The explosive growth of mobile data traffic has prompted operators to deploy a large number of base stations (BSs). However, due to the uneven traffic distribution, many BSs remain underutilized or idle during off-peak periods while still consuming substantial amounts of energy. To tackle this issue, we propose a Proactive Optimization-based (PO-based) BS sleeping strategy for large scale scenarios with hundreds of BSs. Specifically, by analyzing the Autocorrelation Function (ACF) of BS traffic in real-world scenarios, we identify multiple potential periods. Guided by this insight, we introduce multi-time-window mechanism and Graph Convolutional Network (GCN), designing Multi-Time-Window Spatio-Temporal Graph Convolutional Network (MTSGCN) to effectively capture the complex spatio-temporal dependencies present large-scale settings. The forecasted results acquired by MTSGCN serve as inputs to a multiple-BSs cooperative sleeping problem with the objective to minimize the total energy consumption. To tackle this huge problem efficiently, we first use K-means++ to divide the large region into several small cooperative clusters and then adopt the Integral Linear Programming (ILP) algorithm to solve each subproblem. Experimental results demonstrate that MTSGCN reduce the forecasting error by 10.9% compared with the state-of-the-art methods. Furthermore, the proposed MTSGCN-ILP algorithm achieves over 20% energy savings gains compared to the other typical strategies.

Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoWblockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.

As the sixth generation wireless technology evolves, applications such as, holography, autonomous driving, and telemedicine require enhanced data rates, reliability, and spectral efficiency. Unmanned Aerial Vehicles (UAVs) have gained attention due to their flexible deployment, line-of-sight transmission, and dynamic adaptability. However, UAV-assisted communication encounters challenges stemming from UAV position deviations caused by environmental factors such as wind and turbulence, which degrade transmission reliability. To address these problems, we propose a Non-Orthogonal Multiple Access-based full-duplex UAV relay protocol to improve the system transmission rate. The protocol utilizes successive interference cancellation for signal separation and maximal ratio combining for signal enhancement. Considering UAV position uncertainty, we formulate a robust optimization problem for joint UAV position optimization and power allocation. By employing the Bernstein-type inequality, we transform the probabilistic constraints into the deterministic constraints and solve the problem using a block coordinate descent-based algorithm. Simulation results demonstrate that, compared to the benchmark schemes, the proposed strategy improves system throughput and exhibits enhanced robustness, particularly under significant UAV position deviations.

As edge applications demand real-time processing with limited bandwidth and energy, traditional communication systems face challenges to meet performance requirements due to the centralized architecture and redundant data transmission. To address these challenges, we propose a UAV-assisted semantic edge computing network that leverages UAV mobility and semantic communication. We formulate a joint optimization problem involving UAV trajectory, data allocation, and semantic extraction to maximize the semantic processing rate. To solve this problem, we develop a hybrid deep deterministic policy gradient (H-DDPG) algorithm that integrates deep reinforcement learning (DRL) with convex optimization via block coordinate descent (BCD), thereby enabling efficient joint decision-making across tightly coupled variables. Furthermore, we propose a hybrid diffusion deep deterministic policy gradient (H-D3PG) algorithm, which incorporates denoising diffusion models into the DRL framework. By addressing the limited adaptability of deterministic strategies, this design enhances policy expressiveness and stability. As a result, the algorithm enables adaptive trajectory control under time-varying semantic tasks and wireless channel conditions in UAV-assisted edge networks. Simulations show that H-D3PG improves the semantic processing rate by up to 38.8% while reducing energy consumption compared to Raw Data Transmission.

In this paper, we propose a task-oriented semantic communication system based on the cloud-network-end (C-N-E) architecture to improve the energy efficiency of image transmission. Within the system, a cloud server provides storage and computation resources for image data collected by multiple cameras. The semantic information of an image is modeled as a scene graph, enabling the analysis of end-user interests. To reduce communication overhead, only useful semantic information relevant to user interests is transmitted. Considering the delay constraint, we formulate an optimization problem to minimize the total energy consumption by jointly selecting semantic information and allocating computation and communication resources. To solve this problem efficiently, an iterative algorithm based on optimal matching and sequential convex approximation is developed. Comparative simulations validate the efficacy of our algorithm.

Federated Learning (FL) offers promising solutions for deploying AI in wireless networks, allowing resourceconstrained devices to collaboratively train machine learning models, and reducing deployment costs. However, FL faces challenges due to device heterogeneity and unreliable communication links, which extend training time. Unmanned Aerial Vehicles (UAVs), with their flexibility and deployment advantages, have emerged as valuable assets in addressing these limitations by enhancing line-of-sight communication and providing proximal computational resources. This paper proposes a UAV-assisted FL framework that jointly optimizes resource allocation, task loads, and UAV trajectories to minimize FL completion time. Through a block coordinate descent (BCD) approach, our framework addresses the formulated joint optimization problem. Simulation results demonstrate that our proposed framework effectively balances resource allocation and significantly reduces FL completion time compared to benchmark schemes.

The rapid development of 5G technology leads to increasing energy consumption in base stations (BSs). For the vision of green and sustainable communications, we propose a scheme aimed at reducing BS energy consumption through symbol shutdown. This approach reduces BS energy consumption while ensuring the quality of service (QoS) for user equipments (UEs). Our scheme considers highly dynamic channel conditions and formulates a joint optimization problem, including BS shutdown and power allocation, constrained to the long-term average rate demand of all UEs. To address this problem, we design a Lyapunov method-based algorithm (LMBA) that transforms the mixed integer and dynamic optimization problem into a more tractable form and solves it using the Karush-Kuhn-Tucker conditions. Simulations indicate that our proposed strategy significantly reduces the BS energy consumption and the LMBA outperforms several benchmark algorithms.

The space-air-ground integrated network (SAGIN) reveals enormous potential towards ubiquitous access with pros-perous applications for future 6G wireless networks, yet the limited energy presents a significant challenge towards the efficient operation of SAGIN. In this paper, we propose to employ an un-manned aerial vehicle (UAV) to approach the ground nodes to help alleviate the computation burden, where the computed results are then forwarded to the satellite for remote use. We formulate the problem to minimize the weighted energy consumption in terms of data offloading, computation, and results forwarding, along with the UAV propulsion energy, while jointly investigating the transmissions, scheduling, computation, and trajectory strategy design. The problem is then decomposed and solved in a block coordinate descent framework. Simulation results demonstrate that the proposed joint optimization scheme effectively reduces the overall energy consumption compared to benchmark approaches.

This paper propose a graph neural network (GNN) framework to achieve physical layer security. We consider the secure communication between a multi-antenna base station and multiple users, in the presence of multiple eavesdroppers, where the GNN-based beamforming is conducted for secure transmissions. Particularly, we reinterpret the networks roles as graph elements and track the inter-user interference through the graph structure, and thus the secrecy rate maximization is obtained through neural network training. Numerical results indicates that the proposed GNN approach approximate the secrecy performance as compared with the conventional optimization techniques, while obtaining the solution in a more efficient manner, with the ability to adapt and scale in dynamic wireless networks.

Ensuring reliable and secure emergency communications in post-disaster scenarios is challenging, particularly when mobile communication infrastructures are damaged. In response to challenges in post-disaster emergency communications (PDEComs), this article proposes a cooperative relaying system (CRS) enhanced with Internet of Things (IoT) technology. The system features an airship equipped with buffers that serves as an aerial relay, designed to improve data transmission performance and communication security. To achieve this, it incorporates physical layer security techniques. Additionally, we introduce a hybrid mechanism that combines nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA), facilitating flexible resource allocation in IoT-enhanced CRSs. To fully leverage the advantages of the proposed airship-and-buffer aided CRS, we formulate a weighted secure sum rate (WSSR) maximization problem, jointly considering the power control, mode selection, and information security. Initially, we address the formulated WSSR maximization problem using Lyapunov optimization. Subsequently, the primal problem is divided into four cases, from which optimal power control and mode selection policies can be derived. This process is constrained by the stability of buffer queues and privacy transmission requirements. Finally, the simulation results show that the proposed scheme outperforms state-of-the-art schemes in terms of the WSSR. By adopting the airship and the hybrid NOMA/OMA mechanism, the WSSR can be increased by 29.9% and 96.4%, respectively. Moreover, we explore the impact of network parameters (e.g., the distance between the eavesdropper and airship, and decoding thresholds) on information security.

Non-terrestrial network (NTN)-based integrated communication and computation empowers various emerging applications with global coverage. Yet this vision is severely challenged by the energy issue given the limited energy supply of NTN nodes and the energy-consuming nature of communication and computation. In this paper, we investigate the energy-efficient integrated communication and computation for the ground node data through a NTN, incorporating an unmanned aerial vehicle (UAV) and a satellite. We jointly consider ground data offloading to the UAV, edge processing on the UAV, and the forwarding of results from UAV to satellite, where we particularly address the uncertainties of the UAV-satellite links due to the large distance and high dynamics therein. Accordingly, we propose to minimize the weighted energy consumption due to data offloading, UAV computation, UAV transmission, and UAV propulsion, in the presence of angular uncertainties under Gaussian distribution within the UAV-satellite channels. The formulated problem with probabilistic constraints due to uncertainties is converted into a deterministic form by exploiting the Bernstein-type inequality, which is then solved using a block coordinate descent framework with algorithm design. Simulation results are provided to demonstrate the performance superiority of our proposal in terms of energy sustainability, along with the robustness against uncertain non-terrestrial environments.

In this paper, we investigate the secrecy performance of the UAV short-packet communication system assisted reconfigurable intelligent surface (RIS). In this system, based on the phase shift differences of the RIS, the Gamma and exponential distributions are used to match the received signal-to-noise ratio (SNR) at the link terminals. Closed-form expressions for both the probability density function (PDF) and the cumulative distribution function (CDF) are derived. Based on the above PDF and CDF, we derive closed-form expressions for the average achievable rate (ASR) and the average secure block-error rate (SBLER) to evaluate the system’s security and reliability performance. In addition, a novel analytical model is proposed, which can simplify the calculation of the secrecy outage probability (SOP). Furthermore, to explore the performance boundaries, we also derive closed-form expressions for the asymptotic SOP and the asymptotic probability of positive secrecy capacity (PPSC) in high-SNR regions. The accuracy of the derived expressions is validated through simulations and numerical results, which also demonstrate the effectiveness of the proposed SOP analysis framework. In the simulation, we investigate the impact of key system parameters, such as the number of RIS elements, finite block-length channels, UAV altitude, Rician factor, as well as reliability and confidentiality constraints, on the overall system performance. The simulation results show that the proposed system provides significant performance advantages in ensuring secure and reliable transmission under various operating conditions.

Over-the-air computation (AirComp) is a promising technique that addresses big data collection and fast wireless data aggregation. However, in a network where wireless communication and AirComp coexist, mutual interference becomes a critical challenge. In this paper, we propose to employ an unmanned aerial vehicle (UAV) to enable integrated communication and AirComp, where we capitalize on UAV mobility with alleviated interference for performance enhancement. Particularly, we aim to maximize the sum of user transmission rate with the guaranteed AirComp accuracy requirement, where we jointly optimize the transmission strategy, signal normalizing factor, scheduling strategy, and UAV trajectory. We decouple the formulated problem into two layers where the outer layer is for UAV trajectory and scheduling, and the inner layer is for transmission and computation. Then, we solve the inner layer problem through alternating optimization, and the outer layer is solved through soft actor–critic-based deep reinforcement learning. Simulation results show the convergence of the proposed learning process and also demonstrate the performance superiority of our proposal as compared with the baselines in various situations.

This paper investigates the integration of the non-orthogonal multiple access (NOMA) technique and autonomous aerial vehicles (AAVs) in Internet of Vehicles (IoV), aiming to provide flexible access and improve communication coverage for vehicle platooning. The goal is to accurately analyze performance and reasonably optimize network design for AAV-assisted vehicle platooning in NOMA-enhanced IoV. To achieve this, an analytical solution is derived for the average achievable rate from the lead vehicle to follower vehicles over Rician fading channels. Leveraging this analytical solution, the Gauss-Chebyshev integration is employed to obtain the approximate solution. Then, we formulate a problem of maximizing the sum of secure rates by optimizing the trajectory and spectrum allocation. The formulated problem is constrained by the security requirement and imperfect channel state information. Addressing the NP-hard nature of this problem, an iterative optimization algorithm is developed, incorporating Q-learning and the graph theory to alternately adjust the trajectory and spectrum allocation. Finally, the simulation results show that the approximate solution matches well with the analytical solution, and the gap is less than 6%. Moreover, the proposed scheme has a significant performance improvement in the sum of secure rates compared with the state-of-the-art schemes.

The emergence of new application scenarios has enabled integrated sensing and communication (ISAC) as one of the potential technologies of 6-th generation mobile communication (6G). To meet both communication efficiency and sensing efficiency, a satisfactory ISAC waveform is essential. In this paper, we primarily investigate the impact of different modulation schemes on sensing performance for UAV-to-ground scenarios. Firstly, we analyze the sensing performance differences of modulation schemes in the sensing algorithm based on orthogonal frequency division multiplexing (OFDM) systems. Secondly, we examine the periodic auto-correlation functions (PACFs) with different modulation schemes and modulation orders. We observe that the waveforms modulated by phase shift keying (PSK) exhibit the lower sidelobes compared to waveforms modulated by quadrature amplitude modulation (QAM). Finally, we simulate the probability of detection (Pd) with different modulation schemes for UAV-to-ground scenarios. Numerical results demonstrate that the modulated waveform with constant modulus exhibit superior sensing performance. For the modulated waveform with non-constant modulus, the higher modulation orders result in the poorer sensing performance. This inspires us to change the sensing performance by designing the power spectrum of the modulated waveform with non-constant modulus. This work is helpful for the waveform design and performance analysis of ISAC.

Studying the characteristics of the satellite-toground (S2G) channel model is essential for the development and assessment of satellite communication systems. In this study, we introduce a unique approach by combining a low-earth-orbit (LEO) random geometric satellite channel model with line-of-sight (LoS) and single-bounced (SB) non-line-of-sight (NLoS) components for the S2G multiple-input multiple-output (MIMO) channel. By utilizing the coaxial cylinders reference model in a lightly shadow environment occluded by terrain features, we compute the space correlation function (SCF) and time correlation function (TCF). To simplify the simulation process, we present a deterministic simulation model using the finite number of scatterers and analyze the various factors that influence channel characteristics. The findings from the simulation indicate that the orientation of both the satellite and terrestrial receiver antennas, as well as their respective movement directions, distances, and the density of scatterers’ azimuth angles, all play a significant role in shaping the statistical properties of the channel model.

To facilitate UAV-based wireless communications, a comprehensive understanding of the wireless channel characteristics is critical. However, most existing studies focuse on the high-altitude channel, while limited attention is paied to the ultra-low altitude channels given the evident difficulties in the measurement of the latter scenario. In this paper, we have tackled the challenges of lightweight channel sounder system design enabling aerial mounting and flying to facilitate the ultra-low altitude UAV channel measurement. We specially focus on the ultra-low altitude channel at 1.4 GHz, which is recently authoried in China to particularly facilitate UAVs applications. With extensive measurements in a riverside area, we analyze the propagation properties and enlighten the large-scale fading and small-scale fading characteristics, where the large-scale parameters are revealed and the Log-logistic distribution is evaluated as the best fit to characterize the small-scale fading. These findings provide important guidance for the ultra-low altitude UAV communication particularly in the riverside scenarios.

In blockchain systems characterized by computation competition, allocating computation resources is of paramount significance for the economic benefits of nodes. Besides, uncer-tainties of computation resources also affect the node's profits. In this paper, we address the computation resource allocation issue within a proof-of-work (PoW) blockchain system without exact information on the available resources, which impedes the direct investigation of the maximum mining profit. Correspondingly, we establish the chance-constrained threshold for maximum achievable profit through the blockchain in an uncertain environment and maximize this threshold under a given outage probability. Particularly, the uncertain computation resource is modeled only with its first and second statistics, which lack the exact distribution information. In this respect, we propose the distributionally robust approach to tackle the chance-constrained resource allocation strategy, which guarantees the intended profit threshold regardless of the actual distribution. We show that the considered problem admits a conditional value-at-risk (CVaR) approximation reformulation, which can be handled by alternately optimizing the resource allocation strategy and the profit threshold. Simulation results demonstrate that the proposed design is robust against the uncertainty distribution, and effectively guarantees the profits of miners.

Satellite communication is considered as one of the key technologies to achieve global seamless coverage and has attracted wide attention. It is crucial to establish an accurate clutter loss model for satellite-ground communication. Clutter loss refers to the extra path loss caused by the obstruction of the terrain and objects on the ground, especially when a satellite has low elevation angle. The clutter loss model proposed by ITU-R P.2108-0 only considers the influence of the elevation angle, and the traditional prediction model for the clutter loss is limited in accuracy and stability. In this work, we used a satellite ground station to carry out the channel measurement at 8.25 GHz on the clutter loss of the X-band satellite-to-ground (S2G) links in the suburban campus environment, and extracted the clutter loss data set involved in the process of satellite inbound and outbound from the received signal strength. We utilize the multi-layer perceptron (MLP), long short-term memory (LSTM), and bidirectional-long short-term memory (Bi-LSTM) neural networks to build channel models to predict the clutter loss based on the measurement data. The model prediction results show that the Bi-LSTM-based model has higher prediction accuracy than the MLP-based and LSTM-based models.

Over-the-air computation (AirComp) emerges as a promising method to integrate computation and communication in 5G and beyond network architecture. Nevertheless, the performance of AirComp, measured by mean-square error (MSE), can be severely bottlenecked by the availability of channel information. In this paper, we investigate the AirComp design in presence of channel uncertainties. Particularly, we consider the case that only the first and second moments of the channel, which can be easily obtained through actual measurement, are available, without the exact statistical information. Then, we establish the chance-constrained AirComp with a thresholded MSE under a given outage probability. Correspondingly, we address the distributionally robust AirComp design to guarantee the intended threshold regardless of the channel distribution. By leveraging conditional value-at-risk (CVaR), we reformulate the probabilistic-form constraint into its deterministic counterpart to facilitate the analysis. Then, the reformulated problem is decomposed to optimize the transmit and receive scaling factors alternatively. Simulation results demonstrate that our proposal rigorously ensures robustness amid uncertainties and effectively reduces computation distortion when compared to the baseline methods.

Integrating the unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) network with the blockchain technology emerges its superiority in the network utilization, differentiated service, and security, which has been regarded as a promising technique for time-critical applications. In this paper, we propose a UAV-assisted MEC network architecture and a comprehensive data processing flow, where the UAVs cooperate with the base station in computation as edge servers and act as blockchain nodes. We formulate an optimization problem that jointly considers UAVs’ position, data offloading, and resource allocation for minimizing the total time consumption of data processing. To address this problem, we decouple it as three tractable subproblems and propose a Block Coordinate Descent (BCD)-based iterative algorithm. In addition, we analyze the task migration and resource allocation problem in computation, and obtain analytical solutions by the Karush-Kuhn-Tucker (KKT) conditions. The simulated results indicate that the proposed algorithm leads to substantial performance gains.

How to improve the coverage capability of network including connectivity and throughput is vital for enabling the Internet of Everything (IoE) in B5G/6G. However, the traditional terrestrial networks are confronted with the high-cost and inflexible challenges especially in the remote area and emergency applications. In order to solve these challenges, we consider a heterogeneous aerial network (HetAN), where some low-altitude base stations (LBSs) are deployed as access points for wireless coverage and a high-altitude base station (HBS) hovers as the hub for backhaul of LBSs. Furthermore, we apply the non-orthogonal multiple access (NOMA) to uplink transmission for the terrestrial users, which enable massive connectivity in the IoE. To maximize connectivity and throughput, we jointly optimize the LBSs’ deployment, power control, channel allocation, and rate control by fully exploiting the potential of the HetAN in wide-area coverage. For solving the formulated problem efficiently, we propose an iterative algorithm based on the methods of graph theory, bionic algorithm, and theoretical analysis. Simulation results are provided to reveal the influence of the control variables on network performance and indicate that our algorithm can greatly improve the connectivity and throughput with the other schemes.

Efficient data processing and computation are essential for the Industrial Internet of Things (IIoT) to empower various applications, which can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this article, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

Unmanned aerial vehicle mounted base stations (UAV-BSs) are expected to become an integral component of future intelligent transportation systems, which can provide seamless coverage for vehicles on highways with poor cellular infrastructures. Motivated by the above, this paper proposes an aerial-ground integrated vehicular networking architecture, based on which a UAV-vehicle collaboration perspective is proposed. Specifically, an emerging vehicle-to-UAV (V2U) and vehicle-to-vehicle (V2V) collaboration framework is first presented to facilitate diverse vehicular applications. Next, we investigate the coverage radius maximization problem by optimizing the UAV-BS altitude. Meanwhile, by taking the channel state information (CSI) feedback delay into account, we formulate a V2U communication sum rate maximization problem by optimizing the power control and spectrum allocation, which is constrained by the capacity and reliability requirements. Then, we derive the closed-form expression of optimal UAV-BS altitude. Afterwards, we decouple the formulated sum rate maximization problem, and devise an efficient algorithm with polynomial complexity, where the optimal power control and spectrum sharing are solved. Finally, simulation results demonstrate that the maximum coverage radius and optimal UAV-BS altitude can be achieved by our proposed scheme in different urban environments. In addition, our designed scheme can effectively improve the V2U communication sum rate in comparison with the current works.

Integrated sensing and communication (ISAC) is considered one of the key technologies for next-generation wireless communication. To achieve satisfactory communication and sensing performance simultaneously, it is necessary to maximize compatibility with the existing communication waveform. In this paper, we mainly investigate the ISAC constellation design based on communication waveforms (random waveforms). Firstly, we derive the modulated waveform with constant modulus has a smaller side lobe of periodic auto-correlation function (PACF), i.e., the modulated waveform with constant modulus is more suitable for sensing. To improve the sensing performance of the modulated waveform with non-constant modulus, we propose a ISAC constellation design method based on PCS, which designs the power spectrum of the modulated waveform by adjusting the probabilities of constellation points, thereby reducing the side lobe of PACF. In addition, we design a joint optimization problem between the weighted variance of normalized energy and the communication information entropy (CIE) to obtain the tradeoff between communication and sensing. Finally, we simulate the communication performance and sensing performance of the reshaped waveform, and obtain some interesting conclusions. The simulation results show that, for the modulated waveform with non-constant modulus, the proposed method can reduce the side lobe of PACF, and increase the probability of detection (Pd), along with a minimal loss for CIE and a tiny growth for bit error rate (BER). This work is helpful for the theoretical exploration and system design for ISAC.

The emerging artificial intelligence (AI) puts forward high requirement for big data acquisition, which is difficult to be met with the existing communication technologies in real time. In this paper, we investigate new graph learning based access management scheme for supporting the real-time big data acquisition in the sixth-generation mobile communication system (6G). We model the network scene with a mass of communication links as a fully connected graph which takes into account the accumulative interference of all links. Then, the joint admission and power control problem is formulated as a combinatorial optimization problem. We propose a graph attention network (GAT) based algorithm which can learn the system features by weighted aggregation of neighbor nodes. In addition, we construct a differentiable loss function that can accurately express the optimization objective and train the network by the change of loss. Based on the output of the GAT, we iteratively optimize the link admission and power to active more links. Simulation results demonstrate that the proposed algorithm is superior to the traditional convex optimization based algorithms and the nonmodified GAT based algorithms in the number of activated links. Moreover, the training of the constructed network is unsupervised with high computational efficiency, which makes them suitable for the big data access management.

In 5G Radio Access Networks (RANs), the energy consumption of the ground base station (GBS) accounts for more than 80%. Therefore, reducing the energy consumption of GBSs has been an important research direction for building green and environment-friendly communication networks. In view of this, we formulate an unmanned aerial vehicle (UAV)-assisted GBS sleep strategy for energy saving, which utilizes the mobility of UAVs to fill the wireless coverage holes caused by the sleeping of GBSs. To further enhance the effect of the formulated strategy, we propose a joint GBS sleeping, UAV trajectory planning, and UAV transmission power allocation problem to minimize the energy consumption of the entire system. To address the intractable problem, we first devise an iterative algorithm to optimize the trajectory and power of the UAV based on the block coordinate descent (BCD), and then nest it into the branch and bound (BaB) to obtain the GBSs operation status. Simulations demonstrate that the formulated strategy efficiently reduces the energy consumption of the network compared with other schemes.

Unmanned aerial vehicles (UAVs) combining with mobile edge computing (MEC) networks have promoted the application of Internet of Things (IoT) devices, providing enhanced coverage with flexible computing services. But the energy consumption of data processing is still a shortage in the UAV-assisted MEC architecture. Motivated by that, we propose a dynamic UAV-assisted MEC network and formulate a problem and jointly optimize association strategies, UAV trajectory, data offloading, and resource distribution for minimizing total energy consumption. To deal with this tricky problem, we devise a dichotomy-based joint iterative optimization algorithm. Specifically, we divide the problem into three sub-problems, solving by the integer programming, successive convex optimization, and dichotomy method. Finally, the simulation consequences prove that the devised network and algorithm significantly reduce total energy consuming.

The unmanned aerial vehicle (UAV) communication with the multiple-input–multiple-output (MIMO) system has attracted lots of attention to improve spectral efficiency and channel capacity. The large antenna spacing is often required to reduce the spatial correlation between subchannels. However, this requirement cannot be well satisfied in practical scenarios due to the limited dimension of UAV platforms. Therefore, polarization diversity is a promising approach for UAV MIMO systems. In this work, we propose a three-dimensional (3-D) geometrical-based polarized model for UAV-correlated MIMO channels. By utilizing the geometrical theory of polarization, we describe the channel depolarization caused by the terrestrial scattering environment based on the multicylinder geometrical model and then acquire the channel polarization function and polarized channel impulse response (CIR). Furthermore, we investigate the impact of the key factors, such as the UAV tilt rotation, cross-polarization, and limited antenna spacing on the UAV MIMO channel capacity. To validate the proposed UAV channel model, we compare the spatial correlations of the numerical simulation and UAV field measurement results for the co- and cross-polarized channels, and the close agreements between them are observed. This work can provide useful guidance and support for the design and performance evaluation of UAV communication networks.

In this article, we present a drone-relayed vehicular networking architecture, which aims to improve the achievable data rate of cell-edge vehicles in rural highway scenarios. Specifically, we first incorporate the decode-and-forward (DF) relay protocol with the nonorthogonal multiple access (NOMA) and maximum ratio combining (MRC) techniques, based on which an NOMA- and MRC-Enabled framework is proposed. Next, to fully exploit the advantages of the proposed framework, we separately formulate the total achievable data rate maximization and energy consumption minimization problems by jointly considering the height and 2-D trajectory optimization of relaying drone. The formulated energy consumption minimization problem is transformed into a trajectory optimization problem with obstacle avoidance constraints. Then, for the total achievable data rate maximization problem, we utilize the golden section method to design a height optimization scheme with polynomial complexity. Afterward, we improve the particle swarm optimization (PSO) algorithm, and present an effective 2-D optimization scheme. In addition, the performance superiority of the proposed NOMA- and MRC-Enabled framework is analyzed theoretically. Finally, simulation results verify the efficacy of the proposed height and trajectory optimization schemes. For instance, by using the NOMA and MRC techniques, the total achievable data rate can be improved by 24.4%. Moreover, within the same running time, a shorter trajectory can be obtained by adopting our presented trajectory optimization scheme in comparison with the current works.

Air-to-ground (A2G) communication based on Unmanned aerial vehicle (UAV) is an important part of the future communication system. In this paper, an A2G channel model with UAV three-dimensional (3D) wobbles (pitch, roll, and yaw) based on the geometry-based stochastic model (GBSM) is proposed. On this basis, the UAV’s internal vibration is modeled as a sinusoidal random process, and the UAV wobble caused by the atmospheric flow is modeled as the uniform distribution random process. We derive the channel temporal correlation function (CF) with UAV 3D wobbles, analyze the variation of the temporal CF with different carrier frequencies, and amplitudes of the wobble angles. It is found that, even if the UAV wobbles slightly, the channel temporal correlation will be significantly affected. Numerical results show that the channel CF will decrease rapidly with the increase of the amplitudes of wobble angles and the carrier frequency. Therefore, the coherence time of millimeter wave (mmWave) band is significantly less than that of sub-6 GHz band. The consistency of simulation results and measurement results in published papers ensures the availability of the proposed model. For the MUAVs scenario, when the distance between different UAVs is much greater than the wavelength, the A2G channels between different UAVs and user equipment (UE) on the ground are not correlated to each other, and the temporal auto-correlation function (ACF) of each UAV is the same as that of the SUAV scenario. This work contributes to the theoretical exploration and system design of A2G communication based on UAV.

Medical-care sensor networks promote the rapid development of telemedicine applications. However, in poverty-struck, disaster-struck or remote areas with limited infrastructures, it is difficult to provide fast and timely medical-care services. To address this challenge, we propose an aerial base station (ABS)-assisted medical-care sensor network, based on which the data transmission problem is investigated by jointly considering the anti-interference and anti-collision requirements. Specifically, in order to reduce the bit error rate caused by electromagnetic interferences, we first design an anti-interference method based on M-ary spread spectrum and multi-carrier modulation. Then, by introducing a multi-frequency sensor identification mechanism, an anti-collision method based on time division multiple access and frequency division multiple access is presented. Finally, simulation results demonstrate that our proposed scheme has significant advantages in anti-collision and anti-interference compared with current schemes. In quad-interference scenarios, the anti-interference performance is improved by 5.3 dB. Moreover, the anti-collision performance is also increased by 17.2%. Furthermore, in scenarios with a large number of sensors, the successful sensor identification percentage is always greater than 50%.

Wireless communication based on Unmanned aerial vehicle (UAV) is one of the important technologies in the future communication system. It is necessary to establish an accurate air-to-ground (A2G) wireless channel model. In this paper, a A2G channel model with UAV three-dimensional (3D) wobbles (pitch, roll, and yaw) is proposed. The internal vibration of the UAV is modeled as a sinusoidal random process, and the UAV wobble caused by the random air fluctuations is modeled as the uniform distribution random process. We derive the A2G channel temporal auto-correlation function (ACF) with UAV 3D wobbles, analyze the variation of the temporal ACF with different time instants, carrier frequencies, and amplitudes of the wobble angles. It is found that, even if the UAV wobbles slightly, the channel temporal correlation will be significantly affected. Numerical results show that the channel ACF will decrease rapidly with the increase of the amplitudes of the wobble angles and the carrier frequency. This work contributes to the establishment of the next generation wireless channel model and the design of communication system.

The Internet of Things (IoT) has gained rapid development, but due to the limited battery capacity and access capacity, there are many complex problems in the application. In this paper, we consider an air-and-ground cooperative wireless network, which can provide dynamic coverage for sensor equipments (SEs). Jointly considering the dynamic deployment of the aerial base stations (ABSs) and the admission-and-power control of the SEs, we formulate a two time-scale network control problem to minimize the long-term power consumption of all SEs under their individual rate requirement. On large time scales, we propose a particle swarm optimization algorithm (PSOA) to adjust the positions of the ABSs. On small time scales, we devise a joint admission-and-power control algorithm (JACA). Simulation results indicate that the air-and-ground network incorporated with the proposed algorithms can significantly reduce the total power consumption of the SEs compared with the other schemes.

Unmanned aerial vehicles (UAVs)-enabled multicasting network has attracted significant attention in recent years. However, there are still some disadvantages of existing multicasting schemes used in these systems, such as low transmission efficiency and high feedback overhead. Accordingly, we propose a sliding coding window (SCW)-based random network coding (SCWRNC) scheme for a UAV multicasting network where one UAV base station is dispatched to the multicast data stream to multiple user equipments (UEs). The proposed scheme includes an SCW scheduling original packets for encoding, a lower triangular coding structure enabling UEs to decode out information even without receiving a full set of coded packets, and a feedback-compete mechanism requiring only one UE to send feedback information. The packet scheduling process is described as a five-tuple Markov decision process. Then, we give a theoretical analysis of the proposed scheme, based on which the sliding steps of SCW and the UAV hovering location are jointly optimized to maximize the system throughput. The optimal sliding steps are obtained by applying the Greedy scheduling technique, while the UAV optimal position is obtained by minimizing the maximum outage probability of all UEs. Furthermore, we also propose a flexible feedback mechanism, which enables more than one UE to send feedback for systems with sufficient resources and a “F-SCWRNC” scheme for systems where no UE is allowed to send feedback. Numerical results show that both the proposed SCWRNC scheme and F-SCWRNC scheme could achieve significant throughput gain over the existing ones.

Medical-care Internet of Things enables rapid medical assistance by providing comprehensive and clear healthy information. However, due to the limited infrastructure, it is difficult to quickly and securely transmit medical-care information in poverty-stricken or disaster-stricken areas. To tackle the above situation, in this article, we propose a delay-sensitive secure nonorthogonal multiple access (NOMA) transmission scheme with the high-altitude platform (HAP) and low-altitude platforms (LAPs) cooperated to securely provide delay-sensitive medical-care services. In the proposed scheme, we first design a novel HAP–LAP secure transmission framework to provide NOMA communication services to multiple hotspots. Constrained by the limited power and spectrum, we formulate an optimization problem, such that the privacy information delay is minimized. For thisnonconvex optimization problem, we design an alternating optimization framework, where the power, spectrum, and LAPs’ location are tackled in turn. In addition, we theoretically analyze the performance superiority compared with the orthogonal multiple access scheme and derive the secrecy outage probability closed-form expression. Finally, numerical results show the performance superiority of the proposed scheme compared with the current works with respect to the secure information delay.

In this paper, we focus on the energy saving problem for the wireless sensor networks (WSNs). Specifically, we propose a UAV-assisted wireless network architecture, where the cell-edge sensor devices (SDs) can access the aerial access points (AAPs) instead of the terrestrial access point (TAP). Since the transmitter-to-receiver distance is shortened and the ground-to-air channel is usually line-of-sight, the SDs can use lower power to transmit data and thereby prolong their lifetime. To fully exploit the potential of the network architecture, we jointly optimize the AAPs' position, channel allocation, and power control to minimize the total transmission power of all SDs. In order to solve the complex joint optimization problem, we reformulate it as three tractable subproblems and use the methods in graph theory to design low-complex algorithms. Simulation results indicate that the proposed network architecture greatly outperforms the traditional WSNs, and the proposed algorithms can further reduce the total power consumption.

In this paper, we consider a UAV-enabled two-way relaying system where the UAV relay assists the information exchange between two ground users (GUs). The two-slot physical network coding scheme (PNC) for information exchange is adopted. The rate region of this scheme in this UAV-enabled relaying system is firstly analyzed. Then, we maximize the system average sum rate under this scheme by jointly optimizing the time resources allocation, transmission powers of the transceivers, and the UAV trajectory subject to the UAV mobility constraints and the information causality constraints. The formulated problem is a nonconvex optimization problem which is hard to solve directly. We propose an iterative algorithm by applying the successive convex approximation and block coordinate descent techniques to solve this problem. Specifically, the time resources allocation, transmission powers and the UAV trajectory are alternatively optimized in each iteration. In addition, the non-convex trajectory optimization problem is solved by successively solving an approximate convex optimization problem. To gain more insights, we also investigate the effects of traffic pattern, which is defined as the ratio between the traffic in two directions, on the system performance by considering a new traffic pattern constraint. Numerical results show that the proposed relaying scheme with moving relay can achieve great throughput gains as compared to the conventional scheme with static relay.

In this paper, we consider an unmanned aerial vehicle (UAV)-assisted relay network for B5G, where the UAV and the base station (BS) cooperated with each other to serve the cell-edge users. Aiming at this network, we propose a new decode-and-forward (DF) relay protocol incorporated with Non-orthogonal multiple access (NOMA) and maximum ratio combining (MRC), by which the coverage for the cell-edge users is enhanced. Theoretical analysis demonstrates that the proposed NOMA based relay protocol is superior to the traditional orthogonal multiple access (OMA) based relay protocol in terms of channel capacity. In order to make full use of the advantages of the proposed protocol, we formulate the 3D spatial position optimization problem of the UAV relay with the objective to maximize the sum-rate of all the cell-edge users. Based on the genetic algorithm, we propose an effective algorithm to solve the formulated problem. simulation results indicate that the proposed relay protocol greatly outperforms the traditional protocols, and the proposed algorithm achieves orders of magnitude speedup in computational time with only slight loss of performance.1

Unmanned aerial vehicles (UAVs) have played an important role in wireless communications due to the advantages, such as highly controllable mobility in 3-D space, swift deployment, Line-of-Sight (LoS) aerial–ground links, and so on. In this article, we consider a UAV-enabled two-way relaying system, where the UAV relay assists the information exchange between two ground users (GUs) under three different schemes, i.e., four-slot multihopping (FSMH) without network coding (NC), three-slot NC, and two-slot physical NC (PNC). First, the capacity region of each scheme in this relaying system is analyzed. Then, we maximize the system average sum rate by jointly optimizing the time resource allocation, transmission powers of the transceivers, and the UAV trajectory subject to the constraints on UAV mobility and information causality under each scheme. To solve those problems, we propose an iterative algorithm by applying the successive convex approximation and block coordinate descent techniques. Specifically, the time resource allocation, transmission powers, and the UAV trajectory are alternatively optimized in each iteration. In addition, the nonconvex trajectory optimization problem is solved by successively solving an approximate convex optimization problem. To gain more insights, we also investigate the performance of those three schemes with symmetric and asymmetric traffic, respectively, by introducing a new traffic pattern constraint. Numerical results show that the proposed relaying schemes with moving relay can achieve great throughput gains as compared to the conventional scheme with static relay. The three relay schemes also show great performance heterogeneity under different traffic patterns.

In this paper, we investigate the application of the non-orthogonal multiple access (NOMA) technique into the unmanned aerial vehicle (UAV) aided relay networks. Specifically, we first incorporate the NOMA protocol with the decode-and-forward (DF) relay protocol to enhance the performance of the cell edge users in a macrocell network. Theoretical analysis indicates that the NOMA-DF-relay protocol outperforms the conventional orthogonal multiple access (OMA) based DF-relay protocol in terms of data rate. To fully exploit the advantages of the proposed protocol, we formulate a joint UAV height optimization, channel allocation, and power allocation problem with the objective to maximize the total data rate of the cell edge users under the coverage of the UAV. For solving the formulated problem effectively, we first analyze its property and employ the golden section method to propose a general framework to obtain the optimal height of the UAV. Then, we design a low-complexity iterative algorithm to solve the joint channel-and-power allocation problem based on the matching theory and the Lagrangian dual decomposition technique. Finally, simulation results demonstrate that the NOMA-DF-relay protocol is superior to the OMA-DF-relay protocol even when the system parameters are not optimized, and the proposed algorithms can further significantly improve the network performance in comparison with the other schemes.

In this article, we address the security issue in a tiered small-cell network aiming at security optimization for small-cell users (SUEs) to defend against eavesdropping. Meanwhile, the transmissions from small-cell base stations (SBSs) are subject to the aggregate interference constraints of macro-cell users (MUEs). In particular, we consider two-fold information uncertainties in small cells, i.e., the uncertainties regarding the eavesdroppers and interference channels to the MUEs. As such, the SBSs compete for robust secrecy rate with robust protection for the MUEs. We adopt the generalized robust Nash equilibrium problem (GRNEP) formulation, for which we confirm the existence of equilibrium and analyze the condition for the uniqueness with variational inequality-assisted analysis. Furthermore, to solve for the equilibrium, we introduce the pricing mechanism and decompose the original GRNEP as a nonlinear complementarity problem with a priced NEP, where the former provides solution of price coefficients and the latter for resource allocation strategies based on given prices. Finally, extensive simulation results are provided to demonstrate the impacts of the interference constraint and uncertainties upon the security performance of an individual SUE and the overall network, which also corroborate the effectiveness of our proposal in security provisioning for the SUEs and interference protection for the MUEs.

Non-Orthogonal Multiple Access (NOMA) has recently been conceived as a promising technology for the fifth-generation mobile communication system. In this paper, we apply NOMA into the device-to-device (D2D) and cellular hybrid networks to improve the data rate of the D2D links. Specifically, we formulate a D2D transmission rate maximization problem by jointly considering user pairing and power control under the constraints of the decoding threshold of cellular users. To solve the formulated problem, we first analyze the optimal transmission power of the D2D users. According to the obtained power control strategy, the user pairing problem is transformed into a bipartite graph matching problem, which can be solved optimally by the Hungarian algorithm. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of data rate.

In this paper, we performed a measurement campaign at 3.5 GHz using two ±45° polarized antenna arrays in two typical industrial scenarios, the manufacturing district and workshop corridor. We obtain averaged power delay profiles (APDP) at a number of positions and fit the APDPs by power-decaying curves. We have found that the dense metallic facilities cause large excess delay of the multipath component arrival. On the other hand, we model the small-scale fading by Lognormal distribution. We also obtain root-mean-square delay spread (RMS-DS) and model it by Nakagami distribution. Futhermore, the positive correlation between RMS-DS and spatial separation distance between the transmitter and receiver is modeled by a linear function. Dense metallic facilities in workshops leads to complicated propagation environments. Channel coherent bandwidth and energy dispersion should be considered carefully in the design of wireless communication systems in industrial environments.

Cooperative communication in wireless networks has received much attention in both academia and industry. How to effectively allocate and schedule radio resources to improve system performance becomes an important issue of cooperative communication. This paper mainly studies the ultra-low complexity wireless channel allocation algorithm for interference-limited networks. Firstly, we use the traditional sequential convex approximation (SCA) technique to design the channel allocation algorithm. Then, we utilize the characteristics of deep neural network (DNN) that can approximate a complex function with multiple layers of mapping to approximate the SCA-based algorithm. Based on DNN, we design an ultra-low complexity algorithm. Simulation results indicate that the DNN-based algorithm can achieve good performance with ultra-low computation time, which is a feature for practical application.

Non-orthogonal multiple access (NOMA) and device-to-device (D2D) are two key technologies of the fifth-generation wireless networks. In this paper, we propose a new D2D-and-NOMA integrated framework, where the D2D users (DUEs) can reuse the spectrum of the cellular users (CUEs) in four NOMA-aided spectrum-sharing modes. In order to fully exploit the potential of the proposed framework, we jointly optimize user pairing and power control to maximize the number of accessed D2D links and meanwhile reduce the total power consumption under the constraints of the decoding thresholds of the DUEs and CUEs. We first analytically obtain the optimal transmission power for each DUE-CUE pair. Then, based on the power control policy, we reformulate the user pairing problem as a min-cost max-flow problem in graph theory and solve it efficiently. Specifically, our proposed algorithm can solve the formulated problem optimally with low complexity. Finally, simulation results indicate that our algorithm can significantly improve the number of accessed D2D links and reduce the power consumption in comparison with the other schemes.

Nonorthogonal multiple access (NOMA) and device-to-device (D2D) are two promising technologies that have great potential in improving user connectivity. In this paper, we incorporate NOMA into the D2D-capable cellular networks and propose a new NOMA-aided D2D access scheme. In the proposed scheme, the D2D users (DUEs) can operate in four spectrum-sharing modes, which are the extension of the traditional underlay mode. To fully exploit the advantages of the NOMA-and-D2D integrated framework, we formulate a connectivity-maximization problem by jointly considering user pairing, mode selection, and power control under the constraints of the decoding thresholds of cellular users and DUEs. Based on the graph theory, we devise an efficient algorithm with polynomial complexity to solve the formulated problem optimally. We first analytically obtain the optimal transmission power and spectrum-sharing mode for every possible user pair through a graphical method. Based on the power control and mode selection policies, we transform the user pairing problem into a min-cost max-flow problem which can be tackled by the Ford-Fulkerson algorithm. Finally, simulation results indicate that the NOMA-aided D2D access scheme outperforms the traditional underlay mode, and the proposed algorithm yields a large performance gain in comparison with other schemes in terms of user connectivity and power consumption.

Simultaneous wireless information and power transfer (SWIPT) technique offers a potential solution to ease the contradiction between high data rate and long standby time in the fifth generation (5G) mobile communication systems. In this paper, we focus on the SWIPT network design and optimization with 5G new frequencies. To design an efficient SWIPT network, we first investigate the propagation properties of 5G low-frequency (LF) and high-frequency (HF) channels. Specifically, a measurement campaign focusing on 3.5 GHz and 28 GHz is conducted in both outdoor and outdoor-to-indoor scenarios. Motivated by the measurement results, we design a dual-band SWIPT network, where the HF band is used for short-distance information delivery, while the LF band is used for short-distance energy transfer and long-distance information delivery. The designed network has a win-win architecture which can enhance the throughput of cell-edge users and improve the energy-harvesting efficiency of cell-center users. To further boost the network performance, we devise a joint power-and-channel allocation algorithm, which has the advantages of low complexity and fast convergence. Finally, simulation results demonstrate that the designed dual-band network outperforms the conventional single-band network in terms of energy-harvesting efficiency and user fairness, and the proposed algorithm can further upgrade the network performance significantly.

In this paper, by using a dual-band channel sounder equipped with omnidirectional antennas, we have measured and investigated the propagation parameters including path loss and shadowing at 1.79 and 4.9 GHz in the typical non-line-of-sight (NLOS) urban macrocell (UMa) scenario. Two different large-scale channel models, the alpha-beta-gamma (ABG) model and the close-in model with a frequency-weighted path loss exponent (CIF), are derived according to the channel measurement data. Finally, we compare the parameter values, shadow fading (SF) standard deviations, and prediction errors of the two models, and evaluate the accuracy and sensitivity with respect to the measurement data. The numerical results show the improvement in the channel fading estimation of the CIF model compared with the ABG model.

Space wireless networks composed of a large number of low-cost miniaturized nanosatellites enable many promising astronautical applications such as the distributed and cooperative sensing. Data collection via multihop relay is critical for nanosatellite networks. However, due to the small size of onboard solar panels and limited attitude control, energy harvesting (EH) capability of nanosatellites is quite low and unstable. In addition, the space networks have 3-D topologies due to various orbit altitudes. In this paper, we propose a novel 3-D EH-aware routing (3DEHR) scheme for nanosatellite networks. The main idea is to establish the 3-D artificial potential field (APF) based on the EH capabilities and geographic positions of neighboring nanosatellites. A nanosatellite node determines the next hop among its neighbors based on the composite force direction obtained from the APF for data forwarding. We also design the methods in 3DEHR to solve the routing void problem and to circumvent sensitive nodes such as malicious satellites. Simulation results show that 3DEHR outperforms the existing routing schemes in hop stretch and packet delivery rate. Since the routing paths are planned based on the EH capabilities of nanosatellites, the harvested energy in the network is fully utilized and the network lifetime is extended. 3DEHR is suitable for nanosatellite networks in space and other 3-D ad hoc networks with dynamic EH capabilities and topologies.

Nonorthogonal multiple access (NOMA) exhibits superiority in spectrum efficiency and device connections in comparison with the traditional orthogonal multiple access technologies. However, the nonorthogonality of NOMA also introduces intracell interference that has become the bottleneck limiting the performance to be further improved. To coordinate the intracell interference, we investigate the dynamic user scheduling and power allocation problem in this paper. Specifically, we formulate this problem as a stochastic optimization problem with the objective to minimize the total power consumption of the whole network under the constraint of all users' long-term rate requirements. To tackle this challenging problem, we first transform it into a series of static optimization problems based on the stochastic optimization theory. Afterward, we exploit the special structure of the reformulated problem and adopt the branchand-bound technique to devise an efficient algorithm, which can obtain the optimal control policies with a low complexity. As a good feature, the proposed algorithm can make decisions only according to the instantaneous system state and can guarantee the long-term network performance. Simulation results demonstrate that the proposed algorithm has good performance in convergence and outperforms other schemes in terms of power consumption and user satisfaction.

It is known that random network coding (RNC) can be used to improve the performance of wireless multicast networks. However, in the delay sensitive applications, no useful information can be recovered if a user cannot collect a full set of the encoded packets. This becomes more severe for multicast hard deadline constrained prioritized data because of the delivery time limitation and packet interdependency. Meanwhile, the performance of a multicast network is also limited by its bottleneck user(s) due to the heterogeneity of the underlying physical channels. Accordingly, we propose a cross-layer transmission scheme that utilizes beamforming at physical layer and adaptive RNC (ARNC) at network layer to maximize the overall network throughput. Under this joint-design, a smart antennas array that operates the beamforming is used to dynamically allocate the transmitting power among users, and the ARNC is adopted to achieve the network coding gain. In this way, the performance of each user is well balanced and the overall network throughput is increased observably. Furthermore, we propose a sample-based feedback scheme to reduce the system overhead. The analytical and simulation results are shown that the throughput of the network has increased by 30%~40% under our schemes compared with other RNC-based schemes.

Pilot contamination attack is an important activity of active eavesdropping conducted by a malicious user during channel training phase. This attack is potentially harmful to the physical layer security. In this paper, motivated by the fact that frequency asynchronism could introduce divergence of the transmitted pilot signals between intended user and attacker, we propose a new uncoordinated frequency shift (UFS) scheme for detecting pilot contamination attack in multiple antenna system. During the reverse training phase of the UFS scheme, the legitimate user Bob deliberately introduces multiple random frequency shifts in the publicly known pilot sequence. Since eavesdropper Eve has no knowledge of these random frequency shifts, it is almost impossible for her to pretend exactly like Bob. This provides the opportunity to detect the presence of Eve. An attack detection algorithm is then developed based on source enumeration method. Both the asymptotic performance analysis and numerical results are provided to verify the proposed detection scheme. The proposed scheme is also enhanced based on noise power estimation to cope with attacks from a multi-antenna Eve. Furthermore, the proposed UFS scheme is extended by introducing general parameterized phase shifts. It is demonstrated that the proposed UFS scheme can achieve comparable detection performance as the existing superimposed random sequence based scheme, without sacrifice of legitimate channel estimation performance.

Connecting unmanned aerial vehicles (UAVs) by using cellular networks can significantly expand the civil applications of UAVs, due to much a larger connectivity range and low cost. However, the low-altitude air-to-ground (ATG) channels are different from the traditional terrestrial mobile access channels, and hence need to be characterized specifically. In this paper, a measurement campaign and modeling of the aerial access channels between a UAV and base station (BS) in the typical urban macro-cell (UMa) and rural macro-cell (RMa) scenarios are presented. The channels were sounded at 2.412 GHz and 919 MHz with the horizontal ranges of 420 meters and 10 kilometers, respectively, in the two scenarios. The transmitter equipped with two omnidirectional antennas was fixed on a fourrotor UAV which flew horizontally at various altitudes from 25 to 150 meters. The receiver was installed on the top of five-storey buildings to emulate a BS. The path loss (PL) was measured and the statistical models have been proposed which are based on the corresponding 3GPP territorial channel models but adjusted by adding correction factors (CFs). The CF is relevant to the UAV height in the UMa scenario. For the RMa scenario there is a breaking point with respect to the horizontal distance and hence a segmented function for the CF is proposed. The new PL models can help to establish the ATG channel models and support the design of air-borne access technologies for the LTE networks.

Utilization of millimeter wave (mmWave) frequencies in the 5G has driven great efforts on measurement and modeling of the propagation channels, which are critical for the system evaluation and deployment. To compensate the large path loss, rotational scanning using high-gain horn antennas is usually employed for spatial channel characterization. However, it is a challenging issue to de-embed the antenna effect from captured channel profiles, especially for the wideband sounding. In this paper, a new convolutional modeling approach is first proposed, by which a synthesized spatial channel response is expressed by the consecutive convolutions of an antenna-free propagation model and directional antenna patterns. Then, based on the convolutional model, a simple two-step antenna de-embedding algorithm is designed. Furthermore, an indoor measurement campaign was performed using a steering receiver antenna and frequency-sweeping from 72.5 to 73.5 GHz. The high similarity between the measured and reproduced spatial channel responses and multipath impulse responses has indicated that the proposed approach can effectively de-embed the antenna effect and mitigate the system noise. It is also illustrated that the sparse impulse propagation model composed of only a few significant paths can sufficiently describe an mmWave channel. The channel angular, frequency, and time responses can be conveniently reproduced by the convolution of antenna patterns and sparse propagation models.

Tag collision occurs easily when the multiple tags respond to the same reader query simultaneously over a shared wireless channel. The solution of tag collision is a significant issue in the mobile RFID environment, because of the random mobility of tags. For the purpose of object tracking, locating and monitoring, the reader will repeatedly identify the same tags which still stay within its read range, which will waste a lot of resources and add some new collisions. To handle the above case well, we propose a tag anti-collision protocol based on adaptive ternary query splitting (ATQS), which can effectively avoid the collisions between the new arriving tags and the original staying tags by the blocking technique. Using the formula for the conversion of binary to ternary, the number and length of queries are simplified by a ternary query tree. Using the ternary resolution technique, the new processing procedure of tag identification is designed, in which the reader can use only one prefix of ID to identify at most three staying tags having the same prefix. From the simulation and performance analysis, it obtains that our protocol can effectively decrease the identification delay, which outperforms the previous protocols with the impacts of the staying ratio, the arriving radio and the number of tags.

Network coding offers a promising platform for multicast transmission to improve the throughput. However, the users may not collect a full set of the encoded packets to recover the useful information because of its block processing property, leading to the low throughput under wireless fading channel. While the schemes that using feedback from all the receivers to cover the problem above always causes the huge feedback overhead in large network. Accordingly, in this paper we proposed an adaptive sliding window random network coding(ASWRNC) that introduce RNC with lower triangular coefficient matrix and sliding window to achieve higher network throughput and lower feedback overhead. Particularly, we introduce an adaptive encoding scheme that can let the user decode out partial original packets without collecting the full set of encoded packets. On the other hand, the window sliding scheduling helps the user with better channel decode out more original packets. Further, we discuss in detail the feedback-compete mechanism to decline the feedback overhead.

Random network coding (RNC) is an efficient coding scheme at the network layer to improve the performance of the wireless multicast networks. However, the full set of encoded packets may not be collected to correctly decode the original information in wireless fading channels, leading to the low network throughput. This issue will be more severe in multimedia transmission. On the other hand, cooperative transmission scheme that fully utilizes the space diversity can effectively mitigate channel impairments. Accordingly, we proposed an all-user cooperation scheduling scheme based on the adaptive random network coding to improve the transmission efficiency in wireless networks with fading channels. The cooperative and adaptive random network coding (CARNC) scheme schedules one user as a relay to help the transmitter by maximizing the immediate network throughput based on the packet reception status of all users. This cooperation can effectively repair the encoded packet set due to deep channel fading and improve the throughput of the whole network. Extensive simulations have been conducted to evaluate and compare the performance of the proposed CARNC and other scheduling schemes. The results have shown that CARNC has a better performance in the whole network throughput due to the utilization of both adaptive coding and user cooperation.

With the recent developments in RFID systems, many readers are needed to apply to the same work place, which easily leads to the reader collision. When the communication with tags of a reader is disturbed by the operation of other readers, a reader collision will occur, which is achieved less attention than the tag collision in previous studies. To solve several types of reader collisions, we propose an efficient multi-channel reader collision avoidance (EMRCA) protocol, which is suitable for the multiple readers RFID system. After analyzing the relationship between the distance of two readers and the types of reader collisions, we give the solutions of reader collisions, and present the principle of choosing data channels. Using several data channels, the new processing procedure of communication with tags is designed, in which the neighbor readers and the relevant readers have the different communication principles. The simulation results show that the proposed protocol has many better performances than the previous protocols in terms of the number of failed interrogations, the process time of interrogation, and the network overhead.

To collect data from large-scale wireless sensor networks (WSNs) is a challenging issue and there are mainly two approaches to increase the efficiency: 1) by hierarchical routing based on node clustering and 2) by mobile elements (MEs). Since either method has pros and cons, this paper presents a hybrid approach, called node-density-based clustering and mobile collection (NDCMC), to combine the hierarchical routing and ME data collection in WSNs. A number of cluster heads (CHs) gather information from cluster members and then an ME visits these CHs to collect data. First, for a randomly deployed WSN, a new CH selection scheme based on the node density is proposed. The advantage is that the nodes which are surrounded by more deployed nodes are more likely to be CHs. Thus, the efficiency of both intracluster routing and ME data collection is improved. Second, a low-complexity traveling track planning algorithm is designed for an ME to pass by all CHs. The analytical model of NDCMC is also developed and the expectation of the sensor power consumption and network lifetime are derived. In addition, a simple random clustering and mobile collection (RCMC) scheme is introduced by which a number of CHs are selected randomly in a WSN. Although RCMC yields performance degradation, it has much less complexity. Extensive simulations show that the proposed hybrid NDCMC scheme leads to not only remarkable performance improvement but also convenient tradeoff between the network energy saving and the data collection latency.

Multipath propagation and power arrival profiles in three-dimensional (3-D) space determine the performance of the full-dimensional MIMO (FD-MIMO) systems. Field channel measurements are crucial in characterizing wireless channel properties. Nevertheless, in spatial channel measurements, estimating the direction-of-arrivals (DoAs) of multipath components (MPCs) is a challenging issue, because of the large number of propagation paths and the correlation among the multipath signals. The number of incidence angles and estimation precision in traditional methods is limited by the sensor array size and signal correlation. In this paper, we propose a scheme for measuring and estimating the 2-D DoAs of propagation paths called multipath angular estimation using the array response of PN-sequences (MAPS). By using a receiving planar antenna array (PAA), MAPS first extracts the complex path array response vector (PARV) for each propagation path and then estimates the DoAs of the paths individually and independently. The subspace-decomposition theory for MAPS is proved and extensive simulations are conducted to compare MAPS with other algorithms. Furthermore, a channel sounder using two PAAs and the probing signal of 2.6 GHz carrier modulated by PN-sequences has been developed. The simulation and field tests show that MAPS can estimate arbitrary number of resolved MPCs in a channel snapshot and effectively suppress the multipath interference.

Random liner network coding (RLNC) is an efficient coding scheme to improve the performance of the wireless multicast networks. However, traditional RLNC comes with a sacrifice in service delay because if the users are not able to collect a full size of the encoding packets, the useful information can not be recovered under the wireless fading channel. This effect is more severe for transmitting the hard deadline constrained prioritized (HDCP) data due to the limited delivery time and data interdependencies. On the other hand, relay scheme provides a useful way to overcome the fading problem in wireless network. Accordingly, we propose a BS-relay competitive scheduling algorithm (BRCS) which introduced adaptive RLNC in the rely-aided wireless network to deliver the HDCP data, to overcome the problem above. BRCS fully considered the property of the multimedia data and design a scheduling scheme, in which BS and relay compete with other to choose the best encoding packet for transmission so as to maximize the network throughput for the given hard deadline. Moreover, to show the performance of BRCS, we compared with other scheduling schemes and the simulation results are provided to corroborate the effectiveness of the proposed technique.

In dense wireless local area networks (WLANs), the hidden stations (HSs) cause severe collisions and performance degradation. Although cooperative communications can achieve spatial diversity, how to efficiently cooperate in a wireless network is a challenging issue due to the distributed nature of the stations. The contributions of this paper are two-fold. First, we establish an analytical model for the IEEE 802.11 WLANs with HSs using the mean value analysis method, which can provide the theoretical results of collision probability, frame service time and network throughput. Second, we propose a new medium access control (MAC) protocol, named network coding cooperative MAC (NCC-MAC) to utilize cooperation to relieve the HS problem. Different from the traditional RTS/CTS scheme, NCC-MAC utilizes random linear coding (RLC) to realize opportunistic cooperative transmission without extra control messages. Furthermore, the ACK-triggering mechanism is introduced which can help to avoid repeated collision caused by HS(s) in dense WLANs and also improve the efficiency of the coded cooperation. Simulations have been conducted to compare the performance of NCC-MAC with DCF, simple cooperative MAC (SC-MAC) without network coding, and other cooperative MAC in the literature. The results show that NCC-MAC can effectively realize station cooperation and reduce the effect of HS(s), improving the network throughput and delay performance considerably.

The full-dimensional MIMO (FD-MIMO) technology is expected to increase the system capacity significantly by elevation beamforming and sectorization, especially for the users on different floors in buildings. To design and evaluate the FD-MIMO requires the indoor-to-outdoor (I2O) channel modeling in the elevation domain, with the user equipments (UEs) located at different heights. However, such channel models are still lacking. In this paper, using a channel sounder equipped with two uniform planar antenna arrays, we have performed the spatial channel measurement in an urban micro-cell (UMi) environment where the UE is located from the 1st to the 5th floors and on each floor both LOS and NLOS scenarios are considered. The power and elevation angle of arrival (EoA) of each propagation path at the base station are estimated, and the distribution of the EoA and elevation spread of arrival (ESA) at each floor are obtained. The stochastic models of EoA and ESA with respect to the UE height are also proposed. This work can help to extend the current 2-dimensional channel models by combining the elevation propagation statistics to support the FD-MIMO system design.

How to minimize the energy dissipation and extend the lifetime of wireless sensor networks (WSNs) is still an active research topic nowadays. Hierarchical routing based on node clustering is an effective method, while using mobile elements (MEs) to gather data can prevent huge energy consumption of the sensors from long-distance transmission. Considering that both methods have pros and cons, this paper presents a hybrid approach, called Node Density based Clustering and Mobile Collection (NDCM), to combine the hierarchical routing and ME data collection in WSNs. A number of Cluster Heads (CHs) first gather information from the cluster members and then the ME visits these CHs to collect data. A new CH selection scheme based on the node density is proposed. Thus, a node at the center of an area where nodes are densely deployed is more likely to be a CH, which can improve the efficiency of both intra-cluster routing and ME data collection. We also introduce a simple Random Clustering and Mobile Collection (RCM) scheme according to which a number of CHs are selected randomly throughout the network. In addition, the nodes which are covered by the radio range of the ME, called Virtual Heads (VHs), can also send/relay packets directly to the ME. The different mobility schemes are compared through extensive simulations and the results show that the proposed hybrid NDCM scheme leads to remarkable improvement in network lifetime and convenient trade off between the network energy saving and packet latency.

In WSNs, accidents like traffic congestion and sensor node running out of energy may occur frequently. These accidents are usually geographically localized, resulting in some groups of nodes unusable temporarily or even forever, which are called sensitive clusters in this paper. Although the data collection methods have been intensively studied, how to dynamically optimize the routing to bypass the sensitive clusters is an interesting and open issue. A new distributed, location-based routing protocol, named sensitive artificial potential field routing (SAPFR), is proposed to deliver packets efficiently while bypassing the sensitive clusters adaptively. SAPFR can build the multihop route from a sensor node to the sink with high energy efficiency and power consumption balancing. In particular, SAPFR is highly adaptive to bypass the dynamic sensitive clusters. Simulation results show that the obtained routes proactively bypass the sensitive clusters and the transmission efficiency is improved as well. SAPFR provides a high routing success rate in the WSNs even with a large proportion of sensitive sensor nodes.

In recent years, several random key pre-distribution schemes have been proposed for wireless sensor networks. However, the problem of key and node revocation has received fewer attentions. In this paper, we present a novel random key revocation protocol, which is suitable for large scale networks and removes compromised information efficiently. The proposed revocation protocol can guarantee network security and has less memory consumption and communication overhead. With the combination of centralized and distributed revocations, the protocol achieves both timeliness and accuracy for revocation. The simulation results show that our protocol has better performance than existing protocols in terms of increasing revocation validity and revocation velocity, and prolonging the network lifetime.

Routing is critical for WSNs due to the nature of multi-hop message delivery and the restricted power supply and computation capacity. Routing mechanisms with efficient power utilization and low computation complexity are of fundamental importance to meet the future application demand of WSNs. In this paper, a novel location-based routing technique, the Artificial Potential Field based Routing (APFR), is proposed. APFR establishes an Artificial Potential Field (APF) for the tagged sensor node and then uses a greedy selection algorithm to determine the next hop based on the direction obtained from the APF. In addition, a backup scheme is presented to recover the route in the case of routing voids. The advantages of APFR are three-fold. First, the routing tends to point to the areas with high node density, so the transmission load is balanced among the sensor nodes. Second, when some nodes are running out of energy, the APF will be updated and the routing will change automatically according to the new APF. Third, APFR only requires local geographic information and is of low computational complexity. The simulation results show that APFR can prolong the network lifetime and improve the routing success rate compared to other routing protocols such as GEAR and GPSR, especially for densely deployed networks.

The new multichannel/multicarrier technologies have drawn great attention recently to support high data rate in mobile multiple-access environments, such as the carrier aggregation (CA) as a part of 4G LTE-Advanced enhancement. On the way to the development of multicarrier technologies, there is a considerable need to understand and utilize the correlation properties of distinct subchannels, which, however, has not been reported in the literature. Based on the extensive indoor ultra-wideband (UWB) channel measurement, this paper develops a new approach to analyze the correlation of the large and small-scale fading of arbitrarily separated subchannels. The approach avoids the high complexity and cost of the conventionally probing the multiple subchannels simultaneously using channel sounders which would be too complicated to implement. We first propose a modified CLEAN algorithm to reliably extract channel impulse responses (CIRs) from the measured pulse response waveforms with substantially varying noise floor. Then the cross-correlation of the fading of two distinct subchannels, the autocorrelation of the fading of a single subchannel and the correlation of the multipath profile over a small area are analyzed. The study has revealed how significantly the channel response would change when the receiver travels from room to room or just over a distance of dozen of wavelength, and how two separated subchannels are correlated. In our view, the acquired new insights on subchannel correlations could provide guidelines for efficient designs and performance predictions of multicarrier communication systems.

In wireless networks using hybrid MAC, nodes can reserve time periods inside scheduling cycles, and the time which is not reserved can be used by all the nodes through contention-based access. The hybrid MAC is attractive because it can provide satisfactory QoS by resource reservation and also achieve high channel utilization by multiplexing gain in the contention periods. However, we are still lacking a clear understanding of its performance and the optimization design scheme. In this paper, we propose an analysis framework for hybrid MAC using soft-reservation, where the unused reserved time can be released and accessed by the other nodes through contention. By the mean value analysis approach, the collision probability and average service time of one frame are obtained. The hybrid MAC based on the WiMedia ECMA-368 standard has been simulated to validate the analysis and compared to the conventional contention-based MAC and the hard-reservation hybrid MAC, which shows the soft reservation has much better performance and higher capacity when the network is relatively heavily loaded.

Second-order statistical parameters of wireless channels, such as level crossing rate (LCR) and average fade duration (AFD), determine how frequent and the burst length of the channel in bad conditions, so they play an important role in the performance of wireless communication systems. For user-cooperative wireless systems, due to the interactions of multiple channels, it is non-trivial to determine the LCR and AFD of the received signals, which is an open issue. In this paper, we develop an analytical framework to quantify the LCR and AFD of the amplify-and-forward (AF) cooperative system using selection combining (SC) over Rayleigh fading channels. We first analyze the statistics of the two independent fading paths, the AF relay path with a mobile-to-mobile (M2M) channel and a mobile-to-fixed (M2F) Rayleigh fading channel, and the direct path with a M2F Rayleigh fading channel. Then, we derive the expressions of the second-order statistical parameters of the AF cooperative system with SC. Numerical results verify the correctness of our model. The analytical and simulation results reveal the different effect of the motions of the source and the relay nodes, and they can be used to select better relay nodes and assist the design and optimization of error control mechanisms in different layers in wireless cooperative networks.

Hybrid media access control (MAC) protocols use reservation and contention-based approaches simultaneously, so they can provide satisfactory quality-of-service to multimedia applications by resource reservation, and achieve high resource utilization with multiplexing gain during the contention periods. However, reservation can significantly affect the behavior of the contention-based access. How to split channel time between reservation periods and contention periods and how to adjust the contention scheme for hybrid MAC are important, open issues. In this paper, an analytical model for the hybrid MAC with saturated traffic is first proposed and then extended to the unsaturated traffic case. Based on the mean value analysis, the proposed models give the average frame service time and throughput for the contention-based MAC with the presence of reserved channel periods. They are also applicable to online admission control due to their low computational complexity.

To reserve or not for bursty video traffic over wireless access networks has been a long-debated issue. For uplink transmissions in infrastructure-based wireless networks and peer-to-peer transmissions in mesh or ad-hoc networks, reservation can ensure the Quality-of-Service (QoS) provisioning at the cost of a lower degree of resource utilization. Contention-based Medium Access Control (MAC) protocols are more flexible and efficient in sharing resources by bursty traffic to achieve a higher multiplexing gain, but the performance may degrade severely when the network is congested and collisions occur frequently. More and more wireless standards adopt a hybrid approach, which allows the coexistence of resource reservation and contention-based MAC protocols. However, how to cost-effectively support video traffic using hybrid MAC protocols is still an open issue. In this paper, we first propose how to use hybrid MAC protocols to support video streaming over wireless networks. Then, we quantify the performance of video traffic over wireless networks with contention-only, reservation-only, and hybrid MAC protocols, respectively. Admission regions for video streams with these three approaches are obtained. Using the standard WiMedia MAC protocols as an example, extensive simulations with a commonly-used network simulator (NS-2) and real video traces are conducted to verify the analysis. The analytical and simulation results reveal the tradeoff between reservation and contention-based medium access strategies, and demonstrate the effectiveness of the hybrid approach.

Orthogonal frequency-division multiplexing (OFDM) is a promising technique for high data rate wireless access networks. Modeling OFDM systems for the analysis of network performance is very challenging, because of the complexity of the modulation/coding schemes and the wideband wireless channel fading in both the time and frequency domains. In this paper, a novel packet-level model based on a two-dimensional Markov chain is proposed for OFDM systems over time-varying (Nakagami-m fading), frequency-selective channels. First, the level cross rate (LCR) of the amplitude of channel frequency response is derived. Then, we develop a methodology to map the received signal-to-noise ratio (SNR) of the subcarriers into a finite number of channel states with different packet error rate (PER). The proposed model presents directly the performance of the OFDM systems and incorporates the time- and frequency-domain correlations of the fading channels. Channel coding is also considered in evaluating PER. Simulations have verified that the statistics of the BER presented by our model are consistent with those of waveform simulations. The proposed Markov model can be an effective tool to study and optimize upper-layer protocols of OFDM-based wireless networks, via both analysis and simulation.

Ultra-wideband (UWB) wireless communication technologies have been proposed to support high data rate multimedia services in office or residential environments. Due to the low transmission power of UWB, the shadowing effect by moving people can considerably reduce the received signal quality and thus significantly degrade the quality of service (QoS) of on-going transmissions. An open issue is to build a simple model which captures the temporal variation of UWB channels and the packet error rate (PER) due to the people shadowing effect (PSE), which will be a useful tool for upper layer protocol performance analysis and simulation. This paper presents an analytical study of the PSE and the temporal variation of UWB channels induced by the motion of a person. First, we derive the angular power spectral density (APSD) of the indoor UWB channel impulse response (CIR), and the PSE in terms of signal power attenuation. Second, based on a two-dimensional random walk mobility model, the PER variation due to people shadowing is modeled as a finite-state Markov chain (FSMC). The investigation of APSD provides important insights on the spatial propagation characteristics of UWB signals. The proposed packet-level channel model can be conveniently incorporated into analytical frameworks and simulation tools for evaluating upper-layer protocols of UWB networks.

Delayed acknowledgment (Dly-ACK) and packet fragmentation are link-layer policies for ultra-wideband (UWB) based wireless personal area networks (WPANs) to improve the channel utilization, defined in both the IEEE 802.15.3a and ECMA-368 standards. On the other hand, the shadowing effect caused by people moving between the transmitter and receiver may severely degrade the received signal power and thus introduce channel variation. In this paper, we develop an analytical framework for studying the performance of the Dly-ACK and fragmentation over UWB fading channels. A Markov model is used to capture the time-variation of the UWB shadowing channel. The distribution of transmission delay of fragmented packets and the queuing behavior of the sender's buffer are derived. The system performance of packet delay and loss are obtained. Validated by simulations, the analytical results provide important insights and guidelines for better supporting high data rate, delay sensitive traffic in UWB-based WPANs.

In wireless networks, the physical layer adaptive modulation and coding (AMC) scheme has been proposed to improve bandwidth efficiency over the time-varying channel. In this paper, we study the performance of ultra-wideband (UWB) based wireless personal area network where AMC is coupled with the distributed reservation protocol (DRP) and the delayed- acknowledgement (Dly-ACK) schemes at the link layer. Considering the channel variation caused by the people shadowing effect, we first propose an analytical model using an embedded Markov chain to investigate the queuing behavior at sender's buffer. Second, the throughput optimization problem is formulated and the optimal transmission mode and payload length are obtained. Simulation results are given to validate the analysis. By jointly considering channel characteristics, physical layer and link layer transmission schemes, the analytical results of the paper can provide useful guidelines for cross-layer optimization, which is essential to ensure quality of services in UWB networks.

Ultra-wideband (UWB) technology is expected to provide high data rate services for future wireless personal area networks (WPANs). The WiMedia Alliance recently has launched its standard for UWB-based WPANs, where the distributed reservation protocol (DRP) is specified to allow the channel time being reserved in a distributed manner. In view of the urgent need of using DRP to support high data rate multimedia applications, we investigate the delay performance of DRP in this paper. Since the negotiation of channel time is fully distributed without centralized coordination, the reserved channel time may be non-evenly spaced. In addition, the channel dynamics due to shadowing that is notable in indoor environments can greatly affect the protocol performance. In this paper, we study the delay performance of DRP under different reservation patterns and take into account the dynamics of UWB shadowing channel. The system is modeled as a discrete-time single server queue with vacation, which can be represented by the quasi-birth and death (QBD) process and solved by the well-established matrix-geometric approach. We use numerical results to validate the accuracy of the mathematical modeling. The proposed analytical model can be useful to understand the actual performance of DRP, thereby further performance improvement can be guided.

In this paper, we develop a novel packet-level channel model for orthogonal frequency-division multiplexing (OFDM) systems over frequency-selective Nakagami-m fading channel. Since the subcarriers are correlated, the Level Cross Rate (LCR) in frequency response is introduced and derived. A complete model including finite state Markov chains in both the time domain and the frequency domain is proposed for the multi- carrier system, which models the received signal to noise ratio (SNR) of each subcarrier and further presents the packet error behavior of the OFDM system. This model is useful for upper layer protocol design and analysis over OFDM systems, and it speeds up significantly the simulation for OFDM networks over fading channels. The model is validated by simulations, which confirm that the statistical properties of the frequency-selective fading channel have been maintained in our model.

In an indoor ultra-wideband (UWB) communication environment, the line-of-sight (LOS) between the transmitter and receiver may be frequently blocked by moving people. Blocking of LOS may significantly affect the quality of service (QoS) of on-going UWB communications. Based on the Angular Power Spectrum and the human blocking models, we build a packet-level UWB channel model considering the shadowing processes. The model is simple enough to be incorporated into existing network simulators and it can be used to facilitate protocol design and QoS analysis for UWB based wireless personal area networks (WPANs).