Design and FPGA-realization of a self-synchronizing chaotic decoder in the presence of noise

This paper describes requirement for wireless transmission of Chaotic Code Division Multiplexed Access (Chaotic CDMA) and it focuses on real-time synchronization algorithm embedded into electronic programmable device. CDMA with quasi-orthogonal codes is used to allow multi-users to transmit simultaneously in the same channel. Since the channel is shared between all users, the receiver system has to cope with channel noise and overall with interference from other users. As a result, one of the main problems of communication with quasi-orthogonal chaotic codes is to implement a real time decoder in presence of noise. Even if set-membership algorithm are efficient in real time synchronization of chaotic discrete generators in the presence of noise, these algorithms require a large memory resource. In this paper, we propose an evolution of set-membership algorithm toward genetic algorithm to be implemented into electronic programmable device. The advantage of genetic algorithm compared with set-membership algorithm is that they require a fixed size of memory.     

无线通信中的最优资源分配―复杂性分析与算法设计

最优资源分配问题是无线通信系统设计中的基本问题之一.最优地分配功率、传输波形和频谱等资源能够极大地提高整个通信系统的传输性能.目前,相对于通信技术在现实生活中的蓬勃发展,通信系统优化的数学理论和方法显得相对滞后,在某些方面已经成为影响其发展和应用的关键因素.无线通信中的最优资源分配问题常常可建模为带有特殊结构的非凸非线性约束优化问题.一方面,这些优化问题常常具有高度的非线性性,一般情况下难于求解;另一方面,它们又有自身的特殊结构,如隐含的凸性和可分结构等.本文着重考虑多用户干扰信道中物理层资源最优分配问题的复杂性刻画,以及如何利用问题的特殊结构设计有效且满足分布式应用等实际要求的计算方法.     

Group Scheduling with Controllable Setup and Processing Times: Minimizing Total Weighted Completion Time

The following single machine scheduling problem is studied. A partition of a set of n jobs into g groups on the basis of group technology is given. The machine processes jobs of the same group contiguously, with a sequence independent setup time preceding the processing of each group. The setup times and the job processing times are controllable through the allocation of a continuously divisible or discrete resource to them. Each job uses the same amount of the resource. Each setup also uses the same amount of resource, which may be different from that for the jobs. Polynomial-time algorithms are constructed for variants of the problem of finding an optimal job sequence and resource values so as to minimize the total weighted job completion time, subject to given restrictions on resource consumption. The algorithms are based on a polynomial enumeration of the candidates for an optimal job sequence and solving the problem with a fixed job sequence by linear programming. This research was supported in part by The Hong Kong Polytechnic University under grant number G-T246 and the Research Grants Council of Hong Kong under grant number PolyU 5191/01E. In addition, the research of M.Y. Kovalyov was supported by INTAS under grant number 00-217.     

Traffic assignment in communication satellites

A high capacity communication satellite interconnects scores of ground stations simultaneously. Under the Satellite-Switched/Time Division Multiple Access (SS/TDMA) system, each channel of the satellite is allocated to a pair of ground stations for a certain time period, after which the whole set of allocations (called a switch) is changed simultaneously. The problem we address is to minimize the time length of the entire sequence of switches, subject to a limit on the number of switches. We formulate this as a 3-index bottleneck-sum assignment problem, and solve it by a heuristic that obtains consistently better results than earlier methods based on different formulations.     

Throughput-oriented channel assignment for opportunistic spectrum access networks

Cognitive radio (CR) is a revolutionary technology in wireless communications that enhances spectrum utilization by allowing opportunistic and dynamic spectrum access. One of the key challenges in this domain is how CR users cooperate to dynamically access the available spectrum opportunities in order to maximize the overall perceived throughput. In this paper, we consider the coordinated spectrum access problem in a multi-user single-transceiver CR network (CRN), where each CR user is equipped with only one half-duplex transceiver. We first formulate the dynamic spectrum access as a rate/power control and channel assignment optimization problem. Our objective is to maximize the sum-rate achieved by all contending CR users over all available spectrum opportunities under interference and hardware constraints. We first show that this problem can be formulated as a mixed integer nonlinear programming (MINLP) problem that is NP-hard, in general. By exploiting the fact that actual communication systems have a finite number of available channels, each with a given maximum transmission power, we transfer this MINLP into a binary linear programming problem (BLP). Due to its integrality nature, this BLP is expected to be NP-hard. However, we show that its constraint matrix satisfies the total unimodularity property, and hence our problem can be optimally solved in polynomial time using linear programming (LP). To execute the optimal assignment in a distributed manner, we then present a distributed CSMA/CA-based random access mechanism for CRNs. We compare the performance of our proposed mechanism with reference CSMA/CA channel access mechanisms designed for CRNs. Simulation results show that our proposed mechanism significantly improves the overall network throughput and preserves fairness.     

Knapsack problems with sigmoid utilities: Approximation algorithms via hybrid optimization

We study a class of non-convex optimization problems involving sigmoid functions. We show that sigmoid functions impart a combinatorial element to the optimization variables and make the global optimization computationally hard. We formulate versions of the knapsack problem, the generalized assignment problem and the bin-packing problem with sigmoid utilities. We merge approximation algorithms from discrete optimization with algorithms from continuous optimization to develop approximation algorithms for these NP-hard problems with sigmoid utilities.     

Fixed interval scheduling: Models,applications, computational complexity and algorithms

The defining characteristic of fixed interval scheduling problems is that each job has a finite number of fixed processing intervals. A job can be processed only in one of its intervals on one of the available machines, or is not processed at all. A decision has to be made about a subset of the jobs to be processed and their assignment to the processing intervals such that the intervals on the same machine do not intersect. These problems arise naturally in different real-life operations planning situations, including the assignment of transports to loading/unloading terminals, work planning for personnel, computer wiring, bandwidth allocation of communication channels, printed circuit board manufacturing, gene identification and examining computer memory structures. We present a general formulation of the interval scheduling problem, show its relations to cognate problems in graph theory, and survey existing models, results on computational complexity and solution algorithms.     

Greedy approaches for a class of nonlinear Generalized Assignment Problems

The traditional Generalized Assignment Problem (GAP) seeks an assignment of customers to facilities that minimizes the sum of the assignment costs while respecting the capacity of each facility. We consider a nonlinear GAP where, in addition to the assignment costs, there is a nonlinear cost function associated with each facility whose argument is a linear function of the customers assigned to the facility. We propose a class of greedy algorithms for this problem that extends a family of greedy algorithms for the GAP. The effectiveness of these algorithms is based on our analysis of the continuous relaxation of our problem. We show that there exists an optimal solution to the continuous relaxation with a small number of fractional variables and provide a set of dual multipliers associated with this solution. This set of dual multipliers is then used in the greedy algorithm. We provide conditions under which our greedy algorithm is asymptotically optimal and feasible under a stochastic model of the parameters.     

Resource level minimization in the discrete–continuous scheduling

A discrete–continuous problem of non-preemptive task scheduling on identical parallel processors is considered. Tasks are described by means of a dynamic model, in which the speed of the task performance depends on the amount of a single continuously divisible renewable resource allotted to this task over time. An upper bound on the completion time of all the tasks is given. The criterion is to minimize the maximum resource consumption at each time instant, i.e., the resource level. This problem has been observed in many industrial applications, where a continuously divisible resource such as gas, fuel, electric, hydraulic or pneumatic power, etc., has to be distributed among the processing units over time, and it affects their productivity. The problem consists of two interrelated subproblems: task sequencing on processors (discrete subproblem) and resource allocation among the tasks (continuous subproblem). An optimal resource allocation algorithm for a given sequence of tasks is presented and computationally tested. Furthermore, approximation algorithms are proposed, and their theoretical and experimental worst-case performances are analyzed. Computer experiments confirmed the efficiency of all the algorithms.     

Viable set computation for hybrid systems

In this paper, we revisit the problem of computing viability sets for hybrid systems with nonlinear continuous dynamics and competing inputs. As usual in the literature, an iterative algorithm, based on the alternating application of a continuous and a discrete operator, is employed. Different cases, depending on whether the continuous evolution and the number of discrete transitions are finite or infinite, are considered. A complete characterization of the reach-avoid computation (involved in the continuous time calculation) is provided based on dynamic programming. Moreover, for a certain class of automata, we show convergence of the iterative process by using a constructive version of Tarski’s fixed point theorem, to determine the maximal fixed point of a monotone operator on a complete lattice of closed sets. The viability algorithm is applied to a benchmark example and to the problem of voltage stability for a single machine-load system in case of a line fault.     

Decentralized Algorithm for Centralized Variational Inequalities in Network Resource Allocation

Decentralized algorithms would be useful for making network resource allocations in large-scale and complex system networks because such networks tend to lack centralized operators and are subject to continuous infrastructure improvements. In this paper, we consider a variational inequality for network resource allocation and devise a decentralized allocation algorithm for it. The proposed algorithm enables each user in the network to decide its own optimal resource allocation in cooperation with other users without using other users’ private information such as their utility functions. Moreover, we present a convergence analysis on the algorithm and apply it to the network resource allocation problem.     

Optimal Base Station Positioning and Channel Assignment for 3G Mobile Networks by Integer Programming

In this paper, discrete mathematical programming approaches are used to solve the frequency allocation and cell site selection problem in an integrated setup. Both CDMA (code division multiple access) and FD/TDMA (frequency/time division multiple access) technologies will be important for 3rd generation mobile systems. If all users share the same bandwidth, base transmitter stations should be placed such that a maximum of traffic can be carried at low interference rates. The expected traffic is represented by spatially scattered weighted nodes. The problem to select an optimal set of base station locations from a given pool of configurations is formulated as an integer linear program and solved by combinatorial optimization methods. For systems which employ FD/TDMA schemes, the cell site optimization process depends on the assignment of channels. We suggest an integrated linear programming approach to solve both objectives in a single planning step. Because of the problems' tremendous complexity, special branch-and-bound procedures are developed as exact and approximate solution methods. An examples is given for a typical urban scenario with base transmitters below roof tops.     

Computation of lower bounds on the network cost in location problems subject to distance constraints

Methods for the computation of lower bounds on the cost of the connecting network for the continuous and discrete variants of the problem of location of interconnected objects subject to minimal or maximal distances between them are proposed. For the continuous variant, the bound is found by solving a linear programming problem. For the discrete variant, an assignment problem with a rectangular matrix containing forbidden entries is constructed. An application of the assignment problem for locating objects of various sizes is described.     

Optimizing bandwidth allocation in elastic optical networks with application to scheduling

We study a problem of optimal bandwidth allocation in the elastic optical networks technology, where usable frequency intervals are of variable width. In this setting, each lightpath has a lower and upper bound on the width of its frequency interval, as well as an associated profit, and we seek a bandwidth assignment that maximizes the total profit. This problem is known to be NP-complete. We strengthen this result by showing that, in fact, the problem is inapproximable within any constant ratio even on a path network. We further derive NP-hardness results and present approximation algorithms for several special cases of the path and ring networks, which are of practical interest. Finally, while in general our problem is hard to approximate, we show that an optimal solution can be obtained by allowing resource augmentation. Some of our results resolve open problems posed by Shalom et al. (2013) [28]. Our study has applications also in real-time scheduling.     

A mathematical model for personalized advertisement in virtual reality environments

We consider a personalized advertisement assignment problem faced by the manager of a virtual reality environment. In this online environment, users log in/out, and they spend time in different virtual locations while they are online. Every time a user visits a new virtual location, the site manager can show the ad of an advertiser. At the end of a fixed time horizon, the manager collects revenues from all of the advertisers, and the total revenue depends on the number of ads of different advertisers she displays to different users. In this setup, the objective of the manager is to find an optimal dynamic ad display policy in order to maximize her expected revenue. In the current paper, we formulate this problem as a continuous time stochastic optimization problem in which the actions of users are represented with two-state Markov processes and the manager makes display decisions at the transition times of these processes. To our best knowledge, no formal stochastic model and rigorous analysis has been given for this practical problem. Such a model and its analysis are the major contributions of this paper along with an optimal solution.     

Location of a discrete resource and its allocation according to a fractional objective

The problem considered is that of the location of a discrete resource and its allocation to activities with concave return functions in such a way as to maximize the ratio of ‘return’ to ‘cost’, the total cost being the sum of a fixed cost and linearly variable costs. It is assumed that each resource has an effectiveness of 0 or 1 against each activity. It is demonstrated that an optimal solution can be determined by the rounding to integers of the solution of an associated problem in continuous variables. Solutions with objective values arbitrarily close to the optimal value can be generated by resource-wise optimizations. An upper bound of the number of non-zero integer allocations in an optimal solution is derived.     

The incorporation of fixed cost and multilevel capacities into the discrete and continuous single source capacitated facility location problem

In this study we investigate the single source location problem with the presence of several possible capacities and the opening (fixed) cost of a facility that is depended on the capacity used and the area where the facility is located. Mathematical models of the problem for both the discrete and the continuous cases using the Rectilinear and Euclidean distances are produced. Our aim is to find the optimal number of open facilities, their corresponding locations, and their respective capacities alongside the assignment of the customers to the open facilities in order to minimise the total fixed and transportation costs. For relatively large problems, two solution methods are proposed namely an iterative matheuristic approach and VNS-based matheuristic technique. Dataset from the literature is adapted to assess our proposed methods. To assess the performance of the proposed solution methods, the exact method is first applied to small size instances where optimal solutions can be identified or lower and upper bounds can be recorded. Results obtained by the proposed solution methods are also reported for the larger instances.

     

Heuristic approaches to discrete-continuous project scheduling problems to minimize the makespan

In this paper some discrete-continuous project scheduling problems to minimize the makespan are considered. These problems are characterized by the fact that activities of a project simultaneously require for their execution discrete and continuous resources. A class of these problems is considered where the number of discrete resources is arbitrary, and one continuous, renewable, limited resource occurs. A methodology for solving the defined problems is presented. The continuous resource allocation problem is analyzed. An exact, as well as a heuristic approach to the problem is discussed. The idea of the continuous resource discretization is described, and a special case of the problem with identical processing rate functions is analyzed. Some computational experiments for evaluating the efficiency of the proposed heuristic approaches are presented. Conclusions and directions for future research are given.