This paper studies the achievable rate region for a two-way relay network with collaborative beamforming under the assumption that the source-to-relay and relay-to-source channels are reciprocal. Specifically, we investigate the achievable rate regions for two cases where the relay cluster is subject to a sum-power constraint or to individual-power constraints. We can 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 forms 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.
As a new application form of sustainable energy generation, building-integrated photovoltaics provide great flexibility for energy conservation and emission reduction. However, due to the randomness and volatility from photovoltaic generation, the full exploitation of the advantages of building-integrated photovoltaics has become an important question for the utilities. To address this issue, this paper proposes a novel and comprehensive (combined) water-energy supply system applied to the smart community with building-integrated photovoltaics and two-stage electric-hydraulic dispatching method. It was designed to minimize the electricity consumption cost and power fluctuation while meeting the water demand loads, followed by the iterative algorithm as well as linearized techniques to solve the optimisation model. The Shapley value method was introduced to ensure the fair revenue between the property company and owners. Simulation studies on a building-integrated-photovoltaics community and a 15-node water distribution system indicates that the annual operation cost of the water-electricity combined supply system was reduced by 29.6% while the external grid obtained a better prediction regarding the real-time exchange power. The results confirm the effectiveness of the proposed model on peak shaving and valley filling and fluctuation smoothing.
Unmanned aerial vehicle (UAV) as an aerial base station is a promising technology to rapidly provide wireless connectivity to ground users. Given UAV's agility and mobility, a key question is how to adapt UAV deployment to best cater to the instantaneous wireless traffic in a territory. In this paper, we propose an adaptive deployment scheme for a UAV-aided communication network, where the UAV adapts its displacement direction and distance to serve randomly moving users' instantaneous traffic in the target cell. In our adaptive scheme, the UAV does not need to learn users' exact locations in real time, but chooses its displacement direction based on a simple majority rule by flying to the spatial sector with the greatest number of users in the cell. To balance the service qualities of the users in different sectors, we further optimize the UAV's displacement distance in the chosen sector to maximize the average throughput and the successful transmission probability, respectively. We prove that the optimal displacement distance for average throughput maximization decreases with the user density: the UAV moves to the center of the chosen sector when the user density is small and the UAV displacement becomes mild when the user density is large. In contrast, the optimal displacement distance for success probability maximization does not necessarily decrease with the user density and further depends on the target signal-to-noise ratio (SNR) threshold. Extensive simulations show that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, especially when the user density is not large.
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 user-AP 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 rate-profile 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.
Intelligent reflecting surface (IRS) is a promising new technology for achieving spectrum and energy efficient wireless communication systems in the future. By adaptively varying the incident signals' phases/amplitudes and thereby establishing favorable channel responses through a large number of reconfigurable passive reflecting elements, IRS is able to enhance the communication performance of mobile users in its vicinity cost-effectively. In this paper, we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system in which an IRS is deployed to assist the communication between a nearby user and its associated base station (BS). We aim to maximize the downlink achievable rate for the user by jointly optimizing the transmit power allocation at the BS and the passive array reflection coefficients at the IRS. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the BS's power allocation and the IRS's passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the cases without the IRS or with other heuristic IRS designs.
Inflow control device (ICD) restricts flows of certain sections to achieve a uniform push of water oil contact along a horizontal well. The efficiency of ICD depends on its configuration (location, number of nozzles, and nozzle sizes). Traditionally, optimal ICD configurations have been selected using a trial and error approach simulating different configurations. This method is time-consuming and the outcome can be limited by pre-set candidates, thus may result in non-optimal configurations. In this paper, a method involving several optimization objectives is proposed to obtain the optimal ICD configuration, in order to reach an optimal total production before water breakthrough. A flow coupling model (permeation flow – nozzle flow – inner tubing flow) of horizontal wells completed by ICDs is established to calculate the inflow profile and the pressure distribution along the horizontal wellbore. The production rate and the water breakthrough time are the optimization objectives, and the sand control condition (production pressure drawdown < critical pressure drawdown of sanding onset) is taken as one of the constraints of optimization. Then the NSGA-II algorithm is employed to search for the Pareto frontier based on the model of coupled flow. Selected by the TOPSIS method from the Pareto frontier, the optimal ICD configuration ensuring optimal total production and no sanding is acquired. The original data of well A1 in SL oilfield are used for case study. The optimal configuration is compared with single-objective optimization results and open hole completion in terms of production rate and water breakthrough time. By predicting the total productions of different configurations before water breakthrough, we think the optimization method is capable of determining the optimal configuration. This research can provide some theoretical support for the determination of ICD configuration in horizontal wells.
Multi-antenna precoding effectively mitigates the interference in wireless networks. However, the resultant performance gains can be significantly compromised in practice if the precoder design fails to account for the inaccuracy in the channel state information (CSI) feedback. This paper addresses this issue by considering finite-rate CSI feedback from receivers to their interfering transmitters in the two-user multiple-input-multiple-output (MIMO) interference channel, called cooperative feedback, and proposing a systematic method for designing transceivers comprising linear precoders and equalizers. Specifically, each precoder/equalizer is decomposed into inner and outer components for nulling the cross-link interference and achieving array gain, respectively. The inner precoders/equalizers are further optimized to suppress the residual interference resulting from finite-rate cooperative feedback. Further- more, the residual interference is regulated by additional scalar cooperative feedback signals that are designed to control transmission power using different criteria including fixed interference margin and maximum sum throughput. Finally, the required number of cooperative precoder feedback bits is derived for limiting the throughput loss due to precoder quantization.
Multiuser multiple input multiple output (MU‐MIMO) wireless communication system provides substantial downlink throughput in millimeter wave (mmWave) communication by allowing multiple users to communicate at the same frequency and time slots. However, the design of the optimum beam‐vector for each user to minimise interference from other users is challenging. In this paper, based on the concept of signal‐to‐leakage plus noise ratio (SLNR), we analyze the ergodic sum‐rate capacity using statistical Eigen‐mode (SE) and zero‐forcing (ZF) models with Ricean fading channel. In the analysis, the orthogonality of channel vectors between users is assumed to guarantee interference cancelation from other cochannel users. The impact of the number of antenna elements on the achievable sum‐rate capacity obtained by dirty paper coding (DPC) method considered as a nonlinear scheme for approximating average system capacity is studied. A power iterative precoding scheme that iteratively finds the most dominant eigenvector (optimum weight vector) for minimising cochannel interference (CCI), that is, maximising the SLNR for all users simultaneously, is designed resulting in enhancement of average system capacity. The average system capacities achieved by the proposed power iterative technique in this study compared with the singular value decomposition (SVD) method are in the ranges of 5–11 bps/Hz and 1–6 bps/Hz, respectively. Therefore, the proposed power iterative method achieves higher performance than the SVD regarding achievable sum‐rate capacity.
Characterizing the global maximum of weighted sum-rate (WSR) for the if-user Gaussian interference channel (GIC), with the interference treated as Gaussian noise, is a key problem in wireless communication. However, due to users' mutual interference, this problem is in general non-convex and thus cannot be solved directly by conventional convex optimization techniques. In this paper, by jointly utilizing the monotonic optimization and rate profile techniques, we develop a new approach to obtain the globally optimal power control solution to the WSR maximization problem for the GIC with single-antenna transmitters and receivers. Different from prior work, this paper proposes to maximize the WSR in the achievable rate region of the GIC directly by exploiting the facts that the achievable rate region is a "normal" set and the users' WSR is a "strictly increasing" function over the rate region. Consequently, the WSR maximization problem belongs to the class of monotonic optimization over a normal set and thus can be solved globally optimally by the outer polyblock approximation algorithm. However, an essential step in this algorithm hinges on how to efficiently characterize the intersection point on the Pareto boundary of the achievable rate region with any prescribed "rate profile" vector. This paper shows that such a problem can be transformed into a sequence of signal-to-interference-plus-noise ratio (SINR) feasibility problems, which can be solved efficiently by existing techniques. Furthermore, this paper sheds some lights on how the proposed algorithm can be applied to solve WSR maximization problems for the multi-antenna GICs.
For opportunistic spectrum sharing in cognitive radio (CR) networks, maximizing own transmit rate and minimizing interference powers at primary receivers are in general two conflicting goals to be accomplished by a secondary user. This paper studies this fundamental tradeoff from an information-theoretic perspective by characterizing the secondary user's channel capacity under both its own transmit-power constraint and an interference-power constraint at each primary receiver. In particular, this paper exploits multi-antennas at the secondary transmitter to balance effectively between spatial multiplexing and interference avoidance. Both optimal and suboptimal algorithms for design of the secondary user's transmit spatial spectrum are presented and their performances are compared.
As service-oriented environments grow in size and complexity, managing their performance becomes increasingly difficult. To assist administrators, autonomic techniques have been adopted to permit these environments to be self-managing (problem localization, workload management, etc.). These techniques need a sense of system state and the ability to project a new state given some change within the environment. Recent work addressing this issue frequently used statistically learned models which were derived entirely from data. However, many environments already have management facilities in place that could provide precise and useful insights (e.g. workflows) into the system. This paper introduces a method of modeling service-oriented system performance using Bayesian networks and specifically addresses the benefits obtained by incorporating these insights into the model learning process. To further minimize model building costs, we devise a decentralized method to concurrently learn parts of the model where knowledge inclusion is impossible. Simulations and applications in actual environments show significant reductions in learning time, better accuracy and stronger tolerance to small learning data sets.
This article introduces the computer supervisory control system which can monitor all instantaneous states of wind pressure demanded in production under well,water levels of the whole mine's suppying－water pools and load of every feeder wire circuit by using computers. It is greatly convenient that mangers adjust production in time according to the information above mentioned. This effectively reduces consuming of electricity energy and greatly improves economic benefits.
With the rapid development of network technology and computer technology, network applications can be seen everywhere. From life, finance to politics, the development of all walks of life has been inseparable from computer communication networks. Once there is a problem with the computer communication network, it will cause serious economic losses and may even cause national turmoil. Therefore, the reliability requirements of computer communication networks are getting higher and higher, and the reliability analysis and application of computer communication networks have become the focus of research. Based on genetic algorithm, this paper optimizes the reliability of computer communication networks. Firstly, the theory of reliability of computer communication network is briefly introduced, and the factors affecting reliability are simply analysed. Secondly, based on the influencing factors, the reliability optimization analysis model of computer communication network is established. Finally, the genetic algorithm is used to solve the established reliability optimization analysis model. The experimental results show that with the increase in the number of iterations of the genetic algorithm for data regression calculation, the algorithm reaches more than 0.99, and the optimal solution is obtained, which improves the reliability of the network, and the adaptability of the network link also increases. The genetic algorithm is applied to the reliability analysis of computer communication network, and the corresponding reliability optimization method is designed, which can improve the reliability of computer network while reducing the cost.
In this paper, we study a single-user multiple-input single-output (MISO) system with constant envelope (CE) transmission. To enable the nonlinear mapping from a fixed receiver signal constellation to the transmitter CE signal vectors, the availability of perfect channel state information at the transmitter (CSIT) is assumed in existing literature. However, traditionally, CSIT needs to be acquired at the cost of additional channel training and feedback overhead, which increases with the number of transmit antennas. In this paper, we propose a novel adaptive online signal constellation design for MISO CE transmission with significantly reduced training time and feedback complexity compared to the traditional training with fixed constellation set. Numerical results show that our proposed scheme outperforms the traditional scheme in terms of average throughput and yet with less training time required.
This paper studies a new unmanned aerial vehicle (UAV)-enabled wireless power transfer system, where a UAV-mounted mobile energy transmitter is dispatched to deliver wireless energy to a set of energy receivers (ERs) at known locations on the ground. We investigate how the UAV should optimally exploit its mobility via trajectory design to maximize the amount of energy transferred to all ERs during a finite charging period. First, we consider the maximization of the sum energy received by all ERs by optimizing the UAV's trajectory subject to its maximum speed constraint. Although this problem is non-convex, we obtain its optimal solution, which shows that the UAV should hover at one single fixed location during the whole charging period. However, the sum-energy maximization incurs a "near-far" fairness issue, where the received energy by the ERs varies significantly with their distances to the UAV's optimal hovering location. To overcome this issue, we consider a different problem to maximize the minimum received energy among all ERs, which, however, is more challenging to solve than the sum-energy maximization. To tackle this problem, we first consider an ideal case by ignoring the UAV's maximum speed constraint, and show that the relaxed problem can be optimally solved via the Lagrange dual method. The obtained trajectory solution implies that the UAV should hover over a set of fixed locations with optimal hovering time allocations among them. Then, for the general case with the UAV's maximum speed constraint considered, we propose a new successive hover-and-fly trajectory motivated by the optimal trajectory in the ideal case and obtain efficient trajectory designs by applying the successive convex programing optimization technique. Finally, numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmark schemes.
