithm is insensitive to multi-antenna structure, not like space-time coding (STC), layered space-time (LST), and also obtains receive diversity by combining extrinsic information or log-likelihood ratio (LLR) information. Simulation results verify the proposed receiver algorithm

Conventional automatic repeat request (ARQ) schemes perform poorly in wireless fading channels. In this paper, variable packet size adaptive modulation (AM) ARQ scheme is proposed. The performance of discrete packet size AM selective repeat (SR) ARQ schemes for Rayleigh fading channels is analyzed and simple analytical expressions of its throughput are presented. The optimum switching thresholds that maximize the throughput are found. Theoretic analysis and numerical results indicate that a small number of packet sizes per modulation mode can get good performance in fading channels.

Unlike wired networks, random packet loss due to bit errors may cause significant performance degradation of the transmission control protocol (TCP). We propose and study a novel end-to-end congestion control mechanism called TCP/spl I.bar/LD (lose detection) that is simple and effective for dealing with random packet loss . The simulation results show that our scheme can achieve significant throughput improvements without adversely affecting other concurrent TCP connections, including other concurrent Reno connections.

This contribution proposes a novel synchronization approach for cyclic prefix (CP) based systems such as OFDM and CP-based single carrier systems. Short-time FFT is applied to the received signal, which results the its 2-D spectrum. By detecting the flat region on the 2-D spectrum in time direction, the instants where inter-symbol interference (ISI) is absent are determined for symbol timing and carrier synchronization. Simulations results on its performance in AWGN and multipath fading environment as well as its robustness against duration of channel impulse response (CIR) and frequency offset are presented.

In this paper, we apply pilot-symbol-aided estimation technique in time domain to estimate the fractional part of the normalized frequency offset in OFDM system. Moreover, we correct the frequency offset according to a general theoretical model for OFDM with frequency offset. The simulation results prove the estimation of frequency offset is precise, and the correction of frequency offset is very effective.

ARQ can guarantee an almost error free reception. But in traditional ARQ schemes, all packets are transmitted with the same modulation mode, which makes its performance poor in fading channels. Adaptive modulation (AM) is an efficient way to improve spectral efficient of the system. Combining ARQ and AM will give a more promising performance. In this paper, the throughput of SR-ARQ schemes with adaptive modulation systems is analyzed. Simple analytical expressions are presented. Numerical results from the analysis and some discussion are given.

We present a squaring method to simplify the decoding of orthogonal space-time block codes in a wireless communication system with an arbitrary number of transmit and receive antennas. Using this squaring method, a closed-form expression of signal-to-noise ratio after space-time decoding is also derived. It gives the same decoding performance as the maximum-likelihood ratio decoding while it shows much lower complexity.