Integrating unmanned aerial vehicles (UAVs) into the cellular network as new aerial users is a promising solution to meet their ever-increasing communication demands in a plethora of applications. Due to the high UAV altitude, the channels between UAVs and the ground base stations (GBSs) are dominated by the strong line-of-sight (LoS) links, which brings both opportunities and challenges. On one hand, a UAV can communicate with a large number of GBSs at the same time, leading to a higher macro-diversity gain as compared to terrestrial users. However, on the other hand, severe interference may be generated to/from the GBSs in the uplink/downlink, which renders the interference management with coexisting terrestrial and aerial users a more challenging problem to solve. To deal with the above two trade-off, this paper studies the uplink communication from a multi-antenna UAV to a set of GBSs in its signal coverage region. Among these GBSs, we denote available GBSs as the ones that do not serve any terrestrial users at the assigned resource block (RB) of the UAV, and occupied GBSs as the rest that are serving their respectively associated terrestrial users in the same RB. We propose a new cooperative interference cancellation strategy for the multi-beam UAV uplink communication, which aims to eliminate the co-channel interference at each of the occupied GBSs and in the meanwhile maximize the sum-rate to the available GBSs. Specifically, the multi-antenna UAV sends multiple data streams to selected available GBSs, which in turn forward their decoded data streams to their backhaul-connected occupied GBSs for interference cancellation. To draw useful insights, this paper focuses on characterizing the maximum degrees-of-freedom (DoF) achievable by our proposed strategy for UAV's sum-rate maximization in the high signal-to-noise ratio (SNR) regime, subject to the stringent constraint that all the occupied GBSs do not suffer from any interference in the UAV's uplink transmission. Numerical examples validate the DoF performance of our proposed strategy, as compared to benchmark schemes with fully cooperative, local or no interference cancellation.
Magnetic resonant coupling (MRC) is a practically appealing method for realizing the near-field wireless power transfer (WPT). The MRC-WPT system with a single pair of transmitter and receiver has been extensively studied in the literature, while there is limited work on the general setup with multiple transmitters and/or receivers. In this paper, we consider a point-to-multipoint MRC-WPT system with one transmitter sending power wirelessly to a set of distributed receivers simultaneously. We derive the power delivered to the load of each receiver in closed-form expression, and reveal a "near-far" fairness issue in multiuser power transmission due to users' distance-dependent mutual inductances with the transmitter. We also show that by designing the receivers' load resistances, the near-far issue can be optimally solved. Specifically, we propose a centralized algorithm to jointly optimize the load resistances to minimize the power drawn from the energy source at the transmitter under given power requirements for the loads. We also devise a distributed algorithm for the receivers to adjust their load resistances iteratively, for ease of practical implementation.
6DMA (six-dimensional movable antenna) is a new and revolutionizing technology that fully exploits the wireless channel spatial variation at the transmitter/receiver by flexibly adjusting the three-dimensional (3D) positions and 3D rotations of distributed antennas/antenna surfaces (arrays). In this article, we provide an overview of 6DMA for unveiling its great potential in wireless networks, including its motivation and competitive advantages over existing technologies, system/channel modeling, and practical implementation. In particular, we present a variety of 6DMA-enabled performance enhancement in terms of array gain, spatial multiplexing, interference suppression, and geometric gain. Furthermore, we illustrate the main applications of 6DMA in wireless communication and sensing, and elaborate their design challenges as well as promising solutions. Finally, numerical results are provided to demonstrate the significant capacity improvement of 6DMA-aided communication in wireless network.
Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the quasi-static channels among the fixed BS, IRS, and two anchors are estimated off-line only, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.
A model-based channel capacity prediction scheme is proposed to provide statistical quality-of-service (QoS) guarantees under a medium or high traffic load for IEEE 802.11 based wireless multimedia networks. The proposed scheme perceives the state of wireless link from the MAC retransmission information and calculates the statistical channel capacity especially in saturated traffic load. Based on the capacity prediction model, resource reservation and QoS routing optimization are carried out. Via a cross-layer design approach, the scheme allocates network resource and forwards data packet by taking into consideration of the interference among flows and the link state. Simulation results show that the proposed scheme can achieve more accurate capacity prediction and higher resource utilization than previous QoS routing schemes, and improve network performance in terms of end to end transmission delay, packet delivery ratio and network throughput.
Wireless power transfer (WPT) technology enables a cost-effective and sustainable energy supply in wireless networks. However, the broadcast nature of wireless signals makes them non-excludable public goods, which leads to potential free-riders among energy receivers. In this study, we formulate the wireless power provision problem as a public goods provision problem, aiming to maximize the social welfare of a system of an energy transmitter (ET) and all the energy users (EUs), while considering their heterogeneous valuations, private information, and self-interested behaviors. We propose a two-phase all-or-none scheme involving a low-complexity Power And Taxation (PAT) mechanism, which ensures voluntary participation, truthfulness, budget balance, and social optimality at every Nash equilibrium (NE). We propose a distributed PAT (D-PAT) algorithm to reach an NE, and prove its convergence by connecting the structure of NEs and that of the optimal solution to a related optimization problem. We further extend the analysis to a multi-channel system, which brings a further challenge of non-strictly concave agents' payoffs. We propose a Multi-Channel PAT (M-PAT) mechanism and a distributed M-PAT (D-MPAT) algorithm to address the challenge. Simulation results show that, our design is most beneficial when there are more EUs and more homogeneous channel gains.
The Power Distribution Internet of Thing (PDIoT) is faced with mass access and increasingly complicated tasks. To manage mass access and timely process tasks, PDIoT needs to configure plenty of computing resources and set up corresponding resource management strategy. In this paper, PDIoT based cloud-edge system is applied to solve the above problem, and with the requirement to analyse its performance, a simulation software EdgeCloudSim is proposed to model such PDIoT. Firstly, the key elements of PDIoT modelling, including cloud-edge architecture and task processing are analysed. Cloud-edge architecture is the carrier of computing resources, while task processing consists of uploading task requests, processing tasks and downloading instructions. Then, the methods to model PDIoT by EdgeCloudSim are researched. EdgeCloudSim has a modular architecture. Thus, by tuning parameter or editing function in a certain modular, a wanted PDIoT can be modelled. Finally, a fault handling scenario and three different task offloading strategies are set in simulation. The simulation result verifies the effectiveness of EdgeCloudSim.
Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed \emph{energy beamforming}, is an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the \emph{net} harvested energy at the ER, which is the average harvested energy offset by that used for channel training. An optimization problem is formulated for the training design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when training should be employed to improve the net transferred energy in MIMO WET systems.
This paper studies the spatial capacity of wireless ad hoc networks. We propose a transmission scheme with threshold-based scheduling, where each transmitter decides to transmit in the data transmission phase if the signal-to-interference-ratio (SIR) at its receiver in the preceding pilot phase is no smaller than a predefined threshold. For comparison, we also consider a reference scheme, where all transmitters transmit independently in both the pilot and data transmission phases. For both schemes, we assume a homogeneous Poisson Point Process (PPP) to model the locations of transmitters that have the intention to transmit. However, for the proposed scheme, the point process formed by the retained transmitters in the data transmission phase is generally not a PPP due to the SIR-based scheduling. First, we show how to set the SIR threshold in the proposed scheme to assure that it outperforms the reference scheme in terms of network spatial capacity. Then, we present exact/approximate spatial capacity expressions for the proposed scheme with different SIR-threshold values. Finally, we provide simulation results to validate our analysis.
This paper considers an unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) in urban areas, where a UAV is deployed to collect data from distributed sensor nodes (SNs) within a given duration. To characterize the occasional building blockage between the UAV and SNs, we construct the probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using the combined simulation and data regression method, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. We assume that only the knowledge of SNs' locations and the probabilistic LoS channel model is known a priori, while the UAV can obtain the instantaneous LoS/Non-LoS channel state information (CSI) with the SNs in real time along its flight. Our objective is to maximize the minimum (average) data collection rate from all the SNs for the UAV. To this end, we formulate a new rate maximization problem by jointly optimizing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Although the optimal solution is intractable due to the lack of the complete UAV-SNs CSI, we propose in this paper a novel and general design method, called hybrid offline-online optimization, to obtain a suboptimal solution to it, by leveraging both the statistical and real-time CSI. Essentially, our proposed method decouples the joint design of UAV trajectory and communication scheduling into two phases: namely, an offline phase that determines the UAV path prior to its flight based on the probabilistic LoS channel model, followed by an online phase that adaptively adjusts the UAV flying speeds along the offline optimized path as well as communication scheduling based on the instantaneous UAV-SNs CSI and SNs' individual amounts of data received accumulatively.
The use of insulators and operation experience in the Jindongnan-Nanyang-Jingmen 1000 kV a.c.project are presented.By analyzing the contamination of UHV insulator,the electrical,mechanical and aging performances etc.,the reliability of insulators and the porcelain insulator aging rate,hydrophobicity,and mechanical properties of composite insulators,periodic testing of steel corrosion and so on are studied,and the relevant operation for 1000 kV UHV insulators and optimal selection of insulators are formulated.It is indicated that the requirements of insulator disc diameter,structure height,creepage distance and specified mechanical load are higher than those in 500kV transmission line,and the requirements of insulator disc diameter and structure height are two times higher than those of 500kV substation,so the long-term operation reliability of insulators is important.The experimental verification and quality control were performed so as to make the insulators design and performance to meet the requirements of UHV project.
To support large-scale deployment of unmanned aerial vehicles (UAVs) in future, a new wireless communication paradigm, namely, cellular-connected UAV, has recently received an upsurge of interests in both academia and industry. Specifically, cellular base stations (BSs) and spectrum are reused to serve UAVs as new aerial user equipments (UEs) for meeting their communication requirements. However, compared to traditional terrestrial UEs, the high altitude of UAVs results in more frequent line-of-sight (LoS) channels with both their associated and non-associated BSs in a much wider area, which causes stronger aerial-ground interference to both UAVs and terrestrial UEs. As such, conventional techniques designed for mitigating the terrestrial interference become ineffective in coping with the new and more severe UAV-terrestrial interference. To tackle this challenge, we propose in this article new interference mitigation solutions for achieving spectral efficient operation of the cellular network with co-existing UAVs and terrestrial UEs. In particular, we exploit the powerful sensing capability of UAVs and inactive BSs in the network for interference mitigation/cancellation. Numerical results are presented to verify the efficacy of the proposed solutions and show their significant spectrum efficiency gains over terrestrial interference mitigation techniques.
