44 results about "Subgradient method" patented technology

The invention discloses a D2D user resource distribution method based on multicarrier communication, comprising steps of calculating signal to noise ratio on different subcarriers of each D2D user, distributing a subcarrier to each user according to the calculated signal to noise ratio value to enable the signal to noise ratio of each user to be greatest on the distributed subcarrier, performing distribution on the rest of subcarriers according to the signal to noise ratio of a weighing cellular user and the signal to noise ratio of the D2D user until all the subcarriers are distributed to the users, utilizing a subgradient method to perform iteration on lagrange factors and distributing emission power of the D2D user on each subcarrier in order to ensure the cellular user to meet the requirement of the lowest signal to noise ratio and maximization of D2D user system capacity. The D2D user resource distribution method performs subcarrier distribution on the D2D user on the premise that the cellular user lowest signal to noise ratio is guaranteed and establishes an optimized function which targets maximization of system capacity of the D2D user system and uses the cellular user signal to noise ratio and the D2D user emission power as the constraint condition so as to realize the subcarrier distribution and power distribution of the D2D user.

The invention discloses a sharing spectrum access-based carrier and power combined allocation method. By the method, the problem of spectrum allocation in cognitive radio is solved by combining a game correlation theory, a duality principle, a subgradient method and the like; when an overlay spectrum accesses, an iterative algorithm for power allocation is provided, the convergence and the availability of the iterative algorithm are verified, and the problem of carrier and power allocation is solved when an underlay spectrum accesses; and when the underlay spectrum accesses, due to the constraint of the interference tolerance, strategy spaces among users are mutually coupled, and a coupling strategy game is decomposed into an independent strategy non-cooperative game and an optimization problem by introducing the duality principle and the subgradient method, so that the problem of carrier and power allocation is solved when the underlay spectrum accesses.

The invention discloses a joint path selection and power distribution method for energy collection nodes in a wireless sensor network, and belongs to the field of cooperative communication technologies. The method comprises the steps of analyzing a system scene, describing problems; establishing a system mathematic model; and then finding the optimal solution by using an optimization method. The method aims at the special application scene and is derived from the actual application, and being different from the traditional independent sensor node or gateway resource distribution, the method comprehensively considers the joint power distribution and path selection of sensor nodes and gateways, and maximizes the handling capacity performance of the communication nodes by using the gateways as relay stations. According to the method of the invention, the solution of an optimization problem is processed by using convex optimization to convert a target function of the optimization problem without approximate calculation, so that computation complexity is greatly reduced while accuracy of the problem is not influenced, and delay generated by system overheads is reduced; a Lagrangian multiplier method is used in an optimizing process, and thus an optimizing speed is rapid; a subgradient method and an incremental step length are used in an iterative process, so that optimizing is more accurate.

The invention discloses a power allocation algorithm in a multi-beam satellite communication system. The power allocation algorithm comprises the following steps: at first, creating a downlink capacity model of the multi-beam satellite communication system for a user terminal according to a power bandwidth balancing principle in the multi-beam satellite communication system; then, creating a constrained optimization problem model for the power allocation problem; and finally, solving through the Lagrangian duality theory and a subgradient algorithm. The power allocation algorithm provided by the invention has excellence on the aspects of total capacity of the system and the total refusal amount of two-order services, good system performance can be obtained, and valuable power resources can be effectively utilized.

The invention discloses a hydropower station group terminal energy storage maximum short-term optimized dispatching method, and belongs to the field of hydropower dispatching. First, restraint of uniformity of gross capability and total load requirements of each time interval in an original model is replaced by restraint that the maximum difference value of the gross capability and the total load requirements of each time interval is zero; and then the simplified maximum difference value is converted to be in an easily-processed form through a coherency function method. On the basis, an antithesis optimized model is established through a Lagrangian relaxation method, and solving is carried out in a secondary gradient method. According to the method, a terminal energy storage maximum optimized dispatching problem of a Lagrangian multiplier vector with the existing scale of 96 is converted to a problem of only one Lagrangian multiplier, complexity of the problem is lowered, effective solving can be achieved through a combined method of the secondary gradient method and a successive approximation search algorithm, the method has strong practicability, and application is wide.

The invention provides a power control method based on SWIPT (Simultaneously Wireless Information and Power Transfer) in a heterogeneous cellular network. The method comprises the steps of initializing system parameters, establishing a maximum energy efficiency optimization model according to a system scene, converting the model into a power control problem of a base station, and converting an original problem into a joint optimization problem between base station transmission power and a user side power distribution coefficient through variable substitution; under the condition that the userside power distribution coefficient rhok is fixed, carrying out solution through adoption of a subgradient method based on a Lagrange multiplier, and obtaining the optimum power control scheme P<k><*>of the base station according to a KKT condition; and obtaining the optimum energy efficiency value lambda<k><*> of a system through combination of a bisection method according to the obtained powercontrol scheme P<k><*>. According to the optimum power control method provided by the invention, the energy efficiency of the cellular system can be improved, the energy limited problem of a mobile device also can be effectively solved, and the method has certain guiding significance in a practice aspect.

The invention aims to provide an interference-based combined resource allocation method for a macro base station and a family base station. Combined channel allocation is performed for a macro user and a family base station based on a heuristic type dynamic clustering method, and cross-layer interference between a family base station system and a macro cellular system is effectively reduced while clustering is performed on the family base station. While ensuring constraint conditions of macro user and family user QoS, an optimization problem with minimization of family base station transmitting power being a target function is constructed, and a subgradient method is utilized to solve the optimization problem. The method reduces interference of the family base station system, decreases interruption probability of the family user, and improves energy efficiency of the family base station.

The invention discloses a bandwidth and power coordinated allocation method in inter-satellite communication links. The bandwidth and power coordinated allocation method is implemented based on a Lagrangian duality theory and a subgradient method, and comprises the steps of: determining that satellite are in a normal communication stage; establishing an inter-satellite communication system model according to channel parameters, and determining an optimization object; comprehensively considering system capacity and service requirement of each communication link, conducting modeling for the optimization object; adopting a gradient descent method for acquiring a calculation method of power and bandwidth allocated to each communication link; and solving the optimal power and the optimal bandwidth required by an inter-satellite communication link system. The bandwidth and power coordinated allocation method disclosed by the invention has the advantages of being fast, efficient and stable, increasing system capacity of the satellite system, reducing calculation workload, and has relatively high utilization rate of on-satellite resources.

The invention provides a renewable energy optimization method of an energy collection type wireless relay network with maximum throughput. The method comprises the following steps: 1) realizing the maximum throughput in the energy collection type wireless relay network via renewable energy optimization management, wherein the optimization problem is described as a multi-variable optimization; 2) decomposing P1 problem into two sub optimization problems due to the convex optimization properties: power sub optimization and time slot sub optimization; in view of the power sub optimization, optimizing the variable pi by using a regular convex optimization algorithm (interior point method); and in view of the time slot sub optimization, optimizing the variable (the formula is as shown in the specification) by using a prediction subgradient method. The invention provides a renewable energy optimization method for realizing the maximum throughput and maximum system efficiency in the energy collection type wireless relay network by combining time scheduling with power allocation.

The invention aims at providing a combined channel assignment and power control method of a home base station system. According to the method, the channel assignment and power control of a home network are combined through an optimized object, and compared with an interference-minimum principle, a convex optimization and subgradient method is adopted to solve the closed form of power, so that the maximization of the network capacity of a home base station is ensured; and in addition, the optimization of the network capacity of the home base station is correlated with the optimization of the capacity of a macro base station, and channels are reasonably distributed for a macros user, so that the capacity of a macros base station achieves maximum under a same condition. Compared with a simple power control algorithm, the combined channel assignment and power control method provided by the invention has the advantages that not only can the network capacity of the home base station be greatly increased, but also the capacity of the macro base station is increased.

A ruled surface adaptively measuring method based on constant contact force relates to measurement of curved parts. The method comprises performing grid division on a surface to be measured, obtaining the contact force of the unit node of a complex curved surface to be measured as well as the coordinate of a measuring head, and predicting the displacement change of a probe with constant contact force according to subgradient size and direction change; during the surface measurement, reutilizing the position coordinates of unit nodes on a parallel measurement route to effectively improve the measurement efficiency; adaptively completing measurement of the complex curved surface composed of ruled surfaces without changing measurement hardware to obtain high practicality, to effectively improve the measurement efficiency and to reduce the operating processes. Meanwhile, according to the ruled surface adaptively measuring method based on the constant contact surface, direct radius compensation is performed on measured points through an element method, the adjusting displacement of a second contact point is predicated through a subgradient method, and during route planning, measured data of unit cross nodes are reutilized, so that the measurement efficiency can be effectively improved.

The invention discloses a relay selection method of an energy acquisition based multi-relay cooperative communication system, and belongs to the technical field of cooperative communication. The relay selection method comprises steps of analyzing a system scene and summarizing problems; establishing a system mathematical model; and using an optimization method to solve an optimal solution. The relay selection method provided by the invention is practically applied in special application scenes, so compared with previous multi-relay selection methods, the relay selection method is characterized in that multi-relay cooperative communication based on energy acquisition is taken into consideration, and an expression of the throughput capacity is deduced, so the throughput capacity performance among communication nodes is maximized, solving of optimization problems is performed and the relay selection method has practical guidance meanings. Aiming at the solving of the optimization problem, convex optimization processing is adopted and a target function of an optimization problem is converted without approximate calculation, so precision of the problem is not affected, calculation complexity is greatly reduced, and time delays generated by expense of the system are reduced. During an optimization process, a lagrangian multiplier method is adopted, so optimization speed is fast. A subgradient method is adopted in an algorithm iteration process, and gradual step lengths are selected, so optimization is more accurate.

The invention discloses a high resolution SAR image classification method based on a co-sparse model and solves a technical problem of high classification time complexity caused by utilizing only an integrated sparse model to represent images during SAR image classification in the prior art. The method comprises steps that an initial pixel value matrix X is selected in to-be-classified SAR images; an analysis operator is selected to learn an initial sample; a projection subgradient method and a unified row specification tight frame method are combined to learn an analysis operator omega; an augmented lagrangian method is utilized to solve a co-sparse coefficient Z; a co-sparse coefficient vector of a pixel block corresponding to each pixel point and a pixel value vector of the pixel block are combined to acquire a characteristic vector; classification is carried out based on an SVM classifier to acquire a prediction label of each pixel point characteristic vector of the whole graph; the prediction label result is displayed in a gray image. The method is advantaged in that sparse expressions of the images can be rapidly acquired, classification timeliness and classification accuracy of the SAR images can be guaranteed, and the method is for high resolution SAR image classification.

The invention discloses a method for positioning a fault between an information network and a communication network. The method comprises the steps of deleting the redundant information in a cause-and-effect graph fault propagation model to generate a bipartite graph fault propagation model; removing information network-associated alarms from the bipartite graph fault propagation model to establish the direct relationship between fault source of the communication network and the associated fault event of the information network; determining a final objective function for a network fault positioning problem; based on the Lagrangian Relaxation and Subgradient method, solving the optimal solution of the final objective function. According to the technical scheme of the invention, the mutual influence mechanism between the information network and the communication network is analyzed, and then a bipartite graph-based network fault positioning model is provided. Meanwhile, the optimal solution problem of the model is solved, so that the network fault positioning efficiency is improved. Therefore, fault positioning results are more accurate.

The invention discloses a GFDM-based wireless energy-carrying network uplink and downlink resource joint allocation method. Each user performs information decoding and energy collection on a received signal by adopting a power division receiver structure in a downlink, and then uses the collected energy for uplink information transmission. The weighted reachable sum rate of the uplink and the downlink is maximized under the condition of meeting the constraint of energy collection and total transmitting power by allocating GFDM subcarriers and subsymbols of the uplink and the downlink and allocating power allocation and power division factors. In order to solve the non-convex optimization problem, a Lagrange duality method, a sub-gradient method and a greedy algorithm are respectively adopted to solve the joint optimization of uplink and downlink power distribution and power segmentation factors. The invention is superior to a fixed subcarrier distribution algorithm. According to the invention, multi-resource optimal allocation of uplink and downlink subcarriers, subsymbols, power and power segmentation factors of the wireless energy-carrying network based on GFDM is effectively realized.

The invention relates to a welding robot motion model subgradient method. The method is a non-smooth optimizing method proposed based on a non-smooth motion planning model question of a welding robot,the non-smooth motion planning model question is a motion planning question, and after motion planning question variation is conducted, the non-smooth motion planning model of the welding robot can be obtained, and the motion optimizing problem of the welding robot is solved on the basis by applying the subgradient method. A power system of the welding robot can be obtained, a welding robot controller is obtained, a control system of the welding robot is allowed to be stable by the controller obtained through computation under an optmizing model taking the welding robot energy as a performance indicator, and lower needed energy consumption can be achieved. The method has the advantages that the algorithm structure is simple, the algorithm speed is linearly convergent, and the time consumption is lower.

The invention relates to a computing power network service function chain resource allocation method, which belongs to the field of edge computing and resource allocation, and comprises the following steps: S1, establishing a user uncertainty service function chain demand model; s2, based on the user uncertainty service function chain demand model, service node computing resources and transmission link network bandwidth resource allocation are jointly considered, and a computing power network service cost optimization problem model is established; s3, converting the optimization problem into a convex problem by using a variable relaxation method, solving by using a dual decomposition method and a subgradient method to obtain an optimal user node association and service link selection result, and obtaining a corresponding resource allocation scheme; s4, restoring the linear solution in the step S3 into a discrete solution by using a heuristic rounding algorithm; and S5, solving the residual traffic engineering problem based on the discrete solution, updating the continuous solution, and obtaining feasible solutions of all variables of the problem. According to the invention, the joint resource cost of the service node and the transmission link can be reduced.

The invention discloses an optimal power distribution scheme in a multi-channel cognitive wireless network based on convex optimization. A multi-channel cognitive radio network model based on a frequency spectrum sharing mechanism is established. An optimization problem of maximizing the total communication rate of the secondary user under the constraint of the total transmitting power in a peak form and the interference power of each channel is established; the convexity of the optimization problem is verified by using a convex optimization theory, the optimization problem is decomposed intosub-problems in a specific channel state, the KKT condition satisfied by the optimal solution is given by using a Lagrange dual method, the expression of the optimal solution about dual variables is given, and finally, the optimal power allocation scheme is solved by using a dichotomy and a sub-gradient method. According to the method, multiple channels are jointly considered for frequency spectrum sharing, instantaneous peak interference power constraints are adopted to strictly ensure that the primary user is not interfered, and meanwhile, multiple channels are utilized to enable the secondary user to transmit with higher transmitting power, so that the comprehensive utilization rate of frequency spectrum resources is further improved.

The invention discloses an optimization problem processing method and device for machine learning. The method comprises the steps: determining an objective function and a constraint condition according to a to-be-solved problem, and continuing a next step in response to the fact that the objective function is a non-differentiable convex function; updating the current solution by using a projection subgradient method based on the number of iterations and the constraint condition, and substituting the current solution into the target function to obtain a current function value; in response to the fact that the current function value does not meet the convergence condition and the number of iterations does not exceed the preset upper limit, accumulating the number of iterations and re-executing the previous step; and determining a solution corresponding to an optimal value in the current function values as an optimal solution of the to-be-solved problem in response to the condition that the current function values meet the convergence condition or the number of iterations exceeds a preset upper limit. According to the method, the optimal solution problem of the non-differentiable function of machine learning can be solved, and the convergence speed is very high.