50 results about "Utility maximization" patented technology

The invention relates to a coverage and capacity self-optimization device of a self-organization network in LTE/LTE-A and a method thereof. A fault detection unit monitors faults related to the coverage and the capacity in real time in a mobile communication network, a learning and matching unit learns and matches with history event knowledge, and wireless parameters are initially configured according to the most similar event. Then a parameter decision unit adjusts the initially configured wireless parameters on the premise of taking account of the mutual influence of different wireless configuration parameters, and a utility function is introduced to evaluate the efficiency of the self-optimization process to finally finish the utility maximization on the premise of comprehensively taking account of network/business and the requirements of operators, and realize the coverage and capacity automatic optimization of the self-organization network in the LTE/LTE-A. The invention has the advantages that the invention realizes the accurate position of fault sources and the correct decision by using the existing coverage and capacity self-optimization model, thereby achieving the target of coverage and capacity self-optimization.

The invention discloses a frequency division multiplexing multi-user MIMO energy efficiency optimization method which can be applied to a multi-user MIMO system. The optimization goal is to design a precoding scheme for each user to maximize a total energy efficiency utility function. The frequency division multiplexing multi-user MIMO energy efficiency optimization method is implemented through the following steps that firstly, optimization preprocessing is conducted on energy efficiency functions of all the users, wherein the optimization preprocessing process is specifically divided into three steps of noise whitening and channel diagonalization, determination of a water-filling solution of a link power allocation scheme and acquisition of the optimized energy efficiency functions; secondly, the energy efficiency functions optimized in advance are used for determining the sending power of each user to maximize the total energy efficiency utility function of the multiple users; finally, a user group energy efficiency utility maximization algorithm is used for solving the problem. According to the frequency division multiplexing multi-user MIMO energy efficiency optimization method, the definition of the multi-user energy efficiency utility function has universality and can cover various indexes including the weighted average, the exponentiation product, the weighed harmonic average, the weighed minimum value and the like for maximizing the energy efficiency of all the users, and the efficient algorithm put forward later is applicable to all the indexes.

The invention discloses a cognitive radio network master user spectrum allocation method based on a game theory, belongs to the technical field of wireless communications, and particularly relates to the field of cognitive radio networks. According to the cognitive radio network spectrum allocation method based on the game theory, the master user combined utility maximization is achieved by optimizing and pricing a sharing frequency spectrum in a cognitive radio network. Various component channel cognition user demand functions Dkl of master users and master user combined utility functions Utotal are established, a plurality of master users of the cognitive radio network are modeled to be a cooperative game model aiming at the optimizing and pricing problem of the cognition user sharing frequency spectrum, the master user combined benefits are maximized, a master user optimal frequency spectrum pricing strategy is obtained, the combined utility of the master users in the cognitive network and recognition user access performance are comprehensively considered, optimizing and sharing of spectrum resources can be effectively achieved, the utilization rate of system spectrum resources is improved, and the problem of lacking of the spectrum resources is solved.

The invention discloses a cognitive cooperative network joint resource allocation method based on utility optimization. According to the method, master and slave users of a cognitive radio network share spectrum resources and perform information transmission, the master users occupy corresponding authorized frequency bands to communicate, and the slave users communicate through a direct transmission or cooperative transmission mode under the condition of without interfering with communication of the master users. A network resource scheduler receives service requests of the slave users, models a slave user joint utility function, and realizes optimized allocation of sub channels and transmission power of the slave users and optimized selection of relay modes in cooperation based on the rule of utility maximization. According to the invention, cognitive cooperative network spectrum resource sharing can be effectively realized, and the overall network performance can be improved.

The invention relates to the technical field of communication, in particular to a task unloading method of a heterogeneous cellular network. The method comprises the steps as follows: a user unloadingtask request is established and parameters are preset; a lyapunov function and a target function are constructed; the queue backlog is minimized while the user unloading utility is maximized, a method for solving the optimal unloading amount is selected according to the effect of network heterogenization on user unloading tasks and whether the WIFI connection lime is determined, that is, if the set WIFI connection time is determined, a lagrangian optimization method is adopted to solve the optimal unloading amount; otherwise, stochastic programming is adopted to solve the optimal unloading amount. The user system unloading utility maximization is considered, the unloading queue backlog is reduced, and the user mobility and the effect of network heterogenization on the user unloading tasksare considered, so that the backlog of user unloading tasks is substantially reduced and system jam is reduced while the user unloading utility is maximized.

The invention discloses a perceived utility maximization-based crowd perception motivation method. The method comprises the following steps of: S1, sending a perception task set to a perception service platform by a task publisher; S2, publishing a message to each user in a target user side set in a perception area by the perception service platform after receiving the task set; S3, judging whether the tasks can be completed or not by the users, and if the judging result is positive, returning bidding prices to the perception service platform; S4, selecting the users that win the bidding by the perception service platform on the basis of a motivation model, and distributing the perception task set to the corresponding users that win the bidding; S5, executing perception tasks and sending perception results to the perception service platforms by the users that win the bidding; and S6, integrating the received perception results by the perception service platform, returning the perception results to the task publisher and paying rewards to the users that win the bidding. The method is capable of recruiting more users in a wider range under the condition that the budget constraints and user costs are non-uniformly distributed, so that the overall quality of perception services is improved.

The invention relates to a user access load balancing method based on self-backhaul perception, and belongs to the technical field of wireless communication networks. According to the method disclosed by the invention, a user access load balancing strategy is provided according to various small base station access and backhaul resource load states under a dense heterogeneous network; various small base station in-band wireless access and backhaul bandwidth distribution is learned by utilization of a Q-Learning algorithm; users are accessed again under different bandwidth distribution factors according to the user access load balancing strategy; the system utility under different access conditions is obtained; therefore, the optimal bandwidth distribution strategy is obtained; and the system utility maximization is realized while the load balancing is ensured. By means of the user access load balancing method based on self-backhaul perception disclosed by the invention, a solution is provided for the load balancing work mode in the self-backhaul scene of the dense heterogeneous network; and thus, the user rate experience is improved while the network load balancing is improved.

Methods and systems for allocating resources in a cellular uplink include iteratively allocating uplink resource blocks (RBs) to a plurality of user devices in a multiple-input multiple-output network. Allocation is performed subject to constraints and incremental utility maximization according to an approximation that has a worst-case guarantee.

A system and method which integrates a greedy allocation for consumer admission control with a Lagrangian allocation to compute flow rates and which links the results of the two approaches to allow a tradeoff between consumer admission control and flow rates. The Lagrangian Rates, Greedy Populations (hereinafter, “LRGP”) solution is a scalable and efficient distributed solution for maximizing the total utility in an event-driven distributed infrastructure. The greedy population, consumer portion generates prices used in the LaGrangian rate flow approach. The method is iterative including a regular exchange of information for ongoing optimization, dynamically adjusting producer rates in response to changes to consumer service and dynamically adjusting the service to consumer populations in response to changes in the producer rates.

The invention provides a redundant data utility maximization method. In the step that a platform selects a wining biding document from a biding document set and determines remuneration of a mobile phone user corresponding to each wining biding document, a credible combinatorial auction method in game theory is adopted, a task allocation method which has credibility, individual rationality, computational effectiveness and budget rationality is provided, redundant data utility is maximized, further task allocation of the platform has credibility, individual rationality, computational effectiveness and budget rationality.

Methods and a system are provided that, in turn, are for providing security between a user device and a computer related device. A method includes providing a distributed registry service that specifies a plurality of services available to support communications between the user device and the computer related device. The method further includes at least one of dynamically constructing and altering one or more multi-node transient processing pathways between the user device and the computer related device based on respective selected ones of the plurality of services. For at least one node in each of the one or more transient processing pathways, an address thereof and a time period the at least one node is active and capable of being used is set or changed, based on at least one of an application programming interface type and a data request type implicated by a received packet.

The invention discloses a workload matching and utility optimization-based intelligent work order dispatching method, and relates to a dispatching method. The operation and maintenance of electricity acquisition systems are insufficient in operation and maintenance ability on one hand and extensive in dispatching on the other hand, so that the problems of long-term operation and maintenance resource shortage and the like are caused. The method comprises the following steps of: obtaining all the abnormal work orders in a dispatching area of a selected dispatching unit according to the selected dispatching unit, and calculating distances from a station to all the abnormal work order points and each abnormal work order point by using a shortest path algorithm; generating a repeated path matrix through a repeated path matrix algorithm; generating a preliminary path chain list through scanning the repeated path matrix, and carrying out descending-order sorting according to total emergency degrees of the paths, wherein the paths with high total emergency degrees are dispatched preferentially; and finally carrying out refined dispatching on the basis of preliminary paths. According to the method, the workload of each dispatching path is saturated and does not exceed a specified workload, the space positions of abnormal points on the dispatched paths are ensured to be compact, the total path length is as short as possible, and the total emergency degrees of the paths are as high as possible.

The embodiment of the invention discloses an evaluation method and an evaluation device for evaluating the investment benefit of a power distribution network. The method comprises the following stepsof establishing a power distribution network investment benefit overall evaluation index system, and deriving a monomer evaluation index system according to the overall evaluation index system; according to the data of each monomer evaluation index in each power distribution network monomer item in a target area, determining the score of the power distribution network monomer item; according to the data of an overall evaluation index in the target area, determining the score of each overall evaluation index; and ranking all power distribution network monomer items according to the scores of all power distribution network monomer items. According to the invention, the method and the device are designed for the insufficient fund condition during the construction of the power distribution network. According to the score of each overall evaluation index, the score of each power distribution network monomer item or the ranking result of power distribution network monomer items in the targetarea, an optimal combination of power distribution network monomer items is determined. Therefore, the monomer item construction of the power distribution network is further optimized on the basis ofthe time sequence. The contradiction between the power development demand and the limited investment fund can be relieved, and the utility maximization of investment benefits is realized.

The invention provides a wireless charging sensor network total utility maximization method. The method comprises following steps of determining an optimal data rate of nodes; finding out a node fusion outward link rate according to the optimal data rate of the nodes; determining the average energy consumption of the nodes in data transmission and receiving; determining a link allocation flow rate; determining the electric quantity supplemented for the nodes; and determining the delay time of data collection. The method has following several advantages that a mobile collector directly performs energy transmission through wireless energy transmission, so that energy transmission is not affected by environment change any more, such as weather change and seasonal change; on approaching the nodes, the mobile collector can have stable high charging rate, thereby guaranteeing high efficient data service; and due to the mobility of the mobile collector, the router burden of the sensor can be relieved, so that the energy can be saved and the network life cycle can be prolonged.

The invention discloses a utility maximization resource allocation method of an electric power telecommunication network driven by QoS. The method comprises a step of constructing a two-stage resource allocation model based on trilateral game for a resource allocation problem of the electric power telecommunication network, wherein an Infranet provider (InP) reports resource supply information for a resource allocation center; a service provider (SP) proposes resource demand information to the resource allocation center; the resource allocation center allocates the resource for the SP; the resource allocation center detects the quality of service of the InP, and performs punishment on the InP when the resource allocation center detects the fact that the InP cannot ensure the SP business thereon to run as the QoS requirement; the resource allocation center computes an utility value of the InP to finish this transaction; each participant waits for the next turn of resource competition. By using the resource allocation method disclosed by the invention, the dominant strategy excitation compatibility character is satisfied, and a goal of for maximizing system profit can be realized.

The invention belongs to the technical field of biochemical environment protection, and concretely provides a resource comprehensive utilization method of refining alkaline slag. The method mainly ischaracterized in that the refining alkaline slag is subjected to pretreatment, biochemical treatment, post treatment, evaporative crystallization and tail gas treatment to finally realize the organicphase reprocessing utilization; the tail gas is exhausted in a standard reaching way; meanwhile, sodium sulfate products are recovered at the same time; the utilization maximization of resources is realized. The method provided by the invention has the advantages that the process is reasonable; the cost is low; the implementation is easy; good economic benefits, environment benefits and social benefits are realized.

An approach to large-scale portfolio optimization for asset returns represented by factor models is disclosed. Factor models can be used within general portfolio optimization problems, such as mean-variance optimization, expected utility maximization, and mean-risk optimization, with various measures of risk, including conditional Value-at-Risk, as well as the representation of risk constraints and constraints on higher moments of the asset return distribution. Both expected utility maximization and mean-risk optimization are more general than mean-variance optimization and can consider fat tails in the asset return distribution and, thus, allow for better control of downside risk. Explicit risk constraints especially constraints on conditional Value-at-Risk, limit downside risk in either mean-variance optimization, expected utility maximization, or mean-risk optimization. Constraints on higher moments limit fat tails of the asset return distribution. Equilibrium returns in expected utility maximization and mean-variance optimization based on factor models of asset returns are obtained. Active management of portfolios of financial assets based on factor exposures is provided.

The invention provides a task optimum allocation method in a participatory sensing system. On the one hand, a load balancing objective and a utility maximization objective are combined through Nash negotiation so as to acquire a Nash objective, so that the participatory sensing system can fairly optimize load balancing and utility maximization to reach a Pareto optimal point and realize task optimum allocation when taking load balancing and utility maximization as a fair balancing objective for task allocation; on the other hand, as pairing Nashi objective is broken down into a pairing load balancing objective and a pairing utility maximization objective so as to perform distributed computing in the process of acquiring a task optimum allocation strategy according to the Nash objective, the computing burden of a platform is relieved, and the execution speed of task allocation is improved.

A system and method efficiently solve the expected utility maximization problem in large-scale financial asset portfolio optimization. The system and method solve the expected utility maximization problem employing a factor representation of asset returns. Additionally, the system and method calibrate the optimization model to a benchmark to obtain unconditional mean returns and enable active management based on conditional expected return predictions. The system and method also enable options to be considered as part of the portfolio.