Publications

Mobile-edge computation offloading (MECO) is envisioned as a promising technique for enhancing mobiles' computation capabilities and prolonging their battery lives, by offloading intensive computation from mobiles to nearby servers such as base stations. In this paper, we investigate the energy-efficient resource-management policy for asynchronous MECO systems, where the mobiles have heterogeneous input-data arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival- deadline orders. An optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource- management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is obtained by using the block coordinate decent method. To gain further insights, we further study the special case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems to find the optimal scheduling order and then jointly optimize the data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to balance the defined effective computing power among offloading mobiles via time sharing.

In this paper, we pursue a unified study on energy harvesting and coordinated multi-point (CoMP) enabled wireless communication by investigating a new joint energy and communication cooperation approach. We consider a practical CoMP system with clusters of multiple-antenna base stations (BSs) each powered by hybrid power supplies (including both the conventional grid and renewable energy sources) cooperatively transmitting to multiple single-antenna mobile terminals (MTs). We propose a new design paradigm termed energy cooperation among BSs within each cluster, which share energy for more efficient cooperative transmission via injecting/drawing power to/from the grid with a zero-sum net energy transfer. We maximize the downlink sum-rate for all MTs in one particular CoMP cluster with cooperative zero-forcing precoding at BSs subject to a new type of transmit power constraints featuring energy cooperation among BSs with a given loss ratio. To jointly optimize the precoders at BSs and the amount of energy transferred among them, we propose an efficient algorithm by applying the techniques from convex optimization. By simulations, we show that the proposed joint communication and energy cooperation solution substantially improves the downlink throughput for energy harvesting CoMP systems, as compared to suboptimal designs without communication and/or energy cooperation.

No abstract is provided for this article.

This paper studies user cooperation in the emerging wireless powered communication network (WPCN) for throughput optimization. For the purpose of exposition, we consider a two-user WPCN, in which one hybrid access point (H-AP) broadcasts wireless energy to two distributed users in the downlink (DL) and the users transmit their independent information using their individually harvested energy to the H-AP in the uplink (UL) through time-division-multiple-access (TDMA). We propose user cooperation in the WPCN where the user that is nearer to the H-AP and in general has a better channel for DL energy harvesting as well as UL information transmission uses part of its allocated UL time and DL harvested energy to help relay the far user's information to the H-AP, in order to achieve more balanced throughput. We maximize the weighted sum-rate (WSR) of the two users by jointly optimizing the time and power allocations in the network for both wireless energy transfer in the DL and wireless information transmission and relaying in the UL. Simulation results show that the proposed user cooperation scheme can effectively improve the achievable throughput in the WPCN with desired user fairness.

Intelligent reflecting/refracting surface (IRS) has recently emerged as a promising solution to reconfigure wireless propagation environment for enhancing the communication performance by tuning passive signal reflection or refraction. In this paper, we study a new IRS-aided high-mobility communication system by employing the intelligent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">refracting</i> surface with a high-speed vehicle to aid its passenger's communication with a remote base station (BS). Due to the environment's random scattering and vehicle's high mobility, a rapidly time-varying channel is typically resulted between the static BS and fast-moving IRS/user, which renders the channel estimation for IRS with a large number of passive refracting elements more challenging, as compared to that for the conventional slow fading IRS channels with low-mobility users. In order to reap the high IRS passive beamforming gain with low channel training overhead, we propose a new and efficient two-stage transmission protocol to achieve both IRS channel estimation and refraction optimization for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed as well as the line-of-sight (LoS) dominant channel between the BS and IRS, the user first estimates the LoS component of the cascaded BS-IRS-user channel in Stage I, based on which IRS passive refraction is designed to maximize the corresponding IRS-refracted channel gain. Then, the user estimates the resultant IRS-refracted channel as well as the non-IRS-refracted channel in Stage II for setting an additional common phase shift at all IRS refracting elements so as to align these two channels for maximizing the overall channel gain for data transmission. Simulation results show that the proposed design can efficiently achieve the full IRS passive beamforming gain in the high-mobility communication scenario, which also converts the overall BS-user channel from fast to slow fading for more reliable transmission. The proposed on-vehicle IRS system is further compared with a baseline scheme of deploying fixed IRSs (intelligent reflecting surfaces) on the roadside to assist high-speed vehicular communications, which achieves significant rate improvement due to its greatly saved channel training time.

The rapidly growing wave of wireless data service is pushing against the boundary of our communication network's processing power. The pervasive and exponentially increasing data traffic present imminent challenges to all the aspects of the wireless system design, such as spectrum efficiency, computing capabilities and fronthaul/backhaul link capacity. In this article, we discuss the challenges and opportunities in the design of scalable wireless systems to embrace such a "bigdata" era. On one hand, we review the state-of-the-art networking architectures and signal processing techniques adaptable for managing the bigdata traffic in wireless networks. On the other hand, instead of viewing mobile bigdata as a unwanted burden, we introduce methods to capitalize from the vast data traffic, for building a bigdata-aware wireless network with better wireless service quality and new mobile applications. We highlight several promising future research directions for wireless communications in the mobile bigdata era.

To enhance the signature of an extended target in a SAR image, a robust waveform design method is presented for spectrally dense environments. First, the problem is formulated by maximizing the worst-case signal-to-clutter ratio (SCR) over the uncertainty set of statistics for both target and background scattering characteristics, subject to energy, similarity, and spectrum constraints. Second, the closed-form solutions for the uncertain statistics are derived. The problem of maximizing worst-case SCR is boiled down to a nonconvex fractional quadratically constrained quadratic problem (QCQP). Resorting to the Dinkelbach’s algorithm and Lagrange duality, the QCQP is split into a series of solvable semidefinite programming problems. A convergence analysis is conducted, where a sufficient condition for global convergence is derived. Finally, numerical examples are presented to demonstrate the performance of the proposed scheme.

Ship power system continuously expanding its scale makes simulation test to grow its importance for the safety evaluation. This platform integrates object-oriented technology, visualization technology and database technology to build ship power system graphical simulation platform. We establish the simulation model of ship power system component by using object-oriented technology. The user just needs to click on the icons and input necessary parameters to model a system, Visualization technology makes the user to draw ship power system components and the electric network. The system will carry out network topology analysis after entering some parameters and forming nodal admittance matrix for simulation. Thus a comprehensive ship power system simulation platform was established according to above procedures. Finally, based on the sample system simulation the validity and reliability of this analyzed topology was verified.

Low-earth orbit (LEO) satellite communication plays an important role in assisting/complementing terrestrial communications by providing worldwide coverage, especially in harsh environments such as high seas, mountains, and deserts which are uncovered by terrestrial networks. Traditionally, the passive reflect-array with fixed phase shifts has been applied in satellite communications to compensate for the high path loss due to long propagation distance with low-cost directional beamforming; however, it is unable to flexibly adapt the beamforming direction to dynamic channel conditions. In view of this, we consider in this paper a new intelligent reflecting surface (IRS)-aided LEO satellite communication system, by utilizing the controllable phase shifts of massive passive reflecting elements to achieve flexible beamforming, which copes with the time-varying channel between the high-mobility satellite (SAT) and ground node (GN) cost-effectively. In particular, we propose a new architecture for IRS-aided LEO satellite communication where IRSs are deployed at both sides of the SAT and GN, and study their cooperative passive beamforming (CPB) design over line-of-sight (LoS)-dominant single-reflection and double-reflection channels. Specifically, we jointly optimize the active transmit/receive beamforming at the SAT/GN as well as the CPB at two-sided IRSs to maximize the overall channel gain from the SAT to each GN. Interestingly, we show that under LoS channel conditions, the high-dimensional SAT-GN channel can be decomposed into the outer product of two low-dimensional vectors. By exploiting the decomposed SAT-GN channel, we decouple the original beamforming optimization problem into two simpler subproblems corresponding to the SAT and GN sides, respectively, which are both solved in closed-form. Furthermore, we propose an efficient transmission protocol to conduct channel estimation and beam tracking, which only requires independent processing of the SAT and GN in a distributed manner, thus substantially reducing the implementation complexity. Simulation results validate the performance advantages of the proposed IRS-aided LEO satellite communication system with two-sided cooperative IRSs, as compared to various baseline schemes such as the conventional reflect-array and one-sided IRS.

In conventional terrestrial cellular systems, mobile terminals (MTs) at the cell edge often pose the performance bottleneck due to their long distance from the ground base station (GBS), especially in hotspot areas. This paper proposes a new hybrid network architecture by leveraging the use of unmanned aerial vehicle (UAV) as an aerial mobile base station, which flies cyclically along the cell edge to serve the cell-edge MTs and help offloading the traffic from the GBS. To achieve user fairness, we aim to maximize the minimum throughput of all MTs in a single cell by jointly optimizing the UAV's trajectory, as well as the bandwidth allocation and user partitioning between the UAV and GBS. Numerical results show that the proposed hybrid network with optimized spectrum sharing and cyclical multiple access design significantly improves the spatial throughput over the conventional cellular network with the GBS only.

No abstract is provided for this article.