In wireless sensor networks, utilizing the unmanned aerial vehicle (UAV) as a mobile data collector for the sensor nodes (SNs) is an energy-efficient technique to prolong the network lifetime. In this letter, considering a general fading channel model for the SN-UAV links, we jointly optimize the SNs' wake-up schedule and UAV's trajectory to minimize the maximum energy consumption of all SNs, while ensuring that the required amount of data is collected reliably from each SN. We formulate our design as a mixed-integer non-convex optimization problem. By applying the successive convex optimization technique, an efficient iterative algorithm is proposed to find a sub-optimal solution. Numerical results show that the proposed scheme achieves significant network energy saving as compared to benchmark schemes.
This paper revisits the optimal power allocation for outage minimization in the classic point-to-point fading channels with the channel distribution information (CDI) known at the transmitter. The channel state information (CSI) is assumed to be perfectly known at the receiver, but not available at the transmitter. In particular, we consider a finite horizon of N-block transmissions subject to an average power constraint at the transmitter. Although minimizing the time-averaged per-block outage probability over each N-block transmission is shown to be a non-convex problem for a large class of practical fading channels, we show that the globally optimal power allocation is obtainable by a simple one-dimensional search. It is shown that if the average transmit power is above a certain threshold determined by the distribution of the fading channel and the target transmission rate, the uniform power allocation is optimal; otherwise, an on-off power allocation is optimal. Moreover, a suboptimal low-complexity power allocation scheme is proposed, which is shown to be asymptotically optimal as N goes to infinity. Finally, numerical results are provided to validate our analysis.
The famous Moore's Law states that chip density doubles almost every 18 months. The Semiconductor Industries Association (SIA) roadmap predicts that complementary metal-oxide semiconductor (CMOS) chips will continue to fuel the need for high-performance systems for another 10--15 years. However, new devices and technologies must be introduced to continue the scaling of chips beyond that time-frame. With rapid advancements in electronic materials and devices, many new nanoscale devices have been created, such as resonant tunneling diode (RTD), quantum cellular automata (QCA), single electron transistor (SET), single electron box (SEB), and tunneling phase logic (TPL), which have novel structures and properties. Such devices offer the opportunity to further improve the compactness and speed of very large scale integrated (VLSI) systems. While it is easier to implement Boolean gates using CMOS, it is easier for many nanoscale devices to implement threshold and majority gates. As progress is made in the material and physical understanding of nanoscale devices, research must be done at the logic level to fully harness the potential offered by these devices. The main purpose of the work in this dissertation is to bridge the current wide gap between research on nanoscale devices and research on synthesis methodologies for generating optimized networks utilizing these devices. We present the first comprehensive threshold network synthesis methodology for multi-output multi-level threshold networks starting from Boolean descriptions. It does threshold logic synthesis through binate and unate splitting and by calling a linear program solver. We have implemented the methodology in a tool called TELS, on top of an existing logic synthesis tool, and validated it with a large number of benchmarks. We also present a methodology for efficient majority/minority network synthesis of arbitrary multi-output Boolean functions, which is based on the concept of admissible patterns of majority gates. We have implemented this methodology in a tool called MALS, on top of the same existing logic synthesis tool. We expanded our research from combinational circuits to sequential circuits by addressing the problem of state encoding of finite-state machines targeting threshold/majority logic based implementations. We propose a methodology for state encoding based on an evolutionary algorithm. Besides solving the logic synthesis and state encoding problems for threshold and majority circuits, we also evaluated several factorization methods, targeting threshold/majority logic circuits specifically. Nanotechnology-based circuits are usually more vulnerable to both permanent and transient faults. Therefore, we evaluated threshold/majority totally self-checking (TSC) circuits to impart concurrent error detection capability to such circuits. We establish the efficacy of all the above methods through experimental results on a large number of benchmarks.
Intelligent reflecting surface (IRS) is a new promising technology that is able to manipulate the wireless propagation channel via smart and controllable signal reflection. In this paper, we investigate the capacity region of a multiple access channel (MAC) with two users sending independent messages to an access point (AP), aided by M IRS reflecting elements. We consider two practical IRS deployment strategies that lead to different userAP effective channels, namely, the distributed deployment where the M reflecting elements form two IRSs, each deployed in the vicinity of one user, versus the centralized deployment where all the M reflecting elements are deployed in the vicinity of the AP. For the distributed deployment, we derive the capacity region in closed-form; while for the centralized deployment, we derive a capacity region outer bound and propose an efficient rateprofile based method to characterize an achievable rate region (or capacity region inner bound). Furthermore, we compare the capacity regions of the two cases and draw useful insights into the optimal deployment of IRS in practical systems.
Massive multiple-input multiple-output (MIMO) techniques have been recently advanced to tremendously improve the performance of wireless networks. However, the use of very large antenna arrays brings new issues, such as the significantly increased hardware cost and signal processing cost and complexity. In order to reap the enormous gain of massive MIMO and yet reduce its cost to an affordable level, this paper proposes a novel system design by integrating an electromagnetic (EM) lens with the large antenna array, termed \emph{electromagnetic lens antenna} (ELA). An ELA has the capability of focusing the power of any incident plane wave passing through the EM lens to a small subset of the antenna array, while the location of focal area is dependent on the angle of arrival (AoA) of the wave. As compared to conventional antenna arrays without the EM lens, the proposed system can substantially reduce the number of required radio frequency (RF) chains at the receiver and hence, the implementation costs. In this paper, we investigate the proposed system under a simplified single-user uplink transmission setup, by characterizing the power distribution of the ELA as well as the resulting channel model. Furthermore, by assuming antenna selection used at the receiver, we show the throughput gains of the proposed system over conventional antenna arrays given the same number of selected antennas.
Wireless power transfer (WPT) technology enables a cost-effective and sustainable energy supply in wireless networks, where energy users (EUs) can remotely harvest energy from the wireless signal transmitted by energy transmitters (ETs). However, the broadcast nature of wireless signal makes wireless power a non-excludable public good, which renders the traditional market mechanisms inefficient due to the possibility of the free-riders. In this study, we formulate the transmit power provision problem in a single-channel WPT network as a public good provision problem, aiming to maximize the social welfare of all the ET and EUs considering their private information and selfish behaviors. The considered problem also brings both economic and technical challenges in ensuring voluntary participation and distributed algorithm design. To this end, we propose a two- phase all-or-none procedure involving a low-complexity Power And Taxation (PAT) Nash mechanism, which ensures voluntary participation, incentive compatibility, and budget balance, and yields the socially optimal transmit power at all Nash equilibria. We further propose a distributed D-PAT Algorithm and prove its convergence by exploiting the connection between the structure of Nash equilibria and that of the optimal solutions to a related optimization problem. Finally, our simulation results validate the PAT Mechanism and the practical algorithm. We show that our design can significantly improve the social welfare compared to the benchmark market mechanism, especially when there are many and relatively comparable EUs.
In order to be efficient in service-oriented environments, performance management efforts must be focused on those services that are critical from an end-to-end perspective. This paper overviews a flexible approach to identifying these services and quantifying their criticality, via a statistical extension to the classic critical path algorithm for DAGs. The solution is preliminarily illustrated in a service-oriented Grid.
Moisture has a great influence on power transformers, because high moisture will seriously degrade the insulation performance, especially when the moisture is suspended in the oil in an emulsified state. In this paper, a multi-physics simulation model coupled the flow field, electric field and phase field was established to investigate the effect of the dynamic behavior of water droplets on the insulation properties of liquid dielectrics. The results showed that the suspended micro-droplets under electric field exhibited tensile deformation along the direction of the electric field. The degree of deformation increased with the increase of electric field strength and the droplet radius. The deformation ratio can be expressed as a function only related to the dimensionless electric capillary number Boe. When the electric field intensity exceeded a certain value, the micro-droplets would be unstable and the head would become pointed, forming the Taylor cone. When the electric field was further enhanced, the tip of the water droplet emitted micro-jets. According to the existing theory, the critical electric field strength required for the instability of water droplets of different radius was calculated. The influence of the dynamic behavior of water droplets on the breakdown characteristics of the liquid was discussed. When the water droplet was unstable, the electric field near its head was severely distorted, which would easily lead to partial discharge and vaporization of the water drop. The micro-jets emitted from the tips and the arrangement of the micro droplets along the electric field direction would easily form a water bridge, which could induce breakdown of liquid dielectric.
Intelligent reflecting surface (IRS) that enables the control of wireless propagation environment has recently emerged as a promising cost-effective technology for boosting the spectrum and energy efficiency in future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming the full signal reflection by each of the elements regardless of its phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper the practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection coefficient. Based on the proposed model and considering an IRS-aided multiuser system with an IRS deployed to assist in the downlink communications from a multi-antenna access point (AP) to multiple single-antenna users, we formulate an optimization problem to minimize the total transmit power at the AP by jointly designing the AP transmit beamforming and the IRS reflect beamforming, subject to the users' individual signal-to-interference-plus-noise ratio (SINR) constraints. Iterative algorithms are proposed to find suboptimal solutions to this problem efficiently by utilizing the alternating optimization (AO) or penalty-based optimization technique. Moreover, we analyze the asymptotic performance loss of the IRS-aided system that employs practical phase shifters but assumes the ideal phase shift model for beamforming optimization, as the number of IRS elements goes to infinity. Simulation results unveil substantial performance gains achieved by the proposed beamforming optimization based on the practical phase shift model as compared to the conventional ideal model.
No abstract is provided for this article.
In this paper, we propose a new multiple-input multiple-output (MIMO) communication system with movable antennas (MAs) to exploit the antenna position optimization for enhancing the capacity. Different from conventional MIMO systems with fixed-position antennas (FPAs), the proposed system can flexibly change the positions of transmit/receive MAs, such that the MIMO channel between them is reconfigured to achieve higher capacity. We aim to characterize the capacity of MA-enabled point-to-point MIMO communication systems, by jointly optimizing the positions of transmit and receive MAs as well as the covariance of transmit signals. First, we develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix and the position of each transmit/receive MA with the other variables being fixed. Next, we propose alternative algorithms of lower complexity for capacity maximization in the low-SNR regime and for the multiple-input single-output (MISO) and single-input multiple-output (SIMO) cases. Numerical results show that our proposed MA systems significantly improve the MIMO channel capacity compared to traditional FPA systems as well as various benchmark schemes, and useful insights are drawn into the capacity gains of MA systems.
Simultaneous wireless information and power transfer (SWIPT) has recently drawn significant interests for its dual use of radio signals to provide wireless data and energy access at the same time. However, a challenging secrecy communication problem arises in the SWIPT system since the messages sent to information receivers (IRs) may be eavesdropped by energy receivers (ERs), which are presumed to receive energy from the same signals broadcast by the transmitter. To tackle this problem, we propose in this paper an artificial noise (AN) aided transmission scheme to facilitate the secrecy information transmission to IRs and yet meet the energy harvesting requirement for ERs, under the assumption that the AN can be cancelled at IRs but not at ERs. Specifically, the proposed scheme splits the power at the transmitter into two parts, to send the confidential message to the IR and an AN to interfere with the ER against eavesdropping, respectively. Under a simplified three-node wiretap channel setup, the transmit power allocations and power splitting ratios over fading channels are jointly optimized to maximize the average secrecy information rate for the IR subject to a combination of average and peak power constraints at the transmitter as well as an average energy harvesting constraint at the ER. The formulated problem is shown to be non-convex, for which we propose an efficient algorithm by iteratively optimizing the transmit power allocations and power splitting ratios over fading channels. Finally, the performance of the proposed scheme is evaluated by simulations and compared against other heuristic schemes in terms of achievable (secrecy) rate-energy trade-off.
