Universal service is an important method, which can eliminate the digital gap of telecommunication industry. It also is the bridge of information superhighway in the information network convergence era. The paper analyzes the auction policy for universal service. It finds that the auction can solve the asymmetry information obstacle between regulator and enterprise. It can promote the development of carrier market for network convergence. It designs the auction mechanism for universal service, and hopes it provide the theory guidance for the future development.
Apple Inc. is one of the world's leading technology companies. Its success depends not only on its innovative products and excellent design, but also on its deep competitive advantages. Competitive advantage refers to the competitive advantages that a company has established in the market, making it difficult for other companies to enter or compete. This paper will explore Apple's competitive advantages, including its brand loyalty, ecosystem, innovation capabilities, and supply chain management.
Today, customers expect to run triple-play services through BGP/MPLS IP-VPNs. Some service providers will deploy services that request Quality of Service (QoS) guarantees from a local Customer Edge (CE) to a remote CE across the network. As a result, the application (e.g., voice, video, bandwidth-guaranteed data pipe, etc.) requirements for an end-to-end QoS and reserving an adequate bandwidth continue to increase. Service providers can use both an MPLS and an MPLS Traffic Engineering (MPLS-TE) Label Switched Path (LSP) to meet their service objectives. This document describes service-provider requirements for supporting a customer Resource ReSerVation Protocol (RSVP) and RSVP-TE over a BGP/MPLS IP-VPN. This document is not an Internet Standards Track specification; it is published for informational purposes.
This paper studies the energy management problem for two self-interested microgrids with integrated renewable energy and energy storage systems, which can exchange energy with each other through the transmission line connecting them. Microgrids are willing to cooperate if and only if both can benefit from the energy cooperation, e.g. achieve lower energy costs as compared to the without energy cooperation case, while sharing limited information due to privacy considerations. We thus propose an iterative algorithm for the partially cooperative energy management problem, which aims to reduce energy costs of both microgrids simultaneously, while sharing limited information. To provide performance benchmark, we also consider the fully cooperative energy management problem, for which we ideally assume that microgrids share all their information to minimize their total energy cost. Last, we evaluate our proposed algorithms for the partially and fully cooperative energy management via simulations based on the Tucson power system data.
This paper studies the achievable rate region for an amplify-and-forward (AF)-based two-way relay network with collaborative beamforming. With different assumptions of channel reciprocity between the source-relay and relay-source channels, the achievable rate region is characterized under two setups. First, with reciprocal channels, we investigate the achievable rate regions when the relay cluster is subject to a sum-power constraint or individual-power constraints. We show that the optimal beamforming vectors obtained from solving the weighted sum inverse-SNR minimization (WSISMin) problems are sufficient to characterize the corresponding achievable rate region. Furthermore, we derive the closed-form solutions for those optimal beamforming vectors and consequently propose the partially distributed algorithms to implement the optimal beamforming, where each relay node only needs the local channel information and one global parameter. Second, with the nonreciprocal channels, the achievable rate regions are also characterized for both the sum-power constraint case and the individual-power constraint case. Although no closed-form solutions are available under this setup, we present efficient numerical algorithms by solving a sequence of semi-definite programming (SDP) problems after semi-definite relaxation(SDR), where the optimal beamformer can be obtained under the sum-power constraint and approximate optimal solutions can be obtained under the individual power constraints.
Frequency control is essential to maintain the stability and reliability of power grids. For decades, generation side controllers, e.g., governors and automatic generation controllers, have been used to stabilize the frequency of power systems, which incur high operational costs. In smart grids, utilizing demand response is an appealing alternative to control the system frequency at the demand side, which can reduce the dependency of grids on expensive generation side controllers. Despite of economic advantages, the frequency oscillation problem, which occurs when smart appliances simultaneously respond to the system frequency by varying their power consumptions, is the main barrier to realize demand response enabled frequency control in practice. In this paper, we investigate a new distributed control algorithm by randomizing smart appliances' responses to solve this problem. We provide a comprehensive analysis to characterize various impacts of the randomized demand response on the system frequency in terms of its mean and variance over time. Furthermore, based on the frequency dynamics analysis, we determine the average frequency recovery time, the average number of responded smart appliances, and the probability of frequency overshoot, which provide important guidelines for designing our control algorithm. Finally, we validate our analysis via simulations under practical setups.
In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in orthogonal frequency division multiple access (OFDMA) systems with the coexistence of information receivers (IRs) and energy receivers (ERs). The IRs are served with best- effort secrecy data and the ERs harvest energy with minimum required harvested power. To enhance physical- layer security and yet satisfy energy harvesting requirements, we introduce a new frequency-domain artificial noise based approach. We study the optimal resource allocation for the weighted sum secrecy rate maximization via transmit power and subcarrier allocation. The considered problem is nonconvex, while we propose an efficient algorithm for solving it based on Lagrange duality method. Simulation results illustrate the effectiveness of the proposed algorithm as compared against other heuristic schemes.
This letter proposes a novel UAV-enabled mobile jamming scheme to improve the secrecy rate of ground wiretap channel. Specifically, a UAV is employed to transmit jamming signals to combat against eavesdropping. Such a mobile jamming scheme is particularly appealing since the UAV-enabled jammer can fly close to the eavesdropper and opportunistically jam it by leveraging the UAV's mobility. We aim to maximize the average secrecy rate by jointly optimizing the UAV's trajectory and jamming power over a given flight period. To make the problem more tractable, we drive a closed-form lower bound for the achievable secrecy rate, based on which the UAV's trajectory and transmit power are optimized alternately by an efficient iterative algorithm applying the block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that the proposed joint design can significantly enhance the secrecy rate of the considered wiretap system as compared to benchmark schemes.
Constant envelope (CE) precoding is an appealing transmission technique, which enables highly efficient power amplification, and is realizable with a single radio frequency (RF) chain at the multiantenna transmitter.In this paper, we study the transceiver design for a point-to-point multiple-input multiple-output (MIMO) system with CE precoding.Both single-stream transmission (i.e., beamforming) and multi-stream transmission (i.e., spatial multiplexing) are considered.For single-stream transmission, we optimize the receive beamforming vector to minimize the symbol error rate (SER) for any given channel realization and desired constellation at the combiner output.By reformulating the problem as an equivalent quadratically constrained quadratic program (QCQP), we propose an efficient semi-definite relaxation (SDR) based algorithm to find an approximate solution.Next, for multi-stream transmission, we propose a new scheme based on antenna grouping at the transmitter and minimum mean squared error (MMSE) or zero-forcing (ZF) based beamforming at the receiver.The transmit antenna grouping and receive beamforming vectors are then jointly designed to minimize the maximum SER over all data streams.Finally, the error-rate performance of single-versus multi-stream transmission is compared via simulations under different setups.
This paper considers the use of energy harvesters in delay-constrained point-to-point wireless communications, where the source transmits with power drawn periodically from a device that harvests energy from the environment. In particular, the source is assumed to transmit over a block-fading channel with a constant transmission rate. It is also assumed that the channel state information (CSI) is unknown at the source but perfectly known at the destination, and the energy harvesting process is deterministic and known a priori at the source. The optimal power allocation is studied to minimize the receiver outage probability over a finite horizon of N energy-harvesting periods, each of which contains M communication blocks with independent channel fading coefficients. Although the outage minimization problem is shown to be non-convex, the optimal power allocation solution is obtained by the proposed forward search algorithm, which corresponds to an on-off transmission scheme. Moreover, a threshold-based sub-optimal low-complexity power allocation algorithm is proposed, which is shown to be asymptotically optimal as M goes to infinity. Finally, numerical results are provided to validate our analysis.
Energy harvesting is a promising solution to prolong the operation of energy-constrained wireless networks. In particular, scavenging energy from ambient radio signals, namely wireless energy harvesting (WEH), has recently drawn significant attention. In this paper, we consider a point-to-point wireless link over the flat-fading channel subject to the time-varying co-channel interference. It is assumed that the receiver has no fixed power supplies and thus needs to replenish energy via WEH from the unintended interference and/or the intended signal sent by the transmitter. We further assume a single-antenna receiver that can only decode information or harvest energy at any given time due to the practical circuit limitation. As a result, it is important to investigate when the receiver should switch between the two modes of information decoding (ID) and energy harvesting (EH), based on the instantaneous channel and interference conditions. In this paper, we derive the optimal mode switching rule at the receiver to achieve various tradeoffs between the minimum transmission outage probability for ID and the maximum average harvested energy for EH, which are characterized by the boundary of a so-called "outage-energy" region. Moreover, for the case when the channel state information (CSI) is known at the transmitter, we investigate the joint optimization of transmit power control and scheduling for information and energy transfer with the receiver's mode switching. Our results provide useful insights to the optimal design of emerging wireless communication systems powered by opportunistic WEH.
To achieve the more promising passive beamforming gains in the double-intelligent reflecting surface (IRS) assisted system over the conventional single-IRS system, channel estimation is practically indispensable but also a more challenging problem to tackle, due to the presence of not only the single- but also double-reflection links that are intricately coupled. In this paper, we propose a new and efficient channel estimation scheme for the double-IRS assisted uplink multiple-input multiple-output (MIMO) communication system to resolve the cascaded channel state information (CSI) of both its single- and double-reflection links. First, for the single-user case, the higher-dimensional double-reflection channel is efficiently estimated at the multi-antenna base station (BS) with low training overhead by exploiting the fact that its cascaded channel coefficients are scaled versions of those of a lower-dimensional single-reflection channel. Then, the proposed channel estimation scheme is extended to the multi-user case, where given an arbitrary user's cascaded channel estimated as in the single-user case, the other users' cascaded channels are scaled versions of it and thus can be estimated with reduced training overhead. Simulation results verify the effectiveness of the proposed channel estimation scheme as compared to the benchmark scheme.
The bit error rate (BER) performance of single code and multicode channelization schemes for high rate data transmission in direct-sequence code division multiple access (DS/CDMA) systems is compared. The multipath interference (MPI) effects, which become significant as the data rate increases, are accurately included in the analysis. It is shown that notable performance improvement can be achieved by using multicode scheme in the multipath fading channel.
The use of unmanned aerial vehicles (UAVs) as communication platforms is of great practical significance in future wireless networks, especially for on-demand deployment in temporary events and emergency situations. Although prior works have shown the performance improvement by exploiting the UAV's mobility, they mainly focus on delay-tolerant applications. As delay requirements fundamentally limit the UAV's mobility, it remains unknown whether the UAV is able to provide any performance gain in delay-constrained communication scenarios. Motivated by this, we study in this paper a UAV-enabled orthogonal frequency division multiple access (OFDMA) network where a UAV is dispatched as a mobile base station (BS) to serve a group of users on the ground. We consider a minimum-rate ratio (MRR) for each user, defined as the minimum instantaneous rate required over the average achievable throughput, to flexibly adjust the percentage of its delay-constrained data traffic. Under a given set of constraints on the users' MRRs, we aim to maximize the minimum average throughput of all users by jointly optimizing the UAV trajectory and OFDMA resource allocation. First, we show that the max-min throughput in general decreases as the users' MRR constraints become more stringent, which reveals a fundamental throughput-delay tradeoff in UAV-enabled communications. Next, we propose an iterative parameter-assisted block coordinate descent method to optimize the UAV trajectory and OFDMA resource allocation alternately, by applying the successive convex optimization and the Lagrange duality, respectively. Furthermore, an efficient and systematic UAV trajectory initialization scheme is proposed based on a simple circular trajectory. Finally, simulation results are provided to verify our theoretical findings and demonstrate the effectiveness of our proposed designs.
