Optimize the Communication Cost of 5G Internet of Vehicles through Coherent Beamforming Technology

Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long-distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP-hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.

MCRA: Multicost Rerouting Algorithm in SDN

A software-defined network (SDN) partitions a network into a control plane and data plane. Utilizing centralized control, an SDN can accurately control the routing of data flow. In the network, links have various costs, such as bandwidth, delay, and hops. However, it is difficult to obtain a multicost optimization path. If online rerouting can be realized under multiple cost, then network performance can be improved. This paper proposes a multicost rerouting algorithm for elephant flow, as the latter is the main factor affecting network traffic. By performing path trimming, the algorithm can obtain the approximate optimal solution of (1+e) in polynomial time. Simulation results show that the proposed algorithm yields good performance.

Cutting Path Optimization of Pattern Pieces in Garment Automatic Cutter

In view of the defect and shortage in cutting path automatic optimization of 2D pattern pieces in current garment automatic cutter, a new optimization method of computer is explored. If there is no cutting path optimization implemented by garment automatic cutter before cutting, some problems will be caused, such as too much unless travel and too long processing time. At present, both at home and abroad, the studies on automatic optimization in cutting preprocessing are relatively weak. According to the “segment cutting from left to right” feature of automatic cutter in cutting process, an algorithm which can be summarized as “segment and reducing point” was proposed. This algorithm combined with the solution of shortest path problem, its purpose is to seek for the approximate optimal solution of cutting path. The algorithm implemented through Visual C++ 6.0 programming. Used in production by enterprise shows that the program is simple to operate, and has a high compute speed. Averagely, unless travel in cutting process reduced more than 10%. It proves that the algorithm is feasible and efficient. Using this algorithm achieved the purpose of reducing unless travel, improving cutting efficiency and lowering the cost.

On strong consistency of a class of recursive stochastic Newton-Raphson type algorithms with application to robust linear dynamic system identification

The recursive stochastic algorithms for estimating the parameters of linear discrete-time dynamic systems in the presence of disturbance uncertainty has been considered in the paper. Problems related to the construction of min-max optimal recursive algorithms are demonstrated. In addition, the robustness of the proposed algorithms has been addressed. Since the min-max optimal solution cannot be achieved in practice, an approximate optimal solution based on a recursive stochastic Newton-Raphson type procedure is suggested. The convergence of the proposed practically applicable robustified recursive algorithm is established theoretically using the martingale theory. Both theoretical and experimental analysis related to the practical robustness of the proposed algorithm are also included. .

A Primal-Dual Interior-Point QP Method and its Extension for Engineering Optimization

Presented in this paper is a primal-dual infeasible-interior-point quadratic programming (QP) algorithm and its extension for nonlinear programming that is suited for engineering design and structural optimization, where the number of variables are very large and function evaluations are computationally expensive. The computational experience in solving both test problems and optimal structural design problems using the algorithm demonstrated that the algorithm finds an approximate optimal solution in fewer iterations and function evaluations, the obtained solution usually is an interior feasible solution, and so the resulting method is very efficient and effective.